Search Results (3,602)

Individuals across their lifespan may experience hearing loss from medications or chemicals, prompting concern about ototoxic environmental exposures. This study applies computational modeling as a screening-level hazard identification and chemical prioritization approach and is not intended to constitute a human health risk assessment or to estimate exposure- or dose-dependent ototoxic risk. We evaluated in silico drug-induced ototoxicity models on 80 environmental chemicals, excluding 4 with known ototoxicity, and analyzed 76 chemicals using fingerprinting, similarity assessment, and machine learning classification. We compared predicted environmental ototoxicants with ototoxic drugs, paired select polychlorinated biphenyls with the antineoplastic drug mitotane, and used PCB 177 as a case study to construct an ototoxicity pathway. A systems biology framework predicted and compared molecular targets of mitotane and PCB 177 to generate a network-level mechanism. The consensus model (accuracy 0.95 test; 0.90 validation) identified 18 of 76 chemicals as potential ototoxicants within acceptable confidence ranges. Mitotane and PCB 177 were both predicted to disrupt thyroid-stimulating hormone receptor signaling, suggesting thyroid-mediated pathways may contribute to auditory harm; additional targets included AhR, transthyretin, and PXR. Findings indicate overlapping mechanisms involving metabolic, cellular, and inflammatory processes. This work shows that integrated computational modeling can support virtual screening and prioritization for chemical and drug ototoxicity risk assessment. Full article

After nearly two decades of developments in Historic/Heritage Building Information Modeling (HBIM), the field has reached a stage of maturity that calls for a critical reassessment of its evolution, achievements, and remaining challenges. Digital representation has become a central component of contemporary heritage conservation, enabling advanced methods for analysis, management, and communication. This review examines the maturation of HBIM as a comprehensive framework that integrates extended reality (XR), artificial intelligence (AI), machine learning (ML), semantic segmentation and Digital Twin (DT). Six major research domains that have shaped recent progress are outlined: (1) the application of HBIM to restoration and conservation workflows; (2) the expansion of public engagement through XR, virtual museums, and serious games; (3) the stratigraphic documentation of building archaeology, historical phases, and material decay; (4) data-exchange mechanisms and interoperability with open formats and Common Data Environments (CDEs); (5) strategies for modeling geometric and semantic complexity using traditional, applied, and AI-driven approaches; and (6) the emergence of heritage DT as dynamic, semantically enriched systems integrating real-time and lifecycle data. A comparative assessment of international case studies and bibliometric trends (2015–2025) illustrates how HBIM is transforming proactive and data-informed conservation practice. The review concludes by identifying persistent gaps and outlining strategic directions for the next phase of research and implementation. Full article

Despite the growing adoption of hybrid energy systems integrating solar photovoltaic (PV), pumped storage hydropower (PSH), and battery energy storage (BES), comprehensive studies on their dynamic stability and interaction mechanisms remain limited, particularly under weak grid conditions. Due to the high impedance of weak grids, ensuring stability across varied operating scenarios is crucial for advancing grid resilience and energy reliability. This paper addresses these research gaps by examining the interaction dynamics between PV, PSH, and BES on the DC side and the utility grid on the AC side. The study identifies operating-region-dependent instability mechanisms arising from negative incremental resistance behavior and weak grid interactions and proposes a virtual-impedance-based active damping control strategy to suppress poorly damped oscillatory modes. The proposed controller effectively reshapes the converter output impedance, shifts unstable eigenmodes into the left-half plane, and improves phase margins without requiring additional hardware components or introducing steady-state power losses. System stability is analytically assessed using root-locus, Bode, and Nyquist criteria within a developed small-signal state-space model, and further validated through large-signal real-time simulations on an OPAL-RT platform. The main contributions of this study are threefold: (i) a comprehensive stability analysis of a utility-scale grid-connected hybrid PV–PSH–BES system under weak grid conditions, (ii) identification of operating-region-dependent instability mechanisms associated with DC–link interactions, and (iii) development and real-time validation of a practical virtual-impedance-based active damping strategy for enhancing system stability and grid integration reliability. Full article

Background: Three-dimensional virtual surgical planning (Three-dimensional VSP) has become standard practice in the treatment of mandibular oral squamous cell carcinoma (OSCC) in the last decade. Dutch guidelines recommend a care pathway interval (CPI) of a maximum of 30 days, and a free bone margin of at least 5 mm. Fused MRI and CT data are used for accurate tumor delineation. Based on this data, a virtual surgical plan is created and transferred to the operating room using resection guides and patient-specific implants (PSIs). Long-term evaluation is needed to further optimize its clinical use. Objectives: This study evaluates adherence to bone margin and CPI guidelines in mandibular OSCC. Additionally, it assesses the accuracy of tumor resection and reconstruction using 3D-VSP and compares the complications of 3D-planned mandibular reconstruction using different kinds of osteosynthesis plates. Methods: All patients who underwent a segmental mandibulectomy between 2014 and 2024 at the University Medical Center Groningen were included. CPI, clinical outcomes, and complications were analyzed. The preoperative virtual plan was compared with the postoperative outcome to assess accuracy. Results: The median CPI was 34 days, and 93.7% of bone margins were tumor-free. Mean absolute resection deviation was 1.63 mm (±1.42). PSI reconstructions were significantly more accurate in intergonial distance and coronal angle compared to conventional plates. Plate-related complications were more common in non-bony reconstructions; PSI reconstructions showed significantly more plate exposure. Conclusions: 3D-VSP leads to high accuracy in resection and reconstruction and favorable bone margins. Shortening the CPI and reducing biological complications are essential to further improve oncological outcomes. Full article

This study aimed to develop an immersive virtual reality (VR) program and conduct preliminary evaluation of its feasibility and learner perception for enhancing nursing students’ clinical practicum education. The VR program was designed using the ADDIE model (analysis, design, development, implementation, and evaluation) and implemented on the UNITY 3D platform. Expert evaluation was conducted through a VR application, and its effectiveness was further assessed among 25 fourth-year nursing students in terms of immersion, presence, and satisfaction. The expert evaluation yielded a mean score of 6.54 out of 7, indicating acceptable content validity. Among learners, evaluation demonstrated immersion at 42.28 ± 2.37 out of 50 (95% CI: 41.30–43.26), presence at 81.36 ± 7.32 out of 95 (95% CI: 78.34–84.38), and satisfaction at 13.48 ± 1.26 out of 15 (95% CI: 12.96–14.00). Overall, the developed VR program demonstrated acceptable expert validity and positive learner perceptions. These preliminary findings suggest feasibility as a supplementary practicum. However, the single-group design without control comparison and reliance on self-reported measures preclude conclusions about educational effectiveness. Full article

Autonomous vehicles (AVs) must dynamically maintain sufficient safe distances from surrounding vehicles to ensure safety. Many existing studies have focused on collisions avoidance, such as the safety ranges in a rectangular shape that consider only longitudinal safe distance. A safety envelope is proposed herein, which is geometrically constructed from four quarter ellipses that account for longitudinal and lateral safe distances. The origin of the safety envelope is placed at the AV’s center of gravity. Using the safety envelope, a potential collision is identified when any surrounding vehicle enters it. To sustain the safety envelope even under hazardous situations, a collision avoidance strategy is introduced. In this strategy, the AV dynamically adjusts its velocity or changes lanes with velocity adjusting by assessing the risk level, complexity level, and riding comfort. For the lane-changing maneuvers, a virtual vehicle is introduced to be placed in the target lane to guide the AV’s movement. The efficacy of this strategy is verified via a simulation under a hazardous situation involving an AV and six human-driven vehicles driving on a highway. Results show that the proposed collision avoidance strategy utilizing safety envelope effectively ensures the safety of AV and surrounding vehicles, even under hazardous situations. Full article

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were improved, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations. Full article

Background: Reconstruction of the maxilla and midface remains one of the most demanding challenges in craniofacial surgery, requiring precise planning and a clear understanding of defect geometry to achieve functional and esthetic restoration. Advances in computer-assisted surgery (CAS) and virtual surgical planning (VSP), based on 3D segmentation of radiologic imaging, have significantly improved the management of maxillary deformities, allowing for further knowledge of patient-specific information, including anatomy, pathology, surgical planning, and reconstructive issues. The integration of computer-aided design and manufacturing (CAD/CAM) and 3D printing has further transformed reconstruction through customized titanium meshes, implants, and surgical guides. Methods:This systematic review, conducted following PRISMA 2020 guidelines, synthesizes evidence from clinical studies on CAD/CAM-assisted reconstruction of maxillary and midfacial defects of congenital, acquired, or post-resection origin. It highlights the advantages and drawbacks of maxillary reconstruction with patient-specific implants (PSISs). Primary outcomes are represented by accuracy in VSP reproduction, while secondary outcomes included esthetic results, functions, and assessment of complications. Results: Of the 44 identified articles, 10 met inclusion criteria with a time frame from April 2013 to July 2022. The outcomes of 24 treated patients are reported. CAD/CAM-guided techniques seemed to improve osteotomy accuracy, flap contouring, and implant adaptation. Conclusions: Although current data support the efficacy and safety of CAD/CAM-based approaches, limitations persist, including high costs, technological dependency, and variable long-term outcome data. This article critically evaluates the role of PSISs in maxillofacial reconstruction and outlines future directions for its standardization and broader adoption in clinical practice. Full article

Human–machine shared control has been widely adopted to enhance driving performance and facilitate smooth transitions between manual and fully autonomous driving. However, existing authority allocation strategies often neglect real-time assessment of scenario complexity and driver workload. To address this gap, we leverage non-invasive eye-tracking devices and the 3D virtual driving simulator Car Learning to Act (CARLA) to collect multimodal data—including physiological measures and vehicle dynamics—for the real-time classification of scenario complexity and cognitive workload. Feature importance is quantified using the SHAP (SHapley Additive exPlanations) values derived from Random Forest classifiers, enabling robust feature selection. Building upon a Hidden Markov Model (HMM) for workload inference and a Model Predictive Control (MPC) framework, we propose a novel human–machine shared control architecture with adaptive authority allocation. Human-in-the-loop validation experiments under both high- and low-workload conditions demonstrate that the proposed strategy significantly improves driving safety, stability, and overall performance. Notably, under high-workload scenarios, it achieves substantially greater reductions in Time to Collision (TTC) and Time to Lane Crossing (TLC) compared to low-workload conditions. Moreover, the adaptive approach yields lower controller load than alternative authority allocation methods, thereby minimizing human–machine conflict. Full article

To ensure the safety of vehicles and occupants under failures or functional limitations of ego vehicles, a minimum risk maneuver (MRM) has been proposed as a key automated driving system (ADS) function. However, executing an MRM may pose certain potential risks when sensor failures occur. This study proposed an MRM strategy designed to enhance highway-driving safety during MRM execution under multiple sensor-failure conditions. A hazard and operability study analysis, based on an ADS behavior model, is conducted to systematically identify hazards, determine potential hazardous events, and categorize the associated safety risks arising from sensor failures. Within the proposed strategy, virtual objects are generated to account for potential hazards and support risk assessments. Adaptive MRM behavior is determined in real time by analyzing surrounding objects and evaluating time-to-collision and time headway. The strategy is verified by using a MATLAB–CARLA co-simulation environment across three representative highway scenarios with combined sensor failures. The result demonstrates that the proposed MRM strategy can mitigate collision risk in hazardous scenarios while effectively leveraging the remaining functional sensors to guide the ego vehicle toward an appropriate minimum risk condition during MRM execution. Full article

This study presents a methodology used in the design certification of a battery module for electric vehicle applications. The methodology combines virtual and physical techniques to assess the structure under mechanical shock testing, according to the standards’ requirements. Virtual prototyping is used to quantify parameters as stresses and deformations. Performed simulations using the virtual prototype are validated by testing the physical prototype, which allows for assessing various design parameters with a high level of confidence. The testing of a physical prototype is performed using specialized equipment—a mechanical shock test bench—which is developed and manufactured especially for this task. The presented methodology is demonstrated in an industrial use case, and the main contribution of this study is related to the way the combination of virtual and physical prototyping and testing is used. Full article

Background/Objectives: Anxiety disorders (ADs) affect up to 20% of mothers in the postpartum period, characterized by psychological symptoms (e.g., emotion dysregulation; ER) and physical symptoms (e.g., disrupted bodily awareness). Although Cognitive Behavioural Therapy effectively reduces anxiety and mood symptoms, it shows limited efficacy in addressing ER difficulties and rarely targets interoceptive dysfunction—both common in postpartum ADs. This study evaluates the effectiveness of a brief mindfulness-based intervention in improving anxiety, ER, and interoception in mothers with postpartum ADs. A secondary aim is to examine changes in brain connectivity associated with these domains. Methods: This protocol describes a proof-of-concept randomized controlled trial involving 50 postpartum mothers with ADs. Participants will be randomized to receive either a 4-week mindfulness intervention plus treatment-as-usual (TAU) or TAU alone. Participants in the mindfulness + TAU group will complete a virtual 4-week group intervention adapted from Mindfulness-Based Cognitive Therapy. The TAU group will receive usual care for 4 weeks and then be offered the mindfulness intervention. Self-report measures of anxiety, ER, and interoception will be collected at baseline, post-intervention, and at a 3-month follow-up. Resting-state functional MRI will be conducted at baseline and post-intervention to assess functional connectivity changes. This trial has been registered on ClinicalTrials.gov (NCT07262801). Results: Improvements in anxiety, ER, and interoception are anticipated, along with decreased default mode network, and increased salience network connectivity post-intervention is hypothesized. Conclusions: This study will be the first to examine the combined psychological and neural effects of mindfulness in postpartum ADs, offering a potentially scalable mind–body treatment. Full article

Background: Effective preoperative patient counseling is essential to shared decision-making. In thyroid surgery, patient communication can be complicated by the complex anatomy and variable surgical approaches, which may not be fully conveyed through conventional verbal explanations or schematic drawings. Virtual three-dimensional (3D) thyroid models may provide an intuitive tool to enhance patient comprehension. Methods: We conducted a randomized controlled trial at Peking Union Medical College Hospital with 94 newly-diagnosed thyroid cancer patients scheduled for thyroidectomy. Participants were assigned to either the control group (n = 47), which received preoperative drawing-based counseling, or the intervention group (n = 47), which utilized a virtual 3D model for communication. The Thyroid Navigator app, developed by Kuma Hospital, was used to provide dynamic 3D representation of the thyroid gland, surrounding structures, and potential surgical procedures. After standardized preoperative consultations, patients were surveyed to assess their understanding in pertinent anatomy and postoperative complications. Results: Patients in the 3D model group demonstrated similar correct response rates in lesion localization (p = 0.536) or parathyroid gland recognition (p = 0.071), but significantly higher accuracy in identifying the recurrent laryngeal nerve and the extent of lymph node dissection compared with the control group (p < 0.05). Moreover, comprehension of the causes of major postoperative complications—including hoarseness (recurrent laryngeal nerve injury, p = 0.004), hypocalcemia (parathyroid gland impairment, p = 0.015), and bleeding (inadequate hemostasis, p = 0.008)—was significantly improved in the 3D model group. Conclusions: Use of a virtual 3D thyroid model significantly improves patient comprehension of thyroid anatomy, surgical procedures, and potential complications, thereby enhancing clinician–patient communication. Virtual 3D models represent a practical and cost-effective supplement to conventional counseling in thyroid surgery, offering clear benefits in patient education and shared decision-making. Full article

Background/Objectives: Sarcopenia is an age-related decline in muscle mass and strength, reducing mobility and functional independence and increasing the risk of falls. Non-pharmacological interventions remain the most effective strategies to prevent or delay its progression, with exercise recognized as the primary approach. Virtual reality (VR)-based training has recently emerged as a promising tool to promote physical activity; however, its application among the oldest-old individuals remains underexplored. This is a randomized controlled pilot study to evaluate the effects of VR-based intervention using the game “Beat Saber” on muscle strength and selected physical performance indicators related to sarcopenia risk in older adults. Methods: Thirty-eight residents (mean age: 87.2) of a long-term care facility were randomly assigned to either a VR group or a control group. The VR group participated in 12 supervised VR-based training sessions of 20 min per session, three times per week for four weeks. Handgrip strength, the arm curl test, 30-s chair stand, a 2-min step-in-place test, and an 8-foot up-and-go test were assessed before and after the intervention. Results: Linear mixed-model analyses revealed significant group-by-time interactions for upper- and lower-limb strength (handgrip, arm curl, chair stand; p < 0.05), favoring the VR group. Agility and endurance (8-foot up-and-go, 2-min step-in-place) showed no significant interactions. In the VR group, the 30-s chair stand performance correlated positively with the arm curl and the 2-min step-in-place tests results, while handgrip strength correlated with the arm curl performance. In the control group, the 30-s chair stand test results correlated strongly with the 8-foot up-and-go and 2-min step-in-place tests, but no significant correlations were found for handgrip strength. Conclusions: The findings indicate short-term functional benefits of VR exercise among the oldest-old adults. VR-based training appears to be an effective and well-tolerated method to enhance physical performance in individuals aged 80 and older and may represent a valuable strategy for improving functional performance indicators associated with sarcopenia risk in adults aged 80 years and older. Full article

This paper provides a comprehensive overview of mobile network softwarization, emphasizing the technological foundations and its transformative impact on the energy efficiency of modern and future mobile networks. In the paper, a detailed analysis of communication concepts known as software-defined networking (SDN) and network function virtualization (NFV) is presented, with a description of their architectural principles, operational mechanisms, and the associated interfaces and management frameworks that enable programmability, virtualization, and centralized control in modern mobile networks. The study further explores the role of cloud computing, virtualization platforms, distributed SDN controllers, and resource orchestration systems, outlining how they collectively support mobile network scalability, automation, and service agility. To assess the maturity and evolution of mobile network softwarization, the paper reviews contemporary research directions, including SDN security, machine-learning-assisted traffic management, dynamic service function chaining, virtual network function (VNF) placement and migration, blockchain-based trust mechanisms, and artificial intelligence (AI)-enabled self-optimization. The analysis also evaluates the relationship between mobile network softwarization and energy consumption, presenting the main SDN- and NFV-based techniques that contribute to reducing mobile network power usage, such as traffic-aware control, rule placement optimization, end-host-aware strategies, VNF consolidation, and dynamic resource scaling. Findings indicate that although fifth-generation (5G) mobile network standalone deployments capable of fully exploiting softwarization remain limited, softwarized SDN/NFV-based architectures provide measurable benefits in reducing network operational costs and improving energy efficiency, especially when combined with AI-driven automation. The paper concludes that mobile network softwarization represents an essential enabler for sustainable 5G and future beyond-5G systems, while highlighting the need for continued research into scalable automation, interoperable architectures, and energy-efficient softwarized network designs. Full article