The Application Degree of Administrative Accountability and Organizational Governance, and the Relationship between them in the Directorates of Education in Jordan from the VieWpoint of its Administrative Leaders

This study aimed to identify the application degree of administrative accountability and organizational governance, and the relationship between them in the directorates of education in Jordan from the viewpoint of its adm nistrative leaders. The researchers used descriptive statistics- correlation through two tools. A survey for administrative accountability and consisted of -20- items, while the second survey was for organizational governance, it consisted of 40 items concerned with five areas, which are: Revelation and transparency, effective participation, control and administrative responsibility, justice and integrity, and efficiency and effectiveness. The sample consisted of 272 educational leaders from 6 directorates. This represents 14% of the sample community. The results showed that the application degree of administrative accountability and organizational governance in the directorates of education in Jordan was moderate. The results also indicated that there was a significant positive relationship between the application degree of administrative accountability and the application of administrative governance in the directorates of education

Comparative Analysis of Predictive Performance in Nonparametric Functional Regression: A Case Study of Spectrometric Fat Content Prediction

Objective: This research aims to compare two nonparametric functional regression models, the Kernel Model and the K-Nearest Neighbor (KNN) Model, with a focus on predicting scalar responses from functional covariates. Two semi-metrics, one based on second derivatives and the other on Functional Principle Component Analysis, are employed for prediction. The study assesses the accuracy of these models by computing Mean Square Errors (MSE) and provides practical applications for illustration. Method: The study delves into the realm of nonparametric functional regression, where the response variable (Y) is scalar, and the covariate variable (x) is a function. The Kernel Model, known as funopare.kernel.cv, and the KNN Model, termed funopare.knn.gcv, are used for prediction. The Kernel Model employs automatic bandwidth selection via Cross-Validation, while the KNN Model employs a global smoothing parameter. The performance of both models is evaluated using MSE, considering two different semi-metrics. Results: The results indicate that the KNN Model outperforms the Kernel Model in terms of prediction accuracy, as supported by the computed MSE. The choice of semi-metric, whether based on second derivatives or Functional Principle Component Analysis, impacts the model's performance. Two real-world applications, Spectrometric Data for predicting fat content and Canadian Weather Station data for predicting precipitation, demonstrate the practicality and utility of the models. Conclusion: This research provides valuable insights into nonparametric functional regression methods for predicting scalar responses from functional covariates. The KNN Model, when compared to the Kernel Model, offers superior predictive performance. The selection of an appropriate semi-metric is essential for model accuracy. Future research may explore the extension of these models to cases involving multivariate responses and consider interactions between response components

An automated text summarization methodology

Most of the information is embedded in a long text documents.Having a summarizer that can produce a summary from the texts automatically is very desirable.This paper presents an introduction of an automated text summarization system by addressing the history of summarization and its existing application tools, and proposes a methodology for an automated text summarization.The proposed methodology utilized possibility and probability theory in the sentence extraction and sentence abstraction.The possibility and probability are also utilized in identifying relevant words and term occurrences techniques

The Experimentally Studying of Solid Desiccant Wheel Performance Combined with the System of Air Conditioning

تهدف هذه الدراسة إلى دراسة أداء نظام إزالة الرطوبة المجففة الصلب لتقليل الحمل الكامن على ملف التبريد لنظام تكييف الهواء وتحسين الراحة الحرارية، وبالتالي تقليل استهلاك الطاقة. تحتوي العجلة الدوارة المجففة على هلام السيليكا كما تم استخدام مادة صلبة ماصة للرطوبة في هذه الدراسة. العجلة قطرها 550 مم وسمكها 200 ملم. تنقسم مساحة المقطع العرضي للعجلة إلى جزئين في نسبة العرض إلى الارتفاع. الجزء الكبير يمثل عملية إزالة الرطوبة أو الامتصاص، في هذا القسم تتم إزالة الرطوبة من الهواء الرطب بواسطة هلام السيليكا. بينما يمثل الجزء الآخر عملية الامتصاص أو التجديد في هذا القسم تم امتصاص الرطوبة من الهواء الرطب بواسطة هلام السيليكا في العملية الأولى سيتم إزالتها منه. أظهرت النتائج التجريبية أن استخدام عجلة التجفيف سيقلل بشكل كبير الحمل الحراري على ملف التبريد عن طريق تقليل الحمل الكامن للهواء الرطب الذي يمر عبر العجلة ولفائف التبريد.The aim of this study was to study the performance of the system of solid desiccant dehumidification to decrease the latent load on cooling coil for the system of air-conditioning and advance the thermal comfort, thus reduce the energy consumption. Rotary desiccant wheel contains a silica gel as a solid moisture absorbent material has been utilized in this study. The wheel was a diameter of 55 cm and a thickness of 20 cm. The wheel cross sectional area is divided into two parts in aspect ratio. The large part represent dehumidification or absorption process, in this section moisture is removed from the humid air by the silica gel. While the other part represent desorption or regeneration process in this section moisture was absorbed from the humid air by the gel of silica in the first process will be removed from it. The experimental results demonstrates that the utilizing of the desiccant wheel will reduce significantly the thermal load on the cooling coil by reducing the latent load of the passing of humid air respectively via the wheel and the cooling coi

Economic Diversification and the Urban Image; Changing the Narrative on Street Vending

Street vending is a dynamic phenomenon of network of events, socio-economic and cultural factors while remaining a narration of place. At the metropolitan level, the narrative is negatively skewed towards street vending and its aesthetic reality, contemporaneously exploring hostile environmental interventions within the informal sector. This paper attempted to explore a counter-narrative asking; based on aesthetic experience, can the “desired” urban image be achieved by allowing street vendors proliferate in public spaces? This question was asked within the scope of the political-economy of diversification in Nigeria. Mapping over google satellite images over critical periods leading to demolitions and/or developments, this paper documented the spatial distribution of vendors to determine the urban centres that are hostile to vending activities and those that were not. The paper argued that, around public spaces such as parks and sidewalks, the precarious nature of vending activities lead to their diffidence in upgrades to stalls, tables and kiosks. With pictures from spaces that appear to approve of street vending tacitly, a pattern of upgrades in vending apparatus and kiosks were established. This paper proposes an integrative model of passive, active and tacit support that is required to influence the discourse of vending activities within the context of urban images produced in Nigerian. In conclusion and using sing Gouverneur (2014) concepts of receptors and transformers, this paper revealed that potential existing parks within a dense urban area could serve as transformers, creating an urban image that defies that “out of place” narrative associated with vendors

COACHES’ REQUIRED LEADERSHIP STYLES AND ATHLETES’ MOTIVATION IN TEAM SPORTS, ADAMAWA STATE SPORTS COUNCIL, NIGERIA

The study was assessed relationship between coaches’ required leadership style and athletes’ motivation in selected team sports in Adamawa State Sports Council, Nigeria. Correlational design was used for the study. The population for the study comprised all the male and female programme athletes in team sports of basketball, football, handball and volleyball in Adamawa State Sports Council, Nigeria. One hundred and eight copies of questionnaires were administered but only one hundred and four copies of questionnaires were well completed making 96.3% return rate. Purposive sampling technique was used to select one hundred and eight athletes that make the first and second teams of each of the four sports of basketball, football, handball and volleyball in Adamawa State Sports Council, Nigeria. Two instruments were adopted, modified and used for the study. The first was the Leadership Scale for Sports (LSS) used to determine coaches’ leadership styles. The second was the Sport Motivation Scale (SMS) utilized to measure athletes’ motivation in team sports. Descriptive statistics (mean, standard deviation, frequency, percentage) was used to analyze the demographic information of the respondents and research questions while inferential statistics (Pearson Product Moment Correlation Coefficient) was utilized to test the research hypotheses at 0.05 level of significance. The result of the study revealed that coaches’ required leadership style is significant to athletes’ motivation in team sports of basketball, football, handball and volleyball in Adamawa State Sports Council, Nigeria. Therefore, the following recommended that coaches should be mindful of situational consideration and type of leadership style they employ or use to coach their teams.  Article visualizations

Artificial Intelligence based Robotic Platforms for Autonomous Precision Agriculture

Robotic applications are continuously expanding into every aspect of human livelihood, it becomes paramount to leverage this trend for precision agriculture. The agricultural sector despite being an important sector for human is slowly evolving in terms of technology. Crude and manual processes which are conventionally used for agriculture have severe economic and social impacts. The inefficiencies and less productiveness of these methods results to food wastage amidst food shortage, inconsistencies, time consumption, higher labour expenses, and low yield. The world will benefit from automating the processes in agriculture. In bid of addressing such, it becomes necessary to build on existing platforms and develop intelligent autonomous vehicles for precision agriculture. This should include development of intelligent drones for precision agriculture, development of intelligent ground robots for precision agriculture, and other systems working cooperatively. To achieve this, we leverage on Artificial Intelligence (AI) and mathematical methods to impact sufficient intelligence on robotic platforms to make them suitable for precision agriculture. This thesis explores the capabilities of AI for weed classification and detection, weed relative position estimation, fruit 6D pose estimation and virtual reality for teleoperated systems in fruit picking. Infestation of weeds diminishes the yield of crops in agriculture. Deep learning is becoming a more popular approach for identifying weeds on farmlands. However, precision agriculture requires that the object of interest (weed) is precisely classified and detected to facilitate removal or spraying. An approach for this is presented and involves cascading a classification network (ResNet-50) with a detection network (YOLO) for weed classification and detection which we termed Fused-YOLO. Thus, weeds can precisely be located and classified (type) within an image frame. Inspired by the precision of this detection model, the work extends to presenting a novel monocular vision-based approach for drones to detect multiple types of weeds and estimate their positions autonomously for precision agriculture applications. A drone is subjected to an elliptical trajectory while acquiring images from an onboard monecular camera. The images are fed to the fused-YOLO model in real-time. The centre of the detection bounding boxes is leveraged to be the centre of the detected object of interest (weeds). The centre pixels are extracted and converted into world coordinates forming azimuth and elevation angles from the target to the UAV and are effectively used in an estimation scheme that adopts the Unscented Kalman Filteration to estimate the exact relative positions of the weeds. The robustness of this algorithm allows for both indoor and outdoor implementation while achieving a competitive result with affordable off-the-shelf sensors. Artificial intelligence for autonomous 6D pose estimation has valuable contributions to agricultural practices rallying around fruit picking, harvesting, remote operations and other contact-related applications. Conventionally, Convolutional Neural Networks (CNNs) based approaches are adopted for pose estimation. However, precision agriculture applications are demanding on higher accuracy at lower computational costs for real-time applications. Motivated by this, a novel architecture called Transpose is proposed based on transformers. TransPose is an improved Transformer-based 6D pose estimation with a depth refinement. More modalities often result in higher accuracy at the expense of computational cost. TransPose takes in a single RGB image as input without extra modality. However, an innovative light-weight depth estimation network architecture is incorporated into the model to estimate depth from an RGB image using a feature pyramid with an up-sampling method. A transformer model having proven to be efficient, regress the 6D pose directly and also outputs object patches. The depth and the patches are utilised to further refine the regressed 6D pose. The performance of the model is extensively assessed and compared with state-of-the-art methods. As part of this research, a first-ever fruit-oriented 6D pose dataset was acquired. Lastly, a seamless teleoperation pipeline that interfaces virtual reality with robots for precision agriculture tasks is proposed to pave the way for virtual agriculture. This utilises the Transpose model to estimate the 6D pose of a fruit and render it in a virtual reality environment. A robotic manipulator is which is then controlled from within the virtual reality environment to pick/harvest the fruit while being guided by the Transpose AI model. The robustness of the pipeline is tested over simulation and real-time implementation with a physical robotic manipulator is also investigated