Search Results (4,411)

The valorization of agricultural residues helps improve crop economic efficiency and alleviate environmental pressures. Owing to the merits of simplicity, high efficiency, low costs, and scalability, adsorption removal of contaminants using biochar has been widely investigated. The adsorption removal of organic and inorganic contaminants from wastewater using biochar derived from agricultural residue follows the principles of the circular economy and green chemistry, facilitating both environmental remediation and agricultural development. Due to the distinctive precursors—agricultural residues—biochar exhibits unique physicochemical properties, enabling it to interact differently with contaminants in real wastewater. Herein, this review addresses the knowledge gap in wastewater remediation using agricultural residue-based biochar. It compiles the principles of adsorption with agricultural waste-derived biochar, including general concepts, interactions between biochar and wastewater contaminants, and selective adsorption. The preparation, activation, modification, functionalization, and regeneration of such biochar, as well as their application to wastewater remediation, are comprehensively outlined. Furthermore, the economic evaluation and environmental impacts, as well as the future directions and challenges in this field, have also been presented. Full article

This study introduces the Badland Dissection Index (BDI), a new morphometric parameter that quantifies the internal dissection and drainage maturity of badland basins. The index was applied to 87 calanchi basins developed on marine clays in the Ionian sector of Basilicata (southern Italy). BDI values range from 0.13 to 0.62, with approximately 65% of the basins exhibiting values lower than 0.30, indicating mature geomorphic stages dominated by organized fluvial incision. Pearson correlation analysis shows that BDI is strongly correlated with compactness and shape indices (r = −0.71 with circularity ratio, r = 0.74 with Gravelius compactness index, GCI), and moderately with relief (r = 0.46 with Melton ratio), highlighting the primary control exerted by basin geometry on badland dissection. A principal component analysis shows that compactness-related variables and BDI dominate the first component, which explains 38.6% of the variance, while hydrological indices define an independent second component; together the first two components account for 57.4% of total variance. A multiple regression model confirms GCI as the dominant predictor of BDI (R² = 0.58), with relief variables playing a secondary role. Owing to its simplicity, limited data requirements and clear geomorphic meaning, BDI provides a robust and scalable tool for comparing badland morphodynamics across semiarid settings and for monitoring landscape evolution where only medium-resolution topographic data are available. Full article

Change detection (CD) with synthetic aperture radar (SAR) images remains pivotal for environmental monitoring and disaster management. Deep learning has powerful feature extraction capabilities for CD, but suffers from complex architectures and limited interpretability. While BLSs demonstrate advantages in structural simplicity and interpretability, their feature representation capacity remains constrained. In high-precision CD with SAR images, strong feature representation capability is required, along with an uncomplicated framework and high interpretability. Therefore, a novel paradigm named PC-BiBL is proposed which achieves seamless integration of deep learning and broad learning. On the one hand, it employs a hierarchical cross-convolutional encoding (HCCE) module that uses pseudo-random cross-convolution (PCConv) for hierarchical cross-feature representation, aggregating contextual information. PCConv is an untrained convolution layer, which can utilize specialized pseudo-random kernels to extract features from bitemporal SAR images. On the other hand, since back-propagation algorithms are not required, the features can be directly fed into the bifurcated broad learning (BiBL) module for node expansion and direct parameter computation. BiBL constructs dual-branch nodes and computes their difference nodes, explicitly fusing bitemporal features while highlighting change information—an advancement over traditional BLS. Experiments on five SAR datasets demonstrate the state-of-the-art performance of PC-BiBL, surpassing existing methods in accuracy and robustness. Quantitative metrics and visual analyses confirm its superiority in handling speckle noise and preserving boundary information. Full article

Hydrogen energy has been recognized as the most promising secondary energy source due to high energy density, abundance, and environmental friendliness. Among hydrogen production techniques, water electrolysis has emerged as a key research focus, owing to its high efficiency, operational simplicity, controllability, and pollution-free nature. However, the anodic oxygen evolution reaction (OER) involves a high overpotential and sluggish kinetics, which severely constrain the overall efficiency of water electrolysis. Transition metal oxide (TMO) catalysts are regarded as promising substitutes for noble-metal-based catalysts, given their advantages of low cost, elemental abundance, tunable electronic structures, and favorable stability. This review systematically elaborates on the reaction mechanisms of TMO catalysts, including the adsorbate evolution mechanism (AEM) and lattice oxygen mechanism (LOM), and summarizes various performance-enhancement strategies, such as morphology control, doping engineering, support engineering, and heterostructure construction. Furthermore, it outlines current challenges and future research directions, covering precise synthesis and structural control, identification of active sites and mechanistic elucidation, and stability and degradation issues, as well as multifunctional applications and broad-pH-range adaptability. The aim is to offer theoretical guidance and technical insights for designing and developing high-performance TMO electrocatalysts. Full article

This work addresses the challenge of realizing broadband, low-loss fiber-to-waveguide coupling in the short-wavelength near-infrared range (700–1050 nm), where the required fine structural dimensions and taper tips approach or even exceed current fabrication limits, resulting in tight fabrication tolerances and degraded coupling efficiency. We propose a broadband trilayer adiabatic edge coupler on a thin-film lithium tantalate platform that requires only two standard lithography and etching steps. The design integrates a crossed bilayer taper and a dual-core mode converter to achieve adiabatic mode transformation from a ridge to a thin strip waveguide, ensuring excellent fabrication tolerance and process simplicity. Simulations predict a minimum coupling loss of 0.57 dB at 850 nm, which includes the transmission through the complete edge-coupler structure, along with a 0.5-dB bandwidth exceeding 140 nm. The proposed structure provides a broadband, low-loss, and fabrication-tolerant interface for short-wavelength photonic systems such as quantum photonics, biosensing, and visible-light communications. Full article

This study compares and evaluates the performance of a statistical model, Multiple Linear Regression (MLR), and a deep learning model, Long Short-Term Memory (LSTM), for predicting monthly mean temperature in the Han River Basin, South Korea. Predictor variables were dynamically selected based on lagged correlation analysis between climate indices and temperature over the past 40 years, identifying the top ten variables with the highest correlations for lag times ranging from 1 to 18 months. The MLR model was developed through stepwise regression with cross-validation, while the LSTM model was constructed using an 18-month input sequence to capture temporal dependencies in the data. Model performance was evaluated using percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (r), and tercile-based probability metrics. Both models reproduced the seasonal variability of monthly temperature with high accuracy (NSE > 0.97, r > 0.98). The LSTM model showed slightly higher predictive skill in several periods but also exhibited larger prediction variance, reflecting the sensitivity of nonlinear architectures to variations in predictor–response relationships. In contrast, the MLR model demonstrated more stable predictive behavior with narrower uncertainty bounds, particularly under low signal-to-noise conditions, owing to its structural simplicity. These findings indicate that the two approaches are complementary; the LSTM model better captures nonlinear temporal dynamics, while the MLR model provides interpretability and robustness. Future work will explore advanced hybrid architectures such as CNN–LSTM and Transformer-based models, as well as multi-model ensemble methods, to further enhance the accuracy and reliability of medium-range temperature prediction. Full article

A stripline-type circulator is essential for the initial low-power characterization of vacuum electron devices such as magnetrons, enabling accurate measurements of startup behavior, oscillation frequency, and mode structure while minimizing reflections and protecting diagnostic equipment. In this study, two broadband S-band stripline circulator prototypes operating in the 2–4 GHz and 3–4 GHz bands were designed, fabricated, and experimentally characterized. A unified design methodology was implemented by using the same ferrite material and coupling angle in both structures, providing procurement simplicity, cost reduction, and technological standardization. This approach also enabled a direct assessment of how bandwidth variations influence circulator behavior. The design goals targeted a transmission efficiency above 90%, isolation exceeding 15 dB, and a voltage standing-wave ratio (VSWR) of 1.2:1. Experimental evaluations, including magnetic field mapping, low-power S-parameter measurements, and high-power tests, confirmed that both prototypes satisfy these specifications, consistently achieving at least 90% transmission across their respective operating bands. Additionally, a comparative analysis between a locally fabricated ferrite and a commercial ferrite sample was conducted, revealing the influence of material properties on transmission stability and high-power behavior. The results demonstrate that broadband stripline circulators employing a common ferrite material can be adapted to different S-band applications, offering a practical, cost-effective, and reliable solution for RF systems. Full article

The accurate determination of trace metals in aqueous matrices necessitates robust sample preparation techniques that enable selective preconcentration of analytes while ensuring compatibility with subsequent instrumental analysis. Dispersive solid-phase extraction (d-SPE), a suspension-based variant of conventional solid-phase extraction (SPE), facilitates rapid sorbent–analyte interactions and enhances mass transfer efficiency through direct dispersion of the sorbent in the sample solution. This approach offers significant advantages over traditional column-based SPE, including faster extraction kinetics and greater operational simplicity. When supported by appropriately engineered sorbents, d-SPE exhibits considerable potential for the selective enrichment of trace metal analytes from complex aqueous matrices. In this work, a fibrous silica-based chelating material, DSA-KCC-1, was synthesized by grafting 3,5-Di-tert-butylsalicylaldehyde (DSA) onto aminopropyl-modified KCC-1. The dendritic KCC-1 scaffold enables fast dispersion and short diffusion pathways, while the immobilized phenolate–imine ligand introduces defined binding sites for transition-metal uptake. Characterization by FTIR, TGA, BET, FESEM/TEM, XRD, and elemental analysis confirmed the successfulness of functionalization and preservation of the fibrous mesostructured. Adsorption studies demonstrated chemisorption-driven interactions for Pb(II) and Co(II) from water, with Langmuir-type monolayer uptake and pseudo-second-order kinetic behavior. The nano-adsorbent exhibited a markedly higher affinity for Pb(II) than for Co(II), with maximum adsorption capacities of 99.73 and 66.26 mg g⁻¹, respectively. Integration of the DSA-KCC-1 nanosorbent into a d-SPE–ICP-OES workflow enabled the reliable determination of trace levels of the target ions, delivering low limits of detection, wide linear calibration ranges, and stable performance over repeated extraction cycles. Analysis of NIST CRM 1643d yielded results in good agreement with the certified values, while the method demonstrated high tolerance toward common coexisting ions. The combined structural features of the KCC-1 support and the Schiff-base ligand indicate the suitability of DSA-KCC-1 for d-SPE workflows and demonstrate the potential of this SPE format for selective preconcentration of trace metal ions in aqueous matrices. Full article

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

The growing demand for robust food authentication methods has driven the establishment of fast, sensitive, and field-based detection systems for identifying meat species. This study presents a colorimetric-based PSR approach for identifying yak (Bos grunniens) meat within fresh, thermally processed, and blended meat samples. Targeting the mitochondrial D-loop locus, the assay incorporates a simple alkaline lysis (AL) procedure for efficient DNA extraction, eliminating the requirement for specialized instrumentation. The PSR assay demonstrated high specificity, showing no evidence of cross-reactivity with closely associated food animals such as buffalo, cattle, goat, sheep, mithun, and pig. Sensitivity assessment revealed the assay’s capability to detect 1 pg of yak DNA, with reliable performance in samples exposed to thermal conditions up to 121 °C. Additionally, the technique detected yak meat down to a concentration of 0.1% in binary beef mixtures. This method provides a significant improvement in sensitivity over end-point PCR and is particularly well-suited for field applications due to its practical simplicity, affordability, as well as no reliance on sophisticated instrument. This is, to the best of our understanding, the first reported PSR-based approach developed for the identification of yak meat, offering a robust tool for food origin verification, regulatory enforcement, and product integrity monitoring. Full article

Establishing a correlation between the Cone Penetration Test (CPT) and the Standard Penetration Test (SPT) is of significant importance for geotechnical engineering practice. A novel correlation between CPT-q_t and SPT-N₆₀ based on a genetic algorithm (GA) and linear regression was proposed in this study. Based on the soil behavior type index (I_c), a GA was first applied to divide the dataset into different I_c intervals. Subsequently, linear regression was performed separately for the data in each interval to establish a correlation between CPT-q_t and SPT-N₆₀. Concurrently, the Segmented Information Criterion (SIC) was introduced to perform dual-objective optimization of complexity and prediction accuracy. The results indicate that the proposed model achieved an R² of 0.60 and an RMSE of merely 8.10. Specifically, the R² values improved by 33% and 5% compared to the traditional models and the AI models, respectively. On the validation dataset, the proposed model achieved an R² of 0.67 and an RMSE of 4.33, demonstrating higher accuracy compared to the traditional models. In summary, a method for investigating the CPT-SPT correlation is proposed in this study, characterized by simplicity, efficiency, and enhanced reliability. Additionally, a novel criterion (SIC) for mitigating overfitting is introduced. These two research findings can provide more reliable input parameters for SPT-based design, thereby supporting geotechnical engineering applications, and offer a valuable reference for relevant studies in other regions. Full article

Coastal sandbars play a crucial role in shoreline protection, yet monitoring their dynamics remains challenging due to the cost and limited temporal coverage of traditional surveys. This study assesses the feasibility of using Sentinel-2 multispectral imagery combined with the logarithmic band ratio method to automatically detect submerged sandbar crests along three morphologically distinct beaches on the northwestern Mediterranean coast. Pseudo-bathymetry was derived from log-transformed band ratios of blue-green and blue-red reflectance used to extract the sandbar crest and validated against high-resolution in situ bathymetry. The blue-green band ratio achieved higher accuracy than the blue-red band ratio, which performed slightly better in very shallow waters. Its application across single, single/double, and double shore-parallel bar systems demonstrated the robustness and transferability of the approach. However, the method requires relatively clear or calm water conditions, and breaking-wave foam, sunglint, or cloud cover conditions limit the number of usable satellite images. A temporal analysis at a dissipative beach further revealed coherent bar migration patterns associated with storm events, consistent with observed hydrodynamic forcing. The proposed method is cost-free, computationally efficient, and broadly applicable for large-scale and long-term sandbar monitoring where optical water clarity permits. Its simplicity enables integration into coastal management frameworks, supporting sediment-budget assessment and resilience evaluation in data-limited regions. Full article

Evacuated blood collection tubes are widely used in clinical and laboratory settings due to their simplicity and reliability. However, their performance is influenced by factors such as ambient pressure, temperature, tube design, and procedural conditions. This study systematically investigates and quantifies these effects on draw volume and filling dynamics, with a particular emphasis on high-altitude applications. A combination of theoretical modeling, experimental validation, and qualitative analysis was employed to identify critical parameters and assess their significance. The results demonstrate that standard tubes designed for sea-level conditions, particularly those with low fill ratios, may exhibit substantial deviations in draw volume at high altitudes. Factors such as blood temperature and venous pressure were found to have a considerable impact, while others, such as material creep, were negligible under typical conditions. By consolidating and analyzing these effects, this study provides a valuable resource for manufacturers and medical personnel, offering valuable insights to improve the design and use of evacuated blood collection tubes. The findings emphasize the importance of considering environmental conditions during production and clinical application, particularly for high-altitude scenarios. Future work should refine the models and expand testing under realistic conditions to enhance reliability and applicability. Full article

Due to their simplicity and ease of visualization, lattice models can be useful to illustrate basic concepts in thermodynamics. The recipe to obtain classical thermodynamic expressions from lattice models is usually based on invoking the thermodynamic limit, and the ideal gas law can easily be obtained as the density of non-interacting particles vanishes. We present a lattice-based analysis that shows that, when a gas consisting of non-interacting particles evolves towards mechanical equilibrium with the environment, the ideal gas law can be obtained with no recourse to unnecessary assumptions regarding the size or particle density of the lattice. We also present a statistical mechanical analysis that considers a quantized volume and reproduces the process obtained for the discrete lattice model. We show how the alternative use of a well-known and accessible model (the non-interacting lattice gas) can give microscopic insights into thermal systems and the assumptions that underlie the laws used to describe them, including local vs. global equilibrium, irreversible processes, and the sometimes subtle difference between physical assumptions and mathematically convenient approximations. Full article

Background and Objectives: A surgical method is rarely very effective and simple to perform. A Lichtenstein Repair (LR) is one such exception. Because of the very high incidence of inguinal hernia, LR has become the global gold standard in inguinal hernia repair—not only due to its relative simplicity and reproducibility but also because it can be performed under local anesthesia. These attributes facilitated its worldwide adoption, including in underdeveloped and resource limited settings. Today, many variations are performed under the common name “Lichtenstein Repair”. The extent to which these modifications influence outcomes—particularly recurrence and chronic pain—remains unclear. Materials and Methods: To evaluate reasons for variation in the LR technique, a literature review of seven major surgery textbooks was performed. In addition, a questionnaire comprising 17 questions addressing the key steps of the LR was sent to 90 surgeons across 19 different hospitals in Austria (6) and Slovenia (13). The questionnaire focused on core principles described by Lichtenstein and later refined by his successors. The overall response rate was 78%. Results: Descriptions of the LR in major hernia textbooks vary substantially, partly due to the evolution of the technique over time and partly because any subaponeurotic anterior-canal mesh repair is often labeled as “Lichtenstein”. Survey responses demonstrated considerable variation and lack of standardization or uniformity in several critical steps of the LR. More than 50% of respondents reported using pre-formed meshes that they excessively trim, limiting adequate coverage of the inguinal region. Furthermore, routine patient follow-up is lacking in the majority of cases. Conclusions: The contemporary umbrella term “Lichtenstein Repair” encompasses many different anterior mesh techniques. While some surgeon-specific preferences may not compromise integrity, strict adherence to the evidence-based key principles of the original repair remains essential to minimize recurrences and chronic inguinal pain. Standardization with meticulous adherence to the key principles of the LR is critical to ensure the data submitted into registries, RCTs, and meta-analyses are accurate, comparable, and meaningful. Full article