Search Results (1,106)

Lidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems. Full article

Background/Objectives: The study aims to identify key cognitive and non-cognitive variables (e.g., clinical, neuroimaging, and genetic data) predicting cognitive decline in Parkinson’s disease (PD) patients using machine learning applied to a sample (N = 618) from the Parkinson’s Progression Markers Initiative database. Traditional research has mainly employed explanatory approaches to explore variable relationships, rather than maximizing predictive accuracy for future cognitive decline. In the present study, we implemented a predictive framework that integrates a broad range of baseline cognitive, clinical, genetic, and imaging data to accurately forecast changes in cognitive functioning in PD patients. Methods: An artificial neural network was trained on baseline data to predict general cognitive status three years later. Model performance was evaluated using 5-fold stratified cross-validation. We investigated model interpretability using explainable artificial intelligence techniques, including Shapley Additive Explanations (SHAP) values, Group-Wise Feature Masking, and Brute-Force Combinatorial Masking, to identify the most influential predictors of cognitive decline. Results: The model achieved a recall of 0.91 for identifying patients who developed cognitive decline, with an overall classification accuracy of 0.79. All applied explainability techniques consistently highlighted baseline MoCA scores, memory performance, the motor examination score (MDS-UPDRS Part III), and anxiety as the most predictive features. Conclusions: From a clinical perspective, the findings can support the early detection of PD patients who are more prone to developing cognitive decline, thereby helping to prevent cognitive impairments by designing specific treatments. This can improve the quality of life for patients and caregivers, supporting patient autonomy. Full article

The insulin-like growth factor binding protein, acid-labile subunit (IGFALS), plays a crucial role in glucose metabolism and immune regulation, key processes in recovery from surgery. Here, we studied the perioperative serum IGFALS dynamics and explored potential clinical implications. A total of 79 patients undergoing elective cardiac surgery with implementation of cardiopulmonary bypass had their serum isolated at baseline, 24 h, seven days, and three months postoperatively to assess serum concentrations of IGFALS and insulin growth factor 1 (IGF-1). Markers of perioperative injury included troponin I (TnI), high-mobility group box 1 (HMGB-1), and heat shock protein 60 (Hsp-60). Inflammatory status was assessed via interleukin-6 (IL-6) and interleukin-8 (IL-8). Additionally, we measured in vitro cytokine production to viral stimulation of whole blood and monocytes. Surrogates of neuronal distress included neurofilament light chain (NF-L), total tau (τ), phosphorylated tau at threonine 181 (τp181), and amyloid β40 and β42. Renal impairment was defined by RIFLE criteria. Cardiac dysfunction was denoted by serum N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Serum IGFALS levels declined significantly after surgery and remained depressed even at 3 months. Administration of acetaminophen and acetylsalicylic acid differentiated IGFALS levels at the 24 h postoperatively. Serum IGFALS 24 h post-operatively correlated with production of cytokines by leukocytes after in vitro viral stimulation. Serum amyloid-β1-42 was significantly associated with IGFALS at baseline and 24 h post-surgery Patients discharged home had higher IGFALS levels at 28 days and 3 months than those discharged to healthcare facilities or who died. These findings suggest that IGFALS may serve as a prognostic biomarker for recovery trajectory and postoperative outcomes in cardiac surgery patients. Full article

In the wide range of human diseases, Parkinson’s disease (PD) has a high incidence, according to a recent survey by the World Health Organization (WHO). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. PD is a degenerative disorder of the brain, characterized by the impairment of the nigrostriatal system. A wide range of symptoms of motor and non-motor impairment accompanies this disorder. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V microcontroller unit (MCU) was designed for the voice-controlled human-machine interface (HMI). With the help of signal processing and feature extraction methods, the digital signal is impaired by the impairment of the nigrostriatal system. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals as normal or abnormal to identify PD. We use Matrix Laboratory (MATLAB R2021a_v9.10.0.1602886) to analyze the data, develop algorithms, create modules, and develop the RISC-V processor for embedded implementation. Machine learning (ML) techniques are also used to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs). Full article

Blind and visually impaired (BVI) people face significant challenges in perception, navigation, and safety during travel. Existing infrastructure (e.g., blind lanes) and traditional aids (e.g., walking sticks, basic audio feedback) provide limited flexibility and interactivity for complex environments. To solve this problem, we propose a real-time travel assistance system based on deep learning. The hardware comprises an NVIDIA Jetson Nano controller, an Intel D435i depth camera for environmental sensing, and SG90 servo motors for feedback. To address embedded device computational constraints, we developed a lightweight object detection and segmentation algorithm. Key innovations include a multi-scale attention feature extraction backbone, a dual-stream fusion module incorporating the Mamba architecture, and adaptive context-aware detection/segmentation heads. This design ensures high computational efficiency and real-time performance. The system workflow is as follows: (1) the D435i captures real-time environmental data; (2) the processor analyzes this data, converting obstacle distances and path deviations into electrical signals; (3) servo motors deliver vibratory feedback for guidance and alerts. Preliminary tests confirm that the system can effectively detect obstacles and correct path deviations in real time, suggesting its potential to assist BVI users. However, as this is a work in progress, comprehensive field trials with BVI participants are required to fully validate its efficacy. Full article

Background: Psychological problems post-stroke can negatively impact stroke survivors. Although general psychological services exist (e.g., NHS Talking Therapies), access remains limited, particularly for individuals with post-stroke communication and cognitive impairments. Stroke service staff report low confidence in managing psychological distress. This study is the first to explore the barriers and facilitators to implementing a novel intervention package comprising a cross-service care pathway and staff training to enhance post-stroke psychological provision. Methods: Staff from stroke and mental health services in four UK regions, recruited through purposive sampling to ensure diversity of services and professional roles, participated in semi-structured interviews or focus groups, guided by the Theoretical Domains Framework (TDF), before and after implementation of the intervention package. Pre-implementation interviews/groups identified anticipated barriers and facilitators to implementation and training needs, informing the development of site-specific intervention packages; post-implementation interviews/groups explored experienced barriers, facilitators and perceptions of the intervention. Interviews underwent thematic analysis using the TDF. Results: Fifty-five staff participated pre-implementation and seventeen post-implementation, representing stroke (e.g., nurse, physiotherapist, consultant) and psychology (e.g., counsellor, psychological therapist) roles across acute, rehabilitation, community, and voluntary services. Challenges anticipated pre-implementation included: limited specialist post-stroke psychological support; low staff confidence; and fragmented service pathways. Post-implementation findings indicated increased staff knowledge and confidence, enhanced screening and referral processes, and stronger inter-service collaboration. Implementation success varied across sites (with some sites showing greater ownership and sustainability of the intervention) and across staff roles (with therapy staff more likely than nursing staff to have received training). Conclusions: Effective implementation of an intervention package to increase psychological provision post-stroke requires staff engagement at all levels across all services. Staff investment influenced ownership of the intervention package, beliefs about priorities and overall enhancement of service capability. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

Pain is a common condition among older adults and a key factor influencing daily functioning. This cross-sectional study examined how pain presence and distribution (no pain, localized pain [LP], and widespread pain [WP]) are related to limitations in Basic and Instrumental Activities of Daily Living (BADLs and IADLs). Data were drawn from the Survey of Health, Aging, and Retirement in Europe (SHARE) Wave 9, including 68,839 participants aged 50 or older. A clear gradient of functional limitation was observed: Individuals with WP reported the highest number of limitations, followed by those with LP, while those with no pain showed minimal impairment. These associations remained significant after adjusting for age, sex, cognitive status, physical health, and psychosocial factors, with adjusted prevalence ratios (aPRs) for WP of 1.77 for BADLs and 1.22 for IADLs (p < 0.001). Notably, depression, perceived loneliness, long-term illness, physical inactivity, and mobility limitations were especially relevant among participants with WP. The findings suggest the clinical value of assessing and implementing interventions not only in the presence but also in the extent of pain to better identify individuals at greater risk of losing independence in daily life. Full article

Dementia is a neurodegenerative disorder characterized by progressive cognitive impairment that significantly affects daily living. Early detection of Alzheimer’s disease—the most common form of dementia—remains essential for prompt intervention and treatment, yet clinical diagnosis often requires extensive and resource-intensive procedures. This article explores the effectiveness of automated Natural Language Processing (NLP) methods for identifying Alzheimer’s indicators from audio transcriptions of the Cookie Theft picture description task in the PittCorpus dementia database. Five NLP approaches were compared: a classical Tf–Idf statistical representation and embeddings derived from large language models (GloVe, BERT, Gemma-2B, and Linq-Embed-Mistral), each integrated with a logistic regression classifier. Transcriptions were carefully preprocessed to preserve linguistically relevant features such as repetitions, self-corrections, and pauses. To compare the performance of the five approaches, a stratified 5-fold cross-validation was conducted; the best results were obtained with BERT embeddings (84.73% accuracy) closely followed by the simpler Tf–Idf approach (83.73% accuracy) and the state-of-the-art model Linq-Embed-Mistral (83.54% accuracy), while Gemma-2B and GloVe embeddings yielded slightly lower performances (80.91% and 78.11% accuracy, respectively). Contrary to initial expectations—that richer semantic and contextual embeddings would substantially outperform simpler frequency-based methods—the competitive accuracy of Tf–Idf suggests that the choice and frequency of the words used might be more important than semantic or contextual information in Alzheimer’s detection. This work represents an effort toward implementing user-friendly software capable of offering an initial indicator of Alzheimer’s risk, potentially reducing the need for an in-person clinical visit. Full article

Shaboo is a street name commonly used in parts of Asia, particularly the Philippines and Thailand, to refer to methamphetamine, a powerful and highly addictive stimulant. Its long-term effects are related to chronic exposure to the drug effects, primarily neurotoxicity phenomena, which could lead to cognitive impairment, or psychiatric symptoms. We aim to present one case of problematic shaboo use in a patient referring to an addiction outpatient clinic in Northern Italy. This case highlights that the treatment of these patients involves careful multidisciplinary management. An accurate knowledge of the physical and psychological effects of New Psychoactive Substances is essential, as well as the implementation of a tailored psychological and social support program. Full article

Introduction: Visual impairment impacts millions of people around the world, with the vast majority of problems being treatable. Disadvantaged communities are unable to utilize the same resources to treat these problems due to a lack of knowledge or resources, in addition to the presence of barriers preventing access. The objective of this paper is to assess eye health awareness and evaluate the barriers for individuals from disadvantaged communities in order to inform future interventions and increase access to care. Methods: This is a pilot study utilizing an online anonymous questionnaire designed to assess the demographics, eye health awareness, and access to eye care of community-based patients. A comprehensive literature review was also conducted using PubMed, Scopus, and Google Scholar to evaluate barriers to eye care and methods to improve community health outcomes. The primary goal was to improve understanding of eye health awareness and access in order to inform future strategies that can help in improving eye health awareness and service availability. Results: The results indicated that 61.2% of respondents believed that eye exams are very important, and only 7.7% of participants believed that regular eye exams are not important. The majority of participants (75%) agree that regular eye exams help prevent serious eye conditions and 84.5% believe that eye health can affect quality of life. 35.6% of participants reported they had their eyes checked by a healthcare professional within the last year, while 21.2% reported never having an eye exam. Although the majority of participants found access to eye care services in their community somewhat or very easy, 8.6% and 9.5% of participants found access difficult and very difficult, respectively. Even though 45.6% of participants reported not facing any barriers regarding access to eye care, the cost of services, long waiting times, and lack of nearby eye care providers were often cited as barriers from the remainder of the participants. Moving forward, local interventions such as mobile eye clinics, public health workshops, and telehealth are viable options to obtain an understanding of the community’s health status in addition to creating opportunities to educate and provide health screenings. Conclusion: The results indicate that although there is awareness of the importance of eye health for the majority of participants, there is still a sizable minority who have insufficient understanding. Barriers to healthcare such as cost, waiting times, and proximity to providers are common problems that are preventing many from seeking eye care. Future interventions should be created to increase access and literacy amongst the community through telehealth, mobile eye clinics, and public health workshops. Additional efforts should be taken by healthcare stakeholders to enhance care delivery, implement policies, and improve awareness. Full article

Platinum-induced ototoxicity constitutes a significant adverse effect in pediatric oncology, frequently resulting in permanent hearing impairment with profound implications for quality of life, language acquisition, and scholastic performance. This comprehensive review critically evaluates contemporary ototoxicity monitoring practices across various pediatric oncology settings, analyzes current guideline recommendations, and formulates strategies for implementing standardized surveillance protocols. Through examination of recent literature—encompassing retrospective cohort investigations, international consensus recommendations, and functional outcome assessments—we present an integrated analysis of challenges and opportunities in managing chemotherapy-associated hearing loss among childhood cancer survivors. Our findings demonstrate marked heterogeneity in monitoring methodologies, substantial implementation obstacles, and considerable impact on survivors’ functional status across multiple domains. Particularly concerning is the persistent absence of an evidence-based consensus regarding the appropriate duration of audiological surveillance for this vulnerable population. We propose a structured framework for comprehensive ototoxicity management emphasizing prompt detection, standardized assessment techniques, and integrated long-term follow-up care to minimize the developmental consequences of platinum-induced hearing impairment. This approach addresses critical gaps in current practice while acknowledging resource limitations across diverse healthcare environments. Full article

Background/Objectives: Partial maxillectomy frequently results in severe impairments of oral functions, such as difficulties in chewing, speech, swallowing, and facial appearance. Immediate prosthetic rehabilitation is challenging because soft tissue healing is typically required before impression taking. This study aimed to (1) develop a comprehensive digital workflow for fabricating immediate obturator prostheses using preoperative data and (2) assess their early clinical effectiveness in restoring oral functions after surgery. Methods: In this prospective clinical study, 20 patients undergoing partial maxillectomy from January 2023 to January 2025 were enrolled. A digital workflow combining cone-beam computed tomography (CBCT), intraoral scanning, CAD/CAM design, and 3D metal printing was implemented. Obturator prostheses were digitally designed preoperatively and inserted immediately post-resection. Functional outcomes were postoperatively evaluated after one month using the Obturator Functioning Scale (OFS), which measures functional, speech, esthetic, and psychosocial aspects. Results: The digitally fabricated immediate obturator prostheses were successfully placed intraoperatively in all patients. Most participants reported mild to moderate difficulties, with speech-related issues being the most common, while esthetic concerns were minimal. Masticatory function was satisfactorily restored in 75% of cases, and 60% of patients experienced minimal fluid leakage during swallowing. No significant differences were found between genders. Patients with larger defects tended to report greater functional challenges. Conclusions: The digitally planned immediate obturator prosthesis provides a practical and effective solution for early rehabilitation following partial maxillectomy. This digital workflow reduces patient discomfort, restores key oral functions, and facilitates psychosocial recovery. Full article

Stroke is one of the leading causes of long-term disability worldwide, often resulting in motor impairments that limit the ability to perform daily activities independently. Conventional rehabilitation exoskeletons, while effective, are typically rigid, bulky, and expensive, limiting their usability outside of clinical settings. In response to these challenges, this work presents the development and validation of a novel soft exosuit designed for elbow flexion rehabilitation, incorporating a multi-wire Shape Memory Alloy (SMA) actuator capable of both position and force control. The proposed system features a lightweight and ergonomic textile-based design, optimized for user comfort, ease of use, and low manufacturing cost. A sequential activation strategy was implemented to improve the dynamic response of the actuator, particularly during the cooling phase, which is typically a major limitation in SMA-based systems. The performance of the multi-bundle actuator was compared with a single-bundle configuration, demonstrating superior trajectory tracking and reduced thermal accumulation. Surface electromyography tests confirmed a decrease in muscular effort during assisted flexion, validating the device’s assistive capabilities. With a total weight of 0.6 kg and a fabrication cost under EUR 500, the proposed exosuit offers a promising solution for accessible and effective home-based rehabilitation. Full article

Obstructive sleep apnea (OSA) is a highly prevalent sleep-related breathing disorder, primarily characterized by recurrent episodes of upper airway obstruction during sleep. Individuals affected by OSA are at increased risk for a variety of adverse health outcomes, particularly neurocognitive impairments and cardiovascular complications, highlighting the clinical significance of this condition. A defining feature of OSA is intermittent hypoxemia, which contributes to the excessive production of reactive oxygen species (ROS) and the subsequent development of oxidative stress. The primary objective of this narrative review was to comprehensively investigate the intricate mechanisms of oxidative stress and elucidate their complex interplay in the development and progression of OSAS. Subsequently, we examined the current literature to identify the most promising biomarkers and pharmacological treatments related to OSA and oxidative stress. We found that biomarkers of oxidative stress have shown potential in assessing disease severity and tracking individual responses to therapy. However, none have yet to be incorporated into standard clinical practice. Continuous positive airway pressure (CPAP) is the gold standard treatment for OSA. Nevertheless, antioxidant therapy has emerged as a potential adjunctive approach that may help address residual dysfunctions not fully resolved by CPAP alone. Both the use of oxidative stress biomarkers and antioxidant-based therapies require further validation through robust clinical studies before they can be routinely implemented in clinical settings. Full article