Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Organizational Climate Mediation InServant Leadership Relations with Employee Job Satisfaction and Organizational Commitment at Polda Aceh

This study aims to determine the mediating role of organizational climate in the relationship of servant leadership on job satisfaction and organizational commitment of the Aceh Regional Police officers. Because all the populations of this study were identified, the sample design used in this study was probability sampling by using the approach of stratified random sampling. Data were collected from 258 Aceh Police officers, which were then processed using Structural Equation Modeling (SEM). The results showed that Servant Leadership affects Job Satisfaction, Organizational Commitment, and Organizational Climate; Organizational climate affects job satisfaction but does not affect Organizational Commitment; Organizational Climate mediates the Servant Leadership effect on Job Satisfaction partially, and mediates Servant Leadership effect on Organizational Commitment fully. These findings also reveal that the model of increasing job satisfaction and organizational commitment in Aceh Regional Police employees is a function of increasing Servant Leadership and the suitability of the organizational climate. This model can be a basis for conducting research related to existing variables
MR-FNC: A Fake News Classification Model to Mitigate Racism

—One of the most challenging tasks while processing natural language text is to authenticate the correctness of the provided information particularly for classification of fake news. Fake news is a growing source of apprehension in recent times for hate speech as well. For instance, the followers of various beliefs face constant discrimination and receive negative perspectives directed at them. Fake news is one of the most prominent reasons for various kinds of racism and stands at par with individual, interpersonal, and structural racism types observed worldwide yet it does not get much importance and remains to be neglected. In this paper, to mitigate racism, we address the fake news regarding beliefs related to Islam as a case study. Though fake news remained to be a concerning factor since the beginning of Islam, a significant increase has been noticed in it for the last three years. Additionally, the accessibility of social media platforms and the growth in their use have helped to propagate misinformation, hate speech, and unfavorable views about Islam. Based on these deductions, this study intends to categorize such anti-Islamic content and misinformation found in Twitter posts. Several preprocessing and data enhancement steps were employed on retrieved data. Word2vec and GloVe were implemented to derive deep features while TF-IDF and BOW were applied to derive textual features from the data respectively. Finally, the classification phase was performed using four Machine-based predictive analysis (ML) algorithms Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), and a custom deep CNN. The results when compared with certain performance evaluation measures show that on average, ML-models perform better than the CNN for the utilized dataset
DEVELOPMENT OF COMIC-BASED LIVE WORKSHEET STUDENT WORKSHEETS TO IMPROVE LEARNING OUTCOME OF SOLAR

One of the uses of technology in education is to use e-learning-based learning media. E-learning media is a learning media equipped with web access. E-learning can make it easier for teachers to access various materials, worksheets and teaching materials through various media, one of which is by using Live Worksheets. The cause of low learning achievement is that students are less creative in learning, lack of practice questions and less interesting questions. Based on this, learning media is needed that can support the teaching process through comic-based student worksheets. The development of comic-based student worksheets aims to determine the validity of the media, students' responses to the media, and the effectiveness of the media developed in improving student learning outcomes. The method used in media development is ADDIE. Data collection techniques used in this study include: interviews, questionnaires or questionnaires and test techniques. The data analysis in this study is descriptive analysis and N-gain test. The sample used in this study is one lecturer who is a material expert, design expert, media expert, and 30 seventh grade students at the state junior high school 24 Medan. The validation results obtained from material experts, design experts, media experts are feasible to use. The responses obtained from students are also very good and learning media can improve student learning outcomes, it can be seen from the N-gain test calculation score, which is 0.71 in the high category
Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

Introduction: Stroke is the second leading cause of death globally and a primary factor contributing to disability. Unilateral limb motor impairment caused by stroke is the most common scenario. The bilateral movement pattern plays a crucial role in assisting stroke survivors on the affected side to relearn lost skills. However, motion compensation often lead to decreased coordination between the limbs on both sides. Furthermore, muscle fatigue resulting from imbalanced force exertion on both sides of the limbs can also impact the rehabilitation outcomes. Method: In this study, an assessment method based on muscle synergy indicators was proposed to objectively quantify the impact of motion compensation issues on rehabilitation outcomes. Muscle synergy describes the body’s neuromuscular control mechanism, representing the coordinated activation of multiple muscles during movement. 8 post-stroke hemiplegia patients and 8 healthy subjects participated in this study. During hand-cycling tasks with different resistance levels, surface electromyography signals were synchronously collected from these participants before and after fatigue. Additionally, a simulated compensation experiment was set up for healthy participants to mimic various hemiparetic states observed in patients. Results and discussion: Synergy symmetry and synergy fusion were chosen as potential indicators for assessing motion compensation. The experimental results indicate significant differences in synergy symmetry and fusion levels between the healthy control group and the patient group (p ≤ 0.05), as well as between the healthy control group and the compensation group. Moreover, the analysis across different resistance levels showed no significant variations in the assessed indicators (p > 0.05), suggesting the utility of synergy symmetry and fusion indicators for the quantitative evaluation of compensation behaviors. Although muscle fatigue did not significantly alter the symmetry and fusion levels of bilateral synergies (p > 0.05), it did reduce the synergy repeatability across adjacent movement cycles, compromising movement stability and hindering patient recovery. Based on synergy symmetry and fusion indicators, the degree of bilateral motion compensation in patients can be quantitatively assessed, providing personalized recommendations for rehabilitation training and enhancing its effectiveness.
Non-Destructive Testing of a Fiber-Web-Reinforced Polymethacrylimide Foam Sandwich Panel with Terahertz Time-Domain Spectroscopy

Terahertz (THz) non-destructive testing can detect internal defects in dielectric materials. However, this technology is mainly used for detecting thin and simple structures at present, lacking validations for the detection effectiveness of internal defects in thicker and more complex structures, such as fiber-web-reinforced composite sandwich panels. In this study, samples of fiber-web-reinforced polymethacrylimide foam sandwich panels, which are, respectively, 20 mm and 30 mm thick, were made to detect the internal debonding, inclusion, pore, and crack defects by the THz time-domain spectroscopy system (THz-TDS). The peak-to-peak-imaging algorithm, maximum-amplitude-imaging algorithm, minimum-amplitude-imaging algorithm, pulse-width-imaging algorithm, and time-of-flight-imaging algorithm were used to process and image the collected THz signals. The results showed that the peak-to-peak-imaging algorithm had the best performance. To address the low imaging resolution of THz-TDS, a block-based super-resolution reconstruction method—SSSRGAN—is proposed, which can improve image resolution while maintaining the clear edge contours of defects. The defect-detection results of the samples showed that THz-TDS could detect all pore, debonding, and crack defects, with a minimum size of 3 mm for pores and debonding and a minimum thickness of 1 mm for cracks. The method showed poor detection performance for inclusions with a thickness of 0.053 mm, but could still extract the defect features. Based on the THz-TDS reflection mode measurement principle, the thickness information of the panel, foam core, and web of the samples was calculated: the measurement error was no more than 0.870 mm for Sample #1 and no more than 0.270 mm for Sample #2, demonstrating the accuracy of THz-TDS in measuring the dimensions of sandwich panel structures. In general, THz technology shows potential for detecting internal defects and performing dimensional measurements in complex structures. With the advancement of portable devices and enhancements in detection speed, real-time on-site detection is anticipated in the future.
Munich Papers in Political Economy

The world’s population will continue to age significantly in the near future. One strategy to address the growing gap between supply and demand of professional caregivers in many regions is the use of care robots. Although there have been numerous ethical debates about the use of robots in elderly care, the important question of how (potentially) affected people perceive situations with care robots compared to situations with human caregivers has not yet been systematically examined. Using a large-scale experimental vignette study, we investigated the influence of the nature of the caregiver on participants’ perceived well-being when confronted with different care situations in nursing homes. Our results show that the views of people already affected by care dependency regarding care robots differ substantially from the views of people not affected by care dependency. The non-affected strongly devalued care robots compared to human caregivers, especially in a service context. This devaluation was not found among those affected; their perceived well-being was not influenced by the nature of the caregiver. These findings also proved robust when controlling for people’s attitudes toward robots, gender, and age.
Digital Economic Transformation: In-depth Analysis of Smart Economy in the Implementation of Smart Village in Ketapang Village

Weak technology and lack of human resources caused global e-government to show that Indonesia is below the world average. It is challenging for Indonesia to have an E-Government initiative program towards Good Governance, one of which is Smart City innovation. The community economy is crucial in determining community welfare in moving towards a Smart City. With a Smart Village in Ketapang Village, Banyuwangi Regency, it is hoped that it can create community welfare through a Smart Economy. This research aims to determine the success of Smart Economy in implementing Smart Village in Ketapang Village, Banyuwangi Regency. This research uses a qualitative method with a descriptive approach. Data collection techniques were carried out using observation, interviews and documentation. If the previous research only discussed Smart Cities in general, then in this research, the author examines one of the success factors of smart cities, namely, the Smart Economy. This update discusses the Smart Economy in depth. The study was carried out based on seven Smart Economy indicators using the Giffinger approach. The research results obtained show that the Smart Economy in Ketapang Village has not been maximized due to several obstacles, such as the uneven distribution of facilities provided by the regional government, the lack of ability of the Ketapang Village community to utilize technology, and the lack of government support for technology weak responsibility towards society. However, in general, its implementation is exemplary, manifested in developing technological innovations for the community and government facilities that continue to be built.
Ecological and Oceanographic Influences on Leatherback Turtle Behavior and Scyphozoan Jellyfish Distributions in the Gulf of Mexico

ii ACKNOWLEDGMENTS . v LIST OF TABLES . x LIST OF ILLUSTRATIONS . xi CHAPTER I – General Introduction. 1 1.1 Leatherbacks, Jellyfish, and the Gulf of Mexico . 2 1.2 Research Objectives . 7 CHAPTER II – Space Use and Movements of Leatherback Turtles (Dermochelys coriacea) in the Gulf of Mexico . 9 2.
Optimization of Calorific Value in Briquette made of Coconut Shell and Cassava Peel by varying of Mass Fraction and Drying Temperature

Coconut shells and cassava peels are agricultural residues that are abundantly produced in places where coconut and cassava processing activities are prevalent. formerly these waste products have been disposed of through incineration or natural decomposition, hence exacerbating air pollution and triggering degradation of the environment. The objective of this research is to determine the optimum combination of mixed mass fraction and drying temperature for briquettes produced from coconut shell and cassava peel. Observed variable on this research was Mass fraction with the ratio of coconut shell and Cassava peel 75:25 as level 1, 70:30 as level 2 and 65:35 as level 3. Drying temperature has 3 level 150oC, 200oC and 250oC. The lowest calorific value achieved in briquette made of 65% coconut shell mix with 35% Cassava peel and drying temperature is 200oC on second replication The highest calorific value achieved in briquette made of 65% coconut shell mix with 35% Cassava peel and drying temperature is 150oC on third replication. Coconut shell has a greater effect than Cassava peel on the calorific value of briquettes, but the chemical content of Cassava peel makes the burning rate longer. According to the outcomes of the normality test, versus fits, histograms, and versus order plots indicate that the data has a normal distribution. based on optimization results using Taguchi L9 and ANOVA optimizer the optimal combination using rule larger better is the mass fraction of coconut shell 70:30 Cassava peel with a drying temperature of 250oC.
Health and environmental effects to wildlife from radio telemetry and tracking devices—state of the science and best management practices

This paper discusses the potential health risks and benefits to tagged wildlife from the use of radio tracking, radio telemetry, and related microchip and data-logger technologies used to study, monitor and track mostly wildlife in their native habitats. Domestic pets, especially canids, are briefly discussed as radio-tagging devices are also used on/in them. Radio tracking uses very high frequency (VHF), ultra-high frequency (UHF), and global positioning system (GPS) technologies, including via satellites where platform terminal transmitters (PTTs) are used, as well as geo-locating capabilities using satellites, radio-frequency identification (RFID) chips, and passive integrated responder (PIT) tags, among others. Such tracking technologies have resulted in cutting-edge findings worldwide that have served to protect and better understand the behaviors of myriad wildlife species. As a result, scientists, field researchers, technicians, fish and wildlife biologists and managers, plus wildlife and other veterinarian specialists, frequently opt for its use without fully understanding the ramifications to target species and their behaviors. These include negative physiological effects from electromagnetic fields (EMF) to which many nonhuman species are exquisitely sensitive, as well as direct placement/use-attachment impacts from radio collars, transmitters, and implants themselves. This paper provides pertinent studies, suggests best management practices, and compares technologies currently available to those considering and/or using such technologies. The primary focus is on the health and environmental risk/benefit decisions that should come into play, including ethical considerations, along with recommendations for more caution in the wildlife and veterinarian communities before such technologies are used in the first place.
A decision support system to recommend appropriate therapy protocol for AML patients

Introduction Acute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions. Methods This study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions. Results The proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance. Discussion Our system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.
Leveraging Machine Learning for Enhanced Cyber Attack Detection and Defence in Big Data Management and Process Mining

— The rapidly developing field of "Commercial Operation Divergence Analysis," this research seeks to identify and understand differences in commercial systems that exceed expected results. Approaches in this domain aim to identify the characteristics of process implementations that are associated with changes in process effectiveness. This entails identifying the features of procedural behaviours that result in unpleasant results and figuring out which behaviours have the biggest impact on increased efficiency. As the scale and complexity of big data management and process mining continue to expand, the threat of cyber-attacks poses a critical challenge. This research leverages machine learning techniques for the detection and defence against cyber threats within the realm of big data management and process mining. The study introduces novel metrics such as Skewness, Coefficient of Variation, Standard Deviation, Maximum, Minimum, and Mean for assessing the security state, utilizing variables like SPI, SPEI, and SSI. The research addresses prior issues in cyber-attack detection by integrating machine learning into the specific context of big data and process mining. The novelty lies in the application of Skewness and other statistical metrics to enhance the precision of threat detection. The results demonstrate the effectiveness of the proposed methodology, showcasing promising outcomes in identifying and mitigating cyber threats in the given dataset and which makes use of Support Vector Regression (SVR), has a standard deviation of 0.9, which is consistent with the variability shown in SVM. The results demonstrate a significant achievement, with a Mean Absolute Error (MAE) of 0.98, indicating the efficacy of the proposed approach in providing accurate and timely insights for cyberattack detection and defense, thereby enhancing the overall security posture in data-intensive systems. The results highlight how well the proposed method extracts significant insights from complicated event data, with important ramifications for real-world application and decision-making procedures.
NUMERICAL INVESTIGATIONS OF THE MECHANISMS ASSOCIATED WITH THE ONSHORE RIPPLE MIGRATION

Quantification of cross-shore sediment transport is one of most intriguing challenges in shoreline and coastal geomorphology. During the past decades, several key mechanisms associated with onshore/offshore sediment transport have been identified, such as wave skewness/asymmetry, progressive wave streaming and undertow current. However, applying these mechanisms to the migration of wave formed bedforms (ripples) is not straightforward. For example, recent field observations off Fire Island, NY showed that ripples migrated onshore even during periods of offshore directed wave skewness, which is contradictory to the prediction of empirical sediment transport formulations. The physical processes driving ripple vortex formation, ejection and boundary layer streaming associated with rippled bed can further complicate the bedload/suspended load sediment transport over ripples. To fully understand these mechanisms, a comprehensive model that can resolve the ripple dynamics and interactions between free surface wave and rippled bed is examined.
Integra® Dermal Regeneration Template in Complex Scalp Reconstruction

Background/Objectives: The need for surgical reconstruction of scalp defects following the excision of cutaneous skin cancers is an increasingly common procedure. Particular challenges arise when considering options for reconstruction of large defects not amenable to local skin flap coverage. The use of skin grafts poses the risk of donor site morbidity. This paper investigates the emerging use of Integra®, a synthetic acellular dermal regeneration template, as an alternative or adjunct to skin grafting in scalp reconstruction. Methods: The study presents a retrospective analysis of 101 patients who underwent Integra®-based reconstruction of scalp defects. Demographics, procedure details, complications, need for further surgery, and time to healing were evaluated. Results: The overall success rate of the one-stage Integra®-only procedure was 95%, with a minor complication rate of 30.7%. Anticoagulation medication was identified as an independent risk factor for post-operative infection, while previous head and neck radiotherapy and increased defect depth were associated with the requirement for a second-stage skin graft. Conclusions: These findings support the consideration of Integra® as a safe and viable alternative for both partial and full thickness scalp defects in a select cohort of complex highly co-morbid patients, reducing complications and the need for additional procedures.