Miracle-dz/UniFAR-FTUs huggingface dataset:
Organizational Citizenship Behavior Predictors: A Mediation Analysis on Savings and Loan Cooperative

Purpose: This study aims to identify the influence of LMX (Leader-Member Exchange), POS (Perceived Organizational Support), and OC (Organizational Commitment) on OCB (Organizational Citizenship Behavior) within the setting of savings and loan cooperatives in Indonesia. Method : The research method involves collecting data from 150 employees working in savings and loan cooperatives using a questionnaire. PLS-SEM (Partial Least Squares Structural Equation Modeling) is used as the statistical method to analyze the data. Results and Conclusion: The research findings indicate that there is a direct positive relationship between LMX and OCB, but no direct positive relationship between POS and OCB. Additionally, there is a direct relationship between LMX and POS with OC. The study also found that OC acts as a mediator in the relationship between LMX and OCB, as well as the relationship between POS and OCB. Research Implications: The results of this study have significant implications for the management of savings and loan cooperatives in Indonesia. Cooperative leaders can pay more attention to the quality of leader-member exchange (LMX) and perceived organizational support (POS) as both directly influence the organizational citizenship behavior (OCB) of employees. Moreover, understanding the role of OC as a mediator can help enhance the effectiveness of employee behavior development programs within cooperatives. Top of Form Originatity/Values: This research represents a pioneering effort to study organizational citizenship behavior in the unique Indonesian context of savings and loan cooperatives. The new insights gleaned from this research make valuable contributions to the existing literature, open opportunities for further research, and offer workable guidance for cooperative management to optimize employee performance and organizational effectiveness.
Exploring the Potential of ChatGPT in Improving Online Marketing and Promotion of MSMEs

In the ever-evolving digital era, the use of technology and the internet has changed the way businesses operate, including in product marketing and promotion. ChatGPT is allegedly an example of AI technology that can improve online marketing and promotion of MSMEs. This research will explore the potential of ChatGPT in improving online marketing and promotion of MSMEs. This research is qualitative in nature. The techniques used to obtain information involve careful observation and meticulous note-taking, followed by analytical procedures such as data reduction, visualisation, and inference. The study arrived at the conclusion that exploring the potential of ChatGPT in enhancing online marketing and promotion of MSMEs offers an exciting opportunity for MSMEs to reach potential customers, improve customer interactions, and optimise marketing efforts efficiently. With ChatGPT's ability to provide natural responses, provide product information, provide recommendations, and run marketing campaigns, MSMEs can expand their market reach and provide a better customer experience.
The effect of heated water on the temperature and evaporation of Hyco Lake, North Carolina, 1966-72

Three levels of thermal loading of Hyco Lake by heat from two steamelectric generators have resulted in higher temperatures and increased evaporation in the 3,750-acre (1,518 square hectometer) lake. During the winter, local temperature increases of 34°F (19°C) were sometimes observed at the surface as heated water spread out on top of the cooler, more-dense, lake water, affecting areas as large as 2,590 acres (1,048 square hectometers) with temperature increases averaging up to 12°F (6.7°C) higher than natural lake temperatures. In the summer average temperature increases at the surface seldom exceeded 6°F (3.3°C) and the maximum area affected was 2,570 acres (1,040 square hectometers). Forced evaporation from the heated part of Hyco Lake increased in proportion to the added heat, reaching a maximum of 23.1 inches (586 millimeters) in 1970, as compared to 55.6 inches (1,412 millimeters) of natural evaporation for that year. In 1969, over the heated area, 210,600 British thermal units per square foot per year or 2,407,510,000 joules per square meter per year of heat were added to the lake. Of this amount, about 58 percent of the heat was utilized for evaporation, 24 percent for back radiation, 8.0 percent was conducted as sensible heat from the lake, 1.0 percent was removed through outflow, and 0.04 percent was advected by the evaporated water. These percentages are fairly typical of the first three stages of thermal loading.
Driving Cultures and Climate Change: Bodies, Space and Affluence.

Climate change scientists have identified the private automobile as one of the most important sources of greenhouse gas emissions. Many civic environmentalists are advocating for alternative forms of transport to the private car in climate change mitigation policies, including walking, cycling and mass transit systems. Yet, many people in affluent societies appear wed to their cars. In this thesis a cultural geography approach is adopted to account for the resistance to changing modes of transport. The thesis adopts a performative framework to explore how particular understandings of climate change, sustainability and place are configured through driving practices. The interpretation offered draws on the concepts of ‘a progressive sense of place’, ‘the spatial imperative of subjectivity’ and ‘the ecology of place’. The data analyses for this research are drawn from forty surveys, as well as a range of qualitative methods, including informal interviews, participant diaries, and ‘drive talk’ experiences with twelve participants from the affluent Sydney suburb of Burraneer Bay. This analysis introduces the importance of embodied knowledge to discussion of climate change mitigation policies. Results illustrate that car mobility in this affluent suburb is not configured by economics – but rather life cycles, and the experiences of comfort, convenience and safety. Tempting drivers to abandon their cars for public transport will remain problematic unless closer consideration is given to how geographical knowledge is configured by driving.
Determination of Kinetic Parameters and Identification of the Rate-Determining Steps in the Oxygen Exchange Process for LaNi0.6Fe0.4O3−δ

The mixed ionic and electronic oxide LaNi0.6Fe0. 4O3−δ (LNF) is a promising ceramic cathode material for solid oxide fuel cells. Since the reaction rate of oxygen interaction with the cathode material is extremely important, the present work considers the oxygen exchange mechanism between O2 and LNF oxide. The kinetic dependence of the oxygen/oxide interaction has been determined by two isotopic methods using 18O-labelled oxygen. The application of the isotope exchange with the gas phase equilibrium (IE-GPE) and the pulsed isotope exchange (PIE) has provided information over a wide range of temperatures (350–800 °C) and oxygen pressures (10–200 mbar), as each method has different applicability limits. Applying mathematical models to treat the kinetic relationships, the oxygen exchange rate (rH, atom × cm−2 × s−1) and the diffusion coefficient (D, cm2/s) were calculated. The values of rH and D depend on both temperature and oxygen pressure. The activation energy of the surface exchange rate is 0.73 ± 0.05 eV for the PIE method at 200 mbar, and 0.48 ± 0.02 eV for the IE-GPE method at 10–20 mbar; for the diffusion coefficient, the activation energy equals 0.62 ± 0.01 eV at 10–20 mbar for the IE-GPE method. Differences in the mechanism of oxygen exchange and diffusion on dense and powder samples are observed due to the different microstructure and surface morphology of the samples. The influence of oxygen pressure on the ratio of contributions of different exchange types to the total oxygen exchange rate is demonstrated. For the first time, the rate-determining step in the oxygen exchange process for LNF material has been identified. This paper discusses the reasons for the difference in the mechanisms of oxygen exchange and diffusion.
Estimating Anthropometric Marker Locations from 3-D LADAR Point Clouds

Abstract : An area of interest for improving the identification portion of the system is in extracting anthropometric markers from a Laser Detection and Ranging (LADAR) point cloud. Analyzing anthropometrics markers is a common means of studying how a human moves and has been shown to provide good results in determining certain demographic information about the subject. This research examines a marker extraction method utilizing principal component analysis (PCA), self-organizing maps (SOM), alpha hulls, and basic anthropometric knowledge. The performance of the extraction algorithm is tested by performing gender classification with the calculated markers.
Impact of Navigation Aid and Spatial Ability Skills on Wayfinding Performance and Workload in Indoor-Outdoor Campus Navigation: Challenges and Design

Wayfinding is important for everyone on a university campus to understand where they are and get to where they want to go to attend a meeting or a class. This study explores the dynamics of mobile navigation apps and the spatial ability skills of individuals on a wayfinding performance and perceived workload on a university campus wayfinding, including indoor-outdoor navigation, by focusing on three research objectives. (1) Compare the effectiveness of Google Maps (outdoor navigation app) and MazeMap (indoor-outdoor navigation app) on wayfinding performance and perceived workload in university campus wayfinding. (2) Investigate the impact of participants’ spatial ability skills on their wayfinding performance and perceived workload regardless of the used navigation app. (3) Highlight the challenges in indoor-outdoor university campus wayfinding using mobile navigation apps. To achieve this, a controlled experiment was conducted with 22 participants divided into a control (using Google Maps) and an experiment group (using MazeMap). Participants were required to complete a time-bound wayfinding task of navigating to meeting rooms in different buildings within the Gløshaugen campus of the Norwegian University of Science and Technology in Trondheim, Norway. Participants were assessed on spatial ability tests, mental workload, and wayfinding performance using a questionnaire, observation notes and a short follow-up interview about the challenges they faced in the task. The findings reveal a negative correlation between overall spatial ability score (spatial reasoning, spatial orientation, and sense of direction) and perceived workload (NASA TLX score and Subjective Workload Rating) and a negative correlation between sense of direction score and total hesitation during wayfinding task. However, no significant difference was found between the Google Maps and the MazeMap group for wayfinding performance and perceived workload. The qualitative analysis resulted in five key challenge categories in university campus wayfinding, providing implications for designing navigation systems that better facilitate indoor-outdoor campus navigation.
Black Gram Disease Classification via Deep Ensemble Model with Optimal Training

Black gram crop belongs to the Fabaceae family and its scientific name is Vigna Mungo.It has high nutritional content, improves the fertility of the soil, and provides atmospheric nitrogen fixation in the soil. The quality of the black gram crop is degraded by diseases such as Yellow mosaic, Anthracnose, Powdery Mildew, and Leaf Crinkle which causes economic loss to farmers and degraded production. The agriculture sector needs to classify plant nutrient deficiencies in order to increase crop quality and yield. In order to handle a variety of difficult challenges, computer vision and deep learning technologies play a crucial role in the agricultural and biological sectors. The typical diagnostic procedure involves a pathologist visiting the site and inspecting each plant. However, manually crop disease assessment is limited due to lesser accuracy and limited access of personnel. To address these problems, it is necessary to develop automated methods that can quickly identify and classify a wide range of plant diseases. In this paper, black gram disease classifications are done through a deep ensemble model with optimal training and the procedure of this technique is as follows: Initially, the input dataset is processed to increase its size via data augmentation. Here, the processes like shifting, rotation, and shearing take place. Then, the model starts with the noise removal of images using median filtering. Subsequent to the preprocessing, segmentation takes place via the proposed deep joint segmentation model to determine the ROI and non-ROI regions. The next process is the extraction of the feature set that includes the features like improved multi-texton-based features, shape-based features, color-based features, and local Gabor X-OR pattern features. The model combines the classifiers like Deep Belief Networks, Recurrent Neural Networks, and Convolutional Neural Networks. For tuning the optimal weights of the model, a new algorithm termed swarm intelligence-based Self-Improved Dwarf Mongoose Optimization algorithm (SIDMO) is introduced. Over the past two decades, nature-based metaheuristic algorithms have gained more popularity because of their ability to solve various global optimization problems with optimal solutions. This training model ensures the enhancement of classification accuracy. The accuracy of the SIDMO, which is around 94.82%, is substantially higher than that of the existing models, which are FPA[Formula: see text]88.86%, SSOA[Formula: see text]88.99%, GOA[Formula: see text]85.84%, SMA[Formula: see text]85.11%, SRSR[Formula: see text]85.32%, and DMOA[Formula: see text]88.99%, respectively.
Informatics for Efficient EST-Based Gene Discovery in Normalized and Subtracted cDNA Libraries

Structured Abstract Motivation: Large-scale Gene Discovery projects based on EST sequencing are important for providing catalogs of cDNAs for various purposes. To provide accurate estimates of gene discovery, the methods used must be robust in the presence of sequence errors. Efficient informatics systems play a vital role in a number of aspects of the process: serial subtraction, clustering, annotation, quality trimming, and clone verification. Subtracted cDNA libraries have been constructed to address the issues of h igh redundancy, and has implemented a sampling-based method for improving the quality of clone naming. Results: A large-scale EST-based gene discovery program at the University of Iowa has led to the discovery of more than 40,000 previously undiscovered genes in three mammalian species in less than two years. The overall system of software is described, statistics concerning performance and throughput are given, and detailed methods of certain aspects of the pipeline are provided. In particular, our sequence editing, clustering, and clone verification methods are described in some detail. Availability: All of the originally produced software described in this paper is available from our project web server at: http://genome.uiowa. edu.
Prospect of Mesenchymal Stem-Cell-Conditioned Medium in the Treatment of Acute Pancreatitis: A Systematic Review

Mesenchymal stem cells (MSCs) have demonstrated potential in both clinical and pre-clinical research for mitigating tissue damage and inflammation associated with acute pancreatitis (AP) via paracrine mechanisms. Hence, there has been a recent surge of interest among researchers in utilizing MSC cultured medium (CM) and its components for the treatment of AP, which is recognized as the primary cause of hospitalization for gastrointestinal disorders globally. A systematic review was conducted by searching the MEDLINE, EMBASE, and Web of Science databases. Studies that involve the administration of MSC-CM, extracellular vesicles/microvesicles (EVs/MVs), or exosomes to AP animal models are included. A total of six research studies, including eight experiments, were identified as relevant. The findings of this study provide evidence in favor of a beneficial impact of MSC-CM on both clinical and immunological outcomes. Nevertheless, prior to clinical trials, large animal models should be used and prolonged observation periods conducted in pre-clinical research. Challenges arise due to the lack of standardization and consensus on isolation processes, quantifications, and purity testing, making it difficult to compare reports and conduct meta-analyses in MSC-CM-based therapies.
Enhanced Adaptive Graph Convolutional Network for Long-Term Fine-Grained SST Prediction

Sea surface temperature (SST) prediction is an important task in monitoring marine environment and forecasting ocean disasters, and has received tremendous popularity in recent years. However, existing methods for SST prediction usually focus on short-term prediction (e.g., predicting the SST in future seven days) or long-term coarse-grained prediction (e.g., predicting the monthly average SST in the next half year), and cannot work well for long-term fine-grained SST prediction (e.g., predicting the daily SST in future 60 days) due to their inability of learning long-term fine-grained spatial and temporal dependencies in SST. To address this issue, we proposed an enhanced adaptive graph convolutional network (EA-GCN) for long-term fine-grained SST prediction. First, EA-GCN introduces a static graph to capture the long-term correlations between the SST in different sea regions and a dynamic graph to learn the short-term correlations. Then, EA-GCN designs the multibranch spatio-temporal embedding to learn the patterns in SST at different time granularities. Finally, the learned patterns are fused to make long-term fine-grained SST predictions. According to the experiments on two real SST datasets, EA-GCN largely outperforms state-of-the-art SST prediction methods when predicting the daily SST in future 60 days.
Performance Evaluation of a Biogas Reactor Processing Chicken Manure with High Solids Content

The chicken sector in Turkey is amongst the most important economic activities. In parallel to the increase in the production of chicken and other side products, tremendous amount of waste material (manure) has also been generated throughout Turkey. This environmental problems associated with this sector is not only a bottleneck in Turkey but also in Europe. The current disposal method for chicken manure in Turkey is unfortunately direct release to receiving media including land and aquatic environment. Common use anaerobic digestion can be used for chicken manure which has high amount of ammonia and ammonium. A continuously stirred tank reactor (2 liters) was used as a mesophilic (37 o C) anaerobic reactor to examine the effect of high lodings of dry matter and ammonia on the methane yield of the system. The retention time was kept at 30 days and the loading rate was maintained in the range 2.2-2.5 kg VS m -3 d -1 . Biochemical methane potential (BMP) was also employed in order to evaluate the effect of varying dry matter loadings. This research was to determine the extent of anaerobic biodegradation of raw manure, methane yields and impact of ammonia on performance of the digestion.
Hierarchical Online Game-Theoretic Framework for Real-Time Energy Trading in Smart Grid

In this paper, the real-time energy trading problem between the energy provider and the consumers in a smart grid system is studied. The problem is formulated as a hierarchical game, where the energy provider acts as a leader who determines the pricing strategy that maximizes its profits, while the consumers act as followers who react by adjusting their energy demand to save their energy costs and enhance their energy consumption utility. In particular, the energy provider employs a pricing strategy that depends on the aggregated amount of energy requested by the consumers, which suits a commodity-limited market. With this price setting, the consumers’ energy demand response strategies are designed under a non-cooperative game framework, where a unique generalized Nash equilibrium point is shown to exist. As an extension, the consumers are assumed to be unaware of their future energy consumption behaviors due to uncertain personal needs. To address this issue, an online distributed energy trading framework is proposed, where the energy provider and the consumers can design their strategies only based on the historical knowledge of consumers’ energy consumption behavior at each bidding stage. Besides, the proposed framework can be implemented in a distributed manner such that the consumers can design their demand responses by only exchanging information with their neighboring consumers, which requires much fewer communication resources and would thus be more suitable for the practical operation of the grid. As a theoretical guarantee, the proposed framework is further proved to asymptotically achieve the same performance as the offline solution for both energy provider and consumers’ optimization problems. The performance of practical designs of the proposed online distributed energy trading framework is finally illustrated in numerical experiments.
Co-Expression of JcNAC1- and JcZFP8-Improved Agronomic Traits of Tobacco and Enhanced Drought Resistance through NbbHLH1 and NbbHLH2

Previous studies have identified numerous transcription factors involved in drought response, each of which play different roles in plants. The objective of the present study was to evaluate the effectiveness of two transcription factors on drought response in Jatropha curcas L. , JcNAC1 and JcZFP8. The overexpression of these transcription factors in tobacco (Nicotiana benthamiana L.) improved drought resistance, but JcZFP8 delayed germination and JcNAC1 reduced biomass and yield. By constitutively co-expressing these two genes in tobacco, drought resistance was improved, and the negative effects of each of them were overcome. The transgenic plants with double-gene co-expression showed stronger drought tolerance with 1.76-fold greater accumulation of proline and lower H2O2 and malondialdehyde (MDA) content to 43 and 65% of wildtype (WT) levels, respectively. The expression levels of NbbHLH1 and NbbHLH2 genes upregulated linearly with the increased drought tolerance of double genes co-expression plants. In drought conditions, the leaf water contents of bhlh1, bhlh2, and bhlh1bhlh2 deletion mutants obtained by CRISPR-CAS9 knockout technique were maintained at 99%, 97%, and 97% of WT. The bhlh1bhlh2 was found with lower germination rate but with higher reactive oxygen levels (1.64-fold H2O2 and 1.41-fold MDA levels). Thus, the co-expression of two transcription factors with different functions overcame the adverse traits brought by a single gene and enhanced the shared drought-tolerant traits, which can provide guidance on theory and selection of gene combinations for the application of multi-gene co-expression in agriculture in the future.