Relevant bibliographies by topics / Resnet Fastai

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Resnet Fastai'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Resnet Fastai"

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Priyanshu, Shrivastava, Singh Karan, and Pancham Ashish. "Classification of Grains and Quality Analysis using Deep Learning." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 244–50. https://doi.org/10.35940/ijeat.A3213.1011121.

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There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils which have the maximum consumption have been selected. Lentils are designated into classes based on colors. The technique of determining the elegance of a lentil is with the aid of seed coat shade. Red lentils can be confirmed through the cotyledon coloration. Lentil types may also have a huge variety of seed coat colors from inexperienced, red, speckled inexperienced, black and tan. The cotyledon colour may be red, yellow or inexperienced. The size and color of every Indian Lentil type (i.e. Red, Green, and Yellow, Black, White) are decided to be large or Medium or small, then size and colour end up part of the grade name. An smart machine is used to perceive the kind of Indian lentils from bulk samples. The proposed machine allows kernel length and coloration size using picture processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with the highest accuracy. Rice is one of most consumed grains in India so its quality is of utmost importance. In this project, we identify and grade five types of rice and grade them with the help of their distinguished features such as size, color, shape, and surface. The project works in three phases viz., Feature Extraction, Training, and Testing. Various rice grain has a different shape, size, surface and various lentils come in different colors, Hence the feature that will be extracted is texture and colors. The method of regression will be adopted for the grading mechanism where the output will be in terms of percentage purity. The methodology for the extraction of the feature will be GLCM and Edge Detection where for supervised learning SVM and Back Propagation will be utilized. The project provides an efficient replacement for the traditional grading mechanism and standardizes the pricing of farm products based on their quality only.
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Vannala, Sai Durga, and Vineela K. "Fastai-Powered Lung Cancer Classification Model." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 146–49. http://dx.doi.org/10.22214/ijraset.2023.55952.

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Abstract: Utilising Fastai and PyTorch, a deep learning model for classifying lung cancer was created. The model, which is based on the ResNet-30 architecture, was trained using a dataset that included the classes "lung_aca," "lung_n," and "lung_scc." Rotation and scaling were added to the dataset as enhancements. The training and validation sets each had 30 samples, and the model attained a high level of accuracy. Loss plots were employed to display the outcomes and the Fastai library was utilised for simple model construction and training. The Fastai and PyTorch versions of this model have been modified for best performance, and it is a potent tool for precise lung cancer categorization.
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Saxena, Pranshu, Sanjay Kumar Singh, Mamoon Rashid, Sultan S. Alshamrani, and Mrim M. Alnfiai. "Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2." PeerJ Computer Science 11 (May 27, 2025): e2903. https://doi.org/10.7717/peerj-cs.2903.

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Background Lung cancer has the highest global fatality rate, with diagnosis primarily relying on histological tissue sample analysis. Accurate classification is critical for treatment planning and patient outcomes. Methods This study develops a computer-assisted diagnosis system for non-small cell lung cancer histology classification, utilizing the FastAI-2 framework with a modified ResNet-34 architecture. The methodology includes stain normalization using LAB colour space for colour consistency, followed by deep learning-based classification. The proposed model is trained on the LC25000 dataset and compared with VGG11 and SqueezeNet1_1, demonstrating modified ResNet-34’s optimal balance between depth and performance. FastAI-2 enhances computational efficiency, enabling rapid convergence with minimal training time. Results The proposed system achieved 99.78% accuracy, confirming the effectiveness of automated lung cancer histopathology classification. This study highlights the potential of artificial intelligence (AI)-driven diagnostic tools to assist pathologists by improving accuracy, reducing workload, and enhancing decision-making in clinical settings.
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Pazhanivelan, Sellaperumal, K. P. Ragunath, N. S. Sudarmanian, S. Satheesh, and P. Shanmugapriya. "Deep Learning-Based Multi-Class Pest and Disease Detection in Agricultural Fields." Journal of Scientific Research and Reports 31, no. 1 (2025): 538–46. https://doi.org/10.9734/jsrr/2025/v31i12797.

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Farmers and agricultural workers would manually inspect crops for signs of pests or use traps to monitor pest populations. The advent of deep learning algorithms such as vision transformers and FastAI ResNet has brought about a significant transformation in pest detection practices. These advanced algorithms leverage the capabilities of artificial intelligence to process vast amounts of data and learn intricate patterns associated with different pest species and their impact on crops. Unlike manual methods, deep learning algorithms can analyze large datasets quickly and accurately, leading to more efficient and effective pest detection. Vision transformers and FastAI ResNet stand out for their ability to continuously learn and adapt to new data, including changes in pest populations over time. This adaptability is crucial in agriculture, where pest dynamics can vary due to factors like climate conditions, environmental changes, and pest control interventions. FastAI ResNet-50 and Vision Transformers have demonstrated remarkable accuracy in classifying various disease classes, indicating their reliability and precision in detecting different pests and diseases affecting crops. Their high accuracies, ranging from 0.95 to 1.00, underscore their effectiveness in agricultural pest detection tasks. However, the study highlights challenges that arise when dealing with more classes in a classification task. Factors such as increased complexity, imbalanced data distributions, and higher-dimensional feature spaces can impact model accuracy. To address these challenges, the study recommends various strategies, including data augmentation, class balancing, robust model architectures, regularization techniques, and transfer learning. Implementing these strategies can help maintain or improve accuracy levels, ensuring that deep learning models remain effective and reliable for agricultural pest detection and disease management applications.
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Zhao, Hongfei. "Automatic Deep Learning-based Histopathologic Image Classification." Journal of Computer Science and Artificial Intelligence 3, no. 2 (2025): 30–34. https://doi.org/10.54097/4m060j94.

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Histopathologic image analysis is a critical component in cancer diagnosis, yet traditional manual inspection methods are often time-consuming, subjective, and error-prone. This study presents a fully automated deep learning framework for the classification of histopathologic images stained with Hematoxylin and Eosin (H&E). Leveraging Convolutional Neural Networks (CNNs), particularly DenseNet and ResNet architectures, the proposed system integrates essential components such as data preprocessing, augmentation, hyperparameter optimization, and training automation using the fastai library. Experiments were conducted on the PCam dataset derived from Camelyon16, comprising over 320,000 labeled image patches. Results demonstrate that DenseNet outperforms ResNet in terms of accuracy and AUC, achieving 84.37% test accuracy and 0.96 AUC. The framework shows high reproducibility, efficiency, and potential for clinical deployment, offering a scalable solution to improve diagnostic accuracy in pathology. Future directions include exploring hybrid models and advanced augmentation techniques to further enhance classification performance.
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Ulu, Banu. "Comparison of deep learning methods in brain tumor diagnosis: High-performance classification with MRI data." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67, no. 1 (2025): 59–73. https://doi.org/10.33769/aupse.1619837.

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Brain tumors are serious health problems that must be diagnosed accurately and in a timely manner in order to provide effective treatment. Magnetic resonance imaging (MRI) is widely used in the detection of brain tumors. The accuracy of MRI results depends on the expertise of the physician and usually requires confirmation with biopsy. In recent years, revolutionary developments in image processing and deep learning technologies have provided significant improvements in the diagnosis and classification of brain tumors using MRI. In this study, it is aimed to classify brain tumors accurately and effectively for four different classes (glioma, meningioma, pituitary, and no tumor) previously created using MRI image data. Four different transfer learning-based deep learning methods for classification; ResNet-18, EfficientNet-B0, DenseNet-121, and ConvNeXt-Tiny, are compared using the Fastai library. Accurate diagnosis of brain tumors is of critical importance in the treatment of patients, and the aim of the study is to achieve high accuracy and speed. Our proposed Fastai library-based EfficientNet-B0 model has achieved both fast and highly successful results in the diagnosis of brain tumors with a 99% accuracy rate and 73 minutes of training performance. In addition, the DenseNet-121 model has achieved highly successful results with 99% accuracy rates, and the ResNet-18 and ConvNeXt-Tiny models have achieved 98% accuracy rates. Our results provide fast and effective insights into the possible uses of deep learning frameworks in the field of medical imaging. In addition, these results provide significant improvements compared to studies in the literature.
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A.S, HARISH, AAGASH SETHUPATHI N,, and VISHAL A.S. "CRICKET POSE PREDICTION USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26048.

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Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes six strokes: pull, square-cut, cover drive, straight drive, flick, and sweep. The study uses the ResNet model to extract features from videos and to predict the strokes. The methodology involves data collection and pre-processing and using pre-trained model ResNet-34 CNN for cricket pose classification.The model captures video frames,preprocess them,predict the strokes.The study achieves an outstanding accuracy of 90.77% using the ResNet model, outperforming the other algorithms used in the study.. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality. KEYWORDS: Cricket shot prediction, deep learning, Fastai CNN, Convolutional Neural Network, real- time video analysis, sports analytics.
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Hubens, Nathan, Matei Mancas, Bernard Gosselin, Marius Preda, and Titus Zaharia. "FasterAI: A Lightweight Library for Neural Networks Compression." Electronics 11, no. 22 (2022): 3789. http://dx.doi.org/10.3390/electronics11223789.

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FasterAI is a PyTorch-based library, aiming to facilitate the use of deep neural network compression techniques, such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick implementation and experimentation. More particularly, compression techniques are leveraging callback systems of libraries, such as fastai and Pytorch Lightning to propose a user-friendly and high-level API. The main asset of FasterAI is its lightweight, yet powerful, simplicity of use. Indeed, because it has been developed in a very granular way, users can create thousands of unique experiments by using different combinations of parameters, with only a single line of additional code. This allows FasterAI to be suited for practical usage, as it contains the most common compression techniques available out-of-the-box, but also for research, as implementing a new compression technique usually boils down to writing a single line of code. In this paper, we propose an in-depth presentation of the different compression techniques available in FasterAI. As a proof of concept and to better grasp how the library is used, we present results achieved by applying each technique on a ResNet-18 architecture, trained on CALTECH-101.
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J, Srilatha, S. Subashini T, and Vaidehi K. "Solid Waste Detection and Recognition using Faster RCNN." Indian Journal of Science and Technology 16, no. 42 (2023): 3778–85. https://doi.org/10.17485/IJST/v16i42.2005.

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Abstract <strong>Objective:</strong>&nbsp;To develop a two-stage object detection method based on convolutional neural networks (CNNs) to identify and classify solid waste, contributing to the creation of intelligent systems for society.&nbsp;<strong>Methods:</strong>&nbsp;The study utilizes a base network, ResNet 101, to generate convolution feature maps. In the first stage, a Region Proposal Network (RPN) is created on top of these convolution features, producing 256-dimensional feature vectors, objectness scores, and bounding rectangles for different anchor boxes. In the next stage, the region proposals are used to train a softmax layer and regressor, enabling the classification and localization of five types of solid waste, namely cardboard, glass, metal, paper and plastic.&nbsp;<strong>Findings:</strong>&nbsp;The proposed Faster RCNN demonstrates nearly real-time object detection rates. Experimental results reveal that the Faster RCNN with ResNet 101 and RPN achieves an accuracy of 96.7%, outperforming the Faster RCNN with a simple CNN, which achieves an accuracy of 86.7%.&nbsp;<strong>Novelty:</strong>&nbsp;Unlike traditional R-CNN, which relies on computationally inefficient selective search, the proposed Faster RCNN employs RPN, a small neural network sliding on the last convolution layer's feature map, predicting object presence and bounding boxes. This approach significantly improves efficiency compared to the exhaustive examination in R-CNN's selective search. <strong>Keywords</strong>: Object Detection, RCNN, Fast RCNN, Faster RCNN, RPN, ROI pooling
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Bayu, Theopilus. "Identifikasi Citra Plasmodium Guna Mendeteksi Parasit Penyakit Malaria dengan Menggunakan Algorithm Convolutional Neural Network." Science Tech: Jurnal Ilmu Pengetahuan dan Teknologi 8, no. 2 (2022): 25–35. http://dx.doi.org/10.30738/st.vol8.no2.a12796.

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Malaria merupakan jenis penyakit yang disebabkan oleh sel protozoa yang dinamakan Plasmodium, dalam perkembangannya sel Plasmodium akan memasuki dan menghancurkan sel-sel darah merah. Deteksi penyakit malaria merupakan sesuatu yang sulit karena membutuhkan waktu yang cukup lama. Parasit malaria dapat di lakukan identifikasi dengan menggunakan microscope untuk melihat apakah ada atau tidaknya Plasmodium dalam sel darah merah. Akan tetapi metode ini sangat bergantung pada kualitas dari microscope. Dikembangkan sebuah model computer vision dengan menggunakan CNN (convolutional neural network). Model dikembangkan dengan melakukan komparasi arsitektur model ResNet-101, AlexNet, dan VGG-19 dengan memberikan dua skenario penentuan learning rate yaitu minimum learning rate dan maximum learning rate with slice. Proses training masing-masing model menggunakan 10 epoch. Digunakan tools/ library fast.ai untuk melakukan pembentukan model yang ada. Hasil dari penelitian yang berjudul Klasifikasi Citra Plasmodium Guna Mendeteksi Parasit Penyakit Malaria dengan menggunakan Algoritma CNN ini didapatkan bahwa arsitektur model ResNet-101 memiliki akurasi yang jauh lebih baik dibandingkan model AlexNet dan VGG-19, baik dengan menggunakan minimum learning rate maupun maximum learning rate, proses training model arsitektur ResNet-101 dengan menggunakan maximum learning rate with slice memiliki akurasi paling baik sebesar 0.97586% dan presisi sebesar 0.98249% dibandingkan dengan arsitektur AlexNet dan VGG-19.
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Dissertations / Theses on the topic "Resnet Fastai"

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Levin, Alexandra, and Najda Vidimlic. "Improving Situational Awareness in Aviation: Robust Vision-Based Detection of Hazardous Objects." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48568.

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Enhanced vision and object detection could be useful in the aviation domain in situations of bad weather or cluttered environments. In particular, enhanced vision and object detection could improve situational awareness and aid the pilot in environment interpretation and detection of hazardous objects. The fundamental concept of object detection is to interpret what objects are present in an image with the aid of a prediction model or other feature extraction techniques. Constructing a comprehensive data set that can describe the operational environment and be robust for weather and lighting conditions is vital if the object detector is to be utilised in the avionics domain. Evaluating the accuracy and robustness of the constructed data set is crucial. Since erroneous detection, referring to the object detection algorithm failing to detect a potentially hazardous object or falsely detecting an object, is a major safety issue. Bayesian uncertainty estimations are evaluated to examine if they can be utilised to detect miss-classifications, enabling the use of a Bayesian Neural Network with the object detector to identify an erroneous detection. The object detector Faster RCNN with ResNet-50-FPN was utilised using the development framework Detectron2; the accuracy of the object detection algorithm was evaluated based on obtained MS-COCO metrics. The setup achieved a 50.327 % AP@[IoU=.5:.95] score. With an 18.1 % decrease when exposed to weather and lighting conditions. By inducing artificial artefacts and augmentations of luminance, motion, and weather to the images of the training set, the AP@[IoU=.5:.95] score increased by 15.6 %. The inducement improved the robustness necessary to maintain the accuracy when exposed to variations of environmental conditions, which resulted in just a 2.6 % decrease from the initial accuracy. To fully conclude that the augmentations provide the necessary robustness for variations in environmental conditions, the model needs to be subjected to actual image representations of the operational environment with different weather and lighting phenomena. Bayesian uncertainty estimations show great promise in providing additional information to interpret objects in the operational environment correctly. Further research is needed to conclude if uncertainty estimations can provide necessary information to detect erroneous predictions.
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Visani, Giacomo. "Sviluppo di nuove formulazioni di resine fast curing e ottimizzazione dei relativi processi di impregnazione e di cura industriale di prepregs." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14389/.

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Una delle tecniche di formatura di materiali compositi più utilizzata è la laminazione a partire da prepregs seguita da un ciclo di cura in autoclave. L’autoclave rappresenta una metodologia di cura abbastanza flessibile nonostante essa sia caratterizzata da elevati tempi di processo a causa della notevole inerzia termica, che comporta una lenta velocità di riscaldamento e raffreddamento. Una delle possibilità per velocizzare il processo in autoclave, ed aumentare quindi la produttività, consiste nell’utilizzare una resina fast curing. Purtroppo, tipicamente le resine fast curing presentano un’elevata reattività, che le rende ideali per processi ad infusione come il Resin Tranfer Molding (RTM) e generalmente altrettanto inadatte alla produzione di preimpregnati. In questo lavoro di Tesi si è ricercato un formulato epossidico contraddistinto da tempi di cura sufficientemente brevi e Tg abbastanza elevate, e si è verificata la possibilità di industrializzare il processo di produzione di un prepreg bastato su tali formulazioni mediante la tecnica dell’impregnazione a solvente, utilizzando una delle resine che hanno mostrato le prestazioni migliori nello screening preventivo. A tale scopo, si è valutato l’effetto del processo, tramite caratterizzazione termica, cinetica e meccanica del prepreg prodotto. In base ai risultati ottenuti è stato, infine, elaborato un ciclo di cura ottimizzato per tale prepreg.
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Westergren, Gisela. "Om jag inte kan göra nog så gör jag istället ingenting." Thesis, Konstfack, Ädellab, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:konstfack:diva-6259.

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It`s blameful to do nothing. You should catch the day and its possibilities. Every second, minute and day, you have the potential to be constantly productive. Do not lose a second of time that you can use to create an imprint of your existence. Do not waste your time when you have the opportunity to create something measurably valuable. Soon you are gone and the competition is great. Doing nothing is a task that fewer and fewer are devoted to, to a lesser extent. We are expected to always be accessible and keep up to date with what is happening around us. There is a constant occupation of our sense of mind, thoughtfulness and rest is unprioritized. We try to maximize the part of our time when we are productive and active to absurdity. The goal seems to make as big imprint of our existence as possible. Fast and more is applauded until the day we burn ourselves out. In my work, I investigate passivity as backlash in a faster world, in a world where focus is on production, efficiency and results. A world where everything worth valuing is measurable. What is really a desired result and is it always possible to measure? Who really gets that gain from our fast life? Can passivity and slowness act as an activist act? Can one passively create something measurable? Is burning time perhaps our time´s most provocative act? With my work, I want to question my constant bad conscience about being insufficient and my feeling of not doing enough. If I can´t do enough, I do nothing instead.<br>Det är skuldbelagt att göra ingenting. Du bör fånga dagen och dess möjligheter. Varje sekund, minut och dag har du potential att vara konstant produktiv. Förlora inte en sekund av tid som du kan använda till att skapa ett avtryck av din existens. Slösa inte med din tid när du har möjlighet att skapa något mätbart värdefullt. Snart är du borta och konkurrensen är stor. Att göra ingenting är en syssla som allt färre ägnar sig åt. Vi förväntas alltid vara tillgängliga och hålla oss uppdaterade om vad som händer runt oss. Det sker ständigt en ockupation av våra sinnesintryck. Eftertänksamhet och vila prioriteras bort. Vi försöker att maximera den del av vår tid då vi är produktiva och aktiva till absurditet. Målet verkar vara att hinna sätta ett så stort avtryck av vår existens som möjligt. Snabbare och mer applåderas till den dagen då vi bränner ut oss. I mitt arbete undersöker jag passivitet som motreaktion till en snabbare värld, i en värld där fokus ligger på produktion, effektivitet och resultat. I en värld där endast det som är mätbart ges ett värde. Vad är egentligen ett önskat resultat och är det alltid möjligt att mäta? Vem är det egentligen som får vinsten av vårt snabba liv? Kan passivitet och långsamhet fungera som en aktivistisk handling? Kan man passivt skapa något mätbart? Är ”att bränna tid”  kanske vår tids mest provokativa handling? Med mitt arbete vill jag ifrågasätta mitt konstanta dåliga samvete om att vara otillräcklig och min känsla av att jag inte gör tillräckligt. Om jag inte kan göra nog så gör jag istället ingenting.
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Santos, Felipe Vieira dos. "Aprendizado profundo aplicado no monitoramento de insetos em culturas de citros." Master's thesis, 2018. http://hdl.handle.net/10198/22839.

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Recentemente, detectou-se em Portugal, o inseto Trioza erytrea, o qual tem a possibilidade de transmitir a doença Huanglongbing (Citrus Greening) prejudicial à cultura dos citrinos. Esta patologia ainda não possui cura e tem alto potencial lesivo ao cultivo. Embora a doença não se manifestou-se em território português, métodos preventivos, baseados no monitoramento da quantidade de Triozas, são indispensáveis para efetivas estratégias de contingência, assim como antecipar uma possível disseminação. O método tradicional de monitoramento é realizado por intermédio de um profissional incumbido de identificar e contabilizar manualmente a Trioza e mais quatro outros insetos (indicadores da presença de Trioza), a partir de armadilhas autocolantes instaladas nas plantações de citros. Esse modo de execução consome tempo considerável, é suscetível a erros e demanda um profissional que nem sempre está disponível. Nesse sentido, o presente trabalho se propôs a desenvolver uma solução computacional, mediante a utilização de Redes Neurais Convolucional (RNC). Isso se deu por meio de uma aplicação, a qual desempenha a função de identificar, classificar e contar automaticamente os insetos citados. Realizou-se o estudo do desempenho de arquiteturas de RNC, as quais possuem a potencialidade de reconhecer insetos a partir de imagens. Como resultado, mostrou-se que a arquitetura SSD com Inception-v2 obteve melhor desempenho na identificação de Trioza. Porém, num primeiro momento, não se tem a viabilização da substituição do método tradicional pela aplicação, isso em decorrência das deficiências nos processos de aprendizagem.<br>Recently, the insect Trioza erytrea was detected in Portugal, which has the possibility of transmitting the disease Huanglongbing (Citrus Greening) harmful to the citrus culture. This pathology has no cure yet and has a high potential for injury to crops. Although the disease did not manifest itself in Portuguese territory, preventive methods, based on monitoring the amount of Triozas, are indispensable for effective contingency strategies, as well as anticipating a possible spread. The traditional method of monitoring is carried out by means of a professional responsible for manually identifying and accounting for Trioza and four other insects (indicators of the presence of Trioza), using self-adhesive traps installed in citrus plantations. This mode of execution consumes considerable time, is susceptible to errors and demands a professional who is not always available. In this sense, the present work proposed to develop a computational solution, through the use of Convolutional Neural Networks (CNN). This was done through an application, which performs the function of automatically identifying, classifying and counting the insects mentioned. The study of the performance of CNN architectures was carried out, which have the potential to recognize insects from images. As a result, it was shown that the SSD architecture with Inception-v2 achieved better performance in identifying Trioza. However, at first, there is no possibility of substituting the traditional method for application, due to deficiencies in the learning processes.
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Books on the topic "Resnet Fastai"

1

Dormann, Gidon. Retsiḥot ba-reshet: Murder in the internet. Tsivʻonim, 2015.

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Ḥafuṭa, Avraham. Sefer Reshit daʻat: Maʼamre maḥshavah ṿe-hashḳafah ṿe-divre ha-taʻarovot. A. Ḥafuṭa, 1996.

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Yonah, Yadid. Sefer Yedid nefesh: Derushim ṿe-ḥidushim be-ṭohar ha-midot, moʻadim ṿe-sefer Be-reshit. Yadid Yonah, 2009.

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Eliʻezer Daṿid ben ʻAmram Grinṿald. Ḳeren le-Daṿid: Ḥidushe Agadah u-derushim le-moʻade H. me-reshit ha-shanah ṿe-ʻad aḥarit ha-shanah. Makhon Amarot ṭehorot Papa, 2008.

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Raiz, Meʼir Aryeh. Sefer Ori ṿe-yishʻi: ʻiyunim u-veʼurim be-ʻinyene yeraḥ ha-etanim u-farshiyot Be-reshit, Noaḥ ṿe-Lekh lekha. Meʼir Aryeh ben Mordekhai Raiz, 2000.

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Huber, Andreas M. Feiertagsschutz für Minderheiten. P. Lang, 2007.

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Kunming yu yun xiang wen hua chuan bo you xian gong si. Kunming shang kong de ying: Zhongguo kong jun Meiguo zhi yuan yuan Hua hang kong dui (fei hu dui) zai Dian yuan Hua kang Ri de chuan qi gu shi = Eagles over Kunming : legend of American volunteer group (flying tigers) in the Chinese air force to assist China and resist Japan. Yunnan mei shu chu ban she, 2015.

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Geisel, Ted. Scrambled eggs super! Random House, 1992.

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Geisel, Ted. Scrambled eggs super! Collins, 2001.

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Be-reshit ha-derekh: ʻal moʻade ha-ḥodesh ha-sheviʻi ha-ʻIvriyim ba-Miḳra = The Hebrew biblical festivals of the seventh month. Reʼuven Mas, 2010.

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Book chapters on the topic "Resnet Fastai"

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Lasko, Wolf W., and Lara M. Lasko. "Result Frames take hold faster than change processes." In International Sales Steering by Result Framing. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-06352-8_4.

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Hallonsten, Olof. "Faster, Better, Stronger." In Empty Innovation. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31479-7_5.

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AbstractInsightful sociological analyses of current society have identified acceleration as its defining feature. A competitiveness ideal, and a constant aspiration to exceed the promise of a better future with the help of technological progress, have led to a broad exploitation of vague but powerful expectations The result is often mere window-dressing, but progress has also become an identity marker for a class of politicians and business leaders who look for roles for themselves to play in the fulfillment of promises, both “moving fast and breaking things” and launching huge and very costly strategic innovation programs. The chapter analyzes these features of contemporary public and economic life and question the validity of the underlying assumptions.
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Devi, S. Kiruthika, and C. N. Subalalitha. "Assisting the Visually Challenged People Using Faster RCNN with Inception ResNet V2 Based Object Detection Model." In Pervasive Computing and Social Networking. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_14.

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DhivyaShree, M., K. R. Sarumathi, and R. S. Vishnu Durai. "An Ensemble Model for Face Mask Detection Using Faster RCNN with ResNet50." In Artificial Intelligence and Speech Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95711-7_48.

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Chowdary, Chittineni Harshitha, and C. Rohith Bhat. "Blade Surface Defect Detection in Images Using Faster ResNet50 and Xception for Better Accuracy." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-5223-5_41.

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Donkers, Huib, Bart M. P. Jansen, and Jari J. H. de Kroon. "Finding k-Secluded Trees Faster." In Graph-Theoretic Concepts in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15914-5_13.

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AbstractWe revisit the k-Secluded Tree problem. Given a vertex-weighted undirected graph G, its objective is to find a maximum-weight induced subtree T whose open neighborhood has size at most k. We present a fixed-parameter tractable algorithm that solves the problem in time $$2^{\mathcal {O} (k \log k)}\cdot n^{\mathcal {O} (1)}$$, improving on a double-exponential running time from earlier work by Golovach, Heggernes, Lima, and Montealegre. Starting from a single vertex, our algorithm grows a k-secluded tree by branching on vertices in the open neighborhood of the current tree T. To bound the branching depth, we prove a structural result that can be used to identify a vertex that belongs to the neighborhood of any k-secluded supertree $$T' \supseteq T$$ once the open neighborhood of T becomes sufficiently large. We extend the algorithm to enumerate compact descriptions of all maximum-weight k-secluded trees, which allows us to count the number of such trees containing a specified vertex in the same running time.
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Poh, Chyong Yi, and Soo Siang Teoh. "Performance Evaluation of Optic Disc Detection Using Faster RCNN with Alexnet, Resnet50 and Vgg19 Convolutional Neural Networks." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8129-5_115.

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Sieksmeyer, Jan, Anne Klepp, Valentina Niccolai, Jacqueline Metzlaff, Alfons Schnitzler, and Katja Biermann-Ruben. "Influence of Manner Adverbs on Action Verb Processing." In Language, Cognition, and Mind. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50200-3_20.

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AbstractLanguage-motor interaction is suggested by the involvement of motor areas in action-related language processing. In a double-dissociation paradigm we aimed to investigate motor cortical involvement in the processing of hand- and foot-related action verbs combined with manner adverbs. In two experiments using different tasks, subjects were instructed to respond with their hand or foot following the presentation of an adverb-verb combination. Experiment 1, which prompted reactions via color changes of the stimuli combined with a semantic decision, showed an influence of manner adverbs on response times. This was visible in faster responses following intensifying adverbs compared with attenuating adverbs. Additionally, an interaction between implied verb effector and response effector manifested in faster response times for matching verb-response conditions. Experiment 2, which prompted reactions directly by the adverb type (intensifying vs. attenuating), revealed an interaction between manner adverbs and response effector with faster hand responses following intensifying compared with attenuating adverbs. Additional electroencephalography (EEG) recordings in Experiment 2 revealed reduced beta-desynchronization for congruent verb-response conditions in the case of foot responses along with faster response times. Yet, a direct modulation of verb-motor priming by adverbs was not found. Taken together, our results indicate an influence of manner adverbs on the interplay of language processing and motor behavior. Results are discussed with respect to embodied cognition theories.
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Bellia, Andrea Francesca, Julian Evans, and Sandro Lanfranco. "A Drone’s Eye View: A Preliminary Assessment of the Efficiency of Drones in Mapping Shallow-Water Benthic Assemblages." In Proceedings e report. Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-147-1.50.

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The study assesses consumer drone efficiency for surveying shallow-water benthic cover. We hypothesised that using a drone would reduce duration, and manpower requirements, while increasing accuracy, relative to manual surveys. Results obtained during this study clearly indicated that automated drone surveys were faster and more accurate than manual survey methods under most circumstances. This result has important implications for the scientific and economic aspects of the process and would have a multiplicative effect in monitoring programs that require regular surveys.
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Ghymers, Christian. "Systemic Potential Aspects of CBDCs." In United Nations University Series on Regionalism. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68475-3_13.

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AbstractCentral Bank Digital Currencies (CBDCs) appear as a necessary step for preserving monetary sovereignty and seizing the benefits of digitalization for the efficiency of payments and financial inclusion. Their blockchain technology permits significantly faster, safer, cheaper payments than cash or bank accounts, particularly for cross-border payments. They will probably affect bank profitability, increasing competition for funding them and they will also increase the effects of interest rate adjustments on the economy (higher efficiency of monetary policy). But, more importantly, CBDCs carry the necessary potential to shake up the status quo of the international monetary system thanks to their technological properties. Basically, CBDCs eradicate the difference between domestic and cross-border transactions, eliminating the intermediary costs, risks (credit and liquidity risks), and delays required by the traditional cross-border payments. Moreover, they might act as a systemic “game-changer” by triggering two mutually supportive developments: (i) a strong increase in currency substitution implying higher exchange-rate volatility and (ii) a fast-growing demand for digital SDRs that will become costless by bundling and unbundling their five CBDCs components, therefore providing the most competitive safe asset and the best vehicle for the Forex and “repo” transactions. This e-SDR could become the missing tool for stabilizing global liquidity and emerge as a multilateral reserve currency, which would upgrade the IMF into a global Lender-of-Last-Resort. The IMF could issue or withdraw e-SDRs for stabilizing global liquidity, and the costly Triffin Dilemma could be efficiently solved.
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Conference papers on the topic "Resnet Fastai"

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Chen, Sirui, Tengfei Yu, Wen He, Yicheng Wang, Qi Lei, and Zhongbao Chen. "Detecting and Classifying Invasive Breast Carcinoma by the Faster R-CNN with Resnet 50." In 2024 IEEE/ACIS 24th International Conference on Computer and Information Science (ICIS). IEEE, 2024. https://doi.org/10.1109/icis61260.2024.10778358.

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Løe, David, Michael Chusid, and Steven H. Miller. "Densify Faster, Densify Cleaner, Densify Greener: Enhancing Polished Concrete Floors." In Paint and Coatings Expo (PACE) 2010. SSPC, 2010. https://doi.org/10.5006/s2010-00029.

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Summary Specifying polished concrete floors can reduce a project’s material- and energy-consumption in both new construction and remodels. As a high-performance, low-maintenance floor, it is sustainable throughout its lifecycle. The only gap in its green profile has been the chemistry used to densify concrete in preparation for polishing. New, less caustic colloidal silica-based densifiers solve this problem, as they are safer for workers and less burdensome on the environment. Their use can also help contain costs. Polished concrete’s growing popularity stems from its aesthetic options, competitive cost benefits, and sustainability.1 In particular, its sustainability derives from being ‘already there.’ Leaving concrete exposed as a finish eliminates the material and energy consumption associated with adding a floorcovering, as well as the volatile organic compound (VOC) emissions that result from the installation and maintenance of many types of flooring materials. The only flaw in the material’s sustainability has been the environmental impact of concrete densifiers used in the polishing process itself. However, a new generation of concrete-densifying chemicals can now rectify this problem. Densifiers based on colloidal silica are replacing the older, highly caustic, silicate-based chemicals—providing polishing systems that are safer to handle for applicators and that eliminate problems associated with disposing hazardous materials after application. Furthermore, colloidal silica-based products penetrate deeply and react more effectively with concrete, producing greater consistency. They are also faster to apply, which reduces the number of steps and potentially lowers finishing costs.
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Xing, Cheng, Chunyu Zhang, Min Zhang, et al. "Alarm Priority Sorting with Multimodal Data Fusion in Optical Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3h.5.

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An alarm priority sorting scheme using multimodal data fusion is proposed, achieving 95.79% fault-related alarm classification accuracy, which has actual alarm priority ranking result and faster running time than traditional method.
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Meli, P. I., and B. N. Morse. "New Developments in 100% Solids Fast Curing, Epoxy Technology for Protecting Tanks and Pipe." In CORROSION 2000. NACE International, 2000. https://doi.org/10.5006/c2000-00179.

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Abstract Epoxy coatings and modified epoxy coatings have been successfully used across a broad spectrum of corrosive environments for almost 50 years. This 'work-horse' technology continues to evolve coating products that offer higher chemical resistance, and faster cure times while reducing health and environmental hazards. The development of solvent-free, thick-film, hybrid cycloaliphatic epoxies is discussed with respect to their laboratory test performance, application requirements, and effectiveness of field and shop applications.
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Trieu, Quang, and George Nehmetallah. "Fast Deep Coherence Holography (FDCH) for 3D Object Reconstruction." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.m2a.3.

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We propose a fast and position independent deep learning based coherence holography method to reconstruct the total 3D object from two interferograms at a fixed camera location yielding a better result in terms of accuracy and time.
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Rastus Shane, J., and V. Vanitha. "Sign Language Detection Using Faster RCNN Resnet." In 2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2023. http://dx.doi.org/10.1109/icaeca56562.2023.10200987.

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FAN, RONG, Sei-ichiro Kamata, and Yawen Chen. "Pulmonary nodule detection using improved faster R-CNN and 3D Resnet." In Thirteenth International Conference on Digital Image Processing, edited by Xudong Jiang and Hiroshi Fujita. SPIE, 2021. http://dx.doi.org/10.1117/12.2599884.

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Ismail, Muhammad Irfan Mohd, and Aun Naa Sung. "Acne lesion and wrinkle detection using faster R-CNN with ResNet-50." In INDUSTRIAL, MECHANICAL AND ELECTRICAL ENGINEERING. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0110831.

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Adole, Anthony, Eran Edirisinghe, Baihua Li, and Chris Bearchell. "Investigation of Faster-RCNN Inception Resnet V2 on Offline Kanji Handwriting Characters." In PRIS 2020: 2020 International Conference on Pattern Recognition and Intelligent Systems. ACM, 2020. http://dx.doi.org/10.1145/3415048.3416104.

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Xie, Qubo, Ke Zhou, and Xiaohu Fan. "A Scene Text Detection Algorithm based on ResNet and Faster R-CNN." In AICS 2019: 2019 International Conference on Artificial Intelligence and Computer Science. ACM, 2019. http://dx.doi.org/10.1145/3349341.3349521.

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Reports on the topic "Resnet Fastai"

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Lehotay, Steven J., and Aviv Amirav. Ultra-Fast Methods and Instrumentation for the Analysis of Hazardous Chemicals in the Food Supply. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7699852.bard.

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Original proposal objectives: Our main original goal was to develop ultra-fast methods and instrumentation for the analysis of hazardous chemicals in the food supply. We proposed to extend the QuEChERS approach to veterinary drugs and other contaminants, and conduct fast and ultra-fast analyses using novel 5MB-MS instrumentation, ideally with real samples. Background to the topic: The international trade of agricultural food products is a $1.2 trill ion annual market and growing. Food safety is essential to human health, and chemical residue limits are legislated nationally and internationally. Analytical testing for residues is needed to conduct risk assessments and regulatory enforcement actions to ensure food safety and environmental health, among other important needs. Current monitoring methods are better than ever, but they are still too time-consuming, laborious, and expensive to meet the broad food testing needs of consumers, government, and industry. As a result, costs are high and only a tiny fraction of the food is tested for a limited number of contaminants. We need affordable, ultra-fast methods that attain high quality results for a wide range of chemicals. Major conclusions, solutions and achievements: This is the third BARD grant shared between Prof. Amirav and Dr. Lehotay since 2000, and continual analytical improvements have been made in terms of speed, sample throughput, chemical scope, ease-of-use, and quality of results with respect to qualitative (screening and identification) and quantitative factors. The QuEChERS sample preparation approach, which was developed in conjunction with the BARD grant in 2002, has grown to currently become the most common pesticide residue method in the world. BARD funding has been instrumental to help Dr. Lehotay make refinements and expand QuEChERS concepts to additional applications, which has led to the commercialization of QuEChERS products by more than 20 companies worldwide. During the past 3 years, QuEChERS has been applied to multiclass, multiresidue analysis of veterinary drug residues in food animals, and it has been validated and implemented by USDA-FSIS. QuEChERS was also modified and validated for faster, easier, and better analysis of traditional and emerging environmental contaminants in food. Meanwhile, Prof. Amirav has commercialized the GC-MS with 5MB technology and other independent inventions, including the ChromatoProbe with Agilent, Bruker, and FUR Systems. A new method was developed for obtaining truly universal pesticide analysis, based on the use of GC-MS with 5MB. This method and instrument enables faster analysis with lower LaDs for extended range of pesticides and hazardous compounds. A new approach and device of Open Probe Fast GC-MS with 5MB was also developed that enable real time screening of limited number of target pesticides. Implications, both scientific and agricultural: We succeeded in achieving significant improvements in the analysis of hazardous chemicals in the food supply, from easy sample preparation approaches, through sample analysis by advanced new types of GC-MS and LCMS techniques, all the way to improved data analysis by lowering LaD and providing greater confidence in chemical identification. As a result, the combination of the QuEChERS approach, new and superior instrumentation, and the novel monitoring methods that were developed will enable vastly reduced time and cost of analysis, increased analytical scope. and a higher monitoring rate. This provides better enforcement, an added impetus for farmers to use good agricultural practices, improved food safety and security, increased trade. and greater consumer confidence in the food supply.
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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the internal twinned model, ResNet50 and MobileNetV2, with the former historically having higher accuracy and the latter historically having faster processing time. When trained and tested on experimentally obtained GPR profiles with synthetically added changes, ResNet50 had a higher accuracy. Thanks to this higher accuracy, less computational processing was needed, leading to ResNet50 needing only 107 s to make a prediction compared to MobileNetV2 needing 223 s. Results imply that twinned models with higher historical accuracies should be investigated further. It is also recommended to test Siamese Twin models further with experimentally produced changes to verify the changed detection model’s accuracy is not merely specific to synthetically produced changes.
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Wei, Shang-Jin, and Xinding Yu. Characterizing Regionalism in Asia: A Modern Global Supply Chain Perspective. Asian Development Bank, 2022. http://dx.doi.org/10.22617/wps220377-2.

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This paper quantifies economic interdependence among Asian economies and between Asia and the rest of the world using a modern global value chain decomposition framework. It shows that global value chain linkages in value-added terms across economies have grown faster than value-added linkages through final goods trade. Stronger value-added linkages between two economies tends to make it more likely for the pair to have membership in the Regional Comprehensive Economic Partnership or Comprehensive and Progressive Agreement for Trans-Pacific Partnership.
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Iurasova, Olga, and Jelena Dementjeva. Impact of Manufacturing Sector Development on Economic Growth. Vilnius Business College, 2024. https://doi.org/10.57005/ab.2024.3.3.

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This study presents a model of GDP development based on production sectors. Analysis of actual data reveals the presence of several oscillatory processes, which are associated with the heterogeneity of investments and the aggregation of various production processes with their unique characteristics in statistical data. Fluctuations in aggregated macroeconomic indicators result from the superposition of different oscillations and do not exhibit strictly defined periods, particularly for long-term fluctuations. The model indicates that these fluctuations are asymmetrical relative to extreme points, consistent with actual data. This means the decline in production volumes after reaching a maximum is faster than the rise before the maximum. The analysis provides a framework for forecasting production development.
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Loecker, Florian, Amanah Ramadiah, and Kimmo Soramäki. Countering Consumer Fraud and Scams with National Fraud Portals. FNA, 2024. http://dx.doi.org/10.69701/oppl1525.

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In this paper, we argue that setting up a new Digital Public Infrastructure - a National Fraud Portal (NFP) - is the only way to address fraud and consumer scams efficiently. NFPs provide a technological solution as a shared facility for banks, law enforcement, the Financial Intelligence Unit (FIU), the central bank, the conduct supervisor, and other stakeholders. Further down the line, NFPs can connect to one another as cross-border criminal activity increases (a likely consequence of suppressing fraud domestically). The National Fraud Portal (NFP) enables: The real-time tracing and tracking of fund movements across the banking system that allow banks to recover funds for victims quickly The validation and prioritization of cases across the economy using data-driven models The faster identification of new mule accounts at a reduced cost More accurate methods for fraud detection and risk scoring that employ Graph AI deployed on network data. The real-time provision of risk scores and features to banks via APIs, allowing them to improve their fraud models and make faster, more accurate decisions about preventing fraudulent payments before settlement. In this paper, we detail the technological components of the National Fraud Portal. The paper is a result of FNA’s work building technology for National Fraud Portals in Southeast Asia, as well as conversations with over 100 institutions across 20 countries that are actively working on, or have an interest in tackling the problem of fraud and scams.
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Micco, Alejandro, and Alberto E. Chong. The Internet and the Ability to Innovate in Latin America. Inter-American Development Bank, 2002. http://dx.doi.org/10.18235/0010801.

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In this paper we provide an overview of the situation of the Internet in Latin America and argue that, although latecomers, Latin American countries could in principle catch up at a faster pace and a lower cost. But that will depend on the environment for innovation in the countries; in that respect, the adoption of the Internet may prove to be no different than other technological changes. The paper also discusses how the degree of innovativeness in a country helps explain the extent to which new technologies may be more effectively absorbed. What is surprising about this relationship is that it is valid even when isolating the fact that countries with better telephone infrastructure are also the ones with more Internet hosts. We found that the capacity to innovate and assimilate new technologies is not just a matter of income or infrastructure endowment.
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López-Valverde, Nansi, Javier Aragoneses, Antonio López-Valverde, Cinthia Rodríguez, and Juan Manuel Aragoneses. Role in the osseointegration of titanium dental implants, of bioactive surfaces based on biomolecules: A systematic review and meta-analysis of in vivo studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.6.0076.

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Review question / Objective: Does the bioactive surface of titanium dental implants, based on biomolecules, influence osseointegration?. The aim of our study was to evaluate the role and efficacy of bioactive surfaces in osseointegration. Our review study limited the research interest to titanium dental implants coated with a biomolecule, i.e., an organic molecule produced by a living organism. Condition being studied: In recent years, much attention has been paid to topographical modifications of dental implant surfaces, as well as to their coating with biologically active substances.a bioactive surface is one capable of achieving faster and higher quality osseointegration, shortening waiting times and solving situations of poor bone quality. Molecules that can be applied for bioactive purposes include bioceramics, ions and biomolecules. Collagen and bone morphogenetic protein have been suggested as bone stimulating agents. Biofunctionalization of the implant surface with a biomimetic active peptide has also been shown to result in a significant increase in bone-to-implant ratios and an increase in peri-implant bone density.
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Granados, Camilo, and Daniel Parra-Amado. Output Gap Measurement after COVID for Colombia: Lessons from a Permanent-Transitory Approach. Banco de la República, 2025. https://doi.org/10.32468/be.1295.

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We estimate the output gap for the Colombian economy explicitly accounting for the COVID-19 period. Our estimates reveal a significant $20$\% decline in the output gap but with a faster recovery compared to previous crises. Our empirical strategy follows a two-stage Bayesian vector autoregressive (BSVAR) model where i) a scaling factor in the reduced form of VAR is used to model extreme data, such as those observed around the COVID-19 period, and ii) permanent and transitory shocks are structurally identified. As a result, we obtain that a single structural shock explains the potential GDP, while the remaining shocks within the model are transitory in nature and thus can be used to estimate the output gap. We elaborate on the relative strengths of our method for drawing policy lessons and show that the improved approximation accuracy of our method allows for inflation forecasting gains through the use of Phillips curves, as well as for rule-based policy diagnostics that align more closely with the observed behavior of the Central Bank.
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Cunningham, Stuart, Marion McCutcheon, Greg Hearn, Mark Ryan, and Christy Collis. Australian Cultural and Creative Activity: A Population and Hotspot Analysis: Sunshine Coast. Queensland University of Technology, 2020. http://dx.doi.org/10.5204/rep.eprints.136822.

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The Sunshine Coast (unless otherwise specified, Sunshine Coast refers to the region which includes both Sunshine Coast and Noosa council areas) is a classic regional hotspot. In many respects, the Sunshine Coast has assets that make it the “Goldilocks” of Queensland hotspots: “the agility of the region and our collaborative nature is facilitated by the fact that we're not too big, not too small - 330,000 people” (Paddenburg, 2019); “We are in that perfect little bubble of just right of about everything” (Erbacher 2019). The Sunshine Coast has one of the fastest-growing economies in Australia. Its population is booming and its local governments are working together to establish world-class communications, transport and health infrastructure, while maintaining the integrity of the region’s much-lauded environment and lifestyle. As a result, the Sunshine Coast Council is regarded as a pioneer on smart city initiatives, while Noosa Shire Council has built a reputation for prioritising sustainable development. The region’s creative economy is growing at a faster rate that of the rest of the economy—in terms of job growth, earnings, incomes and business registrations. These gains, however, are not spread uniformly. Creative Services (that is, the advertising and marketing, architecture and design, and software and digital content sectors) are flourishing, while Cultural Production (music and performing arts, publishing and visual arts) is variable, with visual and performing arts growing while film, television and radio and publishing have low or no growth. The spirit of entrepreneurialism amongst many creatives in the Sunshine Coast was similar to what we witnessed in other hotspots: a spirit of not necessarily relying on institutions, seeking out alternative income sources, and leveraging networks. How public agencies can better harness that energy and entrepreneurialism could be a focus for ongoing strategy. There does seem to be a lower level of arts and culture funding going into the Sunshine Coast from governments than its population base and cultural and creative energy might suggest. Federal and state arts funding programs are under-delivering to the Sunshine Coast.
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Xu, Xun, Shiqi Ou, Tianduo Peng, et al. The Potential Role of Truck-Hailing and Operational Efficiency Improvement in Decarbonizing China’s Medium- and Heavy-Duty Road Freight Transport. King Abdullah Petroleum Studies and Research Center, 2025. https://doi.org/10.30573/ks--2025-dp04.

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Truck-hailing is a relatively new Uber-like business model that connects road freight carriers with shippers via mobile apps. First appeared around 2013, it has achieved fast market uptake in China, involving almost 8 million commercial trucks annually by the end of 2023. With China being one of the world’s largest road transport carbon emitters, it is crucial to understand the potential climate implications of this emergent trend. Here we utilize a large national proprietary truck-hailing sample and a transport-energy-emission model to explore the potential role of truck-hailing and logistics improvement under multiple scenarios. We found that under optimistic scenarios, logistics improvements as a potential result of high market penetration of truck-hailing services could significantly reduce road freight emissions in China, and there could be potential synergies between logistics improvements and technological advancement. We also found that operational performance limitations (range and capacity) of zero-emission vehicles could have moderate emission impacts.
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