Search Results (43,672)

Small rural water utilities in the Appalachia region of the US often experience extreme water loss while struggling to maintain water quality compliance. This study quantifies the impact of reducing water loss on distribution system water quality in Martin County, Kentucky. Hydraulic and water quality models were developed, calibrated, and validated using EPANET for chlorine residuals and KYPIPE for trihalomethane (TTHM) formation. The models evaluated water loss reduction scenarios ranging from the current 70% to the industry target of 15%. Results showed that lowering water loss increased residence times, causing chlorine residual declines of 22–68%, with one site falling to the 0.2 mg/L threshold. TTHM concentrations increased by 12–18% in winter–spring and 26–44% in summer–fall, with two sites exceeding the individual 0.080 mg/L maximum contaminant level. These novel findings indicate that reducing water loss can unintentionally degrade water quality, underscoring the need for integrated planning. Recommended mitigation strategies include seasonal operational adjustments, water source and TTHM precursor management, optimized tank management, targeted flushing, and phased infrastructure upgrades. The modeling framework developed offers potential for broader application in other rural systems facing similar challenges. Full article

High denitration efficiency and strong adaptability to flue gas temperature fluctuations are the core properties of the NH₃-SCR catalyst. In this study, Fe₂O₃ modification is used as a means to explore the mechanism of adding Fe₂O₃ to broaden the temperature range of the 6CeO₂-40MnO₂/TiO₂ catalyst during the preparation process. The results show that the 6Fe₂O₃-6CeO₂-40MnO₂/TiO₂ catalyst exhibits excellent denitration performance, with a denitration efficiency higher than 90%. The temperature range is from 129 to 390 °C. N₂ selectivity and resistance to SO₂ and H₂O are good, and the denitration performance is significantly improved. When the Fe₂O₃ content is 6%, it promotes lattice shrinkage of TiO₂, improves its dispersion, refines the grain size, and increases the specific surface area of the catalyst. At the same time, Fe₂O₃ enhances the chemical adsorption of oxygen on the catalyst surface and increases the proportion of low-cost metal ions, thereby promoting electron transfer between active elements, generating more surface reactive oxygen species, increasing the oxygen vacancy content and adsorption sites for NO_x and NH₃, and significantly improving the redox performance of the catalyst. This effect is particularly conducive to the formation of strong acid sites on the catalyst surface. The NH₃-SCR reaction on the surface of the 6Fe₂O₃-6CeO₂-40MnO₂/TiO₂ catalyst follows both the L-H and E-R mechanisms, with the L-H mechanism being dominant. Full article

This paper focuses on the issue of unmodeled dynamics and large-range parametric uncertainties in air-breathing hypersonic vehicles (AHV), proposing an adaptive dynamic surface control method based on radial basis function (RBF) neural networks. First, the hypersonic longitudinal model is transformed into a strict-feedback control system with model uncertainties. Then, based on backstepping control theory, adaptive dynamic surface controllers incorporating RBF neural networks are designed separately for the velocity and altitude channels. The proposed controller achieves three key functions: (1) preventing “differential explosion” through low-pass filter design; (2) approximating uncertain model components and unmodeled dynamics using RBF neural networks; (3) enabling real-time adjustment of controller parameters via adaptive methods to accomplish online estimation and compensation of system uncertainties. Finally, stability analysis proves that all closed-loop system signals are semi-globally uniformly bounded (SGUB), with tracking errors converging to an arbitrarily small residual set. The simulation results indicate that the proposed control method reduces steady-state error by approximately 20% compared to traditional controllers. Full article

The transmission of Escherichia coli through contaminated fruits and vegetables poses serious public health risks and has led to several national outbreaks in the USA. To enhance food safety, rapid and reliable detection of E. coli on produce is essential. This study evaluated the performance of the CSI-D+ system combined with deep learning for detecting varying concentrations of E. coli on citrus and spinach leaves. Eight levels of E. coli contamination, ranging from 0 to 10⁸ colony-forming units (CFU)/mL, were inoculated onto the leaf surfaces. For each concentration level, 10 droplets were applied to 8 citrus and 12 spinach leaf samples (2 cm in diameter), and fluorescence images were captured. The images were then subdivided into quadrants, and several post-processing operations were applied to generate the final dataset, ensuring that each sample contained at least 2–3 droplets. Using this dataset, multiple deep learning (DL) models, including EfficientNetB7, ConvNeXtBase, and five YOLO11 variants (n, s, m, l, x), were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize the spatial responses of the models to bacterial presence. All YOLO11 models outperformed EfficientNetB7 and ConvNeXtBase. In particular, YOLO11s-cls was identified as the best-performing model, achieving average validation accuracies of 88.43% (citrus) and 92.03% (spinach), and average test accuracies of 85.93% (citrus) and 92.00% (spinach) at a 0.5 confidence threshold. This model demonstrated an inference speed of 0.011 s per image with a size of 11 MB. These findings indicate that fluorescence-based imaging combined with deep learning for rapid E. coli detection could support timely interventions to prevent contaminated produce from reaching consumers. Full article

This study compares the operational performance of membrane capacitive deionization (MCDI) and circulation-MCDI (C-MCDI) under symmetric (2/2, 3/3, 4/4 min) and asymmetric (5/2, 5/3, 5/4 min) adsorption/desorption cycles to identify efficient operating conditions at the pilot scale. A pilot system was tested with a NaCl solution of about 1000 mg/L, and 15 consecutive cycles were conducted to evaluate removal efficiency, specific energy consumption (SEC), and stability. MCDI consistently achieved over 90% removal efficiency with SEC below 0.6 kWh/m³ across all modes, maintaining stable performance over 15 cycles. The 2/2 condition provided the shortest cycle time and the highest treated water productivity, making it the most efficient condition for the pilot-scale MCDI tested. C-MCDI showed stronger dependence on operating conditions, with the number of stable cycles ranging from 3 to 7 depending on desorption duration. Nevertheless, the 5/2 condition achieved about 91% removal efficiency with 0.58 kWh/m³ SEC, and its extended adsorption period yielded about 2.5 times more treated water per cycle than the 2/2 case. Overall, this work provides a comparative pilot-scale evaluation of MCDI and C-MCDI, highlighting their advantages, limitations, and potential applications, and offering practical insights for energy-efficient and sustainable desalination strategies. Full article

The development of cosmetic prostheses benefits significantly from the integration of additive manufacturing technologies, which offer new possibilities for personalization, rapid production, and cost efficiency. This study explores the potential of selected additive manufacturing methods for fabricating prosthetic fingers used in upper limb cosmetic prostheses. Esthetic and mechanical properties of the printed components were assessed alongside production efficiency and material use. Quantitatively, maximum bending loads ranged from 9 to 136 N, maximum compressive loads from 12 to 158 N, and sample mass from 4 to 22 g across configurations. The findings confirm that additive manufacturing enables the creation of prosthetic components that meet basic cosmetic and functional expectations. However, the choice of manufacturing method strongly influences surface quality, structural performance, production time, and economic feasibility. These results highlight the importance of matching technological capabilities with specific clinical and design requirements. The study contributes to the ongoing development of digital fabrication workflows for prosthetics and underscores the need for standardized evaluation criteria to support reliable comparisons across materials and manufacturing processes. Full article

Accurate extraction of single-diode photovoltaic (PV) model parameters is essential for reliable performance prediction and diagnostics, yet five-parameter identification from I-V data is ill-posed and computationally expensive. To develop and validate a hybrid analytical–metaheuristic approach that derives the diode ideality factor, saturation current, and photocurrent analytically while optimizing only series and shunt resistances, thereby reducing computational cost without sacrificing accuracy. I-V datasets were collected from a 9.54 kW grid-connected PV installation in Algiers, Algeria (15 operating points; 747–815 W m⁻²; 25.4–28.4 °C). Nine metaheuristics—Stellar Oscillation Optimizer, Enzyme Action Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, Cuckoo Search, Owl Search Algorithm, Improved War Strategy Optimization, Rüppell’s Fox Optimizer, and Artificial Bee Colony—were benchmarked against full five-parameter optimization and a Newton–Raphson baseline, using root-mean-squared error (RMSE) as the objective and wall-time as the efficiency metric. The hybrid scheme reduced the decision space from five to two parameters and lowered computational cost by ≈60–70% relative to full-parameter optimization while closely reproducing measured I-V/P-V curves. Across datasets, algorithms achieved RMSE ≈ 2.49 × 10⁻² − 2.78 × 10⁻². Rüppell’s Fox Optimizer offered the best overall trade-off (lowest average RMSE and fastest runtime), with Whale Optimization Algorithm a strong alternative (typical runtimes ≈ 107–112 s). Partitioning identification between closed-form physics and light-weight optimization yields robust, accurate, and efficient PV parameter estimation suitable for time-sensitive or embedded applications. Dynamic validation using 1498 real-world measurements across clear-sky and cloudy conditions demonstrates excellent performance: current prediction $R^2=0.9882$, power estimation $R^2=0.9730$, and voltage tracking $R^2=0.9613$. Comprehensive environmental analysis across a 39.2 °C temperature range and diverse irradiance conditions ($0-1014W/m^2$) validates the method’s robustness for practical PV monitoring applications. Full article

This paper presents a method to design and optimize a mimetic, multi-band antenna for direction-finding applications based on multiple IoT mobile nodes for protecting sensitive areas. A set of 84 antenna configurations were selected based on possible resonant paths and simulated using a Method of Moments (MoM)-based tool to compute resonant frequencies, VSWR, and gain across three frequency bands centered on 433 MHz, 877.5 MHz, and 2.4 GHz. Compared to a brute-force approach requiring 8¹⁴ full-wave simulations, our technique dramatically reduces computing time by performing only 84 simulations, followed by a fine-tuning procedure targeting the antenna segments with the highest contribution to the error figure. The final design provides good gain and VSWR figures over almost all the frequency ranges of interest. Full article

Gypsum alabaster as material for European sculpture emerged in the 12th century and soon rivalled marble due to its accessibility, ease of sculpting, and aesthetic qualities. Lack of clear terminology and the visual similarity of the two materials have led to a considerable amount of confusion and deliberate misnomers. Despite attempts, since early modern times, to make a clear physical and chemical distinction between both materials, mistakes persist, even in modern collections. Here we present a non-invasive, cost-effective, reliable technique to differentiate the two, using an ultra-portable near-infrared spectrometer. The characteristic NIR spectrum of gypsum alabaster over the range of 900–1700 nm strongly contrasting with the near-featureless spectra of marble, allows for a simple and straightforward differentiation of these materials. Our technique enables rapid lithological identification of complex composite sculptural ensembles. We illustrate this through two case studies: The 15th century Saint Catherine of Alexandria from Kortrijk, attributed to André Beauneveu, one of the most prominent artists of the late Middle Ages, was supposedly made of alabaster, but is in fact made of marble and restored with alabaster replacement parts. The tomb of Prince-Bishop Julius Echter in Würzburg Cathedral is an example of the variety of materials used for such monuments in the 17th century. Here we highlight a previously undocumented but extensive use of multi-coloured alabaster. Full article

Rupture discs are critical safety devices for pressure vessels, yet defining replacement intervals for discs that have not ruptured remains challenging due to limited quantitative life-prediction methods. This study investigates forward-acting rupture discs made of 316 L stainless steel and Inconel 600 under three test conditions: low pressure at room temperature, low pressure at elevated temperature, and ultra-high pressure at elevated temperature. Static hold life and fatigue life were measured over a range of operating ratios R = P_w/P_b. To model life–ratio relationships while avoiding far-reaching extrapolation, static life was fitted with a log-normal accelerated-life (AFT) model and fatigue life with a Basquin relation following ASTM E739, reporting 95% prediction bands. Predictions were restricted to validated domains (static: R ≥ 0.86) and truncated at five times the groupwise maximum observed life/cycles. Results show a consistent trend for both materials and all conditions: life decreases as R increases, with steep sensitivities within the observed range. At matched R, Inconel 600 generally exhibits longer life than 316 L. Qualitative failure analysis under constant and cyclic loading indicates progressive plastic deformation, local thinning, and a concomitant reduction in bursting pressure until failure. The proposed in-range predictive framework provides actionable guidance for determining conservative replacement intervals for rupture discs. Full article

This research investigates a dual-polished surface plasmon resonance sensor based on dual-core photonic crystal fiber, featuring an innovative design aimed at enhancing hemoglobin concentration detection in blood, providing a valuable tool for diagnosing numerous health issues, such as chronic obstructive pulmonary disease. The sensor makes use of an external sensing mechanism and utilizes gold (Au) coating as the plasmonic material, chosen for its strong plasmonic response and excellent chemical stability, ensuring robust performance across the 1.31–1.42 refractive index range. The electromagnetic characteristics and efficacy of the designed sensor were thoroughly investigated using the finite element method. Our proposed sensor demonstrates outstanding performance metrics, attaining peak amplitude sensitivity of about 734 RIU⁻¹, and wavelength sensitivity of 74,000 nm/RIU along with 1.35 × 10⁻⁶ RIU wavelength resolution. It also exhibits a notable Figure of Merit value of 667 for a corresponding Full width at Half Maximum value of 111 nm. Finally, a machine learning model based on linear regression was employed that enables the prediction of any hemoglobin concentration levels corresponding to analyte RI values. These exceptional performance metrics highlight the potential of our sensor as a reliable, cost-effective and highly sensitive solution for real-time biosensing applications. Full article

Urban mobility plays a critical role in ensuring resilience during natural disasters such as floods, yet developing reliable traffic models remains challenging for medium-sized cities with limited monitoring infrastructure. This study developed a hybrid traffic modeling approach that integrates Google Traffic data with field measurements to address incomplete digital coverage in flood-prone areas of Loja, Ecuador. The methodology involved collecting 1501 field speed measurements and 235,690 Google Typical Traffic observations using exclusively open-source tools and freely available data sources. Adjustment factors ranging from 0.25 to 0.97 revealed systematic discrepancies between Google Traffic estimates and field observations, highlighting the need for local calibration. The resulting traffic network model encompassing 4966 nodes and 5425 edges accurately simulated flood impacts, with the most critical scenario (Thursday 17–19, 100% road impact) showing travel time increases of 1123% and congestion index deterioration from 1.79 to 21.69. Statistical validation confirmed significant increases in both travel times (p = 0.0231) and distances (p = 0.0207) under flood conditions across five representative routes. This research demonstrates that accurate traffic models can be developed through intelligent integration of heterogeneous data sources, providing a scalable solution for enhancing urban mobility analysis and emergency preparedness in resource-constrained cities facing climate-related transportation challenges. Full article

Cancer therapy is rapidly evolving from a one-size-fits-all paradigm toward highly personalized approaches. Traditional chemotherapies and radiotherapies, while broadly applied, often yield suboptimal outcomes due to tumor heterogeneity and are limited by significant toxicities. In contrast, precision oncology tailors prevention, diagnosis, and treatment to the individual patient’s genetic and molecular profile. Key advancements underscore this shift: molecularly targeted drugs (e.g., trastuzumab for HER2-positive breast cancer, EGFR and ALK inhibitors for lung cancer) have improved efficacy and reduced toxicity compared to conventional therapy. Pharmacokinetic (PK) and pharmacodynamic (PD) considerations are central to personalizing treatment, explaining variability in drug exposure and response among patients and guiding dose optimization. Modern strategies like therapeutic drug monitoring and model-informed precision dosing seek to maintain drug levels in the therapeutic range, improving outcomes. Immunotherapies, including checkpoint inhibitors and CAR-T cells, have transformed oncology, though patient selection via biomarkers (such as PD-L1 expression or tumor mutational burden) is critical to identify likely responders. Innovative drug delivery systems, notably nanomedicine, address PK challenges by enhancing tumor-specific drug accumulation and enabling novel therapeutics. Furthermore, rational combination regimens (informed by PK/PD and tumor biology) are being designed to achieve synergistic efficacy and overcome resistance. Key barriers include the high cost of biomarker testing, insufficient laboratory infrastructure, and inconsistent reimbursement policies. Operational inefficiencies such as long turnaround times or lack of clinician awareness further limit the use of precision diagnostics. Regulatory processes also remain complex, particularly around the co-development of targeted drugs and companion diagnostics, and the evidentiary requirements for rare subgroups. Addressing these barriers will require harmonized policies, investment in infrastructure, and educational initiatives to ensure that the promise of personalized medicine becomes accessible to all patients. Ensuring that advances are implemented responsibly—guided by pharmacological insights, supported by real-world evidence, and evaluated within ethical and economic frameworks—will be critical to realizing the full potential of personalized cancer medicine. Full article

Background/Objectives: Pacemaker-associated heart failure (PaHF) is a recognized complication of chronic ventricular pacing, yet its long-term incidence and prognostic impact remain incompletely defined. Previous studies on PaHF have been largely limited by small sample sizes, single-center designs, and insufficient long-term or time-dependent analyses. We aimed to evaluate the incidence, clinical predictors, and mortality risk of PaHF in a nationwide real-world cohort. Methods: Using the Korean National Health Insurance Service database, we identified 32,216 patients who underwent de novo pacemaker implantation between 2008 and 2019 without prior heart failure. Results: During a median follow-up of 3.8 years, 4170 patients (12.9%) developed new-onset PaHF and 6184 (19.2%) died. PaHF was independently associated with increased all-cause mortality (adjusted hazard ratio [HR]: 3.11, 95% confidence interval [CI]: 2.93–3.32, p < 0.001), even after accounting for immortal-time bias and relevant covariates. The incidence of PaHF and its associated mortality risk both peaked within the first six months post implantation and remained persistently elevated throughout follow-up; notably, PaHF-associated mortality showed a late resurgence. Sensitivity and subgroup analyses consistently demonstrated higher mortality among patients with PaHF across a wide range of clinical characteristics. Conclusions: In this large, nationwide cohort, the development of PaHF was associated with a substantial and sustained increase in mortality risk following pacemaker implantation. Given the persistent and dynamic nature of this risk, longitudinal monitoring of cardiac function and individualized pacing strategies may be warranted to mitigate long-term adverse outcomes. Additionally, these findings provide real-world benchmarks to guide future pacing strategies and surveillance efforts. Full article

Condition monitoring and fault detection in induction motors (IMs) are priorities in the industrial environment to secure safe conditions for the processes and production. Convolutional Neural Networks (CNNs) are gaining interest in these tasks as they allow automatic extraction of features from the inputs, sometimes Time–Frequency Distributions (TFDs) obtained with various transforms, directly into large models for data classification. This work presents a proposal for the application of a widely used texture analysis tool in the medical field, the 2D Discrete Orthonormal Stockwell Transform (2D-DOST), to improve the accuracy of a lightweight CNN when using different TFDs and comparing the results to the use of the TFDs in RGB and grayscale. The results show that the use of the 2D-DOST improves the classification accuracy in a two to five percent range for all motor conditions under study, while having minimal variations to the training times when compared to RGB or grayscale images, opening the possibility for the use of image processing tools on TFDs to improve automatic feature extraction while using small CNN models. Full article