This study explores the durability of plasticized polyvinyl chloride (PVC-P) geomembranes in hydraulic engineering anti-seepage structures, particularly under varying operational temperature conditions. Employing accelerated thermal air aging tests on three distinct PVC-P geomembrane variants, the study assesses their mechanical properties, specifically axial tensile strength, using an electronic universal testing machine. A comprehensive thermal air aging model, based on the Arrhenius equation, has been developed, offering insights into the lifespan prediction of these geomembranes. Results demonstrate that factors such as annual average temperature, plasticizer content, and membrane thickness significantly influence the geomembranes' service life. Post-aging observations include a notable yellowing and increased brittleness of the geomembranes, coupled with a decline in tensile strength and elongation. Elongations exhibit a decreasing trend, aligning with a first-order degradation kinetics equation. Under conditions of 50℃ over a period of 120 days, the elongation of polyvinyl chloride (PVC)-HX, PVC2.0-JT, and PVC2.5-JT geomembranes was reduced to 255.88%, 430.11%, and 434.58%, respectively. Predictions indicate that at an operational temperature of 20℃, the expected lifespans for these geomembranes are 19, 45, and 48 years, with material failure correlating to plasticizer loss rates of 58.2%, 32.5%, and 24.8%, respectively. These findings offer valuable guidance for the selection of geomembrane materials in hydraulic engineering projects, considering various designed service durations.

In the domain of intellectual property protection, the embedding of digital watermarks has emerged as a pivotal technique for the assertion of copyright, the conveyance of confidential messages, and the endorsement of authenticity within digital media. This research delineates the implementation of a non-blind watermarking algorithm, utilizing alpha blending facilitated by discrete wavelet transform (DWT) to embed watermarks into genuine images. Thereafter, an extraction process, constituting the inverse of embedding, retrieves these watermarks. The robustness of the embedded watermark against prevalent manipulative attacks, specifically median filter, salt and pepper (SAP) noise, Gaussian noise, speckle noise, and rotation, is rigorously evaluated. The performance of the DWT-based watermarking is quantified using the peak signal-to-noise ratio (PSNR), an objective metric reflecting fidelity. It is ascertained that the watermark remains tenaciously intact under such adversarial conditions, underscoring the proposed method's suitability for applications in digital image security and copyright verification.

This investigation delineates an optimised predictive model for employee attrition within a substantial workforce, identifying pertinent models tailored to the specific context of employee and organisational variables. The selection and refinement of the appropriate predictive model serve as cornerstones for enhancements and updates, which are integral to honing the model's precision in prognosticating potential departures. Through meticulous optimisation, the model demonstrates proficiency in pinpointing the pivotal factors contributing to employee turnover and elucidating the interdependencies among salient variables. A suite of 27 general and eight critical variables were scrutinised. Pertinent correlations were unearthed, notably between monthly income and job satisfaction, home-to-work distance and job satisfaction, as well as age with both job satisfaction and performance metrics. Drawing from prior studies in analogous domains, a three-stage analytical methodology encompassing data exploration, model selection, and implementation was employed. The rigorous training of the optimised model encompassed both attrition factors and variable correlations, culminating in predictive outcomes with a precision of 90% and an accuracy of 87%. Implementing the refined model projected that 113 out of 709 employees, equating to 15.93%, were at a heightened risk of exiting the organisation. This quantitative foresight equips stakeholders with a strategic tool for preemptive interventions to mitigate turnover and sustain organisational vitality.

In the realm of agriculture, crop yields of fundamental cereals such as rice, wheat, maize, soybeans, and sugarcane are adversely impacted by insect pest invasions, leading to significant reductions in agricultural output. Traditional manual identification of these pests is labor-intensive and time-consuming, underscoring the necessity for an automated early detection and classification system. Recent advancements in machine learning, particularly deep learning, have provided robust methodologies for the classification and detection of a diverse array of insect infestations in crop fields. However, inaccuracies in pest classification could inadvertently precipitate the use of inappropriate pesticides, further endangering both agricultural yields and the surrounding ecosystems. In light of this, the efficacy of nine distinct pre-trained deep learning algorithms was evaluated to discern their capability in the accurate detection and classification of insect pests. This assessment utilized two prevalent datasets, comprising ten pest classes of varied sizes. Among the transfer learning techniques scrutinized, adaptations of ResNet-50 and ResNet-101 were deployed. It was observed that ResNet-50, when employed in a transfer learning paradigm, achieved an exemplary classification accuracy of 99.40% in the detection of agricultural pests. Such a high level of precision represents a significant advancement in the field of precision agriculture.

In Sub-Saharan Africa, particularly in Nigeria, Lassa fever poses a significant infectious disease threat. This investigation employed count regression and machine learning techniques to model mortality rates associated with confirmed Lassa fever cases. Utilizing weekly data from January 7, 2018, to April 2, 2023, provided by the Nigeria Centre for Disease Control (NCDC), an analytical comparison between these methods was conducted. Overdispersion was indicated (p<0.01), prompting the exclusive use of negative binomial and generalized negative binomial regression models. Machine learning algorithms, specifically medium Gaussian support vector machine (MGSVM), ensemble boosted trees, ensemble bagged trees, and exponential Gaussian Process Regression (GPR), were applied, with 80% of the data allocated for training and the remaining 20% for testing. The efficacy of these methods was evaluated using the coefficients of determination (R²) and the root mean square error (RMSE). Descriptive statistics revealed a total of 30,461 confirmed cases, 4,745 suspected cases, and 772 confirmed fatalities attributable to Lassa fever during the study period. The negative binomial regression model demonstrated superior performance (R²=0.1864, RMSE=4.33) relative to the generalized negative binomial model (R²=0.1915, RMSE=18.2425). However, machine learning algorithms surpassed the count regression models in predictive capability, with ensemble boosted trees emerging as the most effective (R²=0.85, RMSE=1.5994). Analysis also identified the number of confirmed cases as having a significant positive correlation with mortality rates (r=0.885, p<0.01). The findings underscore the importance of promoting community hygiene practices, such as preventing rodent intrusion and securing food storage, to mitigate the transmission and consequent fatalities of Lassa fever.

In regions characterized by extreme cold and elevated altitudes, notably in the northwest, the mechanical characteristics of construction materials such as Ultra-High Performance Concrete (UHPC) are critically impacted by ambient temperatures. This study investigates the mechanical properties of UHPC subjected to low-temperature curing environments, conducting uni-axial compressive and splitting tensile strength tests on UHPC specimens, which comprise water, dry mix, and steel fibers. These specimens were cured at varied temperatures (-10℃, -5℃, 5℃, 10℃). Utilizing damage theory principles, the loss rate in compressive strength of UHPC post-curing was quantified as a damage indicator, revealing internal degradation. A predictive model for damage under low-temperature maintenance was developed, grounded in the two-parameter Weibull probability distribution and empirical damage models. Parameter estimation for this model was achieved through the least squares method, informed by experimental data. The findings indicate a rapid increase in UHPC’s mechanical strength at all curing temperatures, with 7-day strength achieving approximately 90% of its 28-day counterpart. A positive correlation was observed between the mechanical strength of UHPC, curing temperature, and age. Despite a reduction in mechanical strength due to low-temperature curing, UHPC was found to attain anticipated strength levels suitable for construction in cold environments. The proposed model for predicting UHPC damage under low-temperature conditions demonstrated efficacy in estimating the strength loss rate, thereby offering substantial technical support for UHPC’s application in northwest regions.

The optimization of traffic flow, enhancement of safety measures, and minimization of emissions in intelligent transportation systems (ITS) pivotally depend on the Vehicle License Plate Recognition (VLPR) technology. Challenges predominantly arise in the precise localization and accurate identification of license plates, which are critical for the applicability of VLPR across various domains, including law enforcement, traffic management, and both governmental and private sectors. Utilization in electronic toll collection, personal security, visitor management, and smart parking systems is commercially significant. In this investigation, a novel methodology grounded in the Kanade-Lucas-Tomasi (KLT) algorithm is introduced, targeting the localization, segmentation, and recognition of characters within license plates. Implementation was conducted utilizing MATLAB software, with grayscale images derived from both still cameras and video footage serving as the input. An extensive evaluation of the results revealed an accuracy of 99.267%, a precision of 100%, a recall of 99.267%, and an F-Score of 99.632%, thereby surpassing the performance of existing methodologies. The contribution of this research is significant in addressing critical challenges inherent in VLPR systems and achieving an enhanced performance standard.

An analytical approach was adopted to ascertain the optimal distribution channel for Bingo LLC's final products, deploying a multifaceted decision-making framework that incorporated the Full Consistency Method (FUCOM) and Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) methodologies. Weighting coefficients essential for distribution channel selection were derived using FUCOM, informed by responses to a meticulously designed questionnaire administered to experts from distinct Bingo LLC branches in Maglaj and Kraševo. The gathered data, reflecting a range of pertinent criteria, facilitated the computation of weighting coefficients via the FUCOM technique within a Microsoft Excel environment. These coefficients were subsequently employed in the execution of the MARCOS method to determine the hierarchical positioning of the potential alternatives. This process culminated in the identification of the most advantageous distribution channel alternative for the company. The overarching aim of this analysis was to elucidate the most efficacious distribution channel strategy to enhance Bingo LLC's business operations, underpinned by the hypothesis that proficient management of distribution channels is a critical determinant of commercial success. The implications of this research extend to the broader field of trade, highlighting the significance of strategic distribution channel management. This study stands as a testament to the application of decision-making models in operational enhancements and contributes to the existing body of knowledge with empirical evidence from the case of Bingo LLC.

This study employed Schwartz's basic value theory and the theory of planned behaviour (TPB) to elucidate environmentally sustainable tourist behaviour (ESTB) among Muslim tourists in Indonesia. Two central inquiries were examined. Firstly, the impact of intrapersonal environmental and non-environmental values on the ESTB of Muslim tourists was scrutinised. Secondly, the mediating role of environmental attitude (EA) on these intrapersonal values towards ESTB in Muslim-friendly destinations in Indonesia was assessed. A cross-sectional survey, conducted in June and July 2022, collected data from 300 Muslim tourists at Muslim-friendly destinations in Indonesia. Participants, aged 17 and above, were selected through a purposive sampling approach; they had obtained a CHSE certificate in Malang during the transition from epidemic to the new normal era and had visited at least one of the 26 Muslim-friendly tourist spots. Analysis using the Structural Equation Model (SEM) revealed that while environmental knowledge (EK) and attitudes positively influenced ESTB, environmental concern (EC) and religious value (RGV) did not demonstrate a direct impact. Moreover, it was discerned that EA played a significant mediating role between RGV, EC, EK, and environmentally sustainable behaviours. It was further observed that individuals endorsing egoistic values typically exhibited weaker pro-environmental beliefs and were less inclined towards pro-environmental actions. Conversely, those with altruistic values displayed stronger pro-environmental beliefs, consequently impacting their environmental sustainability behaviour (ESB) in Muslim-friendly tourist destinations.

The burgeoning importance of digitalization and cyber-physical manufacturing systems in the industrial sector is undeniable, yet discussions around viable solutions for small and medium-sized enterprises remain scant. These enterprises often face constraints in replacing extant machinery or implementing extensive IT upgrades, despite the availability of skilled engineering personnel. In response to this gap, an illustrative use case involving the application of Digital Twins (DT) to legacy systems is delineated, encompassing a detailed exploration of necessary hardware and software components, alongside pertinent considerations for implementation design. The establishment of a symbiotic relationship between the physical and digital realms is underscored as imperative, necessitating a granular understanding of the system to uncover opportunities and constraints for intervention. Such understanding is posited as a critical determinant of the DT's utility. This case study, situated within the Cyber-Physical Manufacturing Systems Laboratory at Széchenyi István University, serves to elucidate these principles and contribute to the discourse on smart manufacturing solutions for legacy systems.

This survey aims to offer a thorough and organized overview of research on anomaly detection, which is a significant problem that has been studied in various fields and application areas. Some anomaly detection techniques have been tailored for specific domains, while others are more general. Anomaly detection involves identifying unusual patterns or events in a dataset, which is important for a wide range of applications including fraud detection and medical diagnosis. Not much research on anomaly detection techniques has been conducted in the field of economic and international trade. Therefore, this study attempts to analyse the time-series data of United Nations exports and imports for the period 1992 – 2022 using LSTM based anomaly detection algorithm. Deep learning, particularly LSTM networks, are becoming increasingly popular in anomaly detection tasks due to their ability to learn complex patterns in sequential data.

This paper presents a detailed explanation of LSTM architecture, including the role of input, forget, and output gates in processing input vectors and hidden states at each timestep. The LSTM based anomaly detection approach yields promising results by modelling small-term as well as long-term temporal dependencies.

A notable association between Alzheimer's Disease and Epilepsy, two divergent neurological conditions, has been established through previous research, illustrating an elevated seizure development risk in individuals diagnosed with Alzheimer’s Disease (AD). The hippocampus, fundamental in both seizure and tumour pathology, is intricately investigated herein. The subsequent aberrant electrical activity within this brain region, frequently implicated in seizure onset and propagation, underpins a complex relationship between degenerative cerebral changes and seizure incidence. Symptomatic manifestations in hippocampal glioma include, but are not limited to, seizures, memory deficits, and language difficulties, contingent upon the tumour's location and size. Thus, the cruciality of proficient seizure detection and analysis is underscored. Employing canny edge detection and thresholding to delineate contours and boundaries within images, an analysis was conducted by transmuting grayscale or colour images into a binary format. The input dataset, utilised for the training and testing of machine and deep-learning models, comprised images of seizures. These models were subsequently trained to discern patterns and features within the images, facilitating the differentiation between two predefined classes. Resultantly, the models predicted, with a defined accuracy level, the presence or absence of a seizure within a new image. The Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models demonstrated classification accuracies of 96% and 95%, respectively. By analysing performance metrics on a per-slice basis, the localization of seizure activity within the brain could be visualised, offering valuable insights into regions affected by this activity. The amalgamation of edge detection, feature extraction, and classification models proficiently discriminated between seizure and non-seizure activities, providing pivotal insights for the diagnosis and therapeutic strategies for epilepsy. Further, studying these neurological alterations can illuminate the progression and severity of cognitive and emotional deficits within affected individuals, whilst advancements in diagnostic methodologies, such as Magnetic Resonance Imaging (MRI), facilitate an enriched comparative analysis.