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Large-scale automated systems such as manufacturing systems, transportation systems, the Smart Grid and many others are continuously becoming larger, more distributed, more complex, and more intelligent. There is a growing expectation that their software controller will make real-time intelligent decisions, at all levels of the control hierarchy that make up the enterprise. The need is changing for distributed intelligent controllers that are scalable to arbitrarily large systems. In this paper, we first present the model explosion problem. This problem arises when every controller in the control hierarchy is to have a unique simulation model of its unique control domain to use in its decision-making process. That is, the modeling effort needed to provide intelligence to all controllers in the control hierarchy grows exponentially with the number of controllers in the hierarchy using current modeling technology. Since each controller is in a unique location within the control hierarchy, each will need to have its simulation model custom made for its unique control domain, leading to the scalability issue that we refer to as the model explosion problem. Next, a new modeling paradigm that solves the scalability issue resulting from the model explosion problem is presented, where the simulation models are automatically generated by recycling the models used for control. If the controller models are created using the presented modeling paradigm, then these same models can be used for simulation with no modification or the need to understand the control logic. Furthermore, gathering the state from the physical system being controlled to initialize the simulation models in a real-time control application becomes a trivial operation of simply coping data from one software model to its identical copy, without the need to interpret the meaning of the data. Finally, an example of a hierarchical controller to control a small physical model of a manufacturing plant is presented. We show how we automatically generated all the simulation models in the control hierarchy without any modification and with minimal effort, and used them to make intelligent decisions in real time. [ABSTRACT FROM AUTHOR]

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems. [ABSTRACT FROM AUTHOR]

Successful performance in competitive swimming requires a swimmer to maximise propulsion and minimise drag, which can be assessed using instantaneous swimming velocity. Many systems exist to quantify velocity, and therefore, it is important to understand the agreement between systems. This study examined the agreement between an automated video-based system and a tethered system to measure instantaneous velocity. Twenty-two competitive swimmers (state level or higher) completed 25 m of each stroke at maximal intensity. The tethered speedometer was attached to the swimmer’s waist, while videos of each trial were recorded. The swimmer’s head was then automatically tracked using proprietary software, and instantaneous velocity was determined from each system. Bland-Altman plots showed good agreement between the two systems in backstroke (95% Limits of Agreement (LOA): −0.24–0.26 m.s−1) and freestyle (95% LOA: −0.36–0.38 m.s−1) but poorer agreement in butterfly (95% LOA: −0.51–0.53 m.s−1) and breaststroke (95% LOA: −0.88–0.92 m.s−1). The root mean square error was higher in butterfly (0.27 m.s−1) and breaststroke (0.46 m.s−1) compared to backstroke (0.13 m.s−1) and freestyle (0.19 m.s−1). Results demonstrated that the two systems are comparable for measuring instantaneous swimming velocity; however, larger discrepancies are evident for butterfly and breaststroke. [ABSTRACT FROM AUTHOR]

This article presents the proper organization of the supply chain to meet consumer demand, which is crucial for modern commercial enterprises involved in the sale of various products. Studies indicate that a company's success is linked to the satisfaction of its customers. To optimize the supply chain, this study will consider the use of artificial neural network models. The results of this model will seek a balance between demand and supply, helping determine the necessary quantity of goods to satisfy demand and prevent overproduction. By using this model, the company can fully meet the needs of its customers. Additionally, the company saves its resources and labor costs and reallocates them to other tasks. The model demonstrates the optimization of production and supply business processes, as well as an increase in efficiency. [ABSTRACT FROM AUTHOR]

Large-scale automated systems such as manufacturing systems, transportation systems, the Smart Grid and many others are continuously becoming larger, more distributed, more complex, and more intelligent. There is a growing expectation that their software controller will make real-time intelligent decisions, at all levels of the control hierarchy that make up the enterprise. The need is changing for distributed intelligent controllers that are scalable to arbitrarily large systems. In this paper, we first present the model explosion problem. This problem arises when every controller in the control hierarchy is to have a unique simulation model of its unique control domain to use in its decision-making process. That is, the modeling effort needed to provide intelligence to all controllers in the control hierarchy grows exponentially with the number of controllers in the hierarchy using current modeling technology. Since each controller is in a unique location within the control hierarchy, each will need to have its simulation model custom made for its unique control domain, leading to the scalability issue that we refer to as the model explosion problem. Next, a new modeling paradigm that solves the scalability issue resulting from the model explosion problem is presented, where the simulation models are automatically generated by recycling the models used for control. If the controller models are created using the presented modeling paradigm, then these same models can be used for simulation with no modification or the need to understand the control logic. Furthermore, gathering the state from the physical system being controlled to initialize the simulation models in a real-time control application becomes a trivial operation of simply coping data from one software model to its identical copy, without the need to interpret the meaning of the data. Finally, an example of a hierarchical controller to control a small physical model of a manufacturing plant is presented. We show how we automatically generated all the simulation models in the control hierarchy without any modification and with minimal effort, and used them to make intelligent decisions in real time.

Abstract The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

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The development of smart and innovative software applications in various disciplines has inspired our lives by providing various cutting-edge technologies spanning from online to smart and efficient systems. The proliferation of innovative internet-enabled tools has transformed the nation into a globalized world where individuals can participate on various platforms, collaborate in activities, communicate on issues, and exchange information safely and consistently. Coordination and cooperation are essential in software development. It gathers all software developers in one space, encouraging them to discuss goals and work rationally to accomplish the project goal. In recent years, continuous software development and deployment have become increasingly common in software engineering. Continuous software engineering (CSE) is a method that involves a variety of strategies to increase the regularity of novel and modified software versions. CSE enables a continuous learning and improvement process through rapid software update iteration by combining continuous integration and delivery. Continuous integration is a method that has arisen in order to remove gaps between development and deployment. Software engineers must handle uncertainty and alter stakeholders' requirements, which is possible through continuous software developmental strategies that manage the overall software cycle and produce high-quality software applications. The proposed study is a systematic review related to continuous software development and deployment and focuses on achieving four aims: (1) To explore the impacts of continuous development on software, (2) to pinpoint various tools used to carry out this process, (3) to highlight the challenges faced in adopting continuous approaches for development and (4) to analyze the phases of continuous software engineering. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of Support Vector Machines (SVM) in developing automated systems to detect hate speech on social media platforms, addressing a critical need for scalable solutions to enhance online safety and societal cohesion. By leveraging SVM's proven efficacy in managing high-dimensional data and optimizing the balance between precision and recall, the study offers a comprehensive methodology that includes data collection, preprocessing, model training, deployment, and evaluation. The results demonstrate robust average performance across key metrics, affirming the model's reliability in accurately identifying hate speech while minimizing false positives. This research advances the field by showcasing the practical and theoretical contributions of SVM in automated hate speech detection, highlighting its potential to significantly improve content moderation practices. The findings underscore the necessity for ongoing refinement of detection systems and collaborative efforts among researchers, technology firms, and policymakers to create more inclusive online environments that promote respectful discourse and community well-being.

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

The article highlights the issue of forming and managing customer loyalty in the service sector by using digital tools. The importance of customer loyalty as one of the most powerful components of an enterprise’s intellectual capital is emphasized. The types of loyalty are characterized according to the criteria of attitude to the brand (positive/negative) and the frequency of repeated transactions (absolute, hidden, false, zero). Loyalty indicators and how to calculate them are covered: customer retention rate, customer long-term value, Net Promoter Score, customer satisfaction surveys, customer effort evaluation, cause and effect questions, repeated purchases and referrals, engagement. It was noted that among the various types of loyalty (bonus program, fixed discount, temporary discount, multi-level discount program, product as a gift, partner loyalty program, paid loyalty program) the non-commercial program deserves the most attention, because in the long term it forms and testifies to absolute customer loyalty. The main types of loyalty management in the context of digitalization include transactional, social-network, related to the influence on engagement, emotions, consumer behavior, and intentions to recommend a service. It is emphasized that automated systems, such as software for managing business processes and online services for booking procedures, are becoming an integral part of modern beauty salons and offer a wide range of possibilities: online booking for services through a website or mobile application, reminders about visits by messages or e-mail; a calendar of appointments for services with the possibility of synchronization with personal calendars of beauty masters; automatic control of consumables balances, formation of supply orders, cash book management, income and expense accounting, profitability analysis; newsletters about promotions and special offers, maintenance of pages in social networks; storage of information about customers, their preferences, history of visits through the CRM system. The positive consequences of using automation in beauty salons include increasing efficiency and productivity, improving customer service, increasing profitability, expanding marketing and advertising opportunities, improving ratings and reviews, but among the negative ones, we single out dependence on technology, loss of personal contact, and threats to data privacy. A developed set of advertising events with seasonal specialization using online tools (conducting master classes and virtual consultations, seasonal loyalty programs, charity initiatives, promotions for special dates) is presented.

Wei-Yao Wang,1,2 Yi-Hsin Chen,3– 5 Yu-Lin Lee,1,2 Chen-Feng Chiu,6 Shih-Ming Tsao1,2 1School of Medicine, Chung Shan Medical University, Taichung, Taiwan; 2Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan; 3Department of Nephrology, Taichung Tzu Chi Hospital, Taichung, Taiwan; 4School of Medicine, Tzu Chi University, Hualien, Taiwan; 5Department of Artificial Intelligence and Data Science, National Chung Hsing University, Taichung, Taiwan; 6Department of Internal Medicine, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung, TaiwanCorrespondence: Shih-Ming Tsao, Department of Internal Medicine, Chung Shan Medical University Hospital, &num; 110, Section 1, Jianguo North Road, Taichung City, 40201, Taiwan, Tel +886 4 24739595 ext.34718, Fax +886-4-23248172, Email tsmhwy@ms24.hinet.netBackground: Determining oxacillin susceptibility using reference methods and automated systems is crucial for treating invasive infections caused by Staphylococcus aureus. This study compares the oxacillin susceptibility results from the two automated systems with agar dilution and correlates them with genotypes of invasive S. aureus.Methods: Non-duplicate S. aureus invasive isolates were collected over an 11-year period. The oxacillin susceptibility was determined with Phoenix 100 (Jan 2011 to Aug 2018) or Vitek 2 (Sep 2018 to Dec 2021), and susceptibility for oxacillin and cefoxitin was determined with agar dilution. Methicillin-resistant S. aureus (MRSA) was confirmed with mecA existence, and the genotype was determined using SCCmec. The association between genotype and antibiotic susceptibility using two automated systems and agar dilution was evaluated.Results: A total of 842 invasive S. aureus, including 443 mecA+ MRSA and 399 mecA- MSSA, were collected. The susceptibility rates of oxacillin determined by two automated systems and agar dilution were 68.8% (76.8% for Phoenix 100 and 57.6% for Vitek 2) and 54.0%, respectively. When compared with the oxacillin susceptibility using agar dilution, the categorical agreement for Phoenix 100 and Vitek 2 were 0.46% and 0.88%, respectively (p < 0.001). One hundred and forty-three isolates were misinterpreted as oxacillin-susceptible S. aureus (OSSA) using automated systems while comparing with agar dilution, among which molecularly community-associated MRSA (CA-MRSA) outnumbered healthcare-associated MRSA (HA-MRSA) (99 vs 34, p < 0.001). There were 70 mecA+ OSSA (OS-MRSA) using agar dilution, among which 42 harbored SCCmec types were predominantly categorized as CA-MRSA (38, p < 0.001).Conclusion: The categorical agreement of Vitek 2 in determining oxacillin susceptibility and predicting mecA existence is comparable with agar dilution, whereas Phoenix 100 is not. Most of those ORSA determined by agar dilution but misinterpreted as OSSA by automated systems and OS-MRSA are categorized as CA-MRSA.Keywords: oxacillin susceptibility, automated systems, agar dilution, SCCmec, CA-MRSA

AbstractThe integration of Artificial Intelligence (AI) technologies in the field of education has prompted significant advancements, particularly in the domain of assessment and grading. This research delves into the potential of large language models, specifically OpenAI's ChatGPT, in simulating human-like interactions and accurately grading student assessments. To accomplish its objectives, the study compares the grading performance of ChatGPT with that of human correctors in a sample of second-year university students. The research findings indicate only a moderate correlation between the grades assigned by ChatGPT and those of human correctors, suggesting nuanced capabilities in providing comprehensive feedback and streamlining the grading process. While the study highlights the benefits of AI integration in education, it also addresses potential risks, including the exacerbation of educational inequalities and the limitations associated with AI's automated nature. This research contributes to the ongoing discourse surrounding AI's role in education, emphasizing the importance of striking a balance between AI and human instruction for optimal educational outcomes. [ABSTRACT FROM AUTHOR]

The purpose of the study is to develop an approach to planning the trajectory of the manipulator robot using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of manipulator robots and the review of intelligent control systems made it possible to obtain a complete picture of the current state of this issue. A system is proposed that can perceive the environment and control the movement of the robot by generating the correct control commands. For this, 3 tasks were solved, namely: analysis of the environment in order to determine its features, determination of the trajectory in order to neutralize the collision and determination of controlled influences for the executive authorities in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks are proposed. [ABSTRACT FROM AUTHOR]

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards. [ABSTRACT FROM AUTHOR]

Abstract In the era of automated and digitalized information, advanced computer applications deal with a major part of the data that comprises audio-related information. Advancements in technology have ushered in a new era where cutting-edge devices can deliver comprehensive insights into audio content, leveraging sophisticated algorithms such such as Mel Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) to extract and provide pertinent information. Our study helps in not only efficient audio file management and audio file retrievals but also plays a vital role in security, the robotics industry, and investigations. Beyond its industrial applications, our model exhibits remarkable versatility in the corporate sector, particularly in tasks like siren sound detection and more. Embracing this capability holds the promise of catalyzing the development of advanced automated systems, paving the way for increased efficiency and safety across various corporate domains. The primary aim of our experiment is to focus on creating highly efficient audio classification models that can be seamlessly automated and deployed within the industrial sector, addressing critical needs for enhanced productivity and performance. Despite the dynamic nature of environmental sounds and the presence of noises, our presented audio classification model comes out to be efficient and accurate. The novelty of our research work reclines to compare two different audio datasets having similar characteristics and revolves around classifying the audio signals into several categories using various machine learning techniques and extracting MFCCs and STFTs features from the audio signals. We have also tested the results after and before the noise removal for analyzing the effect of the noise on the results including the precision, recall, specificity, and F1-score. Our experiment shows that the ANN model outperforms the other six audio models with the accuracy of 91.41% and 91.27% on respective datasets.

In the era of automated and digitalized information, advanced computer applications deal with a major part of the data that comprises audio-related information. Advancements in technology have ushered in a new era where cutting-edge devices can deliver comprehensive insights into audio content, leveraging sophisticated algorithms such such as Mel Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) to extract and provide pertinent information. Our study helps in not only efficient audio file management and audio file retrievals but also plays a vital role in security, the robotics industry, and investigations. Beyond its industrial applications, our model exhibits remarkable versatility in the corporate sector, particularly in tasks like siren sound detection and more. Embracing this capability holds the promise of catalyzing the development of advanced automated systems, paving the way for increased efficiency and safety across various corporate domains. The primary aim of our experiment is to focus on creating highly efficient audio classification models that can be seamlessly automated and deployed within the industrial sector, addressing critical needs for enhanced productivity and performance. Despite the dynamic nature of environmental sounds and the presence of noises, our presented audio classification model comes out to be efficient and accurate. The novelty of our research work reclines to compare two different audio datasets having similar characteristics and revolves around classifying the audio signals into several categories using various machine learning techniques and extracting MFCCs and STFTs features from the audio signals. We have also tested the results after and before the noise removal for analyzing the effect of the noise on the results including the precision, recall, specificity, and F1-score. Our experiment shows that the ANN model outperforms the other six audio models with the accuracy of 91.41% and 91.27% on respective datasets. [ABSTRACT FROM AUTHOR]

The article highlights the issue of forming and managing customer loyalty in the service sector by using digital tools. The importance of customer loyalty as one of the most powerful components of an enterprise’s intellectual capital is emphasized. The types of loyalty are characterized according to the criteria of attitude to the brand (positive/negative) and the frequency of repeated transactions (absolute, hidden, false, zero). Loyalty indicators and how to calculate them are covered: customer retention rate, customer long-term value, Net Promoter Score, customer satisfaction surveys, customer effort evaluation, cause and effect questions, repeated purchases and referrals, engagement. It was noted that among the various types of loyalty (bonus program, fixed discount, temporary discount, multilevel discount program, product as a gift, partner loyalty program, paid loyalty program) the non-commercial program deserves the most attention, because in the long term it forms and testifies to absolute customer loyalty. The main types of loyalty management in the context of digitalization include transactional, social-network, related to the influence on engagement, emotions, consumer behavior, and intentions to recommend a service. It is emphasized that automated systems, such as software for managing business processes and online services for booking procedures, are becoming an integral part of modern beauty salons and offer a wide range of possibilities: online booking for services through a website or mobile application, reminders about visits by messages or e-mail; a calendar of appointments for services with the possibility of synchronization with personal calendars of beauty masters; automatic control of consumables balances, formation of supply orders, cash book management, income and expense accounting, profitability analysis; newsletters about promotions and special offers, maintenance of pages in social networks; storage of information about customers, their preferences, history of visits through the CRM system. The positive consequences of using automation in beauty salons include increasing efficiency and productivity, improving customer service, increasing profitability, expanding marketing and advertising opportunities, improving ratings and reviews, but among the negative ones, we single out dependence on technology, loss of personal contact, and threats to data privacy. A developed set of advertising events with seasonal specialization using online tools (conducting master classes and virtual consultations, seasonal loyalty programs, charity initiatives, promotions for special dates) is presented. [ABSTRACT FROM AUTHOR]

Studying single cells opens opportunities for understanding many biological processes and, as a consequence, plays an important role in the development of biomedicine. Scanning ion conductance microscopy (SICM) and nanopipette systems for the analysis of single cells represent a powerful tool for investigating the morphological, mechanical, and biochemical features of living cells with nanometer spatial resolution under near-natural conditions. Over the last decades, SICM has shown great success in studying and manipulating single cells, and the noncontact nature of the system, combined with the unique features of nanopipettes used as probes, has opened new possibilities for the application of such systems in biomedical applications. This review reflects the latest trends and directions in the development of SICM and nanopipette systems for the single-cell analysis. [ABSTRACT FROM AUTHOR]

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

Objective. Development of automated network attack detection systems capable of adapting to the ever-changing nature of network attacks and new types of threats. Such systems should be based on machine learning algorithms and models that are able to identify complex dependencies between data in the learning process.Method. To train the models, a sample with signs of normal and abnormal traffic was prepared, and it was thinned and balanced as a result of preliminary statistical analysis. Five machine learning algorithms were selected and tested, both on a training set of features and on a real test set obtained experimentally. Based on the results of the experiments, a random forest classifier was selected, which showed the best results.Result. A model for detecting network intrusions has been developed, which showed a detection accuracy of 0.99 on real traffic.Conclusion. It is shown that a machine learning-based network intrusion detection system can solve the problem of flexible protection that could adapt to the ever-changing nature of network attacks, since one of the most important advantages of machine learning in detecting network intrusions is the ability to learn the signs of attacks and identify cases that are uncharacteristic of those that were observed earlier.

The transformative capacity of the Internet of Things (IoT) has become evident across various sectors. Despite its potential, a discernible hesitancy exists in its adoption within higher education institutions. This research explores the specific advantages and challenges of implementing IoT in the context of higher education, particularly in optimizing data-driven decision-making processes. The approach focuses on creating a comprehensive IoT framework tailored for higher education, encompassing a foundational data warehouse layer, an intermediary application layer for streamlining data, a message broker layer for data orchestration, a granular message consumer layer for data refinement, and a central data lake that consolidates both real-time and structured data. This system adeptly manages a continuous stream of structured and real-time data. With the integration of Kafka and TensorFlow, real-time video streams are processed, providing enhanced security measures for campuses. Biometric devices, strategically positioned, offer detailed data on institutional dynamics, all converging into a central data reservoir. This vast data collection presents profound insights, shaping administrative strategies and improving institutional efficiency. The adoption of IoT in higher education holds vast potential, yet challenges persist. Striking a balance between surveillance and privacy, ensuring data integrity, and navigating the complexities of scalability are vital considerations. However, with careful and strategic implementation, IoT integration can usher in a revolutionary era of data-driven academic operations, enhancing both security and institutional efficiency. [ABSTRACT FROM AUTHOR]

In the dynamic world of financial markets, the prediction of stock performance and bitcoin trading is undergoing a significant transformation due to the integration of advanced technologies and novel methodologies. The incorporation of Transformer models alongside Time Embeddings significantly improves the precision of stock market predictions by effectively capturing intricate temporal relationships and mitigating the presence of overly simplistic assumptions. The integration of real-time social media data with sentiment analysis based on BERT provides significant value in understanding investor sentiment. Additionally, the application of language model pre-training, as exemplified by BERT, brings about a transformative impact on text classification for predicting stock prices. Within the domain of cryptocurrency, sophisticated algorithms such as Transformers, Long Short-Term Memory (LSTM), Deep Convolutional LSTM (DC-LSTM), and Neural Networks (NN) have demonstrated enhanced capabilities in predicting price movements. These algorithms are further bolstered by the implementation of a comprehensive trading strategy. Automated systems for bitcoin trading introduce elements of personalization and adaptability to the trading process, thereby facilitating broader access to a diverse group of traders. The progress highlights the significant importance of the integration of technology and methodologies in the field of financial analysis. This integration enables investors and traders to possess the necessary resources for making well-informed choices within the ever-changing landscape of financial markets. [ABSTRACT FROM AUTHOR]

The development of information technology has contributed to the rapid process of informatization of all spheres of modern society, including library and information activities – the methods of collecting information, its visual display and providing access to it have changed; the processes of acquisition, processing of documents, preservation of collections have been transformed. The forms of information and library services have become different. The process of informatization has brought the activity of the world’s libraries to a higher level, opened up qualitatively new possibilities of performing technological operations, working via mobile devices, providing access to full-text resources in a remote mode, etc. Naturally, all these changes have also affected libraries in the Republic of Uzbekistan. Since the end of the 1990s, there began the active process of introducing information and communication technologies and automation of libraries in the republic. The work to improve the activities of the library and information institutions of the republic is still going on. The article aims to evaluate the state and degree of development of the informatization process in the leading libraries of Uzbekistan. The results of the reforms on the introduction of information technology in the libraries of the Republic of Uzbekistan are given. An important task is to analyze the main trends and the state of the informatization process in the library-information institutions of the republic and to identify the problems in this area of library activity.

This document is an editorial published in Frontiers in Behavioral Neuroscience titled "Improving reproducibility in behavioral neuroscience." The authors discuss the importance of reproducibility in scientific research, particularly in the field of behavioral neuroscience. They highlight the ethical implications of a lack of reproducibility and the potential waste of animal lives in research. The authors suggest that combining principles of animal welfare with experimental rigor can help combat the reproducibility crisis. They also discuss specific measures and procedures that the scientific community should adopt to improve the reliability and translational potential of pre-clinical research. [Extracted from the article]

In the commercial and industrial sectors, automatic robotic control mechanisms, which include robots, end effectors, and anchors containing components, are often utilized to enhance service quality. Robotic systems must be installed in manufacturing lines for a variety of industrial purposes, which also increases the risk of a robot, end controller, and/or device malfunction. According to its automated regulation, this may hurt people and other items in the workplace in addition to resulting in a reduction in quality operation. With today's advanced systems and technology, security and stability are crucial. Hence, the system is equipped with fault management abilities for the identification of developing defects and assessment of their influence on the system's activity in the upcoming utilizing fault diagnostic methodologies. To provide adaptive control, fault detection, and state estimation for robotic automated systems intended to function dependably in complicated contexts, efficient techniques are described in this study. This paper proposed a fault detection and state estimation using Accelerated Gradient Descent based support vector machine (AGDSVM) and gaussian filter (GF) in automatic control systems. The Proposed system is called (AGDSVM + GF). The proposed system is evaluated with the following metrics accuracy, fault detection rate, state estimation rate, computation time, error rate, and energy consumption. The result shows that the proposed system is effective in fault detection and state estimation and provides intelligent control automatic control.

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. It is thus desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. Traditionally, the parameter tuning has been done manually, which can lead to the tuning process being a maze of subjective choices. In this paper we present how to optimize 20 key physical parameters in the atmospheric model Open Integrated Forecasting System (OpenIFS) that have a strong impact on forecast quality. The results show that simultaneous optimization of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members; the results also show that the optimized parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimized model. We find that the optimization process is dependent on the starting values of the parameters that are optimized (starting from better-suited values results in a better model). The results show also that the applicability of the tuned parameter values across different model resolutions is somewhat limited because of resolution-dependent model biases, and we also found that the parameter covariances provided by the tuning algorithm seem to be uninformative. Significance Statement: The purpose of this work is to show how to use algorithmic methods to optimize a weather model in a computationally efficient manner. Traditional manual model tuning is an extremely laborious and time-consuming process, so algorithmic methods have strong potential for saving the model developers' time and accelerating development. This paper shows that algorithmic optimization is possible and that weather forecasts can be improved. However, potential issues related to the use of the optimized parameter values across different model resolutions are discussed as well as other shortcomings related to the tuning process. [ABSTRACT FROM AUTHOR]

An automated basing system is proposed for large annular components when machined in the field by a mobile robot. [ABSTRACT FROM AUTHOR]

The organization of safe and efficient transportation of goods and passengers by air requires the rationalization of the air traffic control system. It is from the operating results of this system that all the qualitative characteristics of the transportation process depend on. At the same time, a special role in this system is given to the control unit inclusive of one or more air traffic control specialists, whose main professional duty is to control air traffic within their area of responsibility based on continuous monitoring of the air situation. As part of this activity, controllers daily make important decisions, the correctness of which directly affects the integrity of aircraft, crews, and passengers. The stated above causes the crucial significance of high-quality training of air traffic control specialists, encompassing the development of skills and abilities, based on specialized training complexes. Therefore, within the framework of this article, an analysis of modern simulators for training controllers was carried out, which made it possible to emphasize their identical structural elements. Additionally, the weaknesses of modern simulator training were identified, associated with the significant labor costs for instructors and the obvious deficiency of reserves for expanding the typical composition of risk events. With reference to the stated above, the key characteristics of an optimally built training complex were worked out, based on the use of modern decision-making support systems in the process of air traffic control specialists training. Adaptation of these models and algorithms will automate the instructor’s position and ensure the dynamism of the system in the field of replenishment of possible air traffic options.

The article discusses the developed automated system for the research of a liquid crystal cell, which consists of hardware and software parts. Unlike previously developed devices for optical measurements of liquid crystal elements, the automated system under consideration provides signal generation accuracy within 0.5% and SFDR noise immunity of at least 80 dB. The hardware part of the system includes the development of a circuit for generating a voltage signal of a certain amplitude and frequency, the formation of signals for controlling the movement of a stepper motor and controlling the intensity of the luminous flux of four laser LEDs, the development and tracing of a printed circuit board. The software part consists in the development of an applied executive algorithm that builds a sinusoidal signal at the output of a digital-to-analog converter of a microcontroller. This example uses the STM32F746IGT6 microcontroller based on the ARM Cortex-M7 core, which has a superscalar architecture with dynamic prediction, a memory protection module, a floating-point computing unit, as well as a direct memory access controller DMA (direct memory access), which is used for accelerated data exchange between memory and peripherals. The DMA is used to quickly control the digital-to-analog converter.

Objective. Currently, access to the information resource of the ATS AS is implemented through the access control subsystem of the information security facility based on the mandatory and discretionary methods of access control developed in the 70s of the last century and requiring improvement. The purpose of the study is to analyze existing methods and, on this basis, select the optimal method for regular users to access information resources of automated systems of internal affairs bodies.Method. System analysis, graph theory method, and verbal (verbal) modeling.Result. A new aspect of the method of access of regular users to information resources of automated ATS systems, based on the use of hierarchical metrics of semantic proximity, is disclosed.Conclusion. The use of a method based on semantic proximity significantly reduces the processing of a large amount of information, respectively, increasing the performance of automated systems.

The present work details the measurement capabilities of Wave Glider autonomous surface vehicles (ASVs) for research-grade meteorology, wave, and current data. Methodologies for motion compensation are described and tested, including a correction technique to account for Doppler shifting of the wave signal. Wave Glider measurements are evaluated against observations obtained from World Meteorological Organization (WMO)-compliant moored buoy assets located off the coast of Southern California. The validation spans a range of field conditions and includes multiple deployments to assess the quality of vehicle-based observations. Results indicate that Wave Gliders can accurately measure wave spectral information, bulk wave parameters, water velocities, bulk winds, and other atmospheric variables with the application of appropriate motion compensation techniques. Measurement errors were found to be comparable to those from reference moored buoys and within WMO operational requirements. The findings of this study represent a step toward enabling the use of ASV-based data for the calibration and validation of remote observations and assimilation into forecast models. [ABSTRACT FROM AUTHOR]