Traditional Operations Research Articles

Transformer based 3D tooth segmentation via point cloud region partition

Automatic and accurate tooth segmentation on 3D dental point clouds plays a pivotal role in computer-aided dentistry. Existing Transformer-based methods focus on aggregating local features, but fail to directly model global contexts due to memory limitations and high computational cost. In this paper, we propose a novel Transformer-based 3D tooth segmentation network, called PointRegion, which can process the entire point cloud at a low cost. Following a novel modeling of semantic segmentation that interprets the point cloud as a tessellation of learnable regions, we first design a RegionPartition module to partition the 3D point cloud into a certain number of regions and project these regions as embeddings in an effective way. Then, an offset-attention based RegionEncoder module is applied on all region embeddings to model global context among regions and predict the class logits for each region. Considering the irregularity and disorder of 3D point cloud data, a novel mechanism is proposed to build the point-to-region association to replace traditional convolutional operations. The mechanism, as a medium between points and regions, automatically learns the probabilities that each point belongs to its neighboring regions from the similarity between point and region features, achieving the goal of point-level segmentation. Since the number of regions is far less than the number of points, our proposed PointRegion model can leverage the capability of the global-based Transformer on large-scale point clouds with low computational cost and memory consumption. Finally, extensive experiments demonstrate the effectiveness and superiority of our method on our dental dataset.

Integrating Blockchain Technology in Supply Chain Management: A Bibliometric Analysis of Theme Extraction via Text Mining

The integration of blockchain technology into supply chain management (SCM) has emerged as a revolutionary force transforming traditional business operations. This study uses bibliometric analysis on 1069 articles from the Scopus database, using text mining and Python to uncover predominant themes and research trends at the intersection of blockchain and SCM. The key findings revealed three main thematic groups: ‘blockchain to improve transparency and traceability in SCM’ (supported by 323 articles), ‘impact of blockchain on supply chain efficiency and cost reduction’ (295 articles), and ‘blockchain-enabled supply chain resilience’ (191 articles). Furthermore, text mining highlighted prominent themes such as ‘decentralized supply chain networks’ (204 articles), ‘smart contracts for automated processes in SCM’ (234 articles), and ‘blockchain for sustainable supply chain practices’ (227 articles). The inclusion of sustainability themes reflects the growing importance of environmentally conscious strategies within supply chains, driven by the capacity of blockchain to reduce waste, and promote resource efficiency. The study identifies critical literature gaps, advocating for further exploration of the socio-economic impacts of blockchain on SCM. The topic extraction suggests new directions for SCM theory, while the role of blockchain in fostering sustainable and ethical supply chains is underscored. Practically, blockchain and IoT emerge as pivotal in the advancement of SCM, with text mining offering industry foresight and emphasizing blockchain-driven resilient strategies. Limitations include reliance on a single database and the recommendation that future studies incorporate diverse sources and qualitative insights. The findings provide a roadmap for academics and practitioners, highlighting potential avenues in SCM, especially in the context of sustainable and ethical practices.

Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model

Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. Poor judgement and bad path planning may increase safety risks and even cause severe construction safety accidents. To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. A prototype based on the theoretical model was constructed and tested on actual construction projects. Through experimental scenarios, it was found that each tower crane operation poses safety risks to 3–4 workers on average, and the proposed prototype can significantly reduce the safety risks of dropped loads from tower crane operations affecting ground workers and important equipment. A comparison between the proposed model and other regular algorithms was also conducted, and the results show that compared with traditional RRT and APF algorithms, the proposed model reduces the average maximum collision times by 50. This research provides a theoretical model and a preliminary prototype to provide dynamic path planning and reduce safety risks in tower crane operations. Future research will be conducted from the aspects of multiple device monitoring and system optimization to increase the analysis speed and accuracy, as well as on human–computer interactions between tower crane operators and the path planning guidance model.

Exploration of Reliability and Security of E- Banking: Selected Urban Cooperative banks

The rise of E-banking has introduced unprecedented convenience and accessibility for users, transforming traditional banking operations into efficient online experiences. However, this shift brings challenges, particularly in terms of security and reliability, as users now expect robust protections for their financial data and consistent, dependable service. This study investigates the perceptions of security and reliability in digital banking among 290 urban cooperative banking customers. Using factor analysis, we explore key dimensions of e-banking services, focusing on elements like encryption, biometric security, and trust in transaction reliability. The analysis identified two primary factors: security and reliability, each with distinct contributions to overall user satisfaction. The factor loadings revealed that users had mixed confidence in encryption technologies and data privacy measures, with some expressing high levels of satisfaction, while others indicated significant concerns. Additionally, reliability emerged as a critical factor, with findings indicating that a notable portion of customers were dissatisfied with the timeliness and trustworthiness of e-banking services. For data collection, a structured survey measured responses on a five-point Likert scale, assessing variables such as trustworthiness, encryption effectiveness, privacy maintenance, and personal security practices. Through the use of descriptive and factor analysis, we quantified customer sentiments and identified patterns in user satisfaction. The results suggest that while there is moderate confidence in digital banking security, the reliability of service delivery remains an area for improvement. Banks should consider addressing these findings by enhancing transparency in their security protocols and strengthening service consistency to better meet user expectations in digital banking environments.

Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.

Since its introduction in 2015, the U-Net architecture used in Deep Learning has played a crucial role in medical imaging. Recognized for its ability to accurately discriminate small structures, the U-Net has received more than 2600 citations in academic literature, which motivated continuous enhancements to its architecture. In hospitals, chest radiography is the primary diagnostic method for pulmonary disorders, however, accurate lung segmentation in chest X-ray images remains a challenging task, primarily due to the significant variations in lung shapes and the presence of intense opacities caused by various diseases. This article introduces a new approach for the segmentation of lung X-ray images. Traditional max-pooling operations, commonly employed in conventional U-Net++ models, were replaced with the discrete wavelet transform (DWT), offering a more accurate down-sampling technique that potentially captures detailed features of lung structures. Additionally, we used attention gate (AG) mechanisms that enable the model to focus on specific regions in the input image, which improves the accuracy of the segmentation process. When compared with current techniques like Atrous Convolutions, Improved FCN, Improved SegNet, U-Net, and U-Net++, our method (U-Net++-DWT) showed remarkable efficacy, particularly on the Japanese Society of Radiological Technology dataset, achieving an accuracy of 99.1%, specificity of 98.9%, sensitivity of 97.8%, Dice Coefficient of 97.2%, and Jaccard Index of 96.3%. Its performance on the Montgomery County dataset further demonstrated its consistent effectiveness. Moreover, when applied to additional datasets of Chest X-ray Masks and Labels and COVID-19, our method maintained high performance levels, achieving up to 99.3% accuracy, thereby underscoring its adaptability and potential for broad applications in medical imaging diagnostics.

The Impact of an Extended Bleed-to-evisceration Interval on the Microbiological Quality of On-farm Slaughtered Cattle Carcasses

The uncertainties surrounding the microbiological risks of an extended exsanguination-to-evisceration interval have limited the implementation of on-farm slaughter in Europe. On-farm slaughter is increasingly advocated by farmers, consumers, and policymakers as a humane alternative to traditional slaughterhouse operations. However, concerns about hygiene and food safety, particularly bacterial contamination, have led to stringent time limits imposed by Member States on the interval between bleeding and evisceration. Microbiological standards for bovine carcasses in the European Union are governed by Commission Regulation (EC) No 2073/2005, which sets process hygiene criteria for aerobic colony count and Enterobacteriaceae. To investigate whether extending the bleed-to-evisceration interval compromises meat safety, five Holstein dairy cattle were slaughtered on-farm, with samples collected from the internal paralumbar area in contact with the intestines for up to 4 h postmortem. The samples were analyzed for Enterobacteriaceae, aerobic colony count, and Escherichia coli. None of the samples exceeded the established thresholds of 1.5 and 3.5 log CFU/cm2 for aerobic colony count and Enterobacteriaceae, respectively, as defined by Belgian health authorities for the nondestructive sampling method. These preliminary findings suggest that on-farm slaughter with evisceration occurring up to 4 h post-mortem does not pose increased microbiological risks to human health. However, further research is necessary, particularly under warmer environmental conditions and with a larger sample size, to confirm these results and to explore additional factors that may influence bacterial translocation and digestive tract wall integrity.

Knowledge-assisted hybrid optimization strategy of large-scale crude oil scheduling integrated production planning

As modern refinery crude oil scheduling scales up, traditional manual operations and independent optimization methods struggle with complexity and dynamics. This study proposes an empirically assisted integrated planning and scheduling optimization model.The integrated model is solved using a hybrid optimization algorithm combining mathematical programming and particle swarm optimization (MP/PSO). Long-term planning aims to minimize operational and transportation costs while maximizing refinery profits; short-term scheduling, based on initial long-term plans, aims to minimize unit switchovers. In the short-term scheduling phase, heuristic rules based on empirical operational knowledge generate a high-performing initial population to accelerate convergence. This strategy is crucial for enhancing refinery emergency response capabilities, ensuring stable operations, and improving economic benefits. Experimental results show that within a reasonable time frame, MP/PSO performs better than PSO and manual scheduling in large-scale crude oil scheduling scenarios.

Blockchain adoption factors in agricultural supply chains: A PLS-SEM study

The purpose of this study is to explore factors influencing the blockchain adoption in agricultural supply chains, to make a particular focus on how security and privacy considerations, policy support, and management support impact the blockchain adoption intention. it further investigates perceived usefulness as a mediating variable that potentially amplifies the effects of these factors on blockchain adoption intention, and sets perceived cost as a moderating variable to test its influence on the strength and direction of the relationship between perceived usefulness and adoption intention. through embedding the cost-benefit theory into the integrated tam-toe framework and utilizing the partial least squares structural equation modeling (PLS-SEM) method, this study identifies the pivotal factors that drive or impede blockchain adoption in the agricultural supply chains, which fills the gap of the relatively insufficient research on the blockchain adoption in agriculture field. the results further provide empirical evidence and strategic insights that can guide practical implementations, to equip stakeholders or practitioners with the necessary knowledge to navigate the complexities of integrating cutting-edge technologies into traditional agricultural operations, thereby promoting more efficient, transparent, and resilient agricultural supply chains.

Optimizing Taxi-Pooling Operations to Enhance Efficiency and Revenue: A Queuing Model Approach

This study investigates the optimization of taxi-pooling operations using the M/M/1/m queuing model, aiming to enhance efficiency and revenue for taxi service platforms. Traditional taxi operations face challenges during peak periods, including inefficiency and increased operational costs. Taxi-pooling, by accommodating multiple passengers with similar travel demands, offers a solution that reduces travel costs, operational expenses, and urban congestion. The study develops an optimization model to balance operational costs and passenger waiting times, identifying the utilization rate of taxis as a critical factor in platform revenue. By modeling the taxi-pooling service as a queuing system, we derive mathematical expressions for passenger waiting times and platform revenue under varying conditions. Our findings highlight the importance of optimal vehicle investment strategies and pricing decisions to maximize revenue. The study provides theoretical support for improving taxi-pooling platforms’ efficiency and competitiveness, contributing to better urban transportation solutions.

Cost of care associated with utilization of telehealth in clinical trials

ObjectiveDue to COVID-19 pandemic restrictions, telehealth was incorporated into standard oncologic care and clinical trial operations. We sought to analyze whether telehealth changed cost of care compared to traditional clinical trial operations. MethodsWe conducted a retrospective cohort study of gynecologic oncology patients enrolled in therapeutic clinical trials at a National Cancer Institute designated center, comparing the cost of cancer care on trial pre-TELEhealth (9/30/2019 to 3/15/2020) versus during TELEhealth (3/16/2020 to 8/20/2020). Inclusion required trial participation during both study periods, ≥1 telehealth visit, and identifiable billing records. The analysis was from a healthcare sector perspective. Cost per patient per month on trial was calculated for scheduled (per protocol) and unscheduled (non-protocol) encounters using 2020 national Medicare reimbursement rates, not institution-specific prices. Pairwise t-tests between pre-TELE and TELE periods were performed. ResultsTwenty-eight patients were included in the study. The majority of patients (93 %) had ovarian cancer. One patient (4 %) had uterine and 1 (4 %) had concurrent ovarian/uterine cancer. Most patients had advanced-stage disease at diagnosis (93 %). Mean cost per patient per month was similar in pre-TELE and TELE periods ($3797 vs. $4720, p = 0.064). There were no cost differences among scheduled or unscheduled encounters, office or ED visits, admissions, outpatient procedures, nor those billed to study sponsors or patient’s insurer. ConclusionsIncorporating telehealth in gynecologic cancer clinical trials during the COVID-19 pandemic did not increase cost of care and may be a mechanism for decentralizing clinical trials, reducing barriers to trial participation, and improving the value of cancer care.

Energy transition in the oil and gas sector: Business models for a sustainable future

The global energy landscape is undergoing a significant transformation as industries shift towards more sustainable practices, driven by environmental regulations, societal pressure, and technological advancements. The oil and gas sector, traditionally dependent on fossil fuels, is at the forefront of this energy transition. This review explores the evolving business models that facilitate the industry's transition to a low-carbon economy, focusing on strategies for achieving sustainability while maintaining profitability. Key approaches include diversifying energy portfolios by investing in renewable energy sources such as wind, solar, and hydrogen, as well as enhancing operational efficiency through digital technologies like artificial intelligence and the Internet of Things (IoT). The transition is also marked by an increased focus on carbon capture, utilization, and storage (CCUS) technologies, which mitigate the environmental impact of traditional oil and gas operations. Business models are being reshaped to incorporate circular economy principles, where waste and emissions are minimized through innovation and resource optimization. Furthermore, partnerships between oil and gas companies and renewable energy firms are emerging as a critical pathway for mutual growth and sustainability. This energy transition also involves rethinking the workforce and reskilling employees for emerging sectors, emphasizing the need for adaptability in business models. Regulatory frameworks play a pivotal role in shaping these models, as policies increasingly favor green investments and low-carbon initiatives. As oil and gas companies diversify their portfolios, they are creating new revenue streams that align with global decarbonization goals while ensuring financial resilience. This study highlights how these innovative business models are setting the foundation for a sustainable future in the oil and gas sector, ensuring that the industry remains relevant in a rapidly changing energy ecosystem. Keywords: Energy Transition, Oil and Gas, Business Models, Sustainability, Renewable Energy, Carbon Capture, Circular Economy, Digital Transformation, Decarbonization, Low-Carbon Economy.

Christian Ethical Response to The Challenges of Digital Financial Borrowing in Nigeria

Digital financial borrowing is the money borrowed online through digital mobile applications, platforms or phones that are offered by financial lenders using the Internet. Due to the advancement in financial technology, banking and financial services and operations have been digitalized. The emergence of digital finance has rebranded banking and financial borrowing access and opportunities. People can now borrow money online, digitally and seamlessly. This development is applaudable because it eases traditional banking operations burdens and removes bottlenecks in borrowing and lending to finance business and personal needs. However, the attendant experiences from digital financial borrowing are worrisome and pose challenges to human well-being in developing economies, especially among Christians in Nigeria. Some of the experiences of digital financial borrowers are poor loan repayment, which may be due to high interest, breach of personal data policy and social shaming of defaulted borrowers by lending companies’ employees. The unvirtuous motives and characters of some borrowers, professionally unethical attitudes and loan recovery strategies are responsible for these challenges. This paper gives an overview of digital financial borrowing in Nigeria and discusses digital financial borrowing, which is characterized with features such as instant, automation, remote, consumer loans and short maturity. The paper argues that digital financial borrowing has assisted financial inclusion and contributed to Nigeria’s digital economy, it has however negatively impacted on borrowers’ personal, emotional, mental, marital, business, spiritual and moral well-being due to poor repayment or inability to repay, high interest, and social shaming of customers by digital loan officers. The paper concludes that Christians should act virtuously and ethically according to biblical instructions when dealing in financial borrowing, wisdom, honesty, godliness, discretion, patience and mutual respect are among Christian virtues to display in financial borrowing decisions, actions and relationships. It recommends that churches and church leaders should encourage financial education and literacy that enhance credit understanding and boost Christians’ financial wellness. It also recommends that Chartered Institute of Bankers of Nigeria, with other financial regulatory bodies in Nigeria should regulate the operations of lending companies and enforce acceptable professional work ethics and attitudes of digital lending companies’ employees to enhance friendly relationships with customers.

Managing the Adoption of Geospatial Information and Technologies in Multiple Industries

In the era of the 4th industrial revolution, geospatial information has become essential for improving processes and enhancing task performance across the value chain, benefiting sectors such as agriculture, transportation, and logistics. Despite the widespread adoption of geospatial technology, understanding how to manage its adoption is crucial as technology continually evolves. This study aims to elucidate key aspects of managing the adoption of geospatial information and technology through a case study approach. The research involves interviews with Chief Executive Officers (CEOs) and directors from three different industries in Malaysia—agriculture, logistics, and transport—that utilise geospatial technologies. Thematic analysis of the interviews revealed four main themes: managing expectations of adoption, addressing factors of nonadoption, overseeing interactions in the adoption process, and ensuring the immersion of the technology. These findings provide valuable insights into the challenges and strategies for adopting new technologies, particularly those that transform traditional operations into technology-driven practices. The study’s evidence underscores the importance of effective management in fostering the growth and economic expansion driven by geospatial technology.

A model for assessing the economic impact of renewable energy adoption in traditional oil and gas companies

The adoption of renewable energy presents significant economic implications for traditional oil and gas companies. This paper proposes a model for assessing the economic impact of integrating renewable energy into traditional oil and gas operations, focusing on cost efficiency, revenue potential, and overall financial performance. The model evaluates key metrics, including capital investment, operational cost savings, revenue generation from renewable energy projects, and potential impacts on profitability and shareholder value. By incorporating various renewable technologies such as solar, wind, and geothermal, the model provides a comprehensive framework for understanding the financial benefits and challenges associated with transitioning to cleaner energy sources. The model incorporates both direct and indirect economic impacts, such as reduced energy costs, enhanced energy security, and improved corporate reputation. It also considers the effects of regulatory incentives, including tax credits and subsidies, which can influence the financial viability of renewable energy projects. Additionally, the model examines potential risks and uncertainties, such as market volatility and technological limitations, which can affect the economic outcomes of renewable energy investments. Case studies of traditional oil and gas companies that have successfully integrated renewable energy are analyzed to illustrate the practical application of the model. These case studies demonstrate how companies have achieved cost reductions, diversified revenue streams, and enhanced their long-term financial resilience through renewable energy adoption. The paper concludes that while the initial investment in renewable energy technologies can be substantial, the long-term economic benefits—including cost savings, revenue growth, and alignment with sustainability goals—can significantly outweigh the challenges. By adopting the proposed model, oil and gas companies can better assess the economic implications of renewable energy integration and make informed decisions to drive sustainable growth and profitability.

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities.

Research on the Compounding Scheme of High-Efficiency and Environmentally Friendly Water-Based Well Cleaning Fluid and Analysis of Its Application Effect

In the process of oil well production, the deposition and enrichment of heavy oil and wax are common phenomena. The deposition of these high-viscosity organic substances will not only seriously affect the normal production of oil wells but also cause certain pollution to the environment. Traditional well-washing operations usually rely on high temperatures and strong cleaning chemicals, which have the problems of low cleaning efficiency at low temperatures and poor environmental protection. Therefore, it is particularly important to study a more efficient and environmentally friendly water-based well-washing fluid. At present, most oilfields in China use diesel or organic cleaning agents to clean oil wells, which not only increases the cost of well-washing operations but also may cause pollution to the environment. In response to this problem, the core of this study lies in its innovative compounding scheme. The scheme includes preferred alkaline components, surfactants, emulsifiers, and solubilizers, and introduces glass fiber cutting and erosion technology. This well-washing fluid can effectively remove wax and heavy oil attached to the well wall at a lower temperature, which not only improves cleaning ability but also greatly reduces the dependence on high temperature. The water-based well-washing fluid provided in this study is composed of water, alkaline substances, surfactants, emulsifiers, and glass fibers. Emulsifiers such as Tween 80, OP-20, and polyethers form a stable emulsification system to help disperse and encapsulate oil and grease particles.

Integrated train timetable with supply–demand interactions at the Chinese high-speed railway

In traditional Chinese railway operations, train timetable and unit assignment are usually separated from pricing and seat allocation, resulting in a decrease in revenue and an increase in operating costs. In this paper, we study the integrated problem of train timetable, unit assignment, pricing, and seat allocation, where passenger choice behavior is modeled by the multinomial logit (MNL) model. This integration enables us to explicitly capture supply–demand interactions. The problem is formulated as a mixed integer nonlinear programming model with the objective of maximizing total profit. By converting the MNL model into its equivalent convex form, we recast the model as a tractable reformulation, allowing the use of a general-purpose solver to solve practical-sized instances. Based on real data from high-speed railway lines in China, three sets of case studies are conducted to validate the effectiveness and efficiency of the proposed approach.

Sediment-Hosted Rare-Earth Elements Mineralization from the Dian-Qian District, Southwest China: Mineralogy and Mode of Occurrence

The Xuanwei Formation’s claystones in the Dian-Qian District of Southwest China are rich in rare-earth elements (REEs), suggesting their potential as a source of medium and heavy rare earths. However, the REE content in these rocks is lower than other types of rare-earth deposits, and the interrelationship among clay minerals is intricate. There is no direct evidence indicating the mineralization of REEs, limiting their beneficiation and extraction. The objective of this study is the characterization of REE distribution in the Dian-Qian District. The sedimentary rocks in this district are mainly composed of kaolinite, boehmite, quartz, rutile, and pyrite. The results of continuous chemical extraction of REE-rich claystone and transmission electron microscope (TEM) observations have confirmed that REEs occurred as florencite in the rocks, and that the ion-absorption state makes only a negligible contribution to the REE content. A close relationship between florencite and kaolinite makes traditional mineral processing operations very difficult. Combined with the properties of kaolinite, roasting-acid leaching was the efficacious approach for rare-earth resources extracted from the rare earth-rich clay rocks of the Xuanwei Formation.

Unveiling the Impact of Perceived Benefits on Digital Banking Readoption in Malaysia

Malaysia's banking sector has undergone significant transformation driven by digital technologies, revolutionizing traditional banking operations and customer engagement strategies. The introduction of digital banking products has redefined the financial landscape, providing unparalleled convenience, accessibility, and financial control to consumers and businesses. Key advantages such as operational efficiency, cost savings, 24/7 accessibility, and real-time financial management have been crucial in driving the adoption and readoption of digital banking services across various demographic segments in Malaysia. The study leverages theoretical frameworks like the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand user acceptance and behavior towards digital banking readoption. These models highlight constructs such as performance expectancy, effort expectancy, social influence, and facilitating conditions as essential factors influencing technology adoption. Recent research emphasizes the importance of service quality in mobile banking applications as a determinant of customer satisfaction and loyalty, reflecting the advanced expectations of tech-savvy Malaysian consumers. a rigorous methodological approach, the study examines the factors influencing the readoption of digital banking products among Malaysian consumers. Utilizing Smart Partial Least Squares Structural Equation Modelling (Smart PLS-SEM), the study validates the relationships between perceived benefits and digital banking readoption decisions. The findings reveal that effort expectancy, rather than performance expectancy, is a critical factor influencing readoption, underscoring the importance of usability and convenience. The model explains 66.4% of the variance in readoption behavior, highlighting the complexity of consumer decision-making in digital banking. This study concludes that banking institutions must continuously enhance digital platform functionality and security while prioritizing superior user experiences. Trust, perceived security, and customer service quality remain pivotal in shaping consumer attitudes towards digital banking adoption and readoption, reflecting the dynamic and evolving preferences within Malaysia's digital banking ecosystem.

Prescribed performance dynamic surface control based on dual extended state observer for 2-dof hydraulic cutting arm

In tunnel section forming operations, the boom-type roadheader tracking target trajectory with high precision is greatly significant in avoiding over and under excavation and improving excavation efficiency. However, there exist complex cutting loads, measurement noise, and model uncertainties, seriously degrading the tracking performance of traditional nominal model-based controllers. Hence, this study first fully analyzes the kinematics of all members of the cutting mechanism and establishes its complete multi-body dynamic model using the Lagrange method. Furthermore, a dual extended state observer is designed to estimate the mechanical system’s angular velocity and unmodeled disturbances and actuators’ uncertain nonlinearities. In particular, introducing a nonlinear filter replaces the traditional first-order filter in dynamic surface technology, overcoming the “explosion of complexity” while attenuating the conservatism of gains tuning. Then, a dual extended state observer-based prescribed performance dynamic surface controller is developed for roadheaders for the first time. Simultaneously, integrating an improved error transformation function into controller design effectively avoids the online computational burden caused by traditional logarithmic operations. Utilizing Lyapunov theory, the cutting system’s prescribed transient response and steady-state performance are guaranteed. Finally, the proposed controller’s effectiveness is verified by comparative experiments on the roadheader.