Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodological concept of hypothesis building and verification in behavioural research

The purpose of this paper is to strengthen and develop the means of assessing and testing behavioural relationships in order to optimise specific fields of action in human and social sciences. Our objective is to provide a research perspective and methodological guidance for verifying hypotheses in the sphere of behavioural analysis, which studies the influence of the concrete environment on behaviour, as well as the impact of behavioural actions on the environment. How do we analyse behaviour? How can we assess and test it? Current methodology in behavioural study suggests a holistic, global approach, the empirical significance in testing scientific theories having been established at the level of the entire system of statements. The design of our methodological process, which associates, specifies variables and relationships between hypothesis variables, helps to improve the means of building and testing the hypotheses addressed in behavioural research. In the light of the Structural model for validating the research hypotheses, the Synthesised model for the research hypotheses and the causal relationship between variables, and the General formalisation of the conceptual model for verifying the main hypotheses, our approach can meet the needs of developing specific research methods in the sphere of behavioural relationships. Behaviour is examined from the perspective of its complexity to the paradigm stage. For a conclusive scientific research, our study will ultimately be followed by a multistage operational approach based on a separate comparison of the specific behavioural modalities of the subjects and a congruent means of statistical validation.
A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

Background A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. Objective The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. Methods Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. Results Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. Conclusions The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. International Registered Report Identifier (IRRID) DERR1-10.2196/29398
Political Communications Strategy of “Success Team” in Regional Head Election: A Case Study of Bireuen, Aceh

: The dynamics of the implementation of regional head election (Pilkada) in Bireuen district had interesting stages. The victory of the regent pair who held this position gave an interesting political process. The couple was initially declared not qualified for the health test by the Independent Election Commission (KIP), but various efforts were made by the success team The pair was able to follow the 2017 Aceh Pilkada Stage. This study aims to understand the political communication strategy of the winning team. It was conducted in Bireuen District with a qualitative approach and case study methods. Data collection is done by Interview and documentation. The results of this study indicate that the political communication strategy of the winning team was able to stimulate the community to support to the candidate's regent pair. The results yielded 1) the emergence of support groups such as the Youth Poros Team, the Gampong Team and Tim Srikandi (women's representatives) who consolidated each other in winning the Bireuen Regent pair. 2) Political communication carried out using the “Door to Door mechanism” and dialogue by describing the vision and mission and presenting the profile of prospective head district who make people sympathize and chose them in the election.
Development of an optimum proliferation medium via the graph kernel statistical analysis method for genetically stable in vitro propagation of endemic Thymus cilicicus (Turkey)

Thymus cilicicus is an endemic Eastern Mediterranean element that has aromatic-medicinal properties. Its natural population spreads across gravelly ground and open rocky areas of South and Southwest Anatolia. The current study on in vitro propagation of T. cilicicus focused deeply on environmental applications such as the development of an optimum medium composition for efficient and genetically stable micropropagation and improved preservation procedures for long-time conservation of elite germplasms for further studies. For this purpose, MS and OM media were used individually and in combination with cytokinins, charcoal, AgNO3, Fe-EDDHA, and H3BO3. The raw data were statistically analyzed via the graph kernel method to optimize the nonlinear relationship between all parameters. The optimal proliferation medium for T. cilicicus was OM supplemented with a combination of 10 g L-1 charcoal and 1 mg L-1 KIN and the calculated averages of the best regeneration rate, the best shoot number and the best shoot length were 96.89%, 3 and 1.24 respectively on this medium. The determination of genetic stability of in vitro grown plants on the optimum medium compositions obtained by the graph kernel method was carried out with the use of the ISSR-PCR technique. All the ISSR primers produced a total of 192 reproductive band profiles, none of which were polymorphic. Furthermore, the micropropagated plants were successfully rooted and acclimatized to greenhouse conditions. In this study, we present a graph kernel multiple propagation index which considers all the possible parameters needing to be analyzed. Such an index is used for the first time for the determination of the optimum proliferation medium.
Identifying Rock Thin Section Based on Convolutional Neural Networks

This paper uses the convolutional neural networks to train and identify rock thin sections. There are three innovations in this method. Firstly the convolutional neural network is used in the field of geological experiment testing and it can automatically identify and classify the rock thin sections. Secondly the original image of rock thin sections are sliced and segmented, so that the convolutional neural network could learn more details of the rock mineral texture without damaging the original resolution of the image. Thirdly using other image enhancement techniques, such as random flipping and standardization, can expand the sample data set and enhance the robustness of the model. Finally the training model achieves the desired results.
Shared lexical-semantic features and the animacy effect in early-course psychosis

Language deficits in psychosis, and in schizophrenia, are presumed to be due to increased activation and connectivity of the semantic memory, which is determined by lexical-semantic features of concepts. The aim of this study was to analyse the influence of shared lexical-semantic features on language processing in patients with first-episode and early-course psychosis. The study included 15 Croatian-speaking patients from the University Psychiatric Hospital Vrapče, Zagreb, diagnosed with first-episode and early-course psychosis and a healthy control group. The subjects performed a lexical-semantic decision task in which the primes and the target words were either related as hypernym and hyponym or were unrelated, and in which the target words represented either animate or inanimate concepts. Two results analyses were conducted: one on the taxonomic (hyponym–hypernym) condition and one on the animacy (living/non-living stimulus) condition. The patient group was less accurate on the taxonomy condition because the taxonomy relations were dependent on their shared features. The patients’ activation of shared features was higher, and their inhibition was reduced. Consequently, the patients will have a greater number of concepts activated and not inhibited. For the inanimate concepts, a high correlation of distinctive features is characteristic, while the animate concepts have a high correlation of shared features. The presupposition is that the greater activation of shared features influenced the patients’ answers, so the distinctive features had no influence. Thus, the control group had higher accuracy for inanimate concepts.
Modeling Anthropogenic and Environmental Influences on Freshwater Harmful Algal Bloom Development Detected by MERIS Over the Central United States

Human and ecological health have been threatened by the increase of cyanobacteria harmful algal blooms (cyanoHABs) in freshwater systems. Successful mitigation of this risk requires understanding the factors driving cyanoHABs at a broad scale. To inform management priorities and decisions, we employed random forest modeling to identify major cyanoHAB drivers in 369 freshwater lakes distributed across 15 upper Midwest states during the 2011 bloom season (July–October). We used Cyanobacteria Index (CI_cyano)—A remotely sensed product derived from the MEdium Resolution Imaging Spectrometer (MERIS) aboard the European Space Agency's Envisat satellite—as the response variable to obtain variable importance metrics for 75 landscape and lake physiographic predictor variables. Lakes were stratified into high and low elevation categories to further focus CI_cyano variable importance identification by anthropogenic and natural influences. “High elevation” watershed land cover (LC) was primarily forest or natural vegetation, compared with “low elevation” watersheds LC dominated by anthropogenic landscapes (e.g., agriculture and municipalities). We used the top ranked 25 Random Forest variables to create a classification and regression tree (CART) for both low and high elevation lake designations to identify variable thresholds for possible management mitigation. Mean CI_cyano was 3 times larger for “low elevation” lakes than for “high elevation” lakes, with both mean values exceeding the “High” World Health Organization recreational guidance/action level threshold for cyanobacteria (100,000 cells/mL). Agrarian‐related variables were prominent across all 369 lakes and low elevation lakes. High elevation lakes showed more influence of lakeside LC than for the low elevation lakes.
Digital Transformation: Is Gonna Be Culture Shock?

: This paper is conducted to describe the digital transformation using the theory of culture shock. It purposes to generate a mapping of culture shock theories by applying a descriptive qualitative approach and literature review of some previous studies. Successful digital transformation of organizations must be supported by digital talent transformation, digital product transformation, and digital infrastructure. In fact, these changes can lead to culture shock, that affects the performance of individuals and organizations. This paper will analyze digital change using the stage of culture shock which consists of four stages including the honeymoon phase, culture shock phase, adjustment phase, and mastery phase. The results of this study are descriptions of each phase and strategy for handling culture shock. So the development concept of the culture shock concepts can be use to explain the dynamic change to achieve organizational sustainability.
River Nile discharge, the Pacific Ocean and world climate – a seasonal synchronization perspective

Abstract The Nile is the longest river in Africa stretching over around 6650 km through 11 countries. From the times of the ancient Egyptian Pharaonic civilization, the Nile is known to be a blessing, which provides major resources including water and fertile soil for agriculture, and facilitates transportations and international trades in nearby countries. Due to its invaluable importance to local economy and agriculture, it is undoubtedly of paramount importance to know how the variability of the Nile is controlled by local and global climate and its morphological characteristics. Here, we utilize a newly developed time-series analysis method applied to monthly Nile river inflow data to reveal various factors changing the river inflow from seasonal to inter-annual, decadal and beyond. On seasonal time-scales a positive feedback, associated mostly with river’s morphological change driven by summer precipitation, is identified as a main mechanism for maximal variability in September leading to major flooding or drought. In particular, the positive feedback is quite similar in its mechanism to major climate feedbacks observed, e.g. with ice albedo and Bjerknes feedbacks. The slow time-evolution of the positive feedback explains human endeavour history to control nature, such as the control of the Nile annual flooding through dam construction. The analysis of climate association reveals clear link with large-scale and low-frequency forcing. Decadal and multi-decadal timescales of local precipitation and associated teleconnection with atmospheric and oceanic circulation can be traced back to the Pacific Ocean, and involve mostly the El-Nino Southern Oscillation and the Pacific Decadal Oscillation.
THE ROLE OF NEGATIVE AFFECTIVITY AND INCIVILITY BEHAVIOR ON INCIDENCE OF COUNTERPRODUCTIVE WORKPLACE BEHAVIOR AMONG NURSES

Background: Nowadays workplace deviant behaviors considered one amongst the foremost serious issues facing today’s organizations and its productivity as it goes against the legitimate interests of the organization. These behaviors can harm organizations or people in organizations. So, the study aimed to determine role of negative affectivity and workplace incivility on incidence of counterproductive work place behavior among nurses at Main Mansoura University Hospital. Method: A descriptive design was used and the study was conducted on 162 nursing staff working at Main Mansoura University hospital by using three tools as: Negative Affectivity questionnaire, workplace incivility questionnaire and counterproductive workplace behavior questionnaire. Results: the mean score of counterproductive workplace behavior is lowest (1.23±0.26) followed by workplace incivility(1.32±0.35) and negative affectivity(1.89±0.78). There was statistically significant positive correlation between counterproductive workplace behavior and both negative affectivity and workplace incivility (P<0.05) among nursing staff (n=162). Recommendation: design and implement policies to reduce the deviant behaviors in the hospital environment . Organizations need to adopt zero tolerance policies that clearly articulate the types of conduct are not acceptable. These policies need to be clearly defined and communicated to all in the hospital and all who enter. There needs to be channels for employees to communicate issues. There also needs to be commitment to upholding these policies from top management.
The Development of Experimental Sets for Measuring Linear Thermal Expansion Coefficient of Metal Using Digital Video-Based Single Slit Diffraction Method

The development of an apparatus for the measurement of linear thermal expansion coefficient of metal by a single diffraction method has been developed. The development was made based on several ideas from previous researchers, as well as to overcome several limitations which yields insignificant results. The development includes the redesign of the primary holder module, the temperature measurement module (using a thermocouple sensor connected to the PC), and the measurement approach of the diffraction pattern distance by means of digital video analysis techniques using Tracker software. The development of the modules as well as the measurement approach that has been done yields very good experimental results. The measured linear thermal expansion coefficient of aluminum has a high level of accuracy and precision. The result is (23.100 ± 0.2186)×10-6/°C, and compared from a reference, i.e., 23.000 × 10-6/°C, the relative error of is 0.95% and the relative standard deviation is 0.43%.
Generalized estimation methods of turbulent fluctuation of high-speed flow with single-pixel resolution particle image velocimetry

The methods that measure the turbulence statistics distribution of high-speed flow with high spatial resolution using particle-image-velocimetry images are proposed, and their performances are verified. Two methods are proposed, and the problems caused by blurring the particle shape due to the high-speed movements are resolved. While the conventional method approximates the unique particle shapes with a circular distribution for image pairs, the first proposed method approximates the different particle shapes with an ellipse for first and second images of pairs and reduces the effects of the blur in the flow direction of the particles. Meanwhile, the second proposed method treats the general particle shapes such as the blur of temporally changing laser intensity and adopts the deconvolution analysis using a Fourier transform. Synthetic particle images were created and a supersonic jet test was performed, and the proposed methods were evaluated to be superior to the previous method for the estimation of turbulent fluctuation using those data.
Robust RSSI-Based Indoor Positioning System Using K-Means Clustering and Bayesian Estimation

This work proposes a new indoor positioning system, named KLIP, that uses the <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-means clustering algorithm to split the environment into different sets of log-distance propagation models in order to better characterize the indoor environment and further improve the position estimation using Bayesian inference. Our proposed method is validated in a large-scale, real-world scenario composed of Bluetooth Low Energy (BLE)-based devices. We demonstrate, throughout the paper, that the addition of location information of training points to the received signal strength indicator (RSSI) as an attribute for the clustering step improves the positioning accuracy. Moreover, the obtained results show that our solution outperforms the naive Bayesian estimation up to 12% – regarding the positioning accuracy – and the broadly deployed <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NN for reduced training dataset size – regarding both accuracy and online processing time. In this sense, KLIP proves to be an efficient and scalable alternative when both site-survey effort and energy consumption constraints must be taken into account.
Climatic rather than edaphic variables determine leaf C, N, P stoichiometry of deciduous Quercus species

Purpose Leaf stoichiometry of carbon (C), nitrogen (N) and phosphorus (P) is indicative of plant nutrient limitation, community composition, ecosystem function. Understanding leaf stoichiometry patterns of C, N, P of eurytopic species at genus-level across large-scale geographic regions and identifying their driven factors are of great importance to assess and to predict species’ distribution range shifts affected by climate change. Methods Here, we determined the patterns of leaf C, N, P stoichiometry of five deciduous oaks species (Quercus) across China covering ~ 20 latitude (~ 21–41˚ N) and longitude (~ 99–119˚ E) degrees, and detected their relationships with climatic, edaphic variables. Results We found that the concentrations ([ ]) of leaf C, N and the ratios ( / ) of N: P, C: P significantly increased, while leaf [P] and C/N decreased with the increasing latitude. However, leaf stoichiometry had no significant trends along the longitudinal gradient with an exception of leaf [C] which decreased with increasing longitude. The climatic variables, i.e. mean annual temperature precipitation (MAP), the max temperature of the warmest month (Tmax), temperature seasonality (TS), aridity index (AI) were the determinants on the geographic patterns of leaf C, N, P stoichiometry. The studied deciduous Quercus species growing in warm and wet environments tended to increase leaf [C], [N], C/P, and N/P, but to decrease leaf [P] and C/N, which maybe their nutritional strategies to improve adaptability. Conclusion The adaptative mechanisms of leaf stoichiometry should be further studied to assess the fate of deciduous Quercus species affected by climate change.
Simulating Airflow Around Flexible Vegetative Windbreaks

Vegetative windbreaks play an important role in reducing soil erosion and sand storms, which are widely applied in arid/semiarid regions. An optimal design of vegetative windbreaks is essential in aeolian engineering practice. In this study, three‐dimensional airflows around multiple arrangements of vegetative windbreaks were simulated using a modified Reynolds‐Average Navier‐Stokes (RANS)‐based computational fluid dynamics (CFD) model, based on which, optimal designs of vegetative windbreaks were discussed. We proposed a correction factor in the source terms of the momentum and turbulent transport equations representing the effects due to the vibration of the vegetative windbreaks, which was further calibrated using both simulated and wind tunnel experimental results. Moreover, a quantitative expression of the correction factors associated with structural parameters of vegetative windbreaks was established and verified. For optimal designs to reduce wind erosion, a suite of numerical experiments was conducted to find the optimal height, leaf area density, and vegetation fraction of the vegetative windbreaks under different free‐stream wind speeds. The results identified that under low free‐stream wind speed (6.4 m/s), the optimal parameters are 9.0 cm, 0.45 cm2/cm3, and 3.92%. Under moderate free‐stream wind speed (8.0 m/s), those optimal parameters are 12.4 cm, 0.47 cm2/cm3, and 3.92%. Under relatively high free‐stream wind speed (10.8 m/s), the optimal parameters are 12.4 cm, 0.47 cm2/cm3, and 8.00%. The results of the current study will aid in optimizing vegetative windbreak designs and preventing wind erosion in aeolian engineering.