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Characterization of heterogeneous reservoirs such as multilayered or fractured systems is an important issue in different disciplines such as hydrology, petroleum and geothermal systems. One of the popular methods that can be used for this purpose is tracer tests. Better understanding of the mechanisms of mass transfer (convection–diffusion process) is essential for having a proper test interpretation. In this study, the solutions of different scenarios of tracer flow in a pair of high and low-permeable layered reservoirs including convection and diffusion mechanisms are discussed. Although analytical solutions generally provided exact solutions, they involve several assumptions and might be hard to use for complex problems. As a result, numerical methods are selected for the investigation of different scenarios and addressing cases that are beyond access of analytical methods. In this study, several scenarios of considering diffusion and convection in low and high permeable zones and effective parameters on tracer concentration are investigated. According to the results of this study, the higher the porosity ratio of low to high permeable layer, the more time is needed to get the final concentration value. Also, by increasing the value of the dispersivity coefficient, the time needed to increase the concentration decreases. In other words, the sharp increase in concentration for lower times is seen in higher dispersivity values. The concentration profile variation is affected by Peclet number. The difference among concentration profiles in different cases is considerable, especially in low Peclet numbers where the diffusion mechanism is dominant. This behavior is more common in low permeable mediums such as multilayered tight or shale reservoirs.

A novel parallelization method of genetic algorithm (GA) solution of the Traveling Salesman Problem (TSP) is presented. The proposed method can considerably accelerate the solution of the equivalent TSP of many complex vehicle routing problems (VRPs) in the cloud implementation of intelligent transportation systems. The solution provides routing information besides all the services required by the autonomous vehicles in vehicular clouds. GA is considered as an important class of evolutionary algorithms that can solve optimization problems in growing intelligent transport systems. But, to meet time criteria in time-constrained problems of intelligent transportation systems like routing and controlling the autonomous vehicles, a highly parallelizable GA is needed. The proposed method parallelizes the GA by designing three concurrent kernels, each of which running some dependent effective operators of GA. It can be straightforwardly adapted to run on many-core and multi-core processors. To best use the valuable resources of such processors in parallel execution of the GA, threads that run any of the triple kernels are synchronized by a low-cost switching mechanism. The proposed method was experimented for parallelizing a GA-based solution of TSP over multi-core and many-core systems. The results confirm the efficiency of the proposed method for parallelizing GAs on many-core as well as on multi-core systems.

With the rapid growth of Internet-of-Things (IoT) applications, data volumes have been considerably increased. The processing resources of IoT nodes cannot cope with such huge workloads. Processing parts of the workload in clouds could solve this problem, but the quality of services for end-users will be decreased. Given the latency reduction for end-users, the concept of processing in the fog devices, which are at the edge of the network has been evolved. Optimizing the energy consumption of fog devices in comparison with cloud devices is a significant challenge. On the other hand, providing the expected-quality of service in processing the requested workloads is highly dependent on the propagation delay between fog devices and clouds, which due to the nature of the distribution of clouds with the different workloads, is highly variable. To date, none of the proposed solutions has solved the problem of workload allocation given the criteria of minimizing the energy and delay of fog devices and clouds, simultaneously. This paper presents a processing model for the problem in which a trade-off between energy consumption and delay in processing workloads in fog is formulated. This multi-objective model of the problem is solved using NSGAII algorithm. The numerical results show that by using the proposed algorithm for workload allocation in a fog-cloud scenario, both of energy-consumption and delay can be improved. Also, by allocating 25% of the IoT workloads to fog devices, the energy consumption and delay are both minimized.

This article traces the historical evolution of medical negligence from ancient outcome-based penalties to modern tort law. Employing a historical-analytical method, it examines primary legal texts—from the Code of Hammurabi to landmark cases like Bolam and Bolitho—and secondary sources to analyze this transformation. The findings reveal a shift from ancient codes that penalized results, through medieval guild regulation, to the common law's establishment of a duty of care and a professionally defined standard subject to judicial scrutiny. Modern developments, such as the rise of informed consent and defensive medicine, illustrate tort law's ongoing adaptation to the complexities of healthcare. The conclusion underscores that this journey reflects evolving societal expectations for reasonable medical care, balancing patient rights with clinical realities. Understanding this history is vital for contemporary debates on patient safety and professional accountability, pointing to future research into non-Western traditions as well as to challenges such as artificial intelligence.

Accurate detection of pupil position in successive frames of eye videos is finding applications in many areas including assistive systems and E-learning. Processing the big datasets of eye videos in such systems requires robust and fast eye-tracking algorithms that can predict the position of eye pupil in consecutive video frames. As a major technique, particle filters provide adequate speed but have a low detection rate. To solve this problem, the present paper suggests the use of genetic algorithms in the sampling step of the particle filter technique. As a result, in each frame, the variety of particles required for predicting the pupil position in the next video frame is maintained and their uniformity is reduced. Finally, the speed and detection rate of the proposed method, as well as the basic particle filter method in predicting the pupil position in video frames are calculated and compared for various populations. The experimental results indicate that, in comparison with the basic particle filter algorithm, the proposed algorithm detects the pupil more accurately and in a shorter time. Also, by achieving an average detection rate of 79.89% in estimation of the pupil center with an error of five pixels on a variety of eye videos with different situations of occlusion and illumination, not only the robustness of the proposed method is assessed but also its superiority to the state-of-the-art methods is evinced.

In resource-constrained Internet of Things (IoT) scenarios, implementing robust and accurate deep learning inference is problematic due to device failures, limited computing power, and privacy concerns. We present a resilient, completely edge-based distributed convolutional neural network (CNN) architecture that eliminates cloud dependencies while enabling accurate and fault-tolerant inference. At its core is a lightweight Modifier Module deployed at the edge, which synthesizes predictions for failing devices by pooling peer CNN outputs and weights. This dynamic mechanism is trained via a novel fail-simulation technique, allowing it to mimic missing outputs in real-time without model duplication or cloud fallback. We assess our methodology using MNIST and CIFAR-10 datasets under both homogeneous and heterogeneous data partitions, with up to five simultaneous device failures. The system displays up to 1.5% absolute accuracy improvement, 30% error rate reduction, and stable operation even with over 80% device dropout, exceeding ensemble, dropout, and federated baselines. Our strategy combines significant statistical significance, low resource utilization (~ 15 KB per model), and real-time responsiveness, making it well-suited for safety-critical IoT installations where cloud access is infeasible.

Secure and efficient data sharing in Industrial Internet of Things (IIoT) is a continuous difficulty due to the limits of static proxy node selection, centralized designs, and the lack of agility in dynamic situations. Traditional systems often suffer from excessive latency, single points of failure, tight access control, and vulnerability to targeted attacks. To address these limitations, we offer BDEQ (Blockchain-based Dynamic Edge Q-learning), a novel framework combining blockchain smart contracts and deep Q-learning for real-time, trust-aware proxy node selection. Unlike static systems, BDEQ’s reinforcement learning agent dynamically selects appropriate edge nodes based on performance, resource availability, and trust criteria. This ensures secure access control, decentralized auditing, and resilience to security attacks. In a simulated gas-industry IIoT context, BDEQ lowered data access latency by 35% and boosted throughput by 28% over baseline approaches while giving greater resilience to attacks. These results validate BDEQ’s relevance to next-generation IIoT contexts needing adaptive, decentralized, and secure data sharing.

Background Primary Health Care (PHC) systems are pivotal in delivering essential health services during crises, as demonstrated during the COVID-19 pandemic. With varied global strategies to reinforce PHC systems, this scoping review consolidates these efforts, identifying and categorizing key resilience-building strategies. Methods Adopting Arksey and O'Malley's scoping review framework, this study synthesized literature across five databases and Google Scholar, encompassing studies up to December 31st, 2022. We focused on English and Persian studies that addressed interventions to strengthen PHC amidst COVID-19. Data were analyzed through thematic framework analysis employing MAXQDA 10 software. Results Our review encapsulated 167 studies from 48 countries, revealing 194 interventions to strengthen PHC resilience, categorized into governance and leadership, financing, workforce, infrastructures, information systems, and service delivery. Notable strategies included telemedicine, workforce training, psychological support, and enhanced health information systems. The diversity of the interventions reflects a robust global response, emphasizing the adaptability of strategies across different health systems. Conclusions The study underscored the need for well-resourced, managed, and adaptable PHC systems, capable of maintaining continuity in health services during emergencies. The identified interventions suggested a roadmap for integrating resilience into PHC, essential for global health security. This collective knowledge offered a strategic framework to enhance PHC systems' readiness for future health challenges, contributing to the overall sustainability and effectiveness of global health systems.

Background This study aimed to evaluate the effect of dentin pretreatment by Dimethyl Sulfoxide (DMSO) on the bond strength and microleakage of a universal bonding agent to dentin. Methods Fifty-six dentinal discs (thickness = 2 mm) were obtained from the crowns of the human third molars. The disks were assigned into 4 groups and treated as follows; self-etch-control group: G-Premio universal adhesive was used in self-etch mode, total-etch-control: G-Premio universal adhesive was used in total-etch mode, self-etch-DMSO: Water-based DMSO (50% volume) was applied on the samples for 60 s followed by application of G-Premio universal adhesive in self-etch mode, and Total-etch-DMSO: The samples were etched, and then, water-based DMSO was applied on them for 60 s followed by the application of G-Premio universal adhesive in total-etch mode. Afterward, resin composite was placed on all samples and light-cured. The samples were kept in distilled water and subjected to 5000 thermal cycles. Microshear bond strength was measured using the universal testing machine and failure modes were analyzed using a stereomicroscope. Forty-eight human third molars were used for microleakage evaluation and a standardized class five cavity was prepared on the buccal surface of each tooth. The teeth were assigned into 4 groups and received aforementioned surface treatment and the cavities were filled with resin composite. After storing in water for 24 h, the samples were subjected to 5000 cycles of thermocycling and the microleakage level of the samples was evaluated using silver nitrate uptake at the bonded interface. Two-way ANOVA test was used to analyze the effect of bonding technique (self-etch/ total-etch) and DMSO pretreatment on the microshear bond strength and microleakage of G-Premio adhesive to dentin. Results Bonding technique had no effect on the bond strength values (p = 0.17) while DMSO pretreatment significantly decreased the microshear bond strength of the samples (p = 0.001). DMSO application increased microleakage significantly in total-etch (P-value = 0.02) while it had no effect in self-etch mode (P-value = 0.44). Conclusions Pretreatment of dentin using 50% DMSO significantly reduced the bond strength of G-Premio Bond in both self-etch and total-etch modes. DMSO effect on microleakage depended on the etching technique; DMSO increased the microleakage level when the adhesive was used in total-etch mode while did not affect the microleakage in self-etch mode.

Background Migration is a growing global phenomenon and a recognized social determinant of health, contributing to significant health inequities between migrant and host populations. Iran, hosting an estimated 4.5 million migrants—including undocumented individuals—faces persistent challenges in ensuring equitable access to healthcare. This study identifies strategies to inform context-specific interventions within Iran’s health system to improve migrant health. Methods We conducted a realist review, searching PubMed, Science Direct, Scopus, Web of Science, Google Scholar, and grey literature from 2010 to 2024. Using the Intervention-Context-Mechanism-Outcome (ICMO) framework, we analyzed 67 studies to identify effective strategies for enhancing migrant health in Iran. Ritchie and Spencer’s five-stage framework method was applied to analyse the data. Results Twenty-seven strategies were identified. Mechanisms underpinning successful interventions included trust-building through intersectoral governance, reduction of financial barriers via inclusive insurance schemes, increased accessibility through cultural competency training, and improved service reach using digital health and community-based outreach. Iran-specific implications included the potential for piloting migrant-inclusive insurance for vulnerable groups and expanding culturally tailored services through community health workers. Conclusion Contextual adaptation of global strategies can address systemic barriers and improve health equity for migrants in Iran. The findings offer evidence-based, actionable insights for policymakers seeking to localize global best practices within Iran’s healthcare infrastructure.