Hydrogen sulphide (H2S) corrosion is a significant problem in the oil industry. It affects pipeline integrity and generates high maintenance and repair costs. This work aims to evaluate global trends and the effectiveness of different types of H2S corrosion inhibitors applied in oil pipelines through bibliometrics and a systematic review, analysing their future implications for developing anticorrosion strategies during the last decade. This process was developed in three phases: (i) baseline data and focusing, (ii) scientific metrics, and (iii) literature review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. The results show sustained growth in publications, focusing on green inhibitors and nanotechnology-based technologies that achieve efficiencies of more than 90% in the laboratory. However, gaps persist in field validation and designing multifunctional composites for extreme environments. These findings suggest prioritising applied research into new self-healing materials and coatings, as well as industrial-scale evaluation protocols to optimise the protection of critical infrastructure in the oil industry.

Port infrastructure is crucial for inter-island connectivity and marine transportation services in the Thousand Islands Regency, Indonesia. Ensuring better connectivity and accessibility for island residents is essential. This research aims to improve marine transportation services in the Thousand Islands Regency by applying a clustering approach. The goal is to enhance regional transportation services by identifying patterns and gaining insights from historical data. The K-means clustering method was employed in this research to analyse historical data and categorize ports into three distinct clusters: low capacity, medium capability, and high capacity. The research identified three clusters: low-capability ports, medium-capability ports, and high-capability ports. The government has identified these clusters as focal points for improving regional transportation services. The findings highlight the essential role of marine transportation in facilitating connectivity and supporting the tourism industry in the Thousand Islands Regency. This analysis provides a comprehensive understanding of the current situation and offers a basis for informed decision-making in future port management strategies. The research urges stakeholders and policymakers to prioritise improvements at the identified ports to enhance service quality, connectivity, and regional development.

Currently, weapons storage is conducted manually, employing multiple layers of security systems that are time-consuming. This system exhibits numerous vulnerabilities that jeopardize security in the oversight of weapons storage facilities. This research seeks to develop a weapon storage security system utilizing a soldier identity-based identifier and to document its usage through a web-based interface. This system incorporates the MFRC 522 RFID sensor for identification, integrated with a drop-bolt lock, Arduino Uno Ethernet shield, DC buzzer, relay module, DC jack module, and an emergency module, all connected to a web-based interface. The system undergoes testing through multiple scenarios to evaluate response time and robustness. The test results indicate that this system operates efficiently and enhances response time during the laying off and taking off weapons, as well as data recording in real-time. The system identifies the key owner and exhibits a response time of 10 seconds, whereas the web interface records a response time of under 18.2 seconds during heavy usage.

Transformer-based language models have demonstrated remarkable success in few-shot text classification; however, their effectiveness is often constrained by challenges such as high intraclass diversity and interclass similarity, which hinder the extraction of discriminative features. To address these limitations, a novel framework, Adaptive Masking Bidirectional Encoder Representations from Transformers with Dynamic Weighted Prototype Module (AMBERT-DWPM), is introduced, incorporating adaptive masking and dynamic weighted prototypical learning to enhance feature representation and classification performance. The standard BERT architecture is refined by integrating an adaptive masking mechanism based on Layered Integrated Gradients (LIG), enabling the model to dynamically emphasize salient text segments and improve feature discrimination. Additionally, a DWPM is designed to assign adaptive weights to support samples, mitigating inaccuracies in prototype construction caused by intraclass variability. Extensive evaluations conducted on six publicly available benchmark datasets demonstrate the superiority of AMBERT-DWPM over existing few-shot classification approaches. Notably, under the 5-shot setting on the DBpedia14 dataset, an accuracy of 0.978±0.004 is achieved, highlighting significant advancements in feature discrimination and generalization capabilities. These findings suggest that AMBERT-DWPM provides an efficient and robust solution for few-shot text classification, particularly in scenarios characterized by limited and complex textual data.

The effects of adding silicon to shape memory alloy (SMA) (Nitinol) were investigated in the current investigation. Most people think that silicon-based SMAs could be a cheaper alternative to NiTi SMA because they have good shape memory properties, good damping capacity, and other useful properties. The alloys were mechanically tested for Vickers microhardness, compression force, shape memory effect (strain recovery), density, and porosity to estimate the Si effect. Powder metallurgy was used to make the alloys. The base alloy (Nitinol) was prepared after sintering treatment at a temperature of 850°C for a period of 6hr. In addition, alloys were prepared from them to find out the effect of adding silicon. These alloys included the base alloy to which silicon was added in proportions of 0%, 3%, 6%, and 9% wt. of Si as their weight ratios. The results showed that increasing the percentage of silicon resulted in improved mechanical properties while 9.0 wt.% Si showed better shape memory properties.

This paper focuses on the study of the use of fused deposition modeling (FDM) in enhancing the process parameters of formed components. Three variables (fill density, layer height, and printing speed) are considered to have a significant and significant effect on the tensile strength of acrylonitrile butadiene styrene specimens. The methodology of this study is based on experiments using the Taguchi strategy. On the other hand, previous studies have mainly focused on analyzing individual process parameters and their effect on the mechanical properties of FDM-manufactured parts. The results of this study, using Taguchi techniques and analysis of variance, show that the largest and most significant effect on the tensile strength of FDM structures was the fill density among the three process parameters. ANOVA results for the average tensile strength with a confidence interval of 66.595%, while ANOVA results for the Young Modulus at a confidence interval of 36.236% and the ANOVA findings for the fractured strength at a confidence interval of 50.228%. A higher F-value indicates that adjusting a process parameter has a greater impact on performance characteristics. In addition, there is a limited effect of the other process variable with a smaller effect, but it was still effective. Finally, valuable insights could be drawn from the results about the correlation between process parameters and mechanical properties of components. The study confirms encouraging results using FDM technology for researchers and future studies in terms of enhancing the structural integrity of the produced components.

The demand for vehicles has increased significantly in the last two decades as a result of the rise in the global population and improved living capacity. The popularity of using products from bio-based sources has also increased due to the need to reduce air pollution resulting from burning fossil fuels while maintaining or increasing the efficiency of engines. In this study, biodiesel (produced from restaurant waste oil) with small amounts of butanol alcohol was added to conventional Iraqi diesel and tested. Adding butanol as a low-dose stimulant to the diesel-biodiesel mixture to improve engine performance and eliminate pollutants is a modern method that has not yet been approved and requires many studies before it is accepted as a vehicle fuel. The engine showed good performance when operating with the proposed mixtures under different load conditions. The D90W5B5 mixture provided the highest cylinder pressure, which was superior to diesel. The tested blends, D90W5B5, D80W10B10, D70W15B15, and W100, caused a decrease in NOx emissions compared to diesel by 16.57%, 25.48%, 33.14%, and 39.76%, respectively. As well as reduced the total suspended particles by 19.1%, 22.02%, 34.66% and 49.7%, respectively. One of the most important results obtained is that these mixtures reduced the Sulfur dioxide (SO₂) and Hydrogen sulfide (H₂S) emissions by 3.9%, 8.66%, 10.98%, and 97.7%, for the first pollutant and by 6.15%, 8.89%, 15.57%, and 97.8%, for the second one, respectively.

The determinants of successful partnership models between coffee farmers and key stakeholders—comprising private enterprises, cooperatives, and governmental bodies—were investigated to enhance productivity and sustainability within the coffee sector in Mojokerto, Indonesia. A mixed-methods approach was employed, integrating factor analysis and multiple linear regression modeling to examine the predictive influence of partnership dimensions. Four core dimensions—economic, social, cultural, and agroclimatic—were evaluated through exploratory factor analysis to uncover latent structures underpinning partnership success. The analysis resulted in the identification of four principal components: socio-economic exchange dynamics, socio-economic connectivity of agriculture, capital networks and socio-economic experience, and economic and educational networks. These components were subsequently used as independent variables in a multiple linear regression model, where partnership success was operationalized through kernel weight outputs as a proxy for productivity performance. The regression model accounted for 84.19% of the variance in partnership success, indicating strong explanatory power. The findings underscore the critical role of non-economic dimensions—particularly social connectivity and education—in driving effective partnerships, alongside traditional economic considerations. Policy implications include the need to design intervention strategies that enhance farmers' access to capital, strengthen educational and training programs, and encourage participation in socio-economic networks. While the model demonstrates strong internal validity within the context of the coffee industry, its applicability to other agricultural commodities remains to be tested. Further research is recommended to validate these findings across diverse agro-industrial contexts, thereby supporting the development of inclusive and scalable partnership models. This study contributes empirical evidence to inform stakeholder decision-making and promote resilient, equity-driven frameworks for agricultural collaboration.

Ensuring the integrity of goods during cold chain transportation remains a critical challenge in logistics, as it is essential to preserve product quality, freshness, and compliance with stringent safety standards. Strategic decision-making in this context requires the prioritization of customer requirements and the optimal allocation of limited operational resources. In response to these demands, an integrated Multi-Criteria Decision-Making (MCDM) model was developed by combining the Best-Worst Method (BWM), Quality Function Deployment (QFD), and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) approach. Within this framework, BWM was utilized to determine the relative importance of user requirements, which were then mapped onto specific operational resources through QFD to identify critical resource elements and derive their corresponding weights. These weights, subsequently treated as evaluation criteria in the MARCOS method, were applied to assess the performance of Third-Party Logistics (3PL) providers. The proposed methodology was validated through a case study involving eight user requirements and seven key resources. The findings indicated that precise temperature control and delivery speed were the most critical user requirements, whereas advanced temperature sensors and vehicles with cooling systems were identified as the most significant resources. Based on the MARCOS evaluation, Provider 1 emerged as the most optimal 3PL alternative. This integrated decision-making model offers a systematic and data-driven approach for aligning customer priorities with resource capabilities, thereby enabling logistics providers to enhance service quality, operational efficiency, and strategic competitiveness in temperature-sensitive supply chains. The model also demonstrates practical scalability and adaptability across diverse cold chain scenarios.

Globally, heart disease is one of the main causes of death. Clinical data analysis is a huge problem when it comes to accurately predicting cardiovascular disease. This work presents a prediction model that makes use of numerous proven classification algorithms and different combinations of information. The goal of this work is to help in the detection of heart disease by employing a hybrid classification system depending on the Binary Harris hawks algorithm (BHHO) and the Logistic regression approach. Also, the Boruta algorithm with random forest is used and compared with the proposed PCA-BHHO algorithm. In this work, the data is first preprocessed, and missing values are filled with mean values. Then, data is scaled using standard scaler, and the proposed hybrid PCA and BHHO are applied to select the best features. RF and logistic regression are employed to classify the patients as heart disease patients or not. For comparison, Boruta is used for feature selection and RF for classification and compared the results with the proposed PCA-BHHO algorithm. Two datasets are utilized to test the proposed model: Statlog and the Cleveland heart disease datasets. The proposed PCA-BHHO algorithm attained an accuracy of 92.59% and 89.33% on the Statlog and the Cleveland datasets, respectively. At the same time, the Boruta-RF algorithm attained an accuracy of 90.14% and 87.64% on the Statlog and Cleveland datasets, respectively.

Seed Quality is an important area of agriculture and directly influences crop yield and germination percentage. Visual examination forms the foundation of traditional seed testing techniques, which are cumbersome, inflexible, and inefficient for effective assessment. This study proposed an automated approach to seed quality assessment based on physical measurement using machine learning and image processing techniques. Snapshots of the new seeds were captured and underwent feature extraction, segmentation, and image improvement to explore notable morphological attributes, such as size and colour. To tag seeds as "good" or "bad" based on physical characteristics, Support Vector Machines (SVMs) are used as a reference model. Rather, Convolutional Neural Networks (CNNs) have been utilised for deep feature extraction and classification. Experimental findings indicate that CNNs perform better than conventional machine learning models, with a scalable and highly accurate method of seed quality assessment. Future use will utilise quantum machine learning to improve prediction and facilitate sustainable, precision agriculture. The improved framework, optimised with great care for onion seeds, is a major breakthrough in increasing the agricultural productivity of onion cultivation.

Drowsy driving is a significant hazard, often leading to vehicular collisions, personal injuries, and fatalities. Detecting drowsiness signs quickly and accurately is crucial for reducing fatigue-related incidents. In recent years, the domain of artificial intelligence, especially the implementation of Convolutional Neural Network (CNN) frameworks in conjunction with the You Only Look Once (YOLO) algorithm, has attracted considerable academic scrutiny. These sophisticated methodologies enable the evaluation of driver fatigue through video footage or ongoing surveillance in real time. This study employs the YOLO algorithm integrated with a CNN to categorize detected drivers into drowsy and awake, utilizing bounding boxes during analysis. Model parameters, such as batch size (64), network size (416×416), subdivisions (16), max batch (4000), and filters (21), are configured for optimal performance. The dataset is split into four scenarios for training and testing, with learning rates set at 0.00261 and 0.001. Notably, the highest Intersection over Union (IoU) value is achieved with an 80%:20% split dataset and a learning rate of 0.00261, effectively identifying drowsiness in drivers and enhancing proactive safety measures.

Accurate and robust image segmentation remains a fundamental challenge in computer vision, particularly in the presence of intensity inhomogeneity, noise, and weak object boundaries. To address these challenges, we propose a Robust Pythagorean Fuzzy Energy-Based Level Set (RPFELS) model, which integrates a novel fuzzy energy formulation with level set evolution to enhance segmentation precision and resilience against noise. The model introduces a Pythagorean fuzzy divergence term to refine energy optimization, ensuring adaptive boundary preservation and reducing sensitivity to intensity variations. Additionally, a bounded fuzzy energy constraint is incorporated to ensure numerical stability and prevent energy leakage during evolution. Extensive experiments on benchmark datasets, including medical and natural images, validate the effectiveness of RPFELS. The model consistently outperforms recent selective segmentation methods in terms of Dice Score, Jaccard Index, and Hausdorff Distance, achieving superior segmentation accuracy and reduced boundary errors. Furthermore, a detailed statistical significance analysis using paired t-tests confirms that the observed improvements are statistically significant (p-value $<$ 0.01), reinforcing the reliability of the proposed approach. Moreover, RPFELS exhibits higher computational efficiency, achieving faster convergence rates compared to existing methods. These findings highlight the robustness and versatility of the proposed approach in handling challenging segmentation scenarios, making it suitable for applications in medical imaging, remote sensing, and industrial defect detection. By ensuring bounded energy evolution and statistically validated performance gains, our model sets a new benchmark in selective segmentation.

To address the time-delay and nonlinear characteristics of liquid level control in near-infrared spectroscopy-based liquid phase detection equipment, as well as the pipeline cavitation issues caused by improper sample pump speed settings during sample delivery—which may result in air bubble retention within the cuvette and subsequently degrade spectral data quality—a dual-buffer bottle sample delivery system model was established. A Proportional-Integral-Derivative (PID) controller was designed, and an enhanced hybrid algorithm integrating the Particle Swarm Optimization (PSO) algorithm and the Sparrow Search Algorithm (SSA) was proposed. The hybrid algorithm, referred to as the Adaptive Chaotic Mapping Particle Swarm Sparrow Algorithm (ACM-PSSA), incorporates Tent chaotic mapping for population initialization, a nonlinear cosine-based adaptive sparrow classification strategy, and a master–slave optimization mechanism wherein SSA performs global exploration and PSO executes local exploitation to optimize PID parameters. Simulation results demonstrate that ACM-PSSA outperforms traditional SSA and PSO across six benchmark test functions in terms of convergence speed, accuracy, and stability. When applied to the liquid level control of the dual-buffer bottle system, the optimized controller achieved a rise time of 0.188 seconds, a settling time of 1.211 seconds, and an overshoot reduced to 20.98%. By leveraging chaotic mapping, adaptive classification, and a master–slave optimization framework, ACM-PSSA effectively overcomes the limitations of conventional SSA and PSO, significantly enhancing the efficiency of PID parameter optimization and the overall control performance of the dual-buffer bottle sample delivery system.