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We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article. [ABSTRACT FROM AUTHOR]

Simulating mixed-state evolution in open quantum systems is crucial for various chemical physics, quantum optics, and computer science applications. These simulations typically follow the Lindblad master equation dynamics. An alternative approach known as quantum state diffusion unraveling is based on the trajectories of pure states generated by random wave functions, which evolve according to a nonlinear Itô–Schrödinger equation (ISE). This study introduces weak first-order and second-order solvers for the ISE based on directly applying the Itô–Taylor expansion with exact derivatives in the interaction picture. We tested the method on free and driven Morse oscillators coupled to a thermal environment and found that both orders allowed practical estimation with a few dozen iterations. The variance was relatively small compared to the linear unraveling and did not grow with time. The second-order solver delivers a much higher accuracy and stability with bigger time steps than the first-order scheme, with a small additional workload. However, the second-order algorithm has quadratic complexity with the number of Lindblad operators as opposed to the linear complexity of the first-order algorithm. [ABSTRACT FROM AUTHOR]

In 2020, Libraries and Cultural Resources (LCR) at the University of Calgary used Qualtrics XM to design and pilot a point-of-use survey to collect user feedback on the usability of our implementation of Primo, Ex Libris's web-scale discovery service. Over a two-week period, users were presented with the pop-up survey while searching and asked to provide feedback. This article summarizes how we designed and implemented this point-of-use survey and the lessons learned from this project. [ABSTRACT FROM AUTHOR]

The many-body expansion lies at the heart of numerous fragment-based methods that are intended to sidestep the nonlinear scaling of ab initio quantum chemistry, making electronic structure calculations feasible in large systems. In principle, inclusion of higher-order n-body terms ought to improve the accuracy in a controllable way, but unfavorable combinatorics often defeats this in practice and applications with n ≥ 4 are rare. Here, we outline an algorithm to overcome this combinatorial bottleneck, based on a bottom-up approach to energy-based screening. This is implemented within a new open-source software application ("Fragme∩t"), which is integrated with a lightweight semi-empirical method that is used to cull subsystems, attenuating the combinatorial growth of higher-order terms in the graph that is used to manage the calculations. This facilitates applications of unprecedented size, and we report four-body calculations in ( H 2 O) 64 clusters that afford relative energies within 0.1 kcal/mol/monomer of the supersystem result using less than 10% of the unique subsystems. We also report n-body calculations in ( H 2 O) 20 clusters up to n = 8, at which point the expansion terminates naturally due to screening. These are the largest n-body calculations reported to date using ab initio electronic structure theory, and they confirm that high-order n-body terms are mostly artifacts of basis-set superposition error. [ABSTRACT FROM AUTHOR]

Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements. [ABSTRACT FROM AUTHOR]

Hand pose estimation is a fundamental task in computer vision with applications in virtual reality, gesture recognition, autonomous driving, and virtual surgery. Keypoint detection often relies on deep learning methods and high-resolution feature map representations to achieve accurate detection. The HRNet framework serves as the basis, but it presents challenges in terms of extensive parameter count and demanding computational complexity due to high-resolution representations. To mitigate these challenges, we propose a lightweight keypoint detection network called LGCANet (Lightweight Ghost-Coordinate Attention Network). This network primarily consists of a lightweight feature extraction head for initial feature extraction and multiple lightweight foundational network modules called GCAblocks. GCAblocks introduce linear transformations to generate redundant feature maps while concurrently considering inter-channel relationships and long-range positional information using a coordinate attention mechanism. Validation on the RHD dataset and the COCO-WholeBody-Hand dataset shows that LGCANet reduces the number of parameters by 65.9% and GFLOPs by 72.6% while preserving the accuracy and improves the detection speed. [ABSTRACT FROM AUTHOR]

The development of e-commerce faces many problems, among which the real-time information exchange between customers and websites is the most urgent. To study the application of software robots in the real-time processing of e-commerce orders. According to the working mechanism of ALICE, JAVAPROGRAMD, AIML technology, database technology, knowledge base principle, Rule of inference, and reasoning strategies are used to design the structure, workflow, knowledge processing flow, and Inference engine supporting human-computer automatic negotiation of the software robot. Establishing a rule base is the acquisition, induction, and organization of knowledge related to order processing. It requires learning and analyzing the problems and related materials to be solved, simulating the actual process of order processing, and extracting useful processing rules and processes from it. The rules in the system are not organized in a simple list form, but have a certain hierarchical structure. This way, when using these rules for reasoning, the hierarchy is clear and it is easy to modify and add rules. The hierarchical structure of the rules referred to here is to simulate the thinking activities of the human brain in the process of order processing, and organize them in a certain way. Based on the basic rules of order processing, the process of order processing, the rational performance of the order and the possibility of necessary events, so as to realize the structure of the reasoning machine. Expands the application of Chatbot ALICE, and provides a new tool for real-time online negotiation and negotiation of e-commerce order processing. The core part of the e-commerce order processing software robot is the human-machine automatic interaction module. Database module, knowledge base module and rule base module provide the necessary data, knowledge and rule basis for its realization, while the implementation of reasoning machine provides the possibility for the intelligence and dynamic interaction of human-machine automatic interaction module. [ABSTRACT FROM AUTHOR]

Adopting an efficient software process model is critical for building high-quality software applications. An important factor impacting the software development process is an accurate estimate of human effort required to complete the software project. While machine learning methods were historically used to develop estimation models, there has been little investigation into the potential of deep convolutional neural networks (DCNNs) for improving software effort estimation. One of the biggest obstacles in using DCNN for this purpose is the common nature of software datasets, which often consist of vectorized samples rather than matrices. To defeat this obstacle and reduce vagueness in software attribute measurement, this study uses Fuzzy theory to generate an appropriate two-dimensional datapoint representation. The fuzzy clustering is commonly used to split dataset samples into separate clusters, which can help to generate Fuzzy membership functions. This approach makes it easier to generate a two-dimensional array representation for each data sample based on the membership values, allowing it to be used as input to the DCNN model. The efficiency of the proposed model was thoroughly evaluated using PROMISE benchmark datasets. The findings based on mean absolute errors and standardized accuracy show that our proposed model produced very good performance with low error rates and outperformed several current state-of-the-art effort estimation models. Nonetheless, further research is needed to determine the impact of different cluster numbers and features on the performance of our model. In conclusion, this study emphasizes the possibility for incorporating DCNN into software effort estimates and highlights the viability of utilizing fuzzy modeling and clustering techniques to enhance the data representation of software datasets. [ABSTRACT FROM AUTHOR]

We design a quantum method for classical information compression that exploits the hidden subgroup quantum algorithm. We consider sequence data in a database with a priori unknown symmetries of the hidden subgroup type. We prove that data with a given group structure can be compressed with the same query complexity as the hidden subgroup problem, which is exponentially faster than the best-known classical algorithms. We moreover design a quantum algorithm that variationally finds the group structure and uses it to compress the data. There is an encoder and a decoder, along the paradigm of quantum autoencoders. After the training, the encoder outputs a compressed data string and a description of the hidden subgroup symmetry, from which the input data can be recovered by the decoder. In illustrative examples, our algorithm outperforms the classical autoencoder on the mean squared value of test data. This classical-quantum separation in information compression capability has thermodynamical significance: the free energy assigned by a quantum agent to a system can be much higher than that of a classical agent. Taken together, our results show that a possible application of quantum computers is to efficiently compress certain types of data that cannot be efficiently compressed by current methods using classical computers. [ABSTRACT FROM AUTHOR]

The intrusion detection process is important in various applications to identify unauthorized Internet of Things (IoT) network access. IoT devices are accessed by intermediators while transmitting the information, which causes security issues. Several intrusion detection systems are developed to identify intruders and unauthorized access in different software applications. Existing systems consume high computation time, making it difficult to identify intruders accurately. This research issue is mitigated by applying the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM). The method uses concealed service sessions to identify the anonymous interrupts. During this process, the system is trained with the help of different parameters such as origin, session access demands, and legitimate and illegitimate users of various sessions. These parameters help to recognize the intruder's activities with minimum computation time. In addition, the collected data is processed using the deep recurrent learning approach that identifies service failures and breaches, improving the overall intruder detection rate. The created system uses the TON-IoT dataset information that helps to identify the intruder activities while accessing the different data resources. This method's consistency is verified using the metrics of service failures of 10.65%, detection precision of 14.63%, detection time of 15.54%, and classification ratio of 20.51%. [ABSTRACT FROM AUTHOR]

Background: Across the globe, shortages of nurses and midwives in rural regions persist. Advanced practice nursing has been found to address workforce shortages through career progression aimed at retention. A regional health service sought to consult with staff about developing an advanced practice nursing framework. Aims: This study aimed to explore the perspectives of nurses and midwives at a regional health service on (i) how their roles aligned with the modified Advanced Practice Role Delineation (APRD) tool and (ii) the potential for implementation of such a framework at their service. Design: A case study conducted at Goulburn Valley Health (GVH) in southeast Australia used a mixed method design with a survey of all nursing and midwifery staff and focus groups with senior staff. Methods: All nurses and midwives were asked to complete a validated modified ARPD questionnaire and those in Grades 3–6 were asked to participate in a focus group. Results: From 183 questionnaires and 38 participants in the focus group discussions, findings concurred that nurses and midwives at GVH reported spending most time on direct patient care and minimal time on research activities, publication and/or leadership. While education was strongly embraced and advanced practice usually supported, senior staff cited the lack of resources, the culture and staff shortages as restricting opportunities for education, career development and implementation of an advanced practice framework. Conclusions: While an advanced practice framework could address staff shortages at this health service, implementation of such a framework is constrained by the lack of resources and workforce shortages common in rural health services. A national strategy that embeds advanced practice roles and resources implementation of advanced practice nursing and midwifery frameworks in areas of need is recommended. [ABSTRACT FROM AUTHOR]

Objective: To provide an updated evaluation of otolaryngology residency program websites during a socially distanced application cycle. Criteria were selected to evaluate for level of comprehensiveness, new content accommodating a virtual interview cycle, and stated commitment to diversity and inclusion. Study design: Review of otolaryngology residency program websites. Setting: Online websites between December 2020 and January 2021. Methods: Otolaryngology residency program websites were evaluated for selected criteria. Programs were categorized by geographic location, size, and Doximity rankings. Differences between groups were analyzed using Student's t-test and analysis of variance with a significance level of P <.05. Results: 118 otolaryngology residency program websites contained a mean (SD) of 16.3 (4.7) of 31 criteria (52.6%). Fewer than one third included virtual/video tours of facilities or the surrounding area. Only 33% had a stated commitment to diversity and inclusion. There was no difference in number of criteria included when categorized by geographic location (P =.22). Larger programs (17.9 vs 14.5, P <.001) and programs ranked in the top 50 by Doximity (17.7 vs 15.2, P =.003) included a significantly greater number of criteria. Conclusion: While the comprehensiveness of otolaryngology residency program websites has improved, considerable room for improvement remains. Programs should strive to maintain updated websites that highlight training opportunities, program culture, and commitment to diversity and inclusion. Programs also should consider prioritizing the development of new online resources that may be especially useful to applicants during a virtual interview cycle. [ABSTRACT FROM AUTHOR]

System-to-system communication via Application Programming Interfaces (APIs) plays a pivotal role in the seamless interaction among software applications and systems for efficient and automated service delivery. APIs facilitate the exchange of data and functionalities across diverse platforms, enhancing operational efficiency and user experience. However, this also introduces potential vulnerabilities that attackers can exploit to compromise system security, highlighting the importance of identifying and mitigating associated security risks. By examining the weaknesses inherent in these APIs using security open-intelligence catalogues like CWE and CAPEC and implementing controls from NIST SP 800-53, organizations can significantly enhance their security posture, safeguarding their data and systems against potential threats. However, this task is challenging due to evolving threats and vulnerabilities. Additionally, it is challenging to analyse threats given the large volume of traffic generated from API calls. This work contributes to tackling this challenge and makes a novel contribution to managing threats within system-to-system communication through API calls. It introduces an integrated architecture that combines deep-learning models, i.e., ANN and MLP, for effective threat detection from large API call datasets. The identified threats are analysed to determine suitable mitigations for improving overall resilience. Furthermore, this work introduces transparency obligation practices for the entire AI life cycle, from dataset preprocessing to model performance evaluation, including data and methodological transparency and SHapley Additive exPlanations (SHAP) analysis, so that AI models are understandable by all user groups. The proposed methodology was validated through an experiment using the Windows PE Malware API dataset, achieving an average detection accuracy of 88%. The outcomes from the experiments are summarized to provide a list of key features, such as FindResourceExA and NtClose, which are linked with potential weaknesses and related threats, in order to identify accurate control actions to manage the threats. [ABSTRACT FROM AUTHOR]

The adoption and popularity of mobile devices by end-users is partially driven by the increasing development and availability of mobile applications that can aid solving different problems and provide access to services in a wide range of domains or categories, namely healthcare, education, e-commerce or entertainment. While these applications use and benefit from the combination of a wide panoply of technologies from the Internet of Things, fog and cloud computing, data security and privacy are typically not fully taken into account before the creation of many mobile applications or during the software development phases. This paper presents an in-depth approach to modeling attacks on the specific cloud and mobile ecosystem, given its importance in the process of secure application development. Moreover, aiming at bridging the knowledge gap between developers and security experts, this paper presents an alpha version of the security by design for cloud and mobile ecosystem (secD4CloudMobile) framework. secD4CloudMobile is a set of tools that covers cloud and mobile security requirement elicitation (CMSRE), cloud and mobile security best practices guidelines (CMSBPG), cloud mobile attack modeling elicitation (CMAME), and cloud mobile security test specification and tools (CM2ST). The purpose of the framework is to provide cloud and mobile application developers useful readily applicable information and guidelines, striving to bring security engineering and software engineering closer, in a more accessible and automated manner, aiming at the incorporation of security by construction. Finally, the paper presents some preliminary results and discussion. [ABSTRACT FROM AUTHOR]

Objective The aim of this work was the development of an augmented reality system including the functionality of conventional surgical navigation systems. Methods An application software for the Augmented Reality System HoloLens 2 from Microsoft was developed. It detects the position of the patient as well as position of surgical instruments in real time and displays it within the two-dimensional (2D) magnetic resonance imaging or computed tomography (CT) images. The surgical pointer instrument, including a pattern that is recognized by the HoloLens 2 sensors, was created with three-dimensional (3D) printing. The technical concept was demonstrated at a cadaver skull to identify anatomical landmarks. Results With the help of the HoloLens 2 and its sensors, the real-time position of the surgical pointer instrument could be shown. The position of the 3D-printed pointer with colored pattern could be recognized within 2D-CT images when stationary and in motion at a cadaver skull. Feasibility could be demonstrated for the clinical application of transsphenoidal pituitary surgery. Conclusion The HoloLens 2 has a high potential for use as a surgical navigation system. With subsequent studies, a further accuracy evaluation will be performed receiving valid data for comparison with conventional surgical navigation systems. In addition to transsphenoidal pituitary surgery, it could be also applied for other surgical disciplines. [ABSTRACT FROM AUTHOR]

Accessible Summary: What Is Known about the Subject?: One in five mothers and birthing parents are affected by postpartum depression (PPD), yet only a small proportion of those with PPD receive treatment.Given cost and accessibility barriers to treatment, brief therapies (e.g. 1 day) could help address PPD on a large scale, though understanding participants' unique experiences with this novel treatment will help guide its refinement and use. What this Paper Adds to Existing Knowledge?: This paper describes the experiences, perspectives and satisfaction of participants who attended a 1‐day workshop for PPD.Participants appreciate being taught practical techniques and the receipt of social support from facilitators and other attendees in the group setting, while some recommended further 'booster sessions' to enhance effectiveness. What Are the Implications for Practice?: One‐day CBT‐based workshops may be an effective, low‐intensity treatment option that provides helpful skills and builds social networks for individuals with PPD.Additional post‐workshop booster sessions may enhance participant satisfaction with this new treatment by providing a reinforcement of their learning and an opportunity to re‐connect with other birthing parents. Introduction: Postpartum depression (PPD) affects up to one in five mothers and birthing parents. Understanding their perceptions of therapeutic interventions is key to their effectiveness and scalability. Aim: The aim of the study was to understand participants' experiences attending an in‐person or online 1‐day cognitive behavioural therapy (CBT)‐based workshop for PPD and compare their experiences participating in either modality. Method: This descriptive qualitative study was embedded in two separate randomized controlled trials evaluating the effectiveness of 1‐day CBT‐based workshops for PPD delivered in‐person or online. Responses were provided by 94 in‐person and 95 online participants about their experiences. Content analysis was used to code and categorize the data. Results: Participants appreciated learning skills to improve PPD symptoms and joining a community of individuals with shared experiences. Integrating practical coping techniques after the workshop was reported to be most useful, though some wanted additional post‐workshop support. Online participants identified additional barriers to workshop engagement. Discussion: Participants who attended either workshop found them to be valuable and effective. Participants valued psychoeducation, social connections and applying practical CBT techniques. Implications for Practice: One‐day CBT‐based workshops may be an accessible treatment option for individuals with PPD, providing new skills and social support, though some prefer to receive additional post‐workshop support. [ABSTRACT FROM AUTHOR]

This study presents an applied study with a pure experimental design to evaluate the effectiveness of the application, with the objective of reducing the levels of loneliness and depression through the use of a mobile application based on neural networks in older adults in the city of Trujillo. In addition, software development technologies such as Flutter, Flask, and ExpressJs were used. The results indicate a significant decrease in the levels of emotional and social loneliness and depression in the experimental group (EG) compared to the control group (CG). Reductions of 15.03% in the level of emotional loneliness (LEL), 24.74% in the level of social loneliness, and a remarkable 44.12% in the level of depression were observed in the EG. In addition, a significant increase of 6% in the mood index was evidenced in the EG. This led to the conclusion that after mobile implementation based on neural networks, the alternative hypothesis is supported, statistically demonstrating the effectiveness of the application in reducing loneliness and depression in older adults. These findings suggest a positive impact on the emotional well-being of this population, underscoring the utility of technology in addressing mental health issues in this demographic. [ABSTRACT FROM AUTHOR]

Stress is a state of mental tension, which helps us to cope with challenges in our life. It makes us progressive when it is positive, but excessive negative stress that perseveres for a long time leads to a state of depressiveness. Longer stressed stage of a human being changes the size, functionality and frequency of response of many internal and external body parameters. By applying computer vision techniques, these changes of body parameters can be tracked to get useful information about the mental stress for a stress affected person. Many studies show the pupil diameter varies significantly with the effect of stress. Our work is based on the study of variation of pupil diameters of stress affected and not affected university students. With the application of different supervised machine learning algorithms, we have observed that the pupil dilates more in case of stress affected students than non-stressed students. We have also found that the pupils of the students dilates more when they were in positive emotional states than their negative emotional states. This work will be helpful for researchers who are working in the field of emotion detection and recognition and affective disorder analysis. [ABSTRACT FROM AUTHOR]

Offline handwritten Chinese character recognition involves the application of computer vision techniques to recognize individual handwritten Chinese characters. This technology has significantly advanced the research in online handwriting recognition. Despite its widespread application across various fields, offline recognition faces numerous challenges. These challenges include the diversity of glyphs resulting from different writers' styles and habits, the vast number of Chinese character labels, and the presence of morphological similarities among characters. To address these challenges, an optimization method based on a separated pre-training model was proposed. The method aims to enhance the accuracy and robustness of recognizing similar character images by exploring potential correlations among them. In experiments, the HWDB and Chinese Calligraphy Styles by Calligraphers datasets were employed, utilizing precision, recall, and the Macro-F1 value as evaluation metrics. We employ a convolutional self-encoder model characterized by high recognition accuracy and robust performance. The experimental results demonstrated that the separated pre-training models improved the performance of the convolutional auto-encoder model, particularly in handling error-prone characters, resulting in an approximate 6% increase in precision. [ABSTRACT FROM AUTHOR]

Individuals who migrate from their home country face a variety of challenges while adapting to the culture in the United States. Immigrant communities are at a significantly higher risk for poor health outcomes; therefore, assessing healthcare treatment for diverse and resilient immigrant populations, including Hispanic/Latine communities, is crucial to preserving their health, culture, and spirit. A paucity of literature exists surrounding perceptions of well-being in immigrant, Hispanic/Latine adults managing chronic diseases. Past studies have shown a discrepancy between providers' and patients' perceptions of healthcare options for overall well-being. We aim to share varying perspectives found within our work geared towards improving the quality of life for Hispanic/Latine immigrants managing chronic disease, especially type 2 diabetes mellitus and hypertension. The primary objective of this article is to strengthen the understanding of intersections between social, physical, financial, and spiritual health within an (im)migrant Hispanic/Latine community using semi-structured ethnographic interviews. These interviews have highlighted community resilience, demonstrating that individuals can adapt to major life transitions while maintaining balance across dimensions of health. This knowledge could be implemented by actively listening to patient concerns regarding their health dimensions to improve individualized and patient-centric care. [ABSTRACT FROM AUTHOR]

In this research, the modified extended tanh-function (METF) and the extended Jacobi elliptic function expansion (EJEFE) techniques are used to investigate the generation and detection of soliton structures in the Hirota–Maccari (HM) model. Consequently, we obtain soliton solutions with advanced structures, including singular bright soliton, dark soliton, periodic waves, breather waves, periodic breather waves, and multiple bright and dark breather waves. In addition, a lump-type breather wave is also included in the presented solutions. Stability analysis of the obtained solutions is addressed by employing the Hamiltonian technique. 3D surfaces and 2D visuals of the outcomes are represented with the help of a computer application. These findings contribute to understanding nonlinear wave phenomena with potential applications in optics, fluid dynamics, and plasma physics. [ABSTRACT FROM AUTHOR]

Background: Metal ions play vital roles in regulating various biological systems, making it essential to control the concentration of free metal ions in solutions during experimental procedures. Several software applications exist for estimating the concentration of free metals in the presence of chelators, with MaxChelator being the easily accessible choice in this domain. This work aimed at developing a Python version of the software with arbitrary precision calculations, extensive new features, and a user-friendly interface to calculate the free metal ions. Results: We introduce the open-source PyChelator web application and the Python-based Google Colaboratory notebook, PyChelator Colab. Key features aim to improve the user experience of metal chelator calculations including input in smaller units, selection among stability constants, input of user-defined constants, and convenient download of all results in Excel format. These features were implemented in Python language by employing Google Colab, facilitating the incorporation of the calculator into other Python-based pipelines and inviting the contributions from the community of Python-using scientists for further enhancements. Arbitrary-precision arithmetic was employed by using the built-in Decimal module to obtain the most accurate results and to avoid rounding errors. No notable differences were observed compared to the results obtained from the PyChelator web application. However, comparison of different sources of stability constants showed substantial differences among them. Conclusions: PyChelator is a user-friendly metal and chelator calculator that provides a platform for further development. It is provided as an interactive web application, freely available for use at https://amrutelab.github.io/PyChelator, and as a Python-based Google Colaboratory notebook at https://colab.research.google.com/github/AmruteLab/PyChelator/blob/main/PyChelator_Colab.ipynb. [ABSTRACT FROM AUTHOR]

This paper introduces the Fish 3D Locomotion app (F3LA), a Python‐based, Graphical User Interface (GUI)‐equipped tool designed to automate behavioral endpoint extraction in zebrafish locomotion assays. Building on our previous work, which utilized a specialized aquatic tank with a mirror and a single camera for fish movement tracking in three dimensions, F3LA significantly enhances data processing efficiency. Its accuracy was tested by reanalyzing and comprehensively comparing the calculated data with the previously published data from prior publications. From the comparison results, 90% of endpoints showed a similar statistical difference result. These minor differences were due to the different starting points for the dataset and updated calculation formulas that are implemented in F3LA. In addition, shoaling area or shoaling volume calculations are also included in F3LA as a new feature that can serve as sensitive indicators of social cohesion, group dynamics, or stress responses, offering insights into neuropsychological conditions or the effects of pharmacological interventions. Furthermore, F3LA offers a marked improvement over manual operations, being at least five times faster, while maintaining consistent accuracy as it reduces human‐induced errors, ensuring a higher degree of reliability in the results. Finally, the potency of F3LA was tested to evaluate the toxicities of 14 rare earth elements (REEs) to the adult zebrafish behaviors. Based on the results, our findings suggested that each tested REE altered fish behaviors in different patterns and magnitudes to each other. However, among the tested light rare earth elements (LREEs), neodymium was demonstrated to cause more relatively severe behavior alterations than other LREEs, indicated by the statistically higher value of entropy (0.2695 ± 0.04977 (mean with a standard deviation)) than the control group (0.2352 ± 0.05896). Meanwhile, in terms of heavy rare earth elements (HREEs), erbium seemed to lead to more distinct behavior toxicities than other HREEs, which was shown by the statistically lower level of fractal dimension (2.022 ± 0.3412) than the untreated group (2.255 ± 0.1661). Taken together, F3LA's development marks a significant advance in high‐throughput toxicological and pharmacological assessments in zebrafish, leveraging three‐dimensional locomotion data for a more comprehensive analysis of fish behavior performance, providing a significant contribution to research in various fields. [ABSTRACT FROM AUTHOR]

Visual inspection of defective tires post-production is vital for human safety, as faulty tires can lead to explosions, accidents, and loss of life. With the advancement of technology, transfer learning (TL) plays an influential role in many computer vision applications, including the tire defect detection problem. However, automatic tire defect detection is difficult for two reasons. The first is the presence of complex anisotropic multi-textured rubber layers. Second, there is no standard tire X-ray image dataset to use for defect detection. In this study, a TL-based tire defect detection model is proposed using a new dataset from a global tire company. First, we collected and labeled the dataset consisting of 3366 X-ray images of faulty tires and 20,000 images of qualified tires. Although the dataset covers 15 types of defects arising from different design patterns, our primary focus is on binary classification to detect the presence or absence of defects. This challenging dataset was split into 70, 15, and 15% for training, validation, and testing, respectively. Then, nine common pre-trained models were fine-tuned, trained, and tested on the proposed dataset. These models are Xception, InceptionV3, VGG16, VGG19, ResNet50, ResNet152V2, DenseNet121, InceptionResNetV2, and MobileNetV2. The results show that the fine-tuned VGG19, DenseNet21 and InceptionNet models achieve compatible results with the literature. Moreover, the Xception model outperformed the compared TL models and literature methods in terms of recall, precision, accuracy, and F1 score. Moreover, it achieved on the testing dataset 73.7, 88, 80.2, and 94.75% of recall, precision, F1 score, and accuracy, respectively, and on the validation dataset 73.3, 90.24, 80.9, and 95% of recall, precision, F1 score, and accuracy, respectively. [ABSTRACT FROM AUTHOR]

Robotic Process Automation (RPA) is an emerging software technology for automating business processes. RPA uses software robots to perform repetitive and error‐prone tasks previously done by human actors quickly and accurately. These robots mimic humans by interacting with existing software applications through user interfaces (UI). The goal of RPA is to relieve employees from repetitive and tedious tasks to increase productivity and to provide better service quality. Yet, despite all the RPA benefits, most organizations fail to adopt RPA. One of the main reasons for the lack of adoption is that organizations are unable to effectively identify the processes that are suitable for RPA. This paper proposes a new method, called Rule‐based robotic process analysis (RRPA), that assists process automation practitioners to classify business processes according to their suitability for RPA. The RRPA method computes a suitability score for RPA using a combination of two RPA goals: (i) the RPA feasibility, which assesses the extent to which the process or the activity lends itself to automation with RPA and (ii) the RPA relevance, which assesses whether the RPA automation is worthwhile. We tested the RRPA method on a set of 13 processes. The results showed that the method is effective at 82.05% and efficient at 76.19%. [ABSTRACT FROM AUTHOR]

In the field of biology, the 'biomarkers' of the different animal species are of great relevance, as well as the relationships between them and the quality of the habitat. A study was proposed, analysing the antlers of the Iberian deer, to measure the bilateral asymmetry of its branches. The antlers are characterised in a 3D CAD model of their axial structure obtained via photogrammetric restitution from two photographs. This method quantifies the global and partial asymmetries of the antler's structural features. The research evaluated 48 deer antlers from hunting days and taxidermy workshops. To quantify asymmetries, the measurements required are the same as those considered to assess the hunting quality of antlers. Bilateral deviations are estimated for each structural trait and the whole antler. Preliminary results showed a convergence between the values of global asymmetry and hunting quality. The methodology proved to be fast, inexpensive, and easy to implement, which facilitates its use in future asymmetry studies of other biological elements of animal or plant origin. It is only necessary that the starting data, which is the bilateral axial structure of the analysed element, is characterised in a 3D model. [ABSTRACT FROM AUTHOR]

Down syndrome is a lifelong cognitive impairment characterized by lower mental skills and intelligence quotient (IQ) compared to their typical peers. The profile is not curable. However, research has been conducted to supplement and improve cognitive functioning through computing and software applications. Conventional cognitive applications and IQ scales pose significant challenges as they are not developed based on specific cognitive guidelines. Therefore, such methods often fail to accurately assess cognitive profiling, resulting in uncertainty. To overcome these challenges, Interactive Mental Learning Activity Software utilizes tailored guidelines incorporating fuzzy logic rules, ensuring accurate cognitive profiling for Down syndrome. Fuzziness was applied within the machine learning framework across three groups structured based on IQ levels. A total of N=200 individuals with Down syndrome participated in the IQ assessment. The findings revealed that individuals with mild impairment demonstrated a higher degree of improvement in cognitive abilities compared to moderate and severe levels. However, the severe category appears to have an unrealistic probability, leading to a standstill in progress. The implementation of the specific guided system led to improvements of 6%, 5%, and 5% in individuals with mild, moderate, and severe cases, respectively. [ABSTRACT FROM AUTHOR]

In the present paper, the most important aspects of computer algebra systems applications in complicated calculations for classical queueing theory models and their novel modifications are discussed. We mainly present huge computational possibilities of Mathematica environment and effective methods of obtaining symbolic results connected with the most important performance characteristics of queueing systems. First of all, we investigate effective solutions to computational problems appearing in queueing theory such as: finding final probabilities for Markov chains with a huge number of states, calculating derivatives of complicated rational functions of one or many variables with the use of classical and generalized L’Hospital’s rules, obtaining exact formulae of Stieltjes convolutions, calculating chosen integral transforms used often in the above-mentioned theory and possible applications of generalized density function of random variables and vectors in these computations. Some exemplary calculations for practical models belonging both to classical models and their generalizations are attached as well. [ABSTRACT FROM AUTHOR]

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings. [ABSTRACT FROM AUTHOR]

Magmatic oxygen fugacity (fO2) is a fundamental property to understanding the long-term evolution of the Earth's atmosphere and the formation of magmatic-hydrothermal mineral deposits. Classically, the magmatic fO2 is estimated using mineral chemistry, such as Fe-Ti oxides, zircon, and hornblende. These methods, however, are only valid within certain limits and/or require a significant amount of a priori knowledge. In this contribution, a new oxybarometer, constructed by data-driven machine learning algorithms using trace elements in zircon and their corresponding independent fO2 constraints, is provided. Seven different algorithms are initially trained and then validated on a data set that was never utilized in the training processes. Results suggest that the oxybarometer constructed by the extremely randomized trees model has the best performance, with the largest R2 value (0.91 ± 0.01), smallest RMSE (0.45 ± 0.03), and low propagated analytical error (~0.10 log units). Feature importance analysis demonstrates that U, Ti, Th, Ce, and Eu in zircon are the key trace elements that preserve fO2 information. This newly developed oxybarometer has been applied in diverse systems, including arc magmas and mid-ocean ridge basalts, fertile and barren porphyry systems, and global S-type detrital zircon, which provide fO2 constraints that are consistent with other independent methods, suggesting that it has wide applicability. To improve accessibility, the oxybarometer was developed into a software application aimed at enabling more consistent and reliable fO2 determinations in magmatic systems, promoting further research. [ABSTRACT FROM AUTHOR]

The application of computer information management system (IMS for short here) in university management faces problems such as incomplete system software and complex system design. Applying clustering algorithms (CA for short here) to computer student IMS can help optimize the system's overall effectiveness. This article constructed a computer student IMS based on computer technology and applied it to the management of college students. This article also combined CA to conduct relevant effectiveness tests on the system, in order to optimize the overall effectiveness of the system. Under the algorithm in this article, the average connection speed for each user accessing the system was 9.17 Mbps. The average reaction time was 0.34 seconds, the average security level was 92.47%, and the highest memory usage rate of the system was 34.27%; Under the decision tree algorithm, the average connection speed of each user accessing the system was 8.82 Mbps, and the average reaction time reached 0.64 s. The average security level was 88.41%, and the highest memory usage rate was 42.58%. Under the artificial neural network algorithm, the average connection speed of the system was 8.47 Mbps, the average response time was 0.86 s, and the highest memory usage rate was 45.97%. Analyzing the data reveals that the algorithm introduced in this paper significantly enhances system connection speed and reduces reaction time. This improvement not only enhances security measures but also minimizes memory usage, effectively optimizing the overall efficiency of the system. [ABSTRACT FROM AUTHOR]

Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The proposed method, which is an extension of HSCNet, allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image localization on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and the combined indoor scenes. [ABSTRACT FROM AUTHOR]

Importance: Parent recall is the primary method for measuring positioning practices such as tummy time in infants. Concerns regarding the accuracy of parent recall have been raised in the literature. To date, no study has examined the agreement of tummy time recall measures with gold-standard methods. Objective: To assess the agreement between parental recall versus direct observation of tummy time in infants, and to explore the impact of prematurity on this relationship. Design: Cross-sectional observational study, spanning 1 yr. Setting: Participants' homes Participants: Thirty-two infant–parent dyads (19 full-term, 13 preterm), with infants ages 3 to 6 mo and caregivers ages older than 18 yr. Outcome and Measures: Home-recorded videos of infant play across 3 days were used as a proxy for direct observation of tummy time and compared with a 12-item parent recall survey. Results: Parent recall had a significant moderate correlation (ρ =.54, p =.002) with direct observation in full-term infants but was not correlated (p =.23) with direct observation in preterm infants. On average, parents of preterm infants overestimated tummy time by 2.5 times per day compared with direct observation. Conclusions and Relevance: For full-term infants, parent recall measures of tummy time exhibit an acceptable level of agreement with direct observation and can be reliably used over shorter periods. Parents of preterm infants may display a bias in recalling tummy time, leading to overestimations. To accurately assess tummy time in this population, a combination of subjective and objective measures should be explored. Plain-Language Summary: Tummy time is an essential movement experience for infants, especially for preterm infants, who are at a higher risk for motor delays. The most common way to track tummy time is through parent reports, or recall, versus a practitioner directly observing tummy time in the home. Despite the widespread use of parent recall to track tummy time, no study has examined the accuracy of parent recall versus direct observation in the home. Accurately assessing tummy time is crucial for improving and supporting health outcomes for infants. This study found that prematurity may affect the accuracy of parent recall for assessing tummy time in young infants. The authors discuss the implications of this finding and provide suggestions to guide the selection of appropriate methods to measure tummy time in clinical practice and research studies. This study found that infant prematurity may affect the accuracy of parent reports, or recall, of tummy time in the home versus a practitioner directly observing infant tummy time. [ABSTRACT FROM AUTHOR]

Biopharmaceutical manufacturing entails a series of highly regulated steps. The manufacturing of safe and efficacious drug product (DP) requires testing of critical quality attributes (CQAs) against specification limits. DP potency concentration, which measures the dosage strength of a particular DP, is a CQA of great interest. In order to minimize the DP potency out‐of‐specification (OOS) risk, sterile fill finish (SFF) process adjustments may be needed. Varying the potency targets can be one such process adjustment. To facilitate such evaluation, data acquisition and statistical calculations are required. Regularly conducting the OOS risk assessment manually using commercial statistical software can be tedious, error‐prone, and impractical, especially when several alternate potency targets are under consideration. In this work, the development of a novel framework for OOS risk assessment and deployment of cloud‐based statistical software application to facilitate the risk assessment are presented. This application is intended to streamline the assessment of alternate potency targets for DP in biologics manufacturing. The major aspects of this potency targeting application development are presented in detail. Specifically, data sources, pipeline, application architecture, back‐end and front‐end development as well as application verification are discussed. Finally, several use cases are presented to highlight the application's utility in biologics manufacturing. [ABSTRACT FROM AUTHOR]

This study endeavored to design and develop an innovative closed-loop diagnostic and therapeutic system with the following objectives: (a) the noninvasive detection of glucose concentration in sweat utilizing nanonengineered screen-printed biosensors; (b) the management of measured data through a specialized computer system comprising both hardware and software components, thereby enabling the precise control of therapeutic responses via a patch-based nanomedicine delivery system. This initiative addresses the significant challenges inherent in the management of diabetes mellitus, including the imperative need for glucose-level monitoring to optimize glycemic control. Leveraging chronoamperometric results as a foundational dataset and the in vivo hypoglycemic activity of nanoemulsion formulations, this research underscores the efficacy and accuracy of glucose concentration estimation, decision-making mechanism responses, and transdermal hypoglycemic treatment effects, within the proposed system. [ABSTRACT FROM AUTHOR]

This study provides an in-depth analysis of the current status of the promotion and application of BIM software in China, highlighting its differences from the international market. Chinese BIM software shows advantages in data security, cost-effectiveness, user habit adaptability, and localization standards, but faces challenges in terms of technological maturity and ecosystem development. By employing bibliometric methods and utilizing literature resources from CNKI and the Web of Science, the study offers a comprehensive analysis of the market and policy environment and proposes targeted recommendations. The study indicates that Chinese BIM software has significant advantages in enhancing data security and cost-effectiveness but lacks technological maturity and requires improvements in ecosystem development. User habits are crucial for the promotion of BIM software, and the localization and adaptability of the software need further enhancement. To promote the widespread application of BIM in the architecture, engineering, and construction (AEC) industries, a robust market mechanism needs to be established. The government should strengthen policy support and improve regulatory frameworks; software developers should enhance product functionality and work closely with users; and application enterprises should actively provide feedback to promote software improvements. Moreover, the introduction of reasonable capital and the collaboration between educational institutions and enterprises to cultivate professional talent are critical for the promotion and application of BIM technology. The research emphasizes that forming a cross-departmental joint mechanism to promote a virtuous cycle in the AEC market is key to achieving the widespread application of BIM technology. These measures can provide strong market impetus for the development of BIM software, accelerate the maturity and ecosystem construction of Chinese BIM software, and ultimately realize the comprehensive upgrading of informatization and digitization in the construction industry. [ABSTRACT FROM AUTHOR]

The article discusses the European Commission's decision to fine Apple for abusing its dominant position in the market for music streaming apps on its App Store. The Commission found that Apple's anti-steering provisions prevented app developers from informing users about alternative and cheaper music subscription services. The Commission ordered Apple to remove these provisions and comply with the Digital Markets Act (DMA), which requires Apple to allow app developers to communicate and promote offers in-app, permit third-party app stores, and provide access to the App Store on fair terms. Apple's compliance proposal has been criticized for potentially stifling the development of alternative app stores. The Commission has opened non-compliance investigations to determine if Apple's proposal falls short of its obligations under the DMA. The article explores the relationship between fairness and contestability in the provision of app store services and discusses the need to regulate the division of surplus between gatekeepers and business users. The author suggests that compliance with DMA requirements may not guarantee fairness and that the Commission may need to regulate how surplus is divided to ensure fair treatment of business users. [Extracted from the article]

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model's behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners. [ABSTRACT FROM AUTHOR]

The article discusses the use of smart telescopes and digital imaging technology in astronomy education. It highlights the affordability and accessibility of smart telescopes, which can be operated using computer technology and offer features such as automatic tracking of celestial objects and access to online databases. The article also mentions the challenges of teaching astronomy and suggests that introducing astrophotography can make the subject more engaging for students. It provides information on the features to consider when choosing a smart telescope and lists some smart telescopes available on the market. The article concludes by discussing the benefits of smart telescopes in collecting and analyzing data, as well as participating in citizen science projects. [Extracted from the article]

The HYDRUS codes have become standard tools for addressing many soil, agricultural, environmental, and hydrological problems requiring the evaluation of various subsurface physical, chemical, and biological processes. There are now many thousands of HYDRUS users worldwide, with thousands of applications of the HYDRUS models appearing in the peer‐reviewed literature. In this manuscript, we provide an overview of the capabilities of the most recent Version 5 of HYDRUS, focusing primarily on features implemented since 2016. We briefly describe the standard HYDRUS model and its standard and nonstandard specialized add‐on modules that significantly expand the capabilities of the software packages. The standard add‐on modules include HPx, UNSATCHEM, Wetland, Furrow, PFAS, COSMIC, DPU, SLOPE Cube, and Particle Tracking. Recent developments of the HYDRUS Package for MODFLOW are also described, along with additional capabilities incorporated into the graphical user interface supporting HYDRUS. Also discussed are new or improved options to simulate the fate and transport of environmental isotopes, multi‐cropping systems, compensated root water uptake, and hydraulic redistribution within the rootzone, which will be implemented in upcoming add‐on modules. Another objective is to review selected applications of the HYDRUS models, such as evaluations of various irrigation, low‐impact development (LID), and managed aquifer recharge (MAR) schemes. Core Ideas: An overview of the capabilities of the most recent Version 5 of HYDRUS.The description of the standard and nonstandard specialized HYDRUS add‐on modules.A review of selected popular applications of the HYDRUS models.Description of new or improved upcoming HYDRUS add‐on modules. [ABSTRACT FROM AUTHOR]

Software vulnerabilities (SVs) have become a common, serious, and crucial concern due to the ubiquity of computer software. Many AI-based approaches have been proposed to solve the software vulnerability detection (SVD) problem to ensure the security and integrity of software applications (in both the development and testing phases). However, there are still two open and significant issues for SVD in terms of (i) learning automatic representations to improve the predictive performance of SVD, and (ii) tackling the scarcity of labeled vulnerability datasets that conventionally need laborious labeling effort by experts. In this paper, we propose a novel approach to tackle these two crucial issues. We first exploit the automatic representation learning with deep domain adaptation for SVD. We then propose a novel cross-domain kernel classifier leveraging the max-margin principle to significantly improve the transfer learning process of SVs from imbalanced labeled into imbalanced unlabeled projects. Our approach is the first work that leverages solid body theories of the max-margin principle, kernel methods, and bridging the gap between source and target domains for imbalanced domain adaptation (DA) applied in cross-project SVD. The experimental results on real-world software datasets show the superiority of our proposed method over state-of-the-art baselines. In short, our method obtains a higher performance on F1-measure, one of the most important measures in SVD, from 1.83% to 6.25% compared to the second highest method in the used datasets. [ABSTRACT FROM AUTHOR]

The Titanic disaster is a topic that continues to fascinate. As the importance of analytics continues to grow in industry, data literacy skills have become increasingly important in business education. This project allows students to use the passenger data from the Titanic to build their data literacy skills using an engaging, experiential topic. The project requires students to extract, transform, describe, analyze, and draw conclusions regarding the factors that impacted survival on the Titanic. The project can be deployed using various application software and tools. We describe how the assignment can be completed using Excel, Tableau, and Python using the Pandas library. [ABSTRACT FROM AUTHOR]

It is estimated that globally, millions of adolescents experience mental health problems, especially anxiety and depression. Adolescents with high perceptions of stress often encounter mental issues, have negative thoughts or are dissatisfied with daily life events. This study employed a quasi-experimental design with two groups and repeated measures to test an innovative nursing intervention, the Be Aware of Stress Smartphone Application. This application aims to increase positive thinking and reduce perceived stress among at-risk adolescents aged 13 to 16, and we sought to determine their satisfaction with its usability. The study was conducted with 44 junior high school students from an opportunity expansion high school in Eastern Thailand, who were randomly assigned to experimental and comparison groups (22 each). Data were collected between December 2023 and February 2024 using the General Information Questionnaire, Positive Thinking Test, Perceived Stress Questionnaire, and Satisfaction of Application Usability scale. The outcomes were measured at baseline, after completing the program at week 4, and at week 8 after using the application. The chi-square test and repeated measures analysis of variance were used to analyze the data. The results of this study have important implications for future research. They indicate a significant improvement in positive thinking and a reduction in perceived stress within the experimental group, compared to the comparison group, at weeks 4 and 8. Moreover, the experimental group reported high satisfaction with the application's usability. These findings suggest that the Be Aware of Stress application is beneficial for community nurses and others to prevent adolescent stress. The study also highlights the need for future research to enhance the application's functionality and compatibility with various phone operating systems to ensure widespread and effective use. [ABSTRACT FROM AUTHOR]

The development of the education industry benefits from the continuous progress and updates of internet technology, and the organic integration of the two can further promote the emergence of the LMS (Learning Management System) system. Based on the application characteristics of the LMS system and the characteristics of the Software as a Service online education management system, a Software as a Service online education management system based on the extended LMS system was constructed. The service mode of the system was analysed, and a solution to the problem of paper formation and an analysis of the automatic paper formation algorithm in the system were provided. In addition, further research was conducted on the scores of the credibility evaluation of the system's services under different attribute parameters. Through the correlation analysis between different attributes and the credibility evaluation scores of the Software as a Service system, it can be seen that among the selected attribute parameters, Response Time and Latency show a negative correlation with the credibility evaluation, while other parameters show a positive correlation. The Software as a Service system constructed in this article provides relevant technical solutions, which have great practical application value and have reference significance for other similar applications. It provides more support for the application and promotion of the Software as a Service (SaaS) online education management system. [ABSTRACT FROM AUTHOR]

Service-oriented computing (SOC) provides a new framework for designing distributed web applications and software in a flexible, scalable, and cost-effective manner. Its use is widespread to efficiently integrate existing Web services and create high value-added applications. This model, proven in various fields such as e-commerce, also shows significant advantages in the field of e-learning. This approach highlights the discovery and use of Web services listed in specialized directories. In fact, this paper proposes a framework for exploring web services associated with education. This approach is based on the application of a matching algorithm to select the services best suited to the needs of users of the online learning system, as well as the ontology of the e-learning domain and the semantic descriptions of the web services via web ontology language for web services (OWL-S). [ABSTRACT FROM AUTHOR]

Natural image matting has garnered increasing attention in various computer vision applications. The matting problem aims to find the optimal foreground/background (F/B) color pair for each unknown pixel and thus obtain an alpha matte indicating the opacity of the foreground object. This problem is typically modeled as a large-scale pixel pair combinatorial optimization (PPCO) problem. Heuristic optimization is widely employed to tackle the PPCO problem owing to its gradient-free property and promising search ability. However, traditional heuristic methods often encode F/B solutions to a one-dimensional (1D) representation and then evolve the solutions in a 1D manner. This 1D representation destroys the intrinsic two-dimensional (2D) structure of images, where the significant spatial correlations among pixels are ignored. Moreover, the 1D representation also brings operation inefficiency. To address the above issues, this article develops a spatial-aware tensorial evolutionary image matting (TEIM) method. Specifically, the matting problem is modeled as a 2D Spatial-PPCO (S-PPCO) problem, and a global tensorial evolutionary optimizer is proposed to tackle the S-PPCO problem. The entire population is represented as a whole by a third-order tensor, in which individuals are classified into two types: F and B individuals for denoting the 2D F/B solutions, respectively. The evolution process, consisting of three tensorial evolutionary operators, is implemented based on pure tensor computation for efficiently seeking F/B solutions. The local spatial smoothness of images is also integrated into the evaluation process for obtaining a high-quality alpha matte. Experimental results compared with state-of-the-art methods validate the effectiveness of TEIM. [ABSTRACT FROM AUTHOR]