Artificial neural networks (ANNs) are powerful data-oriented “black-box” algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH₃, Ca, Cl, Chl-a, EC, F CaCO₃, Mg, Mn, NO₃, pH, SO₄, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R²) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained. Full article

Maximal resection with the preservation of neurological function are the mainstays of the surgical management of high-grade meningiomas. Surgical morbidity is strongly associated with tumor size, location, and invasiveness, whereas patient survival is strongly associated with the extent of resection, tumor biology, and patient health. A versatile microsurgical skill set combined with a cogent multimodality treatment plan is critical in order to achieve optimal patient outcomes. Continued refinement in surgical techniques in conjunction with directed radiotherapeutic and medical therapies will define future treatment. Full article

With the continuous development of automotive intelligence, vehicle occupant detection technology has received increasing attention. Despite various types of research in this field, a simple, reliable, and highly private detection method is lacking. This paper proposes a method for vehicle occupant detection using millimeter-wave radar. Specifically, the paper outlines the system design for vehicle occupant detection using millimeter-wave radar. By collecting the raw signals of FMCW radar and applying Range-FFT and DoA estimation algorithms, a range–azimuth heatmap was generated, visually depicting the current status of people inside the vehicle. Furthermore, utilizing the collected range–azimuth heatmap of passengers, this paper integrates the Faster R-CNN deep learning networks with radar signal processing to identify passenger information. Finally, to test the performance of the detection method proposed in this article, an experimental verification was conducted in a car and the results were compared with those of traditional machine learning algorithms. The findings indicated that the method employed in this experiment achieves higher accuracy, reaching approximately 99%. Full article

This study is focused on the detailed examination of the combustion properties and kinetic analysis of a cellulose acetate fibrous bundle (CAFB), separated from used cigarette filters. It was shown that the faster rate of CAFB heating allows a large amount of heat to be supplied to a combustion system in the initial stages, where the increase in heating rate has a positive response to ignition behavior. The best combustion stability of CAFB is achieved at the lowest heating rate. Through the use of different kinetic methods, it was shown that combustion takes place through two series of consecutive reaction steps and one independent single-step reaction. By optimizing the kinetic parameters within the proposed reaction models, it was found that the steps related to the generation of levoglucosenone (LGO) (by catalytic dehydration of levoglucosan (LG)) and acrolein (by breakdown of glycerol during CAFB burning—which was carried out through glycerol adsorption on a TiO₂ surface in a the developed dehydration mechanism) represent rate-controlling steps, which are strongly controlled by applied heating rate. Isothermal predictions have shown that CAFB manifests very good long-term stability at 60 °C (which corresponds to storage in a sea shipping container), while at 200 °C, it shows a sudden loss in thermal stability, which is related to the physical properties of the sample. Full article

Supercavitating vehicles present significant issues in controller design due to their multiphase flow-coupling characteristics. This study addresses force analysis and the construction of a 6-degree-of-freedom mathematical model for a supercavitating vehicle. A terminal sliding mode control law is intended to guarantee the quick tracking of the command signal for high-precision attitude control. To drastically lower the frequency of actuation and communication, a mechanism to trigger events is also introduced into the control link. A disturbance observer, which estimates system uncertainty using a non-recursive differentiator, improves the robustness of the system. The Lyapunov approach is used to prove that the system is stable. Numerical simulation results validate that the proposed method enhances control accuracy and robustness. The event-trigger mechanism reduces the execution frequency to 18.59%, effectively reducing the communication burden. Full article

Heart failure is a progressive disease that leads to high mortality rates if left untreated, and inotropes are a class of drugs used to treat a type of heart failure where patients have reduced ejection fraction (HFrEF). This study aims to utilize the Fuzzy-Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE), an effectively used multi-criteria decision making (MCDM) technique. To analyze the characteristics of the most often used inotropes for acute HFrEF and chronic HFrEF, we use the same parameters set with distinct importance factors and aims for each property and, therefore, mathematically demonstrate the strengths and weaknesses of each inotrope alternative. As a result, a detailed ranking list for each HFrEF class was obtained, with supplementary information on how each parameter contributed to the ranking of each inotrope. From these results, it was concluded that the F-PROMETHEE method is applicable for evaluating the risks and benefits of various inotropes to determine a starting point for treating an average patient when making a quick decision without complete patient data. As demonstrated in this study, it is possible to easily use the same data set and only change some preference parameters according to individual needs to produce patient-specific results. In this study, we showed that creating a decision-making system that mathematically assists clinical specialists with their decision-making process is feasible. Full article

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra “discriminator” named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two “discriminators” correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model’s feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual “discriminators”. Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. Full article

With the introduction of deep learning, a significant amount of research has been conducted in the field of computer vision in the past decade. In particular, research on object detection (OD) continues to progress rapidly. However, despite these advances, some limitations need to be overcome to enable real-world applications of deep learning-based OD models. One such limitation is inaccurate OD when image quality is poor or a target object is small. The performance degradation phenomenon for small objects is similar to the fundamental limitations of an OD model, such as the constraint of the receptive field, which is a difficult problem to solve using only an OD model. Therefore, OD performance can be hindered by low image quality or small target objects. To address this issue, this study investigates the compatibility of super-resolution (SR) and OD techniques to improve detection, particularly for small objects. We analyze the combination of SR and OD models, classifying them based on architectural characteristics. The experimental results show a substantial improvement when integrating OD detectors with SR models. Overall, it was demonstrated that, when the evaluation metrics (PSNR, SSIM) of the SR models are high, the performance in OD is correspondingly high as well. Especially, evaluations on the MS COCO dataset reveal that the enhancement rate for small objects is 9.4% higher compared to all objects. This work provides an analysis of SR and OD model compatibility, demonstrating the potential benefits of their synergistic combination. The experimental code can be found on our GitHub repository. Full article

Plumbago indica L. contains a valuable bioactive compound called plumbagin. Elicited regenerated shoots grown in vitro could be another source of high-yielding plumbagin. The purpose of this investigation was to examine the effects of elicitor type and concentration, as well as elicitation period, on plumbagin content in in vitro-regenerated shoots of P. indica. Nodal explants were cultured on Murashige and Skoog (MS) medium containing 1 mg/L benzyladenine (BA) in combination with 0–150 mg/L yeast extract or 50–150 µM salicylic acid for four weeks. Plumbagin levels of 3.88 ± 0.38% and 3.81 ± 0.37% w/w g dry extract were achieved from the 50 and 100 mg/L yeast extract-elicited shoots, which were higher than the value obtained for the control. However, the addition of salicylic acid did not increase the plumbagin content. In the elicitation period experiment, nodal explants were cultured on MS medium supplemented with 1 mg/L BA and 50 mg/L yeast extract for durations of three, four and five weeks. The 4-week yeast extract-elicited shoot had a maximum plumbagin content of 3.22 ± 0.12% w/w g dry extract, greater than that of the control. In summary, the plumbagin content of the in vitro P. indica shoots was enhanced by 4-week elicitation using 50 mg/L yeast extract. Full article

This article explores the significance of simulation-based analysis in understanding the effectiveness of material handling strategies. By utilizing simulation models, businesses can optimize production processes, streamline flows, and enhance overall logistics efficiency. In today’s competitive market landscape, the significance of product manipulation cannot be overstated. It directly influences consumer perception and plays a pivotal role in gaining a competitive advantage. Simulation-based analysis has emerged as a powerful tool for optimizing production processes and enhancing logistics efficiency. Robotics sorting and loading offer increased accuracy, speed, and efficiency over manual processes. Their implementation boost productivity, cuts costs, and enhances working conditions. In today’s competitive market, effective product handling shapes consumer perception and competitiveness. VR simulation-based analysis optimizes manufacturing, logistics, and robotics, driving efficiency. Through advanced VR simulation models, businesses streamline operations, adapt to market dynamics, and embrace automation, enhancing competitiveness. Full article

Spam emails and phishing attacks continue to pose significant challenges to email users worldwide, necessitating advanced techniques for their efficient detection and classification. In this paper, we address the persistent challenges of spam emails and phishing attacks by introducing a cutting-edge approach to email filtering. Our methodology revolves around harnessing the capabilities of advanced language models, particularly the state-of-the-art GPT-4 Large Language Model (LLM), along with BERT and RoBERTa Natural Language Processing (NLP) models. Through meticulous fine-tuning tailored for spam classification tasks, we aim to surpass the limitations of traditional spam detection systems, such as Convolutional Neural Networks (CNNs). Through an extensive literature review, experimentation, and evaluation, we demonstrate the effectiveness of our approach in accurately identifying spam and phishing emails while minimizing false positives. Our methodology showcases the potential of fine-tuning LLMs for specialized tasks like spam classification, offering enhanced protection against evolving spam and phishing attacks. This research contributes to the advancement of spam filtering techniques and lays the groundwork for robust email security systems in the face of increasingly sophisticated threats. Full article

Sustainable humanitarian relief logistics (SHRL) is gaining attention due to increased disasters, unpredictable demand, large volumes, high delivery stakes, and limited resources, evaluated through adaptable performance drivers. This study presents a novel hybrid framework for SHRL, combining the Fuzzy Delphi Method (FDM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL). Initially, FDM is utilized to reach a consensus among experts concerning key performance indicators (KPIs) for humanitarian logistics and supply chains. By incorporating the inherent uncertainty and vagueness in expert judgments, FDM refines the list of key performance indicators that reflect the real-life conditions and constraints in disaster operations. Finally, the fuzzy DEMATEL approach was used to analyze the interrelationships among factors, identifying cause-and-effect behavior and ranking them, forming a robust theoretical framework. Based on the acquired results, the KPIs attached to the Quality (P1) aspect of the proposed framework have gained significant importance and are the main cause in a cause-and-effect relationship which impacts and is helpful to improve the performance of humanitarian organizations in all phases of disaster management. The KPIs prompt delivery (D1), and delivery accuracy (D2) are more significant, while capacity building and training (D19) and delivery compliance (D15) are least significant in SHRL scenarios. This research is expected to support humanitarian organizations in enhancing their capabilities, thereby improving the effectiveness and efficiency of aid delivery in disaster-stricken areas. Full article

Alzheimer’s disease (AD) and diabetes are non-communicable diseases with global impacts. Inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are suitable therapies for AD, while α-amylase and α-glucosidase inhibitors are employed as antidiabetic agents. Compounds were isolated from the medicinal plant Terminalia macroptera and evaluated for their AChE, BChE, α-amylase, and α-glucosidase inhibitions. From ¹H and ¹³C NMR data, the compounds were identified as 3,3′-di-O-methyl ellagic acid (1), 3,3′,4′-tri-O-methyl ellagic acid-4-O-β-D-xylopyranoside (2), 3,3′,4′-tri-O-methyl ellagic acid-4-O-β-D-glucopyranoside (3), 3,3′-di-O-methyl ellagic acid-4-O-β-D-glucopyranoside (4), myricetin-3-O-rhamnoside (5), shikimic acid (6), arjungenin (7), terminolic acid (8), 24-deoxysericoside (9), arjunglucoside I (10), and chebuloside II (11). The derivatives of ellagic acid (1–4) showed moderate to good inhibition of cholinesterases, with the most potent being 3,3′-di-O-methyl ellagic acid, with IC₅₀ values of 46.77 ± 0.90 µg/mL and 50.48 ± 1.10 µg/mL against AChE and BChE, respectively. The compounds exhibited potential inhibition of α-amylase and α-glucosidase, especially the phenolic compounds (1–5). Myricetin-3-O-rhamnoside had the highest α-amylase inhibition with an IC₅₀ value of 65.17 ± 0.43 µg/mL compared to acarbose with an IC₅₀ value of 32.25 ± 0.36 µg/mL. Two compounds, 3,3′-di-O-methyl ellagic acid (IC₅₀ = 74.18 ± 0.29 µg/mL) and myricetin-3-O-rhamnoside (IC₅₀ = 69.02 ± 0.65 µg/mL), were more active than the standard acarbose (IC₅₀ = 87.70 ± 0.68 µg/mL) in the α-glucosidase assay. For α-glucosidase and α-amylase, the molecular docking results for 1–11 reveal that these compounds may fit well into the binding sites of the target enzymes, establishing stable complexes with negative binding energies in the range of −4.03 to −10.20 kcalmol⁻¹. Though not all the compounds showed binding affinities with cholinesterases, some had negative binding energies, indicating that the inhibition was thermodynamically favorable. Full article

In addition to supporting vertical loads from superstructures, piles are frequently subjected to horizontal soil pressures, long-term wind, wave, and current forces, as well as seismic loads. Presently, the p-y curve method is widely employed for calculating the horizontal forces acting on piles due to its ability to replicate the nonlinear interaction between piles and soil. This paper provides a thorough review and analysis of the current research on p-y curve models for piles, examining literature across various conditions such as horizontal static loads, cyclic loads, and seismic loads. Special emphasis is placed on the development, classification, and analysis of the key factors influencing major p-y curve models. It also discusses future research directions and prospects, considering emerging trends and prevailing challenges in the field. For instance, future studies should investigate p-y curves for piles under various combined loads, considering the influence of construction methods and the installation effect. Additionally, the development of a comprehensive p-y curve database and the application of existing research to new foundation systems are essential for advancing pile technology and fostering innovative designs. Full article

Myxozoa, a unique group of obligate endoparasites within the phylum Cnidaria, can cause emerging diseases in wild and cultured fish populations. Recently, the myxozoan Myxobolus bejeranoi has been identified as a prevalent pathogen infecting the gills of cultured hybrid tilapia, leading to systemic immune suppression and considerable mortality. Here, we employed a proteomic approach to examine the impact of M. bejeranoi infection on fish gills, focusing on the structure of the granulomata, or cyst, formed around the proliferating parasite to prevent its spread to surrounding tissue. Enrichment analysis showed increased immune response and oxidative stress in infected gill tissue, most markedly in the cyst’s wall. The intense immune reaction included a consortium of endopeptidase inhibitors, potentially combating the myxozoan arsenal of secreted proteases. Analysis of the cyst’s proteome and histology staining indicated that keratin intermediate filaments contribute to its structural rigidity. Moreover, we uncovered skin-specific proteins, including a grainyhead-like transcription factor and a teleost-specific S100 calcium-binding protein that may play a role in epithelial morphogenesis and cysts formation. These findings deepen our understanding of the proteomic elements that grant the cyst its distinctive nature at the critical interface between the fish host and myxozoan parasite. Full article

This paper presents a novel polarization-insensitive dual-band frequency-selective surface (FSS)-based electromagnetic shield. The miniaturized FSS unit cell consists of a modified Jerusalem crossed loop and a corner-modified square loop. These FSS elements are arranged in a co-planner configuration over a single-layer Rogers 5880 substrate and simultaneously offer effective shielding in the X- and Ku-bands. Moreover, the FSS manifests polarization-independent and angularly stable band-reject filter characteristics over various oblique angles of incidence for both the TE and TM polarizations with virtuous attenuation at both resonances. In addition, the FSS structure is modelled into an equivalent lumped circuit to better analyze the phenomenon of EM wave suppression. A finite prototype of FSS is fabricated and tested. The simulated and measured results are in good agreement, thus making it a potential candidate for RF shielding/isolation applications. Full article

This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council’s open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies—including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm—the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables. Full article

The self-sensing technology of microactuators utilizes a smart material to concurrently actuate and sense in a closed-loop control system. This work aimed to develop a position feedback-control system of nickel electrothermal microactuators using a resistivity self-sensing technique. The system utilizes the change in heating/sensing elements’ resistance, due to the Joule heat, as the control parameter. Using this technique, the heating/sensing elements would concurrently sense and actuate in a closed loop control making the structures of microactuators simple. From a series of experiments, the proposed self-sensing feedback control system was successfully demonstrated. The tip’s displacement error was smaller than 3 µm out of the displacement span of 60 µm. In addition, the system was less sensitive to the abrupt temperature change in surroundings as it was able to displace the microactuator’s tip back to the desired position within 5 s, which was much faster than a feed-forward control system. Full article

The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended. Full article

Variable camber wing technology stands out as the most promising morphing technology currently available in green aviation. Despite the ongoing advancements in smart materials and compliant structures, they still fall short in terms of driving force, power, and speed, rendering mechanical structures based on kinematics the preferred choice for large long-range civilian aircraft. In line with this principle, this paper introduces a linkage-based variable camber trailing edge design approach. Covering coordinated design, internal skeleton design, flexible skin design, and drive structure design, the method leverages a two-dimensional supercritical airfoil to craft a seamless, continuous two-dimensional wing full-size variable camber trailing edge structure, boasting a 2.7 m span and 4.3 m chord. Given the significant changes in aerodynamic load direction, ground tests under cruise load utilize a tracking-loading system based on tape and lever. Results indicate that the designed single-degree-of-freedom Watt I mechanism and Stephenson III drive mechanism adeptly accommodate the slender trailing edge of the supercritical airfoil. Under a maximum cruise vertical aerodynamic load of 17,072 N, the structure meets strength requirements when deflected to 5°. The research in this paper can provide some insights into the engineering design of variable camber wings. Full article

The inheritance of historic human-induced disruption and the fierceness of its impact change aquatic ecosystems. This work reviews some of the main stressors on freshwater ecosystems, focusing on their effects, threats, risks, protection, conservation, and management elements. An overview is provided on the water protection linked to freshwater stressors: solar ultraviolet radiation, thermal pollution, nanoparticles, radioactive pollution, salinization, nutrients, sedimentation, drought, extreme floods, fragmentation, pesticides, war and terrorism, algal blooms, invasive aquatic plants, riparian vegetation, and invasive aquatic fish. Altogether, these stressors build an exceptionally composite background of stressors that are continuously changing freshwater ecosystems and diminishing or even destroying their capability to create and maintain ongoing natural healthy products and essential services to humans. Environmental and human civilization sustainability cannot exist without the proper management of freshwater ecosystems all over the planet; this specific management is impossible if the widespread studied stressors are not deeply understood structurally and functionally. Without considering each of these stressors and their synergisms, the Earth’s freshwater is doomed in terms of both quantitative and qualitative aspects. Full article

Phosphorus availability is a serious problem for plants growing and grown in acidic soils of bogs, poor in macronutrients. The application of phosphorus fertilizers to such soils is unprofitable because of the physical and chemical properties of these soils, where phosphate is firmly bound to organic and inorganic compounds and becomes inaccessible to plants. Plants of the Vaccinium genus both from natural stands and commercial plantations may suffer from phosphorus deficiency, so they need to have a number of adaptations that allow them to efficiently extract phosphorus. This review addresses the following issues in relation to plants of the Vaccinium genus: sources of phosphorus for plants; the release of phosphate ions from soil components; the transport of phosphate ions into plants; and the importance of mycorrhiza in supplying phosphorus to plants. Thus, we sought to draw researchers’ attention to sources and routes of phosphorus supply of plants of the Vaccinium genus and its unexplored aspects. Full article