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The "greenization" of papermaking enterprise behavior is an important approach to transition towards to environmental protection and green development mode. Aiming at the problem that it is difficult to quantitatively evaluate the green behavior performance of papermaking enterprises, a new model of green behavior performance evaluation of papermaking enterprises is proposed based on rough set and cloud model theory. Firstly, the factors influencing the green behavior performance of papermaking enterprises are identified, and an evaluation index system for the green behavior of papermaking enterprises is established. Secondly, using rough set theory, the weight of each layer in the index system is determined. Subsequently, considering the fuzziness and uncertainty of index evaluation, the cloud model is employed to construct the performance evaluation model of green behavior of papermaking enterprises. The weights are combined with the evaluation model to finally calculate the performance level of green behavior of papermaking enterprises. Finally, a comprehensive evaluation is conducted, taking the green behavior performance of a papermaking enterprise as the research object. The results show that: (1) The green behavior performance level of the papermaking enterprise is in a good level, but there are pollution penalties caused by inadequate management in pollutant discharge management. In terms of energy structure, coal is the main source of energy, and a large amount of oxides are emitted. (2) The calculation results of the evaluation method are basically consistent with the actual situation. It can reflect the actual effect of the green behavior of the enterprise, and verify the reliability, rationality and effectiveness of the model [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Networks (MANETs) are dynamic and self-configuring networks characterized by node mobility and decentralized operations. Efficient routing protocols play a crucial role in ensuring reliable communication while optimizing resource usage, particularly energy consumption. This paper presents a comparative performance evaluation of three prominent routing algorithms, such as S-DYMO, M-DYMO, and FB-B-DYMO, focusing on their scalability and energy efficiency in MANET environments. This study utilizes simulation-based experiments to assess the performance metrics of S-DYMO, M-DYMO, and FB-B-DYMO. Key parameters evaluated include packet delivery ratio (PDR), end-to-end delay, routing overhead, and energy consumption under varying network sizes and mobility scenarios. Each algorithm's ability to scale with network size and maintain performance under dynamic conditions is analyzed to understand its suitability for real-world MANET deployments. The results indicate that S-DYMO exhibits robust scalability and efficient energy utilization in small to medium-sized networks, leveraging its proactive route discovery approach. Conversely, M-DYMO demonstrates competitive performance in larger networks due to its reactive route maintenance strategy, which balances energy efficiency with scalability challenges. FB-DYMO, integrating feedbackbased mechanisms, shows promising results in mitigating routing overhead and energy consumption but exhibits sensitivity to network dynamics. The findings provide insights into the trade-offs between scalability and energy consumption inherent in S-DYMO, M-DYMO, and FB-B-DYMO routing algorithms, offering guidance for selecting appropriate protocols based on specific MANET deployment scenarios. Future research directions include enhancing protocol adaptability to dynamic network conditions and optimizing energy-efficient routing strategies tailored for evolving MANET environments. [ABSTRACT FROM AUTHOR]

The image processing field is considered one of the highly sensitive fields for accuracy due to the quality of the processing in view of the visual view of the user and due to the development in modern means of communication and the use of these means in the transfer of images and the impact of these means on several factors, including external, including those related to the quality of the source signal and the impact of the transmitted images by these conditions, digital correction processes have emerged to reach a high quality of the received image. Most of the studies and research on digital image correction have focused on the quality and time required for correction processes, and some have focused on using traditional optimization algorithms to obtain acceptable visual quality, while others have focused on shortening time regardless of quality, and due to the fact that all studies and research that have been viewed were focused on the use of speculative methods and hybrid algorithms to address distortion in images, as all weaknesses were related to time, quality and calculations because the size of the image data is large Very. The research aims to study digital images and then process images, optimization methods, genetic algorithms and accomplish an algorithm with high features. In this paper, the simple genetic algorithm is used in the process of correcting images of the type (.JPG), as this method is characterized by the fact that it includes many of the advantages of the previous methods in addition to additional features that provided quality, accuracy and shortening time in calculations. The paper has been completed in five phases: The first stage: Providing external protection for the system by entering the password. Second Stage: Creating the system's database. Third stage: Create (code book) in a new style based on the size of the file used. Fourth stage: Building the genetic algorithm for correction Fifth stage: Using a mathematical model to add distortion to a clear image, correct it and compare the results. [ABSTRACT FROM AUTHOR]

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring. [ABSTRACT FROM AUTHOR]

This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios. [ABSTRACT FROM AUTHOR]

The Fibre reinforced concrete (FRC) has become popular in construction industry in last few decades. Various natural and artificial fibres are added in concrete to enhance the crack resistance property by developing some bonding between fibre and concrete. FRC is not only performs better than conventional concrete but also the fibre reinforced concrete (FRC) has become popular in construction industry in last few decades. FRC is not only performs better than conventional concrete but also it reduces environmental pollution. Actually in many rural area, people are not concerned about the pollution and hygiene. The unused portion of sugarcane fibre, banana fibre, jute fibre are thrown into pond/ lake. After few days they decompose and rotten, that causes pollution in waterbody and disturb the ecosystem. The fibres can be used as additive in concrete to enhance their overall performance as well as to reduce environmental pollution. In this paper, a state-of-art review has been investigated on FRC and its different benefits. Different fibres such as jute fibre, coconut fibre, polypropylene, basalt, areca leaf, glass, mask, plastic, carbon and steel fibre were incorporated in concrete by several researchers in the past decades that have been highlighted in detail in this paper. The performance has been evaluated in terms of load displacement hysteretic pattern, stiffness, ductility, energy dissipation, crack resistance, durability and workability of FRC. The virtue and limitations of FRC have also been discussed. From the existing literature, it is found that the performance of FRC under dynamic load, Damage assessment, Time dependent assessment of damage, Effect of fibre in high-performance concrete (HPFRC) and Life cycle assessment are found as major literature gap that needs to be fulfilled. A case study on damage assessment of FRC has also been conducted in this paper. From the result it is found that the Carbon fibre reinforced concrete (CFRC), Steel fibre reinforced concrete (SFRC) and Areca leaf sheath fibre reinforced concrete (ALSFRC) are experiencing lesser damage in compared to normal concrete without fibre. Based on the existing literature the future scope and probable direction of research of FRC have also been highlighted. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network's edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. Scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author's knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded. [ABSTRACT FROM AUTHOR]

In recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the concepts and practical applications of the spanning tree protocol (STP). It also covers per-VLAN spanning tree (PVST), multiple spanning tree (MST), and rapid STP (RSTP). Moreover, practical scenarios are presented to help the reader understand the concepts and implementations of these protocols. This study analyzes protocols using seven metrics. All protocols have been evaluated using these metrics in both small and big topology scenarios to obtain the best results. In addition, all metrics are mentioned in the introduction chapter, and the way used to apply tests on the metrics is described in the methodology chapter. Based on the experiments, different STPs performance are compared, including STP, RSTP, PVST, and MST. In summary, findings show that STP is easy to use and performs well overall, but it consistently has high latency issues. RSTP is suitable for small networks and has quick convergence, but it cannot handle as much load as STP. PVST performed the best in the experiments, as it demonstrated high scalability and the ability to handle a lot of pressure, although it requires strong hardware. However, MST did not perform as well as expected, as it struggled with delay problems and high jitter. In conclusion, it is recommended to use RSTP for simple networks that require fast convergence with dependable delay and capacity, or STP for networks that require good scaling and bandwidth. PVST is an excellent option for those who can afford high-performance hardware, while MST is suitable for simple networks or those with outdated hardware. [ABSTRACT FROM AUTHOR]

This paper extends analysis of the 'assessment receipt' to include talk and embodied interaction during 'performance responses' in music masterclass interactions. By grounding the analysis in questions of performance completion and audience applause onset, it details the utility of variously position assessment tokens, during performance, before applause, during applause and after applause. These different verbal assessment positions afford, in different ways, instructional interaction by situating the instructor as next relevant speaker. They also help coordinate performance completions and audience applause onset. The paper also identifies the 'receipt assessment', which reverses the component ordering of the earlier phenomenon. The paper is relevant to those studying performance as interaction and extends and deepens the growing insights into musical instructional settings. [ABSTRACT FROM AUTHOR]

Natural Language Processing holds immense potential for extracting insights from healthcare data, but it demands stringent privacy protection. Privacy-preserving NLP techniques, notably Fully Homomorphic Encryption (FHE), offer a path to advanced analytics while preserving patient data confidentiality. This paper marks the culmination of our initial phase, focusing on data preparation and encryption. We’ve employed FHE with the CKKS scheme to ensure data remains encrypted. Performance evaluation adapts multiclass classification metrics, addressing the distinct nature of healthcare data. As we conclude this phase, we emphasize that this is only the beginning. Subsequent steps will focus on developing NLP models trained on encrypted data. Our work highlights the intersection of data privacy and advanced analytics in healthcare, ultimately benefiting healthcare providers and patients. [ABSTRACT FROM AUTHOR]

A malware is an intrusion that is meant to harm the computer and any network connected devices. Due to the technological advancement in digital era, malware is evolving day by day in different forms. Some of the malware includes viruses, trojan horse, ransomware and many more. Obfuscated malware is one kind of malware that cannot be identified using predefined signature pattern or through normal form of detection strategy. Obfuscated malware is major threat to security infrastructure and it is difficult to detect. To automate the process of obfuscated malware detection, machine learning plays a major role. This paper aims to develop a suitable machine learning model as an ensemble framework to detect obfuscated malware. The goal this paper is to finds the most efficient and highest performing ensemble learning method under stacking and boosting. The stacked ensemble learning classifiers are developed with machine learning models like Random Forest, Decision Tree, K-Nearest Neighbor and Naive Bayes. The boosting ensemble learning classifiers are developed using Adaboost Classifier, Extreme Gradient Boosting Classifier and Histogram Gradient Boosting algorithm. The MalMemAnalysis-2022 dataset from Canadian institute for cyber security is taken for study which consist of 58,598 records with 57 features. The performance of the ensemble model are evaluated using metrices like accuracy, precision, recall and F1 score. Based on the comparative analysis between the models, in stacking methods the highest accuracy is obtained by both Random Forest and Decision Tree with 99.99%. In boosting method, the highest accuracy is obtained by both Histogram Gradient Boosting and Extreme Gradient Boosting models with 100%. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The attention mechanism can extract salient features in images, which has been proved to be effective in improving the performance of person re-identification (Re-ID). However, most of the existing attention modules have the following two shortcomings: On the one hand, they mostly use global average pooling to generate context descriptors, without highlighting the guiding role of salient information on descriptor generation, resulting in insufficient ability of the final generated attention mask representation; On the other hand, the design of most attention modules is complicated, which greatly increases the computational cost of the model. To solve these problems, this paper proposes an attention module called self-supervised recalibration (SR) block, which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask. In particular, a special "Squeeze-Excitation" (SE) unit is designed in the SR block to further process the generated intermediate masks, both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels. Furthermore, we combine the most commonly used ResNet-50 to construct the instantiation model of the SR block, and verify its effectiveness on multiple Re-ID datasets, especially the mean Average Precision (mAP) on the Occluded-Duke dataset exceeds the state-of-the-art (SOTA) algorithm by 4.49%. [ABSTRACT FROM AUTHOR]

The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system's mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments. [ABSTRACT FROM AUTHOR]

Water leakage inspection in the tunnels is a critical engineering job that has attracted increasing concerns. Leakage area detection via manual inspection techniques is time-consuming and might produce unreliable findings, so that automated techniques should be created to increase reliability and efficiency. Pre-trained foundational segmentation models for large datasets have attracted great interests recently. This paper proposes a novel SAM-based network for accurate automated water leakage inspection. The contributions of this paper include the efficient adaptation of the SAM (Segment Anything Model) for shield tunnel water leakage segmentation and the demonstration of the application effect by data experiments. Tunnel SAM Adapter has satisfactory performance, achieving 76.2 % mIoU and 77.5 % Dice. Experimental results demonstrate that our approach has advantages over peer studies and guarantees the integrity and safety of these vital assets while streamlining tunnel maintenance. [ABSTRACT FROM AUTHOR]

Parallel continuum robots (PCRs) have attracted increasing attention in the robotics community due to their simplicity in structure, inherence with compliance, and easiness of realization. Over the past decade, a variety of novel designs have been reported to enrich their diversity. However, there is a lack of systematic review of these emerging robots. To this end, this paper conducts a comprehensive survey on the mechanism design, kinetostatic modeling and analysis, and performance evaluation. For these robots, kinetostatic modeling plays a fundamental role throughout the design, analysis, and control stages. A systematic review of the existing approaches for kinetostatic modeling and analysis is provided, and a comparison is made to distinguish their differences. As well, a classification is made according to the characteristics of structure and actuation. In addition, performance evaluation on the workspace, stability, and singularity is also overviewed. Finally, the scenarios of potential applications are elaborated, and future research prospects are discussed. We believe that the information provided in this paper will be particularly useful for those who are interested in PCRs. [ABSTRACT FROM AUTHOR]

The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios. The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the EGLA (Externally Gaped Line Arresters) and its impact on the back flashover rate of a 400 kV transmission line have been investigated. The frequency behavior of the grounding system and soil resistivity has been modeled. To analyze the EGLA performance in relation to the grounding system's frequency behavior, a rod-shaped grounding system model has been implemented. By placing the EGLA at different phases of the transmission line, the best scenario has been identified to minimize back-flashover occurrences. Furthermore, the performance of the frequency grounding system to that of the nonlinear grounding system has been compared. The results clearly indicate that using a nonlinear grounding system leads to higher back flashover rates compared to the frequency grounding system. Additionally, the EGLA absorbs less energy when connected to a nonlinear resistor compared to the frequency grounding system. It can be concluded that modeling the grounding system's frequency behavior using the frequency grounding model provides more accurate results, especially in investigations related to power grid insulation coordination. [ABSTRACT FROM AUTHOR]

This paper studies a novel robotic warehousing system called the overhead robotic compact storage and retrieval system, which can free up the floor space occupation at a low cost. Bins, as basic storage containers, are stacked on top of each other to form a bin stack. Along overhead tracks, bin-picking robots transport bins between storage/retrieval positions and workstations with the aid of track-changing robots. Little research has been done to study operational policies and performance analysis for this new robotic compact warehousing system. We propose a nested queuing network model that considers two transportation resources and performs reinforcement learning using real data to improve the reshuffling efficiency. We find that reinforcement learning based reshuffling policy greatly reduces the reshuffling distance and saves computation time compared to existing policies. We find that the storage policy of stacks affects the optimal width/length ratio regardless of the system height. Interestingly, we obtain the number of robots that can stabilise the system to avoid an explosion of the order queue; two more robots than that number will produce relatively low throughput times. Compared to an AutoStore system, using our system reduces cost by 30% with a slight increase in throughput time. [ABSTRACT FROM AUTHOR]

Conductive hydrogels, characterized by their excellent conductivity and flexibility, have attracted widespread attention and research in the field of flexible wearable sensors. This paper reviews the application progress, related challenges, and future prospects of conductive hydrogels in flexible wearable sensors. Initially, the basic properties and classifications of conductive hydrogels are introduced. Subsequently, this paper discusses in detail the specific applications of conductive hydrogels in different sensor applications, such as motion detection, medical diagnostics, electronic skin, and human–computer interactions. Finally, the application prospects and challenges are summarized. Overall, the exceptional performance and multifunctionality of conductive hydrogels make them one of the most important materials for future wearable technologies. However, further research and innovation are needed to overcome the challenges faced and to realize the wider application of conductive hydrogels in flexible sensors. [ABSTRACT FROM AUTHOR]

At present, there is still some uncertainty in the evaluation of the performance of the Fengyun 4A Lightning Mapping Imager (LMI), which is mainly limited by the detection performance of the reference detection system and the suitability of the evaluation method. In this paper, a one-to-one performance evaluation of the LMI was performed based on total lightning flash data from the lightning Low-Frequency Electric field Detection Array (LFEDA). It was found that there were significant systematic biases in the discharge results detected via LMI, with a median of −0.946 s, −0.0817°, and −0.0245° in time bias, longitude bias, and latitude bias, respectively. The evaluation results after removing the systematic biases indicated that the relative detection efficiency for flashes of LMI was 17.6%, the mean and median time errors were both 0.647 s, and the mean and median distance errors were 6.09 km and 5.02 km, respectively. The relative detection efficiency for groups of LMI was 9.8%, the mean and median time errors were 0.674 s and 0.660 s, and the mean and median distance errors were 7.19 km and 6.54 km, respectively. The detection efficiency of LMI for both flashes and groups at nighttime was significantly higher than its detection efficiency during the daytime. The relative detection efficiency for flashes of LMI at nighttime was 26.5%, while during the daytime it was 14.4%. The relative detection efficiency for groups of LMI at nighttime was 16.2%, while during the daytime it was only 7.4%. The spatial accuracy for both flashes and groups was always better during the daytime than at nighttime. [ABSTRACT FROM AUTHOR]

Active funds vs passive funds represent one of the most prominent investment decisions that ought to be undertaken by investors over the decades. Our paper seeks to offer an insight into the average performance of equity mutual funds during the period when there were roughly 70 'all-time-highs' recorded by S&P 500 amid the COVID-19 health crisis. Additionally, our paper aims to explore the robustness and persistence of the explanatory powers of Morningstar sustainability globes, ratings from agencies and growth tilt during the health crisis. Our results indicated that the benchmark indexes seemed to dominate mutual funds in most cases during our sampling period. The ratings from Morningstar and/or Lipper together with growth investing appeared to be ideal elements to contemplate when making investment decisions during the COVID-19 health crisis. [ABSTRACT FROM AUTHOR]

This paper introduces a research method for locomotive-assisted driving systems based on simulation platform. Locomotive-assisted driving systems have become a key technology in improving driving safety and efficiency. However, conducting system testing directly on actual locomotives may pose risks and incur high costs. Therefore, the development of a simulation platform suitable for locomotive-assisted driving systems is necessary. This paper provides an overview of existing simulation platforms, analyzes their advantages and limitations, and proposes a design scheme for a simulation platform based on virtual reality technology. This platform can simulate various driving scenarios and traffic conditions, allowing researchers to conduct accurate performance evaluations and functional tests of locomotive-assisted driving systems. Finally, the effectiveness and feasibility of the proposed simulation platform are validated through tests, providing strong support for the development of locomotive-assisted driving systems. [ABSTRACT FROM AUTHOR]

This paper determines the weights of the key factors for evaluating the performance of the banking system in Iran using the Balanced Scorecard (BSC) and Fuzzy Analytic Network Process (FANP) approaches. This paper first identifies key factors for the performance evaluation and then determines the cause-and-effect relationship between using the DEMATEL approach. The key factors were included in the BSC framework using the experts' opinions and following principles, including the need for all communication to lead to profitability, not considering the causal relationship from more tangible dimensions to intangible dimensions, and eliminating feedback causal relationships. The calculated weights show the maximum importance of the learning and growth dimensions and the minimum importance of the financial dimension. Among the key factors, on-time service is the maximum important, with customer retention being the minimum important. The extracted key factors can evaluate the performance of the country's banking system descriptively and based on the data obtained from declarations or official reports provided by different evaluators. [ABSTRACT FROM AUTHOR]

Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous studies have primarily focused on the educational benefits of Hour of Code games, there has been limited research on their support methods. Therefore, this paper aims to address this gap with an empirical study of the experience of 104 students who played the game for one hour. Student progress was tracked by an administering teacher and after the game session, a questionnaire was administered to collect data on the participant's perceptions of the support system, interface efficiency, and overall experience with Hour of Code. The results of the study reveal significant problems with the aforementioned systems, which apply not only to Minecraft Adventurer but also to several other similar serious games. Additionally, the findings showed a correlation between the utilization of the support system and student performance, indicating that student's comprehension of the support system significantly influences their learning outcomes. This paper concludes by providing potential solutions to address the identified insufficiencies, offering valuable insights for future researchers and game developers on the design and evaluation of serious games for educational purposes. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Serverless computing has become very popular in recent years due to its flexibility and cost efficiency. Serverless computing is a cloud computing model that allows developers to write and deploy code without managing the underlying infrastructure. Serverless computing has been welcomed due to its scalability, affordability, and ease of use. However, memory orchestration in serverless computing can be challenging due to the ephemeral nature of serverless functions, which are short-lived and stateless. This article has reviewed memory orchestration mechanisms and then presented a classification of memory orchestration mechanisms in serverless computing, which are classified into three main methods: Machine learning-based approach, Heuristic-based approach, and framework-based approach. The advantages and disadvantages of each mechanism as well as the challenges and performance metrics affecting their effectiveness have been investigated. Each memory orchestration approach, whether heuristic-based, framework-based, or machine learning-based, has its own advantages and disadvantages. Different mechanisms are suitable for different use cases. Therefore, it is important to carefully evaluate the trade-offs between performance, cost, and complexity when choosing a memory orchestration mechanism. Finally, the paper identifies several future research directions for memory orchestration in serverless computing, which include developing memory orchestration mechanisms and dynamic memory management, integrating memory orchestration mechanisms with other resource management mechanisms, security, memory sharing, compression, pricing, and investigating the relationships between memory orchestration with cost and performance in serverless computing. [ABSTRACT FROM AUTHOR]

Simple Summary: HER2-positive breast cancer occurs in 15–30% of cases and has a poor prognosis. Digital image analysis of HER2 is promising, but its implementation in real clinical practice remains unclear. This systematic review evaluates the effectiveness of digital image analysis algorithms for HER2 in breast cancer and their performance, with a focus on testing them in real-world clinical settings. The authors aim to assess the applicability of these algorithms in practical clinical scenarios. By analyzing 25 papers from the period 2013–2024 and emphasizing mostly deep learning approaches, the review underscores the importance of standardized evaluation criteria, study designs tailored for clinical applications, and clinical validation. While direct evidence of clinical application was not found, the findings aim to guide future research and the implementation of digital image analysis in breast cancer diagnosis within clinical settings, potentially impacting the research community by advancing algorithmic applications in real clinical practice. This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer. [ABSTRACT FROM AUTHOR]

Today, the Radio frequency identification system is widely used in various applications on a large scale. The readers must be densely deployed in order to cover their entire work area. If the number of readers is not optimal, some readers will be redundant, which will reduce the efficiency of the whole system. In more detail, redundant readers increase system overhead, unnecessary tag-reader communication, tag collision, and reader collision, and also, decrease the lifetime of RFID networks. The redundant reader elimination problem is a process that finds the least number of readers to cover all system tags. It is proved that the redundant reader elimination problem is NP hard. One of the useful tasks in eliminating redundant readers is improving the performance of existing approximation algorithms. In this paper, we propose a distributed algorithm, the LWFO algorithm, which combines the count base algorithm (CBA) and a weighting algorithm. The simulation results show that the proposed algorithm reduces reader redundancy compared to existing algorithms also processing time is reasonable. [ABSTRACT FROM AUTHOR]

In some real-life situations, we will face restrictions of time and sample size which cause a researcher to not have access to all of the data. Therefore, it is valuable to study the estimation of parameters based on information of available data. In such situations, using appropriate sampling schemes, to more efficient estimators are important. The aim of the present paper is to study the Bayes estimators of parameters of the Pareto type-I model under different loss functions and compare among them as well as with the classical estimator named maximum likelihood estimator based on upper record ranked set sampling scheme. Here the informative Gamma prior is used as the conjugate prior distribution for finding the Bayes estimator. We also used symmetric loss functions such as squared error loss function and asymmetric loss functions such as linear-exponential loss function. We present the analysis of a Monte Carlo simulation to compare the performance of the estimators with respect to their risks (average loss over sample space) based on upper record ranked set sampling. Finally, one real data set is analyzed to illustrate the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]