Search Results (7,616)

This research evaluates Battery Energy Storage Systems (BESS) and Compressed Air Vessels (CAV) as complementary solutions for enhancing micro-grid resilience, flexibility, and sustainability. BESS units ranging from 5 to 400 kWh were modeled using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) neural network, achieving high SOC prediction accuracy with R² > 0.98 and MSE as low as 0.13 kWh². Larger batteries (400–800 kWh) effectively reduced grid purchases and redistributed surplus energy, improving system efficiency. CAVs were tested in pumped-storage mode, achieving 33.9–57.1% efficiency under 0.5–2 bar and high head conditions, offering long-duration, low-degradation storage. Waterhammer-induced CAV storage demonstrated reliable pressure capture when Reynolds number ≤ 75,000 and Volume Fraction Ratio, VFR > 11%, with a prototype reaching 6142 kW and 170 kWh at 50% air volume. CAVs proved modular, scalable, and environmentally robust, suitable for both energy and water management. Hybrid systems combining BESS and CAVs offer strategic advantages in balancing renewable intermittency. Machine learning and hydraulic modeling support intelligent control and adaptive dispatch. Together, these technologies enable future-ready micro-grids aligned with sustainability and grid stability goals. Full article

African swine fever (ASF) is a highly fatal viral disease of domestic pigs and wild boar that continues to threaten pig production across Europe. Genotype II African swine fever virus (ASFV) has been present in Serbia since 2019 and was first confirmed in Bosnia and Herzegovina in 2023, yet recent genetic data from the region have been lacking. This study aimed to update the genetic characterization of ASFV strains circulating in Serbia between 2023 and 2025 and to provide the first sequence data from Bosnia and Herzegovina. A total of 110 isolates were analyzed by partial sequencing of seven genomic regions recommended by the European Union Reference Laboratory. Good-quality sequences were obtained for at least two loci per isolate. All isolates belonged to genotype II and were classified as CVR variant I, IGR-II, O174L-I, MGF I, K145R-I, and ECO2-II, corresponding to cluster 19. No novel genetic changes were identified in the sequenced fragments. These findings indicate the stable, endemic circulation of cluster 19 in both domestic pigs and wild boar, maintained through ecological and human-mediated transmission at the wildlife–livestock interface. The detection of cluster 19 in Bosnia and Herzegovina underscores transboundary spread and highlights the need for continued molecular surveillance and regional cooperation. Full article

Purpose: This study aimed to develop a precise predictive model to assess the risk of radiation pneumonitis (RP) and three-year survival in patients with non-small cell lung cancer (NSCLC) following volumetric modulated arc therapy (VMAT). Radiomics features, ensemble stacking, and explainable artificial intelligence (XAI) were integrated to enhance predictive performance and clinical interpretability. Materials and Methods: A retrospective cohort of 221 NSCLC patients treated with VMAT at Kaohsiung Veterans General Hospital between 2013 and 2023 was analyzed, including 168 patients for RP prediction (47 with ≥grade 2 RP) and 118 patients for survival prediction (34 deaths). Clinical variables, dose–volume histogram (DVH) parameters, and radiomic features (original, Laplacian of Gaussian [LoG], and wavelet filtered) were extracted. ANOVA was used for initial feature reduction, followed by LASSO and Boruta-SHAP for feature selection, which formed 10 feature subsets. The data were divided at an 8:2 ratio into training and testing sets, with SMOTE balancing and 10-fold cross-validation for parameter optimization. Six models—logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), XGBoost, and Ensemble Stacking—were evaluated in terms of the AUC, accuracy (ACC), negative predictive value (NPV), precision, and F1 score. SHAP analysis was applied to interpret feature contributions. Results: For RP prediction, the LASSO-selected radiomic subset (FR) combined with Ensemble Stacking achieved optimal performance (AUC 0.91, ACC 0.89), with SHAP identifying V40 Firstorder_Min as the most influential feature. For survival prediction, the FR subset yielded an AUC of 0.97, an ACC of 0.92, and an NPV of 1.00, with V10 Wavelet Firstorder_Min as the top contributor. The multimodal subset (FC+R) also performed strongly, achieving an AUC of 0.91 for RP and 0.96 for survival. Conclusions: This study demonstrated the superior performance of radiomics combined with Ensemble Stacking and XAI for the prediction of RP and survival following VMAT in patients with NSCLC. SHAP-based interpretation enhances transparency and clinical trust, offering a robust foundation for personalized radiotherapy and precision medicine. Full article

Since 1975, it has been known that the Hurwitz zeta-function has a unique property to approximate by its shifts all analytic functions defined in the strip $\mathfrak{D}=\{s=\sigma +it:1/2<\sigma <1\}$. However, such an approximation causes efficiency problems, and applying short intervals is one of the measures to make that approximation more effective. In this paper, we consider the simultaneous approximation of a tuple of analytic functions in the strip $\mathfrak{D}$ by discrete shifts $(\zeta (s+ik{h}_1,{\alpha }_1),\dots ,\zeta (s+ik{h}_r,{\alpha }_r))$ with positive $h_1,\dots ,h_r$ of Hurwitz zeta-functions in the interval $[N,N+M]$ with $M={max}_{1⩽j⩽r}\left(h_j^{-1}{\left(N{h}_j\right)}^{23/70}\right)$. Two cases are considered: $1^{°}$ the set $\{(h_{j}log(m+{\alpha }_j),m\in {\mathbb{N}}_0,j=1,\dots ,r),2\pi \}$ is linearly independent over $\mathbb{Q}$; and $2^{°}$ a general case, where ${\alpha }_j$ and $h_j$ are arbitrary. In case $1^{°}$, we obtain that the set of approximating shifts has a positive lower density (and density) for every tuple of analytic functions. In case $2^{°}$, the set of approximated functions forms a certain closed set. For the proof, an approach based on new limit theorems on weakly convergent probability measures in the space of analytic functions in short intervals is applied. The power $\eta =23/70$ comes from a new mean square estimate for the Hurwitz zeta-function. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article

Rising global temperatures are driving an urgent need for buildings that consume less energy while maintaining comfort. Cooling demand is surging worldwide, yet conventional air-conditioning remains energy-intensive and carbon-heavy. Against this backdrop, radiative cooling materials have gained attention as a passive solution capable of reflecting incoming solar radiation while emitting thermal energy to the sky. This study aims to establish a climate-informed framework that quantitatively predicts the energy-saving potential of façade-integrated radiative-cooling materials across diverse East Asian climates. By synergizing hour-by-hour building-energy simulation with three novel atmospheric suitability indices, we provide a transferable methodology for selecting and optimizing passive cooling strategies at urban and regional scales. Three façade configurations were tested, i.e., a conventional absorptive surface, a common radiative cooling surface, and an idealized high-reflectance and high-emissivity surface. The results show that the ideal case can reduce wall surface temperatures by up to 20 °C, suppress diurnal heat flux swings by 60–80%, and cut annual cooling demand by 5–80 kWh per square meter, depending on climate conditions. To generalize these findings, three new indices—the Weather Structure Index, Diurnal Temperature Index, and Composite Climate Applicability—were proposed. Regression models with R² values above 0.9 confirm the Composite Climate Applicability index as a robust predictor of energy-saving potential. The outcomes demonstrate that radiative cooling is not only highly effective in hot, humid regions but also unexpectedly beneficial in clear, cold climates, offering a practical, climate-informed framework for advancing low-carbon building design. Full article

Multilevel inverters (MLI) have become the frontier in high-power medium voltage systems because of their unique property of generating sinusoidal voltage through smaller voltage increments. Although many MLI structures have been proposed over the years, most still rely on a large number of switches, which increases complexity and conduction losses. In this work, a reconfigurable, gable-shaped multilevel inverter module, capable of operating in both symmetric and asymmetric modes, is introduced for use in AC microgrid cluster environments. The design employs five DC sources and six semiconductor devices arranged in a gable layout, which helps shorten the conduction path while also reducing the total hardware count. As a result, the inverter becomes more compact, experiences lower switching losses, and proves more suitable for grid-connected operation. In symmetric mode, the inverter delivers an 11-level output, while the asymmetric arrangement produces 19 levels. The proposed concept is examined through MATLAB/Simulink (R2023a) studies, and its practicality is verified using a Hardware-in-the-Loop setup with an integrated data-acquisition system capable of delivering 10 kW of real power and handling up to 50% overload. These results confirm the suitability of the topology for real-time grid applications. Full article

This study developed a rapid and reliable SYBR-Green semiplex PCR assay for simulta-neous detection of major carbapenem resistance genes in Klebsiella pneumoniae and Acinetobacter baumannii. Two primer sets were used: one to detect blaKPC, blaNDM-1, and blaOXA-48 genes in K. pneumoniae and blaOXA-23 in A. baumannii, and another to amplify conserved 16S rRNA gene regions as internal controls. The intra- and inter-assay coeffi-cient of variation ranged from 0.03% to 3.8%. Standard curves exhibited excellent linearity across six logarithmic scales (10¹–10⁶ DNA copies/µL), with detection limits of 10–10² DNA copies/mL. Melting temperatures (Tm) were: 88.85 °C (KPIC), 90.65 °C (NDM-1), 89.45 °C (KPC), 84.23 °C (OXA-48), 87.81 °C (OXA-23), and 80.67 °C (ABIC). The SYBR-Green Semiplex PCR assay offers a rapid (65 min turnaround), cost-effective, and sensitive method for early detection of carbapenem-resistant pathogens, enabling timely targeted therapy and improved infection control by potentially reducing empirical antibiotic use before culture confirmation. Full article

Rationale and Objective: Medical imaging, particularly computed tomography (CT), is the largest man-made contributor to collective radiation exposure. This study compares methods for assessing CT radiation dose, focusing on thoracic examinations. Population investigated: We retrospectively analyzed 3956 non-contrast thoracic CT exams from 1553 females (mean age 70 ± 12 years) and 2403 males (mean age 69 ± 12 years). Methods: Data were acquired using a Siemens Somatom Force CT-Scanner (installed in 2015). Exposure parameters and patient somatic data were recorded and used as inputs for the Virtual Dose Simulator (VDS), which served as the gold standard for effective dose (ED^ref) measurement. Additionally, ED was calculated using two ICRP-103 K-factor methods: Shrimpton et al. (ED^shr) and Romanyukha et al. (ED^rom). Results: Regression analysis demonstrated strong linear relationships between ED^ref and both weight and BMI (R² ≥ 0.84), with ED^ref values ranging from 1.55 to 4.59 mSv. Even stronger linear relationships were observed between ED^ref and CT scanner tube current, particularly for women (R² = 0.93) and men (R² = 0.90). Similar trends emerged for dose-length product (DLP), which showed high correlations for both women (R² = 0.95) and men (R² = 0.94). Compared to VDS, ED^rom underestimated women’s doses by 10% and slightly overestimated men’s doses by 1%, while ED^shr underestimated the effective dose by 18% for women and 9% for men. Conclusion: This study demonstrates that K-factor methods provide a simple, efficient, and clinically practical approach for both individual cumulative dose monitoring (critical for patients requiring repeated imaging) and population-level dose assessment (essential for epidemiological risk evaluation). The high reliability of K-factor-based estimates, as demonstrated in this work, underscores their potential for integration into clinical practice to enhance dose optimization and patient safety. Full article

In view of the remarkable progress in microrheology to monitor the random motion of Brownian particles with a size as small as a few nanometers, and given that de Broglie matter waves have been experimentally observed for large molecules of comparable nanometer size, we examine whether Brownian particles can manifest a particle-wave duality without employing a priori arguments from quantum decoherence. First, we examine the case where Brownian particles are immersed in a memoryless viscous fluid with a time-independent diffusion coefficient, and the requirement for the Brownian particles to manifest a particle-wave duality leads to the untenable result that the diffusion coefficient has to be proportional to the inverse time, therefore, diverging at early times. This finding agrees with past conclusions published in the literature, that quantum mechanics is not equivalent to a Markovian diffusion process. Next, we examine the case where the Brownian particle is trapped in a harmonic potential well with and without dissipation. Both solutions of the Fokker–Planck equation for the case with dissipation, and of the Schrödinger equation for the case without dissipation, lead to the same physically acceptable result—that for the Brownian particle to manifest a particle-wave duality, its mean kinetic energy $k_{B}T/2$ needs to be ½ the ground-state energy, $E_0={\displaystyle \frac{1}{2}}\hslash \omega$ of the quantum harmonic oscillator. Our one-dimensional calculations show that for this to happen, the trapping needs to be very strong so that a Brownian particle with mass m and radius R needs to be embedded in an extremely stiff solid with shear modulus, G proportional to $\left(m/R\right){\left(k_{B}T/\hslash \right)}^2$. Full article

Introduction: Clinical variability within families harbouring disease-causing genetic variants hampers clinical care and risk stratification. We studied a multigenerational family presenting with sinus bradycardia and long QT syndrome type 2 (LQTS2). The family harboured a pathogenic variant in KCNH2, which co-segregated with the observed LQTS2. We studied the genetic cause of the high occurrence of sinus bradycardia in this family. Methods: Clinical data was collected, including heart rate, QT-interval, symptoms, and echocardiographic parameters. QTc was calculated using the Bazett and the Fridericia formula. Sanger sequencing of HCN4 was performed, followed by segregation analysis of the identified variant with sinus bradycardia. The biophysiological consequences of two variants, KCNH2-p.L69P (c.206T>C) and HCN4-p.R666W (c.1996C>T), were assessed by patch-clamp experiments. Therefore, a heterologous model was generated by transfection of HEK293A or CHO-k1 cells, respectively. Results: Sanger sequencing of HCN4 identified HCN4-p.R666W (c.1996C>T), which has a stronger segregation with the observed sinus bradycardia than KCNH2-p.L69P. Patch-clamp experiments revealed that KCNH2-p.L69P and HCN4-p.R666W lead to a decrease in the corresponding current densities, which explains the LQTS and sinus bradycardia observed in the patients. Carriers of both genetic variants have a more severe LQTS2 phenotype, reflected in longer QT and higher incidence of syncope. Conclusions: We identified two (likely) pathogenic variants, KCNH2-p.L69P and HCN4-p.R666W, co-segregating with LQTS2 and sinus bradycardia, respectively. Patients carrying both variants showed a more severe phenotype. These findings highlight the importance of additional genetic testing when discordant features are present, thereby enabling more accurate diagnosis, risk prediction, and management. Full article

This study presents a streamlined and accurate approach for modeling the performance of hermetic reciprocating compressors under variable-speed conditions. Traditional compressor models often neglect the influence of motor frequency, leading to considerable deviations at low-speed operation. To address these limitations, a frequency-dependent numerical framework was developed using one-dimensional (1-D) and two-dimensional (2-D) polynomial regressions to represent volumetric efficiency (${\eta }_v$) and isentropic efficiency (${\eta }_{isentr}$) as functions of compression ratio (r) and motor speed frequency (f). The proposed model integrates manufacturer data and thermodynamic property databases to predict compressor behavior across a wide range of operating conditions. Validation using the Bitzer 4HTE-20K CO₂ compressor demonstrated strong agreement with experimental data, maintaining prediction errors within ±10% for both power input and discharge temperature. Moreover, the model enhanced accuracy by up to 19.4% in the low-frequency range below 40 Hz, where conventional models typically fail. The proposed method provides a practical and computationally efficient tool for accurately simulating the performance of hermetic reciprocating compressors that support improved design, optimization, and control of refrigeration and heat pump systems. Full article

Endometriosis involves oestrogen-dependent chronic inflammation and the abnormal proliferation of ectopic endometrial tissue. Conventional hormonal therapies suppress systemic oestrogen, but do not fully address local oxidative and inflammatory signalling. This review provides a mechanistic synthesis of recent molecular evidence. This evidence is on four FDA-recognized (Food and Drug Administration) medicinal plants. These are Curcuma longa, Zingiber officinale, Glycyrrhiza glabra, and Silybum marianum. The review highlights their capacity to modulate key intracellular pathways. These pathways are implicated in endometriosis. The review covers the integration of phytochemical-specific actions within NF-κB- (nuclear factor kappa-light-chain-enhancer of activated B cells), COX-2-(Cyclooxygenase-2), PI3K/Akt-(PI3K/Akt signaling pathway), Nrf2/ARE-(Nuclear factor erythroid 2–related factor 2) and ERβ-(Estrogen receptor beta) mediated networks, which jointly regulate cytokine secretion, apoptosis, angiogenesis and redox balance in endometrial lesions. Curcumin downregulates COX-2 and aromatase while activating Nrf2 signalling, shogaol from ginger suppresses prostaglandin synthesis and induces caspase-dependent apoptosis, isoliquiritigenin from liquorice inhibits HMGB1-TLR4–NF-κB (High Mobility Group Box 1, Toll-like receptor 4) activation, and silymarin from milk thistle reduces IL-6 (Interleukin-6) and miR-155 (microRNA-155) expression while enhancing antioxidant capacity. Together, these phytochemicals demonstrate pharmacodynamic complementarity with hormonal agents by targeting local inflammatory and oxidative circuits rather than systemic endocrine axes. This mechanistic framework supports the rational integration of phytotherapy into endometriosis management and identifies redox-inflammatory signalling nodes as future translational targets. Full article

Uterine leiomyosarcoma (uLMS) is a rare, highly aggressive malignancy of uterine smooth muscle, associated with early metastasis, frequent recurrence, and poor prognosis. Accurate preoperative diagnosis remains difficult given that clinical and radiologic features often overlap with benign leiomyomas, and no reliable biomarkers are currently available. This review summarizes recent evidence on the role of microRNAs (miRNAs) in the biology and clinical management of uLMS. Literature from molecular and translational studies was examined to identify dysregulated miRNAs, their target pathways, and potential diagnostic and therapeutic applications. uLMS displays a characteristic miRNA profile, including downregulation of tumor-suppressive miRNAs such as the miR-29 and miR-200 families and upregulation of oncogenic miRNAs including miR-21 and the miR-183~96~182 cluster, leading to activation of PI3K/AKT/mTOR signaling and epithelial–mesenchymal transition (EMT). Circulating and tissue miRNAs show promise as minimally invasive biomarkers for differentiating uLMS from leiomyomas, predicting prognosis, and guiding therapy. Emerging therapeutic approaches aim to restore the tumor-suppressive miRNAs or inhibit oncogenic ones using mimics or antagomiRs. Overall miRNAs represent critical regulators of uLMS pathogenesis and hold significant potential for precision diagnosis, prognostication, and targeted therapy, though larger validation studies and improved delivery systems are required before clinical translation. Full article

In order to ensure the stability of the gravity heat pipe (GHP) heat exchanger in the mine return air waste heat recovery project and to explore the influence of the working fluid and filling ratio of the GHP on the heat transfer performance, this paper establishes a computational fluid dynamics (CFD) model of the GHP for mine return air waste heat recovery. The heat transfer characteristics and multiphase flow mechanism of the GHP with R22 and R410a working fluids at 30% to 80% filling ratios were studied using the VOF model from three aspects: two-phase flow, wall temperature, and thermal resistance. The validity of the model was verified through experimental data. The findings of the research indicate that the physical property parameters of the working fluid and the alterations in the filling ratio exert a substantial influence on the liquid-phase boiling heat transfer and the condensation process on the condenser wall. The CFD operation results demonstrate a high degree of congruence with the experimental data. The maximum deviation in the wall temperature is 2.9%. When the filling ratio is in the range of 50% to 60%, the axial distribution of the wall temperature tends to be flat. With regard to thermal resistance, both CFD and experimental results demonstrate a tendency of initially decreasing and subsequently increasing with increasing filling ratio. The average wall temperature of R410a GHP with a 50% filling ratio reached the highest value (20.3 °C), and the thermal resistance reached the lowest value (0.021 K/W), demonstrating superior heat transfer performance and excellent isothermal characteristics. Full article