Search Results (875)

The rapid growth of mobile financial services provides significant opportunities for enhancing digital financial inclusion among older adults. However, elderly consumers often lag in adoption and sustained usage due to psychological barriers (e.g., technology anxiety) and factors related to prior experience and comfort with technology (e.g., technology familiarity). This study investigates how technology anxiety and technology familiarity influence elderly consumers’ continuance intention toward mobile banking, while examining age as a moderator by comparing younger older adults (aged 60–69) and older adults (aged 70+). Using data from an online survey of 488 elderly mobile banking users in South Korea, we conducted hierarchical regression analyses. The results show that technology anxiety negatively affects continuance intention, whereas technology familiarity positively enhances sustained usage. Moreover, age significantly moderated these relationships: adults aged 70+ were notably more sensitive to both technology anxiety and familiarity, highlighting distinct age-related psychological differences. These findings underscore the importance of targeted digital literacy initiatives, age-friendly fintech interfaces, and personalized support strategies. This study contributes to the fintech literature by integrating psychological dimensions into traditional technology adoption frameworks and emphasizing age-specific differences. Practically, fintech providers and policymakers should adopt tailored strategies to effectively address elderly consumers’ unique psychological needs, promoting sustained adoption and narrowing the digital divide in financial technology engagement. Full article

The climbing perch, Anabas testudineus, is one of the most widely distributed freshwater amphibious fishes in South and Southeast Asia, exhibiting terrestrial movements. Our experimental study aimed to investigate endocrinological and biochemical changes in the blood of climbing perch associated with their terrestrial movements. To achieve this, the fish were divided into two groups: one group was exposed to aquatic conditions for twenty minutes, while the other group was subjected to terrestrial conditions for the same duration through rapid water level decrease. In terrestrial conditions, the fish predominantly exhibit movements on land, whereas in aquatic environments, they primarily remain immobile or swim. Elevated levels of stress-induced cortisol and glucose after short-term exposure indicate a high-stress response involving both neuroendocrine and metabolic mechanisms. Changes in the activity of aspartate aminotransferase and increased concentrations of triglycerides in the blood serum suggest energy mobilization through aerobic metabolic pathways. Extreme environmental changes did not affect thyroid axis function, including deiodination, thereby maintaining essential physiological activities under new conditions. Additionally, the anaerobic metabolic pathway appears to be minimally utilized at the onset of terrestrial movement, as no significant changes in lactate dehydrogenase concentrations were observed. Overall, the terrestrial movements of the climbing perch are likely predominantly forced and associated with high stress. Full article

Data security is of the utmost importance in the domain of real-time environmental monitoring systems, particularly when employing advanced context-aware intelligent visual analytics. This paper addresses a significant deficiency in the Global System for Mobile Communications (GSM), a widely employed wireless communication system for environmental monitoring. The A5/1 encryption technique, which is extensively employed, ensures the security of user data by utilizing a 64-bit session key that is divided into three linear feedback shift registers (LFSRs). Despite the shown efficacy, the development of a probabilistic model for assessing the vulnerability of breaking or intercepting the session key (Kc) has not yet been achieved. In order to bridge this existing knowledge gap, this study proposes a probabilistic model that aims to evaluate the security of encrypted data within the framework of the Global System for Mobile Communications (GSM). The proposed model implements alterations to the current GSM encryption process by the augmentation of the quantity of Linear Feedback Shift Registers (LFSRs), consequently resulting in an improved level of security. The methodology entails increasing the number of registers while preserving the session key’s length, ensuring that the key length specified by GSM standards remains unaltered. This is especially important for environmental monitoring systems that depend on real-time data analysis and decision-making. In order to elucidate the notion, this analysis considers three distinct scenarios: encryption utilizing a set of five, seven, and nine registers. The majority function is employed to determine the registers that will undergo perturbation, hence increasing the complexity of the bit arrangement and enhancing the security against prospective attackers. This paper provides actual evidence using simulations to illustrate that an increase in the number of registers leads to a decrease in the vulnerability of data interception, hence boosting data security in GSM communication. Simulation results demonstrate that our method substantially reduces the risk of data interception, thereby improving the integrity of context-aware intelligent visual analytics in real-time environmental monitoring systems. Full article

BGP-15, a poly(ADP-ribose) polymerase-1 (PARP-1) inhibitor exerts cardioprotective effects; however, the underlying mechanisms remain unclear. Therefore, our study aimed to investigate the effects of BGP-15 on the imatinib (Imtb)-induced cardiac inflammation at the biochemical level. Male rats were divided to control, Imtb-treated (60 mg/kg/day for 14 days), and Imtb + BGP-15-treated animals. In this group Imtb was co-administered with BGP-15 at the dose of 10 mg/kg/day. At the end of the experiment, nuclear factor-kappa B/p65 (NF-κB/p65), nuclear transcription factor erythroid-2 related factor (Nrf2), heme oxygenase-1 (HO-1), high mobility group box 1 (HMGB1), and myeloperoxidase (MPO) were measured by Western blot. Chemokine and interleukins (ILs) were determined by Legendplex. Additionally, cardiac specific changes were visualized by immunohistochemistry. We demonstrated that Imtb increased NF-κB/p65, IL-6, IL-1β, IL-18, MCP-1, HMGB1, as well as the expression and activity of MPO. Conversely, the expressions of antioxidant Nrf2 and HO-1 were decreased. Administration of BGP-15 effectively mitigated these inflammatory alterations by significantly reducing pro-inflammatory cytokines and MPO activity, while simultaneously restoring and enhancing the levels of Nrf2 and HO-1, thereby promoting antioxidant defenses. The immunohistochemical staining further supported these biochemical changes. Our study provides new and comprehensive biochemical insight for managing Imtb-induced inflammatory responses via BGP-15-induced PARP1 inhibition. Full article

Urban–rural integration (URI) plays a crucial role in advancing rural revitalization and the modernization of agriculture. Nevertheless, numerous nations encounter persistent obstacles, including inefficient resource mobility across urban–rural divides and uneven industrial distribution, while striving to foster such integration. Agricultural new quality productive forces (ANPFs) offer an innovation-led production framework fueled by advances in agricultural technology, allowing urban–rural integration (URI) through improved resource mobility between cities and rural regions. Utilizing panel data from 30 Chinese provinces (2013–2022), this study employs a two-way fixed effects model, mediation analysis model, threshold regression model, and the spatial Durbin model to investigate the transmission mechanism and spatial spillover effect of agricultural new quality productive forces (ANPFs) on urban–rural integration (URI). The findings show the following: (1) Agricultural new quality productive forces (ANPFs) significantly influence urban–rural integration (URI). (2) The influence is significantly stronger in western China than in the eastern and central regions. (3) Industrial restructuring and upgrading (IND) function as a mediating influence in this connection. (4) The role of informatization (INF) has a dual-threshold effect. (5) Geographically, while these forces promote local integration, they may impede progress in nearby regions. This study provides new empirical insights into the factors that influence urban–rural integration (URI) and proposes policy solutions to promote sustainable regional development. Full article

Wireless sensor networks (WSNs) have found extensive applications in a variety of fields, including military surveillance, wildlife monitoring, industrial process monitoring, and more. The gradual energy depletion of sensor nodes with limited battery energy leads to the dysfunction of some of the nodes, thus creating coverage holes in the monitored area. Coverage holes can cause the network to fail to deliver high-quality data and can also affect network performance and the quality of service. Therefore, the detection and recovery of coverage holes are major issues in WSNs. In response to these issues, we propose a method for detecting and recovering coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit based on two conditions, called an agent. Then, the percentage of each unit covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation experiments show that the algorithm proposed in this paper reduces network energy consumption by 6.68%, decreases the distance traveled by mobile nodes by 8.51%, and increases the percentage of network hole recovery by 2.16%, compared with other algorithms. Full article

To address the dual challenges of achieving high classification accuracy and real-time performance when detecting abnormal traffic in cloud environments, this study introduces an improved lightweight real-time detection model, IM-MobileNetV3. By standardizing features, dividing time windows, mapping spatially, and reshaping images, the model converts traffic features into 224 × 224 × 3RGB images, enabling multi-angle representation of traffic features and adapting to the model’s input requirements. Additionally, the MobileNet-Small model is improved by introducing the ECA module to replace the original SE module, avoiding the information loss that is often caused by channel dimension reduction, and the initial convolutional layer, bottleneck layer, and tail structure are optimized to enhance the feature extraction capability. The experimental results show that, on the CIC-IDS2018 dataset, IM-MobileNetV3 achieves an accuracy rate of 96.9%, reduces the number of parameters by 33% compared to the original model, and has a single-sample inference time of only 2.8 ms, significantly outperforming mainstream models. Full article

In the UAV-assisted mobile edge computing (MEC) communication system, the UAV receives the data offloaded by multiple ground user devices as an aerial base station. Among them, due to the limited battery storage of a UAV, energy saving is a key issue in a UAV-assisted MEC system. However, for a low-altitude flying UAV, successful obstacle avoidance is also very necessary. This paper aims to maximize the system energy efficiency (defined as the ratio of the total amount of offloaded data to the energy consumption of the UAV) to meet the maneuverability and three-dimensional obstacle avoidance constraints of a UAV. A joint optimization strategy with maximized energy efficiency for the UAV flight trajectory and user device task offloading rate is proposed. In order to solve this problem, hybrid alternating metaheuristics for energy optimization are given. Due to the non-convexity and fractional structure of the optimization problem, it can be transformed into an equivalent parameter optimization problem using the Dinkelbach method and then divided into two sub-optimization problems that are alternately optimized using metaheuristic algorithms. The experimental results show that the strategy proposed in this paper can enable a UAV to avoid obstacles during flight by detouring or crossing, and the trajectory does not overlap with obstacles, effectively achieving two-dimensional and three-dimensional obstacle avoidance. In addition, compared with related solving methods, the solving method in this paper has significantly higher success than traditional algorithms. In comparison with related optimization strategies, the strategy proposed in this paper can effectively reduce the overall energy consumption of UAV. Full article

Road infrastructure is the foundation of the predominant modes of transport, and its effective management is crucial to meet mobility needs. Although necessary for reconstruction, maintenance, and expansion projects, roadworks produce negative impacts, resulting in further risk for workers and drivers and failing to ensure sustainable development. The objective of this paper is twofold: Firstly, investigate the contributing factors to the occurrence of crashes in roadworks. Secondly, develop a model to estimate crash numbers in these areas. The results, which could support municipalities at the planning stage and implement policies for safe and sustainable development, are achieved by examining 121 sites, where 549 crashes occurred, and 25 contributing factors. The variables are divided into three categories: technical characteristics of the site, infrastructural, and environmental. Besides the conventional variables, a risk-increasing factor is calibrated. It assesses the impact of roadworks according to the manoeuvres imposed and the number of lanes. Consistent with previous findings, several variables related to the work zone layout, traffic conditions, infrastructure, and surrounding environment are correlated with the crash number. After performing a further statistical analysis, a multiple linear regression model, statistically significant (0.000) and suitable for accurately estimating the possible number of crashes (R²_adj = 0.41), is determined. Full article

In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the Metropolitan City of Florence. The analysis employs a methodology centered on the Debt-to-Income Ratio (DTI), which cross-references real estate market values (source: Agenzia delle Entrate and leading real estate portals) with household income brackets to identify affordable areas. The results reveal a clear divide: households with incomes below EUR 26,000 per year (representing about 69% of the population) are excluded from the central urban property market. This evidence confirms regional and national trends, emphasizing a growing mismatch between housing costs and disposable incomes. The study concludes that affordability is a technical–financial parameter and a valuable tool for supporting inclusive urban planning. Its application facilitates the orientation of effective public policies and the identification of socially sustainable housing solutions. Full article

Introduction: Monteggia fractures, first described by Giovanni Battista Monteggia, involve a fracture of the proximal ulna with anterior dislocation of the radial head. Bado’s 1967 classification divides these injuries into four types. Rare mixed patterns exist, overlapping with other forearm injuries such as Galeazzi and Essex–Lopresti lesions. These complex fractures/dislocations pose significant diagnostic and therapeutic challenges and are not adequately represented in current classification systems. Methods and Case Presentation: We report the case of a 56-year-old woman with a complex forearm injury sustained from a fall, presenting with radial head fracture/dislocation, mid-shaft radial fracture, distal ulna fracture, and ulnar collateral ligament rupture. Intraoperative imaging confirmed DRUJ stability and partial interosseous membrane disruption. Surgical management included radial head prosthesis implantation, radial shaft fixation with an anatomical locking plate, intramedullary nailing of the distal ulna, and ligament reconstruction. At two-year follow-up, the patient demonstrated full recovery of elbow flexion–extension and satisfactory forearm function. A narrative literature review was also conducted, focusing on hybrid injury variants. Results: Intraoperative examination under anesthesia revealed good elbow stability with 130° flexion, 15° extension lag, and forearm pronation/supination of 70°/60°. An initial Mayo Elbow Performance Score (MEPS) of 65 was recorded, limited by range of motion and stability. Pain during passive mobilization was mild, with a Visual Analogue Scale (VAS) score of 3/10. Postoperative recovery included 15 days of immobilization followed by structured rehabilitation. At two years, the patient regained full elbow flexion–extension but had residual deficits in pronation–supination, attributed to pre-existing conditions. Conclusions: This case illustrates a previously unreported hybrid Monteggia variant, combining features of Monteggia, Galeazzi, and Essex–Lopresti injuries. It highlights the limitations of current classification systems and supports the need for an expanded diagnostic framework. Successful management required a multidisciplinary surgical approach tailored to the injury’s complexity. Further studies are warranted to refine classification and treatment strategies for these rare combined injuries. Full article

Climate change (CC) is a major threat to agriculture, the sector that supports the territorial economy in the Pays Haut Languedoc et Vignoble (PHLV) region (south France). In this region, farms have been facing the negative effects of CC for several decades. The implementation of agriculture adaptation policies requires a coherent and integrated tool that mobilizes approaches for territorial development, vulnerability assessments, and feasibility. The purpose of this research is to provide a multi-criteria assessment of farm vulnerability to CC in the PHLV region. An index of farm vulnerability was developed based on the classic model of vulnerability, which is the product of exposure and sensitivity divided by adaptive capacity. This assessment was conducted at the farm level, by combining biophysical variables (such as soil type and irrigation) and socioeconomic variables (such as agricultural experience and crop insurance), selected based on a literature review and interviews with local stakeholders and local experts. To solve the weighting problem, we differentiated between a “calculated vulnerability”, without any specific weighting of the vulnerability variables, and a “declared vulnerability” that integrates the scores assigned to the importance of each variable for each farmer surveyed, based on their awareness. Afterward, a discriminant analysis was used to identify the factors that determine the vulnerability classes. Our results show that (i) the majority of the surveyed farms have a relatively high vulnerability index, but wine farms and cereal farms are the most vulnerable; (ii) for all farms the “declared vulnerability” is lower than the “calculated vulnerability”, showing that farmers underestimate their vulnerability; (iii) there is an interesting link between the low level of vulnerability and the adaptation efforts already made over the past ten years by certain farms that have changed or introduced crops and improved their agricultural practices. Full article

Background: Pain manifestations as well as increased muscle tone and stiffness noted in the course of delayed-onset muscle soreness (DOMS) are reflected in altered values of the biomechanical and visco-elastic parameters of muscles. This study aimed to compare the effects of soft tissue mobilization with foam rolling and percussive massage on symptoms of DOMS induced by a standardized muscle fatigue protocol. Methods: Healthy volunteers (n = 60) were divided into three groups: FR group—foam rolling (n = 20), PM group—percussive massage (n = 20) and CON group—control/passive rest (n = 20). The fatigue protocol for the gastrocnemius muscle was carried out for development of DOMS in subsequent days. Therapeutic procedures were applied to participants for 3 consecutive days. The results of therapy were assessed by means of myotonometry, performed five times (before, three times during the treatment procedure, and after the end of the procedure). Results: Foam rolling significantly reduced the onset and duration of increased muscle tone (p = 0.006) and stiffness (p < 0.001), unlike percussive massage. The control group exhibited higher tone and stiffness after 48 h, at the peak of DOMS-related pain symptoms. Only foam rolling improved elasticity (decrement, p < 0.001), while visco-elastic properties (relaxation, creep) varied inversely with tone and stiffness. Foam rolling led to significantly lower stiffness (day 2) and reduced decrement and relaxation (day 4) compared to the control. Neither therapy was more effective than passive rest for pain relief during the observation period. Conclusions: Foam rolling and percussive massage accelerate recovery of muscle tone, stiffness, and elasticity after DOMS as compared to passive rest but offer no added benefit for pain relief. Full article

This study investigates the convergence of blockchain, artificial intelligence (AI), near-field communication (NFC), and mobile technologies in electronic payment (e-payment) systems, proposing an innovative integrative framework to deconstruct the systemic innovations and transformative impacts driven by such technological synergy. Unlike prior research, which often focuses on single-technology adoption, this study uniquely adopts a cross-technology convergence perspective. To our knowledge, this is the first study to empirically map the multi-technology convergence landscape in e-payment using scientometric techniques. By employing bibliometric and thematic network analysis methods, the research maps the intellectual evolution and key research themes of technology convergence in e-payment systems. Findings reveal that while the integration of these technologies holds significant promise, improving transparency, scalability, and responsiveness, it also presents challenges, including interoperability barriers, privacy concerns, and regulatory complexity. Furthermore, this study highlights the potential for convergent technologies to unintentionally deepen the digital divide if not inclusively designed. The novelty of this study is threefold: (1) theoretical contribution—this study expands existing frameworks of technology adoption and digital governance by introducing an integrated perspective on cross-technology adoption and regulatory responsiveness; (2) practical relevance—it offers actionable, stakeholder-specific recommendations for policymakers, financial institutions, developers, and end-users; (3) methodological innovation—it leverages scientometric and topic modeling techniques to capture the macro-level trajectory of technology convergence, complementing traditional qualitative insights. In conclusion, this study advances the theoretical foundations of digital finance and provides forward-looking policy and managerial implications, paving the way for a more secure, inclusive, and innovation-driven digital payment ecosystem. Full article

With the dynamic development of imaging technologies and increasing demands in various industrial fields, neural networks are playing a crucial role in advanced design, monitoring, and analysis techniques. This review article presents the latest research advancements in neural network-based imaging, thermal, infrared, and X-ray technologies from 2005 to 2024. It focuses on two main research categories: ‘Technology’ and ‘Application’. The ‘Technology’ category includes neural network-enhanced image sensors, thermal imaging, infrared detectors, and X-ray technologies, while the ‘Application’ category is divided into image processing, robotics and design, object recognition, medical imaging, and security systems. In image processing, significant progress has been made in classification, segmentation, digital image storage, and information classification using neural networks. Robotics and design have seen advancements in mobile robots, navigation, and machine design through neural network integration. Object recognition technologies include neural network-based object detection, face recognition, and pattern recognition. Medical imaging has benefited from innovations in diagnosis, imaging techniques, and disease detection using neural networks. Security systems have improved in terms of monitoring and efficiency through neural network applications. This review aims to provide a comprehensive understanding of the current state and future directions of neural network-based imaging, thermal, infrared, and X-ray technologies. Full article