Search Results (1,829)

To improve the separation efficiency of chili seeds, heat treatment on the adhesion between the seeds, peel, and embryo seat was studied. This study was conducted to explore the separation effect of heat treatment on chili seeds based on different temperature conditions. Firstly, the physical properties and thermal properties parameters of the materials (chili seed, peel, and embryo seats) were measured. These physical data were imported into ANSYS 2022 software to carry out a thermal steady-state simulation experiment. And the effects on seed activity were studied with different temperature conditions. The results indicated that it can effectively reduce the adhesion force between seeds, fruit peels, and embryo seats at 120 °C for 60 s. The maximum thermal stresses of the chili peel, seed, and embryo seat were 3.687 MPa, 0.878 MPa, and 0.662 MPa, respectively. At the same time, the germination rate of seeds under this treatment condition remained above 80%, ensuring the high activity of the seeds. This study provided a theoretical basis for the separation technology of chili seeds, and it was expected to bring practical guidance for the efficient utilization and extraction of chili seeds. Full article

Wildlife rescue centres face considerable challenges in promoting animal welfare and enhancing the care and housing conditions of animals under professional supervision. These challenges are further compounded by the diversity of species admitted, each with distinct specific needs. In Colombia and other Latin American countries, primates are among the most frequently rescued and behaviourally complex mammalian taxa, requiring particular attention. In response, this study aimed to assess the content validity of proposed animal welfare indicators for Cebus albifrons through a Delphi consultation process and to develop two species-specific assessment protocols: a daily-use tool for keepers and a comprehensive protocol for professional audits. A panel of 23 experts in primate care and rehabilitation participated in two consultation rounds to evaluate and prioritise the indicators based on their content validity, perceived reliability, and practicality. Indicators were classified as either animal-based (direct measures) or resource- and management-based (indirect measures). After each round, experts received summarised feedback to refine their responses and facilitate consensus building. Of the 39 initially proposed indicators, 28 were validated for inclusion in the extended protocol and 10 selected for the daily-use checklist. Among these, 20 indicators in the extended protocol and 6 in the daily protocol were resource- or management-based—such as adequate food provision, physical enrichment, and habitat dimensions—highlighting their practical applicability and relevance in identifying welfare issues and risk factors. Although these indirect indicators were more numerous, the top-ranked indicators in both protocols were animal-based, including signs of pain, affiliative behaviours, and abnormal repetitive behaviours. These are essential for accurately reflecting the animals’ welfare state and are therefore critical components of welfare assessment in captive non-human primates. This study demonstrates that welfare assessment tools can be effectively tailored to the specific needs of wildlife rescue centres, providing a robust foundation for enhancing welfare practices. These protocols not only offer practical approaches for assessing welfare but also underscore the importance of embedding animal welfare as a priority alongside conservation efforts. Future research should aim to refine these tools further, assess their implementation, and evaluate inter- and intra-observer reliability to ensure consistency across different settings. Full article

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification of physiological states using Heart Rate Variability (HRV), combined with unsupervised machine learning techniques. This approach is particularly valuable when annotated datasets are scarce or unavailable—as is often the case in real-world wearable and IoT-based health monitoring. In this study, data were collected from participants under controlled conditions representing rest, stress, and physical exertion. Core HRV parameters such as the SDNN (Standard Deviation of all Normal-to-Normal intervals), RMSSD (Root Mean Square of the Successive Differences), DFA (Detrended Fluctuation Analysis) were extracted. Principal Component Analysis was applied for dimensionality reduction. K-Means, hierarchical clustering, and Density-based spatial clustering of applications with noise (DBSCAN) were used to uncover natural groupings within the data. DBSCAN identified outliers associated with atypical responses, suggesting potential for early anomaly detection. The combination of HRV descriptors enabled unsupervised classification with over 90% consistency between clusters and physiological conditions. The proposed approach successfully differentiated the three physiological conditions based on HRV and fractal features, with a clear separation between clusters in terms of DFA α1, α2, LF/HF, and RMSSD (with high agreement to physiological labels (Purity ≈ 0.93; ARI = 0.89; NMI = 0.92)). Furthermore, DBSCAN identified three outliers with atypical autonomic profiles, highlighting the potential of the method for early warning detection in real-time monitoring systems. Full article

Industrial Control Systems (ICSs) are fundamental to critical infrastructure, yet they face increasing cybersecurity threats, particularly data integrity attacks like replay and data forgery attacks. Traditional IT-centric security measures are often inadequate for the Operational Technology (OT) environment due to stringent real-time and reliability requirements. This paper proposes an endogenous security defense mechanism based on the Luenberger observer and residual analysis. By embedding a mathematical model of the physical process into the control system, this approach enables real-time state estimation and anomaly detection. We model the ICS using a linear state-space representation and design a Luenberger observer to generate a residual signal, which is the difference between the actual sensor measurements and the observer’s predictions. Under normal conditions, this residual is minimal, but it deviates significantly during a replay attack. We formalize the system model, observer design, and attack detection algorithm. The effectiveness of the proposed method is validated through a simulation of an ICS under a replay attack. The results demonstrate that the residual-based approach can detect the attack promptly and effectively, providing a lightweight yet robust solution for enhancing ICS security. Full article

Background: Training planning in military environments is complex due to diverse operational demands and constant exposure to stressors. When combined with high training volumes and insufficient recovery, this can result in physical and mental overload. Regular assessments are crucial to monitor the condition of personnel and adjust training accordingly, though more research is needed to effectively track performance in real operational settings. Objectives: This study aims to monitor neuromuscular and psychological performance in relation to training load in a military school, addressing the research gap in tracking performance in operational settings. Methods: Overall, 27 marines (age: 27.9 ± 4.8 years; height: 178.1 ± 6.3 cm; weight: 79.1 ± 7.8 kg) were monitored over a 13-week academic-military training period to assess neuromuscular performance and psychological fatigue. Results: Laboratory tests included the countermovement jump (p = 0.002), isometric mid-thigh pull (p = 0.001), and handgrip strength for both dominant (p = 0.947) and non-dominant hands (p = 0.665). Field tests involved maximum pull-ups (p = 0.015), push-ups (p = 0.001), and the medicine ball throw (p = 0.334). Psychological evaluation via the POMS questionnaire showed the highest negative mood scores in Tension–Anxiety, Depression–Melancholia, and Fatigue–Inertia, while Vigor–Activity was the highest positive state. RESTQ-Sport results indicated total recovery was 68.9% greater than total stress. Conclusions: Despite improvements in some field tests, no significant neuromuscular gains were observed, likely due to excessive training loads, limited recovery, and sustained stress. Full article

This paper presents a resilient observer-based and event-triggered control scheme for discrete-time Cyber–Physical Systems (CPS) under Markovian Cyber-Attacks (MCA). The proposed framework integrates a Luenberger observer for cyberattack detection with a state-feedback controller designed to preserve system stability in the presence of Denial-of-Service (DoS) and False Data Injection (FDI) attacks. Attack detection is achieved through residual signal generation combined with Markovian modeling of the attack dynamics. System stability is guaranteed by formulating relaxed Linear Matrix Inequality (LMI) conditions that incorporate relaxation variables, a diagonal Lyapunov function, the S-procedure, and congruence transformations. Moreover, the Event-Triggered Mechanism (ETM) efficiently reduces communication load without degrading control performance. Numerical simulations conducted on a three-tank system benchmark confirm enhanced detection accuracy, faster recovery, and strong robustness against uncertainties. Full article

Facial features are critical visual indicators for understanding what a person is experiencing, providing valuable insights into their emotions and physical states. However, accurately detecting these features under diverse conditions remains a significant challenge, especially in computationally constrained environments. This paper presents a facial feature extraction method designed to identify regions of interest for detecting facial cues, with a focus on improving the accuracy of eye and mouth detection. Addressing the limitations of standard Haar cascade classifiers, particularly in challenging scenarios such as droopy eyes, red eyes, and droopy mouths, this method introduces a correction algorithm rooted in normal human facial anatomy, emphasizing symmetry and consistent feature placement. By integrating this correction algorithm with a feature-based refinement process, the proposed approach enhances detection accuracy from 67.22% to 96.11%. Through this method, the accurate detection of facial features like the eyes and mouth is significantly improved, offering a lightweight and efficient solution for real-time applications while maintaining computational efficiency. Full article

Stone cleaning for cultural heritage monuments is a critical conservation intervention that must effectively eliminate harmful surface contaminants while preserving the material’s physical, chemical, and historical integrity. This study investigated the removal of tenacious black biofilms formed by Nocardia species previously isolated from deteriorated limestone from the Bastet tomb in Tell Basta, Zagazig City, Egypt, using a Q-switched 1064 nm Nd:YAG laser. Experimental limestone specimens were systematically inoculated with Nocardia sp. under controlled laboratory conditions to simulate biodeterioration processes. Comprehensive testing revealed that a laser fluence of 0.03 J/cm² with a 5 ns pulse duration, applied under wet conditions with 500 pulses, achieved the complete elimination of the biological black film without damaging the underlying stone substrate. The cleaning efficacy was evaluated through an integrated analytical framework combining stereomicroscopy, scanning electron microscopy coupled with energy-dispersive X-ray analysis (SEM-EDX), X-ray diffraction (XRD), and laser-induced plasma spectroscopy (LIPS). These analyses demonstrated a remarkable transformation from a compromised mineralogical composition dominated by gypsum (62%) and anhydrite (13%) to a restored state of 98% calcite, confirming the laser treatment’s effectiveness in reversing biodeterioration processes. SEM micrographs revealed the complete elimination of mycelial networks that had penetrated to depths between 984 μm and 1.66 mm, while LIPS analysis confirmed the restoration of elemental signatures to near-control levels. The successful application of LIPS for real-time monitoring during cleaning provides a valuable tool for preventing overcleaning, addressing a significant concern in laser conservation interventions. This research establishes evidence-based protocols for the non-invasive removal of Nocardia-induced black biofilms from limestone artifacts, offering conservation professionals a precise, effective, and environmentally sustainable alternative to traditional chemical treatments for preserving irreplaceable cultural heritage. Full article

This study explores the behavior of phantom dark energy within the framework of Weyl-type $f(Q,T)$ gravity, considering a spatially flat FLRW universe under observational constraints. The field equations are analytically solved for a dust-like fluid source. To determine the present values of the model parameters, we utilize observational data from the Hubble parameter measurements via cosmic chronometers (CC) and the apparent magnitude data from the Pantheon compilation of Type Ia supernovae (SNe Ia). With these obtained parameter values, we analyze the model’s physical characteristics by evaluating the effective and dark energy equation of state parameters ${\omega }_{eff}$ and ${\omega }_{de}$, the deceleration parameter $q\left(z\right)$, and energy conditions. Additionally, we conduct the $O_m$ diagnostic test for the model. We estimate the transition redshift $z_t\le 0.5342, 0.6334$ and the present age of the universe $t_0=13.46, 13.49$ Gyrs with $H_0=67.4\pm 3.6, 68.8\pm 1.9$ Km/s/Mpc, ${\mathsf{\Omega }}_{m0}={0.41}_{-0.24}^{+0.13}, {0.299}_{-0.077}^{+0.042}$, and ${\omega }_{eff}=-0.6447,-0.696$, ${\omega }_{de}=-1.0347,-1.0284$. We find a transit phase accelerating and physically acceptable phantom dark energy model of the universe. Full article

The Terracotta Army, an integral part of China’s cultural heritage, has suffered physical erosion like cracks and notches over time. Manual inspection methods are inefficient and subjective. This study proposes an automated defect detection system based on computer vision to enhance the efficiency and precision of detecting these defects. The system includes the following core modules: (1) high-resolution image acquisition, which ensures comprehensive and detailed data capture; (2) sophisticated image illumination processing, which compensates for varying lighting conditions and improves image quality; (3) advanced image data augmentation techniques, which enrich the dataset and improve the generalization ability of the detection model; and (4) accurate defect detection, which leverages state-of-the-art algorithms. In the experimental phase, the efficacy of the proposed approach was evaluated. Illumination-enhanced low-light images were used for data augmentation, and the generated images showed high similarity to the original images, as measured by PSNR and SSIM. The YOLOv10 algorithm was employed for defect detection and achieved average detection rates of 91.71% for cracks and 93.04% for abrasions. This research provides a scientific and efficient solution for cultural relic protection and offers a valuable reference for future research in heritage conservation. Full article

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks. Full article

This paper presents a physics-informed digital twin designed for real-time thermal monitoring and visualization of a metallic plate. The system comprises a physical layer consisting of an aluminum plate equipped with thermistors to capture boundary conditions, a computational layer that implements the steady-state Laplace equation using the finite difference method, and an embedded execution framework deployed on a microcontroller that utilizes Direct Memory Access-driven ADC for efficient concurrent acquisition. The computed thermal field is transmitted through a serial interface and displayed in real time using a Python-based visualization interface. The Steinhart–Hart model was used to experimentally characterize the sensors, ensuring accuracy in the boundary condition acquisition. While the current formulation is restricted to steady-state conditions, it enables accurate spatial reconstructions with acceptable error margins and demonstrates operational concurrency with the physical system. The compact and modular architecture allows adaptation to other physical domains governed by elliptic PDEs, making it suitable for educational applications, diagnostic prototyping, and embedded edge deployments. Full article

Background: Celiac disease (CD) is a chronic, immune-mediated condition requiring lifelong adherence to a gluten-free diet. In children, CD can negatively impact not only physical health but also psychological well-being and quality of life. The burden of dietary restrictions, social limitations, and emotional stress may lead to increased anxiety and depressive symptoms. This study aims to compare the quality of life, anxiety, and depression levels in children with celiac disease to those of healthy peers without chronic illness. Methods: The research involved a total of 129 individuals aged 8–18 years (64 with celiac disease and 65 healthy volunteers) and their parents. To assess children with celiac disease and healthy children, we used a sociodemographic form that we created, along with the State-Trait Anxiety Inventory (STAI), Trait Anxiety Inventory (TAI), Children’s Depression Inventory (CDI), Pediatric Quality of Life Inventory (PedsQL), and Parent Quality of Life Inventory tests. Results: Celiac patients’ diet adherence, parental education level, and family income were found to be significantly associated with quality of life, as well as levels of depression and anxiety. (p < 0.037, p < 0.04, p < 0.004, respectively). Celiac patients had significantly lower BMI SDS (mean −0.55 ± 1.13, p < 0.001) and height SDS scores (mean −0.49 ± 1.28, p < 0.017). Key factors negatively affecting the quality of life in individuals with celiac disease were difficulty adhering to the diet and low family income levels. Conclusions: Elevated anxiety with reduced quality of life highlights the importance of integrating psychosocial support into the routine care of children with celiac disease. A holistic treatment approach that considers the psychosocial well-being of children can significantly improve their quality of life. Full article

Accurate parametric modeling of lithium-ion batteries is essential for battery management system (BMS) design in electric vehicles and broader energy storage applications, enabling reliable state estimation and effective thermal control under diverse operating conditions. This study presents a detailed characterization of lithium-ion cells to support advanced BMS in electric vehicles and stationary storage. A second-order equivalent circuit model is developed to capture instantaneous and dynamic voltage behavior, with parameters extracted through Hybrid Pulse Power Characterization over a broad range of temperatures (−10 °C to 45 °C) and state-of-charge levels. The method includes multi-duration pulse testing and separates ohmic and transient responses using two resistor–capacitor branches, with parameters tied to physical processes like charge transfer and diffusion. A weakly coupled electro-thermal model is presented to support real-time BMS applications, enabling accurate voltage, temperature, and heat generation prediction. This study also evaluates open-circuit voltage and direct current internal resistance across pulse durations, leading to power capability maps (“fish charts”) that capture discharge and regenerative performance across SOC and temperature. The analysis highlights performance asymmetries between charging and discharging and confirms model accuracy through curve fitting across test conditions. These contributions enhance model realism, thermal control, and power estimation for real-world lithium-ion battery applications. Full article

Gas well production prediction is an important means to determine the economic benefits of gas field development, and it is the key to realize the optimization of gas well production. However, with the continuous development of gas fields, the increasing number of low-yield and low-efficiency wells disrupted the original symmetry in the overall well distribution and production structure. Traditional production capacity prediction methods are difficult to adapt to complex geological conditions and dynamic production characteristics and cannot meet the requirements of refined management of gas fields. In this paper, a CNN-LSTM-attention hybrid prediction model incorporating physical constraints (P-C-L-A) is proposed to predict production per well. The P-C-L-A model integrates CNN’s local feature capture capability, LSTM’s time-dependent modeling, and the attention mechanism’s critical state focusing function. Moreover, the gas well decline law is embedded into the loss function to realize the joint drive of physical constraints and data of the decline curve. Compared with the traditional BP neural network, the model in this paper has higher accuracy, and the root mean square error of the proposed method is reduced by 24.41%. Furthermore, this paper proposes a full life cycle intelligent optimization production strategy of “initial static similar production + historical data-driven rolling production”. For wells in the early stage of production, static production allocation is carried out by matching wells with similar geological engineering parameters based on the symmetry of the characteristic parameters of similar production wells through the k-nearest neighbor value algorithm. For stable production wells, a machine learning model is built to predict short-term production and dynamic production optimization is achieved by rolling updates of production data. The proposed method can be extended to the production prediction of other tight gas wells using similar technical processes. Full article