Search Results (5,864)

Background/Objectives: Antimicrobial resistance (AMR) constitutes a major public health and economic burden in contemporary society. This work aims to identify the patterns and temporal trends of Klebsiella pneumoniae resistance to carbapenems across several European countries. Methods: Data on K. pneumoniae resistance to carbapenems in several countries from 2005 to 2021 (with data available up to 2023) were retrieved from the public ECDC website in 2023. Agglomerative Hierarchical Cluster Analysis (HCA), based on the new generalised affinity coefficient, was applied to the estimated ARIMA models. Results: The dendrogram at the cut-off presented four clusters: the first cluster with Slovenia, Luxembourg, Italy and Estonia; the second cluster with Romania, Malta, Greece, Portugal and Spain; the third cluster Denmark, France and Czechia; and finally the fourth cluster with Lithuania, Cyprus, Finland, Austria, Belgium and Slovakia. Although the resistance values showed an increasing trend over the years in clusters II and III, the increase was more pronounced in cluster II. Conclusions: The application of data analysis, for the first time, using HCA based on a new similarity coefficient, namely, the generalised affinity coefficient, enables the evaluation of time-series patterns in different regions and enables the identification of clusters of countries showing similar temporal patterns of antimicrobial resistance. Carbapenem resistance in K. pneumoniae among countries belonging to cluster II showed increasing trends throughout the study period. Full article

The growing penetration of renewable energy sources introduces significant uncertainties into integrated energy systems (IESs). Conventional single-timescale management strategies, typically designed for static power balance, fail to address the symmetry of source-load uncertainties arising from both supply and demand sides. To address this challenge, this paper proposes a multi-timescale optimal scheduling framework that integrates demand response (DR) and multi-energy flow coupling. The framework adopts a hierarchical progressive strategy across day-ahead, intra-day, and real-time stages. The day-ahead stage optimizes the economic baseline with an hourly resolution. The intra-day stage conducts rolling correction at 15 min intervals to activate slow-response equipment flexibility, boosting combined heat and power (CHP) generation by 40.70% and increasing waste-heat cooling consumption by 41.12%. The real-time stage employs energy storage at 5 min resolution to suppress fluctuations, maintaining electricity, heat, and cooling load deviations, respectively, at remarkably low levels of 0.17%, 0.10%, and 0.06%. Comparative results show that with power-to-gas (P2G) integration, the system purchases off-peak electricity for synthetic natural gas production, cutting gas procurement costs by 12.70% and reducing net carbon emissions from 5.14 t to 4.91 t. DR mechanisms enable a gas–electricity substitution strategy that lowers electricity purchase costs by 9.97%, reduces evening peak electric vehicle (EV) charging load by 8.32%, and decreases charging expenses by 15%. Full article

Vehicle-to-grid (V2G) technology lets a parked electric vehicle push power back to the network, so a fleet of cars can act as distributed storage. Many studies report large benefits from this, such as peak shaving and better use of renewable energy, but most describe their simulation only in words, name no test network, and compare a single charging behavior against a do-nothing case. It is then hard to separate what V2G delivers from what the control strategy delivers. This paper builds and compares dispatch strategies on one fully specified system: a price-following rule with no knowledge of the network, a stronger randomized time-of-use baseline, and a receding-horizon model predictive control (MPC) strategy that re-plans every hour with the distribution network’s per-line thermal limits and voltage bounds embedded directly in the optimization, not merely checked afterward. All run on the IEEE 33-bus feeder at low (10%), medium (30%), and high (50%) EV shares, with a full AC power flow solved every hour. The central result is a critical-penetration effect. At a 50% share, the naive price rule makes the system peak 23.7% worse than having no V2G at all, because the whole fleet reacts to one price signal at once, while the MPC cuts the peak by 18 to 28% in every case. Embedding the network limits removes the worst-line-loading side-effect a system-peak-only objective creates, bringing high-share worst-line loading down from 86.3% to 81.8% while preserving the full peak reduction. A workplace daytime-charging scenario shows renewable use becoming a real, separating metric (up to 2.8 MWh/day of EV demand met directly by rooftop solar, against zero for overnight charging), a quadratic wear cost smooths the profit-cycling frontier that a linear cost makes step-shaped, the controller is robust to forecast error up to 20%, and its solve time is set by network size rather than fleet size. Results are given as they came out of the model. Full article

Establishing an explicit correlation between dynamic machining instabilities and the terminal functional performance of difficult-to-cut materials remains challenging. Herein, a wireless sensory BT50 toolholder was deployed to in situ monitor the dynamic cutting signatures during the milling process of commercial Inconel 718 superalloy. Real-time spectral analyses captured the intensified thermo-mechanical coupling and dynamic load escalations (e.g., axial force increased from 0.56 kN to 0.65 kN) induced by progressive tool wear and anomalous grain coarsening (average grain size expanding from 45.45 μm to 56.18 μm), driven by a thermal-dominant regime at advanced wear stages and accompanied by pronounced lattice rotations and a sustained predominance of high-angle grain boundaries (HAGBs). This microstructural evolution critically governed the macroscopic corrosion degradation. The diminished grain boundary density hindered the rapid diffusion channels essential for robust passivation kinetics, whereas the highly energetic HAGB networks and reoriented crystallographic planes triggered intense localized micro-galvanic dissolution. The results formulate a comprehensive sensing-microstructure-performance closed-loop framework, offering profound mechanistic insights into the functional deterioration of critical machined components. Full article

Introduction: Home-based spirometry has emerged as a potential component of decentralized clinical trials (DCTs) in respiratory medicine, but its real-world feasibility in patients with chronic obstructive pulmonary disease (COPD) and other respiratory diseases remains insufficiently characterized. This study evaluated the feasibility of remote pulmonary function monitoring using a portable spirometer and mobile application in adults with COPD or other respiratory diseases. Methods: This exploratory single-arm study enrolled adults aged 19 years or older with COPD or other respiratory diseases. Participants were asked to perform home-based spirometry at six prespecified time points (Days 15, 29, 43, 57, 71, and 85). Feasibility was assessed using visit-level and participant-level adherence, spirometry quality grades, and patient-reported usability and satisfaction. Participants completing at least 5 of 6 scheduled measurements were classified as the high-adherence group; this cut-off was an operational, study-specific definition. Results: A total of 30 participants were enrolled (19 with chronic obstructive pulmonary disease, 5 with asthma, 2 with asthma–COPD overlap, and 4 with other respiratory conditions), and 23 completed the study. Visit-level completion rates declined over time, from 88.5% on Day 15 to 47.8% on Day 71 and 56.5% on Day 85. Nine participants completed all six measurements, while 13 completed at least five. Portable spirometry quality showed variability across visits, with both high-quality and low-quality measurements observed. Compared with the low-adherence group, the high-adherence group reported significantly higher scores for convenience, communication with the research team, overall satisfaction, willingness to participate in future telemedicine-based trials, device training adequacy, ease of device use, and application usability. Conclusions: Home-based spirometry may be a useful decentralized trial component in patients with COPD and other respiratory diseases; however, the decline in adherence over time and the variability in measurement quality indicate that substantial operational refinement is needed before broader implementation. Because no feasibility threshold was prespecified, no concurrent laboratory spirometry comparison was performed, and the study population was diagnostically heterogeneous, these findings are exploratory and address operational feasibility rather than measurement validity. Full article

This article presents a low-cost, high-voltage excitation circuit (EC) for capacitive ultra-sonic transducers (CUTs) based on a dual-path architecture. The proposed design comprises two independent AC excitation channels (AC-branch) that share a regulated DC-bias voltage (DC-branch). The circuit was developed to satisfy a fundamental operational requirement of CUTs: simultaneous application of a static bias voltage and a time-varying drive voltage. Because the electrostatic force depends nonlinearly on the applied voltage, efficient first-harmonic actuation requires the superposition of DC and AC voltage components. To reach this objective, the circuit and the transducer must be treated as a coupled electrical, electrostatic, mechanical, and acoustic system. In the proposed implementation, the DC-branch uses a TL494-PWM controller, a TIP50 switching transistor, a step-up transformer, and a rectifier-filter stage to generate the high-voltage bias of up to 200 V_DC. Each AC-channel employs an LM3886TF amplifier followed by a 1:15 step-up transformer, enabling the generation of excitation signals of up 180 V_peak. A key feature of the proposed architecture is the electrical independence of the two AC-channels, which allows for distinct excitation frequencies with minimal mutual interference. Experimental validation, performed with and without ultrasonic loads, demonstrates the relation between excitation conditions and the acoustic performance of the CUTs. Full article

Breast cancer-related lymphedema (BCRL) is a chronic disease that stems from damage to the lymphatic system due to breast cancer treatment leading to interstitial fluid buildup in the affected extremity. Patients with axillary lymph node dissection in combination with radiation therapy are at the highest risk of developing lymphedema. The mainstay non-surgical treatment involves participation in complete decongestive therapy with certified lymphedema therapists, daily compression garment usage, at-home pump treatments, and diligent skin care. Surgical treatments options can help alleviate symptoms associated with the disease by improving lymphatic drainage and removing fibrofatty tissue. Immediate lymphatic reconstruction (ILR) involves reconstructing cut lymphatics prophylactically at the time of axillary dissection with lymphovenous bypass (LVB) to decrease the risk of developing lymphedema. Delayed reconstruction addresses clinically diagnosed lymphedema and is divided into physiologic and debulking surgical techniques. Physiologic surgeries include lymphovenous bypass and vascularized lymph node transplantation (VLNT) aimed to treat fluid buildup. Debulking surgeries include lymphatic sparing liposuction or direct excision of fibrofatty tissues in the affected extremity to treat excess fibrofatty tissue secondary to lymphedema. This review discusses the pathophysiology of BCRL, diagnosis and staging of the disease, as well as provides an algorithmic overview on delayed lymphatic reconstruction options for BCRL. Full article

Background/Objectives: Change-of-direction (COD) movements represent a fundamental component of performance in multidirectional sports and constitute one of the primary mechanisms of knee injury. The biomechanical assessment of such movements has traditionally relied on optoelectronic systems and force platforms, tools characterized by high accuracy but limited applicability in field settings. Inertial measurement units (IMUs) represent a promising alternative; however, operational doubts and uncertainties frequently persist. Therefore, the purpose of this systematic review was to critically evaluate the concurrent validity and practical utility of IMU sensors for detecting the biomechanical characteristics of COD maneuvers in sport. Methods: The bibliographic search was conducted in March 2024 across the PubMed MEDLINE, Scopus, and Web of Science databases, using Boolean combinations of the following keywords: IMU, “inertial measurement unit”, CoD, “change of direction”, “cutting maneuvers”, sport, and soccer. Study selection and analysis were performed, and methodological quality was assessed using the QUADAS-2 tool. Results: In the original search (March 2024), ten studies were included, involving 208 participants; a formal update of the search (through August 2026), conducted with the same protocol, identified two additional candidate studies (14 and 30 participants), bringing the total to twelve studies and 252 participants; both supplementary studies underwent the same four-reviewer independent screening, full-text data extraction and QUADAS-2 appraisal applied to the original ten studies. The findings indicate that IMUs demonstrate good concurrent validity for kinematic variables in the sagittal plane, particularly at the hip and knee joints, with ICC values up to 0.99 and a mean RMSE below 2°. Performance decreases substantially in the frontal and transverse planes. Regarding ground reaction forces, IMUs provide acceptable estimates of mean values, but not of instantaneous forces. For temporal variables, IMUs proved comparable to timing gates in the assessment of explosive actions. Conclusions: Based on a limited and heterogeneous evidence base of twelve studies, IMUs appear to be a promising and accessible technology for specific field-based applications, particularly for the assessment of CODs at 0°, 45°, and 90° in the sagittal plane, for monitoring right-left asymmetries, and for counting explosive actions. Given the small number of included studies and their methodological heterogeneity, these findings should be interpreted with caution. IMUs cannot yet be recommended as a complete replacement for gold-standard systems in applications requiring high precision or detailed analysis of instantaneous forces. Full article

Today, the mechanical processing and treatment of metals are of great importance in the development of industrial sectors. Therefore, in the process of mechanical metalworking, a need arises to create new modern equipment or improve existing devices. In industrial sectors, there are several problems with devices and machine tools (such as the 1K62 lathe-screw cutting machine) used in the mechanical processing of metals, including the turning of cylindrical and conical parts, machining their external and internal surfaces, thread cutting, and surface polishing. Specifically, it is difficult to precisely control the spindle speed during the metal-cutting process; energy consumption is high due to the high starting current (5–7 times the nominal value) of asynchronous motors during machine operation; the vibration level is high during operation; and there is no torque control. These problems can be eliminated by using scalar (simple) and vector control methods of frequency converters. In this case, the vector control method was used in the research because it has several advantages as a solution to these problems. As a result, it was demonstrated during the research process that the spindle speed can be reduced to 12.5 rpm. This made it possible to precisely control the spindle speed, maintain torque at low speeds, increase energy efficiency, reduce the load on mechanical transmissions, and ensure the stability of the cutting process. In addition, by operating the asynchronous electric motor through a frequency converter, it is possible to increase the efficiency and service life of such machine tools. This is of significant importance in the process of optimizing the electrical power supply system of the machine tool. Full article

Manufacturing-as-a-service (MaaS) platforms require fast and accurate manufacturing cost estimation to support quotation and resource allocation across distributed networks. However, existing machine learning approaches are often constrained by limited data availability, proprietary information, and insufficient interpretability. This work presents an engineering-based framework for generating high-fidelity synthetic data to develop scalable and interpretable cost estimation models, culminating in a SHAP-interpreted XGBoost model. A parameterized prismatic workpiece was sampled using a Hammersley sequence design of experiments, producing 1355 geometrically feasible parts with representative drilling and pocket milling features. For each configuration, cutting tools, machining parameters, and CNC toolpaths were automatically generated and optimized through an integrated SolidWorks, MATLAB, and VERICUT workflow. Over 70,000 physics-based simulations were performed to obtain reference values for machining time, tool usage, and manufacturing cost. The resulting dataset integrates geometric descriptors and optimized process parameters, enabling the training and evaluation of machine learning models. The proposed methodology demonstrates that engineering-driven synthetic data provides a physically consistent foundation for fast and interpretable AI-enabled cost estimation. While applied to a specific family of three-axis milled parts, this approach serves as a proof-of-concept for predictive micro-services required in next-generation platform-based manufacturing systems. Full article

TinyML models now run inference directly on microcontrollers with only kilobytes of memory, inside sensors, wearables, and industrial devices that often have no cloud link and no way to receive a patch once deployed. This makes them an easy target, since compressed models resist adversarial and extraction attacks less than full-precision models, but most TinyML research still treats security as a step added after compression rather than during it. This paper proposes a three-layer security-by-design architecture built to close that gap. The first layer scores candidate compressed models on a joint fitness function that combines clean accuracy, adversarial robustness, and model size before the final architecture is fixed. The second layer signs and verifies model weights with the lightweight ASCON cipher at boot and during over-the-air updates. The third layer is a runtime monitor that computes a live distribution-drift score on incoming queries and raises local defense strength once that score crosses a fixed threshold, without needing a cloud round trip. The full pipeline was validated on real microcontroller hardware, not simulation, and stays inside a 62 KB RAM budget with inference latency under 10 ms. Against real baseline models on Edge-IIoTset, the proposed method reaches 94.3% accuracy, the highest of the models tested, while cutting attack success rate from over 60–84% down to 38.9% and raising the query cost for model extraction more than five times over the weakest baseline. An ablation study and a five-seed statistical test confirm the gain holds and comes from the combination of all three layers, not from any single part. Full article

Mountainous forest-fire surveillance with multiple UAVs is constrained by rugged terrain, time-varying winds, and temperature-dependent battery derating, which jointly affect endurance and route feasibility. Most existing patrol models simplify these effects through planar routing and constant energy-consumption assumptions. This study develops an energy-aware mixed-integer linear programming model that integrates terrain-induced climbing, period-dependent wind conditions, and battery derating into multi-UAV mission scheduling. Two valid inequalities—a symmetry-breaking cut and a fleet-size lower-bound cut—are embedded in a branch-and-cut framework to improve exact solution efficiency. The proposed method is applicable to year-round patrol planning under season-dependent meteorological conditions. In the Jinyun Mountain, Chongqing, case study, it is evaluated under three representative seasonal scenarios (summer, autumn, and winter), with particular emphasis on the summer pre-fire period as the primary high-risk operating scenario. Within the same computational time limit, the proposed approach reduces total flight distance by approximately 36–49% and energy consumption by approximately 61–74% relative to large neighborhood search and ant colony optimization, while using fewer UAVs to complete the same patrol tasks. These results demonstrate that explicitly coupling environmental and battery constraints can improve the operational efficiency and energy feasibility of multi-UAV wildfire surveillance, and can provide practical decision support for daily wildfire-prevention patrol planning in mountainous environments. Full article

Background/Objectives: Catch-up vaccination is essential for reaching zero-dose and under-vaccinated children. However, community participation depends on several factors whose interactions remain insufficiently understood. This study aimed to analyze the dimensions structuring community participation in catch-up vaccination and to develop a conceptual framework describing their relationships. Methods: An analytical qualitative study was conducted between July and November 2025 in the health districts of Abobo and Yopougon, Abidjan, Côte d’Ivoire. Data were gathered from 106 participants through 26 semi-structured individual interviews and eight focus group discussions involving 80 parents or caregivers of children aged 0–59 months. Interviews and focus group discussions were transcribed in French, and thematic analysis was conducted using NVivo 14 (Lumivero, Denver, CO, USA). A standardized thematic codebook with operational definitions was used by three coders to ensure consistent coding across transcripts. Results: Thematic analysis across participant groups identified seven components shaping community participation: exposure to information, understanding of the message, trust in actors and services, social acceptance, parents’ availability and capacity, participation in the activity, and effective vaccination of the child. Five cross-cutting factors influenced progression along this pathway. Participation could break down when information was insufficient or unclear, trust was undermined by perceived costs or rumors, social acceptance was constrained, or parents faced competing work and time demands. These interactions informed the Community Participation Pathway for Catch-up Vaccination (CPP-CV), which identifies potential disruption points between information exposure and effective vaccination and highlights that non-participation may result from the interaction of informational, social, individual, structural, and organizational factors rather than vaccine hesitancy alone. Conclusions: The findings suggest that participation in catch-up vaccination goes beyond information alone and results from the interaction between community and organizational factors. Non-participation should not be treated as a single problem or assumed to reflect vaccine hesitancy. The CPP-CV provides a programmatically oriented framework for identifying where the pathway from information to vaccination breaks down and for tailoring catch-up strategies accordingly. Full article

Large language models (LLMs) can turn a flood of cross-layer industrial logs into a fluent incident narrative, but a narrative that cites only real, resolvable events can still be wrong in every relation that matters: the login came from a different workstation, the write command occurred after the physical change it supposedly caused, the action fell inside a planned maintenance window, and the controller does not even actuate the affected process. A cited event is not necessarily supporting evidence. When such a narrative drives automated response, the error propagates into isolating the wrong controller or revoking a legitimate operator. We present EviGuard, a system that decides when an LLM’s understanding is trustworthy enough to act on. EviGuard stores auditable cross-layer evidence in a provenance graph, lets the LLM propose only hypotheses, compiles each hypothesis into atomic machine-checkable claims in an Incident Claim Language, and has an ensemble of deterministic verifiers label every claim supported, contradicted, or unknown against the graph—honoring interval time, event-time policy and credential versions, network reachability, and physical control dependencies. A response gate forbids any high-impact action whose critical preconditions are not all supported. On EviCPS-Bench (42 hardware-in-the-loop attack chains, 9600 claim-level labels, $\kappa =0.87$), EviGuard cuts the unsupported-claim rate from 12.6% to 1.7%, raises relation-edge $F_1$ from 0.64 to 0.89, holds prompt-injection success to 0.4%, and executes zero unverified high-impact actions across 3200 response decisions, at a median end-to-end latency of 0.44 s. Full article

In this research, a computer vision model was developed that enables object detection, shape identification, and orientation prediction using image segmentation. The GrabCut algorithm was used to segment images. GrabCut was initialized in two ways: first, by initializing the input image with a bounding box (a box surrounding the object’s position in the image), or second, by initializing the input image with a suggested approximation mask (a mask resembling the segmentation). Three steps are performed iteratively: first, the color distribution of the foreground and background is estimated using a mixed Gaussian model (GMM). Second, a random Markov field is generated to manipulate the pixels (foreground/background). Finally, a histogram optimization algorithm is applied to achieve the desired final segmentation. Although the iterative steps may require time and effort to work within the algorithm, GrabCut consistently delivers high-quality results in segmenting the input image and producing an output image that closely resembles the original. Full article