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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]

United States law bars using peremptory strikes during jury selection because of prospective juror race, ethnicity, sex, or membership in certain other cognizable classes. Here, we extend a Bayesian approach for detecting such illegal strike bias by showing how to incorporate historical data on an attorney's use of peremptory strikes in past cases. In so doing, we use the power prior to adjust the weight of such historical information in the analysis. Using simulations, we show how the choice of the power prior's discounting parameter influences bias detection (how likely the credible interval for the bias parameter excludes zero), depending on the degree of incompatibility between current and historical trial data. Finally, we extend this approach with a prototype software application that lawyers could use to detect strike bias in real time during jury-selection. We illustrate this application's use with real historical strike data from a convenience sample of cases from one court. [ABSTRACT FROM AUTHOR]

Web search is a crucial technology for the digital economy. Dominated by a few gatekeepers focused on commercial success, however, web publishers have to optimize their content for these gatekeepers, resulting in a closed ecosystem of search engines as well as the risk of publishers sacrificing quality. To encourage an open search ecosystem and offer users genuine choice among alternative search engines, we propose the development of an Open Web Index (OWI). We outline six core principles for developing and maintaining an open index, based on open data principles, legal compliance, and collaborative technology development. The combination of an open index with what we call declarative search engines will facilitate the development of vertical search engines and innovative web data products (including, e.g., large language models), enabling a fair and open information space. This framework underpins the EU‐funded project OpenWebSearch.EU, marking the first step towards realizing an Open Web Index. [ABSTRACT FROM AUTHOR]

Background: The results of high-throughput biology ('omic') experiments provide insight into biological mechanisms but can be challenging to explore, archive and share. The scale of these challenges continues to grow as omic research volume expands and multiple analytical technologies, bioinformatic pipelines, and visualization preferences have emerged. Multiple software applications exist that support omic study exploration and/or archival. However, an opportunity remains for open-source software that can archive and present the results of omic analyses with broad accommodation of study-specific analytical approaches and visualizations with useful exploration features. Results: We present OmicNavigator, an R package for the archival, visualization and interactive exploration of omic studies. OmicNavigator enables bioinformaticians to create web applications that interactively display their custom visualizations and analysis results linked with app-derived analytical tools, graphics, and tables. Studies created with OmicNavigator can be viewed within an interactive R session or hosted on a server for shared access. Conclusions: OmicNavigator can be found at https://github.com/abbvie-external/OmicNavigator [ABSTRACT FROM AUTHOR]

Droplet microfluidics is a highly sensitive and high-throughput technology extensively utilized in biomedical applications, such as single-cell sequencing and cell screening. However, its performance is highly influenced by the droplet size and single-cell encapsulation rate (following random distribution), thereby creating an urgent need for quality control. Machine learning has the potential to revolutionize droplet microfluidics, but it requires tedious pixel-level annotation for network training. This paper investigates the application software of the weakly supervised cell-counting network (WSCApp) for video recognition of microdroplets. We demonstrated its real-time performance in video processing of microfluidic droplets and further identified the locations of droplets and encapsulated cells. We verified our methods on droplets encapsulating six types of cells/beads, which were collected from various microfluidic structures. Quantitative experimental results showed that our approach can not only accurately distinguish droplet encapsulations (micro-F1 score > 0.94), but also locate each cell without any supervised location information. Furthermore, fine-tuning transfer learning on the pre-trained model also significantly reduced (>80%) annotation. This software provides a user-friendly and assistive annotation platform for the quantitative assessment of cell-encapsulating microfluidic droplets. [ABSTRACT FROM AUTHOR]

BACKGROUND: Talimogene laherparepvec (T-VEC) is an FDA-approved oncolytic herpesvirus therapy used for unresectable stage IIIB through IV metastatic melanoma. However, the correlation between clinical complete response (cCR) and pathologic complete response (pCR) in patients treated with T-VEC is understudied. STUDY DESIGN: We conducted a retrospective study from a prospectively maintained IRB-approved melanoma single-center database in patients treated with T-VEC from October 2015 to April 2022. Patients were categorized into 3 groups: cCR with pCR, cCR without pCR, and less than cCR. The primary endpoint was overall survival. We used descriptive statistics, chisquare tests, and Wilcoxon rank-sum tests to compare key covariates among exposure groups. We used survival analysis to compare survival curves and reported hazard ratio of death (95% CI) across exposure groups. RESULTS: We included 116 patients with a median overall survival (interquartile range) of 22.7 (14.8-39.3) months. The majority were men (69%) and White (97.4%), with a median age of 74.5 years. More than half of patients (n = 60, 51.6%) achieved cCR. Distribution among the groups was as follows: cCR with pCR (35.3%), cCR without pCR (16.3%), and less than cCR (48.4%). Median overall survival time (interquartile range) was 26.5 (18.6-36.0) months for cCR with pCR, 22.7 (14.4-35.5) months for cCR without pCR, and 17.8 (9.2-47.0) months for less than cCR (log-rank p value = 0.0033). CONCLUSIONS: Patients achieving cCR with pCR after T-VEC therapy have the most favorable overall survival outcomes, whereas those achieving cCR without pCR have inferior survival and those achieving less than cCR have the poorest overall survival outcomes. These findings emphasize the importance of histological confirmation and provide insights for optimizing T-VEC therapy in patients with advanced melanoma. [ABSTRACT FROM AUTHOR]

3D shape processing is a fundamental computer application. Specifically, 3D mesh could provide a natural and detailed way for object representation. However, due to its non-uniform and irregular data structure, applying deep learning technologies to 3D mesh is difficult. Furthermore, previous deep learning approaches for 3D mesh mainly focus on local structural features and there is a loss of information. In this paper, to make better mesh shape awareness, a novel deep learning approach is proposed, which aims to full-use the information of mesh data and exploit comprehensive features for more accurate classification. To utilize self-attention mechanism and learn global features of mesh edges, we propose a novel attention-based structure with the edge attention module. Then, for local feature learning, our model aggregates edge features from adjacent edges. We refine the network by discarding pooling layers for efficiency. Thus, it captures comprehensive features from both local and global fields for better shape awareness. Moreover, we adopt spatial position encoding module based on spatial information of edges to enhance the model to better recognize edges and make full use of mesh data. We demonstrate effectiveness of our model in classification tasks with numerous experiments which show outperforming results on popular datasets. [ABSTRACT FROM AUTHOR]

The School of Engineering and Technology of Central Queensland University focuses on the continuous development and innovation of best learning and teaching practices to increase student retention and improve their learning experiences. Face to face and distance learning (teaching delivery models) are fundamental aspects of providing quality support to the students' learning. One incredibly important aspect of students' learning is to provide relevant industry-related projects and applications of relevant simulation software to mimic the systems. This paper develops students' essential problem-solving skills and control strategies of fluid power systems in mechanical engineering through the Master of Engineering programs by employing Simulink, SimScape Fluid applications in Matlab software and Energy Plus with Design Builder. The main focus is to ensure that the students achieve the required skills of building fluid circuit models based on physical connections that directly integrate with appropriate symbols and modelling paradigms in fluid power applications to model the appropriate physical models to mimic the industrial fluid power projects selected. The basic ideas and content are to be delivered through weekly lectures and tutorial sessions, and the students' skills in fluid power systems and software are developed and monitored through weekly workshops scheduled for all projects. The key outcomes of this study are the level of understanding of fluid power systems, development of simulation skills using the software indicated, interpretation of the results the students have obtained and validations of those results. The students must show they have developed appropriate problem-solving skills using simulation software, professional presentation and effective team-building skills. As the students develop appropriate problem-solving and engineering practice skills, their satisfaction and feedback rates improve significantly. [ABSTRACT FROM AUTHOR]

The EUSCREEN project concerns the study of European vision and hearing screening programmes. Part of the project was the development of a cost-effectiveness model to analyse such programmes. We describe the development and usability of an online tool to enable stakeholders to design, analyse or modify a newborn hearing screening (NHS) programme. Data from literature, from existing NHS programmes, and observations by users were used to develop and refine the tool. Required inputs include prevalence of the hearing impairment, test sequence and its timing, attendance, sensitivity, and specificity of each screening step. Outputs include the number of cases detected and the costs of screening and diagnostics. Eleven NHS programmes with reliable data. Three analyses are presented, exploring the effect of low attendance, number of screening steps, testing in the maternity ward, or screening at a later age, on the benefits and costs of the programme. Knowledge of the epidemiology of a staged screening programme is crucial when using the tool. This study presents a tool intended to aid stakeholders to design a new or analyse an existing hearing screening programme in terms of benefits and costs. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such systems have the potential to address a wide range of vision tasks simultaneously, without being limited to specific problems or data domains. This universality is crucial for practical, real-world computer vision applications. In this study, our focus is on a specific challenge: the large-scale, multi-domain universal object detection problem, which contributes to the broader goal of achieving a universal vision system. This problem presents several intricate challenges, including cross-dataset category label duplication, label conflicts, and the necessity to handle hierarchical taxonomies. To address these challenges, we introduce our approach to label handling, hierarchy-aware loss design, and resource-efficient model training utilizing a pre-trained large vision model. Our method has demonstrated remarkable performance, securing a prestigious second-place ranking in the object detection track of the Robust Vision Challenge 2022 (RVC 2022) on a million-scale cross-dataset object detection benchmark. We believe that our comprehensive study will serve as a valuable reference and offer an alternative approach for addressing similar challenges within the computer vision community. The source code for our work is openly available at https://github.com/linfeng93/Large-UniDet. [ABSTRACT FROM AUTHOR]

Computer simulation has been in the toolkit of industrial engineers for over 50 years and its value has been enhanced by advances in research, including both modeling and analysis, and in application software, both commercial and open source. However, "advances" are different from paradigm shifts. Motivated by big data, big computing and the big consequences of model-based decisions, it is time to reboot simulation for industrial engineering. [ABSTRACT FROM AUTHOR]

With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications. [ABSTRACT FROM AUTHOR]

Purpose: Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted. Design/methodology/approach: A systematic mapping study was conducted using 51 papers retrieved from the Scopus database published between 2005 and 2022 that reported on usability testing of mobile learning applications. Findings: The key findings suggest that research is expected to expand in the near future. User-based testing is the commonly used method, while data are collected mainly through questionnaires, observation and interviews. Testing is mainly conducted in a controlled environment. Originality/value: The study provides (1) an evidence-based discussion on usability testing of mobile learning applications, (2) an up-to-date map on state of the art on usability testing of mobile learning applications and (3) providing direction for further research to scientifically strengthen the field. [ABSTRACT FROM AUTHOR]

As its first objective, this article carries out a bibliometric analysis aimed at serving as a guide on how to conduct bibliometrics using Biblioshiny, an interface of the Bibliometrix statistical software. To achieve this, a search has been used as an example, detailing the methodology, procedures, and strategies employed to address research questions and, as a secondary objective, to understand which works jointly develop Computational Thinking, Modelling, and Inquiry in the field of sciences. By utilizing quantitative statistical methods for data visualization, the intention is to identify the most relevant authors and journals, along with their impact indices, as well as the main thematic areas and their temporal evolution, in order to gain insights into what is being studied and how in this field. The results indicate that modelling is one of the salient themes among the 31 selected articles, accomplished through research, construction, and design of simulations in digital environments using block-based programming. [ABSTRACT FROM AUTHOR]

The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder's lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct short code blocks containing mistakes. This makes code composition easier and more time-efficient, thus improving the efficacy of large-scale development environments. Because Python is becoming more popular, the goal of our study is to enhance the efficiency of writing Python code by offering an automatic code-correcting approach. Furthermore, automatic program evaluation has been performed to assist in the debugging of small code blocks, which will ultimately be employed in the creation of real-time computer applications. The proposed technique would be useful for new learners who wish to create small Python code blocks for ease of writing and debugging on online platforms (like edX, Coursera, and Udacity). One of the major contributions of the project is to create an erroneous dataset of Python coding that contains all potential forms of probable syntax errors. The dataset induces variety through the use of multiple coding templates and is used to train deep learning models. We used the state-of-the-art text-to-text T5 transformer network model to automatically repair and evaluate the incorrect code. The outcomes of auto-correction are examined using the ROUGE and BLEU scores, as well as accuracy. The model corrects Python code with single, double, and the multiple number of errors with greater than 80% accuracy. Similarly, the performance of the basic T5 transformer network for program auto-evaluation with and without mistakes has been examined, and the model achieves greater than 65% accuracy in both cases. The proposed T5 base transformer outperforms the SOTA auto-correction models in terms of accuracy, according to a comparison study of the proposed method with the earlier techniques for auto-correcting codes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Under the Workforce Innovation and Opportunity Act of 2014 (WIOA), vocational rehabilitation (VR) agencies are required to evaluate their students' need for pre-employment transition services and use this information to inform their goals, priorities and strategies. OBJECTIVE: This manuscript presents the development and field testing of the Transition Self-Assessment Tool (TSAT), a web-based instrument designed to support VR agencies' evaluation and strategic planning initiatives by measuring the availability, accessibility, and coordination of school-based transition services. METHOD: Our methods describe the TSAT development procedures and the methods of conducting a TSAT field test. RESULTS: Findings suggest high schools offer a broad array of career-related services for their students. However, not all potentially eligible students with disabilities have access to those services and when these services are provided, they are frequently delivered without coordination with VR. Additionally, data show more services are available, accessed, and coordinated in high schools participating in a statewide transition program. CONCLUSION: Results from the TSAT field test suggest it is feasible to conduct a statewide needs assessment of school-based transition services using this instrument. Furthermore, the findings from the TSAT align with prior knowledge about school-based transition services, and can be used to inform a state's goals, priorities and strategies. [ABSTRACT FROM AUTHOR]

In the past, the focus of developing software applications was mainly on collecting, analyzing, and implementing user and business requirements. Nowadays, with the unlimited variety of software applications that serve the same purpose, it has become essential to go beyond user requirements to incorporate their emotions and values to ensure the use of those applications. However, the paucity of addressing the incorporation of human values into software engineering practices, in the literature and in the industry, makes it challenging to understand how to do it. Hence, in this study, we attempted to understand the level of adopting human values in software engineering activities, perceived usefulness, opportunities, and challenges in practice. In addition, we empirically investigated the relationship between the maturity level of the Requirements Engineering (RE) practices and the adoption of human values. To achieve those goals, we designed a survey that was distributed to software industry practitioners; 51 complete responses were received. The results showed that there is a positive relationship between the maturity level of RE and the adoption of human values. Also, most participants agreed that incorporating human values into the software design cycle is important; however, the lack of proven effective techniques and practices to capture and analyze the values are two of the main obstacles to adopting human values in practice. [ABSTRACT FROM AUTHOR]

Objective: This study is to determine whether a needle guidance device combining a 3D-printed component with a smartphone would decrease the number of passes and time required to perform a standard CT-guided needle procedure in a phantom study. Materials and methods: A 3D-printed mechanical guide with built-in apertures for various needle sizes was designed and printed. It was mounted on a smartphone and used to direct commercially available spring-loaded biopsy devices. A smartphone software application was developed to use the phone's sensors to provide the real-time location of a lesion in space, based on parameters derived from preprocedural CT images. The physical linkage of the guide, smartphone, and needle allowed the operator to manipulate the assembly as a single unit, with real-time graphical representation of the lesion shown on the smartphone display. Two radiology trainees and 3 staff radiologists targeted 5 lesions with and without the device (50 total procedures). The number of passes and time taken to reach each lesion were determined. Results: Use of the smartphone needle guide decreased the mean number of passes (with guide, 1.8; without guide, 3.4; P < 0.001) and mean time taken (with guide, 1.6 min; without guide, 2.7 min; P = 0.005) to perform a standard CT-guided procedure. On average, the decreases in number of passes and procedure time were more pronounced among trainees (P < 0.001). Conclusion: The combination of a mechanical guide and smartphone can reduce the number of needle passes and the amount of time needed to reach a lesion in a phantom for both trainees and experienced radiologists. [ABSTRACT FROM AUTHOR]

Real-time stereo matching with high accuracy is a dynamic research topic; it is attractive in diverse computer vision applications. This paper presents a stereo-matching algorithm that produces high-quality disparity map while maintaining real-time performance. The proposed stereo-matching method is based on three per-pixel difference measurements with adjustment elements. The absolute differences and the gradient matching are combined with a colour-weighted extension of complete rank transform to reduce the effect of radiometric distortion. The disparity calculation is realized using improved dynamic programming that optimizes along and across all scanlines. It solves the inter-scanline inconsistency problem and increases the matching accuracy. The proposed algorithm is implemented on parallel high-performance graphic hardware using the Compute Unified Device Architecture to reach over 240 million disparity evaluations per second. The processing speed of our algorithm reaches 98 frames per second on 240 × 320-pixel images and 32 disparity levels. Our method ranks fourth in terms of accuracy and runtime for quarter-resolution images in the Middlebury stereo benchmark. [ABSTRACT FROM AUTHOR]

This paper presents a flexible family of 2- and 3-parameter distributions whose support is the closed interval [0,1], with explicit density, cumulative density, and quantile functions. These distributions are suited to modeling quantiles, thereby expanding the toolbox of distributions for doubly-bounded random variables. The densities at the boundaries are determined by dispersion and skew parameters, and a third parameter exclusively influences location. The paper also discusses practical issues of parameter estimation and assesses estimation bias, Type I error-rate accuracy, and parameter-estimate collinearity. It also provides three examples of applications to real data using new packages implementing the distributions in R and Stata. [ABSTRACT FROM AUTHOR]

In computer artificial intelligence, there is great potential in research on the protection of Suzhou's traditional gardens, a world cultural heritage site. As a special material in Suzhou's traditional garden architecture, shedthin tile is widely used in roof base laying and is one of the important materials for building roofs. However, professionals need to reach the roof and spend much time and effort assessing the damage before repairing it. Therefore, the main goals of this study are to investigate a machine learning-based method for finding targets and determining the type of surface damage on a shedthin tile using the YOLOv4 model trained in this study. Using 500 shedthin tile on-site photos as training samples, the model was trained for 750 epochs. The main results of this study are as follows: (1) An object detection method based on machine learning can efficiently and accurately identify damage content, overcoming the manpower and time–cost limitations of traditional assessment methods. (2) The detection model in this study has an accuracy of 85.89% for water stain recognition of shedthin tiles, 93.29% for surface scaling, 87.37% for color aberration, and 96.15% for too wide a gap. The comprehensive accuracy is 90.20%, which meets the basic testing requirements. (3) The model demonstrated its robustness and reliability in complex environments in application tests in actual scenarios, providing a methodological reference for computer vision and target detection technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

To address information ambiguities, this study suggests using neutrosophic sets as a tactical tool. Three membership functions (called T r , I n , and F i ) that indicate an object's degree of truth, indeterminacy, and false membership constitute the neutrosophic set. It becomes clear that the neutrosophic connectivity index (CIN) is an essential tool for solving practical problems, especially those involving traffic network flow. To capture uncertainties, neutrosophic graphs are used to represent knowledge at different membership levels. Two types of C I N s , mean CIN and CIN, are investigated within the framework of neutrosophic graphs. In the context of neutrosophic diagrams, certain node types-such as neutrosophic neutral nodes, neutrosophic connectivity reducing nodes (NCRN) , and neutrosophic graph connectivity enhancing nodes (NCEN) , play important roles. We concentrate on two types of networks, specifically traffic network flow, to illustrate the real-world uses of CIN. By comparing results, one can see how junction removal affects network connectivity using metrics like Connectivity Indexes (CIN) and Average Connectivity Indexes (ACIN). A few nodes in particular, designated by ACIN as Non-Critical Removal Nodes (N C R N s) , show promise for increases in average connectivity following removal. To fully comprehend traffic network dynamics and make the best decisions, it is crucial to take into account both ACIN and CIN insights. This is because different junctions have different effects on average and overall connectivity metrics. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes. [ABSTRACT FROM AUTHOR]

Head Pose Estimation (HPE) has a wide range of applications in computer vision, but still faces challenges: (1) Existing studies commonly use Euler angles or quaternions as pose labels, which may lead to discontinuity problems. (2) HPE does not effectively address regression via rotated matrices. (3) There is a low recognition rate in complex scenes, high computational requirements, etc. This paper presents an improved unconstrained HPE model to address these challenges. First, a rotation matrix form is introduced to solve the problem of unclear rotation labels. Second, a continuous 6D rotation matrix representation is used for efficient and robust direct regression. The RepVGG-A2 lightweight framework is used for feature extraction, and by adding a multi-level feature fusion module and a coordinate attention mechanism with residual connection, to improve the network's ability to perceive contextual information and pay attention to features. The model's accuracy was further improved by replacing the network activation function and improving the loss function. Experiments on the BIWI dataset 7:3 dividing the training and test sets show that the average absolute error of HPE for the proposed network model is 2.41. Trained on the dataset 300W_LP and tested on the AFLW2000 and BIWI datasets, the average absolute errors of HPE of the proposed network model are 4.34 and 3.93. The experimental results demonstrate that the improved network has better HPE performance. [ABSTRACT FROM AUTHOR]

Mobile learning apps are widely acknowledged for their effectiveness in enhancing learning results. This study aims to develop and validate an Android-based mobile learning app for computer operating system principles. Using the userfriendly App Inventor platform known for visual programming, it integrates interactive modules and multimedia for diverse learning styles. The study adopted a Research and Development approach following the ADDIE model (analysis, design, development, implementation, and evaluation). The research was conducted at Universitas Negeri Padang and Universitas Muhammadiyah Muara Bungo and involved 10 participants. The analysis of questionnairebased data conducted by media and material experts categorized the Android-based learning media as "Appropriate", achieving an over-all average of 82.51% and 82.29%, respectively. Meanwhile, student evaluations rated this media as "Feasible" with a score of 82.72%. In conclusion, the development of this Androidbased mobile learning application for computer operating system material demonstrates significant potential as an innovative educational tool. The validation process highlighted its potential effectiveness and offered valuable recommendations to enhance its educational value. These findings make a meaningful contribution to the ongoing discourse surrounding the integration of technology-driven learning approaches into traditional education, benefiting educators and developers seeking to create impactful mobile learning experiences. [ABSTRACT FROM AUTHOR]

Cancer may affect any person. Amongst possible techniques to treat some cancers is ablation, through the use of the irreversible electroporation (IRE) as one of the methods. Medical University of Varna is implementing a project on irreversible electroporation aiming to develop a hardware prototype. The first step is review of existing models and the development of an own model, which will be used in the device prototyping. To find the existing models, we reviewed articles related to electroporation models. Based on the literature review, we developed own model, where we used open-source software OpenEP and Embarcadero C++. The review of the available literature publications indicates that initial models employed a simplified cell structure. Despite this simplicity, these models effectively reflected important characteristics of the cell behaviour and the interdependencies between field features and cells. Recent investigations created better models and optimized parameters for specific cases. The review models were used in the development of dedicated software application for simulation, visualization, and educational purposes in the field of IRE. DOI: 10.18421/TEM131-54 34TUhttps://doi.org/10.18421/TEM131-54U34T Corresponding author: Tihomir Georgiev, Department of Medical Equipment, Electronic and Information Technologies, Medical University “Prof. Dr Paraskev Stoyanov” – Varna, Bulgaria Email: 34TUt.georgiev@mu-varna.bgU34T Received: 20 October 2023. Revised: 02 February 2024. Accepted: 07 February 2024. Published: 27 February 2024. © 2024 Tihomir Georgiev, Kristina Bliznakova, Zhivko BliznakovP P& Georgi Todorov; published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. The article is published with Open Access at https://www.temjournal.com/ Developed software application allows investigation of various IRE protocols. Experimental investigation of implemented models and software is undergoing. The software will allow further advancement in our capacity to explore and comprehend cellular dynamics. [ABSTRACT FROM AUTHOR]

This study delineated the development of an augmented reality (AR+Core) mobile application to enhance computer hardware education. By offering 3D visualizations of computer internals, the application enables students to immerse in hardware-related learning without the need of actual devices. This virtual approach ensures accessibility from anywhere, making tangible components unnecessary. The Multimedia Development Life Cycle (MDLC) informed the design process, while the Hedonic Motivation System Adoption Model (HMSAM) offered insights into technology adoption. Feedback from 7th-grade participants was overwhelmingly positive, with them acknowledging the app’s utility, engagement value, and user-friendliness. Notably, the app scored 85 of 100 for its perceived ease of use, underlining its efficacy for the target age group. [ABSTRACT FROM AUTHOR]

The timely and cost-effective identification of the onset of corrosion and its progress would be critical for effectively maintaining structural integrity. Consequently, a series of fundamental experiments were conducted to capture the corrosion process on a steel plate using a new type of plastic optical fiber (POF) sensor. Electrolytic corrosion experiments were performed on a 5 mm thick steel plate immersed in an aqueous solution. The POF sensor installed on the upper side of the plate and directed downward detected the upward progression of the corrosion zone that formed on the underside of the plate. The results showed that the POF sensors could detect the onset of the upward-progressing corrosion front as it passed the 1 and 2 mm marks related to the thickness of the corroded zone. The POF sensors were designed to optically identify corrosion; therefore, the data obtained by these sensors could be processed using a newly developed graphic application software for smartphones and also identified by the naked eye. This method offered an easy and cost-effective solution for verifying the corrosion state of structural components. [ABSTRACT FROM AUTHOR]

The one‐stage object detector has been widely applied in many computer vision applications due to its high detection efficiency and simple framework. However, one‐stage detectors heavily rely on Non‐maximum Suppression to remove the duplicated predictions for the same objects, and the detectors produce detection confidence to measure the quality of those predictions. The localisation quality is an important factor to evaluate the predicted bounding boxes, but its role has not been fully utilised in previous works. To alleviate the problem, the Quality Prediction Block (QPB), a lightweight sub‐network, is designed by the authors, which strengthens the effect of localisation quality evaluation on detection confidence by leveraging the features of predicted bounding boxes. The QPB is simple in structure and applies to different forms of detection confidence. Extensive experiments are conducted on the public benchmarks, MS COCO, PASCAL VOC and Berkeley DeepDrive. The results demonstrate the effectiveness of our method in the detectors with various forms of detection confidence. The proposed approach also achieves better performance in the stronger one‐stage detectors. [ABSTRACT FROM AUTHOR]

The robustness of qualitative comparative analysis (QCA) results features high on the agenda of methodologists and practitioners. This article aims at advancing this debate on several fronts. First, in line with the extant literature, we take a comprehensive view on robustness arguing that decisions on calibration, consistency, and frequency thresholds should all be tested. Second, we introduce the notion of "sensitivity range" as the range of values for any of these parameters within which the solution formula remains unchanged. Third, we argue that interpreting robustness is more intricate than simply checking if solutions remain unchanged. Beyond sensitivity ranges, researchers should assess robustness by evaluating changes in parameters of fit and the classification of cases as robust, shaky, or possible. Fourth, we enable researchers to perform more than one robustness test at a time by proposing the notions of a "test set": the overlap between conceptually plausible alternative solutions that can be generated; and of a "robust core": that part of a QCA solution that withstands the robustness checks. Fifth, we present functionalities implemented in the R package SetMethods that enable researchers to put in practice our proposals. These advancements are integrated into a comprehensive QCA Robustness Test Protocol consisting of three main tests: sensitivity ranges, fit-oriented robustness, and case-oriented robustness. We illustrate the protocol's implementation with an example on high life expectancy across the globe. [ABSTRACT FROM AUTHOR]

Objective. There is limited evidence about how legal frameworks that underpin end-of-life decisions are applied in practice. This study aimed to identify how end-of-life decisions are made and documented in emergency departments and intensive care units. The secondary aim was to explore the extent to which the legal processes featured in these decisions. Methods. A retrospective chart audit of 85 adult patients who died in the emergency departments and intensive care units of a Queensland health service was undertaken. Quantitative data were analysed and reported using descriptive statistics. Qualitative textual data were analysed using inductive content analysis. Results. Nearly all admissions were unplanned (97.6%), and most patients (74.1%) were admitted from home. Only one patient had an advance health directive, although all had an eligible substitute decision-maker. The qualitative analysis revealed two main concepts -- 'healthcare professionals choreograph the end of life' and 'patients and families are carried on an unplanned journey'. Conclusions. There was limited documentation related to the application of the legal framework in these decisions. Healthcare professionals relied on their clinical judgment about what was in the best interest of the patient. It was common for there to be a substantial effort to achieve consensus in decision-making which coincidentally complied with the law. [ABSTRACT FROM AUTHOR]

To assess the experience and perceived added value of an e-Health application during the physical therapy treatment of patients with temporomandibular disorders (TMD). A mixed-methods study including semi-structured interviews was performed with orofacial physical therapists (OPTs) and with TMD patients regarding their experience using an e-Health application, Physitrack. The modified telemedicine satisfaction and usefulness questionnaire and pain intensity score before and after treatment were collected from the patients. Ten OPTs, of which nine actively used Physitrack, described that the e-Health application can help to provide personalised care to patients with TMD, due to the satisfying content, user-friendliness, accessibility, efficiency, and ability to motivate patients. Ten patients, of which nine ended up using Physitrack, felt that shared decision-making was very important. These patients were positive towards the application as it was clear, convenient, and efficient, it helped with reassurance and adherence to the exercises and overall increased self-efficacy. This was mostly built on their experience with exercise videos, as this feature was most used. None of the OPTs or patients used all features of Physitrack. The overall satisfaction of Physitrack based on the telemedicine satisfaction and usefulness questionnaire (TSUQ) was 20.5 ± 4.0 and all patients (100%) showed a clinically relevant reduction of TMD pain (more than 2 points and minimally 30% difference). OPTs and patients with TMD shared the idea that exercise videos are of added value on top of usual physical therapy care for TMD complaints, which could be delivered through e-Health. Physical therapists and patients with temporomandibular disorders do not use all features of the e-Health application Physitrack in a clinical setting. Exercise videos were the most often used feature and seen as most valuable by physical therapists and patients. Based on a small number of participants, e-Health applications such as Physitrack may be perceived as a valuable addition to the usual care, though this would need verification by a study designed to evaluate the therapeutic effect (e.g., a randomised clinical trial). [ABSTRACT FROM AUTHOR]

Recent research on transformer-based models have highlighted particular methods for medical image segmentation. Additionally, the majority of transformer-based network designs used in computer vision applications have a significant number of parameters and demand extensive training datasets. Inspired by the success of transformers in recent researches, the unet-transformer approach has become one of the de-facto ideas in overcoming the above challenges. In this manuscript, a novel unet-transformer approach was proposed for heart image segmentation to solve parameters, limited dataset, over segmentation, sensitivity noise and higher training time problems. A framework in which a novel width and height wise axial attention mechanism is incorporated into the design to effectively give positional embeddings and encode spatial flattening. Furthermore, a novel local and global spatial attention mechanism is proposed to effectively learn the local and global interactions between encoder features. Finally, we introduce a mechanism to fuse both contexts for better feature representation and preparation into the decoder. The results demonstrate that our prototype provides a robust novel axial-attention mechanism. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

Image dehazing has become a crucial prerequisite for most outdoor computer applications. The majority of existing dehazing models can achieve the haze removal problem. However, they fail to preserve colors and fine details. Addressing this problem, we introduce a novel high-performing attention-based dehazing model (ADMC 2 -net)that successfully incorporates both RGB and HSV color spaces to maintain color properties. This model consists of two parallel densely connected sub-models (RGB and HSV) followed by a new efficient attention module. This attention module comprises pixel-attention and channel-attention mechanisms to get more haze-relevant features. Experimental results analyses can validate that our proposed model (ADMC 2 -net) can achieve superior results on synthetic and real-world datasets and outperform most of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training. In the context of computer vision, image classification is a crucial task that typically requires a substantial training dataset. This research paper introduces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets while minimizing the number of required training data. The proposed methods, namely Similari-ty-based Selection, Prediction Probability-based Selection, and Competence-based Active Learning, have been extensively evaluated through experiments conducted on popular datasets like Cifar10 and Cifar100. The experimental results demonstrate that the proposed methods outperform random selection and conventional selection techniques. The superior performance of the novel selection methods underscores their effectiveness in enhancing the Active Learning process for image classification tasks. [ABSTRACT FROM AUTHOR]

Estimation of baseflow is a complex hydrographic task. Baseflow techniques and coefficients vary from basin to basin, stream to stream, and year to year. In this study, meta-heuristic optimization is used to automatically identify baseflow. The Particle Swarm Optimization (PSO), a meta-heuristic optimization approach, is chosen. The constraint and cost functions were determined using the PSO algorithm, Lyne and Hollick techniques, and a computer application. Over the period 1980–2015, the data were collected at the Kale station in the Yesilirmak River basin to validate the study model. The results show that the hydrographs and baseflow dividing line were separated effectively. It has also been revealed that the PSO has a high speed as well as a high level of precision. In the research, in addition to the baseflow separation, the hydrograph, baseflow, and ratio of the baseflow to the streamflow at the station No. 1402 were assessed using the Mann–Kendall test and Innovative Trend Test (ITA), and as a result, their trends have been found. By the use of both of these methods, it has been shown that all parameters have an unfavorable trend. In addition, the research came to some other significant conclusions, such as the fact that the baseflow declines in tandem with the flow values and that the baseflow rates are low in years with high peak values of the hydrograph. [ABSTRACT FROM AUTHOR]