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الملخص: أهداف البحث: أجريت هذه الدراسة لتحديد ما يفضله الطلاب في اختيار العلاج الطبيعي كخيار مهني والعوامل التي تلعب أدوارا رئيسة في اختيارهم. طرق البحث: أجريت دراسة مقطعية شملت ٢٩٧ شخصا بأعمار تتراوح بين ١٤-٢١ سنة بين طلاب جامعات محتملين. وقد تم استقطابهم من ٧ مدارس ثانوية في مناطق حكومية محلية مختارة من ولاية لاغوس باستخدام تقنية المعاينة الهادفة. تم تبني استبانة من الأدبيات المناسبة مكون من ١٤ بندا يبحث في العلاج الطبيعي مقارنة بـ ١١ مهنة مختارة على ستة أبعاد، هي مستويات الإجهاد البدني، والرواتب، والفائدة للمجتمع، والمسؤولية، والوضع الاجتماعي، والتعرف الشخصي، بالإضافة إلى التحقيق في عوامل تعتبر مهمة في اختيار المهنة. ولخصت البيانات باستخدام المتوسط والانحراف المعياري ومعامل كيندال للوفاق. النتائج: تم استقطاب ما يصل إلى ١٨٤ من الذكور (٦٢٪)، و١١٣ من الإناث (٣٨٪) في هذه الدراسة. صُنّف العلاج الطبيعي في المرتبة الرابعة على مستوى الإجهاد البدني، والثالثة على مستوى الراتب، والخامسة على مستوى الفائدة، والرابعة على مستوى المسؤولية، والرابعة على المكانة الاجتماعية والرابعة على الاعتراف الشخصي. وبصورة عامة، تم تصنيف العلاج الطبيعي رابع تخصص من ناحية الأكثر تفضيلا. وينظر طلاب الجامعات المحتملين إلى العلاج الطبيعي على أنه يحمل مكانة مهنية عالية وذلك بتصنيفه في المرتبة الرابعة بعد الأطباء والمهندسين والمحامين. الاستنتاجات: تُعتبر مهنة العلاج الطبيعي ذات تقديرعالٍ ويمكن أن ينظر إليها على أنها مهنة مرغوبة في المستقبل. Abstract: Objectives: This study was carried out to determine the preferences of students in selecting physiotherapy as a career choice and the factors that play key roles in their selection. Methods: A cross-sectional study involving 297 subjects ranging from 14 to 21 years of age was conducted among prospective university students. They were recruited from seven secondary schools in selected local government areas of Lagos state using a purposive sampling technique. A 14-item questionnaire, comparing physiotherapy to 11 selected occupations on six dimensions, that is, level of physical stress, salary, usefulness to society, responsibility, social status, and personal recognition as well as investigating factors considered important in selecting a career, was adapted from relevant literature. The data was analysed using mean, standard deviation, and Kendall's coefficient of concordance. Results and discussion: The responses of 184 males (62.0%) and 113 females (38.0%) were used in the data analysis of this study. Physiotherapy was ranked fourth in terms of level of physical stress, third in terms of salary, fifth in terms of usefulness, fourth in terms of responsibility, fourth in terms of social status, and fourth in terms of personal recognition. Overall, physiotherapy was rated as the fourth-most preferred occupation. Prospective university students perceived physiotherapy as having a high level of occupational prestige, rating it fourth after medicine, engineering, and law. Conclusions: The profession of physiotherapy is held in high esteem and is seen as a desirable career choice among Nigerian high school students. الكلمات المفتاحية: المكانة المهنية, طالب جامعي محتمل, العلاج الطبيعي, Keywords: Occupational prestige, Physiotherapy, Prospective university students

Introduction: Delayed onset muscle soreness (DOMS) is associated with temporary morbidity as a result of pain, soreness, and reduced muscular performance that affects the overall performance of the individual. Several interventional modalities have been studied without a conclusive evidence of efficacy. Objective: This study was carried out as part of a continuous attempt to find an effective and quick relief for DOMS. The study investigated the comparative effects of warm-up exercises and infrared radiation in ameliorating the symptoms associated with DOMS. Materials and Methods: Sixty volunteer subjects between the age of 16 years and 35 years had DOMS induced in the left biceps brachii muscle. They were randomized into three groups with 20 subjects in each group. The groups were allocated as follows: Group A as control that received no treatment, Group B had a bout of warm-up exercises consisting of 10 sets of maximal elbow flexion and extension after which DOMS was experimentally induced, and Group C had infrared radiation for 20 min after which DOMS was experimentally induced. Assessment for muscle soreness, pain, and joint range of motion (ROM) was carried out immediately after inducing DOMS and it was repeated 24 h and 48 h later using the Numerical Pain Rating Scale (NPRS), deep palpation (DP), and goniometry, respectively. Results: The infrared group (Group C) had significantly lower scores on NPRS and DP throughout the 3 days compared with the control while the significant effects of warm-up exercises were seen from day 2. There was, however, no significant difference (P > 0.05) in the range of movement (ROM) scores. Compared with warm-up exercises (Group B), infrared resulted in significantly lower NPRS and DP scores and greater ROM only on day 1. Conclusion: Infrared radiation (IRR) is effective in ameliorating the symptoms of DOMS immediately after treatment on day 1 while the effect of warm-up exercises is seen 24 h later. IRR, thus, has a better effect than warm-up exercises in ameliorating the symptoms associated with DOMS.

In this study, we present the Bayesian estimates of the unknown parameters of the Topp-Leone Lindley distribution using the maximum likelihood and Bayesian methods. In this study, the Bayes theorem was adopted for obtaining the posterior distribution of the shape parameter and scale parameter of the Topp-Leone Lindley distribution assuming the Jeffreys' (non-informative) prior for the shape parameter and the Gamma (conjugate) prior for the scale parameter under three different loss functions namely: Square Error Loss Function, Linear Exponential Loss Function and Generalized Entropy Loss Function. The posterior distribution derived for both parameters are not solvable analytically, it requires a numerical approximation techniques to obtain the solution. The Lindley approximation techniques was adopted to obtain the parameters of interest. The loss function were used to derive the estimates of both parameters with an assumption that the both parameters are unknown and independent. To ascertain the accuracy of these estimators, the proposed Bayesian estimators under different loss functions are compared with the corresponding maximum likelihood estimator using a Monte Carlo simulation on the performance of these estimators according to the mean square error and BIAS based on simulated samples simulated from the Topp-Leone Lindley distribution.. It was also observed for any fixed value of the parameters, as sample size increases, the mean square errors of the Bayesian Estimates and maximum likelihood estimates decrease. Also, the maximum likelihood estimates and Bayesian estimates converge to the same value as the sample gets larger except for Generalized Entropy Loss Function. [ABSTRACT FROM AUTHOR]

In this study, empirical prior Dirichlet allocation (epLDA) model that uses latent semantic indexing framework to derive the priors required for topics computation from data is presented. The parameters of the priors so obtained are related to the parameters of the conventional LDA model using exponential function. The model was implemented and tested with benchmarked data and it achieves a prediction accuracy of 92.15%. It was observed that the epLDA model consistently outperforms the conventional LDA model on different datasets with an average percentage accuracy of 6.33%; this clearly demonstrates the advantage of using side information obtained from data for the computation of the mixture components. [ABSTRACT FROM AUTHOR]

Gastrointestinal parasitism is one of the major health problems affecting productivity of small ruminants worldwide. A dry season study was undertaken to determine the prevalence of Strongyle ova in goats and their faecal egg counts, as well as to compare different faecal culture methods for larval counts and identification. Out of 200 faecal samples examined, 114 were from male and 86 from female goats. An overall prevalence of 42(21%) for Strongyle ova was observed with a mean EPG of 91.67 + 6.12. The prevalence was higher in the male 26(22.8%) than in female 16(18.6%) with mean EPG of 90.38 ± 7.35 and 93.75 ± 11.06 respectively (p > 0.05). It was also higher in adults 38(22.22%) compared with the young 4(13.79%). All positive goats were of the Sahelian breed. No significant difference (p > 0.05%) was observed between sexes and age but a significant difference (p < 0.05) was observed between breeds. The infection was higher from samples collected in the abattoir 36 (25.35%) compared with 6(18.75%) in Mairi Village and none was positive from the University of Maiduguri Farm. A significant difference (p<0.05) was thus observed between locations. Only Strongyle ova was seen throughout the study and on subjection of the positive samples to larval recovery, Oesophagostomum columbianum was the only larva recovered. The test tube method yielded the highest larval recovery with mean larval count of 9.14 + 0.72 (p<0.05), compared with bottle with no charcoal; bottle with charcoal and Baermann's techniques. [ABSTRACT FROM AUTHOR]

It is increasingly difficult to identify complex cyberattacks in a wide range of industries, such as the Internet of Vehicles (IoV). The IoV is a network of vehicles that consists of sensors, actuators, network layers, and communication systems between vehicles. Communication plays an important role as an essential part of the IoV. Vehicles in a network share and deliver information based on several protocols. Due to wireless communication between vehicles, the whole network can be sensitive towards cyber-attacks.In these attacks, sensitive information can be shared with a malicious network or a bogus user, resulting in malicious attacks on the IoV. For the last few years, detecting attacks in the IoV has been a challenging task. It is becoming increasingly difficult for traditional Intrusion Detection Systems (IDS) to detect these newer, more sophisticated attacks, which employ unusual patterns. Attackers disguise themselves as typical users to evade detection. These problems can be solved using deep learning. Many machine-learning and deep-learning (DL) models have been implemented to detect malicious attacks; however, feature selection remains a core issue. Through the use of training empirical data, DL independently defines intrusion features. We built a DL-based intrusion model that focuses on Denial of Service (DoS) assaults in particular. We used K-Means clustering for feature scoring and ranking. After extracting the best features for anomaly detection, we applied a novel model, i.e., an Explainable Neural Network (xNN), to classify attacks in the CICIDS2019 dataset and UNSW-NB15 dataset separately. The model performed well regarding the precision, recall, F1 score, and accuracy. Comparatively, it can be seen that our proposed model xNN performed well after the feature-scoring technique. In dataset 1 (UNSW-NB15), xNN performed well, with the highest accuracy of 99.7%, while CNN scored 87%, LSTM scored 90%, and the Deep Neural Network (DNN) scored 92%. xNN achieved the highest accuracy of 99.3% while classifying attacks in the second dataset (CICIDS2019); the Convolutional Neural Network (CNN) achieved 87%, Long Short-Term Memory (LSTM) achieved 89%, and the DNN achieved 82%. The suggested solution outperformed the existing systems in terms of the detection and classification accuracy.

The concept of randomized neural networks (RNNs), such as the random vector functional link network (RVFL) and extreme learning machine (ELM), is a widely accepted and efficient network method for constructing single-hidden layer feedforward networks (SLFNs). Due to its exceptional approximation capabilities, RNN is being extensively used in various fields. While the RNN concept has shown great promise, its performance can be unpredictable in imperfect conditions, such as weight noises and outliers. Thus, there is a need to develop more reliable and robust RNN algorithms. To address this issue, this paper proposes a new objective function that addresses the combined effect of weight noise and training data outliers for RVFL networks. Based on the half-quadratic optimization method, we then propose a novel algorithm, named noise-aware RNN (NARNN), to optimize the proposed objective function. The convergence of the NARNN is also theoretically validated. We also discuss the way to use the NARNN for ensemble deep RVFL (edRVFL) networks. Finally, we present an extension of the NARNN to concurrently address weight noise, stuck-at-fault, and outliers. The experimental results demonstrate that the proposed algorithm outperforms a number of state-of-the-art robust RNN algorithms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)