Miracle-dz/UniFAR-FTUs huggingface dataset:
Intersecting Faults Simulation for Three-Dimensional Reservoir-Geomechanical Models

Faults are geological entities with thicknesses (of the order cm) several orders of magnitude smaller than the grid blocks (of the order 10 m) typically used to discretize reservoir and/or overunder-burden geological formations. Introducing faults in a complex reservoir and/or geomechanical mesh therefore poses significant meshing difficulties. In this paper, we introduce faults in the mesh without meshing them explicitly, by using the extended finite element method (X-FEM) in which the nodes whose basis function support intersects the fault are enriched within the framework of partition of unity. For the geomechanics, the fault is treated as an internal displacement discontinuity that allows slipping to occur using a Mohr-Coulomb type criterion. For the reservoir, the fault is either an internal fluid flow conduit that allows fluid flow in the fault as well as to enter/leave the fault or is a barrier to flow (sealing fault). Equal-order displacement and pressure approximations are used. Twoand three-dimensional benchmark computations are presented to verify the accuracy of the approach, and simulations are presented that reveal the influence of the rate of loading on the activation of faults.
Oppositional Chemical Reaction Optimization Applied to Renewable-Hydro-Thermal Energy Systems

Renewable wind energy has always been extremely helpful to minimizing generation costs as well as reducing emissions to develop green/clean environments, in response to the environmental challenges. The high-proportional integration of renewable power presents challenges to the safe and stable operation of power networks because of its randomness, intermittency, and volatility. However, renewable wind power integrated hydro-thermal power plant is a complex optimization problem due to the water discharge rate, variable wind speed, water transport delay, valve point effect, scheduling time linkage and constraints related with hydraulic continuity and generational limits. The stochastic wind speed is evaluated using Weibull probability density function (W-pdf). For resolving complex economic problems, an effective chemical reaction optimization (CRO) is introduced, for the Hydro-Thermal-Wind (HTW) scheduling problem. Additionally, opposition-based learning (OBL) is integrated with the primary CRO algorithm to increase the solution superiority and convergence speed. The proposed optimization (OCRO) is applied to four hydro, three thermal, and two wind power plants systems. Moreover, proposed technique is compared to other current approaches and the suggested method is determined to be more effective than other methods described in the literature. Lastly, the output of cascade hydropower plants is stable, highly adjustable, and responds quickly.
Design and Simulation of a 500-kW Battery Charger Using Dual Active Bridge Converter

This study focuses on the design and simulation of a robust 500 kW charger for electric vehicle (EV) charging applications using MATLAB SIMULINK software. The charger consists of two channels, each comprising five dual active bridge (DAB) converters arranged in a series configuration at the input side and interconnected in parallel at the output terminals. Each DAB converter can transfer up to 50 kW of power, resulting in a total power transfer capacity of 250 kW per channel. Precise control is achieved through a phase shift control strategy employed by each DAB converter. The system’s performance was thoroughly tested within a dynamic power range of −50 kW to 50 kW for each DAB converter, showcasing excellent controller response. The results demonstrate the effectiveness and suitability of the DAB converter for EV charging stations.
Electromagnetic-Circuital-Thermal-Mechanical Multiphysics Numerical Simulation Method for Microwave Circuits

A electromagnetic-circuital-thermal-mechanical mu- ltiphysics numerical method is proposed for the simulation of microwave circuits. The discontinuous Galerkin time-domain (DGTD) method is adopted for electromagnetic simulation. The time-domain finite element method (FEM) is utilized for thermal simulation. The circuit equation is applied for circuit simulation. The mechanical simulation is also carried out by FEM method. A flexible and unified multiphysics field coupling mechanism is constructed to cover various electromagnetic, circuital, thermal and mechanical multiphysics coupling scenarios. Finally, three numerical examples emulating outer space environment, intense electromagnetic pulse (EMP) injection and high power microwave (HPM) illumination are utilized to demonstrate the accuracy, efficiency, and capability of the proposed method. The proposed method provides a versatile and powerful tool for the design and analysis of microwave circuits characterized by intertwined electromagnetic, circuital, thermal and stress behaviors.
National Options for a Sustainable Nuclear Energy System: MCDM Evaluation Using an Improved Integrated Weighting Approach

While the prospects look bright for nuclear energy development in China, no consensus about an optimum transitional path towards sustainability of the nuclear fuel cycle has been achieved. Herein, we present a preliminary study of decision making for China’s future nuclear energy systems, combined with a dynamic analysis model. In terms of sustainability assessment based on environmental, economic, and social considerations, we compared and ranked the four candidate options of nuclear fuel cycles combined with an integrated evaluation analysis using the Multi-Criteria Decision Making (MCDM) method. An improved integrated weighting method was first applied in the nuclear fuel cycle evaluation study. This method synthesizes diverse subjective/objective weighting methods to evaluate conflicting criteria among the competing decision makers at different levels of expertise and experience. The results suggest that the fuel cycle option of direct recycling of spent fuel through fast reactors is the most competitive candidate, while the fuel cycle option of direct disposal of all spent fuel without recycling is the least attractive for China, from a sustainability perspective. In summary, this study provided a well-informed decision-making tool to support the development of national nuclear energy strategies.
Investigating the Effect of Dust on the Performance of Rooftop Solar Photovoltaic System

The deposition of particulate matter on the surface of photovoltaic (PV) modules, such as dust, soil, microfibers, and biological organisms like as moss and fungus, is a key contributor to module soiling. Soiling inhibits effective light absorption by the PV module, lowering power output. The aim of this work is to compute the power output when dust accumulates on a Solar Photovoltaic (SPV) system in order to solve the issue of lower power generation caused by soiling. To achieve this, the paper utilizes the soiling ratio, which is determined using the Dust IQ soil monitoring system this device is essential in estimating the soiling ratio, allowing for a thorough investigation of the influence of dust deposition on power output. The primary aim of this study is to undertake a thorough examination of the performance of the Dust IQ soil monitoring system device on a real time data during a span of 4 months. The soiling ratio helps in monitoring the SPV power output when dust is accumulated on the modules and further helps in enabling appropriate cleaning of solar PV arrays. In this paper natural cleaning effect on SPV system performance is considered. The maximum soiling loss assessed due to dust deposition from the analysis is 13.44% emphasizing the significance of minimizing soiling impacts.
Detection of Pulmonary Nodules in CT Images Based on 3D Multi-Scale Retina Vnet

Currently, one of the most common and lethal malignancy is lung cancer, the early diagnosis and treatment of which are critical for prognoses and improving the survival rates of patients. Pulmonary nodules are the symptoms of lung cancer. And clinical distinguish between benign and malignant nodules is measuring their volumes and forms after segmentation. However, it is very inefficient to perform lung nodule segmentation by conventional manual methods. In this paper, a lung nodule identification system based on Retina VNet is suggested. The network inherits the structure of the original VNet while introducing Retina Net to extract multi-scale features of pulmonary nodule images. A deep supervision technique is also used for feature tuning, which may significantly increase the model's detection performance. The LIDC-IDRI lung nodule public dataset is used to assess the model's performance in this research. Results showed that the method proposed in this article is superior to the existing detection methods in lung nodule detection performance (especially in detection of lung nodules of various sizes). Average Precision can reach up to 88.7%.
Adaptive dynamic programming for optimal tracking control of stochastic linear continuous-time systems

This paper investigates optimal tracking control on stochastic continuous-time systems with multiplicative state-dependent and input-dependent noise. Preliminaries of stochastic systems and optimal tracking control are presented and an augmented stochastic differential equation of Ito's type has been constructed. To minimize the quadratic cost function, a model-dependent algorithm is shown to solve the stochastic algebraic Riccati equation. An adaptive dynamic programming (ADP) algorithm has been developed to scrap the reliance on the knowledge of system dynamics. The convergence and stability of the algorithm has been studied following. Finally, numerical simulations are performed to demonstate the effectiveness of the proposed ADP methodology on the tracking performance.
Four novel KVLQT1 and four novel HERG mutations in familial long-QT syndrome.

BACKGROUND Familial long-QT syndrome (LQTS) is characterized by prolonged ventricular repolarization. Clinical symptoms include recurrent syncopal attacks, and sudden death may occur due to ventricular tachyarrhythmias. Three genes responsible for this syndrome (KVLQT1, HERG, and SCN5A) have been identified so far. We investigated mutations of these genes in LQTS families. METHODS AND RESULTS Thirty-two Japanese families with LQTS were brought together for screening for mutations. Genomic DNA from each proband was examined by the polymerase chain reaction-single-strand conformation polymorphism technique followed by direct DNA sequencing. In four of the families, comprising 16 patients, mutations were identified in KVLQT1; five other families (9 patients) segregated mutant alleles of HERG. All 25 of these patients carried the specific mutations present in their respective families, and none of 80 normal individuals carried these alleles. Mutations were confirmed by endonuclease digestion or hybridization of mutant allele-specific oligonucleotides. No mutation in SCN5A was found in any family. CONCLUSIONS We identified nine different mutations among 32 families with LQTS. Eight of these were novel and account for 25% of all types of mutations reported to date. Such a variety of mutations makes it difficult to screen high-risk groups using simple methods such as endonuclease digestion or mutant allele-specific amplification.
Multi‐centre comparison of five eye movement detection algorithms

Although various investigators have suggested algorithms for the automatic detection of eye movements during sleep, objective comparisons of the proposed methods have previously been difficult due to different recording arrangements of different investigators. In this study the results of five eye movement detection algorithms applied to the same data were compared to visually scored data. The percentages of true and false detections are given for various threshold levels in rapid and slow eye movement detections. The methods gave best results when they were used with the same electrode montage they were designed for but the performance decreased when other montages were used. Subtracting the cross‐talk of EEG delta activity improved the correctness of eye movement detections.
Visual Positioning System of Intelligent Robot Based on Improved Kalman Filter

The paper presents improved Kalman Filter for visual positioning of the robot. Firstly, the process of robot visual position system is studied in detail. Secondly, the improved Kalman Filter based on Cubature Kalman Filter is researched and used to fuse the data and obtain the positioning data with high precision. The experiments and simulations provide calibration coefficients for camera calibration and verify the improved Cubature Kalman Filter method can effectively improve the positioning accuracy of robot.
Megacystis Microcolon Intestinal Hypoperistalsis Syndrome: Case Reports and Discussion of the Literature

Megacystis microcolon intestinal hypoperistalsis syndrome (MMIHS) is a rare intestinal dysmotility condition that also involves a dilated urinary bladder. It was believed to be an autosomal recessive condition, but genetic studies have suggested possibly an autosomal dominant inheritance pattern. Prenatal diagnosis can be challenging, but MRI and amniotic fluid/digestive fluid studies may be complementary investigations to improve diagnostic accuracy. Prognosis of MMIHS is generally poor and treatment is mostly supportive. To date, bowel transplantation remains the only viable treatment to restore bowel motility. Here we present two additional cases to contribute towards the scant literature on this condition.
Nested Circular Ring Based FSS-Composite for Low Observable Aerospace Platforms

This paper describes the electromagnetic design, simulation, and experimental studies of Circular ring-based customized absorber working for broadband frequency 8-21 GHz. The proposed design consists of continuous and split arms-based circular rings associated with four circular arm cut geometry. The proposed absorber has been designed using customized glass fabric reinforced plastics (GFRP) having thickness of 0.2 mm, which is separated to a metallic ground plane (CFRP) by a Roha Cell foam spacer of 2mm. Proposed composite has been designed using customized developed 200 ohms resistive materials, design has been simulated using a full-wave electromagnetic simulator under periodic boundary conditions. The simulation result shows minimum 10 dB reflection loss value (90% absorption) in 8-21 GHz, which covers X, Ku, and some portion of K bands having a full-width absorption bandwidth of 13 GHz with 93% fractional bandwidth (FBW). Further, the proposed absorber design has been fabricated and validated experimentally with the simulated results. The proposed absorber is thinner in nature, polarization insensitive, possesses good angular stability, and features of fabricability using customized substrate. These attributes encompass it for potential usages in aerospace platforms.
DRL-APNS: A Deep Reinforcement Learning-Powered Framework for Accurate Predictive Network Slicing Allocation

Wireless networks have undergone significant advancements with the rapid progress in Software Defined Networks (SDN), Open RAN (O-RAN) and 5G technology. Among the most notable developments is the emergence of network slicing, which leverages the concept of virtual networks to separate the end-to-end network and computational resources into individual network slices per one or more services/tenants. However, determining the appropriate allocation of computational and network resources for each network slice to meet the specific requirements of each service remains a challenge, especially with continuously changing demands and varying Key Performance Indicators (KPIs) per service and the risk of insufficient or excessive resource allocation. To address these issues, this paper proposes an intelligent predictive framework based on Deep Reinforcement Learning (DRL). Our framework utilizes historical demand and KPI requirements to predict and reserve optimized network slices for multiple services in the future, considering unique constraints, dynamic pricing and under-provisioning. Using different datasets, we validated our system's effectiveness, scalability, and adaptiveness by comparing it with different baselines and state-of-the-art approaches. Our results affirm the efficiency of the proposed solution, showcasing a minimum cost reduction of 15% compared to different baselines and state-of-the-art solutions while incurring less than 2% additional resource consumption. Furthermore, our system demonstrates excellent scalability and adaptability across varying network conditions.
Estimation of Crowd Density in Surveillance Cenes Based on Deep Learning Techniques

Crowd density estimation is a significant research area in artificial intelligence applications as it is an effective tool for crowd monitoring, control, and behavior comprehension. Unstructured big data includes a substantial contribution from surveillance videos. Manual surveillance appears to be time-consuming and tedious. As a result, many CNN oriented detecting people and estimating population models were published in recent years. The enormous growth in crowd counting methods observed during the last several period is typically due to significant advancements in CNNs and datasets. Large crowd datasets and the recent advent of deep learning have made it possible for most crowd counting techniques to be remarkably successful. We have reviewed current articles on deep learning-based algorithms to recognize and categorize the regions with high crowd density in surveillance scenes to estimate the crowd density for the provided crowd scene image. This study analyzes current efforts and briefly discusses regression, detection and conventional-based density estimation methods. On video surveillance systems, several techniques mainly focus on accurately identifying and classifying crowd density. Considering a focus on using camera footage to estimate crowd volume, this overview intends to summarize studies that are crucial to the wider region of video analysis using deep learning methods. This work presents a thorough assessment along with an examination of the best current developments and efficiency improvements for assessing the density of crowds in observation settings. To ascertain the outstanding effectiveness and accuracy of the current modern techniques for deep learning population density estimation, their effectiveness is evaluated.
Speeding Up QA: An Index Structure for Question Queries

Scalability is a major disadvantage of Web question-answering systems (QA), which produces slow response and tedious search time. The bottleneck lies in the commercial search engine used in simple QA systems. In normal architecture of QA, after finding the related documents in the text corpus, analysis of these documents and retrieval of the answers will cost much time. The reason is that the traditional inverted index used in the commercial search engine is not optimal for the QA systems. One of the solutions is to index the position of answers of QA systems directly in the corpus, not index only the single meaningless words. Thus, the response time of the improved search engine to QA queries will be expected to be almost at the same level as the commercial search engines to searching issued by users. In this paper, we will propose a new inverted index structure for QA systems. By indexing the possible meaningful phrases relative to the position of items (words), our approach can improve the response time without losing the advantages of the inverted index. Due to the larger and larger cheap amount of storing space available in nowadays computers, the extra space used by the approach may be regarded neglectable. Thus, our approach can be used in any large-scale QA system, which always produces enormous quantity of possible answers.
High Throughput Log-Based Replication for Many Small In-Memory Objects

Online graph analytics and large-scale interactive applications such as social media networks require low-latency data access to billions of small data objects. These applications have mostly irregular access patterns making caching insufficient. Hence, more and more distributed in-memory systems are proposed keeping all data always in memory. These in-memory systems are typically not optimized for the sheer amount of small data objects, which demands new concepts regarding the local and global data management and also the fault-tolerance mechanisms required to mask node failures and power outages. In this paper we propose a novel two-level logging architecture with backup-side version control enabling parallel recovery of in-memory objects after node failures. The presented fault-tolerance approach provides high throughput and minimal memory overhead when working with many small objects. We also present a highly concurrent log cleaning approach to keep logs compact. All proposed concepts have been implemented within the DXRAM system and have been evaluated using two benchmarks: The Yahoo! Cloud Serving Benchmark and RAMCloud's Log Cleaner benchmark. The experiments show that our proposed approach has less memory overhead and outperforms state-of-the-art in-memory systems for the target application domains, including RAMCloud, Redis, and Aerospike.
MPower: Towards an Adaptive Power Management System for Mobile Devices

Due to their wide distribution and their growth in functionalities, mobile devices are the interaction point between users and the surrounding environment. Nevertheless, their resources are limited and variable over time, both in terms of performance and power. Especially when dealing with power consumption, mobile devices cannot disregard the environment conditions and the user habits. To manage at the best one of the most important mobile device resource, battery lifetime, we are proposing MPower, an adaptive power management system for Android based mobile devices. The ultimate goal of this research is to automatically generate and select user-specific power profiles, to best optimize the device power consumption given the device features, the environmental conditions and the user needs. MPower is a project currently under development and this paper aims at presenting the overall vision of the research, focusing on the data collection phase, which is a central activity to fulfill the proposed objectives.
Driver's Facial Expression Recognition Using Global Context Vision Transformer

Driver's facial expression recognition plays a critical role in enhancing driver safety, comfort, and overall driving experience by proactively mitigating potential road risks. While most existing works in this domain rely on CNN - based approaches, this paper proposes a novel method for driver facial expression recognition using Global Context Vision Transformer (DFER-GCViT). With its inherent capabilities of transformer-based architectures and global context modeling, the proposed method handles challenges commonly encountered in real-world driving scenarios, including occlusions, head pose variations, and illumination conditions. Our method consists of three modules: preprocessing for face detection and data augmentation, facial feature extraction of local and global features, and expression classification using a modified GC-ViT classifier. To evaluate the performance of DFER-GCViT, extensive experiments are conducted on two benchmarking datasets namely the KMU-FED driver facial expression dataset and FER2013 general facial expression dataset. The experimental results demonstrate the superiority of DFER-GCViT in accurately recognizing driver's facial expressions, achieving an average accuracy of 98.27 % on the KMU-FED dataset and 73.78% on the FER2013 dataset, outperforming several state-of-the-art methods on these two benchmarking datasets.