Search Results (265)

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

Drought and related climate features (aridity, water balance) in Romania since 1961 are well documented, but studies spanning longer periods are limited and typically rely on modelled or sparse observational data. This study presents an analysis of drought, water balance and aridity in Romania over 123 years (1901–2023), using monthly data from 156 weather stations included in the RoCliHom dataset. Drought evolution is analyzed using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Aridity is examined with the De Martonne Aridity Index. The non-parametric Mann–Kendall test is used for trend detection, which allows a fair comparison with previous studies on drought and aridity in Romania. Trend magnitude is calculated with Sen’s slope estimator. Our results show a clear increase in evapotranspiration as a sign of climate warming over the country since the beginning of the 20th century. Annual precipitation amount presents no major changes. Water balance has decreased in July and August at 40% and 85% of the locations, respectively. During the growing season, drought has intensified within the last seven, six and five decades, but there are no significant changes over the full period of study in this respect. We found strong negative correlations between SPEI and North Atlantic Oscillation, Northern Annular Mode and Arctic Oscillation teleconnection indices. The evolution over the 123-year period shows that the drought episodes that occurred in recent decades are not without precedent in the long-term climatic context. Full article

Background: Community-based combined lifestyle interventions (CLIs) are used to help children with overweight or obesity achieve a healthier lifestyle. CLIs utilize the combined knowledge and expertise of professionals from a variety of disciplines. Here, we describe the composition of teams of professionals and their approach to collaboration in four community-based CLIs designed for children with overweight or obesity (focusing on children 4–12 years of age) living in the Netherlands. Methods: A descriptive cross-case comparison was conducted in which four community-based CLIs implemented in ten communities were conceptualized as “cases”. Quantitative data regarding the frequency of contact within the teams, topics addressed in meetings of the CLI teams, the perceived importance of other relevant disciplines in the team, and perceived satisfaction with the collaboration between professionals within the team were collected via questionnaires answered by the professionals (n = 82 respondents). Descriptive analyses including frequencies, percentages, and cross-case comparisons of team characteristics and collaboration were also conducted. Results: The CLI teams differed in composition, size, and background disciplines. The frequency of contact was higher in small teams (<6 professionals) compared to large teams. Larger teams appeared to report a lower perceived satisfaction regarding collaboration. Moreover, the role of coordinator or central healthcare provider was perceived as more important in the large teams than in the small teams. Conclusions: Variation was observed in professional expertise and collaboration within CLI teams. Moreover, professionals in a team should collaborate based on the local possibilities. In large teams (>6 professionals) in particular, a coordinator or trained central healthcare provider can help facilitate collaboration. Full article

Background: Accurate identification and positioning of thoracic devices on chest radiographs is critical for patient safety in intensive care. Multimodal large language models (LLMs) offer potentially generalizable automated evaluation, but their performance in this domain is underexplored. Methods: Three multimodal LLMs (GPT-4o, gpt-4o-2024-08-06; Gemini 3.1 Flash Lite Preview; Claude Sonnet 4.6) were evaluated on 4813 chest radiographs from the RANZCR CLiP dataset for device presence and positioning of ETT, NGT, CVC, and Swan–Ganz catheters. Performance was quantified with 95% Wilson confidence intervals, balanced accuracy, MCC, Cochran’s Q, Bonferroni-corrected McNemar, and Cohen’s/Fleiss’ kappa. Six additional analyses were performed: a blinded paired reader study (n = 377; two board-certified radiologists, blinded to ground truth and to all LLM outputs), external validation on PadChest (n = 200, device-presence detection only—PadChest lacks granular position labels), three-variant prompt-sensitivity analysis (n = 103), repeat-inference stability across three runs (n = 50), systematic error taxonomy, and a failure-case analysis. Results: Device-presence performance varied widely across models; abnormal-position sensitivity was uniformly poor (MCC ≤ 0.028; balanced accuracy 0.41–0.53). Inter-model agreement was poor to slight (Fleiss’ κ: 0.005–0.383 for presence; −0.280 to −0.025 for classification). Radiologists numerically outperformed all three LLMs in 42/42 paired comparisons; the superiority was statistically significant after Bonferroni correction in 33/42 (32/42 at p < 0.001). PadChest replicated the negative finding for device-presence detection (malposition not externally validated). Prompts and inference stochasticity introduced 2–3× sensitivity swings and run-to-run κ from 0.20 to 0.85. Case failures concentrated systematically in multi-device cases (p < 0.0001) but not in abnormal-position cases (p = 0.14). Conclusions: Current general-purpose multimodal LLMs are not yet reliable for autonomous thoracic-device assessment; their failure patterns are structurally characterizable across models, prompts, and case types and support, at most a circumscribed role, as adjunct device-presence screening tools. The findings do not generalize to purpose-built, regulator-approved clinical AI systems. Full article

The Model Context Protocol (MCP) has become the de facto standard for connecting large language models (LLMs) to external tools, and its remote deployment mode lets users add third-party servers with a single URL—shifting a substantial portion of the host’s attack surface to infrastructure operated by anonymous parties. Existing MCP security work has concentrated on tool-description poisoning and studied individual techniques in isolation, leaving it unclear what a malicious remote server can accomplish across its full surface. In this paper, we explore the malicious-server threat space along the axis of whether the host LLM participates in producing the harmful outcome, yielding two categories: LLM-passive attacks, which complete inside the server, and LLM-active attacks, which require the LLM to deliver the malicious content. We implement five scenarios spanning both categories—realizing each LLM-active scenario with both description-based and response-based variants against the same goal—and evaluate all configurations on ChatGPT, Claude Desktop, and Gemini CLI. We find that host-side filtering of MCP-bound data varies sharply across platforms (95% vs. 50% ASR on the same email request), that the description and response channels succeed on disjoint scenarios, and that successful attacks are almost never disclosed to the user. These findings suggest that defending remote MCP deployment requires a multi-layer approach combining host-side filtering, LLM-level response auditing, and user-visible output transparency. Full article

Because textured contact lenses obscure the iris’s natural texture, they pose a serious threat to the accuracy of iris recognition systems and may make identity theft possible. Therefore, this work proposes a reliable method for textured contact lens detection that uses efficient global texture descriptors and effective feature selection with classification techniques. Run-Length and Zernike Moments are effective global texture descriptors that have been extracted from preprocessed iris images that were acquired from the IIIT-D CLI dataset. To improve classification performance, Ant Colony Optimization (ACO) was used to decrease the dimensionality of the feature vectors. Support Vector Machine (SVM) and Logistic Regression (LOG), two classifiers, have been evaluated with different descriptor pairings. According to findings from experiments, Zernike features optimized by ACO and paired with LOG produced the greatest accuracy of 98.04%, greatly surpassing previous methods. The efficacy of the presented approach for safe and dependable iris-based biometric systems is demonstrated by its exceptional results with regard to accuracy, recall, precision, and F1-score. Full article

Software data is widely used to predict attributes of software systems; however, obtaining reliable datasets from commercial companies remains challenging due to confidentiality constraints. GitHub has emerged as a data source, offering access to diverse applications and development statistics. Nevertheless, concerns about the reliability and representativeness of public repositories persist. Star count is a widely accepted indicator of repository popularity, and existing studies mainly rely on time-dependent platform metrics. In this study, we propose using static software metrics extracted from source code, along with GitHub statistics. To our knowledge, this study is among the first to use ELM for popularity prediction with static software metrics. Repositories from different application domains are selected to ensure dataset diversity and representativeness. An automated tool has been developed to collect data via the GitHub API and SourceMonitor CLI. In addition, several baseline machine learning models, including LR, SVM, RF, and LSBoost, are evaluated for comparison. Experimental results show that ELM achieves competitive performance across datasets. In terms of R² scores, ELM performs best in four datasets, RF in three, and LR in one. These results indicate that ELM is an effective method for popularity prediction and highlight the potential of incorporating static software metrics into GitHub-based predictive modeling. Full article

Background: Aging, especially after menopause, reduces the quantity and function of adult stem cells. Estrogen deficiency impairs proliferation, differentiation, and regenerative capacity. This study evaluated whether estrogen enhances endothelial differentiation of adipose-derived stromal cells (ADSCs) and improves therapeutic efficacy in critical limb ischemia (CLI). Methods: Male-derived ADSCs were assessed in vitro for endothelial differentiation using flow cytometry, biochemical assays, and angiogenesis analyses. Therapeutic effects were tested in a rat CLI model using endothelial-differentiated ADSCs (ED-ADSCs) with or without 17β-estradiol (E2). An ovariectomized (OVX) model examined estrogen deficiency and supplementation in vivo. Results: E2 promoted endothelial differentiation, increasing ERα/ERβ expression and activating PI3K/Akt/eNOS and MAPK signaling. This led to elevated VEGF expression, enhanced tube formation, and increased CD34⁺, KDR⁺, and CD31⁺ cell populations. In vivo, E2-pretreated ED-ADSCs significantly improved blood flow recovery. Estrogen deficiency reduced endothelial progenitor populations, which were restored by E2 supplementation. Conclusions: Estrogen modulates endothelial-associated characteristics and angiogenesis-related responses of ADSCs via ER-associated signaling, and may contribute to improved functional outcomes in ischemic conditions. E2 preconditioning may represent a potential strategy for stem cell-based therapy in estrogen-deficient settings. Full article

Human performance in critical environments is frequently degraded by mistimed communication delivered during periods of visual–cognitive saturation. In such settings, failures arise not only from individual limitations but also from poor coordination between operators under rapidly changing workload conditions. We present a dual-loop neuro-adaptive simulation framework based on real-time spectral–topographic EEG representations, in which multichannel cortical activity is transformed into dynamic spatial maps and decoded to regulate both operator assistance and team communication. The system integrates 14-channel wireless EEG (Emotiv EPOC X, 256 Hz), gaze tracking, telemetry, and communication events through an LSL-based multimodal synchronization pipeline. A hybrid CNN–LSTM model processes sequences of spectral-topographic EEG maps to classify three operationally actionable neurocognitive states—Channelized Attention, Diverted Attention, and Surprise/Startle—while also estimating a continuous Cognitive Load Index (CLI). These representation-derived features are then used by a multi-agent proximal policy optimization (MAPPO) controller to generate two coordinated outputs: (i) adaptive haptic guidance for the pilot, designed to reduce reliance on overloaded visual and auditory channels, and (ii) a traffic-light communication gate for the telemetry engineer, regulating whether radio intervention should proceed, be delayed, or be withheld. In a high-fidelity dual-station simulation with 25 pilot–engineer pairs, the proposed framework was associated with a reduction of more than 30% in communication breakdown errors relative to open-loop telemetry, with the strongest effects observed during peak-load windows, while preserving realistic task progression. It also improved pilot reaction time to time-critical warnings and reduced engineer decision load under the tested conditions. These findings support the use of spectral-topographic EEG representations as a practical basis for combining multimodal neurophysiological sensing, spatiotemporal pattern decoding, and adaptive coordination in high-pressure human–machine teams. At the same time, the study should be interpreted as evidence of controlled feasibility in a simulated setting rather than as definitive proof of field-level generalization. We further discuss deployment constraints and propose privacy-by-design safeguards to ensure that neurocognitive signals are used exclusively for operational adaptation rather than employability assessment or performance scoring. Full article

Miscanthus lutarioriparius exhibits strong potential for cadmium (Cd) accumulation, making it a promising candidate for the phytoremediation of Cd-contaminated soils. However, its full remediation potential remains underexploited, highlighting the need for targeted genetic improvement This study presents a comprehensive genome-wide identification and systematic characterization of 20 Ml4CL (4-coumarate: CoA ligase genes) in the M. lutarioriparius. Results indicate that the Ml4CL gene family has undergone substantial evolutionary divergence and expansion. Phylogenetic classification is highly consistent with gene structures ad conserved motifs suggesting potential functional diversification. Promoter analysis revealed a complex cis-regulatory landscape enriched in n ABA- and light-responsive elements, frequently co-occuring with hormone-responsive elements associated with jasmonic acid (JA), gibberellins (GAs), salicylic acid (SA), and strigolactones (SLs) signaling. This pattern suggests that the Ml4CL family may function as an integrative regulatory node linking multiple stress and hormonal signaling pathways. Importantly, under Cd stress, Ml4CL genes exhibited diverse expression dynamics, including gene-specific repression and dose-dependent biphasic responses. Notably, Ml4CL4 showed strong repression, while other members displayed “induction-then-repression” or “repression-then-induction” patterns, suggesting a staged or hierarichical transcriptional response. These findings further suggest that Cd-responsive signaling networks may involve non-linear or threshold-dependent mechanismsthat activate distinct transcriptional programs depending on stress levels. Collectively, this study highlights the regulatory role of the Ml4CL family in plant adaptation to complex environments and identifies candidate dose-resonsive regulatory elements and key allelic variations. These findings provide valuable targets for molecular breeding and synthetic biology aimed at improving crop stress resilience. Full article

High-resolution Köppen–Geiger projections indicate that several cold desert (BWk) regions are likely to transition toward hot desert (BWh) regimes during the twenty-first century, challenging the environmental logic of vernacular architecture. Despite extensive simulation-based research on passive cooling in established BWh contexts, limited attention has been given to climate-type transition zones and to the morphological continuity of traditional housing systems. This study investigates the adaptive capacity of Bukhara’s courtyard houses under projected BWk–BWh reclassification. Employing an analytical generalization approach, the research integrates systematic literature mapping, typological morphological analysis, and a threshold-based compatibility matrix. Findings reveal that climate transition produces a form of morphological discontinuity by weakening diurnal discharge assumptions embedded in high thermal mass systems. However, courtyard typologies retain a resilient passive core when recalibrated through microclimatic amplification strategies. The proposed staged adaptation framework contributes a heritage-sensitive decision model that reconciles climatic performance with spatial integrity, offering transferable guidance for cli-mate-intensifying desert regions. Full article

Lithium sulfide (Li₂S) is indispensable for lithium–sulfur batteries and sulfide solid-state batteries. However, its high preparation cost and strict process conditions represent core bottlenecks restricting large-scale commercial application. To address this issue, a novel process featuring a low-cost, high-safety, and controllable reaction is proposed in this work. Compared with the commercial H₂S-based route for Li₂S production, the developed process presents distinct advantages, including accessible raw materials, high safety, low overall cost, and low environmental load. Using Li₂SO₄·H₂O as the raw material and Ti as the reducing agent, high-purity T-Li₂S (>99.9%) is successfully synthesized via solid-state sintering and purification, yielding a higher purity level than that of commercial C-Li₂S (>99.7%). Furthermore, sulfide all-solid-state electrolytes T-Li_5.3PS_4.3ClBr_0.7 and C-Li_5.3PS_4.3ClBr_0.7 are prepared using the as-obtained T-Li₂S and commercial C-Li₂S as precursors, respectively. The room-temperature Li-ion conductivities are determined to be 14.5 mS/cm and 11.0 mS/cm, revealing faster ion migration and efficient ion transport in T-Li_5.3PS_4.3ClBr_0.7 without high-temperature assistance, which fully validates the feasibility of the proposed strategy. Overall, this work provides a new technical route for the preparation of high-purity Li₂S, showing promising application prospects. Full article

The use of excimer laser atherectomy (ELA) has significantly evolved from the mid-1990s to the present, showing substantial improvements in both coronary and peripheral artery interventions. Initially associated with suboptimal outcomes due to low-energy settings and limited techniques, advancements such as high-energy delivery, improved catheter designs, contrast injection protocols, and refined procedural approaches have greatly enhanced clinical efficacy. In coronary applications, ELA has become an established technique for treating intracoronary thrombus, under-expanded stents, in-stent restenosis, and heavily calcified lesions, offering favorable procedural and clinical outcomes with low complication rates. The excimer laser operates through photochemical, photothermal, and photomechanical mechanisms, enabling precise plaque ablation with minimal collateral damage. In peripheral interventions, especially in critical limb ischemia (CLI), ELA has emerged as a viable option for complex, non-crossable lesions and in-stent restenosis, demonstrating high technical success, improved patency, and promising limb salvage rates. Multiple clinical trials and registries support the safety and effectiveness of ELA, particularly in high-risk patient populations. This narrative review summarizes current evidence and practical considerations on the use of excimer laser atherectomy in coronary and peripheral interventions. Full article

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

Background/Objectives: Although triple antibiotic paste is effective in managing infected primary teeth, its incomplete removability from tooth structure remains a major limitation, prompting the search for alternative drug-delivery systems. The aim of this study was to obtain a multi-antibiotic porous composite system for tailored drug delivery, to develop a formulation strategy, and to characterize the physicochemical properties and drug release. Methods: The developed composites consisted of a porous composite matrix (PCM; chitosan/bioactive filler) and two or three antibiotics (ciprofloxacin [CIP], metronidazole [MET], clindamycin [CLI]). Three methods of incorporating antibiotics were used: applying an antibiotic solution to the stabilized PCM; introducing an antibiotic solution into the polymer matrix; and introducing an antibiotic into the polymer matrix as nanoparticles. The physicochemical properties of the composites, including microstructure, compressive strength, and swelling, were assessed. The antibiotic release profile was assessed for up to 168 h. Results: The most advantageous method for introducing MET and CLI, in terms of release profile, was applying them to the PCM surface, whereas ciprofloxacin exhibited stable release when incorporated directly into the polymer matrix and entrapped during the stabilization process. The composites with nanoparticles, including MET or CIP, did not release any active substances during the experimental period. Conclusions: The results demonstrate that the developed formulation strategy enables the production of composites that rapidly release substantial amounts of the active substances within a short time frame and maintain their concentration for an extended period, which may be beneficial for the treatment of bacterial infections. Full article