Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydraulic stimulation of natural fractures as revealed by induced microearthquakes, Carthage Cotton Valley gas field, east Texas

We produced a high‐resolution microseismic image of a hydraulic fracture stimulation in the Carthage Cotton Valley gas field of east Texas. We improved the precision of microseismic event locations four‐fold over initial locations by manually repicking the traveltimes in a spatial sequence, allowing us to visually correlate waveforms of adjacent sources. The new locations show vertical containment within individual, targeted sands, suggesting little or no hydraulic communication between the discrete perforation intervals simultaneously treated within an 80‐m section. Treatment (i.e., fracture‐zone) lengths inferred from event locations are about 200 m greater at the shallow perforation intervals than at the deeper intervals. The highest quality locations indicate fracture‐zone widths as narrow as 6 m. Similarity of adjacent‐source waveforms, along with systematic changes of phase amplitude ratios and polarities, indicate fairly uniform source mechanisms (fracture plane orientation and sense of slip) over .
Enhancing Learners’ EFL Writing Performance through Extensive Reading Strategy Training into the Writing Process

It is logical to anticipate that teaching one will lead to the student’s proficiency in the other because the reading and writing processes are correlated and, in many cases, combined. The goal was to find out how extended reading strategy training (ERST) affected first-year undergraduate students’ writing abilities. Thirty-one of the 61 undergraduate participants from Wollega University were placed in the control group and 30 were placed in the experimental group. Interviews and tests on the ability to write paragraphs were used to gather the data over the course of 10 weeks. The following criteria were evaluated: the capacity to handle the specified issue, well-developed thoughts, well-organised concepts, acceptable transitions, unity, coherence, syntactic diversity, and idiomaticity with few lexical or grammatical faults. The interview data were evaluated thematically, while the quantitative results were primarily studied using analysis of covariance. The results show that ERST significantly improves the writing abilities of the students. This outcome is consistent with past research that showed extensive reading to be helpful in enhancing students’ writing abilities and offers valuable information on the significance of including the ERST programme into the writing process in writing courses.
Investigating the Impact of Community-Based Health Insurance Scheme among the Rural Dwellers of Sokoto State, Nigeria

The purpose of this paper is to examine a few sample data on the investigation into the impact of community-based health insurance scheme among the rural dwellers of Sokoto state. Thus instrument such as content and face validity, reliability and data normality were also examined based on the revised version by expert, a few data were analyzed using the statistical software SPSS version 18. The result reveals that the instruments are reliable and the data for pilot study show evidence of rational normality. Keywords: Policy Process, Economic Growth, Health Financing, Health Care Delivery, Scheme Design, Scheme Mobilization, Institutional design, Communication, Pilot Test, Sokoto, Nigeria.
Adaptive Service Placement, Task Offloading and Bandwidth Allocation in Task-Oriented URLLC Edge Networks

Recently, the advances of low-latency communication technologies and edge intelligence have enabled a wide range of task-oriented time-sensitive applications. This paper aims at designing adaptive service placement, task offloading, and bandwidth allocation for ultra-reliable and low-latency communication (URLLC)-aided edge networks. The main objective is to minimise both the total end-to-end (e2e) latency and number of installed services at edge servers. The optimal solutions are obtained by jointly optimising service placement decisions, task offloading portions and bandwidth allocation at dynamic timescales subject to network budgets and application requirements under uncertain environment. Selective simulation results are provided to validate the effectiveness of the proposed solution in term of reducing the latency as well as optimising service placement decisions.
An Evaluation of the Impact of Air Pollution on the Lung Functions of High School Students Living in a Ceramic Industrial Park Zone

Santa Gertrudes (SG) and Rio Claro (RC), Sao Paulo, Brazil, are located in a ceramic industrial park zone, and their particulate matter with an aerodynamic diameter of less than 10 µm (PM10) concentration levels has been among the highest in recently monitored cities in Brazil. Local PM10 was mostly composed of silica. A cross-sectional study was designed to evaluate the lung functions of public high school students in SG, RC, and São Pedro (SP) (control location), Brazil, in 2018. The prevalence of asthma, mean PM10, FVC (forced vital capacity), and FEV1 (forced expiratory volume in the first second) were compared between the locations, and regression analyses were performed. A total of 450 students were included (SG: 158, RC: 153, and SP: 139). The mean FVC% (SG: 95.0% ± 11.8%, RC: 98.8% ± 12.9%, SP: 102.4% ± 13.8%, p < 0.05), the mean FEV1% (SG: 95.7% ± 10.4%, RC: 99.7% ± 12.0%, SP: 103.2% ± 12.0%, p < 0.05) and the mean PM10 (SG: 77.75 ± 38.08 µg/m3, RC: 42.59 ± 23.46 µg/m3, SP: 29.52 ± 9.87 µg/m3, p < 0.01) differed between locations. In regression models, each increase in PM10 by 10 µg/m3 was associated with a decrease in FVC% by 1.10% (95% CI 0.55%–1.65%) and a decrease in FEV1% by 1.27% (95% CI 0.75%–1.79%). Exposure to high levels of silica-rich environmental PM10 was found to be associated with lower FVC and FEV1.
Acquisition method improvement for Bossa Nova Technologies' full Stokes, passive polarization imaging camera SALSA

For some applications, the need for fast polarization acquisition is essential (if the scene observed is moving or changing quickly). In this paper, we present a new acquisition method for Bossa Nova Technologies’ full Stokes passive polarization imaging camera, the SALSA. This polarization imaging camera is based on “Division of Time polarimetry” architecture. The use of this technique presents the advantage of preserving the full resolution of the image observed all the while reducing the speed acquisition time. The goal of this new acquisition method is to overcome the limitations associated with Division of Time acquisition technique as well as to obtain high-speed polarization imaging while maintaining the image resolution. The efficiency of this new method is demonstrated in this paper through different experiments.
Role of Micro Finance & Self Help Groups in Women Empowerment: A Case Study of District Mewat

A process in which women challenge the existing norms and culture, to effectively promote their well being is known as women empowerment. The participation of women in Self Help Groups (SHGs) made a significant impact on their empowerment both in social and economical aspects. This study examines women empowerment through self help groups in district Mewat of Haryana. The data required for the study has been collected from both the primary and secondary sources. A multistage random sampling method has been followed. Average and percentage analysis was carried out to draw meaningful interpretation of the results. Garret ranking technique was used to find the reasons for joining the Self help group. Factor analysis was used to measure and to determine the relationship between the observed variables. The results of the study revealed that the SHGs have had greater impact on both economic and social aspects of the beneficiaries.
Advancing Autonomous UAV Target Localization in GPS-Denied Environments

This research introduces a novel approach to enhance the robustness of autonomous UAV target localization in GPS-denied environments. Typically, localization in such environments relies on utilizing received signal strength (RSS) transmitted by the target and employing the Q-learning algorithm to determine agent actions. Usually, there are two approaches to defining Q-learning's state, both using only a single variable as the Q-learning's state. One of those approaches is potentially hard to converge, while the other is susceptible to unrealistic assumptions in a real-world deployment. This study proposes an improved Q-learning algorithm by incorporating multiple variables as states, addressing the limitations of existing approaches that use a single variable. In this research, it will be proven that if the assumptions don't hold, the performance of the proposed method by another research will be messed up. In addition, it's proved that a new approach is proposed in this research able to outperform the approach using only single-variable as Q-learning state in terms of speeds of convergence and robustness, even when the assumptions are not met.
Hybrid Precoding for Integrated Communications and Localization in Massive MIMO LEO Satellite Systems

The future sixth generation (6G) networks will feature great importance on the integration of communications and localization, to realize the Internet of Everything (IoE). In this paper, we investigate the hybrid precoding design for the integrated communications and localization (ICAL) in the massive multiple-input multiple-output (MIMO) low Earth orbit (LEO) systems. In particular, we first derive an upper bound of the communication spectral efficiency (SE) and the squared position error bound (SPEB) of localization. Then, we formulate a multi-objective optimization problem to simultaneously operate communications and localization. Simulation results demonstrate the satisfactory performance of the proposed massive MIMO LEO ICAL system for typical setups.
RemoteGesture: Room-scale Acoustic Gesture Recognition for Multiple Users

As a promising way, controlling smart devices through gestures offers the benefits of non-contact interaction, efficiency and convenience. Previous researches on acoustic-based gesture recognition have mostly focused on near-field gestures within 1 meter and for a single user only. However, such a nearfield sensing scheme is inadequate to meet the growing demands for multi-person human-computer interaction in far-field spaces. In this paper, we present a novel acoustic-based room-scale gesture recognition system that is capable of recognizing gestures simultaneously performed by multi-user. Our approach achieves far-field sensing by examining the relationship between acoustic signal frame length and sensing range, and overcoming a series of practical challenges incurred by far-field sensing. To simultaneously detect and distinguish gestures of multiple persons, we divide the sensing area into multiple beamforming sub-scanning areas and apply binary search to detect multiple users, which allows for an efficient scanning process and facilitates real-time detection. Finally, we conduct a data augmentation scheme to enlarge the training data and apply a lightweight deep learning framework to classify different gestures. Extensive experiments confirm that our system enables multi-user gesture detection and can recognize nine gestures at a distance up to 7 meters.
Doctors for the Truth: Echo Chambers of Disinformation, Hate Speech, and Authority Bias on Social Media

The COVID-19 pandemic has been the catalyser of one of the most prolific waves of disinformation and hate speech on social media. Amid an infodemic, special interest groups, such as the international movement of “Doctors for the Truth”, grew in influence on social media, while leveraging their status as healthcare professionals and creating true echo chambers of COVID-19 false information and misbeliefs, supported by large communities of eager followers all around the world. In this paper, we analyse the discourse of the Portuguese community on Facebook, employing computer-assisted qualitative data analysis. A dataset of 2542 textual and multimedia interactions was extracted from the community and submitted to deductive and inductive coding supported by existing theoretical models. Our investigation revealed the high frequency of negative emotions, of toxic and hateful speech, as well as the widespread diffusion of COVID-19 misbeliefs, 32 of which are of particular relevance in the national context.
Repeatability of Ad Libitum Water Intake during Repeated 1 h Walking/Jogging Exercise Sessions Conducted under Hot Ambient Conditions

A drinking strategy aiming to replace a given percentage of the sweat losses incurred during exercise should result in reproducible fluid intake volume and, hence, fluid balance from one exercise session to the other performed under similar scenarios. Whether this may also be the case with ad libitum drinking during exercise is unclear. We characterized the repeatability of ad libitum water intake during repeated 1 h exercise sessions and examined its effect over time on fluid balance and selected physiological functions and perceptual sensations. Twelve (3 women) healthy individuals participated in this study. At weekly intervals, they completed four 2 × 30 min walking/jogging exercise bouts (55% V˙O2max, 40 °C, 20–30% relative humidity) interspersed by a 3 min recovery period. During exercise, participants consumed water (20 °C) ad libitum. There were no significant differences among the four exercise sessions for absolute water intake volume (~1000 mL·h−1), percent body mass loss (~0.4%), sweat rate (~1300 mL·h−1) and percent of sweat loss replaced by water intake (~80%). Heart rate, rectal temperature, and perceived thirst and heat stress did not differ significantly between the first and fourth exercise sessions. Perceived exertion was significantly lower during the fourth vs. the first exercise session, but the difference was trivial (<1 arbitrary unit). In conclusion, ad libitum water intake during four successive identical 1 h walking/jogging sessions conducted in the heat will result in similar water intake volumes and perturbations in fluid balance, heart rate, rectal temperature, and perceived thirst, heat stress and exertion.
FEMALE SUBALTERNITY IN HANNA RAMBE'S NOVEL MIRAH DARI BANDA

Subaltern reading begins with an author's view of the conditions that characterize subaltern groups. Subaltern falls within the realm of postcolonial feminism which is a form of alliance between postcolonial theory and feminism, seeks to beat back existing gender, cultural, or racial hierarchies, and rejects binary opposition to patriarchalism or colonialism's constructs of authority. This research tries to reveal the subalternity of women during colonialism in the novel Mirah dari Banda. The method used in this research is descriptive qualitative, namely by examining texts that contain subaltern elements. The data in this study are in the form of text sentences or dialogues that reflect subaltern women due to the domination of colonialism. The findings of this study that there are several forms of subaltern experienced by indigenous women (Mirah), including degrading dignity, sexual harassment, physical/verbal violence, obedience to the invaders, and control over others. From some of the research findings, subaltern women do not have a conceptual language to fight against colonial groups or indigenous men, and also women do not have the power to carry out resistance to the hegemonic power of groups in power.
Spine Posture Detection for Office Workers with Hybrid Machine Learning

This study aims to detect bad spine posture using an al-ternative approach that doesn't rely on deep learning or excessive energy. The goal is to improve accuracy and effectiveness without disrupting workflow. A custom dataset was created, numerical inferences were made from posture values, and a hybrid approach using Light Gradient Boosting achieved a 96 % success rate.
IoT Network-Based Intrusion Detection Framework: A Solution to Process Ping Floods Originating From Embedded Devices

Internet of things (IoT) devices are gaining traction around the globe. These devices are sometimes hijacked and turned into zombies or botnets. One risk posed by hijacked devices is a ping flood attack, also known as an internet control message protocol echo request flood. Current literature lacks a ping flood attack dataset generated from an IoT device. This paper contributes by developing an IoT network intrusion detection framework for ping flood attacks. This framework deploys an IoT testbed using embedded devices to emulate two datasets, normal ping traffic and malicious ping flood attack traffic. Features are extracted from the captured traffic using the Zeek tool. Attacks are detected using three machine learning algorithms: logistic regression, K-nearest neighbor, and support vector machine. These models are compared using evaluations such as the confusion matrix, accuracy, precision, recall, F1-score, and misclassification (error rate). The models are validated using split validation and cross-fold validation. The time consumed in training and testing the models across various data levels is also analyzed, along with the time required for feature extraction. The discrepancies between capturing tools are discussed. The use of criteria based on the time difference between requests to detect malicious traffic is considered, as is the impact of machine learning models on memory usage. Our work is compared with similar research. The testbed concluded that the K-nearest neighbor algorithm achieved 99.67% detection accuracy, with an error rate of 0.33% and an F1-score of 99.67%, which is the best amongst the three algorithms.
A Proactive Intrusion Detection and Mitigation System for Grid-Connected Photovoltaic Inverters

The breach of data confidentiality, integrity, and availability due to cyberattacks can adversely impact the operation of grid-connected Photovoltaic (PV) inverters. Detecting such attacks based on their signatures or behavior-based analytics and adopting corrective actions to prevent security breaches for grid-connected PV systems requires the implementation of an intelligent Intrusion Detection System (IDS). In this paper, a Proactive Intrusion Detection and Mitigation System (PIDMS) based on real-time stability boundary identification at the Point of Common Coupling (PCC) for grid-connected PV systems is presented to identify the potentially compromised grid-connected PV systems in Cyber-Physical Power and Energy Systems (CPPES). The proposed PIDMS correlates the variations in the active power and reactive power measurements to power grids voltage at the PCC in real-time and accurately identifies compromised grid-connected PV systems, and enhances the resilience of CPPES. The performance of the proposed PIDMS is validated through dynamic simulations under different operating conditions. The obtained results verify the applicability and effectiveness of the proposed PIDMS.
Beyond 5G-Assisted Automatic Guided Vehicles in Seaport Environments

Location awareness is vital for several fifth generation (5G) and beyond wireless applications. In particular, the use of 5G localization to assist or control automatic guided vehicles (AGVs) in seaport environments is a relevant use case conceived by the 3rd Generation Partnership Project (3GPP) standardization body. However, AGVs control systems require accurate localization, which is challenging in dynamic and cluttered environments like seaports. This paper proposes the use of soft information (SI)-based localization for providing location awareness to assist the motion of AGVs. The localization and navigation performance is determined considering the digital twin of a real seaport environment. Results show that SI-based localization can provide reliable AGVs navigation in 5G and beyond seaport environments.
Impact of Traditional Environmental Management Measures and Ecological Product Diversification on MSME Performance: Evidence of the Batik Industry in Central Java, Indonesia

Research aims: This research aims to investigate the influence of traditional environmental management practices on corporate performance and determine the mediating role of ecological product diversification in this regard.Design/Methodology/Approach: To achieve these objectives, a research design was developed and tested using a sample of 138 micro, small and medium enterprises (MSMEs) operating in the batik industry in Central Java, Indonesia. Observation data were then analyzed using Structural Equation Modeling (SEM) utilizing Amos software.Research findings: The analysis results demonstrated that traditional environmental management and ecological product diversification had a direct positive impact on the performance of small and medium enterprises. However, these results did not confirm the mediating role of ecological product diversification.Theoretical contribution/Originality: This study provides convincing evidence regarding the impact of environmentally friendly product diversification on MSME performance. Therefore, MSMEs, especially those operating in the batik industry, are encouraged to diversify environmentally friendly products to increase competitiveness and adapt to people's concern for the environment in the current era (green economy). Practitioners/Policy implications: It is acknowledged that the overall results obtained from this research provide a significant contribution to the understanding of the importance of environmental management practices and environmentally-friendly product diversification to increase business efficiency in the context of MSMEs.Research limitations/Implications: This study had a limited scope and only covered the Central Java region. Subsequent research can further expand the scope of the research field.
Comprehensive Analysis Over Centralized and Federated Learning-Based Anomaly Detection in Networks with Explainable AI (XAI)

Many forms of machine learning (ML) and artificial intelligence (AI) techniques are adopted in communication networks to perform all optimizations, security management, and decision-making tasks. Instead of using conventional blackbox models, the tendency is to use explainable ML models that provide transparency and accountability. Moreover, Federate Learning (FL) type ML models are becoming more popular than the typical Centralized Learning (CL) models due to the distributed nature of the networks and security privacy concerns. Therefore, it is very timely to research how to find the explainability using Explainable AI (XAI) in different ML models. This paper comprehensively analyzes using XAI in CL and FL-based anomaly detection in networks. We use a deep neural network as the black-box model with two data sets, UNSW-NB15 and NSLKDD, and SHapley Additive exPlanations (SHAP) as the XAI model. We demonstrate that the FL explanation differs from CL with the client anomaly percentage.
The classification of hydrostratigraphic units of North Mahallat based on fuzzy logic

The objective of this research was to classify the lithologic units of North Mahallat in Central Iran into separate hydrostratigraphic units and survey the degree of their similarity to each other. To do so, the study area karst has been divided into seven separate lithologic units based on structural and hydraulic lithologic properties, and then, the data obtained become as fuzzy numbers, and seven lithologic units were classified into relevant and common classes in the form of fuzzy clustering. The results show that generally, the Mahallat area karst consists of seven separate hydrostratigraphic units that all of them can be divided into two main classes. Meanwhile, the best karstic water reservoirs have been allocated to the class which includes massive limestone units (KL 1 ) and (KL 2 ).