107 research outputs found

    Constructing a criterion-referenced test in psychometric subjects according to item response theory

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    The article aimed to construct criterion-referenced in the psychometric subject based on item response theory. The sample consisted of (121) participants (54 male & 67 female) selected from the Department of Psychology at Isra University (Jordan) during the second semester of the academic year 2021/2022. The criterion-referenced test consisted of (36) items following a multiple-choice shape in which each item has 4 options. The results showed the assumptions of items response theory in the study data and matched the responses to (34) items. The results found that there were two items that did not match the model of item response theory that were deleted. Finally, the results of the parameter assessments of the items (discrimination, difficulty, and estimation) indicated that they were agreeable with the test criteria mentioned in psychometric literature and educational. In light of the results obtained, the study recommended using the test, which was developed by the researchers to assess student’s achievement in the subject of psychometrics for students of Psychology, because it demonstrates acceptable validity and reliability and complies with the requirements of the logarithmic three-parameter model, and the possibility of using the same methods as assessments for other courses after ensuring their psychometric qualities

    Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing

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    Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem

    ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

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    In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each dataset over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. The Genetic algorithm (GA) is used to solve the multi-objective optimization problem due to its evolutionary strategy. The performed experiments using the Mobile Data Challenge (MDC) dataset and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data

    Building a Criterion-Referenced Test in Measurement and Evaluation and Determining Its Cut-Off Score by Several Methods

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    The current study aimed to build a criterion-referenced test in measurement and evaluation and determine its cut-off score by several methods. The primary test form had 45 items, which a group of professors and measurement and evaluation experts reviewed; their comments and feedback were taken into account, and the final test form had 40 items. The test has been presented to 174 university students to examine its psychometric characteristics. Multiple statistical techniques were later performed using the SPSS program, and the results show that the discrimination and difficulty coefficients ranged from 0.36 to 0.82. Additionally, the test reliability was calculated using the Kuder-Richardson -20 and Spilt half statistical methods, and the concurrent validity was 0.76. The results showed that the value of the Kuder-Richardson -20 method was 0.81, while the value of the Spilt-Half method was 0.79. Finally, the cut-off score has been calculated using four methods, and the results indicate that the Angoff method value was 65%, the Nedelsky method was 64%, the contrasting groups’ method was 68%, and the criterion groups’ method was 62%. Keywords: criterion-referenced test, measurement, and evaluation, university student, the cut-off score. DOI: 10.7176/JEP/14-1-06 Publication date: January 31st 202

    Nutritional and physicochemical characteristics of innovative high energy and protein fruit- and date-based bars

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    With the increasing global nutritional bar market, developing and formulating innovative high-energy and protein bars to compensate for nutrients using date fruits is beneficial for health-conscious individuals. The current research was undertaken to study the composition and physicochemical characteristics of innovative high-energy and high-protein bars using two combinations of Sukkari dates or fruit mixtures as a base. Fifty percent of either Sukkari date paste or dried fruit mixture (25% raisin, 12.5% fig, and 12.5% apricot) combined with other different ingredients was used to produce a date-based bar (DBB) or fruit-based bar (FBB). Proximate composition, sugar content, amino and fatty acid profiles, minerals and vitamins, phytochemicals, antioxidant activity, and visual color parameters of the DBB and the FBB were determined and statistically compared. Proximate analysis revealed higher moisture and fat content in the FBB than the DBB, while ash and crude fiber were higher in the DBB than the FBB. The protein content in the DBB and the FBB was not statistically different. Both prepared bars exuded around 376–378 kcal 100 g−1 fresh weight. Sugar profile analysis of the DBB and the FBB showed dependable changes based on date or fruit content. Fructose, glucose, and maltose contents were higher in the FBB than in the DBB, while sucrose content was higher in the DBB than in the FBB. The DBB showed significantly higher content in Ca, Cu, Fe, Zn, Mn, and Se and significantly lower content in Mg, K, and Na than the FBB, with no variation in phosphorus content. The DBB and the FBB contained both essential (EAA) and non-essential (NEAA) amino acids. The DBB scored higher Lysine, Methionine, Histidine, Threonine, Phenylalanine, Isoleucine, and Cystine contents than the FBB, while the FBB scored only higher Leucine and Valine contents than the DBB. Seventeen saturated fatty acids were identified in the DBB and the FBB, with Palmitic acid (C16:0) as the predominant fatty acid. Oleic acid (C18:1n9c) was predominant among seven determined monounsaturated fatty acids. Linoleic fatty acid (C18:2n6c) was predominant among eight identified polyunsaturated fatty acids. In addition, α-Linolenic (C18:3n3) was detected in a considerable amount. However, in both the DBB and the FBB, the content and distribution of fatty acids were not remarkably changed. Regarding phytochemicals and bioactive compounds, the FBB was significantly higher in total phenolic content (TPC), total flavonoids (TF), and total flavonols (TFL) contents and scavenging activity against DPPH and ABTS free radicals than the DBB. The DBB and the FBB showed positive a* values, indicating a reddish color. The b* values were 27.81 and 28.54 for the DBB and the FBB, respectively. The DBB is affected by the lower L* value and higher browning index (BI) to make its color brownish. Sensory evaluation data showed that panelists significantly preferred the DBB over the FBB. In conclusion, processing and comparing these bars indicated that using Sukkari dates is a nutrient-dense, convenient, economical, and better sugar alternative that helps combat the calorie content. Thus, scaling up the use of dates instead of fruits in producing high-energy and protein bars commercially is highly recommended

    Green Synthesis of Zn(OH)<sub>2</sub>/ZnO-Based Bionanocomposite using Pomegranate Peels and Its Application in the Degradation of Bacterial Biofilm

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    The ability and potency of bacterial species to form biofilms, which show antibiotic resistance thereby avoiding antibiotic surfaces, is a major cause of prolonged infections. Various advanced approaches have been employed to prevent or damage bacterial biofilms, formed by a variety of bacterial strains, to help prevent the associated infectious disease. In this context, zinc-based nanostructures have been recognized as a potential antibiotic agent against a broad spectrum of bacterial communities. As a result, a sustainable and green synthesis method was adapted in the present study to synthesize a Zn(OH)(2)/ZnO-based bionanocomposite, in which aqueous extracts of waste pomegranate peels (Punica granatum) were employed as a natural bioreducing agent to prepare the bionanocomposite at room temperature. Furthermore, FT-IR, XRD, DLS, UV-Visible, PL spectroscopy, FE-SEM, and TEM were used to characterize the green route synthesized a Zn(OH)(2)/ZnO bionanocomposite. The average crystallite size was determined using the Scherrer relation to be 38 nm, and the DLS results indicated that the Zn(OH)(2)/ZnO bionanocomposite had a hydrodynamic size of 170 nm. On the other hand, optical properties investigated through UV-Vis and PL spectroscopy explored the energy bandgap between 2.80 and 4.46 eV, corresponding to the three absorption edges, and it covered the blue spectrum when the sample was excited at 370 nm. Furthermore, the impact of this green route synthesized a Zn(OH)(2)/ZnO bionanocomposite on the biofilm degradation efficiency of the pathogenic bacterial strain Bacillus subtilis PF_1 using the Congored method was investigated. The Congored assay clearly explored the biofilm degradation efficiency in the presence of a 50 mg/mL and 75 mg/mL concentration of the Zn(OH)(2)/ZnO bionanocomposite against the bacterial strain Bacillus subtilis PF_1 grown for 24 h. This study can be further applied to the preparation of bionanocomposites following a low-cost green synthesis approach, and thus prepared nanostructures can be exploited as advanced antimicrobial agents, which could be of great interest to prevent various infectious diseases

    Current Management Strategies in Breast Cancer by Targeting Key Altered Molecular Players

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    Breast Cancer is second largest disease affecting women worldwide. It remains the most frequently reported and leading cause of death among women in both developed and developing countries. Chemoprevention is one the promising approaches which reduces breast cancer. Tamoxifen and raloxifene are commonly used for treatment of breast cancer in women with high risk, although resistance occurs by tamoxifen after five years of therapy and both drugs cause uterine cancer and thromboembolic events. Aromatase inhibitors are coming up as potential option for prevention in treatment with adjuvant trials in practice. The combination of aromatase inhibitors along with tamoxifen can also be beneficial. For this, clinical trials based on large number of patients with optimal dose and lesser side effects have to be more in practice. Despite the clinical trials going on, there is need of better molecular models which can identify high risk population and new agents with better benefit having less side effects and improved biomarkers for treating breast cancer

    Effect of addition of different levels of pomegranate peel powder to concentrate diet on productive performance of Awassi lambs

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    This study was conducted to investigate the effect of the addition of pomegranate peel powder to concentrate diet at a level of zero (T1), 1.5 (T2) and 3% (T3) on productive performance of Awassi lambs. The concentrate was offered to lambs at a rate of 2.5% of live body weight and ground wheat straw on the adilbitum basis. Results revealed that there was a significant (P<0.05) increase in straw dry matter, organic matter and nitrogen intakes by lambs fed the low level of pomegranate peel powder (T2), whereas, lower values were recorded by lambs fed the high level (T3). With the similar trend of change, total dry matter intake were 1056.03, 954.61 and 841.48 g/day, and 975.35, 896.24 and 793.92 g/day of total organic matter intake, and 8.49, 7.73 and 6.70 g/day of total nitrogen intake for treatments 2, 1 and 3 respectively. Although there was no significant effect in growth parameters, lambs fed T2 gained better final weight, total and daily gains, 34.20, 8.15 and 145.53 g/day respectively, however, lambs fed T3 recorded 31.52, 5.72 and 101.34 g/day for these parameters respectively. Lambs fed T1 and T2 achieved better values of feed conversion ratio as compared with lambs in T3. Lower digestion coefficients were recorded by lambs fed T2 as compared with lambs fed T1 and T3 with a slight difference in dry matter digestibility (58.39%) and organic matter digestibility (59.68%), and relatively high differences in crude protein digestibility (51.78%) and nitrogen-free extract digestibility (63.85%). Lambs fed T2 were prior in ether extract digestibility in comparison with lambs fed T3, where digestion coefficients were 59.92% and 55.09% respectively vs. 60.17% for control treatment. Crude fiber digestion coefficients were closed among the three treatments

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Early and accurate detection of melanoma skin cancer using hybrid level set approach

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    Digital dermoscopy is used to identify cancer in skin lesions, and sun exposure is one of the leading causes of melanoma. It is crucial to distinguish between healthy skin and malignant lesions when using computerised lesion detection and classification. Lesion segmentation influences categorization accuracy and precision. This study introduces a novel way of classifying lesions. Hair filters, gel, bubbles, and specular reflection are all options. An improved levelling method is employed in an innovative method for detecting and removing cancerous hairs. The lesion is distinguished from the surrounding skin by the adaptive sigmoidal function; this function considers the severity of localised lesions. An improved technique for identifying a lesion from surrounding tissue is proposed in the article, followed by a classifier and available features that resulted in 94.40% accuracy and 93% success. According to research, the best method for selecting features and classifications can produce more accurate predictions before and during treatment. When the recommended strategy is put to the test using the Melanoma Skin Cancer Dataset, the recommended technique outperforms the alternative
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