Search Results (11,858)

This study investigates the application of Bayesian probability models in the diagnostic assessment of learning disabilities. The objective of this study was to determine whether specific conditions identified in expert reports could predict subsequent diagnoses. The sample consisted of 201 expert reports on children diagnosed with learning disabilities, which were analysed using qualitative content analysis, fuzzy set qualitative comparative analysis (fsQCA), and Bayesian conditional probability models. Variables such as vocabulary, working memory index, processing speed, and visuomotor coordination were examined as potential predictors. The analysis demonstrated that Bayesian networks captured conditional links, such as the strong association between working memory and perceptual inference, as well as an unexpected negative link between vocabulary and verbal comprehension. The study concludes that Bayesian networks provide a transparent and data-driven framework for pre-screening and risk assessment in special education settings. The limitations of this study include the absence of a control group and exclusive reliance on SNI cases. Future research should explore the integration of abductive reasoning into automated diagnostic software to enhance inclusivity and support decision-making. Full article

Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of the object, its pose, and changes in the process itself. Unfortunately, the use of standard solutions is limited to a relatively small space in which the robot’s vision system operates. The use of the latest solutions in the field of Artificial Intelligence (AI) and external vision systems, in combination with the closed structures of industrial robot control systems, provides advantages by enhancing the digital awareness of the environment of robotic systems. This article presents an example of solving the problem of low digital awareness of the environment of robotic systems resulting from the limited field of view of vision systems used in industrial robots, while maintaining high precision of the systems consisting of the combination of a 3D vision system using a stereovision camera and software with AI elements with the control system of an industrial robot from FANUC and an integrated Robot Vision (iRVision) system to maintain the positioning accuracy of the robot tool. Full article

Introduction: Ambiguous loss is a profound yet underexplored phenomenon in the lives of aging migrants. Older adults who have experienced migration often face disruptions to their sense of belonging, identity, and continuity across borders. These losses are compounded by aging, health challenges, and social isolation. Despite its significance, ambiguous loss among aging migrants has not been conceptually analyzed in depth, limiting the development of culturally responsive care practices. Aim: This concept analysis aimed to identify the defining attributes of ambiguous loss among aging migrants and to develop a conceptual definition that enhances our understanding of the phenomenon and informs future research and practice. Method: Walker and Avant’s eight-step concept analysis framework was applied to examine the concept of ambiguous loss in the context of aging migrants. A systematic keyword search was conducted across four databases (CINAHL, Medline, SCOPUS, PsycINFO), Google Scholar, and relevant gray literature, covering the years of 2010–2024. Covidence software supported the screening process. From 367 records identified, 146 underwent full-text review, and 74 met inclusion criteria. The analysis drew on literature synthesis, case exemplars, antecedents, consequences, and empirical referents. This review followed PRISMA (2020) reporting guidelines. Results: Four defining attributes of ambiguous loss among aging migrants were identified: (a) physical, social, and emotional loss; (b) displacement and loss of homeland; (c) erosion of social identity and agency; and (d) cultural and transnational bereavement. A conceptual definition emerged, describing ambiguous loss as a multifaceted experience of disconnection, intensified by aging, illness, economic hardship, and social isolation. The analysis also highlighted antecedents such as forced migration and health decline, as well as consequences including diminished well-being, resilience challenges, and barriers to integration. Conclusions: Ambiguous loss among aging migrants is a complex construct encompassing intertwined physical, social, and cultural dimensions of loss. This conceptual clarity provides a foundation for developing culturally responsive care models that promote adaptation, resilience, and social inclusion among older migrants. Full article

Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machines (VMs) from the outside. Asynchronous access to the VM’s memory can be insufficient for efficient monitoring of what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep performance overhead as low as possible. In this paper, we present an empirical study that compares the performance of four VMI breakpoint-implementation variants—EPT switching (SLAT view switching) with and without fast single-stepping acceleration, instruction repair, and instruction emulation—from two VMI applications (DRAKVUF, SmartVMI) with the XEN hypervisor on 20 Intel Core i processors ranging from the fourth to the thirteenth generation. Instruction emulation was the fastest method across all 20 tested platforms. Modern processors such as the Intel Core i7 12700H and Intel Core i9 13900HX achieved median breakpoint-processing times as low as 15 µs for the emulation mechanism. The slowest method was instruction repair, followed by EPT switching and EPT switching with FSS. The order was the same for all measurements, indicating that this is a strong and generalizable result. Full article

The functional performance of porous metals and alloys is dictated by pore features such as size, connectivity, and morphology. While methods like mercury porosimetry or gas pycnometry provide cumulative information, direct observation via scanning electron microscopy (SEM) offers detailed insights unavailable through other means, especially for microscale or nanoscale pores. Each scanned image can contain hundreds or thousands of pores, making efficient identification, classification, and quantification challenging due to the processing time required for pixel-level edge recognition. Traditionally, pore outlines on scanned images were hand-traced and analyzed using image-processing software, a process that is time-consuming and often inconsistent for capturing both large and small pores while accurately removing noise. In this work, a software framework was developed that leverages modern computing tools and methodologies for automated image processing for pore identification, classification, and quantification. Vectorization was implemented as the final step to utilize the direction and magnitude of unconnected endpoints to reconstruct incomplete or broken edges. Combined with other existing pore analysis methods, this automated approach reduces manual effort dramatically, reducing analysis time from multiple hours per image to only minutes, while maintaining acceptable accuracy in quantified pore metrics. Full article

In order to break the bottleneck in the teaching and research of superconducting current limiting technology, this paper proposed an integrated platform based on a resistive-type superconducting current limiter (RSFCL). Through a user-programmable software interface, the dynamic working process of the RSFCL was simulated and analyzed, along with the self-triggered quench characteristics, internal current distribution, and instantaneous temperature evolution process under different fault conditions. This platform employed a superconductor–circuit–temperature coupling model to simulate the current limiting characteristics of the RSFCL under various AC/DC and transient conditions. This effectively helps the users understand the electrothermal coupling mechanisms of the RSFCL but also provides the researchers with an efficient simulation tool to analyze superconducting properties, optimize fault current limiter topologies, and validate system-level fault protection strategies. The platform’s simulation results align well with theoretical analyses, offering a reliable auxiliary tool for teaching and research in superconducting power technology. Full article

Addressing the problem of lacking accurate and reliable contact parameters and bonding parameters in the simulation of the mashing process during the harvesting of alfalfa, this study takes the stems of alfalfa at the harvesting stage as the research object. The geometric dimensions and related intrinsic parameters of the stems were measured. Using the Enhanced Discrete Element Method (EDEM) software, a multi-scale discrete element flexible bonding model of alfalfa stems was established based on region-specific parameters. The entire alfalfa stem was divided into three parts: the top, middle, and root sections. A multi-scale particle aggregation model of hollow stems was created using the Hertz-Mindlin with bonding model. The contact parameters between alfalfa stems at the harvesting stage and PU rubber were determined using a mathematical model based on quadratic polynomial fitting curves. The results showed that the shear modulus of the top, middle, and root sections of the alfalfa stems were 24.96 MPa, 29.60 MPa, and 10.48 MPa, respectively. The coefficients of restitution between the top, middle, and root sections of the alfalfa stems and PU rubber were 0.426, 0.375, and 0.386, respectively; the static friction coefficients were 0.613, 0.667, and 0.422, respectively; and the rolling friction coefficients were 0.213, 0.226, and 0.292, respectively. The relative error between the simulated and measured values of the angle of repose was less than 3%, effectively representing the mechanical characteristics of alfalfa stems at the harvesting stage bending and breaking under impact. This study aims to establish a discrete element flexible model of alfalfa stems at the harvesting stage and accurately calibrate the contact parameters with typical rubber materials, thereby addressing the lack of reliable bonding and contact parameters in existing simulations of the mashing process. Full article

Background/Objectives: Legacy planning should respect te care preferences of people facing serious illness and integrate palliative care (PC). Legacy creation with the guidance of health professionals in PC assumes a therapeutic nature and aims to respond to the psychospiritual needs of patients and their families. To date, research on professional experiences to create legacy in PC remains scarce. Therefore, this study sought to explore the experiences of PC professionals in legacy creation with the ill person and their family during EoL care. Methods: A descriptive qualitative study was performed through in-person semi-structured interviews with PC professionals from Portugal. Data collection was conducted from February to May 2025. Transcripts from the interviews were thematically analyzed with qualitative data management software WebQDA. The study adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines. Results: Sixteen PC professionals participated in the study. Most participants were nurses (n = 8), followed by six physicians and two psychologists. The mean age of participants was 44.93 ± 10.46 years. Data analysis yielded three themes: (1) the worth of legacy in EoL; (2) enablers of legacy-building process; and (3) challenges of legacy-building process. Conclusions: Legacy is a meaningful resource that gives professionals the opportunity to connect with patients and their families, and to enact value-concordant person-centered care. By providing a greater grasp of legacy construction, our findings may help healthcare providers better understand how to provide dying patients and their families with dignity-conserving care. Full article

In complex working environments where early fault diagnosis of mechanical equipment is required, interference signals such as ambient vibrations and motor noise can significantly affect the acquisition and analysis of vibration signals and meshing force signals, making it difficult to capture early fault features. This paper provides a method for fault diagnosis and identification of typical gear tooth faults by analyzing the influence of meshing stiffness on dynamic transmission error in the gear transmission process. Three-dimensional models of both normal and faulty gear pairs were built using SolidWorks 2021 software and imported into Adams for dynamic simulation to obtain the system’s dynamic transmission error and meshing force data. By training and identifying these two different types of data, the experimental results demonstrate that the identification accuracy using dynamic transmission error is higher than that based on meshing force. Full article

To address the prominent issues in current potato peeling processes (such as high labor intensity, excessive flesh loss, hard-to-remove peel from bud eyes/concaves), a non-contact waterjet method was proposed. Based on the computational fluid dynamics (CFD) method, the Fluent software was used to simulate and analyze the flow field of fan-shaped nozzle models with different slot angles. The simulation results indicated that the 25° scattering angle nozzle had excellent performance: it ensured effective potato surface coverage and minimized jet energy loss, fitting peeling needs. A one-way fluid–structure interaction (FSI) model of the nozzle–potato system was built to study waterjet–potato mechanical interactions. Surface stress distribution under waterjet impact was analyzed, and jet dynamic pressure was mapped to solid stress via FSI interface load transfer. Simulations revealed that with a 25° scattering angle, 200 mm standoff distance, and 5 MPa pressure, the maximum shear stress at potato surface characteristic points was 0.032 MPa—within the 0.025–0.04 MPa target range and matching potato skin–substrate peeling strength threshold. This confirmed the energy–mechanical response coordination, validated by experiments. The research results can provide an effective technical reference for potato peeling processing. Full article

Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge persists: the lack of practical tools required for calibrating source-detector separation (SDS) to maximize sensitivity at depth (SAD) for monitoring specific cortical regions of interest to neuroscience and neuroimaging studies. This study presents DrSVision version 1.0, a standalone software developed to address this limitation. Monte Carlo (MC) simulations were performed using segmented magnetic resonance imaging (MRI) data from eight cadaveric heads to realistically model light attenuation across anatomical layers. SAD of 10–20 mm with SDS of 19–39 mm was computed. The dataset was used to train a Gaussian Process Regression (GPR)-based machine learning (ML) model that recommends optimal SDS for achieving maximal sensitivity at targeted depths. The software operates independently of any third-party platforms and provides users with region-specific calibration outputs tailored for experimental goals, supporting more precise application of fNIRS. Future developments aim to incorporate subject-specific calibration using anatomical data and broaden support for diverse and personalized experimental setups. DrSVision represents a step forward in fNIRS experimentation. Full article

Given the projection of the impact of climate change and the uncertainty caused by geopolitical volatility, minimising emissions has become an urgent priority for the shipping industry. In this context, the aim of the present study is the calculation and estimation of emissions generated by ship operations within a maritime transportation network, as well as the identification of the optimal route that minimises both emissions and travel time. Emission estimation is carried out using methodologies and assumptions from the Fourth IMO GHG Study. The decision-making, along with the optimisation process, is performed through backward dynamic programming, following a multi-objective optimisation framework. Specifically, the analysis is carried out on both a theoretical and a realistic network. In both cases, various scenarios are examined, including different approaches to vessel speed, some of which incorporate probabilistic speed distributions, as well as scenarios involving uncertainty regarding port availability. Additionally, the resilience of the network is examined, focusing on the additional burden in terms of emissions and travel time when a port is unexpectedly unavailable and a route adjustment is required. The calculations and optimisation are carried out using Excel and the @Risk software by Palisade, with the latter enabling the incorporation of probability distributions and the execution of Monte Carlo simulations. Full article

This article explores how Text-to-Speech (TTS) and Speech-to-Text (STT) technologies are being harnessed in education to enhance accessibility, language development, and overall learner engagement. Drawing upon theoretical frameworks in linguistics and educational psychology, we highlight the benefits TTS and STT can offer to diverse student populations, including students with disabilities, language learners, and those seeking personalized or self-paced instruction. We discuss methods for integrating TTS and STT into the classroom (hardware, software, and practical considerations) and offer case studies of effective implementations in areas such as literacy support, foreign language acquisition, and assessment. We then address the pedagogical benefits these tools provide—such as differentiated instruction, immediate feedback, and a heightened sense of learner autonomy—along with limitations and challenges that educators may encounter. In conclusion, we suggest future directions for research and practice, underscoring the importance of teacher training, ethical considerations, and ever-evolving advancements in natural language processing. Full article

At present, traditional X-ray inspection is used to inspect the welds of the bottom, barrel section and short shell parts of the launch vehicle, which has the disadvantages of low automation, complicated process and low efficiency, and cannot meet the fast-paced development needs of multiple models at present. Moreover, the degree of digitization is low, the test results are recorded in the form of negatives, data statistics, storage and access are difficult, and the circulation efficiency is low, which is not conducive to product quality control and traceability; At the same time, it cannot adapt to and meet the needs of digital and intelligent transformation and development. In this paper, a dual-robot collaborative digital radiographic inspection system for rocket tank welds is developed by combining dual-robot control technology and digital radiographic inspection technology. The system can be directly applied to digital radiographic inspection of tank bottom, barrel section and short shell welds of multiple types of launch vehicles; meanwhile, the dual-robot path planning technology based on the dual-mode is studied. Finally, the imaging software platform based on VS and Twincat3.0 VS2015 software combined with QT upper computer is designed. Experiments show that compared with the existing traditional ray detection methods, the detection efficiency of the system is improved by 5 times, the image sensitivity reaches W14, the resolution reaches D10, and the standardized signal-to-noise ratio reaches 128, which far exceeds the requirements of process technology A, and meets the current non-destructive detection work of multi-model rocket tank welds. Full article

Peptide mapping is a well-established method for confirming the identity of therapeutic proteins as part of batch release testing and product characterization for regulatory filings. Traditionally based on enzymatic digestion followed by reversed-phase liquid chromatography and UV detection, the method has evolved with technological advancements to incorporate mass spectrometry (MS), enabling more detailed structural insights. Residue-level confirmation of amino acid sequences requires MS/MS fragmentation, which produces large amounts of data that must be processed using specialized software. In regulated environments, the use of academic algorithms is often limited by validation requirements, making it necessary to rely on commercially approved tools, although their built-in scoring systems have limitations that can affect sequence assignment accuracy. Here, we present representative examples of incorrect peptide assignments generated by commercial software. In antibody sequence analysis, misidentifications resulted from isobaric and near-isobaric dipeptides (e.g., SA vs. GT). Additional examples from the analysis of SARS-CoV-2 spike protein variants revealed software-induced artifacts, including artificial succinylation of aspartic acid residues to compensate for sequence mismatches, and incorrect deamidation site assignments due to misinterpretation of isotopic peaks. These findings underscore the necessity for expert manual review of MS/MS data, even when using validated commercial platforms, and highlight the molecular challenges in distinguishing true sequence variants from software-driven artifacts. Full article