Search Results (745)

Allergenic proteins in natural rubber latex (NRL) pose significant health risks, particularly in rubber gloves. This study evaluated RNA interference (RNAi) technology for silencing HbREF (rubber elongation factor) and HbSRPP (small rubber particle protein) genes in Hevea brasiliensis to reduce latex allergen content. Double-stranded RNA (dsRNA) targeting these genes demonstrated high stability at 25–37 °C for 6 h and under UV/outdoor conditions for 72 h, but degraded rapidly above 50 °C. Among the three delivery methods tested, direct injection achieved the highest efficiency (>90% gene silencing within 12 h), followed by root drenching (54–84%) and foliar spray (46–70%). HbREF silencing achieved 98–99% expression reduction within 3 h, while HbSRPP showed dose-dependent responses (70–90% silencing) without off-target effects. Gene silencing affected downstream rubber synthesis genes HbCPT (cis-prenyltransferase) and HbRME (rubber membrane elongation protein) (37–58% reduction) while upstream genes remained unaffected. HbREF silencing reduced Hev b1 allergen by 64.04% and Hev b3 by 12.51%, whereas HbSRPP silencing decreased Hev b3 by 71.54% and Hev b1 by 13.48%. Both treatments caused only a 11–13% reduction in dry rubber content. This RNAi approach effectively reduces major latex allergens while maintaining rubber production, demonstrating commercial potential for developing hypoallergenic rubber products through precision agriculture biotechnology. Full article

Every day, countless individuals with hearing or speech disabilities struggle to communicate effectively, as their conditions limit conventional verbal interaction. For them, sign language becomes an essential and often sole tool for expressing thoughts and engaging with others. However, the general public’s limited understanding of sign language poses a major barrier, often resulting in social, educational, and professional exclusion. To bridge this communication gap, the present study proposes a smart wearable glove system designed to translate Arabic sign language (ArSL), especially Moroccan sign language (MSL), into a written alphabet in real time. The glove integrates five MPU6050 motion sensors, one on each finger, capable of capturing detailed motion data, including angular velocity and linear acceleration. These motion signals are processed using an Artificial Neural Network (ANN), implemented directly on a Raspberry Pi Pico through embedded machine learning techniques. A custom dataset comprising labeled gestures corresponding to the MSL alphabet was developed for training the model. Following the training phase, the neural network attained a gesture recognition accuracy of 98%, reflecting strong performance in terms of reliability and classification precision. We developed an affordable and portable glove system aimed at improving daily communication for individuals with hearing impairments in Morocco, contributing to greater inclusivity and improved accessibility. Full article

(1) Background: With the increasing incidence of cancer, the need for handling cytotoxic drugs has also grown. However, manipulating these drugs exposes healthcare professionals to significant risks, including occupational exposure to hazardous chemicals. Therefore, it is important to adopt protective measures, including personal protective equipment (PPE) and correct ergonomic practices, to ensure safe drug preparation and minimize health risks for the operators. However, while chemical exposure and PPE have been extensively addressed in the literature, the combined impact of ergonomic practices and protective measures remains insufficiently emphasized, representing a critical gap this review aims to address. Accordingly, the objective of this literature review was to analyze the ergonomic and individual protection practices during the handling of cytostatic drugs and all the implications that bad ergonomic practices and/or poor individual protection have on the operator’s health; (2) Methods: In order to perform this integrative review, a structured literature search was conducted using online databases (Web of Science^®, Google Scholar^®, and PubMed^®) from January 2005 to June 2025. (3) Results: A total of 19 articles were analyzed, with 17 focusing on PPE and 17 on ergonomics. The findings emphasize that PPE, such as gloves, masks, gowns, sleeves and safety glasses, plays a critical role in the safe handling of cytotoxic drugs, particularly when combined with other safety measures. Additionally, maintaining correct ergonomic posture is important in preventing musculoskeletal disorders; (4) Conclusions: This review emphasizes the significance of integrating appropriate PPE use with sound ergonomic procedures. Although PPE is still the secondary line of defense against occupational exposure, ergonomic issues must also be addressed to avoid chronic musculoskeletal problems. Continuous training, rigorous attention to safety procedures, and ergonomic enhancements should be prioritized by healthcare facilities as a key element of occupational safety programs to reduce the short-term and long-term health hazards for personnel handling dangerous drugs. Full article

Intelligent sensing systems are increasingly used in wearable devices, enabling advanced tasks across various application domains including robotics and human–machine interaction. Ensuring these systems are energy autonomous is highly demanded, despite strict constraints on power, memory and processing resources. To meet these requirements, embedded neural networks must be optimized to achieve a balance between accuracy and efficiency. This paper presents an integrated approach that combines Hardware-Aware Neural Architecture Search (HW-NAS) with optimization techniques—weight reshaping, quantization, and their combination—to develop efficient classifiers for a multisensory glove. HW-NAS automatically derives 1D-CNN models tailored to the NUCLEO-F401RE board, while the additional optimization further reduces model size, memory usage, and latency. Across three datasets, the optimized models not only improve classification accuracy but also deliver an average reduction of 75% in inference time, 69% in flash memory, and more than 45% in RAM compared to NAS-only baselines. These results highlight the effectiveness of integrating NAS with optimization techniques, paving the way towards energy-autonomous wearable systems. Full article

Ensuring hygiene compliance in regulated environments—such as food processing facilities, hospitals, and public indoor spaces—requires reliable detection of personal protective equipment (PPE) usage, including gloves, face masks, and hairnets. Manual inspection is labor-intensive and unsuitable for continuous, real-time enforcement. This study benchmarks three lightweight object detection models—YOLOv8n, YOLOv10n, and YOLOv12n—for automated PPE compliance monitoring using a large curated dataset of over 31,000 annotated images. The dataset spans seven classes representing both compliant and non-compliant conditions: glove, no_glove, mask, no_mask, incorrect_mask, hairnet, and no_hairnet. All evaluations were conducted using both detection accuracy metrics (mAP@50, mAP@50–95, precision, recall) and deployment-relevant efficiency metrics (inference speed, model size, GFLOPs). Among the three models, YOLOv10n achieved the highest mAP@50 (85.7%) while maintaining competitive efficiency, indicating strong suitability for resource-constrained IoT-integrated deployments. YOLOv8n provided the highest localization accuracy at stricter thresholds (mAP@50–95), while YOLOv12n favored ultra-lightweight operation at the cost of reduced accuracy. The results provide practical guidance for selecting nano-scale detection models in real-time hygiene compliance systems and contribute a reproducible, deployment-aware evaluation framework for computer vision in hygiene-critical settings. Full article

Background and Objectives: Distal upper limb injuries are frequent in winter sports, but their functional impact is often underestimated. This study aimed to describe the epidemiology, mechanisms, and risk factors for injuries involving the forearm, wrist, hand, and fingers sustained during two consecutive winter seasons in the Italian Dolomites. Materials and Methods: All adult and willing patients presenting to the Emergency Department of Brixen Hospital after ski- or snowboard-related accidents between December 2023 and March 2025 completed a standardized 23-item questionnaire on demographics, experience level, environmental factors, equipment, and trauma mechanism. For the aim of this study only distal upper limb injuries were extracted and analyzed. Statistical analyses compared fracture versus non-fracture injuries, “good” versus “bad” fractures (AO classification and surgical complexity), and isolated ulnar collateral ligament (UCL) injuries. Results: A total of 195 patients were analyzed: 96 (49.2%) sustained a fracture and 33 (16.9%) presented with isolated UCL lesions. Fractures occurred more frequently on blue slopes (56.2% vs. 33.3%, p < 0.001), whereas non-fracture injuries predominated on red and off-piste slopes. Age, BMI, and skill level did not differ significantly between groups. Surgically classified complex distal forearm fractures were significantly more frequent in females (p < 0.005) but were not associated with environmental factors. UCL injuries occurred mainly on red slopes (54.5%) and were often related to pole entrapment during falls. None of the injured patients reported the use of protective wrist or thumb supports. Conclusions: Distal upper limb injuries are a common pattern of alpine sports trauma, with wrist fractures and skier’s thumb being predominant lesions. Low-speed falls on easy slopes are associated with wrist fractures, while UCL injuries are linked to intermediate slopes. Preventive strategies should include fall technique education, protective gloves, and improved pole ergonomics. Full article

This study investigates the challenges of promoting accessibility for deaf teachers and students in higher education, focusing on the development of inclusive teaching materials. A qualitative case study was conducted in ten teacher training programmes at the Federal University of Alagoas (Brazil), including nine distance learning courses and one face-to-face LIBRAS programme. Analysis of the Virtual Learning Environment revealed a predominance of text-based content, with limited use of Libras videos, visual resources, or assistive technologies. The integration of Brazilian Sign Language into teaching practices was minimal, and digital translation tools were rarely used or contextually appropriate. Educators reported limited training, technical support, and institutional guidance for the creation of accessible materials. Time constraints and resource scarcity further hampered inclusive practices. The results highlight the urgent need for institutional policies, continuous teacher training, multidisciplinary support teams, and the strategic use of digital technologies and Artificial Intelligence (AI). Compared with previous studies, significant progress has been made. The present study highlights the establishment of an Accessibility Centre (NAC) and an Accessibility Laboratory (LAB) at the university. These facilities are designed to support the development of policies for the inclusion of people with disabilities, including deaf students, and to assist teachers in designing educational resources, which is essential for enhancing accessibility and learning outcomes. Artificial intelligence tools—such as sign language translators including Hand Talk, VLibras, SignSpeak, Glove-Based Systems, the LIBRAS Online Dictionary, and the Spreadthesign Dictionary—can serve as valuable resources in the teaching and learning process. Full article

The sensory glove (also known as data or instrumented glove) plays a key role in measuring and tracking hand dexterity. It has been adopted in a variety of different domains, including medical, robotics, virtual reality, and human–computer interaction, to assess hand motor skills and to improve control accuracy. However, no particular technology has been established as the most suitable for all domains, so that different sensory gloves have been developed, adopting different sensors mainly based on optic, electric, magnetic, or mechanical properties. This work investigates the performances of the MANUS Quantum sensory glove that sources an electromagnetic field and measures its changing value at the fingertips during fingers’ flexion. Its performance is determined in terms of measurement repeatability, reproducibility, and reliability during both quasi-static and dynamic hand motor tests. Full article

This research demonstrates the high efficiency of thermal plasma gasification for the treatment of simulated operational radioactive waste (SORW), representative of the low-level radioactive waste (LLW) generated in Argentina. A prototype system with a 4.8 kW plasma torch was used to process SORW composed of nitrile gloves, laboratory paper, and stable non-radioactive elements to simulate ⁶⁰Co, ⁹⁰Sr, ¹³⁷Cs, and ¹⁴⁴Ce. The process achieved vast volume reduction (99.6%), converting 5625 mL of waste into minimal volumes of four different solid residues (SR): SR1 (20 mL), SR2 (1 mL), SR3 (1 mL), and SR4 (3 mL), resulting in a volume reduction factor (VRF) of 225. Elemental analysis showed clear differences in retention behavior: excellent retention for Co (96 ± 10% inside the plasma reactor) and Ce (59 ± 6%), while more volatile Sr (39 ± 4%) and Cs (26 ± 3%). The latter were partially captured in downstream components (22.8 ± 1.1% Sr and 2.9 ± 0.15% Cs in quencher). The gases treatment system achieved >97% reduction for most plasma generated pollutants: NO_x (98.9 ± 0.6%), CO (98.2 ± 0.8%), H₂S (97.6 ± 0.6%), and H₂ (98.1 ± 0.9%), with 80.6 ± 2.5% for SO₂ and 75.0 ± 1.1% reduction for CO₂. Full article

Melanin is one of the key light absorbers in skin and is responsible for the colour of the skin. This study evaluates the responsivity of different skin tones to white light within the visible spectral range of 300–700 nm on 12 participants. The results show that the peak amplitude of the reflected light signal decreased by 90% for darker skin tones, compared to 70% for lighter skin tones. There were also visible differences at the 460 nm and 570 nm wavelengths between the skin tones, suggesting that the standard one-glove-fits-all pulse oximeter might not be ideal. Full article

The Signal Amplification By Reversible Exchange (SABRE) technique provides enhancement of Nuclear Magnetic Resonance (NMR) signals up to several orders of magnitude using chemical exchange of a substrate and parahydrogen on an iridium complex. Therefore, the availability of such a catalytic complex to a broader community is an absolutely vital step for dissemination of the groundbreaking SABRE methodology. The most common SABRE catalyst, which is activated in situ, is based on Ir-IMes system (IMes = 1,3-Bis(2,4,6-trimethylphenyl)imidazol-2-ylidene). Earlier approaches for the synthesis of this catalyst often relied on specialized equipment and were limited to a comparatively small scale. This, in turn, increased the barrier of entry for new scientists to the area of SABRE hyperpolarization. Here, we present a robust, inexpensive, and easy to reproduce synthetic procedure for the preparation of this SABRE catalyst, which does not require specialized inert atmosphere equipment like a glove box or Schlenk line. The synthesis was validated on the scale of several grams vs. tens of milligrams scale in the reported approaches. The resulting SABRE catalyst, [Ir(IMes)(COD)Cl], was activated in situ and further evaluated in hyperpolarization experiments resulting in signal enhancements comparable to (or higher than) those for the catalyst prepared using Schlenk line equipment. Full article

Safety helmets and gloves are indispensable personal protective equipment in power grid operation environments. Traditional detection methods for safety helmets and gloves suffer from reduced accuracy due to factors such as dense personnel presence, varying lighting conditions, occlusions, and diverse postures. To enhance the detection performance of safety helmets and gloves in power grid operation environments, this paper proposes a novel algorithm, YOLO-DCRCF, based on YOLO11 for detecting the wearing of safety helmets and gloves in such settings. By integrating Deformable Convolutional Network version 2 (DCNv2), the algorithm enhances the network’s capability to model geometric transformations. Additionally, a recalibration feature pyramid (RCF) network is innovatively designed to strengthen the interaction between shallow and deep features, enabling the network to capture multi-scale information of the target. Experimental results show that the proposed YOLO-DCRCF model achieved mAP50 scores of 92.7% on the Safety Helmet Wearing Dataset (SHWD) and 79.6% on the Safety Helmet and Gloves Wearing Dataset (SHAGWD), surpassing the baseline YOLOv11 model by 1.1% and 2.7%, respectively. These results meet the real-time safety monitoring requirements of power grid operation sites. Full article

The theological rendering of economics in the Bahá’í Faith—particularly from ‘Abdu’l-Bahá—advocated progressive taxation, a strong welfare state, the abolition of trusts, and the redistribution of wealth. These orientations directly diverge from “neoliberal” economic theory, especially as articulated by Frederick Hayek: concerns that social justice exacerbates poverty and claims that progressive taxation is “discrimination.” Despite these seemingly antithetical orientations, there has been a slow and tentative, if not uneasy, meeting of Bahá’í and neoliberal ideals in global organizations and scholarship. Through a comparative analysis of the writings of both ‘Abdu’l-Bahá and Friedrich Hayek, I first illuminate the fundamental disagreements on economy and society between Bahá’í theology and neoliberalism. Second, I cover recent scholarship on the moralization of markets and the sacralization of financial actors in order to contextualize the historical and contemporary unions of theology and economy. Third, I outline how ‘Abdu’l-Bahá’s theological vision and Hayek’s neoliberal theories accrete around four mutual worldviews, which can tempt hermeneutic deemphases of the fundamental divergences in Bahá’í and neoliberal logics: (1) the duality of human nature, (2) the limits of materialist reason, (3) the apotheosis of the market and self-love, and (4) sacrificial submission to transcendent authority. Full article

Over the last decade, soft robotic gripper systems, such as grippers, have been used in a variety of applications, particularly in human rehabilitation. This study aims to enhance the rehabilitation process by creating a mirroring system glove for hand paralysis patients due to injury, stroke, hemiplegia, and others. A soft and flexible liquid silicone rubber (LSR) was used to develop and build a pair of gloves to improve comfort and safety compared with rigid rehabilitation equipment. The non-affected hand’s sensory glove, equipped with flex sensors, detects motion by measuring the bending angle at each finger. The other glove uses Arduino and a pneumatic system to help the afflicted hand accomplish training exercises. The new design of a gripper is important for manufacturing gloves that provide acceptable gripping behavior. Full article

This study investigates the evolution of online public opinion during the COVID-19 pandemic by integrating topic mining with sentiment analysis. To overcome the limitations of traditional short-text models and improve the accuracy of sentiment detection, we propose a novel hybrid framework that combines a GloVe-enhanced Biterm Topic Model (BTM) for semantic-aware topic clustering with a RoBERTa-TextCNN architecture for deep, context-rich sentiment classification. The framework is specifically designed to capture both the global semantic relationships of words and the dynamic contextual nuances of social media discourse. Using a large-scale corpus of more than 550,000 Weibo posts, we conducted comprehensive experiments to evaluate the model’s effectiveness. The proposed approach achieved an accuracy of 92.45%, significantly outperforming baseline transformer-based baseline representative of advanced contextual embedding models across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These results confirm the robustness and stability of the hybrid design and demonstrate its advantages in balancing precision and recall. Beyond methodological validation, the empirical analysis provides important insights into the dynamics of online public discourse. User engagement is found to be highest for the topics directly tied to daily life, with discussions about quarantine conditions alone accounting for 42.6% of total discourse. Moreover, public sentiment proves to be highly volatile and event-driven; for example, the announcement of Wuhan’s reopening produced an 11% surge in positive sentiment, reflecting a collective emotional uplift at a major turning point of the pandemic. Taken together, these findings demonstrate that online discourse evolves in close connection with both societal conditions and government interventions. The proposed topic–sentiment analysis framework not only advances methodological research in text mining and sentiment analysis, but also has the potential to serve as a practical tool for real-time monitoring online opinion. By capturing the fluctuations of public sentiment and identifying emerging themes, this study aims to provide insights that could inform policymaking by suggesting strategies to guide emotional contagion, strengthen crisis communication, and promote constructive public debate during health emergencies. Full article