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1
Kurniawan, Albert. "Sequential Pattern Mining Data Transaksi Penjualan Supermarket menggunakan Algoritme Generalized Sequential Pattern." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (2022): 126–36. http://dx.doi.org/10.35957/jatisi.v9i1.1460.
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Data transaksi penjualan supermarket online merupakan sequence dataset. Data ini menyimpan data transaksi pembelian yang dilakukan oleh pelanggan, sehingga dapat dianalisis menggunakan pendekatan Market Basket Analysis (MBA). Masalah yang sering dialami oleh pihak supermarket adalah sulitnya menerapkan strategi penjualan yang akurat kepada para konsumen. Berdasarkan masalah tersebut, pada penelitian ini akan dilakukan analisis terhadap dataset supermarket West Superstore berdasarkan pendekatan MBA. Algoritme yang digunakan adalah algoritme Generalized Sequential Pattern (GSP), di mana algoritme ini dapat membangkitkan frequentitem dan sequencepattern, sehingga aturan yang dihasilkan dapat lebih akurat. Algoritme GSP pada penelitian ini diimplementasikan dalam bahasa pemrograman Python. Hasil pengujian menunjukkan bahwa keluaran dari Python sudah sesuai dengan keluaran dari perhitungan algoritme GSP. Waktu komputasi yang diperlukan untuk pembangkitan aturan pada algoritme GSP juga bergantung pada jumlah record yang ada. Semakin banyak jumlah transaksi penjualan yang akan dianalisis, maka waktu komputasinya juga semakin lama. Analisis yang dilakukan pada dataset penjualan di West Superstore menghasilkan 391 aturan, di mana aturan tersebut dapat dimanfaatkan oleh pihak supermarket untuk penerapan strategi penjualan.
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Astuti, Tri, and Lisdya Anggraini. "Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm." IJIIS: International Journal of Informatics and Information Systems 2, no. 1 (2019): 17–23. http://dx.doi.org/10.47738/ijiis.v2i1.10.
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As a center for learning and information services, STMIK Amikom Purwokerto Library is a source of learning and a source of intellectual activity that is very important for the entire academic community in supporting the achievement of the college Tridharma program. Book lending transaction data, can produce information that is important as supporting decision making when further analyzed. One useful information is that it can provide information in the form of user behavior patterns in borrowing books that are used to maintain the availability of related book stocks to be balanced. This study uses the Generalized Sequential Pattern (GSP) algorithm, which can be used to determine the behavior patterns of users in each transaction and can show relationships or associations between books, both requested simultaneously and sequentially. From the calculations that have been done, 295 frequent sequences are consisting of 3 sequence patterns that are formed from the minimum support of 0.53% or the minimum number of books borrowed, namely 2 books. Three book items have very strong linkages in book lending transactions, namely book code 6690, 2026, and 8131.
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Al-Refaie, Abbas, Banan Abu Hamdieh, and Natalija Lepkova. "Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining." Buildings 13, no. 4 (2023): 946. http://dx.doi.org/10.3390/buildings13040946.
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This study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. In data collection, historical data were collected regarding maintenance activities and product attributes. The generalized sequential pattern (GSP) and association rules were then applied to predict maintenance activities with and without attributes to determine the frequent sequential patterns and significant rules of maintenance activities. Finally, a comparison was performed between the sequences of maintenance activities with and without attributes. A real case study of washing machine products was presented to illustrate the developed framework. The results showed that the proposed framework effectively predicted the next maintenance activities and planning preventive maintenance based on product attributes. In conclusion, the data mining approach is found effective in determining the maintenance sequence that reduces downtime and thereby enhancing productivity and availability.
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Setiawan, Esther Irawati, Valerynta Natalie, Joan Santoso, and Kimiya Fujisawa. "Sequential Pattern Mining to Support Customer Relationship Management at Beauty Clinics." Bulletin of Social Informatics Theory and Application 6, no. 2 (2022): 168–76. http://dx.doi.org/10.31763/businta.v6i2.602.
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 The increasing competition for beauty clinics, makes management need to think of methods to survive in this competition. For that, the company needs to improve CRM in its service to customers. Customer Relationship Management is a series of activities managed in an effort to better understand, attract attention, and maintain customer loyalty.
 Sequential Pattern Mining is one of the data mining techniques that is useful for finding patterns sequential / sequence of a set of items. The algorithm that is used is the Generalized Sequential Pattern (GSP). GSP performs candidate generation and support counting processes that is, the union of L1−k with itself which generates a candidate sequence that cannot exist as twin candidate, after that deletion candidate who does not meet the minimum support. While carrying out the process through existing data, is also carried out increasing the number of supports from the included candidates in data sequences. The output to be produced by the program are all frequent itemsets that satisfy minimum support in the form of rules.
 Sales transaction data will be processed by using the Generalized Sequential Pattern algorithm so that it can produce a rule, namely the purchase order that meets the minimum support. The result of the rule used by management to support enterprise CRM activities such as acquiring new customers, increasing the profits from existing customers, and retaining existing customers.
 
 
 
 
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Ramos, Somya, Winarko Edi, and Priyanta Sigit. "A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3422~3432. https://doi.org/10.11591/eei.v11i6.4021.
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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.
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Wijaya, Raven, and Ramos Somya. "Analisis Dataset Transaksi Penjualan Minimarket Menggunakan Algoritma Generalized Sequential Pattern Berbasis Web." Jurnal Pendidikan Teknologi Informasi (JUKANTI) 5, no. 2 (2022): 8–15. http://dx.doi.org/10.37792/jukanti.v5i2.516.
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Minimarket merupakan salah satu bidang usaha ritel yang menjual berbagai macam barang. Minimarket dapat dijumpai di berbagai tempat seperti di stasiun pengisian bahan bakar, stasiun kereta api, dan di pinggiran jalan. Usaha bisnis ini mengalami pertumbuhan yang sangat pesat. Berdasarkan hasil wawancara dari Katadata.co.id dengan Ketua Umum Asosiasi Perusahaan Retail Indonesia (Aprindo). Pertumbuhan minimarket lebih dari 15 persen per tahunnya. Berdasarkan data tersebut para pemilik bisnis ini membutuhkan strategi untuk dapat bersaing dengan kompetitornya. Oleh karena itu diperlukan berbagai macam strategi agar pemilik bisnis minimarket dapat bersaing dengan kompetitornya. Penelitian ini bertujuan untuk menganalisis dataset transaksi penjualan pada minimarket dengan menggunakan algoritma Generalized Sequential Pattern (GSP) berbasis website untuk menemukan pola sekuensial yang diperlukan untuk menyusun berbagai macam strategi. Penelitian ini menggunakan sebuah data transaksi salah satu minimarket di kota Salatiga yang berisi 321 data transaksi. Pencarian rule dengan nilai support sebesar 2 dan berhasil membangkitkan 53 rule. Salah satu rule yang didapatkan adalah <{‘Cap Lang Minyak Kayu Putih 30Ml’, ‘Antangin Jrg Obat Masuk Angin Sirup 5X15ml’}>. Rule tersebut dapat dipertimbangkan untuk menentukan letak produk agar mempermudah pembeli menemukan barang sehingga meminimalisir kerugian yang diakibatkan karena barang tidak laku.
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Leece, Michael, and Arnav Jhala. "Sequential Pattern Mining in StarCraft: Brood War for Short and Long-Term Goals." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 2 (2021): 8–13. http://dx.doi.org/10.1609/aiide.v10i2.12736.
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A wide variety of strategies have been used to create agents in the growing field of real-time strategy AI. However, a frequent problem is the necessity of hand-crafting competencies, which becomes prohibitively difficult in a large space with many corner cases. A preferable approach would be to learn these competencies from the wealth of expert play available. We present a system that uses the Generalized Sequential Pattern (GSP) algorithm from data mining to find common patterns in StarCraft:Brood War replays at both the micro- and macro-level, and verify that these correspond to human understandings of expert play. In the future, we hope to use these patterns to learn tasks and goals in an unsupervised manner for an HTN planner.
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Puertas, Eloi, Sergio Escalera, and Oriol Pujol. "Generalized multi-scale stacked sequential learning for multi-class classification." Pattern Analysis and Applications 18, no. 2 (2013): 247–61. http://dx.doi.org/10.1007/s10044-013-0333-y.
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Kumar, Anil, Sonal Chawla, and Supreet Kaur Mann. "Sequential Pattern Mining and Hybrid Sentiment-based Collaborative Architecture for Rating Prediction." International Journal of Mathematical, Engineering and Management Sciences 10, no. 1 (2025): 148–62. https://doi.org/10.33889/ijmems.2025.10.1.009.
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This research presents a novel approach named “Sequential Pattern Mining and Hybrid Sentiment-based Collaborative Architecture for Rating Predictions". This approach overcomes the limitations of traditional techniques by considering multi-dimensional data, including users' past behaviour, buying patterns, and sentiments to enhance the rating predictions and recommendations. The proposed prediction approach incorporates users’ past behaviour i.e. ratings through the Collaborative Filtering technique. The users’ sentiments are included by implementing Hybrid Sentiment Analysis and the sequential buying patterns are considered through the Generalized Sequential Pattern Mining technique. The Hybrid Sentiment Analysis technique combines Lexicon-based and Deep Learning-based Sentiment Analysis methodologies for more comprehensive sentiment evaluation. The proposed Hybrid Rating Prediction System is evaluated using a standardized public dataset and standard evaluation metrics including Accuracy, Precision, Recall, and F1-Score. Therefore, the research study has three primary objectives. The first objective is to identify the existing recommendation techniques through a literature review. The second objective is to propose a hybrid approach that mitigates the limitations of traditional systems by incorporating multi-dimensional information about the user and items. The third objective is to evaluate, validate, and compare the proposed approach against existing state-of-the-art systems and possible hybrid systems. The results demonstrate that the proposed hybrid approach achieves an Accuracy of 79.79%, with significant improvements in Precision and F1-Score compared to existing systems.
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Ou-Yang, Chao, Shih-Chung Chou, Yeh-Chun Juan, and Han-Cheng Wang. "Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan." Applied Sciences 9, no. 12 (2019): 2434. http://dx.doi.org/10.3390/app9122434.
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Medication is designed to cure diseases, but serious risks can arise from severe adverse drug reactions (ADRs). ADRs can lead to emergency room visits and hospitalization, straining healthcare resources and, thus, they have strong implications for public health. Stevens–Johnson Syndrome (SJS) is one ADR and comprises the highest proportion of all drug relief cases in Taiwan. Pharmacovigilance involves the collection, detection, assessment, monitoring, and prevention of ADRs, including SJS. Most medical specialists are not fully aware of the risk of drug-induced SJS. Consequently, various drugs may be prescribed to susceptible patients for a great variety of diseases and, in turn, cause SJS. In this research, medical records of SJS patients were retrieved from the Taiwan National Health Insurance Research Database, and the Generalized Sequential Patterns (GSP) algorithm was used to find the sequential patterns of diseases before SJS onset. Then we mined the sequential patterns of medications prescribed in each disease pattern. Afterwards, we detected significant associations of each pattern of diseases and medications prescribed among age groups with statistical analysis. We found that, first, most patients developed SJS after being prescribed the causative medications fewer than four times. Second, Respiratory System Diseases (RSDs) appeared in disease sequential patterns of all lengths. Patterns involving RSDs were more frequent than others. Third, NSAIDs, H2-antagonists for peptic ulcer, penicillin antibiotics, theophylline bronchodilators, and cephalosporin antibiotics were the most frequent medications prescribed. Fourth, we found that patients in certain age groups had higher risks of developing SJS. This study aimed to mine the sequential patterns of diseases contracted and medications prescribed before patients developed SJS in Taiwan. This useful information can be provided to physicians so that they can stop the administration of suspected drugs to avoid evolution towards more severe cases.
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Massari, Juliana Maria, Daniella Jorge de Moura, Irenilza de Alencar Nääs, et al. "Sequential Behavior of Broiler Chickens in Enriched Environments under Varying Thermal Conditions Using the Generalized Sequential Pattern Algorithm: A Proof of Concept." Animals 14, no. 13 (2024): 2010. http://dx.doi.org/10.3390/ani14132010.
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Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, the potential of which has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective here was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through a proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at the comfort temperature showed high motivation to perform the behaviors of preening (P), foraging (F), lying down (Ld), eating (E), and walking (W); the sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}>; and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally “Ll” is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.
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Egorov, E. V., T. A. Naumova, A. I. Gayda, Kh B. Dadasheva, and O. V. Lovacheva. "Comprehensive Treatment of Generalized Tuberculosis with Endobronchial Valves." Tuberculosis and Lung Diseases 102, no. 2 (2024): 70–76. http://dx.doi.org/10.58838/2075-1230-2024-102-1-70-76.
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The article describes a clinical case of generalized tuberculosis manifested by tuberculous spondylodiscitis and disseminated pulmonary tuberculosis with extensive drug resistance and bilateral multiple destruction. The chemotherapy regimen compiled according to the sensitivity pattern of the pathogen, sequential implantation of two endobronchial valves and their long-term (18 months) synergic effect made it possible to cure pulmonary tuberculosis with healing of all multiple bilateral destruction. The same chemotherapy regimens allowed achieving cure of tuberculous spondylodiscitis. Co-infection of hepatitis C provided no negative impact on the treatment course.
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Somya, Ramos, Edi Winarko, and Sigit Priyanta. "A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3422–32. http://dx.doi.org/10.11591/eei.v11i6.4021.
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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.
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Branco, Tatiane, Daniella Jorge de Moura, Irenilza de Alencar Nääs, Nilsa Duarte da Silva Lima, Daniela Regina Klein, and Stanley Robson de Medeiros Oliveira. "The Sequential Behavior Pattern Analysis of Broiler Chickens Exposed to Heat Stress." AgriEngineering 3, no. 3 (2021): 447–57. http://dx.doi.org/10.3390/agriengineering3030030.
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Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird’s initial response to an adverse environment and is capable of providing an indicator of well-being in real-time. The present study aims to identify and characterize the sequential pattern of broilers’ behavior when exposed to thermoneutral conditions (TNZ) and thermal stress (HS) by constant heat. The research was carried out in a climatic chamber with 18 broilers under thermoneutral conditions and heat stress for three consecutive days (at three different ages). The behavior database was first analyzed using one-way ANOVA, Tukey test by age, and Boxplot graphs, and then the sequence of the behaviors was evaluated using the generalized sequential pattern (GSP) algorithm. We were able to predict behavioral patterns at the different temperatures assessed from the behavioral sequences. Birds in HS were prostrate, identified by the shorter behavioral sequence, such as the {Lying down, Eating} pattern, unlike TNZ ({Lying down, Walking, Drinking, Walking, Lying down}), which indicates a tendency to increase behaviors (feeding and locomotor activities) that guarantee the better welfare of the birds. The sequence of behaviors ‘Lying down’ followed by ‘Lying laterally’ occurred only in HS, which represents a stressful thermal environment for the bird. Using the pattern mining sequences approach, we were able to identify temporal relationships between thermal stress and broiler behavior, confirming the need for further studies on the use of temporal behavior sequences in environmental controllers.
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Ahmad, Sayed Sayeed, Rashmi Rani, Ihab Wattar, et al. "Hybrid Recommender System for Mental Illness Detection in Social Media Using Deep Learning Techniques." Computational Intelligence and Neuroscience 2023 (July 8, 2023): 1–14. http://dx.doi.org/10.1155/2023/8110588.
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Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person’s mental stability. With the utilization of the sequential pattern mining algorithm, efficient extraction of frequent patterns from the database is achieved. A candidate sub-sequence generation-and-test method is adopted in conventional sequential mining algorithms like the Generalized Sequential Pattern Algorithm (GSP). However, since this approach will yield a huge candidate set, it is not ideal when a large amount of data is involved from the social media analysis. Since the data is composed of numerous features, all of which may not have any relation with one another, the utilization of feature selection helps remove unrelated features from the data with minimal information loss. In this work, Frequent Pattern (FP) mining operations will employ the Systolic tree. The systolic tree-based reconfigurable architecture will offer various benefits such as high throughput as well as cost-effective performance. The database’s frequently occurring item sets can be found by using the FP mining algorithms. Numerous research areas related to machine learning and data mining are fascinated by feature selection since it will enable the classifiers to be swift, more accurate, and cost-effective. Over the last ten years or so, there have been significant technological advancements in heuristic techniques. These techniques are beneficial because they improve the search procedure’s efficiency, albeit at the potential sacrifice of completeness claims. A new recommender system for mental illness detection was based on features selected using River Formation Dynamics (RFD), Particle Swarm Optimization (PSO), and hybrid RFD-PSO algorithm is proposed in this paper. The experiments use the depressive patient datasets for evaluation, and the results demonstrate the improved performance of the proposed technique.
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Niculescu, Virginia. "On Generalizing Divide and Conquer Parallel Programming Pattern." Mathematics 10, no. 21 (2022): 3925. http://dx.doi.org/10.3390/math10213925.
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(1) Background: Structuring is important in parallel programming in order to master its complexity, and this structuring could be achieved through programming patterns and skeletons. Divide-and-conquer computation is essentially defined by a recurrence relation that links the solution of a problem to the solutions of subproblems of the same type, but of smaller sizes. This pattern allows the specification of different types of computations, and so it is important to provide a general specification that comprises all its cases. We intend to prove that the divide-and-conquer pattern could be generalized such that to comprise many of the other parallel programming patterns, and in order to prove this, we provide a general formulation of it. (2) Methods: Starting from the proposed generalized specification of the divide-and-conquer pattern, the computation of the pattern is analyzed based on its stages: decomposition, base-case and composition. Examples are provided, and different execution models are analyzed. (3) Results: a general functional specification is provided for a divide-and-conquer pattern and based on it, and we prove that this general formulation could be specialized through parameters’ instantiating into other classical parallel programming patterns. Based on the specific stages of the divide-and-conquer, three classes of computations are emphasized. In this context, an equivalent efficient bottom-up computation is formally proved. Associated models of executions are emphasized and analyzed based on the three classes of divide-and-conquer computations. (4) Conclusion: A more general definition of the divide-and-conquer pattern is provided, and this includes an arity list for different decomposition degrees, a level of recursion, and also an alternative solution for the cases that are not trivial but allow other approaches (sequential or parallel) that could lead to better performance. Together with the associated analysis of patterns equivalence and optimized execution models, this provides a general formulation that is useful both at the semantic level and implementation level.
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NAKAMURA, AKIRA. "SOME NOTES ON PARALLEL COORDINATE GRAMMARS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 05 (1995): 753–61. http://dx.doi.org/10.1142/s0218001495000304.
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In a coordinate grammar, the rewriting rules replace sets of symbols having given coordinates by sets of symbols whose coordinates are given functions of the coordinates of the original symbols. Usually, at each step of a derivation, only one rule is applied and only one instance of its left hand side is rewritten. This type is referred to sequential grammars. As a counterpart of this grammar, parallel coordinate grammars are defined as generalized parallel isometric grammars. In the parallel grammars, the rewriting rule are used in parallel in a derivation application. The paper discusses some properties of parallel coordinate grammars and examines a relationship between the sequential coordinate grammars and parallel ones.
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Wang, X., and G. Bertrand. "Some sequential algorithms for a generalized distance transformation based on Minkowski operations." IEEE Transactions on Pattern Analysis and Machine Intelligence 14, no. 11 (1992): 1114–21. http://dx.doi.org/10.1109/34.166628.
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He, Lei, Xiao-Hong Shen, Mu-Hang Zhang, and Hai-Yan Wang. "Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution." Entropy 22, no. 4 (2020): 374. http://dx.doi.org/10.3390/e22040374.
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Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.
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Coole, James, and Greg Stitt. "Traversal Caches: A Framework for FPGA Acceleration of Pointer Data Structures." International Journal of Reconfigurable Computing 2010 (2010): 1–16. http://dx.doi.org/10.1155/2010/652620.
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Field-programmable gate arrays (FPGAs) and other reconfigurable computing (RC) devices have been widely shown to have numerous advantages including order of magnitude performance and power improvements compared to microprocessors for some applications. Unfortunately, FPGA usage has largely been limited to applications exhibiting sequential memory access patterns, thereby prohibiting acceleration of important applications with irregular patterns (e.g., pointer-based data structures). In this paper, we present a design pattern for RC application development that serializes irregular data structure traversals online into a traversal cache, which allows the corresponding data to be efficiently streamed to the FPGA. The paper presents a generalized framework that benefits applications with repeated traversals, which we show can achieve between 7x and 29x speedup over pointer-based software. For applications without strictly repeated traversals, we present application-specialized extensions that benefit applications with highly similar traversals by exploiting similarity to improve memory bandwidth and execute multiple traversals in parallel. We show that these extensions can achieve a speedup between 11x and 70x on a Virtex4 LX100 for Barnes-Hut n-body simulation.
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Suraharta, I. Made. "PENGEMBANGAN MODEL TRANSPORTASI PENUMPANG ANTAR KOTA/KABUPATEN DI PROPINSI JAWA BARAT." Jurnal Teknik Sipil Unaya 1, no. 1 (2015): 77–94. http://dx.doi.org/10.30601/jtsu.v1i1.9.
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Transport models are crucial in the transportation planning process. Transport model is made by adjusting the needs and availability of data and capability models in representing the real conditions and the future. Transportation models commonly used in transportation planning mechanism is the sequential demand models, which include the trip generation, trip distribution, mode choice, and traffic assignment. This model is suitable to be applied to various situations study areas, especially areas of the city. For intercity regional planning needs, modeling the sequential demand can be simplified into a direct demand model, the record is not much involved in modeling mode. In this study, the authors tried to develop a model of a direct demand models to represent the pattern of movement of people with other modes of road in West Java. The proposed transport model is a function of population, GDP, total number of trip generation traffic zone, the total transportation costs (generalized cost). Model results show the validity of the development of significant and can be used as a travel demand model for transportation planning.
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Ang, Zhendong, and Umang Mathur. "Predictive Monitoring against Pattern Regular Languages." Proceedings of the ACM on Programming Languages 8, POPL (2024): 2191–225. http://dx.doi.org/10.1145/3632915.
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While current bug detection techniques for concurrent software focus on unearthing low-level issues such as data races or deadlocks, they often fall short of discovering more intricate temporal behaviours that can arise even in the absence of such low-level issues. In this paper, we focus on the problem of dynamically analysing concurrent software against high-level temporal specifications such as LTL. Existing techniques for runtime monitoring against such specifications are primarily designed for sequential software and remain inadequate in the presence of concurrency — violations may be observed only in intricate thread interleavings, requiring many re-runs of the underlying software in conjunction with the analysis. Towards this, we study the problem of predictive runtime monitoring , inspired by the analogous problem of predictive data race detection studied extensively recently. The predictive runtime monitoring question asks, given an execution σ, if it can be soundly reordered to expose violations of a specification. In general, this problem may become easily intractable when either the specifications or the notion of reorderings used is complex. In this paper, we focus on specifications that are given in regular languages. Our notion of reorderings is trace equivalence , where an execution is considered a reordering of another if it can be obtained from the latter by successively commuting adjacent independent actions. We first show that, even in this simplistic setting, the problem of predictive monitoring admits a super-linear lower bound of O ( n α ), where n is the number of events in the execution, and α is a parameter describing the degree of commutativity, and typically corresponds to the number of threads in the execution. As a result, predictive runtime monitoring even in this setting is unlikely to be efficiently solvable, unlike in the non-predictive setting where the problem can be checked using a deterministic finite automaton (and thus, a constant-space streaming linear-time algorithm). Towards this, we identify a sub-class of regular languages, called pattern languages (and their extension generalized pattern languages ). Pattern languages can naturally express specific ordering of some number of (labelled) events, and have been inspired by popular empirical hypotheses underlying many concurrency bug detection approaches such as the “small bug depth” hypothesis. More importantly, we show that for pattern (and generalized pattern) languages, the predictive monitoring problem can be solved using a constant-space streaming linear-time algorithm. We implement and evaluate our algorithm PatternTrack on benchmarks from the literature and show that it is effective in monitoring large-scale applications.
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Garvita, Ahuja, Priyal, Kaur Ishleen, and Virmani Deepali. "Prostate Cancer Survival Prediction and Treatment Recommendation: A Machine Learning Perspective." Indian Journal of Science and Technology 17, no. 11 (2024): 1097–106. https://doi.org/10.17485/IJST/v17i11.3157.
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Abstract <strong>Objective:</strong> Prostate cancer, a formidable life-threatening ailment predominantly affecting males, ranks as the third most prevalent global tumor. Its formidable nature arises from the persistent challenges encountered in early detection, often leading to delayed diagnoses and more advanced disease stages. The primary objective is to harness the power of advanced machine learning techniques for the prediction of patient survivability in prostate cancer cases. Furthermore, the study aims to identify a set of treatments pivotal for ensuring positive survival rates. <strong>Methods:</strong> This investigation leverages a comprehensive retrospective dataset comprising 410 cases of prostate cancer, collected from a Cancer Centre in New Delhi. This dataset encompasses vital clinical and treatment attributes. Models, including Artificial Neural Networks (ANN), Adaboost, Random Forest, etc., are thoroughly evaluated. In addition, the Generalized Sequential Pattern (GSP) algorithm is utilized to scrutinize the treatment attributes, thereby uncovering frequent patterns and their correlation with survival rates. <strong>Findings:</strong> The ANN model emerges as the most promising, exhibiting an impressive 84.14% accuracy. The findings stemming from these classification techniques, as well as the insights garnered through sequential mining, underscore the pivotal role of machine learning in the prognostication of prostate cancer. This advancement holds the potential to transform precision medicine and enhance patient care strategies on a global scale. <strong>Novelty:</strong> The study used clinical dataset to predict the survival of cancer patients using neural networks. GSP algorithm is also modified to uncover frequent treatment patterns in patients. <strong>Keywords:</strong> Artificial Neural Network, Cancer, Machine learning, Sequence mining, Survival analysis
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Veigel, B., and M. B. Sterman. "Topographic EEG Correlates of Good and Poor Performance in a Signal Recognition Task." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 1 (1993): 147–51. http://dx.doi.org/10.1177/154193129303700134.
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Topographic EEG measures were compared in 12 adult male subjects during performance of a signal recognition task, presented at three difficulty levels. EEG data were recorded from 17 standard cortical sites, referenced to linked earlobes. Digitized mean spectral magnitude values were calculated for sequential 2 second epochs for each condition, log transformed and subjected to statistical analysis. A good and a poor performance group was established on the basis of scores registered at the highest difficulty level and confirmed statistically. Within-group comparisons showed different EEG patterns for the two performance groups, both within and across difficulty level. The poor performance group showed a progressive pattern of disengagement (increase in 8-12 Hz activity) which diminished gradually as difficulty escalated and was replaced by a pattern of increasing engagement (decrease in 8-12 Hz activity). Good performers showed the same level of engagement independent of difficulty. Performance data alone failed to differentiate between groups under low and moderate task demands. Detailed evaluation of the underlying mechanisms revealed a tendency for all subjects to develop brief periods of disengagement after each stimulus presentation. This pattern became increasingly generalized in poor performers during the low gain task but was also present at the most difficult test level. These findings provide some insight into the dynamics of Central Nervous Systems regulatory mechanisms which modulate sustained cognitive performance under varying demand conditions. They document a propensity for some individuals to become disengaged over time, thereby requiring greater cognitive resource mobilization as task demand increases. Assessment of this trait may be useful in the prediction of performance capability under high demand conditions.
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Schneitz, K., S. C. Baker, C. S. Gasser, and A. Redweik. "Pattern formation and growth during floral organogenesis: HUELLENLOS and AINTEGUMENTA are required for the formation of the proximal region of the ovule primordium in Arabidopsis thaliana." Development 125, no. 14 (1998): 2555–63. http://dx.doi.org/10.1242/dev.125.14.2555.
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Our understanding of the molecular mechanisms that regulate and integrate the temporal and spatial control of cell proliferation during organ ontogenesis, particularly of floral organs, continues to be primitive. The ovule, the progenitor of the seed, of Arabidopsis thaliana has been used to develop an effective model system for the analysis of plant organogenesis. A typical feature of a generalized ovule is the linear arrangement of at least three distinct elements, the funiculus, chalaza and nucellus, along a proximal-distal axis. This pattern is supposed to be established during the early proliferative phase of ovule development. We provide genetic evidence that the young ovule primordium indeed is a composite structure. Two genes, HUELLENLOS and AINTEGUMENTA have overlapping functions in the ovule and differentially control the formation of the central and proximal elements of the primordium. The results indicate that proximal-distal pattern formation in the Arabidopsis ovule takes place in a sequential fashion, starting from the distal end. Furthermore, we show that HUELLENLOS also regulates the initiation and/or maintenance of integument and embryo sac ontogenesis and interestingly prevents inappropriate cell death in the young ovule.
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Carlucci, Margherita, Francesco Chelli, and Luca Salvati. "Toward a New Cycle: Short-Term Population Dynamics, Gentrification, and Re-Urbanization of Milan (Italy)." Sustainability 10, no. 9 (2018): 3014. http://dx.doi.org/10.3390/su10093014.
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After sequential cycles of urbanization and suburbanization, European cities underwent a (more or less intense) re-urbanization wave. The present study analyzes short-term population dynamics in the core of a large metropolitan region (Milan, northern Italy), providing evidence of spatially-heterogeneous re-urbanization characterized by spatially-complex population growth (or shrinkage) at a local scale. Population dynamics over 1999–2017 were assessed in 88 urban districts partitioning Milan′s municipal area and projected up to 2036 for the same spatial units. Empirical results identify spatially-complex and temporally non-linear dynamics with expanding or declining districts distributed heterogeneously across the study area. Multivariate analysis outlines a generalized population decline during 1999–2008 and an opposite pattern afterward (2008–2017), with spatially-homogeneous population expansion expected in the near future. Spatial analysis finally highlights that local-scale population growth rates were more clustered in 2008–2017 than in 1999–2008. While the population decreased continuously in the inner districts (<1 km from the city centre), sub-central districts (1–5 km far from the city centre) experienced mixed patterns of population growth and stability. These results confirm the relevance of local-scale policies managing urban renewal and rehabilitation and promoting metropolitan expansion in a spatially-coordinated manner.
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Hashempour, Majid, Mahdi Doostparast, and Zohreh Pakdaman. "Statistical Inference on the Basis of Sequential Order Statistics under a Linear Trend for Conditional Proportional Hazard Rates." Statistics, Optimization & Information Computing 8, no. 2 (2020): 462–70. http://dx.doi.org/10.19139/soic-2310-5070-802.
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This paper deals with systems consisting of independent and heterogeneous exponential components. Since failures of components may change lifetimes of surviving components because of load sharing, a linear trend for conditionally proportional hazard rates is considered. Estimates of parameters, both point and interval estimates, are derived on the basis of observed component failures for s(≥ 2) systems. Fisher information matrix of the available data is also obtained which can be used for studying asymptotic behaviour of estimates. The generalized likelihood ratio test is implemented for testing homogeneity of s systems. Illustrative examples are also given.
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Ekwueme, Chimeme, Ify Nwaogazie, Chiedozie Ikebude, Godwin Amuchi, Jonathan Irokwe, and Diaa Hourani. "Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Umuahia - Nigeria Using Non-Stationary Approach." Hydrology 13, no. 1 (2025): 83–89. https://doi.org/10.11648/j.hyd.20251301.19.
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The aim of this study is to develop non-stationary rainfall Intensity-Duration-Frequency (IDF) models or curves for Umuahia, in South East Nigeria. The IDF model development was actualized using a 31-year rainfall record (1992-2022), obtained from the Nigerian Meteorological Agency, NIMET. The research employed trend analysis using Mann-Kendall test and change point detection through CUSUM and Sequential Mann Kendall tests to establish the presence of non-stationarity in rainfall patterns. Three different General Extreme Value (GEV) distribution models were evaluated to determine the best-fit non-stationary model using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Results revealed a significant increasing trend in rainfall intensity (p-value = 0.006) with change points identified in 2002-2003. The GEVt-I model consistently demonstrated superior performance across all duration intervals (5-1440 minutes) with the lowest AIC values. A generalized non-stationary IDF model was developed, showing excellent predictive capability (R² = 0.992, MSE = 38.09). The findings highlight the importance of adopting non-stationary approaches for infrastructure design in Umuahia, as traditional stationary methods may significantly underestimate rainfall intensities in the context of climate change. The result from the trend and change point revealed that climate change influences rainfall pattern in Umuahia. Interestingly, the findings of this study align with global trends in climate change impacts on precipitation patterns and underscore the urgent need to update design standards and infrastructure planning approaches in Umuahia, South East of Nigeria.
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KIM, SUN, JEONG-HYEON CHOI, AMIT SAPLE, and JIONG YANG. "A HYBRID GENE TEAM MODEL AND ITS APPLICATION TO GENOME ANALYSIS." Journal of Bioinformatics and Computational Biology 04, no. 02 (2006): 171–96. http://dx.doi.org/10.1142/s0219720006001850.
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It is well-known that functionally related genes occur in a physically clustered form, especially operons in bacteria. By leveraging on this fact, there has recently been an interesting problem formulation known as gene team model, which searches for a set of genes that co-occur in a pair of closely related genomes. However, many gene teams, even experimentally verified operons, frequently scatter within other genomes. Thus, the gene team model should be refined to reflect this observation. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraints. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. We also compared the result from our hybrid model to those from the traditional gene team model. We also show that predicted gene teams can be used for various genome analysis: operon prediction, phylogenetic analysis of organisms, contextual sequence analysis and genome annotation. Our program is fast enough to provide a service on the web at . Users can select any combination of 97 genomes to predict gene teams.
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Khazaei, Hadi, Alireza Mobaseri, Danesh Khazaei, John D Ng, and Dr G. Seethapathy. "ORBITAL Ultrasonography a diagnosis tool in early cellulitis." International Journal of Research and Scientific Innovation 09, no. 07 (2022): 127–30. http://dx.doi.org/10.51244/ijrsi.2022.9711.
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The term cellulitis in general parlance refers to non-suppurative invasive infection (most commonly bacterial) of subcutaneous tissue. Spreading infection, poor localization in addition to cardinal signs of inflammation are the hallmark of cellulitis. Cellulitis can be complicated by spread of infection to the underlying deeper structures with progressive tissue destruction & ulceration with release of bacterial toxins. (1) Orbital cellulitis is an infection of the fat and ocular muscles of the orbit posterior to the orbital septum. It is classically distinguished clinically from pre-septal cellulitis by the presence of pain with eye movement and proptosis on physical examination (1, 2). What makes cellulitis in the preseptal, orbital & retro-orbital soft tissue regions different from generalized cellulitis are the transitional anatomical differences from preseptal (Eyelid skin) to adnexal/orbital to intracranial structures and the presence of well recognized anatomical/surgical sub-compartments. Preseptal cellulitis follows pattern similarities to generalized cellulitis characterized by eyelid edema, eyelid erythema, local rise of temperature and tenderness. Unlike pre-septal cellulitis, orbital cellulitis is considered a medical emergency. If left untreated, it can lead to permanent vision loss, brain abscesses, meningitis, and cavernous sinus thrombosis (3). Though the diagnosis of orbital cellulitis can be made clinically, imaging modalities such as computed tomography (CT) and Orbital Ultrasonography are commonly used to confirm the diagnosis. (4) The present study was designed to provide sequential imaging to visualize the disease progression.
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Wanner, Franz, Wolfgang Jentner, Tobias Schreck, Andreas Stoffel, Lyubka Sharalieva, and Daniel A. Keim. "Integrated visual analysis of patterns in time series and text data - Workflow and application to financial data analysis." Information Visualization 15, no. 1 (2015): 75–90. http://dx.doi.org/10.1177/1473871615576925.
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In this article, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which are significantly connected in time to quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a priori method. First, based on heuristics, we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a priori method supports the discovery of such sequential temporal patterns. Then, various text features such as the degree of sentence nesting, noun phrase complexity, and the vocabulary richness, are extracted from the news items to obtain meta-patterns. Meta-patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time, cluster, and sequence visualization and analysis functionality. We provide a case study and an evaluation on financial data where we identify important future work. The workflow could be generalized to other application domains such as data analysis of smart grids, cyber physical systems, or the security of critical infrastructure, where the data consist of a combination of quantitative and textual time series data.
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Wagata, Kensuke, and Andrew Beng Jin Teoh. "Few-Shot Continuous Authentication for Mobile-Based Biometrics." Applied Sciences 12, no. 20 (2022): 10365. http://dx.doi.org/10.3390/app122010365.
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The rapid growth of smartphone financial services raises the need for secure mobile authentication. Continuous authentication is a user-friendly way to strengthen the security of smartphones by implicitly monitoring a user’s identity through sessions. Mobile continuous authentication can be viewed as an anomaly detection problem in which models discriminate between one genuine user and the rest of the impostors (anomalies). In practice, complete impostor profiles are hardly available due to the time and monetary cost, while leveraging genuine data alone yields poorly generalized models due to the lack of knowledge about impostors. To address this challenge, we recast continuous mobile authentication as a few-shot anomaly detection problem, aiming to enhance the inference robustness of unseen impostors by using partial knowledge of available impostor profiles. Specifically, we propose a novel deep learning-based model, namely a local feature pooling-based temporal convolution network (LFP-TCN), which directly models raw sequential mobile data, aggregating global and local feature information. In addition, we introduce a random pattern mixing augmentation to generate class-unconstrained impostor data for the training. The augmented pool enables characterizing various impostor patterns from limited impostor data. Finally, we demonstrate practical continuous authentication using score-level fusion, which prevents long-term dependency or increased model complexity due to extended re-authentication time. Experiments on two public benchmark datasets show the effectiveness of our method and its state-of-the-art performance.
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Kowshik, Kesavarapu, M. V S S Sandeep, Sai Gottipati Mounika, and Animesh Adhikari. "Analytic Method for Estimating the User Behavior Patterns in Multimedia Social Networks." International Journal of Engineering & Technology 7, no. 2.32 (2018): 427. http://dx.doi.org/10.14419/ijet.v7i2.32.15732.
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Now a days the multimedia social networks plays major role in our daily life. All the earlier MSNs are validated and developed very well. The past decade has witnessed the emergence and progress of multimedia social networks (MSNs), which have explosively and tremendously increased to penetrate every corner of our lives, leisure and work. As well as, the users are enabled by Mobile internet & terminals for accessing the MSNs where ever they are and when they want with the help of any identity. It may be a group or a role. So, it become very complicated & comprehensive to provide the behavior’s interaction between MSNs as well as in users. The implemented system having the advancements and developed framework of the analytics in a particular domain; which is called as SocialSitu, And We implemented an algorithm which is named as novel for the analysis of the serialized users intention according to the typical GSP which is the short form of Generalized Sequential Pattern. An enormous number of user’s behavior records were broken for exploring the usual sequence mode. It is mandatory for guessing the intention of the user. We considered the two types of intentions. Those are playing multimedia & sharing multimedia. These 2 are widely used in regular MSNs with the help of intention serialization algorithm in control of various min support threshold (Min_Support). With the help of microscopic behavior analysis of the users, we find out the each user behavior patterns which are in optimized manner in control of the Min_Support. Based on the different identities of the user, the behavior patterns of the users may be varied in session data which is very large.
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SEGUIN, P., and G. BESSONNET. "GENERATING OPTIMAL WALKING CYCLES USING SPLINE-BASED STATE-PARAMETERIZATION." International Journal of Humanoid Robotics 02, no. 01 (2005): 47–80. http://dx.doi.org/10.1142/s0219843605000399.
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Optimal gait cycles are generated for a seven-link biped using a parametric optimization method. A sagittal walking pattern, including a double-support phase divided into two sub-phases, is considered. Generalized joint coordinates are approximated by three-time differentiable spline-functions. These are the concatenation of 4-order polynomials linked together up to their third derivatives at connecting points — or knots — distributed along the motion time of each phase. Optimization parameters are the values of joint coordinates at the knots, plus the joint velocities, and possibly the joint accelerations, at transitions between successive phases. An integral amount of driving torques is minimized throughout the walking cycle. During the double support, constraint forces in the kinematically closed locomotion system are dealt with as additional actuating forces. For this reason, these are also minimized. Using the above optimization parameters, this basic optimal control problem is transformed into an optimization problem of mathematical programming. The latter is efficiently solved using a Sequential Quadratic Programming algorithm. The only kinematic data required for generating a gait cycle is the walking speed. Postural configurations between successive phases, step length, and relative length of single and double supports are optimized with respect to a given walking speed.
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Roy, Kaushik, Christian Simon, Peyman Moghadam, and Mehrtash Harandi. "CL3: Generalization of Contrastive Loss for Lifelong Learning." Journal of Imaging 9, no. 12 (2023): 259. http://dx.doi.org/10.3390/jimaging9120259.
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Lifelong learning portrays learning gradually in nonstationary environments and emulates the process of human learning, which is efficient, robust, and able to learn new concepts incrementally from sequential experience. To equip neural networks with such a capability, one needs to overcome the problem of catastrophic forgetting, the phenomenon of forgetting past knowledge while learning new concepts. In this work, we propose a novel knowledge distillation algorithm that makes use of contrastive learning to help a neural network to preserve its past knowledge while learning from a series of tasks. Our proposed generalized form of contrastive distillation strategy tackles catastrophic forgetting of old knowledge, and minimizes semantic drift by maintaining a similar embedding space, as well as ensures compactness in feature distribution to accommodate novel tasks in a current model. Our comprehensive study shows that our method achieves improved performances in the challenging class-incremental, task-incremental, and domain-incremental learning for supervised scenarios.
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Buhaiov, M. V. "Amplitude Direction Finder of Radio Frequency Emitters for Unmanned Aerial Vehicle." Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, no. 99 (March 30, 2025): 41–48. https://doi.org/10.20535/radap.2025.99.41-48.
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The detection and localization of low-power radio frequency emitters (RFE) in populated areas using stationary spectrum sensing (SS) equipment is ineffective due to the lack of line of sight in shadowing effect. To solve this problem, it is advisable to place the SS equipment on a small unmanned aerial vehicle (UAV). The limitations of this solution are low time for data collection and processing and the low computing power of SS device. This leads to a decrease in search capabilities of SS both in terms of spatial coordinates and frequency, which is critical in conditions of high density of RFE. The aim of this article is to increase the speed of RFE searching using a UAV-mounted SS device under time, weight, and energy constraints by using a system of broadband directional antennas and multichannel sequential signal processing. The amplitude direction finding method was chosen to estimate the direction of radio wave arrival, taking into account the mass and computational limitations of the SS device placed on the UAV. The proposed structure of the antenna system contains six log-periodic antennas spaced in a circle by 60°. To process the received signal, a multichannel sequential analysis scheme is proposed, in which one scanning receiver is alternately connected to each of the antennas. It is shown that for this scheme, the parameter to be optimized is the flight speed of the UAV. A generalized expression for calculating this value is obtained. An expression for approximating the main lobe of the antenna radiation pattern in form of a Gaussian function is obtained. A procedure for calculating the direction on RFE by comparing the amplitudes of the received signals by two neighboring antennas is developed. Recommendations to avoid ambiguities in calculation angle of arrival for the case of several RFE are given. Compared to existing solutions, when a single receiving channel with a single directional antenna is used and space scanning is performed by rotating the UAV, the proposed approach with a multi-antenna system will reduce the time for azimuthal directions scanning.
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Patel, Dhruv, Raymond Lin, Barun Majumder, and Vitaly V. Ganusov. "Brain-localized CD4 and CD8 T cells perform correlated random walks and not Levy walks." F1000Research 12 (October 3, 2023): 87. http://dx.doi.org/10.12688/f1000research.129923.2.
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Background. For survival of the organism, T cells must efficiently control pathogens invading different peripheral tissues. Whether or not such control is achieved by utilizing different movement strategies in different tissues remains poorly understood. Liver-localized CD8 T cells perform correlated random walks --- a type of a Brownian walk -- in liver sinusoids but in some condition these T cells may also perform Levy flights -- rapid and large displacements by floating with the blood flow. CD8 T cells in lymph nodes or skin also undergo Brownian walks. A recent study suggested that brain-localized CD8 T cells, specific to Toxoplasma gondii, perform generalized Levy walks -- a walk type in which T cells alternate pausing and displacing long distances --- which may indicate that brain is a unique organ where T cells exhibit movement strategies different from other tissues. Methods. We quantified movement patterns of brain-localized Plasmodium berghei-specific CD4 and CD8 T cells by using well-established statistical and computational methods. Results. We found that T cells change their movement pattern with time since infection and that CD4 T cells move faster and turn less than CD8 T cells. Importantly, both CD4 and CD8 T cells move in the brain by correlated random walks without long displacements challenging previous observations. We have also re-analyzed the movement data of brain-localized CD8 T cells in T. gondii-infected mice and found no evidence of Levy walks. We hypothesize that the previous conclusion of Levy walks of T. gondii-specific CD8 T cells in the brain was reached due to missing time-frames in the data that create an impression of large movement lengths between assumed-to-be-sequential movements. Conclusion. Our results suggests that movement strategies of CD8 T cells are largely similar between LNs, liver, and the brain and consistent with correlated random walks and not Levy walks.
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Patel, Dhruv, Raymond Lin, Barun Majumder, and Vitaly V. Ganusov. "Brain-localized CD4 and CD8 T cells perform correlated random walks and not Levy walks." F1000Research 12 (January 23, 2023): 87. http://dx.doi.org/10.12688/f1000research.129923.1.
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Background. For survival of the organism, T cells must efficiently control pathogens invading different peripheral tissues but whether such control (and lack of thereof) is achieved by utilizing different movement strategies remains poorly understood. Liver-localized CD8 T cells perform correlated random walks (CRWs)— a type of a Brownian walk – in liver sinusoids but in some conditions, these T cells may also perform Levy flights – rapid and large displacements by floating with the blood flow. CD8 T cells in lymph nodes or skin also undergo Brownian walks. A recent study suggested that brain-localized CD8 T cells, specific to Toxoplasma gondii, perform generalized Levy walks (LWs) – a walk type in which T cells alternate pausing and displacing long distances — which may indicate that brain is a unique organ where T cells exhibit movement strategies different from other tissues. Methods. We quantified movement patterns of brain-localized Plasmodium berghei-specific CD4 and CD8 T cells by using well-established statistical and computational methods. Results. We found that T cells change their movement pattern with time since infection and that CD4 T cells move faster and turn less than CD8 T cells. Importantly, both CD4 and CD8 T cells move in the brain by CRWs without long displacements challenging previous observations. We have also re-analyzed movement data of brain-localized CD8 T cells in T. gondii-infected mice from a previous study and found no evidence of LWs. We hypothesize that the previous conclusion of LWs of T. gondii-specific CD8 T cells in the brain was reached due to missing timeframes in the data that create an impression of large displacements between assumed-to-be sequential movements. Conclusion. Our results suggest that movement strategies of CD8 T cells are largely similar between LNs, liver, and the brain and consistent with CRWs and not LWs.
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White, A., L. Kaiser, S. Nauwelaerts, M. Lavagnino, N. C. Stubbs, and H. Clayton. "Short-term habituation of equine limb kinematics to tactile stimulation of the coronet." Veterinary and Comparative Orthopaedics and Traumatology 21, no. 03 (2008): 211–14. http://dx.doi.org/10.1055/s-0037-1617363.
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SummaryA lightweight bracelet that provides tactile stimulation to the horse’s pastern and coronet induces a higher flight arc of the hoof. This study addresses the pattern of habituation to these devices. Objective: To evaluate short-term habituation to tactile stimulation of the pastern and coronet in trotting horses. Methods: Tactile stimulation was provided by a lightweight (55 g) device consisting of a strap with seven chains that was attached loosely around the pastern. Reflective markers were fixed to the dorsal hoof wall, the forehead and over the tenth thoracic vertebra of eight sound horses. The horses trotted in hand 10 times at a consistent velocity along a 30 m runway under three conditions applied in random order at two-hour intervals: no stimulators, stimulators on both front hooves or stimulators on both hind hooves. One stride per trial was analyzed to determine peak hoof heights in the swing phase. Sequential trials with stimulators were compared with unstimulated trials using a nested ANCOVA and Bonferronni’s post hoc test (P<0.005). Results: Peak hind hoof height increased significantly for all 10 trials when wearing hind stimulators, whereas peak fore hoof height increased during the first six trials only when wearing fore stimulators. The first trial with stimulators showed the greatest elevation, followed by a rapid decrease over the next three trials and then a more gradual decrease. Conclusions: If the goal is to facilitate a generalized muscular response, a short burst of tactile stimulation is likely to be most effective, whereas longer periods of stimulation will be more effective for strength training.
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Smirnov, A. V. "Comparison of algorithms for multi-objective optimization of radio technical device characteristics." Russian Technological Journal 10, no. 6 (2022): 42–51. http://dx.doi.org/10.32362/2500-316x-2022-10-6-42-51.
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Objectives. The selection of a method for solving multi-objective optimization problems has many practical applications in diverse fields. The present work compares the results of applying different methods to the selected classes of problems by solution quality, time consumption, and various other criteria.Methods. Five problems related to the multi-objective optimization of analog and digital filters, as well as multistep impedance-matching microwave transformers, are considered. One of the compared algorithms comprises the Third Evolution Step of Generalized Differential Evolution (GDE3) population-based algorithm for searching the full approximation of the Pareto set simultaneously, while the other three algorithms minimize the scalar objective function to find only one element of the Pareto set in a single search cycle: these comprise Multistart Pattern Search (MSPS), Multistart Sequential Quadratic Programming (MSSQP) method and Particle Swarm Optimization (PSO) algorithms.Results. The computer experiments demonstrated the capability of GDE3 to solve all considered problems. MSPS and PSO showed significantly inferior results than to GDE3 for two problems. In one problem, MSSQP could not be used to reach acceptable decisions. In the other problems, MSPS, MSSQP, and PSO reached decisions comparable with GDE3. The time consumption of the MSPS and PSO algorithms was much greater than that of GDE3 and MSSQP.Conclusions. The GDE3 algorithm may be recommended as a basic method for solving the considered problems. Algorithms minimizing scalar objective function may be used to obtain several elements of the Pareto set. It is necessary to investigate the impact of landscape features of individual quality indices and scalar objective functions on the extreme search process.
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Henriques, Nathália Ribeiro, and Cássio Cardoso Pereira. "Lessons from a tropical deciduous shrub species: leaf fall can play a more important role than rain in leaf budding." Neotropical Biology and Conservation 17, no. 4 (2022): 239–51. http://dx.doi.org/10.3897/neotropical.17.e93846.
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In the Cerrado, the sequential chaining of phenological events during the dry season is a pattern observed in many plant species. In this season, many plants completely lose their leaves, and soon after deciduous, there is an expressive production of leaf buds. In this study, we investigated the effect of irrigation and early defoliation on the triggering of leaf budding of the deciduous species Peixotoa tomentosa A.Juss. in the dry season of a seasonal environment with water restrictions. Therefore, we set up an experiment with three groups of plants: control (n = 15), irrigation treatment (n = 15), and removal treatment (n = 15), and after the complete deciduousness of the plants, we carried out phenological monitoring of the development of leaf buds in these plants. From July to August 2022, the leaf budding phenology of the 45 individuals was evaluated twice a week. To test whether there is a difference in the number of leaf buds between treatments, we built generalized linear mixed models (GLMMs). Plants in the removal treatment had a statistically higher number of leaf buds produced than the plants in the irrigation and control groups (P &lt; 0.05). However, the control group and the irrigation treatment did not differ from each other (P &gt; 0.05). We showed that early defoliation influenced the triggering of leaf buds in P. tomentosa, increasing the production of young leaves in their individuals in a seasonal environment with water restrictions. Irrigation was not able to break the dormancy of leaf buds. Our findings contribute to a better understanding of the triggering of vegetative phenophases in deciduous Cerrado plants, showing that leaf fall may play a more important role than rain in the production of leaf buds in the dry season.
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Höpken, Wolfram, Marcel Müller, Matthias Fuchs, and Maria Lexhagen. "Flickr data for analysing tourists’ spatial behaviour and movement patterns." Journal of Hospitality and Tourism Technology 11, no. 1 (2020): 69–82. http://dx.doi.org/10.1108/jhtt-08-2017-0059.
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Purpose The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios. Design/methodology/approach The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns. Findings The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent). Research limitations/implications As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour. Practical implications From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment. Originality/value The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.
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Branco, Tatiane, Daniella J. Moura, Irenilza A. Nääs, and Stanley R. M. Oliveira. "Detection of broiler heat stress by using the generalised sequential pattern algorithm." Biosystems Engineering 199 (November 2020): 121–26. http://dx.doi.org/10.1016/j.biosystemseng.2019.10.012.
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Florencio, Rafael, and René Escalante. "Alternating Generalized Projection Method for Satellite Reflectarray Synthesis with Sequential Stages of Different Illuminations." Applied Sciences 15, no. 1 (2024): 181. https://doi.org/10.3390/app15010181.
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Satellite reflectarray synthesis using the intersection approach by means of algorithms of the alternating generalized projection (AGP) method to avoid trap points with sequential stages of different illuminations is presented. In this scenario, we examine two distinct sets: the set of radiation patterns producible by the reflectarray and the set of radiation patterns which meet the mission’s criteria. These sets are generally non-convex. Therefore, it is expected that conventional algorithms of the method of alternating projections (MAP) converge to trap points (i.e., local minima of the distance between the involved sets). Thus, the AGP method, which takes into account the trap points, has been considered. When large reflectarrays are considered for satellite applications, several trap points can appear, producing oscillatory behavior between several trap points. A mathematical formalism which supports the idea that a reduction in the edge illumination of an antenna involves fewer trap points is described. Thus, the oscillatory behavior can be avoided by using sequential stages in the synthesis process with different edge illumination levels in each stage. In this work, we demonstrate this synthesis technique with sequential stages, using the proposed algorithm to avoid trap points.
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Yu, Tianhao, Xianghong Zhou, and Xinrong Deng. "Autoregressive models for session-based recommendations using set expansion." PeerJ Computer Science 11 (February 21, 2025): e2734. https://doi.org/10.7717/peerj-cs.2734.
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With the rapid growth of internet technologies, session-based recommendation systems have emerged as a key paradigm in delivering personalized recommendations by capturing users’ dynamic and short-term preferences. Traditional methods predominantly rely on modeling the sequential order of user interactions, deep learning approaches like recurrent neural networks and Transformer architectures. However, these sequence-based models often struggle in scenarios where the order of interactions is ambiguous or unreliable, limiting their real-world applicability. To address this challenge, we propose a novel session-based recommendation model, Deep Set Session-based Recommendation (DSETRec), which approaches the problem from a set-based perspective, eliminating dependence on the interaction sequence. By conceptualizing session data as unordered sets, our model captures the coupling relationships and co-occurrence patterns between items, enhancing prediction accuracy in settings where sequential information is either unavailable or noisy. The model is implemented using a deep autoregressive framework that iteratively masks known elements within a session, predicting and reconstructing additional items based on set data characteristics. Extensive experiments on benchmark datasets show that DSETRec achieves outperforms state-of-the-art baselines. DSETRec achieves a 13.2% and 11.85% improvement in P@20 and MRR@20, respectively, over its sequence-based variant on Yoochoose. Additionally, DSETRec generalizes effectively across both further short and long sessions. These results highlight the robustness of the set-based approach in capturing unordered interaction patterns and adapting to diverse session lengths. This finding provides a foundation for developing more flexible and generalized session-based recommendation systems.
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Evans, William, Elizabeth Andrade, Sandra Goldmeer, Michelle Smith, Jeremy Snider, and Gunilla Girardo. "The Living the Example Social Media Substance Use Prevention Program: A Pilot Evaluation." JMIR Mental Health 4, no. 2 (2017): e24. http://dx.doi.org/10.2196/mental.7839.
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Background Adolescent substance use rates in rural areas of the United States, such as upstate New York, have risen substantially in recent years, calling for new intervention approaches in response to this trend. The Mentor Foundation USA conducts the Living the Example (LTE) campaign to engage youth in prevention using an experiential approach. As part of LTE, youth create their own prevention messages following a training curriculum in techniques for effective messaging and then share them via social media. This paper reports on a pilot evaluation of the LTE program. Objective To conduct a pilot test of LTE in two rural high schools in upstate New York. We hypothesized that positive antidrug brand representations could be promoted using social media strategies to complement the Shattering the Myths (STM) in-person, event-based approach (hypothesis 1, H1), and that youth would respond positively and engage with prevention messages disseminated by their peers. We also hypothesized that exposure to the social media prevention messages would be associated with more positive substance use avoidance attitudes and beliefs, reductions in future use intentions, and decreased substance use at posttest (hypothesis 2, H2). Methods We adapted a previously published curriculum created by the authors that focuses on branding, messaging, and social media for prevention. The curriculum consisted of five, one-hour sessions. It was delivered to participating youth in five sequential weeks after school at the two high schools in late October and early November 2016. We designed a pre- and posttest pilot implementation study to evaluate the effects of LTE on student uptake of the intervention and short-term substance use and related outcomes. Working at two high schools in upstate New York, we conducted a pilot feasibility evaluation of LTE with 9th-grade students (ie, freshmen) at these high schools. We administered a 125-item questionnaire online to capture data on media use; attitudes toward social media; next 30-day personal drug use intentions; personal reasons to use drugs; reasons participants believe their peers would use drugs; self-reported exposure to the LTE program; and receptivity to the LTE program, among those reporting exposure. We constructed multivariable logistic regression models to analyze the relationship between program receptivity and outcomes. First, in a cross-sectional logistic regression model, we regressed self-reported LTE message receipt on drug use intent and actions related to LTE messaging. Then, for analysis of participants with matched pre- and posttest responses, we used multilevel generalized estimating equation (GEE) techniques to model changes in behavior from baseline to follow-up. Results Youth reported increased intentions to use marijuana (odds ratio [OR] 2.134, P=.02) between pre- and posttest. However, youth who reported exposure and receptivity to LTE reported a significant decrease in intentions (OR 0.239, P=.008). We observed a similar pattern for sedatives/sleeping pills—an increase in intentions overall (OR 1.886, P=.07), but a decrease among youth who reported exposure and receptivity to LTE (OR 0.210, P=.02). We saw the same pattern for use of any drug—an increase in reported intentions overall (OR 2.141, P=.02), but a decrease among youth who reported exposure and receptivity to LTE (OR 0.111, P=.004). Conclusions We observed some evidence of significant LTE program effects. Social media may be an effective strategy for peer-to-peer substance use prevention in the future. These findings point both to the potential of LTE and the social media diffusion model and to the need for more research on a larger scale with an expanded youth population in the future.
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Etemad, Sina, Sotiris K. Ntouyas, Ivanka Stamova, and Jessada Tariboon. "On Solutions of Two Post-Quantum Fractional Generalized Sequential Navier Problems: An Application on the Elastic Beam." Fractal and Fractional 8, no. 4 (2024): 236. http://dx.doi.org/10.3390/fractalfract8040236.
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Fractional calculus provides some fractional operators for us to model different real-world phenomena mathematically. One of these important study fields is the mathematical model of the elastic beam changes. More precisely, in this paper, based on the behavior patterns of an elastic beam, we consider the generalized sequential boundary value problems of the Navier difference equations by using the post-quantum fractional derivatives of the Caputo-like type. We discuss on the existence theory for solutions of the mentioned (p;q)-difference Navier problems in two single-valued and set-valued versions. We use the main properties of the (p;q)-operators in this regard. Application of the fixed points of the ρ-θ-contractions along with the endpoints of the multi-valued functions play a fundamental role to prove the existence results. Finally in two examples, we validate our models and theoretical results by giving numerical models of the generalized sequential (p;q)-difference Navier problems.
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Wylie, Tim. "Crazy Sequential Representations of Numbers for Small Bases." Recreational Mathematics Magazine 6, no. 12 (2019): 33–48. http://dx.doi.org/10.2478/rmm-2019-0007.
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Abstract Throughout history, recreational mathematics has always played a prominent role in advancing research. Following in this tradition, in this paper we extend some recent work with crazy sequential representations of numbers− equations made of sequences of one through nine (or nine through one) that evaluate to a number. All previous work on this type of puzzle has focused only on base ten numbers and whether a solution existed. We generalize this concept and examine how this extends to arbitrary bases, the ranges of possible numbers, the combinatorial challenge of finding the numbers, efficient algorithms, and some interesting patterns across any base. For the analysis, we focus on bases three through ten. Further, we outline several interesting mathematical and algorithmic complexity problems related to this area that have yet to be considered.
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Phadnis, Milind A., and Matthew S. Mayo. "Group sequential design for time-to-event data using the concept of proportional time." Statistical Methods in Medical Research 29, no. 7 (2019): 1867–90. http://dx.doi.org/10.1177/0962280219876313.
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Sequential monitoring of efficacy and safety is an important part of clinical trials. A Group Sequential design allows researchers to perform interim monitoring after groups of patients have completed the study. Statistical literature is well developed for continuous and binary outcomes and relies on asymptotic normality of the test statistic. However, in the case of time-to-event data, existing methods of sample size calculation are done either assuming proportional hazards or assuming exponentially distributed lifetimes. In scenarios where these assumptions are not true, as evidenced from historical data, these traditional methods are restrictive and cannot always be used. As interim monitoring is driven by ethical, financial, and administrative considerations, it is imperative that sample size calculations be done in an efficient manner keeping in mind the specific needs of a clinical trial with a time-to-event outcome. To address these issues, a novel group sequential design is proposed using the concept of Proportional Time. This method utilizes the generalized gamma ratio distribution to calculate the efficacy and safety boundaries and can be used for all distributions that are members of the generalized gamma family using an error spending approach. The design incorporates features specific to survival data such as loss to follow-up, administrative censoring, varying accrual times and patterns, binding or non-binding futility rules with or without skips, and flexible alpha and beta spending mechanisms. Three practical examples are discussed, followed by discussion of the important aspects of the proposed design.
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Stobbe, Nina, Gesche Westphal-Fitch, Ulrike Aust, and W. Tecumseh Fitch. "Visual artificial grammar learning: comparative research on humans, kea ( Nestor notabilis ) and pigeons ( Columba livia )." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1598 (2012): 1995–2006. http://dx.doi.org/10.1098/rstb.2012.0096.
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Artificial grammar learning (AGL) provides a useful tool for exploring rule learning strategies linked to general purpose pattern perception. To be able to directly compare performance of humans with other species with different memory capacities, we developed an AGL task in the visual domain. Presenting entire visual patterns simultaneously instead of sequentially minimizes the amount of required working memory. This approach allowed us to evaluate performance levels of two bird species, kea ( Nestor notabilis ) and pigeons ( Columba livia ), in direct comparison to human participants. After being trained to discriminate between two types of visual patterns generated by rules at different levels of computational complexity and presented on a computer screen, birds and humans received further training with a series of novel stimuli that followed the same rules, but differed in various visual features from the training stimuli. Most avian and all human subjects continued to perform well above chance during this initial generalization phase, suggesting that they were able to generalize learned rules to novel stimuli. However, detailed testing with stimuli that violated the intended rules regarding the exact number of stimulus elements indicates that neither bird species was able to successfully acquire the intended pattern rule. Our data suggest that, in contrast to humans, these birds were unable to master a simple rule above the finite-state level, even with simultaneous item presentation and despite intensive training.