Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Job Engagement in Achieving Organizational Excellence: A Field Study at the Iraqi Residency Affairs Directorate

The current research aims to define the role of job engagement in achieving organizational excellence, as the job engagement variable, depending on the (Rich, 2010) model, included the dimensions (cognitive engagement, emotional engagement, physical engagement), while organizational excellence the researcher relied on the model (Baldrige, 2011- 2012) dimensions (organization leadership, strategic planning, customer focus, information and method of analysis, human resources, operations management, business results),These variables have been studied in the Directorate of Residency Affairs, which is one of the specialized directorates in the Iraqi Ministry of Interior, and the research was relied on the questionnaire as a main tool for measurement and data collection, relying on the method of random sample in its distribution, as it included (45) questions, as well as the method of the personal interview. To support the results of the questionnaire and achieve the greatest degree of realism in data collection, the sample included (206) individuals out of a total of (400) and were selected according to the sample calculation formula (Stephen Thompson) and were distributed directly to the respondents as (190) were retrieved and the number of the questionnaires were valid for analysis. Statistical (180) questionnaires, and in order to analyze the research data, the researcher used ready-made statistical programs (SPSS v.25 AMOS v.25), and among the most important statistical methods used in the analysis are "Alpha Cronbach scale, Spearman-Brown correlation coefficient, confirmatory factor analysis, the mean. " Arithmetic, standard deviation, coefficient of variation, relative importance, Pearson correlation coefficient, simple regression coefficient, multiple linear regression. The research reached a number of results and recommendations, including that the Iraqi Residency Affairs Directorate showed interest in organizational excellence, so it directed job engagement to enhance it well in addition to directing emotional absorption, cognitive engagement and physical engagement directly to improve the required organizational excellence, as these results were in conformity with the hypotheses, Where there is a statistically significant relationship between job engagement and organizational excellence.
Research on Simulation and Modeling of Camping Service Based on HLA/MAS

The structures and functions of High Level Architecture and Multiple Agents System were analysed and studied.Then a strategy of joint simulation and modeling based on HLA/MAS was proposed.This strategy took HLA/RTI as the main framework,and divided the simulation model into three layers:bottom layer for Agent Object,middle layer for Mutiple Agents Federate and top layer for Mutiple Agents System Federation,and utilized MAS/Agent technique for intelligent modeling in AO layer.Moreover,the strategy was applied to design the simulation model of camping service.The structure and members of the model were analysed.The main flow of simulation and the communication and control scheme between AOs were designed.The instances of class definition were given.And the implementation of prototype system was discussed.
Survey on the Application of Agent Technology in Electricity Market

It's a novel way to use Agent model to simulate the uncertain electricity market.In order to analyze the actuality of its application,this paper summarizes the lately researches of Agent based analysis of power market from four aspects,the framework of Multi-Agent system,the Single-Agent Learning mode with directly or indirectly exploring learning-algorithm,other strategy models of Agent and the information-interaction between Agents.Agent Technology has brought us an opportunity to predict the potential risk of the market and broaden our way of research.Single Agent model is excellent at prediction and data processing,but almost all of the so-called Multi-Agent models are just a simple combination of many single Agents.This problem is of great value to be solved since the intelligent utility of a Multi-Agent is not shown.
Valorisation of date seed powder (Phoenix dactylifera L.) for tenderizing
properties of different types of meat

Recently, date (Phoenix dactylifera L.) seeds have gained much popularity due to their vast applications that benefit people and the environment, elevated with plenty of research on food product formulation, cosmetics, and medicinal supplements. Date seeds constitute 10-15% of the date fruit weight. Although date seeds are considered waste products, however, they contain many valuable substances such as carbohydrates, oil, dietary fibre, protein, bio-active polyphenol, tannin and natural antioxidants. Thus, valorisation of the date seeds is necessary as the value could be utilized maximally if they are being studied aptly. This study was intended to (i) evaluate the physical properties of beef and lamb in terms of texture, pH, colour, cooking loss, and water holding capacity as affected by date seed powder; and (ii) compare the tendering effect of date seed powder towards beef and lamb. Texture profile analysis indicated significant differences (p≤0.05) in terms of hardness, springiness, chewiness, and gumminess between the different amount of seed powder used in marinated beef and lamb. The lowest pH value belonged to 20 g of date seed powder which was 5.7 and 6.2 for beef and lamb, respectively. The colour of the lamb was found to be significantly different in terms of lightness, redness, and yellowness when date seed powder was used regardless of its amount. Both beef and lamb displayed a statistically significant (p≤0.05) reduction of colour (L*, a*, and b*) from 20 g treatment samples with 29.7 and 26.5, respectively over the control samples. For beef, redness and yellowness demonstrated a significant difference (p≤0.05) for 20 g treatment samples marinated with date seed powder. Both cooking loss and water holding capacity (WHC) was reduced as the amount of date seed powder used was increased. Different amounts of date seed powder affected the physical properties of beef and lamb, while 15 g and 20 g of date seed powder showed a positive result in regards to meat tenderness.
Inter-Fractional Variations in Volume and Radiation Dose to the Organs at Risk, High-Risk Clinical Target Volume and Implication of Image-Guided Adaptive Planning During Intracavitary Brachytherapy of Carcinoma Cervix

Background Geometrical and anatomical variations occur during the brachytherapy of carcinoma cervix and dose optimization is necessary for every fraction of high‑dose rate intracavitary brachytherapy (HDR-ICBT) for carcinoma of the cervix. A single planned treatment is usually delivered for multiple fractions without consideration of inter-fractional applicator positioning variations and organ motion, which may lead to substantial differences between the planned and delivered doses. Aim and objectives This study was aimed at evaluating the inter-fractional variation in volume and radiation dose to organs at risk during ICBT for cervical cancer. Furthermore, the doses to high-risk clinical target volume (HRCTV) and the role of adaptive planning in ICBT were assessed. Materials and methods Twenty-two patients with carcinoma of the cervix Stage IB2-IVA receiving ICBT were enrolled in the study. All the patients were treated with ICBT four fractions in two applications. For the first application, magnetic resonance imaging-based planning was done, and for the next three fractions, computed tomography (CT) scans were done before every treatment fraction. The CT images were contoured and replanned by keeping the First (I) fraction of each application as the reference. Dose-volume histograms (DVH) were generated, and details of D2cc (DVH on a volume of 2cc) of bladder, rectum, and sigmoid colon (organs at risk-OAR) and D90 HRCTV (dose covering 90%) were documented. Results In patients receiving ICBT, variations in OAR D2cc ranged from 1.5 to 2.5Gy for the bladder (p- 0.001), from 2.0 to 3.2Gy (p-0.005) for the rectum and from 1.5 to 3.5Gy for the sigmoid colon (p 0.103). The p-value was significant for D2cc when compared with the OAR volume for the bladder and rectum in both applications, whereas it was not significant for the sigmoid colon. The percentage change in HRCTV coverage was 7% in the first application and by 16% in the second application because of adaptive planning. Conclusion Significant variations in doses received by D2cc of the bladder and rectum as well as significant improvement in HRCTV coverage between the fractions were observed because of replanning. Hence, image-guided HDR-ICBT should be incorporated with adaptive planning when delivering in multiple fractions.
Using Graph Convolutional Neural Networks for NLP tasks

In recent times, Graph Neural Networks are proposed as a paradigm shifting methodology in solving standard Natural Language Processing and Computer Vision tasks. Graph Neural Networks provide a much more generalised method of representing word embeddings, image pixels, etc. Analogous to the standard Deep Learning literature, there have been multiple variants of GNNs proposed - such as Convolutional GNNs, Recurrent GNNs, Au-toEncoder GNNs, etc. Similar to using Convolution Networks in text classification, Graph Convolution Networks can also be used in text classification tasks. In this project, we plan to use Graph Convolutional Neural Networks to solve the open problem of Text Classification and Sentiment Analysis. Using a baseline model from Yao et al., we make certain modifications in the model training procedure keeping the architecture similar to the one proposed. Results on benchmark datasets show that the results improve from what are reported.
Experimental Investigation of Transient Voltage and Current Characteristics on a Grounding Mesh

This paper investigates transient voltage and current characteristics on a grounding mesh based on field measurements. The grounding mesh is composed of a number of hard copper wires (HDCC 60mm 2 ) with the maximum length of 34.1m and the maximum width of 24.8m and is buried at the depth of 1m from the ground surface. A current from a pulse generator (PG) and from an impulse voltage generator (IG) is applied at various positions of the mesh to observe transient voltage and current distributi ons on the mesh. A weather-proof mobile IG with the maximum output voltage of 3MV and the current of 40kA is used to investigate a nonlinear characteristic on the mesh. It is observed in the measured results that the propagation velocity and the attenuation along a buried naked conductor is significantly different from those of a current. A transient grounding impedance of a single counterpoise shows current-dependence which becomes less noticeable as the counterpoise length becomes longer. Voltage and current distributions on a mesh are dependent on the current applied node, and a transient grounding impedance of a mesh shows a polarity effect, and decreases as the applied current increases.
An Overview of the Proportional Navigation

Based on the large amount of study on missile navigation-interception laws and robot control, this paper gives an outline of study results of various proportional navigation laws and addresses the definitions, performances and characteristics of the main type of proportional navigation laws. It is mainly point out, by comparative analysis, that the pure proportional navigation,including it's various improved modes, is the most effective navigation-interception law,which can be effectively used to pursuit or intercept the maneuvering targets. It is expected that this survey paper will help readers realize that PPN is the more practical and effective way than GPN and much more attention should be paid on it such as studies on PPN in other fields, including the design of pursuit-attacking guidance systems of a modern fighter.
Image Link Through Adaptive Encoding Data Base and Optimized GPU Algorithm for Real-time Image Processing of Artificial Intelligence

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive ethernet technology. The proposed system provides a design and optimization algorithm of in-vehicle networking technologies related Ethernet Audio Video Bridge (AVB) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machin to Machine (RLMM) plays a significant role in reducing the latency between devices. The approach of RLMM on realistic test cases indicated that there was a latency reduction about 30.41% It is expected that the optimized settings for the actual automotive network environment can greatly shorten the time period in the development and design process. The results achieved from the experiments on the latency present in each function are trustworthy since average values are obtained via repeated tests for several months. It would considerably benefit the industry because analyzing the delay between each function in a short period of time is tremendously significant. In addition, through the proposed real-time camera and video streaming via optimized settings of AVB system, it is expected that AI (Artificial Intelligence) algorithms in autonomous driving will be of great help in understanding and analyzing images in real time.
The Information Channels of Emotion Recognition: A Review

Humans are emotional beings. When we express about emotions, we frequently use several modalities, whether we want to so overtly (i.e., Speech, facial expressions,.) or implicitly (i.e., body language, text,.). Emotion recognition has lately piqued the interest of many researchers, and various techniques have been studied. A review on emotion recognition is given in this article. The survey seeks single and multiple source of data or information channels that may be utilized to identify emotions and includes a literature analysis on current studies published to each information channel, as well as the techniques employed and the findings obtained. Ultimately, some of the present emotion recognition problems and future work recommendations have been mentioned.
Trickle - Down and Bubble - Up: Relationships Between HCI Theory and Practice

There has been an ongoing conversation about the role and relationship of theory and practice in the HCI community. This paper explores this relationship through a tentative model, which describes a “trickle-down” of theory into practice, and a “bubble-up” of ideas from practice to inform research and theory development. Interviews were conducted with interaction designers, which included their knowledge of two design methods—affinity diagramming and the concept of affordance—as well as a description of their use of design methods in practice. Based on these interviews, potential relationships between theory and practice were explored through this model. Knowledge of the history or key steps of executing a design method was not found to negatively affect the ability of a designer to apply the key concepts of a method in practice. Opportunities for future research, based on the use of the tentative model as a generative model, are considered.
Machine learning methods for classification of sensitive data

In the era of Big Data there are a lot of new challenges – understanding, processing, and securing the data, assuring data quality, dealing with data growth and other challenges. One of the challenges is to identify and classify data sets in different systems which must follow the conditions defined by different regulations. The classification of these data sets can be automated using machine learning methods. The aim of the research is to provide machine learning methods for classifying sensitive data. The research is based on analysis and comparison of European Union legislation and scientific literature, which addresses issues of data classification using machine learning methods. Special attention is paid to sensitive data defined by the General Data Protection Regulation (GDPR). The main focus in this research is on supervised learning algorithms, where one of the most effective is Naïve Bayes classifier. In order to achieve good results, there is a need to find a proper training data set. Usage of hybrid methods provides a new way for increasing performance of classifiers.
Optimization of Draft Schedules With Variable Metric Hybrid Genetic Algorithm During Hot Strip Rolling

An intelligent method named variable metric hybrid genetic algorithm was introduced in order to optimize 1780 mm hot strip mills,and the optimization results show that optimized schedule is better than that was used and the rolling force or motor power distribution mode is better than reduction load distribution mode in keeping optimization performance when strip production changes within hierarchy range.
Granular-scale Magnetic Flux Emergence and its Associated Features in an Emerging Active Region

Using the high-resolution photosphere and chromosphere observations made by the 1 m New Vacuum Solar Telescope, we studied the granular-scale magnetic flux emergence occurring in emerging active region NOAA 12579. Supplementary observations are also provided by the spacecraft Solar Dynamics Observatory. The studied granular-scale flux emergence took place at two different locations. One is completely embedded in the unipolar region of the following sunspots (Case 1), while another is located at the central part in the active region (Case 2). We find that both cases initially emerge from a dark patch like a wide intergranular lane, but showing the different subsequent features. In Case 1, the emerging granule grows in an elongated feature and reaches its maximum size of almost of 5″ × 3″, with an elongated speed of about 2–3 km s−1. An eruption (i.e., surge) with bright footpoints is observed after the emerging granule reaches its maximum scale. There is a time delay of more than 10 minutes between the appearance of the abnormal granule and the Hα surge. Furthermore, its footpoints are clearly rooted at the intergranular lane. We propose that the eruptive surge could be triggered by the reconnection between the emerging magnetic flux and the preexisting ambient field, leading to the localized heating and bidirectional flows. In Case 2, the granular cell emerging is simultaneously associated with bright points with opposite magnetic polarity, showing the separating motion between them and a bunch of newly formed arch filament systems. We infer that the bright points are due to the strong-field magnetic concentration in the dark intergranular lanes rather than the instantaneous Ellerman bombs.
Automatic Information Extraction for Financial Events by Integrating BiGRU and Attention Mechanism

In this paper, an information extraction method for financial events written in Chinese is proposed. The core entities of the causes and results, as well as the verbs and conditions are extracted from the financial events reported by websites. The method takes the original words and the part-of-speech of words as two inputs. BERT encoder is utilized to transform the original sentences to word-embedding vectors, which then are send to BiGRU to extract the sematic features. And a full-connected network is overlapped on BiGRU to reduce the impact of “covariate shift”. For the second input, the original sentences are cut by Chinese cut-word tool ‘jieba’ to get the part-of-speech of words, which are then transformed by self-attention mechanism to get global dependencies. The two outputs for word-embedding vectors and part-of-speech of words are combined and then decoded by CRF. Finally, the Viterbi algorithm is utilized to get the best sequences. The experiment results validate the effectiveness of the proposed method.
Single Document Extractive Summarization Model Based on Heterogeneous Graph Transformer

At present, the graph model-based summary model has problems such as insufficient semantic fusion between nodes and lack of location information. Therefore, this paper proposes a single-document extraction text summary model based on a heterogeneous graph attention neural network, using HGT (Heterogeneous Graph Transformer), Heterogeneous Graph Attention Neural Network to solve the defect of insufficient deep semantic fusion of nodes, and use trainable position coding to solve the defect of missing position information. Experiments show that the model in this paper has improved on the three evaluation indicators of R_1, R_2 and R_L, and the abstracts extracted have better generality.
Short-term Traffic Flow Forecast on Basis of PCA- Interval Type-2 Fuzzy System

According to the time series of urban traffic flow, a prediction method on basis of type-2 fuzzy logic is proposed under the theoretical framework of fuzzy logic. The prediction model of the interval type-2 fuzzy system is established, and the BP algorithm is used to adjust the coefficients of the antecedent and consequent of fuzzy rules. In this paper, the algorithm is verified by the measured data of road network, and compared with other fuzzy methods, BP algorithm and support vector machine (SVM) According to the experimental data that type-2 fuzzy logic system has higher prediction accuracy.
Non-Standard Clothing Detection in Electricity Scenes Based on Adaptive Training Samples Selection Neural Network

Standard clothing in the electricity scene is so important that it can validly prevent workers from injuries. Strengthen non-standard clothing detection can help people correct the bad habits of dressing. However, there are some challenges existing in standard clothing in electricity scenes. There are few numbers in some kinds of clothing (e.g. short sleeves or not wearing helmet) in electricity scenes. The colors of some personnel clothing are similar to those of the background of workshop, such as white helmets and white frames. To handle these issues, in this paper, a novelty clothing detection method in electricity scenes is proposed based on a policy of adaptive training samples selection. The number of clothing can be expanded by rectifying mosaic augmentation, and the information loss of the top features can be compensated by residual feature augmentations. Experimental results show that the model can automatically and accurately identify non-standard clothing from complex electricity scenes, and has better detection performance (mAP=0.433) compared with the other model (mAP=0.419).
Seagrass rehabilitation in Thousand Islands, Jakarta: step to restore

Seagrass plays an essential role in coastal areas with ecological functions as a marine habitat, sediment trap, sediment stabilizer, nutrients filter, and dampen current. Fisheries and tourism are the seagrass ecosystem services that are the primary community livelihood in Thousand Island. However, seagrass condition has decreased in the last decade, influenced by coastal development, mass tourism, and climate change. This study aims to assessed the seagrass status and identify the threat in the last decade. Seagrass coverage was assessed in 2007 and 2018 using plot transect, and then seagrass threats were documented and collected from research articles, manuscripts, proceedings, and unpublished reports. Seagrass coverage has significantly decreased on Thousand Islands. Seagrass threats include pollution, reclamation, vessel activities, mass tourism, coastal development, climate change, and biotic factors (macroalgae and epiphyte coverage). The threats impact seagrass conditions, such as species diversity, area, and ecosystem services. Rehabilitation is needed to recover seagrass conditions using a direct or indirect approach. These findings recommend that managers should arrange the seagrass rehabilitation action plan to minimize seagrass degradation.