This research proposes the use of hybrid machine learning methods to mimic dye removal efficiency. Hyperparameter tuning via differential evolution (DE), genetic algorithm (GA), random search (RS), and grid search (GS) with the XGBoost model was conducted to achieve more accurate results. This study focused on the relationships between the initial dye concentrations of Fast Green, Eosin Y, and Quinine Yellow dyes, their initial pH, ACMOF adsorbent dosage (activated carbons: metal‒organic frameworks), and sonication time as input variables, with the removal percentage as the output data. The analysis emphasized the correlation between the inputs and outputs, resulting in the generation of four scenarios: 4 inputs, 3 inputs, 2 inputs, and 1 input. The correlation analysis revealed a weak input‒output relationship and the presence of outliers in the data. The use of advanced models, such as XGBoost, improved model performance and accurately predicted dye removal efficiency. The models performed well across different input scenarios, demonstrating their reliability and effectiveness. The results also revealed the importance of data preprocessing techniques in improving the structure and relationships within the data. The DE_XGBoost model outperforms all the other methods in terms of R2 (R2 values of 0.977, 0.958, 0.924, and 0.997 for 4-input, 3-input, 2-input, and 1-input, respectively), demonstrating its DE effectiveness in generalizing the model and enhancing its predictivity. This research contributes to the development of more efficient techniques for dye removal and environmental pollution mitigation, addressing the challenges of traditional testing methods. These findings have implications for industries that use dyes and can help mitigate the environmental pollution caused by dye effluents.

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Globally 117.3 million people are displaced due to war, with one in five refugees experiencing psychological distress. Despite the availability of numerous psychological interventions for war refugees, their overall efficacy is still unexplored. Thus, this review examines the effectiveness of psychological interventions in reducing the mental health issues of war refugees.MethodsA systematic search was conducted in PubMed, Web of Science, APA PsycNET, ScienceDirect and Google Scholar for studies published on the effectiveness of psychological interventions for mental health issues among war refugees in July 2024. JBI and NHLBI checklists were employed to appraise the risk of bias in the included studies. Review Manager version 5.4 was used to run the meta-analysis.ResultsOut of 22,197 initially identified records, a total of 21 studies were included for systematic review and eight randomized controlled trials (RCTs) for meta-analysis. Random effects meta-analysis showed significant medium effect size for psychological interventions (SMD = -0.69; 95 % CI:0.87, -0.51; p < .00001) in reducing the mental health issues among war refugees. Sub group analysis based on the mode of delivery revealed that interventions delivered through in-person mode reported a significant large effect size (SMD = -1.03; 95 % CI:1.36, -0.71; p < .00001) while, telehealth interventions showed significant small effect size (SMD = -0.44; 95 % CI:0.61, -0.28; p < .00001).ConclusionIn-person and telehealth based psychological interventions offer promising findings in addressing mental health issues among war refugees. However, given the barriers to accessing face to face treatments and limited evidence on telehealth interventions, future research on digital interventions is recommended

The offset linear canonical transform (OLCT) is an important tool in signal processing and optics. Recently, the quaternion offset linear canonical transform (QOLCT) has been introduced which is the quaternion extension of the OLCT and the generalized form of quaternion Fourier transform(QFT). In this article, the Plancherel’s theorem of the scalar inner product for the two-sided QOLCT is introduced. Also, the quaternion inner product theorems for the right sided and left sided QOLCT have been discussed. Further, as an application of the Plancherel’s theorem, the real Paley-Wiener theorem and Donoho-Stark uncertainty principle have been explored as well as the solution of particular type of quaternion differential equations are discussed using QOLCT. Additionally, the advantages of QOLCT over QLCT and QFT is illustrated graphically using example and the use of Plancherel’s theorem in filter analysis is demonstrated

Coastal aquifers, a critical component of the land-ocean continuum, act as hotspots of fresh groundwater and seawater mixing while reactive processes further govern the elemental flux to the ocean in form of groundwater discharge. Here we report stable Sr (?88/86Sr) and Ca isotope (?44/40Ca) data for groundwater samples collected from multiple depths (14–333 mbgl) from two coastal aquifers in the Ganges River delta (Sundarbans). Significant variability is observed in ?88/86Sr values (range 0.542 ‰) with shallow (up to 42 mbgl) aquifer samples showing high ?88/86Sr values (up to 0.666 ‰ relative to NIST SRM987), which indicates pronounced interaction with seawater and fractionation induced by Sr removal during carbonate precipitation. The ?44/40Ca values of these samples also show large variability (range 0.76 ‰) which loosely follows the ?88/86Sr trend. The ?88/86Sr-?44/40Ca correlation is relatively poor for shallow groundwater samples and reflects differences in fractionation mechanisms during carbonate precipitation for Sr and Ca isotopes. In contrast to the shallow aquifer samples, the deep aquifer samples display limited seawater influence. The depth-bound variability of molar Sr/Ca, ?88/86Sr, and ?44/40Ca suggests considerable removal of solute Sr and Ca due to secondary mineral formation, dominated by carbonates at shallower depths consistent with saturation index (SI) calculations. This study highlights that formation of secondary minerals in coastal aquifers, as inferred from stable Sr and Ca isotopes, can affect the transport of highly soluble elements like Sr and Ca from the continents to the oceans and has implications for solute geochemistry in coastal aquifers.

To address the challenges of heat input in wire arc additive manufacturing (WAAM), this study employed the pulsed cold metal transfer (PCMT) technique to fabricate Incoloy 825 (IN825) components. PCMT, characterized by controlled droplet transfer and reduced heat input, enhanced mechanical performance and microstructural quality. Comprehensive analyses, including microstructural examination, X-ray diffraction, energy-dispersive X-ray spectroscopy (EDS), and element mapping, were performed. Titanium and molybdenum-rich secondary particles were identified through EDS. The mechanical properties of PCMT-fabricated components were compared with both wrought IN825 and those produced by gas metal arc additive manufacturing (GMAAM). Results demonstrated that PCMT components, particularly those fabricated at a 45° orientation, achieved approximately 113% of the ultimate tensile strength (UTS) and 131% of the elongation compared to wrought IN825. This marked a significant improvement over GMAAM-fabricated components. The reduced heat input and enhanced cooling rates in the PCMT process contributed to finer microstructures and superior mechanical properties. Fractography studies revealed that PCMT components exhibited ductile fractures with significant plastic deformation and some brittle regions. These findings underscored the advantages of PCMT in producing high-performance IN825 components compared to traditional GMAAM.

Groundwater prediction in data-scarce and environmentally sensitive regions presents a persistent challenge due to limited observational data, spatial heterogeneity, and the nonlinear nature of hydrogeological processes. In this study, we propose HydroPredictor, a hybrid machine learning framework that integrates the categorical handling efficiency of CatBoost with the nonlinear feature learning capacity of a regularized Multi-Layer Perceptron (MLP). The model was trained on a geo- referenced dataset of 315 samples from the Feija Basin in southeastern Morocco, incorporating ten environmental predictors such as elevation, rainfall, soil permeability, NDVI, and topographic wetness index. The pipeline includes Optuna-based hyperparameter optimization and 5-fold cross-validation to ensure robustness and generalization. HydroPredictor achieved a testing accuracy of 89.23%, with an F1-score of 0.8937 and Area Under the Curve (AUC) values exceeding 0.90 across all groundwater potential classes. Statistical validation using the Friedman and Wilcoxon signed-rank tests (p < 0.05) confirmed its significant outperformance over conventional models, including Random Forest, Support Vector Machine (SVM), and standalone MLP. Furthermore, HydroPredictor demonstrated superior generalization compared to prior models in the literature (e.g., RF-SSA: AUC = 0.840; GBDT: AUC = 0.88), while maintaining minimal overfitting (∆Accuracy = 0.35%). By combining interpretable tree-based embeddings with deep neural representations, HydroPredictor provides a robust and scalable solution for groundwater classification in data-limited settings, offering a reproducible and operationally relevant tool for sustainable groundwater resource management under climatic and environmental uncertainty.

This study introduces a coalesce forecasting model tailored for flood-prone regions, specifically focusing on Bihar, India. Research has revealed significant disparities in rainfall patterns across various zones such as Tirhut, Patna, and Munger zones experiencing greater mean rainfall than Bhagalpur and Kosi. To evaluate the forecasting capabilities, coalescing methods were applied which includes the autoregressive integrated moving average (ARIMA), exponential smoothing state space (ETS), neural network autoregressive (NNAR), and seasonal-trend decomposition. Moreover, Loess (STL) methods, and trigonometric seasonality, Box‒Cox transformation, ARMA errors, and trend and seasonal components (TBATS) were also employed to contrast the benchmark models such as the seasonal naïve, naïve, and mean methods. These methods were evaluated using error evaluators such as residual error, root mean square error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE), and autocorrelation of errors at lag 1 (ACF1) to determine the performance of these techniques. Additionally, statistical tests, such as the Box–Pierce and Box–Ljung tests, supported these findings. Among the error evaluators and forecasting models, the ETS and NNAR models remain the top choices for Saran-Tirhut-Bhagalpur and Munger-Magadh-Kosi, respectively, effectively capturing rainfall patterns and minimizing residual errors, as indicated by low RMSE values. Moreover, ARIMA and TBATS remain the top choices for Patna, Purnia and Darbhanga, respectively, followed by ETS model. In addition, the STL model secured the second position for Saran, Tirhut, Bhagalpur, and Purnia zones. This research emphasizes the importance of understanding regional rainfall dynamics for effective flood risk management and climate adaptation strategies. This study provides valuable tools for water resource management and agricultural planning in Bihar amidst climate variability challenges. It advocates for rainfall trend analysis followed by forecasting to achieve more precise water resource management and planning.

Microplastic contamination of terrestrial and aquatic environment has gained immense research attention due to their potential ecotoxicity and biomagnification property when enterer into food chain. Heterogenous nature of microplastics coupled with their ability to combine with other emerging pollutants have increased the severity of this crisis. Existing detection methods often fails to accurately quantify the amount of microplastic components present in environmental and biological samples. Thus, a great deal of research gap always exists in our current understanding about microplastics including the limitations in screening, detection and mitigation. This review work presents a comprehensive out look on the impact of microplastics on both terrestrial and aquatic environment. Furthermore, an in-depth discussion on various microplastic detection techniques recently used for microplastic quantification along with their significance and limitations is summarised in this review. The review also elaborates various physical, chemical and biological methods used for the mitigation of microplastics from environmental samples.

This study investigates the use of novel flattened-end nylon fibres (FENF) as reinforcement in concrete to improve its mechanical properties. The research addresses inadequate circumferential bonding between macro synthetic fibres and concrete matrix, which can lead to fibre slippage or failure. Through experiments involving 19 concrete mixes with varying fibre dosages (0.5%, 1% and 1.5%), aspect ratios (35, 55 and 75), and shapes (straight and flattened-end), the study examines the impact of FENF on concrete workability and mechanical strengths. The mechanical strength tests illustrate the significance of fibre dosage, aspect ratio and especially the shape as the compressive, split-tensile strength and flexural strengths of the FENF concrete are respectively showing an increase in strength of up to 10.3%, 25.1% and 26.1% when compared with conventional concrete. Similarly, the straight nylon fibre-reinforced concrete also achieved comparable strength increments up to 11.8%, 13.9% and 15.9% respectively for compressive, split-tensile and flexural strengths. This indicates that the positive effect of fibre shape on circumferential bonding helped the better performance of the FENF in split-tensile and flexural strengths. Further, the statistical methods, including regression analysis, Principal Components Analysis, and Response Surface Methodology, are employed to analyse the complex relationships between fibre characteristics and identify optimal fibre configurations. Using the Scanning Electron Microscope (SEM) the microstructural view has been studied to evaluate the interaction between FENF and the concrete matrix.

The primary objective is to select an appropriate aerator that maximizes the benefits while minimizing the operational costs of the aeration system in aquaculture operations. The choice of suitable aerators significantly impacts the cost of aquaculture operations. Therefore, this study focuses on the economic analysis of the paddle wheel aerator (PWA) and its modified counterpart, the spiral aerator (SA). The efficiency of the PWA and SA was assessed with respect to rotational speed (N), based on the efficiency a comparative economic analysis was conducted. The economics of aerators depends on the various pond sizes, initial dissolved oxygen (DO), and aerators running hours. Therefore, the total aeration cost for the chosen aerators was determined at various pond volumes (100, 200, 300, 700, 1000, 5000, and 10,000 m3 with depth of water 1.0 m) and initial DO of pond water denoted as CP = 1 to 4 mg/L. According to market rates, the primary cost for PWA was ₹32,000, while that for SA was ₹42,000. From the findings, pond sizes in the range of 100 to 10,000 m3 PWA are more economical than SA. Therefore, a new selection method of economically feasible aerators in aquaculture pond was developed in this study.

Artificial intelligence (AI) offers innovative and efficient solutions to contemporary challenges in sustainable aquaculture. Machine learning (ML) and deep learning (DL) are integral components of smart aquaculture, driving significant advancements in the field. The integration of AI with ML, and DL technologies is transforming traditional aquaculture practices by enhancing operational efficiency, optimizing fish health management, improving environmental conditions, monitoring water quality and supporting advanced decision-making processes. This review highlights the latest applications of AI, including ML, and DL in aquaculture, emphasizing their roles in real-time water quality monitoring, disease detection, and automated estimation of fish biomass etc. Key techniques, including predictive modeling, image and video processing, and sensor data integration, are enabling these breakthroughs. Moreover, DL algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have emerged as powerful tools for processing complex data and predicting critical events within aquaculture systems. Despite the notable progress, challenges such as the need for large, labeled datasets, high computational costs, and issues related to model interpretability continue to limit broader adoption. The current review aims to offer researchers and practitioners with a comprehensive overview of AI and its subfields such as ML and DL applications in smart aquaculture, discussing both the opportunities and challenges while suggesting future research directions to overcome existing limitations and expand AI-driven innovations in the industry.

This study investigates the use of novel flattened-end nylon fibres (FENF) as reinforcement in concrete to improve its mechanical properties. The research addresses inadequate circumferential bonding between macro synthetic fibres and concrete matrix, which can lead to fibre slippage or failure. Through experiments involving 19 concrete mixes with varying fibre dosages (0.5%, 1% and 1.5%), aspect ratios (35, 55 and 75), and shapes (straight and flattened-end), the study examines the impact of FENF on concrete workability and mechanical strengths. The mechanical strength tests illustrate the significance of fibre dosage, aspect ratio and especially the shape as the compressive, split-tensile strength and flexural strengths of the FENF concrete are respectively showing an increase in strength of up to 10.3%, 25.1% and 26.1% when compared with conventional concrete. Similarly, the straight nylon fibre-reinforced concrete also achieved comparable strength increments up to 11.8%, 13.9% and 15.9% respectively for compressive, split-tensile and flexural strengths. This indicates that the positive effect of fibre shape on circumferential bonding helped the better performance of the FENF in split-tensile and flexural strengths. Further, the statistical methods, including regression analysis, Principal Components Analysis, and Response Surface Methodology, are employed to analyse the complex relationships between fibre characteristics and identify optimal fibre configurations. Using the Scanning Electron Microscope (SEM) the microstructural view has been studied to evaluate the interaction between FENF and the concrete matrix.

This review explores the multifaceted benefits of Myrica species, commonly known as bayberry, highlighting their therapeutic, nutritional, and environmental value. The fruit of the Myrica tree is very good for you. It’s full of important nutrients and bioactive compounds, like myricetin, that help your immune system and your overall health. Tree bark, in addition to fruit, contains essential oils, and locals use the wood for construction. Various species of Myrica, including Myrica esculenta (Myrica Nagi), which is found in India, are distributed across several countries, including China, Nepal, Japan, Singapore, and Malaysia. Although the industrial uses of bayberry have received limited research, its pharmacological and medicinal properties have been the subject of extensive studies. The high nutritional content of fruit includes iron, magnesium, sodium, potassium, calcium, and copper. Additionally, it contains bioactive compounds such as tannins, flavonoids, volatile compounds, saponins, and phenolic acids. his study looks at how research trends changed from 2014 to 2024 using bibliographic tools that discuss the fruit’s uses and its health, nutrition, and environmental benefits. The study also maps the species’ distributions around the world. Despite its numerous benefits, bayberry faces threats from overexploitation and urbanization, leading to its decline. There is a pressing need to explore and develop sustainable uses and byproducts of these wild fruits to preserve their value and ensure their availability for future generations.

The integration of agri-technology with the Internet of Agricultural Things (IoAT) is revolutionizing the field of smart agriculture, particularly in diagnosing and treating Oryza sativa (rice) diseases. Given that rice serves as a staple food for over half of the global population, ensuring its healthy cultivation is crucial, particularly with the growing global population. Accurate and timely identification of rice diseases, such as Brown Leaf Spot (BS), Bacterial Leaf Blight (BLB), and Leaf Blast (LB), is therefore essential to maintaining and enhancing rice production. In response to this critical need, the research introduces a timely detection system that leverages the power of Digital Twin (DT)-enabled Fog computing, integrated with Edge and Cloud Computing (CC), and supported by sensors and advanced technologies. At the heart of this system lies a sophisticated deep-learning model built on the robust AlexNet neural network architecture. This model is further refined by including Quaternion convolution layers, which enhance colour information processing, and Atrous convolution layers, which improve depth perception, particularly in extracting disease patterns. To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. This performance significantly surpasses that of other models, including AlexNet, AlexNet-Atrous, QAlexNet, and QAlexNet-Atrous, which achieved respective accuracies of 75 %, 84 %, 89 %, and 91 %. Moreover, the CHBA optimization algorithm outperforms other techniques like CSO, BSO, PSO, and CJAYA and demonstrates optimal results with an 80–20 % training-testing parameter split. Service latency analysis further reveals that the Fog-Edge-assisted environment is more efficient than the Cloud-assisted model for latency reduction. Additionally, the DT-enabled QAlexNet-Atrous-CHBA model proves to be far superior to its non-DT counterpart, showing substantial improvements in 18.7 % in Accuracy, 17 % in recall, 19 % in F?-measure, 17.3 % in specificity, and 13.4 % in precision, respectively. These enhancements are supported by convergence analysis and the Quade rank test, establishing the model's effectiveness and potential to significantly improve rice disease diagnosis and management. This advancement promises to contribute significantly to the sustainability and productivity of global rice cultivation

A quadral-duty digital pulse width modulation (QDPWM) control-based hardware accelerator for the auxiliary permanent magnet brushed DC (PMDC) motors of electric three-wheelers (E3Ws) is proposed. The proposed accelerator involves a precise motor speed calculation circuit, including a buffer to hold the position encoder signal for a predefined number of clock cycles to eliminate encoder signal noise. The proposed hardware accelerator is described with supporting mathematical models and is implemented on field-programmable gate array (FPGA) as well as application-specific integrated circuit (ASIC) platforms using SCL 180 nm CMOS technology library. The ASIC implementation at 12.5 MHz shows that the proposed design has significantly less area and power consumption than the conventional PI-PWM controller-based architecture and is comparable to the dual-duty digital pulse width modulation (DDPWM) controller. The proposed FPGA prototype-driven motor attains a wider speed range with low-speed ripple than DDPWM controller-based architecture. The position signal buffer circuit also enables the accelerator to tolerate noise or glitches in the position encoder signal, which makes the speed calculation precise and reliable. The proposed hardware accelerator-based PMDC drive performance has been validated regarding settling time, speed tracking ability, tolerance to dynamic speed, and load variations on a laboratory test setup

Strategic alteration of the chelating atoms around the metal center can modify the electronic band structure of the electrocatalyst, improving its performance in oxygen evolution and reduction reactions (OER/ORR). Advancements in the development of catalysts with heteroatoms and axial modifications in the coordination sphere are mostly limited to single?molecule electrocatalysts or elevated temperature?mediated pyrolysis approaches for oxygen electrocatalysis. Inspired by biological catalytic systems with axial coordination, a pyrolysis?free strategic methodology is adopted for the synthesis of an iron?metaled covalent organic polymer matrix axially laminated over cobalt?based metal?organic framework through an imidazole moiety. Precise engineering of coordination atoms in synthesized core?shell material, featuring dual metal sites with distinct neighboring atom exhibits mutual synergy due to the presence of bridging imidazole moiety between two metal sites. Modulated synergism navigates the electronic structure such that it favors specific reactant adsorption on specific metal sites during bifunctional O2 electrocatalysis as confirmed through in situ Raman spectroscopy and in situ attenuated total reflection infrared (ATR?IR) spectroscopy. Through dynamic correlation between the in?situ studies and modified d?band center obtained theoretically, the pivotal role of axial coordination linkage mediated synergism favoring ORR/OER process via target?specific reactant adsorption is demonstrated

In The Clear Line in Comics and Cinema, Pinho Barros puts forth a thought-provokingcontemplation of the use of ligne claire in various forms of media, with particular emphasis on films

Climate change is a global issue with diverse regional impacts threatening the survival of both human and non-human species. While the academic discourse on climate change predominantly focuses on macro-level studies, it often neglects the vital role of local environmental practices and the perspectives of affected communities. This paper presents insights from ethnographic fieldwork conducted among the Bishnoi community in Khejarli Village, Jodhpur, in the Thar Desert of India. This study utilizes participant observations, semi-structured interviews, focus group discussions, and case studies to explore local environmental knowledge and practices aimed at mitigating and adapting to climate change. Findings reveal the Bishnois’ deep-rooted relationship with nature and the pivotal role of religious beliefs in shaping their conservation efforts. Since the 15th century, the Bishnois have been committed to protecting local species, such as plants and animals, which has been crucial for sustaining the desert ecosystem and combating climate change. Moreover, their religious teachings and principles have helped conserve values among younger generations, ensuring a lasting culture of environmental stewardship. This paper supports integrating micro-level ethnographic studies into global climate change dialogues, urging the recognition of local knowledge as an essential resource for addressing contemporary environmental challenges

In the era of machine learning we are solving the classification problems by training the labeled classes. But sometimes due to insufficient data in some of the training classes, the system training is inadequate for these minority classes. In this case the output for the classes obtained from the less amount of trained data are miserably inappropriate and biased towards the classes having more data. This problem is known as a class imbalance problem. In such cases, standard classifiers tend to be overpowered by the expansive classes and disregard the little ones. As a result, the performance of machine learning and the deep learning algorithms are also reducing and sometimes highly unacceptable too, mainly if it is related to crucial data like medical and health related. Though various researchers provided some methods to solve this problem but mostly they are problem specific and suitable with the specific classifier only. To find a generalized and effective solution to this problem, we have applied various smote variants for solving the imbalanced factors in dataset and finally improved the performance of the various machine learning and deep learning algorithms. We have experimented and analyzed the effects of SMOTE variants on various machine learning techniques over six standard medical datasets. We have found that SMOTE variants are very effective, and they improve the standard performance measures (Accuracy, Precision, Recall and F1-Score). Additionally, based on our research, it is feasible to determine which smote variation works best with machine learning methods and datasets