Search Results (3,319)

The growing need for efficient and safe high-energy lithium-ion batteries (LIBs) in electric vehicles and grid storage necessitates advanced internal monitoring solutions. This work presents a comprehensive simulation model of a novel integrated optical sensor based on ethylene carbonate-filled photonic crystal fiber (EC-PCF). The proposed design synergistically combines a chirped fiber Bragg grating (FBG) and an extrinsic Fabry–Pérot interferometer (EFPI) on a multiplexed platform for the multifunctional sensing of refractive index (RI), temperature, strain, and pressure (via strain coupling) within LIBs. By matching the RI of the PCF cladding to the battery electrolyte using ethylene carbonate, the design maximizes light–matter interaction for exceptional RI sensitivity, while the cascaded EFPI enhances mechanical deformation detection beyond conventional FBG arrays. The simulation framework employs the Transfer Matrix Method with Gaussian apodization to model FBG reflectivity and the Airy formula for high-fidelity EFPI spectra, incorporating critical effects like stress-induced birefringence, Transverse Electric (TE)/Transverse Magnetic (TM) polarization modes, and wavelength dispersion across the 1540–1560 nm range. Robustness against fabrication variations and environmental noise is rigorously quantified through Monte Carlo simulations with Sobol sequences, predicting temperature sensitivities of ∼12 pm/°C, strain sensitivities of ∼1.10 pm/$\mathsf{\mu }$$\mathsf{\epsilon }$, and a remarkable RI sensitivity of ∼1200 nm/RIU. Validated against independent experimental data from instrumented battery cells, this model establishes a robust computational foundation for real-time battery monitoring and provides a critical design blueprint for future experimental realization and integration into advanced battery management systems. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article

Water scarcity, driven by over-consumption, population growth, climate change, and pollution, poses severe threats to both human health and ecosystems. Rainwater harvesting (RWH) has emerged as a sustainable solution to mitigate these impacts, offering environmental, social, and economic benefits. Traditional RWH site selection methods rely heavily on GIS-based Multi-Criteria Approaches, such as the Analytical Hierarchy Process, which typically assess runoff potential at the pixel scale using proxy indicators like runoff coefficients or drainage density. However, these methods often overlook horizontal water fluxes and temporal variability, leading to underestimation of the actual runoff available for harvesting. This study introduces an innovative enhancement to AHP/GIS-based methodologies for rainwater harvesting (RWH) site selection by incorporating Aggregated Runoff (AR) as a key criterion. Unlike traditional approaches, the use of AR—representing the total upstream surface water collected at each pixel—enables a more realistic and accurate assessment of RWH potential without increasing data or computational requirements. The proposed criterion is independent of the specific methodology or data layers adopted, making it broadly applicable and easily integrable into existing frameworks. The methodology is applied to the upper Tiber River catchment in Central Italy, demonstrating that AR-based assessments yield more realistic RWH potential maps compared to conventional methods. Additionally, the study proposes a quantile-based scoring system to account for inter-annual hydrological variability, enhancing the robustness of site selection under changing climate conditions. Full article

This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which can enhance the dexterity of traditional rigid actuators while achieving a human-like workspace. Each finger is designed with a specific degree of rotational freedom to mimic natural opening and closing motions. This study also elaborates on the mapping of eight-channel electromyography to finger grasping force through improved TCN, as well as the control algorithm for grasping flexible objects. A functional prototype of the prosthetic hand was fabricated, and a series of experiments involving adaptive grasping and handheld manipulation tasks were conducted to validate the effectiveness of the proposed mechanical structure and control strategy. The results demonstrate that the hand can stably grasp flexible objects of various shapes and sizes. This work provides a practical solution for prosthetic hand design, offering promising potential for developing lightweight, dexterous, and highly anthropomorphic robotic hands suitable for real-world applications. Full article

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential. Full article

Wireless power transfer (WPT) has emerged as a compelling solution for delivering electrical energy without physical connectors, particularly in applications requiring reliability, mobility, or encapsulation. This work presents the modeling, simulation, construction, and experimental validation of an optimized dual-frequency WPT system using magnetically coupled resonant coils. Unlike conventional single-frequency systems, the proposed architecture introduces two independently controlled excitation frequencies applied to distinct transistors, enabling improved resonance behavior and enhanced power delivery across a range of coupling conditions. The design process integrates numerical circuit simulations in PSpice and three-dimensional electromagnetic analysis in ANSYS Maxwell 3D, allowing accurate evaluation of coupling coefficient variation, mutual inductance, and magnetic flux distribution as functions of coil geometry and alignment. A sixth-degree polynomial model was derived to characterize the coupling coefficient as a function of coil separation, supporting predictive tuning. Experimental measurements were carried out using a physical prototype driven by both sinusoidal and rectangular control signals under varying load conditions. Results confirm the simulation findings, showing that specific signal periods (e.g., 8 µs, 18 µs, 20 µs, 22 µs) yield optimal induced voltage values, with strong sensitivity to the coupling coefficient. Moreover, the presence of a real load influenced system performance, underscoring the need for adaptive control strategies. The proposed approach demonstrates that dual-frequency excitation can significantly enhance system robustness and efficiency, paving the way for future implementations of self-adaptive WPT systems in embedded, mobile, or biomedical environments. Full article

With the rapid development of artificial intelligence technology in the field of intelligent manufacturing, convolutional neural networks (CNNs) have shown excellent performance and generalization capabilities in industrial applications. However, the huge computational and resource requirements of CNNs have brought great obstacles to their deployment on low-end hardware platforms. To address this issue, this paper proposes a scalable CNN accelerator that can operate on low-performance Field-Programmable Gate Arrays (FPGAs), which is aimed at tackling the challenge of efficiently running complex neural network models on resource-constrained hardware platforms. This study specifically optimizes depthwise separable convolution and the squeeze-and-excite module to improve their computational efficiency. The proposed accelerator allows for the flexible adjustment of hardware resource consumption and computational speed through configurable parameters, making it adaptable to FPGAs with varying performance and different application requirements. By fully exploiting the characteristics of depthwise separable convolution, the accelerator optimizes the convolution computation process, enabling flexible and independent module stackings at different stages of computation. This results in an optimized balance between hardware resource consumption and computation time. Compared to ARM CPUs, the proposed approach yields at least a 1.47× performance improvement, and compared to other FPGA solutions, it saves over 90% of Digital Signal Processors (DSPs). Additionally, the optimized computational flow significantly reduces the accelerator’s reliance on internal caches, minimizing data latency and further improving overall processing efficiency. Full article

Breast cancer (BC) is the most frequently diagnosed malignancy in women. Currently, BC is treated with a holistic and multidisciplinary approach from diagnostic, surgical, radio-oncological, and medical perspectives, and advances including in early detection and treatment methods have led to improved outcomes for patients in recent years. Yet, BC remains the second most common cause of cancer-related deaths among women and there is an array of gaps to achieve optimal care. To close gaps in cancer care, here we describe a collaborative Request For Proposals (RFP) framework supporting independent initiatives for metastatic breast cancer (MBC) patients and aiming at improving their quality of care. We set up a collaborative framework between Pfizer and Sharing Progress in Cancer Care (SPCC). Our model is based on an RFP system in which Pfizer and SPCC worked together ensuring the independence of the funded projects. We developed a three-step life cycle RFP. The collaborating framework of the project was based on an RFP with a USD 1.5 million available budget for funding independent grants made available from Pfizer and managed in terms of awareness, selection, and monitoring by SPCC. Our three-step model could be applicable and scalable to quality improvement (QI) initiatives that are devoted to tackling obstacles to reaching optimal care. Through this model, seven projects from five different European countries were supported. These projects covered a range of issues related to the experience of patients with MBC: investigator communication, information, and shared decision-making (SDM) practices across Europe; development, delivery, and evaluation of a scalable online educational program for nurses; assessment of disparities among different minority patient groups; development of solutions to improve compliance or adherence to therapy; an information technology (IT) solution to improve quality of life (QoL) of patients with MBC and an initiative to increase awareness and visibility of MBC patients. Overall, an average of 171 healthcare professionals (HCPs) per project and approximately 228,675 patients per project were impacted. We set up and describe a partnership model among different stakeholders within the healthcare ecosystem―academia, non-profit organizations, oncologists, and pharmaceutical companies―aiming at supporting independent projects to close gaps in the care of patients with MBC. By removing barriers at different layers, these projects contributed to the achievement of optimal care for patients with MBC. Full article

Vision is a critical component of daily life, and its loss significantly hinders an individual’s ability to navigate, particularly when using public transportation systems. To address this challenge, this paper introduces a novel approach for accurately identifying bus route numbers and destinations, designed to assist visually impaired individuals in navigating urban transit networks. Our system integrates object detection, image enhancement, and Optical Character Recognition (OCR) technologies to achieve reliable and precise recognition of bus information. We employ a custom-trained You Only Look Once version 8 (YOLOv8) model to isolate the front portion of buses as the region of interest (ROI), effectively eliminating irrelevant text and advertisements that often lead to errors. To further enhance accuracy, we utilize the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to improve image resolution, significantly boosting the confidence of the OCR process. Additionally, a post-processing step involving a pre-defined list of bus routes and the Levenshtein algorithm corrects potential errors in text recognition, ensuring reliable identification of bus numbers and destinations. Tested on a dataset of 120 images featuring diverse bus routes and challenging conditions such as poor lighting, reflections, and motion blur, our system achieved an accuracy rate of 95%. This performance surpasses existing methods and demonstrates the system’s potential for real-world application. By providing a robust and adaptable solution, our work aims to enhance public transit accessibility, empowering visually impaired individuals to navigate cities with greater independence and confidence. Full article

The future of anxiety management lies in bridging traditional evidence-based treatments with intelligent and adaptive digital platforms. Embedding multi-agent systems capable of real-time mood detection and self-management support represents a transformative step towards intelligent care, enabling users to independently regulate acute episodes, prevent relapse, and promote sustained personal well-being. These digital solutions illustrate how technology can improve accessibility, personalization, and adherence, while establishing the foundation for integrating multi-agent architectures into mental health systems. Such architectures can continuously detect and interpret users’ emotional states through multimodal data, coordinating specialized agents for monitoring, personalization, and intervention. Crucially, they extend beyond passive data collection to provide active, autonomous support during moments of heightened anxiety, guiding individuals through non-pharmacological strategies such as breathing retraining, grounding techniques, or mindfulness practices without requiring immediate professional involvement. By operating in real time, multi-agent systems function as intelligent digital companions capable of anticipating needs, adapting to context, and ensuring that effective coping mechanisms are accessible at critical moments. This paper presents a multi-agent architecture for the digital management of anxiety episodes, designed not only to enhance everyday well-being but also to deliver immediate, personalized assistance during unexpected crises, offering a scalable pathway towards intelligent, patient-centered mental health care. Full article

Background/Objectives: Atopic dermatitis (AD), a chronic inflammatory skin condition, significantly impairs quality of life but remains underdiagnosed in primary care. Blood-cell-count-derived inflammatory indices are emerging as cost-effective biomarkers, but their pathological relevance to AD is limited and requires further discussion. Methods: We developed the Atopic Inflammation Index (AII), a novel blood-cell-based biomarker reflecting AD pathogenesis, and initially assessed its levels in AD patients and healthy controls using clinical samples from Shanghai, China. We then analyzed data from the NHANES (National Health and Nutrition Examination Survey) 2005–2006 cohort (n = 6855) to verify the AII-AD association and compared AII’s diagnostic performance with IgE and eosinophils. Results: Clinical analysis showed a nonlinear association between AII and AD severity. AII effectively distinguished AD patients (including mild cases) from healthy controls (p < 0.001) without elevation in psoriasis or urticaria, unlike eosinophils. In NHANES 2005–2006 (n = 720 AD cases, 10.5%), AII levels were higher in AD compared to non-AD patients (2.33 [1.39–4.09] vs. 2.03 [1.19–3.49], p = 0.007) and remained independently associated after adjustment (OR = 1.03, 95%CI = 1.01–1.04, p = 0.003), while IgE/eosinophils showed non-significant trends. Restricted cubic splines confirmed linear prediction (p = 0.006), and subgroup analyses supported consistency (P-interaction > 0.05). AII outperformed eosinophils (AUC:0.568 vs. 0.546, p = 0.025) with improved detection (sensitivity 0.361→0.614). Sensitivity analysis confirmed robustness after excluding medications, chronic diseases and adult populations. Conclusions: AII is stable and reliable in screening and diagnosing AD, offering a low-cost, practical solution for primary care. This verifies the feasibility of integrating existing detection indicators into new biomarkers, providing valuable inspiration for precision medicine research. Full article

Motion of dislocations is a common mechanism of plasticity in many materials. Dislocation-mediated deformation is essentially an inhomogeneous process, which is manifest in the formation of slip lines and complicated cell wall structures. An adequate description of these processes is an important goal of Materials Theory, which aims to describe the mechanical properties of materials and their reliability in service. This publication advances the thermodynamically consistent theory of dislocation-mediated plasticity to include the spatial gradients of the independent variables. We conducted the renormalization group scaling analysis of deformation and obtained the low-energy dislocation structures as ordinary solutions of the equilibrium equations without any arbitrary assumptions. We matched the emerging theoretical structures with the experimentally observed and made several predictions regarding possible experiments. Full article

Additive manufacturing involves numerous independent parameters, often leading to inconsistent print quality and necessitating costly trial-and-error approaches to optimize input variables. Machine learning offers a solution to this non-linear problem by predicting optimal printing parameters from a minimal set of experiments. Using Fused Deposition Modeling (FDM) as a case study, this work develops a machine learning-powered process to predict porosity defects. Specimens in two geometrical scales were 3D-printed and CT-scanned, yielding raw datasets of grayscale images. A machine learning image classifier was trained on the small-cube dataset (~2200 images) to distinguish exploitable images from defective ones, averaging over 97% accuracy and correctly classifying more than 90% of the large-cube exploitable images. The developed preprocessing scripts extracted porosity features from the exploitable images. A repeatability study analyzed three replicate specimens printed under identical conditions, and quantified the intrinsic process variability, showing an average porosity standard deviation of 0.47% and defining an uncertainty zone for quality control. A multi-layer perceptron (MLP) was independently trained on 1709 data points derived from the small-cube dataset and 3746 data points derived from the large-cube dataset. Its accuracy was 54.4% for the small cube and increased to 77.6% with the large-cube dataset, due to the larger sample size. A rigorous grouped k-fold cross-validation protocol, relying on splitting data per cube, strengthened the ML algorithms against data leakage and overfitting. Finally, a dimensional scalability study further assessed the use of the pipeline for the large-cube dataset and established the impact of geometrical scaling on defect formation and prediction in 3D-printed parts. Full article

We address the inverse spectral problem for a PT-symmetric Dirac operator with discontinuity conditions imposed along the entire real axis—a configuration that has not been explicitly solved in prior literature. Our approach constructs fundamental solutions via convergent recursive series expansions and establishes their linear independence through a constant Wronskian. We derive explicit formulas for transmission and reflection coefficients, assemble them into a PT-symmetric scattering matrix, and demonstrate how both spectral and scattering data uniquely determine the underlying complex-valued, discontinuous potentials. Unlike classical treatments, which assume smoothness or limited discontinuities, our framework handles full-axis discontinuities within a non-Hermitian setting, proving uniqueness and providing a constructive recovery algorithm. This method not only generalizes existing inverse scattering theory to PT-symmetric discontinuous operators but also offers direct applicability to optical waveguides, metamaterials, and quantum field models where gain–loss mechanisms and zero-width resonances are critical. Full article

(1) Background: This study aimed to determine the optimum parameters for the treatment of Streptococcus mutans biofilm on clear dental aligners. (2) Methods: A 24-h-old S. mutans biofilm was grown on polyurethane (PU) and poly(ethylene terephthalate glycol) (PETG) aligners. These samples were treated with three chlorhexidine digluconate (CHX)-based antiseptic solutions, manual brushing, and a combination of both, with varying exposure times. The number of adhered bacteria was determined in both untreated and treated samples after sonication. Materials were analyzed with atomic force and scanning electron microscopy, and surface free energy (SFE) values were determined using three different models. (3) Results: Our findings indicated that control strategies do not depend on the type of material. PU and PETG surfaces exhibited similar SFE values (41–45 mJ/m²). Differences in surface roughness were insufficient to cause significant changes in S. mutans behavior. The highest efficacy of all three tested antiseptics was established for the exposure time of 1 min, with efficacy deteriorating just after 3 min. (4) Conclusions: The efficacy of CHX against S. mutans early biofilm is material-independent and time-dependent. The optimal exposure time of 1 min should be combined with brushing, with a general recommendation of the antiseptic-first approach. Full article