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Статті в журналах з теми "Data freshness and consistency"

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Автор: Grafiati

Опубліковано: 28 вересня 2022

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1

Kazantsev, E. V., N. B. Kondratyev, M. V. Osipov, and O. S. Rudenko. "Influence of different types of hydrocolloids on the structure and preservation of sugary confectionery with a jelly structure consistency: a Review." Proceedings of the Voronezh State University of Engineering Technologies 82, no. 2 (September 18, 2020): 107–15. http://dx.doi.org/10.20914/2310-1202-2020-2-107-115.

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Quality is a time-varying, complex property of a confectionery that shows a measure of acceptability for the customer and rapidly or slowly deteriorates after the manufacture of foodstuffs. The safety of raw materials and finished products during storage is the most important task of global importance, according to WHO, in 2020 year. One of the important problems in the confectionery industry is to ensure long shelf life of confectionery products without reducing their taste properties, as exemplified by jelly marmalade. The task of preserving the freshness of the product is to preserve its consistency, taste, smell, appearance by retaining moisture and preventing damage by microorganisms. Freshness criterion for long shelf life is one of the main factors affecting the sales and competitiveness of sugary confectionery. The aspects of the influence of the properties of structure-forming agents (pectins, agars, modified starches) on the formation of a gelatinous consistency and storage of marmalade are considered. The physical and chemical indicators characterizing the process of moisture transfer in the body of the marmalade during storage are indicated. To assess the migration of moisture during storage, the graphical dependence of aw on the mass fraction of moisture in the marmalade is used - the isotherm of moisture sorption. Analysis of the obtained data of desorption isotherms can serve as a useful tool that shows what proportion of moisture a product is capable of receiving or giving away without losing the properties that characterize the quality of a particular confectionery product. Modern methods are indicated in assessing the quality function of marmalade using a mathematical equation to predict its storage capacity. An integrated approach to ensure the safety of marmalade is considered, which allows predicting its shelf life

2

Vinçon, Tobias, Christian Knödler, Leonardo Solis-Vasquez, Arthur Bernhardt, Sajjad Tamimi, Lukas Weber, Florian Stock, Andreas Koch, and Ilia Petrov. "Near-data processing in database systems on native computational storage under HTAP workloads." Proceedings of the VLDB Endowment 15, no. 10 (June 2022): 1991–2004. http://dx.doi.org/10.14778/3547305.3547307.

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Today's Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-growing data in combination with a mixture of transactional and analytical workloads. While optimizing for aspects such as data freshness and performance isolation, they build on the traditional data-to-code principle and may trigger massive cold data transfers that impair the overall performance and scalability. Firstly, in this paper we show that Near-Data Processing (NDP) naturally fits in the HTAP design space. Secondly, we propose an NDP database architecture, allowing transactionally consistent in-situ executions of analytical operations in HTAP settings. We evaluate the proposed architecture in state-of-the-art key/value-stores and multi-versioned DBMS. In contrast to traditional setups, our approach yields robust, resource- and cost-efficient performance.

3

Agiwal, Ankur, Kevin Lai, Gokul Nath Babu Manoharan, Indrajit Roy, Jagan Sankaranarayanan, Hao Zhang, Tao Zou, et al. "Napa." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2986–97. http://dx.doi.org/10.14778/3476311.3476377.

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Google services continuously generate vast amounts of application data. This data provides valuable insights to business users. We need to store and serve these planet-scale data sets under the extremely demanding requirements of scalability, sub-second query response times, availability, and strong consistency; all this while ingesting a massive stream of updates from applications used around the globe. We have developed and deployed in production an analytical data management system, Napa, to meet these requirements. Napa is the backend for numerous clients in Google. These clients have a strong expectation of variance-free, robust query performance. At its core, Napa's principal technologies for robust query performance include the aggressive use of materialized views, which are maintained consistently as new data is ingested across multiple data centers. Our clients also demand flexibility in being able to adjust their query performance, data freshness, and costs to suit their unique needs. Robust query processing and flexible configuration of client databases are the hallmark of Napa design. Most of the related work in this area takes advantage of full flexibility to design the whole system without the need to support a diverse set of preexisting use cases. In comparison, a particular challenge we faced is that Napa needs to deal with hard constraints from existing applications and infrastructure, so we could not do a "green field" system, but rather had to satisfy existing constraints. These constraints led us to make particular design decisions and also devise new techniques to meet the challenges. In this paper, we share our experiences in designing, implementing, deploying, and running Napa in production with some of Google's most demanding applications.

4

Liu, Sheng, Qiyang Chen, and Linlin You. "Fed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes." Electronics 11, no. 9 (April 27, 2022): 1393. http://dx.doi.org/10.3390/electronics11091393.

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Driven by emerging technologies such as edge computing and Internet of Things (IoT), recent years have witnessed the increasing growth of data processing in a distributed way. Federated Learning (FL), a novel decentralized learning paradigm that can unify massive devices to train a global model without compromising privacy, is drawing much attention from both academics and industries. However, the performance dropping of FL running in a heterogeneous and asynchronous environment hinders its wide applications, such as in autonomous driving and assistive healthcare. Motivated by this, we propose a novel mechanism, called Fed2A: Federated learning mechanism in Asynchronous and Adaptive Modes. Fed2A supports FL by (1) allowing clients and the collaborator to work separately and asynchronously, (2) uploading shallow and deep layers of deep neural networks (DNNs) adaptively, and (3) aggregating local parameters by weighing on the freshness of information and representational consistency of model layers jointly. Moreover, the effectiveness and efficiency of Fed2A are also analyzed based on three standard datasets, i.e., FMNIST, CIFAR-10, and GermanTS. Compared with the best performance among three baselines, i.e., FedAvg, FedProx, and FedAsync, Fed2A can reduce the communication cost by over 77%, as well as improve model accuracy and learning speed by over 19% and 76%, respectively.

5

Etuk, Aniebiet, Joseph A. Anyadighibe, Christian Amadi, and Edim James James. "Service quality delivery and consumers’ choice of fast-food outlets." International research journal of management, IT and social sciences 9, no. 2 (February 8, 2022): 264–73. http://dx.doi.org/10.21744/irjmis.v9n2.2038.

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This study examined the effect of service quality delivery on consumer’s choice of fast foods outlets. Cross-sectional survey research design was adopted. Primary data was collected from respondents using structured questionnaire. Simple regression in the Statistical Package for Social Science (SPSS) was adopted to analyze the data collected. Consequently, it was found that service tangibility, reliability, responsiveness, assurance and empathy had significant effects on consumer’s choice of fast foods. Thus, it was recommended amongst others, that fast food outlets should be more responsive to consumers’ service requirements by rapidly eliciting and resolving consumers’ enquiries and complaints; consistently deliver fast, strong and reliable service; ensure their personnel treat consumers with politeness and consideration at every point of service encounter and constantly seek ways to offer freshness in order to remain relevant in the market place.

6

Drejeris, Rolandas. "New Approach to a Modeling Actions of New Dietary Meals Creation." Current Developments in Nutrition 4, Supplement_2 (May 29, 2020): 709. http://dx.doi.org/10.1093/cdn/nzaa051_006.

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Abstract Objectives Objective of the research is to provide a reasonable model of new meals designing for hospitals food departments. Methods On summarizing the information presented in a wide spectrum of special scientific literature, after assessing it from the perspective of practical adaptability, the original model for new dietary meals designing was presented. The model was tested in the two biggest clinical hospitals of Lithuania, then a patient survey was conducted and appropriate decisions were made. Results The model consists of the following key components: research and assessment of the patients’ needs (customs, traditions or hobbies), processing survey results (generalization of them in order to identify unified and general trends for the different groups of population and health disorders), selection and adaptation of appropriate resources according to the nature of the patients disease (according requirements of the dietary nutrition), choice of suitable processing procedures correspondingly a sufferings of the patients, calculation of the portion size (amounts of an ingredients), planning of the quality (decoration, components arrangement, equipment selection), technology description and approval by head of the department. The model was tested in Kaunas clinical hospital. Patients aged 60–70 in the pulmonology department were interviewed about nutrition. Patients had to assess quality in 10 points system. Freshness of the salads was only 7,45, although freshness was checked very carefully. By the model we found, crispness of any food always adds to the impression of freshness. So salads (beets, carrots, parsnips, celery, etc.) were supplemented with dried vegetable ingredient after conformity assessment of products’ energy value. Patients evaluated the new created meal very positively. Conclusions Use of the model reduces the failure chance and affect the decisions of new dietary meals creation. Application of the suggested model will allow food production departments in hospitals to be consistent in new dietary meals creation and increase the likelihood of their patients’ success of recovery. Funding Sources Any funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

7

Guridi Lopategui, Ibai, Julen Castellano Paulis, and Ibon Echeazarra Escudero. "Physical Demands and Internal Response in Football Sessions According to Tactical Periodization." International Journal of Sports Physiology and Performance 16, no. 6 (June 1, 2021): 858–64. http://dx.doi.org/10.1123/ijspp.2019-0829.

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Purpose: The objectives of the present study were (1) to analyze the internal and external load profile of training and competition carried out by semiprofessional football players during a 27-week period and (2) to examine the possible link between this type of periodization and players’ fitness status and their readiness to compete. Methods: Training and match data were obtained from 26 semiprofessional football players belonging to the reserve squad of a Spanish La Liga club during the 2018/19 season. For the purpose of this study, the distribution of external and internal load during a typical training microcycle, with 6 or 7 days between matches, was analyzed. Five types of sessions were considered: strength, duration, velocity, preofficial match, and official match. Results: The results showed a different internal and external load profile for each type of session, with the load being consistently higher during matches when compared with training sessions (28.9%–94% higher), showing significant differences in all the variables. There was a clear tapering strategy in the last days of the week to arrive with enough freshness to compete, shown by the decrease of the values in the 2 days before the match (15%–83% reduction, depending on the variable). Furthermore, the horizontal alternation of the load allowed the players to maintain their fitness level during the 27-week period. Conclusions: Our findings suggest that this weekly periodization approach could help achieve a double conditional target, allowing a short tapering strategy to face the match with enough freshness and serving as a strategy for maintaining or optimizing players’ physical performance during the season.

8

Houhamdi, Zina, and Belkacem Athamena. "Data freshness evaluation in data integration systems." International Journal of Economics and Business Research 11, no. 2 (2016): 132. http://dx.doi.org/10.1504/ijebr.2016.075306.

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Murray, Nicholas J., Emma V. Kennedy, Jorge G. Álvarez-Romero, and Mitchell B. Lyons. "Data Freshness in Ecology and Conservation." Trends in Ecology & Evolution 36, no. 6 (June 2021): 485–87. http://dx.doi.org/10.1016/j.tree.2021.03.005.

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Errickson, Lauren, and Douglas Zemeckis. "Industry Insights on Consumer Receptivity to Aquaculture Products in the Retail Marketplace: Considerations for Increasing Seafood Intake." Current Developments in Nutrition 5, Supplement_2 (June 2021): 553. http://dx.doi.org/10.1093/cdn/nzab043_005.

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Abstract Objectives Americans consistently fail to meet dietary guidelines for seafood intake. Efforts are needed to increase consumption, especially of sustainable seafood that can be supplied by domestic aquaculture. However, consumer receptivity to aquaculture products is mixed. The objective of this study was to elicit industry perspectives regarding influences on consumer purchases of aquaculture products. Methods Key informant interviews (n = 12) were conducted in late 2020 with U.S. salmon, shrimp, and oyster producers, marketers, and industry interest groups. Participants were recruited via snowball sampling. Virtual interviews were conducted by a trained moderator and assistant moderator/notetaker using a semi-structured interview guide. Qualitative data analysis included a thematic review of interview recordings and notes, with key concepts coded according to a priori themes derived from the literature. Results Interviews yielded important insights into consumer receptivity to aquaculture products. Participants believe that outdated misperceptions of aquaculture persist, noting that despite advances in domestic aquaculture production practices to comply with U.S. standards, some consumers perceive aquaculture as environmentally detrimental and unsustainable. Further, participants believe negative attitudes toward genetically modified organisms, corn and soy-based feeds, antibiotics, and chemicals are misplaced, yet contribute to hesitancy among some consumers. Industry opinions on what is important to consumers reflect strong valuation of seafood quality, freshness, local harvest, and sustainability. Participants suggest product labeling efforts be developed accordingly, and that innovative marketing strategies be undertaken, such as aquaculture product promotion through “know your farmer” campaigns, chef education initiatives, and home delivery programs. Conclusions For domestic aquaculture products to have a meaningful impact on U.S. seafood intake, positive consumer receptivity is key. Industry perspectives will inform marketing and educational efforts toward addressing consumer hesitancy to purchase aquaculture products by resolving misguided concerns, with important implications for consumer health and sustainability of the domestic seafood supply. Funding Sources United States Department of Agriculture.

11

Adamchuk, Leonora, Natalia Dudchenko, Natalia Henhalo, Dina Lisohurska, and Kateryna Pylypko. "Characteristics of dew honey from Ukraine." FOOD RESOURCES 9, no. 16 (June 25, 2021): 6–19. http://dx.doi.org/10.31073/foodresources2021-16-01.

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The subject of research – dew honey is highly valued in the food industry, and the authentication of its origin, in particular the differentiation from blossom honey, the assessment of its safety and quality requires the use of adequate parameters. In this context, sensorial indicators are the primary attributes available for use by ordinary consumers. The said indicators, together with melissopalynological analysis, are the indicators of the botanical and geographical origin of honey. Physical and chemical parameters supplement the information on the characteristics of the localization of the samples, as well as assess their safety and quality. The purpose of the study was to determine the differences between the pollen spectrum, sensorial, physical and chemical parameters of Ukrainian dew honey of different regional origin. Methodology. To achieve this goal, the sensorial, physical and chemical characteristics, as well as pollen profile of 8 samples of dew honey were analyzed. The results of the study. For most of the criteria for assessing the physical and chemical parameters obtained during the study, the data can be considered as being within the ranges of parameters values established within the national regulatory framework for safety and quality of honey – DSTU 4497:2005 ‘Natural honey. Specifications’ and the Order of the Ministry of Agrarian Policy and Food of Ukraine of June 19, 2019 № 330. However, all samples did not meet national criteria for the electrical conductivity and more than a half of all samples did not meet national criteria for the mass fraction of glucose, fructose, and sucrose. The spectrum of pollen showed the presence of high content of spores of fungi, yeast, and green algae, the content and ratio of those differed due to the geographical origin of honey samples. The sensorial evaluation was performed for color, taste, aroma, consistency and crystallization, the presence of fermentation signs. The tendency to crystallize was detected in half of the honey samples, all samples were characterized by a brown range of different tinges, the smell and taste of each sample had a unique bouquet determined by the origin of honey. In one sample of honey with the high yeast content, the initial stages of fermentation were observed, which were accompanied by the presence of vinegar odor. The results obtained in this study indicate satisfactory quality, acceptable freshness, as well as the authenticity of each individual sample of honey. The special characteristics of dew honey differ from those of flower honey, therefore, it may be appropriate to revise the national regulatory framework to adjust the requirements for assessing honey safety and quality. Scope of research results is to apply the obtained results for further authentication of dew honeys for further study of their properties and wide application in the field of nutrition.

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Cui, Jinshi, and Chengxun Cui. "Non-Destructive Evaluation of Salmon and Tuna Freshness in a Room-Temperature Incubation Environment Using a Portable Visible/Near-Infrared Imaging Spectrometer." Transactions of the ASABE 64, no. 2 (2021): 521–27. http://dx.doi.org/10.13031/trans.13858.

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HighlightsWhile freshness is a critical value of food quality, its assessment requires complex methods, which are costly and time-consuming.In this work, it is demonstrated that spectral responses obtained from a portable VIS/NIR imaging spectrometer can be used to predict food freshness using a CNN-based machine learning algorithm.In the food industry, the method can assess real-time food freshness nondestructively and cost-effectively.Abstract. There has been strong demand for the development of accurate but simple methods to assess the freshness of foods. In this study, a system is proposed to determine the freshness of fish by analyzing the spectral response with a portable visible/near-infrared (VIS/NIR) imaging spectrometer and a convolution neural network (CNN) machine learning algorithm. Spectral response data from salmon and tuna, which were incubated at 25°C, were obtained every minute for 30 h and were categorized into three stages (fresh, likely spoiled, or spoiled) based on the time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of the experimental samples. The accuracy of the spectral data in predicting the freshness was ~84% for salmon and ~88% for tuna. Keywords: CNN, Fish, Freshness, pH, Spectral data, VIS/NIR.

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Chen, Zhengchuan, Mingjun Xu, Min Wang, and Yunjian Jia. "Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions." Entropy 24, no. 2 (January 28, 2022): 200. http://dx.doi.org/10.3390/e24020200.

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Motivated by big data applications in the Internet of Things (IoT), abundant information arrives at the fusion center (FC) waiting to be processed. It is of great significance to ensure data freshness and fidelity simultaneously. We consider a wireless sensor network (WSN) where several sensor nodes observe one metric and then transmit the observations to the FC using a selection combining (SC) scheme. We adopt the age of information (AoI) and minimum mean square error (MMSE) metrics to measure the data freshness and fidelity, respectively. Explicit expressions of average AoI and MMSE are derived. After that, we jointly optimize the two metrics by adjusting the number of sensor nodes. A closed-form sub-optimal number of sensor nodes is proposed to achieve the best freshness and fidelity tradeoff with negligible errors. Numerical results show that using the proposed node number designs can effectively improve the freshness and fidelity of the transmitted data.

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Medeiros, Erika Carlos, Leandro Maciel Almeida, and José Gilson de Almeida Teixeira Filho. "Computer Vision and Machine Learning for Tuna and Salmon Meat Classification." Informatics 8, no. 4 (October 19, 2021): 70. http://dx.doi.org/10.3390/informatics8040070.

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Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.

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Peralta, Verónika, Raúl Ruggia, and Mokrane Bouzeghoub. "Analyzing and Evaluating Data Freshness in Data Integration Systems." Ingénierie des systèmes d'information 9, no. 5-6 (December 24, 2004): 145–62. http://dx.doi.org/10.3166/isi.9.5-6.145-162.

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16

Jin, Hao, Ke Zhou, Hong Jiang, Dongliang Lei, Ronglei Wei, and Chunhua Li. "Full integrity and freshness for cloud data." Future Generation Computer Systems 80 (March 2018): 640–52. http://dx.doi.org/10.1016/j.future.2016.06.013.

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Yang, Xiao Jing, and Zheng Hu Yan. "Identification of Freshwater Fish Meat Freshness Based on Multi-Sensor Fusion Technology." Applied Mechanics and Materials 303-306 (February 2013): 912–17. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.912.

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In order to explore the application of multi-sensor fusion technology in nondestructive testing of agricultural products and develop new detection methods for agriculture, multi-sensor information fusion technology are performed to identify freshness of freshwater fish meat. Different freshness of grass carp specimens were identified by multi-sensor fusion method which three characteristic data that are PH value, conductance and smell were measured, collected, and then fused by fuzzy theory method. During the experiment process the value of the total volatile basic nitrogen (TVB-N) of standard samples and test samples were measured. The freshness standard was established by the TVB-N value of standard samples. Correctness of the results of multi-sensor data fusion was verified by comparing the TVB-N value of the test samples with the freshness standard. The results show that the freshwater fish meat with different freshness can be identified correctly by multi-sensor data fusion method which is fuzzy theory and the accuracy rate is 94%.

18

Song, Jiawen, Meihua Xiao, Tong Zhang, and Haoyang Zhou. "Proving authentication property of PUF-based mutual authentication protocol based on logic of events." Soft Computing 26, no. 2 (November 12, 2021): 841–52. http://dx.doi.org/10.1007/s00500-021-06163-9.

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AbstractPUF (Physical unclonable function) is a new hardware security primitive, and the research on PUFs is one of the emerging research focuses. For PUF-based mutual authentication protocols, a method to abstract the security properties of hardware by using logic of events is proposed, and the application aspects of logic of events are extended to protocols based on hardware security. With the interaction of PUF-based mutual authentication protocol formally described by logic of events, the basic sequences are constructed and the strong authentication property in protocol interaction process is verified. Based on the logic of events, the freshness of nonces is defined, and the persist rule is proposed according to the concept of freshness, which ensures the consistency of the protocol state and behavior predicate in the proof process, and reduces the complexity and redundancy in the protocol analysis process. Under reasonable assumptions, the security of the protocol is proven, and the fact that logic of events applies to PUF-based mutual authentication protocols is shown.

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Dwivedi, Amit Kumar, Naveen Kumar, and Manik Lal Das. "Group data freshness scheme for outsourced data in distributed systems." Future Generation Computer Systems 133 (August 2022): 141–52. http://dx.doi.org/10.1016/j.future.2022.03.011.

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Weng, Xiaohui, Xiangyu Luan, Cheng Kong, Zhiyong Chang, Yinwu Li, Shujun Zhang, Salah Al-Majeed, and Yingkui Xiao. "A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies." Journal of Sensors 2020 (September 21, 2020): 1–14. http://dx.doi.org/10.1155/2020/8838535.

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The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

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GUAN, XIAO, JING LIU, QINGRONG HUANG, and JINGJUN LI. "Assessing the Freshness of Meat by Using Quantum-Behaved Particle Swarm Optimization and Support Vector Machine." Journal of Food Protection 76, no. 11 (November 1, 2013): 1916–22. http://dx.doi.org/10.4315/0362-028x.jfp-12-161.

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To improve the performance of meat freshness identification systems, we present a new identification method based on quantum-behaved particle swarm optimization (QPSO) and the support vector machine (SVM). Fresh pork, beef, mutton, and shrimp samples were stored in a hypobaric chamber for several days, and the conventional indices of meat freshness, including total volatile basic nitrogen content, aerobic plate count, pH value, and sensory scores, were determined to achieve the identification of sample freshness. However, the experiments showed that it was difficult to obtain an ideal freshness assessment by any single physicochemical or sensory property. Therefore, SVM was introduced to use these data to build a freshness model. Furthermore, QPSO was proposed to seek the optimal parameter combination of SVM. The experimental results indicated that the hybrid SVM model with QPSO could be used to predict meat freshness with 100% classification accuracy.

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Moon, Eui Jung, Youngsik Kim, Yu Xu, Yeul Na, Amato J. Giaccia, and Jae Hyung Lee. "Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer." Sensors 20, no. 15 (August 1, 2020): 4299. http://dx.doi.org/10.3390/s20154299.

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There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.

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Li, Guohui, Chunyang Zhou, Jianjun Li, and Bing Guo. "Maintaining Data Freshness in Distributed Cyber-Physical Systems." IEEE Transactions on Computers 68, no. 7 (July 1, 2019): 1077–90. http://dx.doi.org/10.1109/tc.2018.2889456.

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24

Grassi, Silvia, Simona Benedetti, Luca Magnani, Alberto Pianezzola, and Susanna Buratti. "Seafood freshness: e-nose data for classification purposes." Food Control 138 (August 2022): 108994. http://dx.doi.org/10.1016/j.foodcont.2022.108994.

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25

Zhang, Hua. "Integrity and Privacy Preserving Data Aggregation Algorithm for WSNs." Applied Mechanics and Materials 721 (December 2014): 732–35. http://dx.doi.org/10.4028/www.scientific.net/amm.721.732.

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This paper proposed an integrity and privacy preserving data aggregation algorithm for WSNs, which is called IPPDA. First, it attached a group of congruent numbers to the sensing data in order to execute integrity checking operated by sink node using Chinese remainder theorem (CRT); then it computed the hash function-based message authentication codes with time and key as the parameters to satisfy data freshness; finally, it adopted a homomorphic encryption scheme to provide privacy preserving. The simulation results show that IPPDA can effectively preserve data privacy, check data integrity, satisfy data freshness, and get accurate data aggregation results while having less computation and communication cost than iCPDA and iPDA.

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Gholam Hosseini, Hamid, Dehan Luo, Guanggui Xu, Hongxiu Liu, and Deena Benjamin. "Intelligent Fish Freshness Assessment." Journal of Sensors 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/628585.

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Fish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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Saputra, Sabarudin, Anton Yudhana, and Rusydi Umar. "Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 3 (June 30, 2022): 412–20. http://dx.doi.org/10.29207/resti.v6i3.4062.

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Consumption of fish as a food requirement for the fulfillment of community nutrition is increasing. This was followed by an increase in the amount of fish caught that were sold at fish markets. Market managers must be concerned about the dispersion of huge amounts of fish in the market in order to determine the freshness of the fish before it reaches the hands of consumers. So far, market managers have relied on traditional ways to determine the freshness of fish in circulation. The issue is that traditional solutions, such as the use expert assessment, demand a human physique that quickly experiences fatigue. Technological developments can be a solution to these problems, such as utilizing image processing techniques classification method. Image processing with the use of color features is an effective method to determine the freshness of fish. The classification method used in this research is the Naive Bayes method. This study aims to identify the freshness of fish based on digital images and determine the performance level of the method. The identification process uses the RGB color value feature of fisheye images. The stages of fish freshness identification include cropping, segmentation, RGB value extraction, training, and testing. The classification data are 210 RGB value of extraction images which are divided into 147 data for training and 63 data for testing. The research data were divided into fresh class, started to rot class, and rotted class. The research shows that the Naive Bayes algorithm can be used in the process of identifying the freshness level of fish based on fisheye images with a test accuracy rate of 79.37%.

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Kallgren, Daniel C., and David Beck Ryden. "Data Consistency Checking." Historical Methods: A Journal of Quantitative and Interdisciplinary History 28, no. 1 (January 1, 1995): 66–69. http://dx.doi.org/10.1080/01615440.1995.9955317.

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29

Sun, Yin, and Benjamin Cyr. "Sampling for data freshness optimization: Non-linear age functions." Journal of Communications and Networks 21, no. 3 (June 2019): 204–19. http://dx.doi.org/10.1109/jcn.2019.000035.

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Siska, Siska. "HUBUNGAN TINGKAT KESEGARAN JASMANI DENGAN KECERDASAN SISWA KELAS VII SMP PEMBANGUNAN LABORATORIUM UNIVERSITAS NEGERI PADANG." JURNAL PENDIDIKAN ROKANIA 4, no. 2 (July 10, 2019): 220. http://dx.doi.org/10.37728/jpr.v4i2.214.

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This research aims to know the level of intelligence of students of grade VII SMP Development Laboratory of Padang State University, the level of physical freshness of class VII students SMP Development Laboratory of Padang State University, and relationship level Physical freshness with the intelligence of class VII SMP Pembangunan Laboratories of Padang State University. This research is correlational. The population is a grade VII student of the Padang State University laboratories which amounted to 203 people. Sampling techniques are purposive sampling with a total of 30 people. To measure the level of physical freshness used TKJI test (Indonesian physical freshness test) for sons and daughters aged 13-15 years and the assessment of Intelligence (IQ) is obtained from the Intellegence (IQ) test in cooperation with the Department of Counseling Faculty of Science Education UNP. Data analysis is done using SPSS 13.0. Based on the results of research findings known average (mean) physical freshness of 12.87 and its deviation (Stardar deviation) 2.12, while the average Intellegence (IQ) is 96.77 and its deviation is 6.93. From the results of a correlation analysis of research acquired correlation coefficient r = 0.394 with significance level 0.031 meaning that there is a significant relationship between the level of physical freshness with the intelligence of junior secondary students development class VII Padang State University Laboratory.

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Güney, Selda, and Ayten Atasoy. "Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 03 (July 9, 2019): 2050003. http://dx.doi.org/10.1142/s0218001420500032.

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The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a scala of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.

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Huang, Xingyi, Haixia Xu, Lei Wu, Huang Dai, Liya Yao, and Fangkai Han. "A data fusion detection method for fish freshness based on computer vision and near-infrared spectroscopy." Analytical Methods 8, no. 14 (2016): 2929–35. http://dx.doi.org/10.1039/c5ay03005f.

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33

Beshai, Heba, Gursimran Sarabha, Pranali Rathi, Arif Alam, and M. Deen. "Freshness Monitoring of Packaged Vegetables." Applied Sciences 10, no. 21 (November 9, 2020): 7937. http://dx.doi.org/10.3390/app10217937.

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Smart packaging is an emerging technology that has a great potential in solving conventional food packaging problems and in meeting the evolving packaged vegetables market needs. The advantages of using such a system lies in extending the shelf life of products, ensuring the safety and the compliance of these packages while reducing the food waste; hence, lessening the negative environmental impacts. Many new concepts were developed to serve this purpose, especially in the meat and fish industry with less focus on fruits and vegetables. However, making use of these evolving technologies in packaging of vegetables will yield in many positive outcomes. In this review, we discuss the new technologies and approaches used, or have the potential to be used, in smart packaging of vegetables. We describe the technical aspects and the commercial applications of the techniques used to monitor the quality and the freshness of vegetables. Factors affecting the freshness and the spoilage of vegetables are summarized. Then, some of the technologies used in smart packaging such as sensors, indicators, and data carriers that are integrated with sensors, to monitor and provide a dynamic output about the quality and safety of the packaged produce are discussed. Comparison between various intelligent systems is provided followed by a brief review of active packaging systems. Finally, challenges, legal aspects, and limitations facing this smart packaging industry are discussed together with outlook and future improvements.

34

Dang, Tran Khanh. "Ensuring Correctness, Completeness, and Freshness for Outsourced Tree-Indexed Data." Information Resources Management Journal 21, no. 1 (January 2008): 59–76. http://dx.doi.org/10.4018/irmj.2008010104.

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35

Kim, Hyunsung, and Sung Woon Lee. "Freshness Preserving Secure Data Gathering Protocol over Wireless Sensor Networks." International Journal of Control and Automation 8, no. 6 (June 30, 2015): 411–20. http://dx.doi.org/10.14257/ijca.2015.8.6.39.

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36

Kapadiya, Ridham, and Jignesh Prajapati. "A Novel Approach for an Enhanced Oruta with Data Freshness." International Journal of Computer Applications 130, no. 10 (November 17, 2015): 1–3. http://dx.doi.org/10.5120/ijca2015907047.

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37

Han, Song, Deji Chen, Ming Xiong, Kam-Yiu Lam, Aloysius K. Mok, and Krithi Ramamritham. "Schedulability Analysis of DeferrableScheduling Algorithms for MaintainingReal-Time Data Freshness." IEEE Transactions on Computers 63, no. 4 (April 2014): 979–94. http://dx.doi.org/10.1109/tc.2012.266.

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38

Clark, Patrick G., Jerzy W. Grzymala-Busse, and Wojciech Rzasa. "Consistency of incomplete data." Information Sciences 322 (November 2015): 197–222. http://dx.doi.org/10.1016/j.ins.2015.06.011.

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39

Mohareb, Fady, Olga Papadopoulou, Efstathios Panagou, George-John Nychas, and Conrad Bessant. "Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data." Analytical Methods 8, no. 18 (2016): 3711–21. http://dx.doi.org/10.1039/c6ay00147e.

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40

Wang, Wei, Weizhen Yang, Yungang Liu, Zhaoba Wang, and Zhuanhong Yan. "A Research of Neural Network Optimization Technology for Apple Freshness Recognition Based on Gas Sensor Array." Scientific Programming 2022 (March 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/5861326.

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In the process of growth, apples’ ripening, storage, transportation, and processing, the appearance and internal physiological characteristics will have some changes because of the effect of time and physical attributes. A series of problems, such as the false ripeness and putrefaction of fruit, will bring huge economic losses to fruit vendors and harm to consumers. In this paper, an odor recognition system has been designed for the fast evaluation of the freshness characteristics of apples, which is based on the freshness characteristics of Fuji apple. A series of apple-air mixture with equivalent model was established by studying the change of gas concentration during the growth and storage of apples. The continuous projection algorithm (Successive Projections Algorithm, SPA) is used to optimize the sensor array to solve the problems of collinearity and overlap and also to eliminate the abnormal and redundant sensors. ZigBee wireless sensor network is adopted to send data to host computer, and BP (Error Backpropagation) neural network algorithm optimized by SFLA (shuffled complex evolution, SCE + Particle Swarm Optimization, PSO) algorithm is used to recognize gas data, which greatly improves the training speed and precision of neural network. The experimental results show that the detection accuracy of the Fuji apples freshness is 98.67% and can quickly and comprehensively identify the freshness of apples.

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Chang, Zhi Yong, Dong Hui Chen, Zhi Hong Zhang, Yue Ying Tong, Jin Tong, and Lu Dai. "Design of a Bionic Olfactory, Tactile Integrated System and its Application in Chicken Meat Quality Inspection." Applied Mechanics and Materials 461 (November 2013): 814–21. http://dx.doi.org/10.4028/www.scientific.net/amm.461.814.

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This study is aiming at the practical problem of meat freshness evaluation. Since meat putrefaction is a complex process that is influenced by many factors, it is necessary to have a comprehensive investigation of the various indicators to determine the freshness of meat. This research integrated information from a multisensory system to reduce uncertainty of evaluation. According to the odor mechanism model of rotten chicken, six types of sensors were chosen, which were combined as array for olfactory experiments. WDW-20 electronic universal testing machine (UTM) was adopted as tactile sensing device. As a bionic tactile test part, the UTM head is to obtain pressure characteristic curves of the meat. According to the odor model and elastic mechanics parameters of the chicken, the mechanical parameters were analyzed under the condition of cold storage, as well as time-varying results of fingerprint odor signal and salt base nitrogen volatile signal. Then, established the meat odor, elastic mechanics and freshness parameters, which were integrated into a fusion system and combined with the data through the experimental test. Eventually, established the mathematical model among meat odor, elastic mechanics parameters and meat freshness. This study provides theory reference for the evaluation of meat freshness, and delivers new thought and method for the design of multiphase bionic intelligent electrical measuring equipment.

42

Cardoso, Patrícia G., Odete Gonçalves, Maria F. Carvalho, Rodrigo Ozório, and Paulo Vaz-Pires. "Seasonal Evaluation of Freshness Profile of Commercially Important Fish Species." Foods 10, no. 7 (July 6, 2021): 1567. http://dx.doi.org/10.3390/foods10071567.

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Freshness is considered one of the most important parameters to judge the quality of most fish products. In the current study, the seasonality effect on the freshness profile of different economic fish species was evaluated for the first time, using three different approaches (sensory: Quality Index Method (QIM) and European (EC) Scheme; physical: Torrymeter (TRM) values; and microbiological analyses: Total Viable Counts (TVC) and degradative bacteria). Over a year, individuals of farmed fish Sparus aurata and Dicentrarchus labrax, as well as the wild fish Trachurus trachurus, Scomber colias, and Sardina pilchardus, were sampled seasonally for the evaluation of their freshness profile over 10 days on ice. In general, data showed an increase in QIM values, a decline in TRM, and an increase of spoilage bacteria throughout the storage time, revealing a clear temporal degradation of the quality of the fish. Additionally, some signs of seasonality effect could only be observed for some species. For example, the seabass D. labrax showed lower numbers of degradative bacteria in winter than in the other seasons, suggesting a high potential to be marketed in a fresher condition, especially during that season. On the other hand, S. colias showed higher freshness scores (i.e., higher TRM values in spring and autumn and lower numbers of bacteria in summer) from spring to autumn. However, from the five studied species, S. colias presented the lowest freshness values, indicating a higher fragility of this species. This information is extremely relevant for consumers and retailers that want to invest in higher quality products, as they would thus be able to choose certain species in detriment of others. Additionally, obtained data showed that farmed species reached day 10 of storage time with lower values of QIM and microbial counts (cfu), as well as higher values of TRM, in relation to wild species. These results reinforce the idea that farmed fish can, under proper conditions, present high quality/freshness profile.

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Nafiasari, Nadya Ayu, and Ariesta Martiningtyas Handayani. "PENGANALISIS KESEGARAN DAGING SAPI DAN DAGING BABI MENTAH BERDASARKAN KLASIFIKASI WARNA DAN KELEMBABAN." Jurnal Teknosains 8, no. 1 (January 3, 2019): 66. http://dx.doi.org/10.22146/teknosains.35643.

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There are many frauds involving the trade of beef. Traders do the fraud by replacing the beef it sells with pork. Also, the lack of consumer knowledge of the difference between beef and pork make these traders unscrupulous. Because we research by designing and creating systems that can identify and analyze the freshness of beef and pork. This system uses a color sensor as a color identifier in meat and humidity sensors to measure the level of freshness of meat. Also used is also a microcontroller as an acquirer and data processor. This system is designed to distinguish between beef and pork. This study involved data collection on beef and pork as much as 420 data. These data have shown that this system has an accuracy of 79%, 80%, 80%, 83%, 77% and 81% in identifying fresh beef, fresh pork, less fresh beef, less fresh pork, the cow is not fresh, and the pork is not fresh in sequence. This system is portable because it can be taken anywhere with a battery that supplies the power of this tool. With this system, consumers can distinguish between beef and pork and can know the level of freshness in each meat.

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Shi, Peng, Yulin Cui, Kangming Xu, Mingmei Zhang, and Lianhong Ding. "Data Consistency Theory and Case Study for Scientific Big Data." Information 10, no. 4 (April 12, 2019): 137. http://dx.doi.org/10.3390/info10040137.

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Big data technique is a series of novel technologies to deal with large amounts of data from various sources. Unfortunately, it is inevitable that the data from different sources conflict with each other from the aspects of format, semantics, and value. To solve the problem of conflicts, the paper proposes data consistency theory for scientific big data, including the basic concepts, properties, and quantitative evaluation method. Data consistency can be divided into different grades as complete consistency, strong consistency, weak consistency, and conditional consistency according to consistency degree and application demand. The case study is executed on material creep testing data. The analysis results show that the theory can solve the problem of conflicts in scientific big data.

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Calanche, Juan, Selene Pedrós, Pedro Roncalés, and José Antonio Beltrán. "Design of Predictive Tools to Estimate Freshness Index in Farmed Sea Bream (Sparus aurata) Stored in Ice." Foods 9, no. 1 (January 8, 2020): 69. http://dx.doi.org/10.3390/foods9010069.

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This research studied sea bream freshness evolution through storage time in ice by determining different quality parameters and sensory profiles. Predictive models for freshness index, storage time, and microbial counts were designed from these data. Physico–chemical parameters were assessed to evaluate the quality of fish; microbial growth was controlled to ensure food safety, and sensory analyses were carried out to characterize quality deterioration. Predictive models were developed and improved with the aim of being used as tools for quality management in the seafood industry. Validation was conducted in order to establish the accuracy of models. There was a good relationship between the physico–chemical and microbiological parameters. Sensory analysis and microbial counts allowed for the establishment of a shelf-life of 10 days, which corresponded to a poor quality (according to the European Community’s system of grading fish for marketing purposes), with a freshness index lower than 50%. Sensory profiles showed that gill and flesh texture were the most vulnerable attributes during storage in ice related to spoilage. The predictive models for the freshness index (%) and ice storage time (h) exhibited an accuracy close to 90% following practical validation.

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Zou, Liang, Weinan Liu, Meng Lei, and Xinhui Yu. "An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy." Entropy 23, no. 10 (September 30, 2021): 1293. http://dx.doi.org/10.3390/e23101293.

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Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.

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Sambodo, Reo. "Analysis of Consumer Behavior in The Decision-Making Process of Vegetable Purchase During the Covid-19 Pandemic at Bu Dipo’s Vegetable Store." JIA (Jurnal Ilmiah Agribisnis) : Jurnal Agribisnis dan Ilmu Sosial Ekonomi Pertanian 7, no. 2 (April 30, 2022): 40. http://dx.doi.org/10.37149/jia.v7i2.23832.

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Research to explain the characteristics of vegetable consumers at Bu Dipo’s store, analyze the decision-making process of buying vegetables, and explore the behaviour of vegetable consumers at Bu Dipo’s store. Data analysis used descriptive analysis, Fishbein multi-attribute, and Rank Spearman correlation. The attributes studied include; price, taste, nutritional content, colour, aroma, freshness, packaging, neatness, durability, shape and size, brand, product quality, and expiration information. Vegetables have the advantage of freshness attributes, nutritional content, and expiration information from other features after consuming them. The characteristics of consumers who buy vegetables at BU Dipo’s store are primarily women aged 31-40 years old, working as private employees with an income of IDR3.000.000 - IDR4.000.000 per month, and having two family members to support. In making vegetable purchasing decisions, the freshness factor is an attribute that influences consumers in buying vegetables at Bu Dipo’s store. Characteristics of consumers, namely age, income level, education level, and the number of family members covered, have no significant relationship with consumer behaviour towards purchasing vegetables at Bu Dipo’s store. Researchers can give suggestions to improve the quality of vegetable freshness and open branches to reach more consumers.

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Sun, Jian, Yuhao Liu, Gangshan Wu, Yecheng Zhang, Rongbiao Zhang, and X. J. Li. "A Fusion Parameter Method for Classifying Freshness of Fish Based on Electrochemical Impedance Spectroscopy." Journal of Food Quality 2021 (March 10, 2021): 1–9. http://dx.doi.org/10.1155/2021/6664291.

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Compared with using a single characteristic parameter of electrochemical impedance spectroscopy (EIS) to classify the freshness of fish samples from different origins, more characteristic parameters could bring higher accuracy as well as complexity, subjectivity, and uncertainty. In order to eliminate the disadvantages of the multiparameter model, a data fusion method based on model similarity (DFMS) was proposed in this study. The similarity relation between the freshness models based on EIS characteristic parameters and physicochemical indicator was analyzed and quantified accordingly, and then, the weighting factors of the fusion model were determined. The classification accuracy rate of fish freshness based on DFMS was 9.2∼15% greater than that of a single EIS characteristic parameter. The novel dimensionless fusion parameter method proposed in this article might provide a simple yet effective indicator for EIS-based food quality evaluation.

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Varghese, Lino Abraham, and S. Bose. "Effective Data Storage for e-Learning with Enhanced Freshness Monitoring System." Journal of Computational and Theoretical Nanoscience 15, no. 1 (January 1, 2018): 245–48. http://dx.doi.org/10.1166/jctn.2018.7079.

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50

Meddeb, Maroua, Amine Dhraief, Abdelfettah Belghith, Thierry Monteil, Khalil Drira, and Saad AlAhmadi. "Cache Freshness in Named Data Networking for the Internet of Things." Computer Journal 61, no. 10 (January 27, 2018): 1496–511. http://dx.doi.org/10.1093/comjnl/bxy005.

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