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Internet-based psychological treatments for depression have been around for more than 20 years. There has been a continuous line of research with new research questions being asked and studies conducted. In this paper, the author reviews studies with a focus on papers published from 2020 and onwards based on a Medline and Scopus search. Internet-based cognitive behavior therapy (ICBT) programs have been developed and tested for adolescents, older adults, immigrant groups and to handle a societal crisis (e.g. COVID-19). ICBT works in regular clinical settings and long-term effects can be obtained. Studies on different treatment orientations and approaches such as acceptance commitment therapy, unified protocol, and tailored treatments have been conducted. Effects on quality-of-life measures, knowledge acquisition and ecological momentary assessment as a research tool have been reported. Factorial design trials and individual patient data meta-analysis are increasingly used in association with internet intervention research. Finally, prediction studies and recent advances in artificial intelligence are mentioned. Internet-delivered treatments are effective, in particular if therapist guidance is provided. More target groups have been covered but there are many remaining challenges including how new tools like artificial intelligence will be used when treating depression. [ABSTRACT FROM AUTHOR]

Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: ‘delayed graft function’, ‘kidney transplantation’, ‘renal transplantation’, ‘survival’, and ‘recipients’. These emerging trends include ‘brain death donors’, ‘blood absence re-injection injuries’, ‘tacrolimus’, ‘older donors and recipients’, and ‘artificial intelligence and DGF’. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF. [ABSTRACT FROM AUTHOR]

The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions' space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems. [ABSTRACT FROM AUTHOR]

This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into subcategories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models' performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA). [ABSTRACT FROM AUTHOR]

The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions. [ABSTRACT FROM AUTHOR]

Most manufactured electrical cables suffer from reductions in their physical, mechanical and electrical properties. These setbacks are mainly attributed to the improper control of wire tension during the cable manufacturing process. Hence, this paper systematically reviewed different control algorithms involved in controlling tension in moving webs, which include conventional control, advanced control, observer-based control, artificial intelligence-based control and hybrid control techniques. Thus, the review provided information about existing tension control techniques in moving webs, including their strengths and weaknesses. It was observed in this review that although a significant research effort has been made on web tension control systems, a thorough literature review is still lacking. It was concluded that controller optimisation using hybrid control algorithms is gaining popularity in web tension control due to their improved control response. Hence, its application in wire tension control can better help cable manufacturers improve the quality of manufactured cables. [ABSTRACT FROM AUTHOR]

The current sea monitoring equipment's are being used for a variety of purposes around the world. Currently used vehicles have some drawbacks. The first is the high fuel cost. The Vehicle engines cost more fuel as they have to release more power and environment and pollution. As well as not being able to stay under the sea for long days, there will often be a need for vehicles to come to the surface to refuel. The second is the vibrations and noise of these vehicles. The vibrations caused by these can be detrimental to the biodiversity of the ocean. Also, the noise makes it easier for enemies to identify our vehicles. Similarly when these vehicles go under water, water waves form on the surface. With this in mind, radar can detect what a vehicle under the sea looks like. In this paper, an artificial intelligence based microbiological model was proposed to monitor the sea level. With this biological model can greatly reduce fuels. It can get more capacity than normal vehicles. As fuel consumption decreases, so it does environmental pollution and since it operates quietly and without high vibrations, there is no threat to the biodiversity of the ocean. [ABSTRACT FROM AUTHOR]

The United Nations established the 17 Sustainable Development Goals (SDGs) in 2015 to address issues like gender equality, clean water, health, education, and hunger by 2030. Of the 17 SDGs, health and education have an outsized impact on countries’ socioeconomic development, so providing insights into progress on these two goals is crucial. Machine learning can help solve many real-world problems, including working towards the SDGs. This paper proposes using a metaheuristic ensemble of Cat Swarm Optimization algorithms with Feed Forward Extreme Learning Machines, called Sustainable Health And Educational Goal Development (SHEGD) Prediction, to effectively contribute to countries’ economic growth by achieving health and education SDGs through machine learning. The model is assessed using UN SDG datasets and performance metrics like accuracy, precision, recall, specificity, and F1-score. Comparisons to other machine learning models demonstrate this model’s superiority in designing a recommendation system for progressing towards the health and education SDGs. The proposed model outperforms the other approaches, proving its value for an SDG recommendation system design. [ABSTRACT FROM AUTHOR]

In this study, a bibliometric analysis technique is used for performance analysis and science mapping of artificial intelligence (AI) applications in streamflow research. This paper examines the current trends in the literature using the Scopus database over the last 37 years. RStudio Bibliometrix software was used to analyse the titles, keywords, abstracts, and full texts of 3000 publications to identify trends in AI models, publication types, journals, citations, authors, countries, and regions. The highest frequency AI-related keyword is "artificial neural networks," which was used in a total of 25587 times. The most common publication type, at 82.1%, is journal articles, and the highest rate of country production is 25% for China. In recent years, streamflow research studies have significantly increased their use of AI applications. [ABSTRACT FROM AUTHOR]

The prediction of final semester grades is a crucial undertaking in education, offering insights into student performance and enabling timely interventions to support their academic journey. This paper employs a deep learning approach, specifically gated recurrent unit (GRU), in conjunction with feature optimization using analysis of variance (ANOVA), to forecast final semester grades. The predictive model is trained and evaluated on a handcrafted grade prediction dataset, which contains the academic performance of the students during Plus 2 and from Semester 1 to Semester 4 of a group of Computer science and Engineering majors in Kerala. By processing historical academic records and contextual information, the GRU model learns to predict future performance accurately. To enhance the model's efficacy and interpretability, ANOVA is applied to optimize the feature selection process. This statistical technique identifies the most influential factors contributing to final grades, refining the model's predictive power while reducing dimensionality. The experimental results showcase the model's effectiveness in predicting final semester grades, demonstrating superior accuracy and performance compared to grade prediction using CNN with Bayesian optimization and LSTM with L1-Norm optimization. [ABSTRACT FROM AUTHOR]

The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs. [ABSTRACT FROM AUTHOR]

According to World Health Organization (WHO) data, cardiovascular diseases (CAD) continue to take the lives of more than 17.9 million people worldwide each year. Heart attacks are considered a fatal disease in this category, especially for older adults, which highlights the need to employ artificial intelligence to anticipate this disease. This research faces many challenges, starting with data quality and availability, where AI models require large and high-quality datasets for training. Elderly populations exhibit various health conditions, lifestyle factors, and genetic diversity. Creating AI models that can accurately generalize across such a diverse group can be challenging. Two datasets for CAD diseases were used for this study. Traditional machine learning (ML) techniques were used on these datasets, as well as a neural network method based on extreme learning machines (ELM), which provided varying percentages of accuracy, time, and average estimated error. The ELM algorithm outperformed all other algorithms by attaining the best accuracy, the shortest execution time, and the lowest percentage of average estimated error. Experimental results showed that the Extreme learning machine performed well with 200 hidden neurons, even with the proposed absence of parts of the dataset, with an accuracy of 97.57-99.06%. [ABSTRACT FROM AUTHOR]

Introduction: The rapid advancement of technology and the integration of innovative medical devices are significantly transforming medical education. This review examines the impact of these changes and the importance of adapting educational strategies to leverage these advancements. Areas covered: This narrative review employs a qualitative approach. From an initial pool of 294 articles, researchers conducted independent screenings and identified 134 studies relevant to innovations in technology and their impact on medical education. Following a comprehensive review and consensus, studies deemed to be of low relevance were excluded, resulting in a final selection of 74 articles. An expert panel discussion was held, and the study concludes with a final section that presents the findings and offers brief, clear recommendations. Expert opinion: This study indicates that the utilization of Innovative medical technologies has the potential to enhance learning outcomes. The use of simulations allows students to engage in hands-on practice without risking patient harm. Mobile devices afford students uninterrupted access to educational resources, thereby enabling efficient learning. Artificial intelligence (AI) has the potential to personalize education, enhance diagnostic skills, and foster critical thinking. Further research in this field has the potential to yield significant insights [ABSTRACT FROM AUTHOR]

Introduction: For over 60 years, spinal cord stimulation has endured as a therapy through innovation and novel developments. Current practice of neuromodulation requires proper patient selection, risk mitigation and use of innovation. However, there are tangible and intangible challenges in physiology, clinical science and within society. Areas covered: We provide a narrative discussion regarding novel topics in the field especially over the last decade. We highlight the challenges in the patient care setting including selection, as well as economic and socioeconomic challenges. Physician training challenges in neuromodulation is explored as well as other factors related to the use of neuromodulation such as novel indications and economics. We also discuss the concepts of technology and healthcare data. Expert opinion: Patient safety and durable outcomes are the mainstay goal for neuromodulation. Substantial work is needed to assimilate data for larger and more relevant studies reflecting a population. Big data and global interconnectivity efforts provide substantial opportunity to reinvent our scientific approach, data analysis and its management to maximize outcomes and minimize risk. As improvements in data analysis become the standard of innovation and physician training meets demand, we expect to see an expansion of novel indications and its use in broader cohorts. [ABSTRACT FROM AUTHOR]

Introduction: Due to the complex anatomy of the spine and the intricate surgical procedures involved, spinal surgery demands a high level of technical expertise from surgeons. The clinical application of image-guided spinal surgery has significantly enhanced lesion visualization, reduced operation time, and improved surgical outcomes. Areas covered: This article reviews the latest advancements in deep learning and artificial intelligence in image-guided spinal surgery, aiming to provide references and guidance for surgeons, engineers, and researchers involved in this field. Expert opinion: Our analysis indicates that image-guided spinal surgery, augmented by artificial intelligence, outperforms traditional spinal surgery techniques. Moving forward, it is imperative to collect a more expansive dataset to further ensure the procedural safety of such surgeries. These insights carry significant implications for the integration of artificial intelligence in the medical field, ultimately poised to enhance the proficiency of surgeons and improve surgical outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research. Areas covered: This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors. Expert opinion: The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection. [ABSTRACT FROM AUTHOR]

Theranostics targeting prostate-specific membrane antigen (PSMA) represent a new targeted approach for prostate cancer care that combines diagnostic and therapeutic radiopharmaceuticals to diagnose and treat the disease. Positron emission tomography (PET) is the imaging method of choice and several diagnostic radiopharmaceuticals for quantifying PSMA have received FDA approval and are in clinical use. [68Ga]Ga-PSMA-11 is one such imaging agent and the focus of this article. One beta-emitting radioligand therapy ([177Lu]Lu-PSMA-617) has also received FDA approval for prostate cancer treatment, and several other alpha- and beta-emitting radioligand therapies are in clinical trials. Theranostics targeting PSMA in men with prostate cancer are discussed with a focus on use of [68Ga]Ga-PSMA-11 for imaging PSMA-positive lesions in men with prostate cancer. The review covers [68Ga]Ga-PSMA-11 manufacture, current regulatory status, comparison of [68Ga]Ga-PSMA-11 to other imaging techniques, clinical updates, and emerging applications of artificial intelligence for [68Ga]Ga-PSMA-11 PET. [68Ga]Ga-PSMA-11 is used in conjunction with a PET/CT scan to image PSMA positive lesions in men with prostate cancer. It is manufactured by chelating precursor with68Ga, either from a generator or cyclotron, and has regulatory approval around the world. It is widely used clinically in conjunction with radioligand therapies like [177Lu]Lu-PSMA-617. [ABSTRACT FROM AUTHOR]

As pricing in hospitality and tourism evolves due to the opportunities presented by artificial intelligence (AI), so do the concerns that come with technological advancements. There is a fine line between what is possible, what is profitable, and what is ethical, sustainable and responsible in the use of AI for pricing. This viewpoint article draws attention to the dark side of pricing, and presents a framework towards sustainable pricing, against the backdrop of the current European Union legal framework. The framework includes ethical guidelines, self-regulation, self-protection and technological regulation, which should be considered as a whole. [ABSTRACT FROM AUTHOR]

This article aims to offer a heterodox perspective on the productivity-pay gap in the United States in light of the ongoing disruptive changes within leading industries driven by the widespread adoption of artificial intelligence (AI). We present robust arguments that the application of AI-type technologies across various industries and domains is likely to not only exacerbate the existing productivity-pay gap but also change the way this gap is generated. We suggest that deploying decision-making AI algorithms in economic processes could empower capital-technology owners at the expense of the traditional managerial technostructure, potentially re-establishing the possession of capital as the main driver of income inequality. In addressing the potential economic and social costs of the current trajectory of AI technologies, some of the major concepts in heterodox economics may prove useful. [ABSTRACT FROM AUTHOR]

Determining the optimal generalization operators of city buildings is a crucial step during the building generalization process and an important aspect of realizing cross-scale updating of map data. It is a decision-making behavior of the cartographer that can be learned and simulated using artificial intelligence algorithms. Multi-scale data can provide rich generalization samples to train the determination process. However, previous studies have focused primarily on the intelligent use of each generalization operator separately, neglecting the intelligent scheduling issue between multiple operators when they are used simultaneously. Herein, we propose an improved graph neural network (GNN) called self-neighborhood merged GNN (SNGNN) that selects the optimal generalization operators for different buildings. In SNGNN, node and edge information are passed with different weights through two modules to simulate the effects of a building on itself and the neighborhood on either side. SNGNN has been experimentally validated using sample datasets for Ningbo, China, at 1:10,000 and 1:25,000. The F1-score of the testing dataset was 94.19%, and the classification precision of each operator was ≥87%. Compared with other popular intelligent algorithms, the experimental results for SNGNN revealed better performance in determining the optimal generalization operators. [ABSTRACT FROM AUTHOR]

This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dualsensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities. [ABSTRACT FROM AUTHOR]

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays. [ABSTRACT FROM AUTHOR]

Synchrotron X-ray imaging has been utilised to detect the dynamic behaviour of molten pools during the metal additive manufacturing (AM) process, where a substantial amount of imaging data is generated. Here, we develop an efficient and robust deep learning model, AM-SegNet, for segmenting and quantifying high-resolution X-ray images and prepare a large-scale database consisting of over 10,000 pixel-labelled images for model training and testing. AMSegNet incorporates a lightweight convolution block and a customised attention mechanism, capable of performing semantic segmentation with high accuracy (~96%) and processing speed (< 4 ms per frame). The segmentation results can be used for quantification and multi-modal correlation analysis of critical features (e.g. keyholes and pores). Additionally, the application of AM-SegNet to other advanced manufacturing processes is demonstrated. The proposed method will enable end-users in the manufacturing and imaging domains to accelerate data processing from collection to analytics, and provide insights into the processes' governing physics. [ABSTRACT FROM AUTHOR]

Scientific workflows are a common and critical part of scientific computing, involving complex computations and oversized and distributed computing resources. Efficient workflow execution requires scheduling algorithms considering task dependencies, resource requirements, and deadlines. Cloud computing provides an innovative architecture for extensive heterogeneous computing services. However, scheduling hybrid cloud resources with deadline restrictions while observing QoS standards is an NP-complete task. Mapping workflow tasks to virtual machines and determining the optimal schedule order is a challenging aspect of cloud computing. By executing task requests on the most advantageous virtual machine in the resource pool, energy consumption, overall execution time, and computing costs can be reduced. This research aims to identify the best location to process applications using user's demand and priority. A multi-objective genetic algorithm is proposed to achieve this objective, which considers conflicting objectives such as time, energy, cost, and deadline. The algorithm initializes an efficient ranking heuristic approach and predicts the earliest finish time (PEFT) using the Bayesian approach to improve the Pareto fronts. This approach enhances the VM migration of cloud-based tasks and optimizes the search space for conflicting objectives. Experimental findings show that the proposed approach reduces cost by 5-6% and time delay by 8% compared to existing approaches. The proposed approach offers an effective solution for scheduling scientific workflows on cloud computing resources while considering various QoS standards. The results demonstrate the potential of multi-objective genetic algorithms for optimizing workflow scheduling in cloud computing environments. [ABSTRACT FROM AUTHOR]

Demand forecasting, a crucial aspect of anticipating future customer needs, involves using historical data to predict trends. With the rise of artificial intelligence (AI), companies are increasingly turning to machine learning algorithms to enhance accuracy in forecasting compared to traditional methods. This article delves into the application of machine learning algorithms in demand forecasting, specifically within the realm of supply chain management, addressing both long-term (4-5 years) and short-term (3-4 months) scenarios. The primary focus is on improving prediction accuracy by employing feature selection algorithms and various machine learning and deep learning approaches. Utilizing diverse algorithms, such as time series, traditional machine learning, and advanced deep learning techniques, the study aims to forecast demand for different timeframes. Evaluation metrics like Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are employed to assess the effectiveness of each model. The primary goal is to pinpoint the most effective algorithm tailored to a specific dataset. This empowers companies to make well-informed decisions and enhance their supply chain operations by leveraging precise demand forecasts. The results of this study hold the potential to empower decision-makers and practitioners by enhancing their forecasting capabilities. By integrating both forecast periods a more comprehensive and robust supply chain strategy is ensured. [ABSTRACT FROM AUTHOR]

With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. The study seeks to investigate the research method of design form in the collaborative mode of artificial intelligence, to provide new ideas for design to conduct interdisciplinary research, and to promote design innovation under AI collaboration. This research begins with the design morphology theory, integrates interdisciplinary theories such as bionic design, and topology research, and collaborates with AIGC tools such as Midjourney, Stable Diffusion, and Chilloutmix to conduct case-specific research. To improve the accuracy of the morphological study, parametric design, bi-directional progressive topology optimization, genetic algorithm and simulation analysis, and other methods were also used in the research process to carry out a comprehensive design experiment exploration. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. AI synergy provides new ideas for complex morphology research, extends and complements design, and plays a crucial role in the phases of morphology exploration, concept generation, and solution implementation, thereby assisting in the exploration of the central content of design morphology. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) are gaining momentum due to the advantages it possesses in congestion-free traffic, reduced probability of accidents, and economical transit thus reducing the transit time, saving human life, and helping the growth of the economy. Autonomous vehicles (AV) are an important part of ITS as these are the actual actuators of the ITS. In AVs, the perception system is particularly important as this provides vital information to the motion and planning system. Any error in perception of the environment by the AV will lead to the failure of the entire ITS. This article provides a thorough review of the recent technological advancements in perception systems. In this work, we have considered peer-reviewed journals and conference proceedings on AV from 2019 onwards. The primary focus is on motion prediction models, object detection, localization, sensor data fusion, sensors, autonomous driving (AD), communication technology and AI. This article also discusses the various challenges faced by perception systems in AD and how communication technology and Artificial Intelligence (AI) can help the perception system to overcome the existing challenges. [ABSTRACT FROM AUTHOR]

The artificial intelligence-based allocation of resources can substantially reduce resource wastage and cost. Cloud resource allocation and management have appeared to be the central research direction. Computing through the cloud looks like improvements in network, parallel, and distributed computing. This presentation explains how scheduling works in a cloud environment by comparing it to natural selection, a concept that describes biological evolution. A strategy to reduce the time needed to find a solution by proposing an optimal solution. algorithm adds flexibility, adaptability, parallel processing, and global optimization. Furthermore, the cost function is used to find a beneficial solution by studying the operational completion time, cost of resources, and load balancing. benchmark problems tested through the algorithm show that it performs better than the other algorithms. The algorithm simultaneously solves the scheduling tasks of virtual machines and self-guided vehicles in a cloud computing environment. We Also tested for significant differences among algorithms and the number of jobs using an analysis of variance. Finally, task scheduling using the suggested algorithm indicates an improvement. [ABSTRACT FROM AUTHOR]

Intracranial aneurysm (IA) is a serious threat to human health and can lead to subarachnoid hemorrhage and other serious consequences. If IAs can be detected in advance and treated before rupture, it will greatly reduce the harm of IAs to patients. Since brain arteries are 3D (three-dimensional) structures, point cloud methods can directly process 3D data, which is crucial for tasks that require spatial understanding, such as object detection. The 3D point cloud object detection methods Point-Voxel Feature Set Abstraction (PV-RCNN), Part-Aware and Part-Aggregation (PartA2), and Sparsely Embedded Convolutional Detection (SECOND) were applied to the detection of IAs. The object detection model was trained and tested on the public dataset IntrA. The results indicate that the trained model can be used for the detection and location of IAs and high recall values have been obtained on the testing set. This work also provides important metrics for evaluating object detection models, including average precisions (APs), recall values, and object detection results on the testing set, that is, predicted bounding boxes and corresponding confidence scores. In terms of detection results, the IA detection results of PV-RCNN are the best of these three methods by leveraging both point cloud and image information. The object detection method of 3D point cloud can be integrated into the medical imaging post-processing system and can be used as a subsequent module of the 3D reconstruction module. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are a recent advancement in artificial intelligence that has the potential to revolutionize learning, teaching, and research. Still, there is room for improvement regarding how effectively LLMs could be incorporated into these environments. This study investigated the role of LLMs, specifically ChatGPT, in learning, teaching, and research contexts. To understand how motivation, ability, and triggers influence the behavior of undergraduate students, teachers, and research scholars toward LLMs, the Fogg Behavior Model (FBM) is adopted. The study revealed that the behavior of students and researchers to apply LLMs in their respective domains was greatly influenced by their motivation and ability. However, teachers exhibited little interest in incorporating any LLMs into their pedagogical strategies. In addition to these results, participants identified limitations of ChatGPT in learning, teaching, and research fields. These insights contribute valuable perspectives on the practical implementation and effectiveness of LLMs in diverse academic and research contexts. The study sheds light on the potential benefits and challenges of integrating LLMs into educational and research environments. The findings emphasize the importance of accounting for motivational factors and individual abilities when applying such models. The study's findings offer invaluable insights for educators and researchers to harness the potential of LLMs in educational and research environments while mitigating their limitations. [ABSTRACT FROM AUTHOR]

Gestational diabetes is characterized by hyperglycemia diagnosed during pregnancy. High blood sugar levels are likely to affect both the mother and child. This disease frequently goes undiagnosed due to its fewer prominent symptoms, resulting in severe unmanaged hyperglycemia, obesity, childbirth complications and overt diabetes. Artificial Intelligence is increasingly deployed in the medical field, revolutionizing and automating data processing and decision-making. Machine learning is a subset of artificial intelligence that can create reliable healthcare screening and predictive systems. With the advent of machine learning, detecting gestational diabetes and getting more profound insights about the disease is possible. This study explores the development of a reliable clinical decision support system for gestational diabetes detection using multiple machine learning architectures using combinations of five data balancing methods to detect gestational diabetes. An ensemble stack trained on the synthetic minority oversampling technique with edited nearest neighbor obtained the highest performance with accuracy, sensitivity and precision of 96%, 95% and 99%, respectively. Additionally, a layer of explainable artificial intelligence was added to the best-performing model using libraries such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, Quantum lattice, Explain Like I'm 5 algorithm, Anchor and Feature importance. The importance of factors such as Visceral Adipose Deposit and its contribution toward the prediction of gestational diabetes is explored. This research aims to provide a meaningful and interpretable clinical decision support system to aid healthcare professionals in early gestational diabetes detection and improved patient management. [ABSTRACT FROM AUTHOR]

The implementation of artificial intelligence and 4.0 technologies helps business leaders make better decisions to reduce costs, improve team performance, and increase productivity. It also enhances the quality of work life of workers, allows the optimization of the information obtained from the equipment in real time, in this way even unscheduled maintenance can be avoided. A systematic review of the literature regarding the applications and benefits that artificial intelligence has given to companies is presented, the information was obtained from databases such as Ebsco, Scopus, MDPI, ScienceDirect. Analyses, concepts, and contributions that have benefited the understanding of the need to embrace new technologies 4.0 and artificial intelligence are presented, for the benefit of companies and workers. [ABSTRACT FROM AUTHOR]

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution. This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature. The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists. [ABSTRACT FROM AUTHOR]