Multidisciplinary Digital Publishing Institute (Switzerland)
Multidisciplinary Digital Publishing InstituteNot a member yet
1861300 research outputs found
Sort by
Perceived Impact of Wearable Fitness Trackers on Health Behaviours in Saudi Adults
No full textBackground/Objectives: Wearable fitness trackers (WFTs) are growing in popularity as tools to motivate physical activity and support healthier lifestyles. Evidence suggests that they can have both positive and negative effects on user behaviour and well-being. However, little is known about their impact in Saudi settings, considering its unique cultural context. This study aims to investigate the perceived positive and negative impacts of WFTs on adults’ health behaviours and well-being in Saudi Arabia. Methods: A cross-sectional survey was conducted among Saudi adults aged 18 years or older who currently use or have previously used WFTs, using an online questionnaire distributed via social media platforms in May 2025. The survey was developed based on evidence from the literature. It included demographic items, five-point Likert-scale questions assessing positive (9 items) and negative (10 items) effects of WFTs, and an open-ended question. Responses were analysed using descriptive statistics, independent samples t-tests, and one-way ANOVA. Results: A total of 154 adults participated. The mean composite score for positive effects was 3.26 (SD = 0.73), indicating general agreement on the benefits of WFTs, while the negative effects score was 2.15 (SD = 0.66), showing low endorsement of adverse outcomes. No significant differences appeared between gender (positive: p = 0.34; negative: p = 0.24) or age groups (positive: p = 0.56; negative: p = 0.19). However, users of over two months had higher positive scores (M = 3.43, SD = 0.66) than newer or former users (p = 0.01). Open responses showed 62% positive experiences; 27% reported stress, guilt, or obsessive monitoring. Conclusions: This study provides initial insights into WFT use and perceptions in Saudi Arabia. However, its cross-sectional nature limits the ability to draw causal conclusions. While most users experienced beneficial health outcomes, a significant proportion reported negative psychological experiences. These findings highlight WFT users’ dual experiences and the need for further longitudinal research and diverse recruitment strategies to better understand sustained engagement and psychological effects
Wearable Sensor–Based Gait Analysis in Benign Paroxysmal Positional Vertigo: Quantitative Assessment of Residual Dizziness Using the φ-Bonacci Framework
No full textBackground: Benign Paroxysmal Positional Vertigo (BPPV) is the most common vestibular disorder. Although canalith repositioning procedures (CRPs) typically resolve positional vertigo, several patients still report imbalance or residual dizziness, which remain difficult to quantify with standard tests. Wearable inertial sensors now allow high-resolution, objective gait analysis and may detect subtle vestibular-related impairments. Objectives: This study evaluates the clinical usefulness of sensor-based gait metrics, enhanced by the newly developed φ-bonacci index framework to quantify gait changes and residual dizziness in BPPV before and after CRPs. Methods: Fifteen BPPV patients (BPPV-P) and fifteen age-matched controls completed walking tests under eyes-open (EO) and eyes-closed (EC) conditions using wearable inertial measurement units (IMU). φ-bonacci index components—self-similarity (A1), swing symmetry (A2), and double-support consistency (A4)—were calculated to assess gait harmonicity, symmetry and stability. Results: Before treatment, BPPV-P exhibited significantly higher A1 values than healthy controls (p = 0.038 EO; p = 0.011 EC), indicating impaired gait harmonicity. After CRPs, A1 values normalized to control levels, suggesting restored gait self-similarity. Under visual deprivation, both A1 and A4 showed pronounced increases across all groups, reflecting the contribution of vision to balance control. Among post-treatment patients, those reporting residual dizziness demonstrated persistently elevated A4 values—particularly under EC conditions—indicating incomplete sensory reweighting despite clinical recovery. Conclusions: Wearable sensor–derived φ-bonacci metrics offer sensitive, objective markers of gait abnormalities and residual dizziness in BPPV, supporting their use as digital biomarkers for diagnosis, rehabilitation, and follow-up
LFP-Mono: Lightweight Self-Supervised Network Applying Monocular Depth Estimation to Low-Altitude Environment Scenarios
No full textFor UAVs, the industry currently relies on expensive sensors for obstacle avoidance. A significant challenge arises from the scarcity of high-quality depth estimation datasets tailored for low-altitude environments, which hinders the advancement of self-supervised learning methods in these settings. Furthermore, mainstream depth estimation models capable of achieving obstacle avoidance through image recognition are built upon convolutional neural networks or hybrid Transformers. Their high computational costs make deployment on resource-constrained edge devices challenging. While existing lightweight convolutional networks reduce parameter counts, they struggle to simultaneously capture essential features and fine details in complex scenes. In this work, we introduce LFP-Mono as a lightweight self-supervised monocular depth estimation network. In the paper, we will detail the Pooling Convolution Downsampling (PCD) module, Continuously Dilated and Weighted Convolution (CDWC) module, and Cross-level Feature Integration (CFI) module. All results show that LFP-Mono outperforms existing lightweight methods on the KITTI benchmark, and by evaluating with the Make3D dataset, show that our method generalizes outdoors. Finally, by training and testing on the Syndrone dataset, baseline work shows that LFP-Mono exceeds state-of-the-art methods for low-altitude drone performance
The Silenced Voices of Sanctity: Muteness as a Catalyst for Revelation in the Hagiographies of Saint Mechthild and Saint Gertrude
No full textThis essay explores how sanctity at Helfta was defined not by the perfection of song but by its interruption. The Book of Special Grace and the Herald of Divine Love praise Mechthild of Hackeborn and Gertrude the Great as singers of surpassing sweetness yet linger on the migraines, collapses, and illnesses that silenced their voices in the choir. These moments of suspension disclose muteness as more than absence: they reveal it as the paradoxical condition through which divine presence most fully resounds. Bringing sound studies into dialogue with disability studies, I argue that faltering breath, broken chant, and enforced silence function as theological and literary form. At Helfta, impairment itself becomes a hermeneutic structure, the hinge through which sanctity is revealed and narrative meaning is generated. In this framework, muteness operates as a form of narrative prosthesis—an interruption that both structures the hagiographical imagination and unsettles it by refusing cure or closure. By highlighting the fragility of voice as the very medium of divine disclosure, these texts testify that the sweetest music of Helfta lies not in unbroken chant but in silence transfigured into revelation
Energy Management Revolution in Unmanned Aerial Vehicles Using Deep Learning Approach
No full textUnmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks
Conventional and Intensified Steam Reforming of Bio-Oil for Renewable Hydrogen Production: Challenges and Future Perspectives
No full textThe increasing demand for clean and sustainable energy has driven significant research into hydrogen production from biomass-derived feedstocks. Unlike the gasification route, the pyrolysis of biomass followed by steam reforming of bio-oil (SRBO) offers several advantages, including the liquid nature of bio-oil and the operation at lower temperatures, which facilitate easier transportation and storage compared to raw biomass. The conventional SRBO process faces several limitations, mainly catalyst deactivation due to significant coke formation and metallic sintering, as well as low hydrogen yield and purity. Hence, the intensified sorption-enhanced steam reforming of bio-oil (SESRBO) is a promising strategy to overcome these drawbacks, to simultaneously produce high-purity hydrogen and capture carbon dioxide in situ from the reaction media. This critical review presents an in-depth comparative analysis of conventional and intensified steam reforming of bio-oil, with a focus on associated challenges. Special attention is given to recent developments in the design of bifunctional materials (BFMs), which integrate both catalyst and sorbent into a single particle, along with process optimization focusing on key parameters, i.e., reforming temperature and steam presence. Finally, the review highlights key research gaps and future directions to overcome existing challenges in achieving cost-effective and scalable hydrogen production
Some Bioactive Natural Products from Diatoms: Structures, Biosyntheses, Biological Roles, and Properties: 2015–2025
No full textRecently, as a result of growing interest in diatoms as sources of energy (biofuel) and valuable food components for humans and aquaculture organisms, new data on the structures and properties of diatom natural products have been obtained, including both endo- and exometabolites. Information about their biosynthesis, biological activity and roles, and their beneficial and hazardous properties has also emerged. The application of modern methods of molecular biology, metabolomics, and chemical ecology to the study of diatom natural products has improved the understanding of many important natural phenomena associated with diatoms, such as photosynthesis, harmful algal blooms, interactions of diatoms with other organisms of marine biota, and their impact on biogeochemical cycles and climate regulation. In this paper, we discuss various aspects of research on natural compounds from diatoms, covering the last decade, as well as prospects for their further development, which have become apparent in recent years
Fairness-Aware Face Presentation Attack Detection Using Local Binary Patterns: Bridging Skin Tone Bias in Biometric Systems
No full textWhile face recognition systems are increasingly deployed in critical domains, they remain vulnerable to presentation attacks and exhibit significant demographic bias, particularly affecting African populations. This paper presents a fairness-aware Presentation Attack Detection (PAD) system using Local Binary Patterns (LBPs) with novel ethnicity-aware processing techniques specifically designed for African contexts. Our approach introduces three key technical innovations: (1) adaptive preprocessing with differentiated Contrast-Limited Adaptive Histogram Equalization (CLAHE) parameters and gamma correction optimized for different skin tones, (2) group-specific decision threshold optimization using Equal Error Rate (EER) minimization for each ethnic group, and (3) three novel statistical methods for PAD fairness evaluation such as Coefficient of Variation analysis, McNemar’s significance testing, and bootstrap confidence intervals representing the first application of these techniques in Presentation Attack Detection. Comprehensive evaluation on the Chinese Academy of Sciences Institute of Automation-SURF Cross-ethnicity Face Anti-spoofing dataset (CASIA-SURF CeFA) dataset demonstrates significant bias reduction achievements: a 75.6% reduction in the accuracy gap between African and East Asian subjects (from 3.07% to 0.75%), elimination of statistically significant bias across all ethnic group comparisons, and strong overall performance, with 95.12% accuracy and 98.55% AUC. Our work establishes a comprehensive methodology for measuring and mitigating demographic bias in PAD systems while maintaining security effectiveness, contributing both technical innovations and statistical frameworks for inclusive biometric security research
Eco-Physiological and Molecular Roles of Zinc Oxide Nanoparticles (ZnO-NPs) in Mitigating Abiotic Stress: A Comprehensive Review
No full textMitigation of abiotic stress of crops is currently one of the primary issues for modern agriculture to secure food supply. On that point, it is acknowledged that climate change is leading to an increase in temperature and solar radiation, while also contributing to prolonged drought events. In contrast, saline soil and heavy metal pollution have been globally problematic, affecting a large part of crops. In this review, we have provided an overview of the eco-physiological and molecular aspects of zinc oxide nanoparticles (ZnO-NPs) as a novel technology for alleviating abiotic stress in plants. It is reported that the presence of ZnO-NPs has positive benefits in physiological processes, such as photosynthetic efficiency, osmotic regulation, ionic homeostasis, and the activation of antioxidant defense systems through gene modifications and the regulation of genes that are regulated under stress conditions. These are positive results for yields, nutrition, and resistance levels in cereals, legumes, and horticultural crops. Furthermore, essential details are reported, suggesting that the addition of ZnO-NPs to crops may be involved in regulating plant metabolism. Nonetheless, we recognize that this technology poses significant challenges for validation on a large scale, particularly in uncontrolled environments
Idea vs. Reality: Perspectives and Barriers to the Development of Community-Supported Agriculture in Poland
No full textThe study examines the theoretical and practical dimensions of Community-Supported Agriculture (CSA). Its objective is to assess whether social capital theory explains food producers’ engagement in CSA and whether this is reflected in practice. The research is based on a critical review of the relevant literature and on empirical investigations conducted in Poland among CSA producers using the CAWI method in 2024. The findings indicate that social capital theory plays a fundamental role in explaining the mechanisms underpinning CSA, with significant implications for the development of local food systems and for policies supporting small farms. This suggests the need for stronger institutional support aimed at enhancing trust and cooperation between food producers and consumers. Unfortunately, due to the low level of social capital in Poland, the CSA model remains only a niche complement to traditional forms of agriculture, functioning primarily as an alternative for a narrow group of socially and environmentally conscious consumers and small clusters of producers