Search Results (95)

As robotics prehension systems and virtual reality applications are in constant evolution, the need for high-fidelity haptic interaction increases. This helps ensure and enhance user immersion and handling precision. While commercial haptic interfaces offer high performance, their prohibitive cost limits their widespread adoption in general-purpose robotics. Furthermore, many low-cost solutions suffer from limited transparency, where the operator constantly fights the friction of the actuator even during free motion. This article presents the design and development of an innovative, cost-effective master–slave robotic system aimed at democratizing efficient haptic feedback devices. The solution is intended for remote manipulation of objects with a maximum mass of 1 kg, while limiting the gripping force to 50 N, thus ensuring the integrity of objects being manipulated. The device includes a master haptic module in the form of a clamp that reproduces the thumb–index–middle finger gripping motion performed by the user. The system relies on a custom haptic interface measuring the angular position of the master gripper, which is transmitted in real time to the slave gripper, so as to adjust the position of the clamp accordingly, thus optimizing the grasping control loop. As soon as an object is detected, using a force sensor integrated into the slave gripper, the master motor renders a resistive force, preventing the user from closing the haptic module. The other part of the system is the slave mechanical gripper with three fingers, each with three phalanges based on human anatomy, allowing the clamp to mechanically conform to irregular object geometries with a single actuator. The last but not least innovative aspect lies in the implementation of a current sensor, which provides the haptic feedback. The force applied by the user is reproduced by the slave gripper using current sensors, eliminating the need for expensive force-torque sensors while maintaining a responsive feedback loop. Full article

Water contamination by synthetic dyes such as malachite green (MG) remains a significant environmental and public health challenge due to their high toxicity, chemical stability, and resistance to biodegradation. In this study, a CaO-CuFe₂O₄ composite was synthesized through a sustainable route using eggshells and orange peel as agro-industrial waste precursors. Comprehensive structural, spectroscopic and microscopic analyses confirmed the coexistence of a predominant CaO-based phase with spinel CuFe₂O₄, together with nanometric features, satisfactory elemental dispersion and practical magnetic recoverability. Under the experimental conditions employed, the composite exhibited high adsorption performance towards MG, reaching an equilibrium capacity of 2288.4 mg g⁻¹ and 99.98% decolorization within 60 min. The kinetics were better described by the pseudo-second-order model, while the equilibrium behavior was more satisfactorily fitted by the Langmuir isotherm than by the Freundlich model. Thermodynamic analysis indicated that the adsorption process was favorable over the temperature range studied and became more pronounced at higher temperature. The results suggest that the adsorption behavior arises from the combined influence of surface chemistry, calcium-derived basic sites, ferrite-associated metal centers and interfacial accessibility, rather than from surface area alone. In addition, the material could be readily separated from aqueous solution using an external magnetic field, highlighting its practical post-treatment recoverability. Overall, this work demonstrates a viable waste valorization strategy for the development of a magnetically recoverable CaO-CuFe₂O₄ adsorbent for cationic dye removal. Beyond the specific case of MG, the study underscores the potential of agro-waste-derived hybrid oxides as application-relevant materials for water remediation. Full article

In this work, we report the synthesis and evaluation of a bioink based on marine collagen, chitosan, and silica-doped hydroxyapatite (HA–SiO₂) for extrusion-based 3D bioprinting. FTIR spectroscopy confirmed amide (I–III) and phosphate/siloxane signals, TGA showed initial dehydration and degradation stages compatible with the process’s thermal handling, and SEM revealed an interconnected porous microstructure. Rheologically, the ink exhibited elastic dominance (G′ > G″) within the linear range and pseudoplastic, shear-thinning behavior—consistent with pneumatic extrusion. Process evaluation on a BIO X printer (14 G nozzle, low print speeds, moderate pressure, cartridge at 37 °C to 45 °C, and a cooled build platform) enabled deposition of strands with local shape retention. However, filament continuity was limited and line width varied, indicating only preliminary printability and a narrow operating window. Overall, physicochemical, microstructural, and rheological evidence supports the formulation’s viability as a starting point for scaffold fabrication. Full article

Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has emerged due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other traditional cloud-native orchestration systems are not built to facilitate autonomous, decentralized decision-making across the computing continuum or to seamlessly integrate non-container-native devices. This paper presents the Distributed Adaptive Cloud Continuum Architecture (DACCA), a Kubernetes-native architecture that extends orchestration beyond the data center to encompass edge and Internet of Things infrastructures. Decentralized self-awareness and swarm formation are supported for adaptive and resilient operation, a resource and application abstraction layer is established for uniform resource representation, and a Distributed and Adaptive Resource Optimization (DARO) framework based on multi-agent reinforcement learning is integrated for intelligent scheduling in the proposed architecture. Verifiable identity, access control, and tamper-proof data exchange across heterogeneous domains are further ensured by a zero-trust security framework based on distributed ledger technology. When combined, these elements enable increasingly autonomous workload orchestration, trading centralized control for adaptive, decentralized operation with enhanced interoperability, scalability, and trust. Thus, the proposed architecture enables self-managing and context-aware orchestration systems that support next-generation AI-driven distributed applications across the entire computing continuum. Full article

Rapid removal of chemically diverse organic pollutants remains a major challenge in aqueous decontamination. In this study, atmosphere-controlled defect engineering was used to activate anatase TiO₂ as a rapid adsorbent operating on the minute scale, exhibiting low charge selectivity under the investigated conditions. A reduced black TiO₂ (B–TiO₂), produced by inert annealing, achieved ≈100% removal of cationic methylene blue within ~6 min and ≈91% uptake of anionic methyl orange within ~3 min, whereas pristine and air-annealed TiO₂ showed only marginal adsorption under identical conditions. Correlative structural and surface-sensitive analyses indicated that this behaviour was associated with a chemically activated near-surface region enriched in reduced titanium contributions, defect-associated or non-lattice oxygen environments and a locally perturbed anatase framework, together with finely dispersed carbon-related motifs integrated within the oxide matrix. Adsorption kinetics were described, within experimental resolution, by pseudo-second-order fitting, while intraparticle diffusion analysis supported sequential regimes initiated by rapid interfacial attachment. Equilibrium analysis yielded apparent maximum capacities of 6.116 mg g⁻¹ for methylene blue and 2.950 mg g⁻¹ for methyl orange, reflecting adsorption governed by surface heterogeneity for cationic species and an apparent saturation-type response for anionic uptake. Overall, controlled surface non-stoichiometry emerges as a viable strategy to enhance adsorption kinetics in TiO₂, providing a transferable design framework for developing oxide-based adsorbents for sustainable water-treatment applications. Full article

Introduction: Ultrasound elastography is increasingly used across medical imaging, yet its role in thoracic disease remains poorly defined. While both transthoracic ultrasonography (TUS) and endobronchial ultrasound (EBUS) offer real-time assessment of pleural and pulmonary structures, the diagnostic and clinical value of elastography in this context remains uncertain. Materials and Method: A systematic search of MEDLINE, EMBASE, and the Cochrane Library was conducted according to PRISMA guidelines (April 2023; updated January 2025). Original studies evaluating transthoracic or endobronchial elastography for pleural or pulmonary conditions were included. Data extraction and quality assessment were performed independently by three reviewers, with QUADAS-2 used to evaluate risk of bias. Results: Thirty studies met inclusion criteria. Twenty-eight evaluated TUS elastography and two examined EBUS. Shear wave elastography was most frequently applied, particularly for differentiating malignant from benign pleural effusion or subpleural lesions. Surface wave elastography demonstrated consistently higher stiffness values in patients with interstitial lung disease compared with healthy controls, correlating with radiological and functional disease severity. Elastography-guided pleural biopsy improved diagnostic yield compared with conventional ultrasound-guided biopsy. Overall, substantial methodological variation existed among scanning techniques, elastography modalities, reporting methods, and diagnostic thresholds, limiting cross-study comparison. Conclusions: Ultrasound elastography shows promise for evaluating pleural effusion and pulmonary lesions, procedural guidance, and interstitial lung disease possibly improving diagnostic possibilities with bedside evaluation and reducing patient exposure to radiation. However, methodological variation and limited high-quality evidence preclude clinical implementation. Standardized acquisition protocols and multicentre validation studies are necessary to define its diagnostic utility in thoracic imaging. Full article

Reliable detection of unknown signals is essential for ensuring the robustness of underwater acoustic sensing systems, particularly in maritime security and autonomous navigation. However, Conventional deep learning models often exhibit overconfidence when encountering unknown signals and are unable to quantify predictive uncertainty due to their deterministic inference process. To address these limitations, this study proposes a novelty detection framework that integrates an out-of-distribution detector for neural networks (ODIN) with Monte Carlo (MC) dropout. ODIN mitigates model overconfidence and enhances the separability between known and unknown signals through softmax probability calibration, while MC dropout introduces stochasticity via multiple forward passes to estimate predictive uncertainty—an element critical for stable sensing in real-world underwater environments. The resulting probabilistic outputs are modeled using Gaussian mixture models fitted to ODIN-calibrated softmax distributions of known classes. The Kullback–Leibler divergence is then employed to quantify deviations of test samples from known class behavior. Experimental evaluations on the DeepShip dataset demonstrate that the proposed method achieves, on average, a 9.5% and 5.39% increase in area under the receiver operating characteristic curve, and a 7.82% and 2.63% reduction in false positive rate at 95% true positive rate, compared to the MC dropout and ODIN baseline, respectively. These results confirm that integrating stochastic inference with ODIN significantly enhances the stability and reliability of novelty detection in underwater acoustic environments. Full article

Understanding the economic and psychological values that people assign to threatened species is crucial for their effective protection. The long-tailed macaque (Macaca fascicularis) is an endangered primate currently threatened by habitat destruction, removal from the wild for scientific, commercial, and recreational purposes, and culling due to conflicts with local communities. We conducted on-site interviews with Maguindanao residents in the Philippines (n = 500) to explore the conservation value of the long-tailed macaque and to assess how cognition, emotion, and folklore influence willingness to pay (WTP). Participants showed pro-conservation attitudes and positive emotions toward the long-tailed macaque, had substantial knowledge about their behavior and biology, but did not believe in folklore traditions. An average annual WTP of PHP 46.9 was estimated for macaque conservation, amounting to PHP 10.5 million annually, based on the number of households in the study area. Conservation attitudes, emotions, and biological knowledge about the long-tailed macaque were positively linked to WTP for its preservation. Conversely, dominionistic worldviews were negatively associated with WTP. Younger participants, females, and those with higher incomes demonstrated a higher WTP than older participants, males, and those with lower incomes. These findings can aid in designing and implementing outreach campaigns to raise funds and educate local communities, thereby further improving their attitudes toward this endangered primate. Full article

We present the first study into the long-term effects of radiation on an Event-based Vision Sensor (EVS) using real-world data from orbit. Falcon Neuro is an experimental, first-of-its-kind payload attached to the exterior of the International Space Station (ISS) operating two DAVIS 240C Event-based Vision Sensors. This study considers data gathered by Falcon Neuro between January 2022 and September 2024 over a wide range of scenes from Earth-facing and space-facing sensors. Falcon Neuro contains the first working EVS system in orbit. While EVS radiation degradation has been studied on the ground, this is the first study of degradation for EVS cameras of any kind in a real, uncontrolled environment. EVS pixel circuits are unique, analog, and far more complex than CMOS or CCD cameras. By utilizing distinct and unique features in the data created by the different pixel circuits in the camera, we show that degradation effects over the life of the mission caused by radiation or other sources have been minimal, with only one of the 18 measures displaying a convincing deterioration trend. Ultimately, we demonstrate that DAVIS 240C Event-based Vision Sensors have a high aptitude for surviving long-term space flight. Full article

Multimodal remote sensing object detection has gained increasing attention due to its ability to leverage complementary information from different sensing modalities, particularly visible (RGB) and thermal infrared (TIR) imagery. However, existing methods typically depend on deep, computationally intensive backbones and complex fusion strategies, limiting their suitability for real-time applications. To address these challenges, we propose a lightweight and efficient detection framework named RGB-TIR Multimodal Fusion Network (RTMF-Net), which introduces innovations in both the backbone architecture and fusion mechanism. Specifically, RTMF-Net adopts a dual-stream structure with modality-specific enhancement modules tailored for the characteristics of RGB and TIR data. The visible-light branch integrates a Convolutional Enhancement Fusion Block (CEFBlock) to improve multi-scale semantic representation with low computational overhead, while the thermal branch employs a Dual-Laplacian Enhancement Block (DLEBlock) to enhance frequency-domain structural features and weak texture cues. To further improve cross-modal feature interaction, a Weighted Denoising Fusion Module is designed, incorporating an Enhanced Fusion Attention (EFA) attention mechanism that adaptively suppresses redundant information and emphasizes salient object regions. Additionally, a Shape-Aware Intersection over Union (SA-IoU) loss function is proposed to improve localization robustness by introducing an aspect ratio penalty into the traditional IoU metric. Extensive experiments conducted on the ODinMJ and LLVIP multimodal datasets demonstrate that RTMF-Net achieves competitive performance, with mean Average Precision (mAP) scores of 98.7% and 95.7%, respectively, while maintaining a lightweight structure of only 4.3M parameters and 11.6 GFLOPs. These results confirm the effectiveness of RTMF-Net in achieving a favorable balance between accuracy and efficiency, making it well-suited for real-time remote sensing applications. Full article

Seaweeds represent a vital yet often understudied component of the diet and cultural heritage of many coastal communities globally. This study investigated seaweed consumption practices in coastal communities of Tawi-Tawi, Philippines, through one-to-one interviews (n = 280) and focus group discussions (n = 7). The study revealed that nearly all (99%) of the population consumes seaweeds, with women comprising the majority of consumers who have done so since childhood (68% female vs. 32% male). These consumers were predominantly married (79%), within the 21–40 age group (53%), with families of 5–7 members (43%), practicing Islam (97%), and belonging to the Sama tribe (71%). A significant portion (48%) had resided in the area for 21–30 years, attained elementary to high school education (66%), and had a monthly income ranging from 1000 to 10,000 Philippine pesos (72%). Seaweed consumption was a family-wide practice (88%), including children, who typically started around 4–8 years old (61%), driven by perceived nutritional benefits (43%), preferred flavor (80%), affordability (19%), ease of preparation (33%), and cultural integration (23%). The primary edible seaweeds identified were Kappaphycus alvarezii (63%), K. striatus (58%), Kappaphycus spp. (47%), Eucheuma denticulatum (57%), Caulerpa lentillifera (64%), Caulerpa spp. (51%), C. cf. macrodisca ecad corynephora (45%), C. racemosa (30%), and Solieria robusta (49%), with less frequent consumption of K. malesianus (8%), Chaetomorpha crassa (3%), Gracilaria spp. (0.72%), and Hydroclathrus clathratus (0.36%). Specific plant parts were preferred for certain species, and preparation predominantly involved raw (75%) or cooked (77%) salads with spices, primarily prepared by mothers (72%). Consumers generally avoided seaweeds showing signs of ice-ice disease (95%), pale coloration (91%), or epiphyte infestation (84%). Consumption frequency was typically 1–3 times per week (45%), with knowledge largely passed down through generations (95%). Seaweed salads were primarily consumed as a viand (92%) at home (97%), with locals perceiving seaweed consumption as contributing to a healthy diet (40%) and overall well-being [e.g., aiding hunger (76%), improving digestion (20%), preventing obesity (14%), and aiding brain development (3%)]. The study’s findings emphasize the significant yet often overlooked role of seaweeds in the food systems and cultural heritage of Tawi-Tawi’s coastal communities. Future efforts should prioritize the sustainable management of wild resources, explore the cultivation of diverse edible species, and enhance nutritional awareness. Further research into traditional seaweed knowledge holds broader value. Full article

Background/Objectives: Eating disorders (EDs) are associated with cognitive inefficiencies related to cognitive flexibility, central coherence, and inhibition. Transdiagnostic cognitive remediation therapy (TCRT) is a new adaption of cognitive remediation therapy aimed at addressing these difficulties across ED diagnoses. This study investigates the effects of TCRT as an adjunctive treatment for patients with EDs on cognitive and clinical outcomes. Methods: A randomized controlled trial compared the effect of 9 individual sessions of TCRT in conjunction with treatment as usual (TAU) compared to TAU only for patients with EDs and concurrent cognitive difficulties. Participants were assessed at baseline, post-treatment (12 weeks after baseline), and follow-up (6 months after post-treatment assessment). The outcome measures included neuropsychological tests and self-report questionnaires measuring cognitive difficulties and ED psychopathology. The analysis was in accordance with intention to treat principles. Results: Sixty patients with various ED diagnosis and concurrent cognitive difficulties were recruited. The TCRT group (n = 30) displayed significantly greater improvement in self-reported executive functioning, measured by the Behavior Rating Inventory of Executive Function—Adult version compared to the control group (n = 30). However, no superiority of TCRT was observed on performance-based measures of set shifting, central coherence, or inhibition. Moreover, there was no significant difference in improvement in self-reported ED psychopathology. Conclusions: TCRT may enhance compensatory mechanisms for cognitive inefficiencies rather than improve cognitive effectiveness or ED symptoms directly for patients with EDs and concurrent cognitive difficulties. Further investigation of how these impact everyday functioning may provide valuable insights into TCRT’s role in ED treatment. Full article

Introduction: Ensuring vehicular cybersecurity is a critical challenge due to the increasing connectivity of modern vehicles, and traditional centralised learning approaches for intrusion detection pose significant privacy risks, as they require sensitive data to be shared from multiple vehicles to a central server. Objective: The aim of this study is therefore to develop an in-vehicle intrusion detection system (IVIDS) that integrates federated learning (FL) with neural networks, enabling decentralised and privacy-preserving detection of cyberattacks in vehicular networks. The proposed system extends previous research by detecting a broader range of attacks (eight types) and exploring different deep learning architectures. Methods: This study employs an extended version of the publicly available VeReMi dataset to train and evaluate multiple neural network architectures, including Multilayer Perceptrons (MLPs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks. Federated learning is utilised to enable collaborative model training across multiple vehicles without sharing raw data. Various data preprocessing techniques and differential privacy mechanisms are also explored. Results and Conclusions: The experimental results demonstrate that LSTM networks outperform both MLP and GRU architectures in classifying vehicular cyberattacks. The best LSTM model, trained with two previous message lags and standard normalisation, achieved a classification accuracy of 96.75% in detecting eight types of attacks, surpassing previous studies, and demonstrating the potential of applying neural networks designed to work with time series data. Full article

Background: Vitamin K is a cofactor necessary for the biological activity of proteins like Matrix Gla Protein (MGP), which reduce calcification and help preserve lung function. This study aims to determine, first, whether low vitamin K status is associated with chronic obstructive pulmonary disease (COPD), and secondary, whether the level of vitamin K is associated with COPD severity, smoking exposure, or mortality. Methods: The plasma concentration of dephosphorylated uncarboxylated (dp-uc) MGP was used as an inverse biomarker for vitamin K in 98 COPD patients from the CODEX-P COPD study and 986 controls from the DanFunD study. Low vitamin K status was defined as the upper quartile of dp-ucMGP (>589 pmol/L). Using a logistic regression model, we examined whether low vs. high/moderate vitamin K status increased the odds ratio (OR) of having COPD. Secondary analyses, in the COPD cohort only, examined the association between low vitamin K status and COPD severity, smoking exposure in packyears and all-cause mortality, using a Welch’s t-test and log-rank test, respectively. Results: Low vitamin K status was associated with increased odds of having COPD, OR 9.7 (95% CI [5.5 to 17.5], p < 0.001). We found no associations between low vitamin K and COPD severity (est. −0.03, p = 0.7; 95% CI [−0.2 to 0.1]), smoking exposure (p = 0.7), or all-cause mortality (p = 0.5). Conclusions: Low vitamin K status was associated with substantially higher odds of having COPD compared to high/moderate vitamin K status. No association was found between low vitamin K status and COPD severity, smoking exposure, or all-cause mortality. Further studies are needed to determine if vitamin K plays a role in the pathophysiology of COPD and whether supplement therapy is indicated. Full article

Speaker verification is a common issue that has enumerable biomedical security applications. Speaker verification comes in two different forms: text-independent and text-dependent. Each of these forms can be implemented via many different machine learning and deep learning techniques. From our research, we found that there is significantly less work implementing text-independent speaker verification using machine learning techniques than there is using deep learning techniques. Because of this gap, we were motivated to build our own SVM and CNN model for text-independent speaker verification and compare them to other systems using SVMs or deep learning techniques. We limited ourselves to SVMs because they are commonly used for speech recognition and achieved very high accuracies. The main motivation behind this was two-fold. The first reason is to demonstrate that SVMs can and have been successfully used for text-independent speaker verification at a level comparable to deep learning techniques; the second reason is to make work using SVMs for text-independent speaker verification more accessible so it can be expanded upon easily. The analysis and comparison conducted in this paper will demonstrate how SVMs achieve results comparable to deep learning techniques and allow future researchers to more easily find SVMs used for text-independent speaker verification and derive a sense of what is being implemented in the field. Full article