Search Results (48,680)

Parameter uncertainties and fluctuating disturbances have posed significant challenges to the smooth and precise control of Flexible Joint Robots (FJRs) in industrial environments. To mitigate such disturbances, Disturbance Observers (DOBs) are commonly employed; however, the model uncertainties inherent in FJR systems make accurate dynamic modeling challenging, and the efficacy of DOBs hinges heavily on the accuracy of the dynamic model, which limits their applicability to FJR control. This paper presents a hybrid RBFNN-based Disturbance Observer (RBFNNDOB) state feedback controller for FJRs. By combining a nominal model-based DOB with an RBFNN, this method effectively addresses the unknown dynamics of FJRs while simultaneously compensating for external time-varying disturbances. In this framework, an adaptive neural network weight update law is formulated using Lyapunov stability theory. This enables the RBFNN to selectively estimate the unmodeled uncertainties in FJR dynamics, thereby minimizing computational redundancy in model estimation while allowing dynamic compensation for residual uncertainties beyond the nominal model and DOB estimation errors—ultimately enhancing computational efficiency and achieving robust compensation for rapidly changing disturbances. The boundedness of the tracking error is proven using the Lyapunov approach, and experimental validation is conducted on the FJR system to confirm the efficacy of the proposed control method. Full article

To address the limitations of conventional magnetically coupled resonant wireless power transfer (MCR-WPT) systems in multi-frequency multi-load applications—specifically inadequate load power independence and high complexity inconstant-voltage/constant-current (CV/CC) control—this paper proposes a variable-structure dual-frequency dual-load wireless power transfer system by first establishing its mathematical model and implementing hybrid-frequency modulation for multi-frequency output, then developing an improved T/LCC hybrid resonant topology by deriving parameter design conditions for compensation network reconfiguration under CV/CC requirements, subsequently employing an orthogonal planar solenoid coupling mechanism and frequency-division demodulation to achieve load-independent power regulation across wide load ranges for enhanced stability, and finally constructing a 120 W dual-frequency dual-load prototype to validate the system’s CV/CC characteristics, where simulations and experimental results demonstrate stronger consistency with theoretical predictions. Full article

Background: The depressed population needs to be treated and they do not have access to evidenced-based psychological practices (EBPPs). The consequences lead to significant daily impairments and huge economical costs. A large amount of research has focused on the demand for a more extensive use of EBPPs. However, despite these practices being essential to the mental health system, EBPPs are poorly applied in clinical settings. This situation has led to the development of Implementation Research (IR), a scientific field that aims to address the challenge of translation and identify the factors involved in the implementation process. Several implementation studies have been carried out in the field of health. However, the evidence from implementation studies of psychological treatments addressing depression has not yet been summarized. The aim of this study is to conduct a systematic review to assess implementation studies that use EBPPs to address depression. Methods: A systematic review was conducted following the PRISMA guidelines, including implementation studies that applied EBPPs to address depressive disorders. The following databases were used: PubMed, Embase, APA PsycInfo, Cochrane Central, Scopus, and Web of Science. Two independent reviewers revised the studies to determine whether the eligibility criteria were met. Results: A total of 8797 studies were identified through database searches. After removing duplicates, a total of 3757 studies were screened based on titles and abstracts. Finally, 127 full-text articles were reviewed, yielding 31 studies that satisfied the inclusion criteria. Conclusions: This review offers valuable insights into the current state of IR in the implementation of EBPPs for treating depressive disorders. It underlines the necessity for a standardized nomenclature for study designs within the realm of IR and emphasizes the potential of hybrid efficacy–implementation studies to help close the gap between research and clinical practice. Despite the challenges encountered, this review points to a positive outlook for the use of IR in clinical psychology. A gradual adoption of IR is likely to strengthen its role in psychology and support the development of more effective strategies for implementing evidence-based interventions in clinical settings. Full article

The issue of in-orbit optimal safe surrounding control for service satellite (SSat) formation against a huge unknown target satellite (TSat) with specific structures is solved by using relative measurements only, and an optimal cooperative safe surrounding (OCSS) hybrid controller achieving both target tracking (TT) and configuration tracking (CT) is proposed corresponding to the two equal sub-objectives. Facing the challenges caused by incomplete information of the TSat, by using relative measurements only, the initial-condition-free boundaries are constructed by an arctan-based state transformation to directly constrain the target tracking error to perform prescribed transient and steady-state behaviors. Based on the shared TT control law, optimal collision-free CT controllers for all SSats are further solved via a nonzero-sum differential game, where the collision threat from all SSats and target structures are modeled by a novel circumscribed-sphere model. Finally, the effectiveness and advantages of the proposed OCSS control technique is verified by simulation results. Full article

This study evaluates the physical, mechanical, durability, and environmental properties of geopolymer mortars (GMs) produced using waste tire steel fibers (WTSFs), hemp fibers (HFs), waste marble powder (WMP), and recycled fine aggregates (RFAs). Within the scope of this study, fibers were incorporated as single and hybrid types at 0.5% and 1% by volume. The addition of HFs generally reduced dry unit weight, as well as compressive and flexural strength but increased fracture energy by nearly three times. The addition of WTSFs improved compressive and flexural strengths by up to 42% and enhanced fracture energy by 840%. Hybrid fibers increased the strength values by 21% and the fracture energy by up to four times, demonstrating a clear synergistic effect between HFs and WTSFs in enhancing crack resistance and structural stability. In the durability tests conducted within the scope of this study, HFs burnt at 600 °C, while WTSFs showed signs of corrosion under freeze–thaw and acid conditions; however, hybrid fibers combined the benefits of both materials, resulting in an effective preservation of internal structure. The fact that the materials used in the production of GM samples were waste or recycled products reduced the total cost to 188 USD/m³, and thanks to these materials and the carbon-negative properties of HFs, CO₂ emissions were reduced to 338 kg CO₂/m³. The presented study demonstrates the potential of using recycled and waste materials to create sustainable building materials in the construction industry. Full article

Organosilica films, composed of a silicon oxide network with terminal methyl groups, are widely utilized in various applications, including microelectronics. Many of these applications require high hydrophobicity and good mechanical properties, which pose a significant challenge because the Si–CH₃ groups disrupt the Si–O–Si network. This issue becomes particularly pronounced in porous films. Here, we investigate whether material properties can be tuned by simply altering the spatial arrangement of methyl groups. To achieve this, we prepared copolymer films with one or two methyl groups bonded to a silicon atom, while maintaining a constant total amount of methyl groups. The films were deposited using a sol–gel technique combined with template self-assembly. The precursor content was varied to compare films with different proportions of Si–CH₃ and Si(–CH₃)₂. Film characterization included FTIR, ellipsometric porosimetry, AFM, and WCA measurements and dielectric constant evaluations. Our findings indicate that precursors containing dimethyl groups enhance the connectivity of the Si–O–Si network, resulting in a higher Young’s modulus and smaller pore size compared to films with an equivalent amount of methyl groups. However, the lower thermal stability of dimethyl bonds limits the thermal budget of these films. Thus, the spatial arrangement of organic groups within the polymer structure can be employed to tune material properties. These results expand the understanding of organic–inorganic hybrid materials and offer novel approaches for their applications. Full article

Background: The global rise in the aging population presents significant challenges to healthcare systems, especially with increasing rates of chronic illnesses, mental health issues, and functional decline among older adults. In response, holistic and tech-driven approaches, such as telehealth and remote virtual interactive agents (VIAs), are potential emerging solutions to support the physical, cognitive, and emotional well-being of older adults. VIAs are multimodal digital tools that provide interactive and immersive experiences to users. Despite its promise, gaps still exist in the insights that explore ways of delivering geriatric healthcare remotely. Objective: This systematic review examines the existing literature on remote virtual interventions for older adults, focusing on bibliometrics, study purposes, outcomes, and network analysis of studies extracted from major databases using selected keywords and managed using the Covidence application. Methods and Results: Following five stages, namely, problem identification, a literature search, data evaluation, data analysis, and presentation, the review found that the studies on remote VIAs for older adults (2013–2025) were mostly from a positivist perspective, multi-authored, and U.S.-led, mainly showing positive outcomes for most studies (n = 13/15) conducted in home settings with healthy older participants. The dominance of positivist, US-led studies reflect an epistemological stance that emphasizes objectivity, quantification, and generalizability. VIAs, often pre-programmed and internet-based, supported health promotion and utilized visual humanoid avatars on personal devices. Keyword and network analysis additionally revealed four themes resulting from the review: Health and Clinical, Holistic and Cognitive, Home and Caring, and Hybrid and Connection. Conclusion: The review provides innovative insights and illustrations that may serve as a foundation for future research on VIAs and remote healthcare delivery for older adults. Full article

Micro-accelerometers are in high demand across many due to their compact size, low energy consumption, and excellent precision. Since gravity causes a large movement when the device is positioned vertically, measuring low gravitational acceleration is challenging. This study examines the intrinsic relationship between applied voltage levels and displacement in micro-accelerometers. The study introduces a novel design that integrates hybrid flexures, comprising both linear and angular configurations, with an out-of-plane overlap varying (OPOV) electrostatic actuation mechanism. This design aims to measure the micro-accelerometer’s movement and low frequency response. The proposed device with silicon material is designed and simulated using the IntelliSuite^® software, considering its small dimensions and 25 µm thickness. The norm value of 28.0916 μN from gravity’s reaction forces on the body, a resonant frequency of 179.668 Hz at the first desired mode, and a maximum stress of 24.7 MPa were obtained through the electro-mechanical analysis. A comparison of the proposed design was conducted with other configurations, measuring a frequency of 179.668 Hz at a minimum downward displacement of 7.69916 µm under the influence of gravity without electrostatic mechanisms. Following this, an electrostatic actuation mechanism was introduced to minimize displacement by applying different voltage levels, including 1 V, 1.5 V, and 3 V. At 3 V, a significant improvement in displacement reduction was observed compared to the other applied voltages. Additionally, dynamic and sensitivity analyses were carried out to validate the performance of the proposed design further. Full article

Chemical sensors have undergone transformative advances in recent years, driven by the convergence of nanomaterials, advanced fabrication strategies, and state-of-the-art characterization methods. This review emphasizes recent developments, with particular attention to progress achieved over the past decade, and highlights the role of the United States as a major driver of global innovation in the field. Nanomaterials such as graphene derivatives, MXenes, carbon nanotubes, metal–organic frameworks (MOFs), and hybrid composites have enabled unprecedented analytical performance. Representative studies report detection limits down to the parts-per-billion (ppb) and even parts-per-trillion (ppt) level, with linear ranges typically spanning 10–500 ppb for volatile organic compounds (VOCs) and 0.1–100 μM for biomolecules. Response and recovery times are often below 10–30 seconds, while reproducibility frequently exceeds 90% across multiple sensing cycles. Stability has been demonstrated in platforms capable of continuous operation for weeks to months without significant drift. In parallel, additive manufacturing, device miniaturization, and flexible electronics have facilitated the integration of sensors into wearable, stretchable, and implantable platforms, extending their applications in healthcare diagnostics, environmental monitoring, food safety, and industrial process control. Advanced characterization techniques, including in situ Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS, Atomic Force Microscopy (AFM) , and high-resolution electron microscopy, have elucidated interfacial charge-transfer mechanisms, guiding rational material design and improved selectivity. Despite these achievements, challenges remain in terms of scalability, reproducibility of nanomaterial synthesis, long-term stability, and regulatory validation. Data privacy and cybersecurity also emerge as critical issues for IoT-integrated sensing networks. Looking forward, promising future directions include the integration of artificial intelligence and machine learning for real-time data interpretation, the development of biodegradable and eco-friendly materials, and the convergence of multidisciplinary approaches to ensure robust, sustainable, and socially responsible sensing platforms. Overall, nanomaterial-enabled chemical sensors are poised to become indispensable tools for advancing public health, environmental sustainability, and industrial innovation, offering a pathway toward intelligent and adaptive sensing systems. Full article

To tackle the challenges of complex construction environment interference (e.g., lighting variations, occlusion, and marker contamination) and the demand for high-precision alignment during the hoisting process of bridge-erecting machines, this paper presents a two-stage marker detection–localization network tailored to hoisting alignment. The proposed network adopts a “coarse detection–fine estimation” phased framework; the first stage employs a lightweight detection module, which integrates a dynamic hybrid backbone (DHB) and dynamic switching mechanism to efficiently filter background noise and generate coarse localization boxes of marker regions. Specifically, the DHB dynamically switches between convolutional and Transformer branches to handle features of varying complexity (using depthwise separable convolutions from MobileNetV3 for low-level geometric features and lightweight Transformer blocks for high-level semantic features). The second stage constructs a Transformer-based homography estimation module, which leverages multi-head self-attention to capture long-range dependencies between marker keypoints and the scene context. By integrating enhanced multi-scale feature interaction and position encoding (combining the absolute position and marker geometric priors), this module achieves the end-to-end learning of precise homography matrices between markers and hoisting equipment from the coarse localization boxes. To address data scarcity in construction scenes, a multi-dimensional data augmentation strategy is developed, including random homography transformation (simulating viewpoint changes), photometric augmentation (adjusting brightness, saturation, and contrast), and background blending with bounding box extraction. Experiments on a real bridge-erecting machine dataset demonstrate that the network achieves detection accuracy (mAP) of 97.8%, a homography estimation reprojection error of less than 1.2 mm, and a processing frame rate of 32 FPS. Compared with traditional single-stage CNN-based methods, it significantly improves the alignment precision and robustness in complex environments, offering reliable technical support for the precise control of automated hoisting in bridge-erecting machines. Full article

Verticillium wilt, caused by Verticillium dahliae, severely threatens various crops and trees worldwide. This study aimed to characterize the function of a CUE (coupling of ubiquitin conjugation to endoplasmic reticulum (ER) degradation)-domain-containing protein, VdCUE, in V. dahliae, which exhibits sequence divergence between the defoliating strain XJ592 and the non-defoliating strain XJ511. We generated ∆VdCUE-knockout mutants and evaluated their phenotypes in growth and virulence. Functional analyses included verifying the signal peptide activity of VdCUE, testing its ability to induce cell death or inhibit BAX-induced cell death in Nicotiana benthamiana leaves, and identifying host targets via yeast two-hybrid screening. The ∆VdCUE mutants showed reduced formation of melanized microsclerotia but no other obvious growth defects. Cotton plants infected with the ∆VdCUE mutants exhibited a significantly lower disease index and defoliation rate. VdCUE was confirmed to be secreted via a functional signal peptide, but it neither triggered cell death nor inhibited BAX-induced cell death. Three putative host targets were identified and supported by AI-based three-dimensional structural modeling, including tRNA-specific 2-thiouridylase, peptidyl-prolyl cis-trans isomerase, and 40S ribosomal protein, which may mediate VdCUE-dependent virulence regulation. These findings reveal VdCUE as a key virulence factor in V. dahliae, contributing to our understanding of its pathogenic mechanism. Full article

Two hybrid, entropy-guided node metrics are proposed for the k-hop environment: Entropy-Weighted Redundancy (EWR) and Normalized Entropy Density (NED). The central idea is to couple local Shannon entropy with neighborhood density/redundancy so that structural heterogeneity around a vertex is captured even when classical indices (e.g., degree or clustering) are similar. The metrics are formally defined and shown to be bounded, isomorphism-invariant, and stable under small edge edits. Their behavior is assessed on representative topologies (Erdős–Rényi, Barabási–Albert, Watts–Strogatz, random geometric graphs, and the Zephyr quantum architecture). Across these settings, EWR and NED display predominantly negative correlation with degree and provide information largely orthogonal to standard centralities; vertices with identical degree can differ by factors of two to three in the proposed scores, revealing bridges and heterogeneous regions. These properties indicate utility for vulnerability assessment, topology-aware optimization, and layout heuristics in engineered and quantum networks. Full article

Aware of the nefarious effects of excessive exploitation of natural resources and the greenhouse gases emissions linked to building sector, the concept of smart buildings emerged, referring to a building that uses clean energy efficiently. This requires intelligent control systems to manage the use of residential energy consuming devices, namely the HVAC (Heating, Ventilation, Air-conditioning) system. This system consumes up to 50% of the total energy used by a building. In this paper, we introduce a RL (Reinforcement Learning) and MPC-LSTM (Model Predictive Control-Long-Short Term Memory) hybrid control system that combines DNNs (Deep Neural Networks), through RL, with LSTM’s long-short memory technique and MPC’s control characteristics. The goal of our model is to maintain thermal comfort of residents while optimizing energy consumption. Consequently, to train and test our model, we generate our own dataset using a building model of a corporate building in Casablanca, Morocco, combined with weather data of the same city. Simulations confirm the robustness of our model as it outperforms basic control methods in terms of thermal comfort and energy consumption especially during summer. Compared to conventional methods, our approach resulted in a 45.4% and 70.9% reduction in energy consumption, in winter and summer, respectively. Our approach also resulted in 26 less comfort violations during winter. On the other hand, during summer, our approach found a compromise between energy consumption and comfort with no more than 2.5 °C above ideal temperature limit. Full article

Thin-film thermoelectric generators (TFTEGs) emerge as critical components of self-sustaining agricultural systems because they can utilize temperature gradients to generate plant-transpiration-induced thermovoltage signal quantifiable to plant health status. This study examines the latest developments in TFTEG materials, device structures, manufacturing processes, and their integration into agricultural systems such as plant-wearable, canopy-level and stem-clipped TEGs. Key questions addressed include the ideal materials for TFTEG fabrication, their biocompatibility and eco-stability in agricultural settings, recent design and AI-assisted optimization advancements, and future research directions in non-conventional TEG applications. The analysis consolidates evidence from inorganic, organic, and hybrid thermoelectric materials with respect to performance in terms of flexibility, thermal stability, output power, and biocompatibility. Bibliometric analysis was employed to determine dominant research topics and gaps, especially with respect to sustainability and AI-augmented design. The review emphasizes the latest breakthroughs in structural optimization, flexible substrates, encapsulation strategies, and sensor integration for reliability enhancement in field environments. In addition, applications of AI, including neural network-based conditional Generative Adversarial Network, surrogate modeling, and multi-objective optimization, are discussed in relation to the improvement of thin-film TEG design and simulation processes. This study suggests that TFTEGs exhibit great potential in agricultural monitoring and plant wearable applications but material toxicity, mechanical degradation, and integration with AI are still major obstacles. Full article

Personalized learning environments increasingly rely on learner modeling techniques that integrate both explicit and implicit data sources. This study introduces a hybrid profiling methodology that combines psychometric data from an extended Felder–Silverman Learning Style Model (FSLSM) questionnaire with behavioral analytics derived from Moodle Learning Management System interaction logs. A structured mapping process was employed to associate over 200 unique log event types with FSLSM cognitive dimensions, enabling dynamic, behavior-driven learner profiles. Experiments were conducted across three datasets: a university dataset from the International Hellenic University, a public dataset from Kaggle, and a combined dataset totaling over 7 million log entries. Deep learning models including a Sequential Neural Network, BiLSTM, and a pretrained MLSTM-FCN were trained to predict student performance across regression and classification tasks. Results indicate moderate predictive validity: binary classification achieved practical, albeit imperfect accuracy, while three-class and regression tasks performed close to baseline levels. These findings highlight both the potential and the current constraints of log-based learner modeling. The contribution of this work lies in providing a reproducible integration framework and pipeline that can be applied across datasets, offering a realistic foundation for further exploration of scalable, data-driven personalization. Full article