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1. L2 Motivational Self System and International Posture of Burundi Simultaneous Multiple Language Learners: A Mixed Methods Study

Author

Elvis Nizigama, Ali Mohammad Fazilatfar, and Mohammad Javad Rezai

Abstract

This mixed methods study compares L2 learning motivation of Burundi simultaneous learners of French, English and Kiswahili, mainly focusing on L2 Motivational Self System (L2MSS). A total of 348 junior high school students participated in a 60-item questionnaire survey, and 12 of those learners participated in a follow-up semi-structured interview. The quantitative data were analysed using structural equation modelling (SEM) while the interview data were analysed employing a thematic analysis method. The results support the validity of the L2MSS in comparing and explaining Burundi simultaneous multiple language learners' motivation. For all the three target languages, L2 Learning Experience appeared as the strongest predictor of the learners' intended learning effort, followed by the Ideal L2 Self only in relation to English and French learning. The role of the variable of International Posture on the three components of the L2MSS, which was assumed to be influenced by the Burundi socio-educational context, was also observed in the SEM analysis. The analysis indicated that the impact of International Posture on Ideal L2 Self was only significant in the models of both English and Kiswahili while its impact on L2 Learning Experience was only significant in the model of Kiswahili. The interview data also provided further insight into the quantitative findings. Pedagogical implications are provided based on these key findings.

Published
2024

3. Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network

Author

Ali, Mohammad Wazed, Mustafa, Asif bin, Shuvo, Md. Aukerul Moin, and Sick, Bernhard

Subjects
Computer Science - Machine Learning, 68T07, 62M20, 68T05, I.2.6, I.5.1, G.3, J.2
Abstract

Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62\%, our proposed deep MQ-TCN model exhibited a remarkable 28.93\% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88\% PICP score for the load forecasting task at the NREL site using only two weeks of data., Comment: 11 pages, 10 figures

Published
2024

4. LIMO: Load-balanced Offloading with MAPE and Particle Swarm Optimization in Mobile Fog Networks

Author

Seraj, Yasaman, Fadaei, Soheil, Safaei, Bardia, Javadi, Ali, Monazzah, Amir Mahdi Hosseini, and Hemmatyar, Ali Mohammad Afshin

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Fog computing is essentially the expansion of cloud computing towards the network edge, reducing user access time to computing resources and services. Various advantages attribute to fog computing, including reduced latency, and improved user experience. However, user mobility may limit the benefits of fog computing. The displacement of users from one location to another, may increase their distance from a fog server, leading into latency amplification. This would also increase the probability of over utilization of fog servers which are located in popular destinations of mobile edge devices. This creates an unbalanced network of fog devices failing to provide lower makespan and fewer cloud accesses. One solution to maintain latency within an acceptable range is the migration of fog tasks and preserve the distance between the edge devices and the available resources. Although some studies have focused on fog task migration, none of them have considered load balancing in fog nodes. Accordingly, this paper introduces LIMO; an allocation and migration strategy for establishing load balancing in fog networks based on the control loop MAPE (Monitor-Analyze-Plan-Execute) and the Particle Swarm Optimization (PSO) algorithm. The periodical migration of tasks for load balancing aims to enhance the system's efficiency. The performance of LIMO has been modeled and evaluated using the Mobfogsim toolkit. The results show that this technique outperforms the state-of-the-art in terms of network resource utilization with 10% improvement. Furthermore, LIMO reduces the task migration to cloud by more than 15%, while it reduces the request response time by 18%., Comment: This paper has been accepted in CPSAT 2024 and will be published in the IEEE Xplore Digital Library

Published
2024

5. Deep Learning Based Crime Prediction Models: Experiments and Analysis

Author

Utsha, Rittik Basak, Alif, Muhtasim Noor, Rayhan, Yeasir, Hashem, Tanzima, and Ali, Mohammad Eunus

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Crime prediction is a widely studied research problem due to its importance in ensuring safety of city dwellers. Starting from statistical and classical machine learning based crime prediction methods, in recent years researchers have focused on exploiting deep learning based models for crime prediction. Deep learning based crime prediction models use complex architectures to capture the latent features in the crime data, and outperform the statistical and classical machine learning based crime prediction methods. However, there is a significant research gap in existing research on the applicability of different models in different real-life scenarios as no longitudinal study exists comparing all these approaches in a unified setting. In this paper, we conduct a comprehensive experimental evaluation of all major state-of-the-art deep learning based crime prediction models. Our evaluation provides several key insights on the pros and cons of these models, which enables us to select the most suitable models for different application scenarios. Based on the findings, we further recommend certain design practices that should be taken into account while building future deep learning based crime prediction models., Comment: 20 pages, 6 figures

Published
2024

6. Non-Contact Breath Rate Classification Using SVM Model and mmWave Radar Sensor Data

Author

Ali, Mohammad Wassaf, Gupta, Ayushi, Khan, Mujeev, and Wajid, Mohd

Subjects
Computer Science - Machine Learning
Abstract

This work presents the use of frequency modulated continuous wave (FMCW) radar technology combined with a machine learning model to differentiate between normal and abnormal breath rates. The proposed system non-contactly collects data using FMCW radar, which depends on breath rates. Various support vector machine kernels are used to classify the observed data into normal and abnormal states. Prolonged experiments show good accuracy in breath rate classification, confirming the model's efficacy. The best accuracy is 95 percent with the smallest number of support vectors in the case of the quadratic polynomial kernel., Comment: 6 Pages, 5 figures

Published
2024

7. Coded Computing: A Learning-Theoretic Framework

Author

Moradi, Parsa, Tahmasebi, Behrooz, and Maddah-Ali, Mohammad Ali

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Information Theory
Abstract

Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker node processes a combination of the data, rather than the raw data itself. The final result then is decoded from the collective outputs of the worker nodes. However, there is a significant gap between current coded computing approaches and the broader landscape of general distributed computing, particularly when it comes to machine learning workloads. To bridge this gap, we propose a novel foundation for coded computing, integrating the principles of learning theory, and developing a new framework that seamlessly adapts with machine learning applications. In this framework, the objective is to find the encoder and decoder functions that minimize the loss function, defined as the mean squared error between the estimated and true values. Facilitating the search for the optimum decoding and functions, we show that the loss function can be upper-bounded by the summation of two terms: the generalization error of the decoding function and the training error of the encoding function. Focusing on the second-order Sobolev space, we then derive the optimal encoder and decoder. We show that in the proposed solution, the mean squared error of the estimation decays with the rate of $O(S^4 N^{-3})$ and $O(S^{\frac{8}{5}}N^{\frac{-3}{5}})$ in noiseless and noisy computation settings, respectively, where $N$ is the number of worker nodes with at most $S$ slow servers (stragglers). Finally, we evaluate the proposed scheme on inference tasks for various machine learning models and demonstrate that the proposed framework outperforms the state-of-the-art in terms of accuracy and rate of convergence., Comment: 28 pages, 4 figures

Published
2024

8. Newman's theorem via Carath\'eodory

Author

Lavasani, Ali Mohammad, Li, Yaqiao, and Shakerinava, Mehran

Subjects
Computer Science - Computational Complexity
Abstract

We give a streamlined short proof of Newman's theorem in communication complexity by applying the classical and the approximate Carath\'eodory's theorems.

Published
2024

9. Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines

Author

Rony, Md Main Uddin, Haque, Md Mahfuzul, Ali, Mohammad, Alam, Ahmed Shatil, and Hassan, Naeemul

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

In the digital age, the prevalence of misleading news headlines poses a significant challenge to information integrity, necessitating robust detection mechanisms. This study explores the efficacy of Large Language Models (LLMs) in identifying misleading versus non-misleading news headlines. Utilizing a dataset of 60 articles, sourced from both reputable and questionable outlets across health, science & tech, and business domains, we employ three LLMs- ChatGPT-3.5, ChatGPT-4, and Gemini-for classification. Our analysis reveals significant variance in model performance, with ChatGPT-4 demonstrating superior accuracy, especially in cases with unanimous annotator agreement on misleading headlines. The study emphasizes the importance of human-centered evaluation in developing LLMs that can navigate the complexities of misinformation detection, aligning technical proficiency with nuanced human judgment. Our findings contribute to the discourse on AI ethics, emphasizing the need for models that are not only technically advanced but also ethically aligned and sensitive to the subtleties of human interpretation., Comment: 5 pages, 2 tables, 1st HEAL Workshop at CHI Conference on Human Factors in Computing Systems, May 12, Honolulu, HI, USA 2024

Published
2024

11. NeRCC: Nested-Regression Coded Computing for Resilient Distributed Prediction Serving Systems

Author

Moradi, Parsa and Maddah-Ali, Mohammad Ali

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Information Theory
Abstract

Resilience against stragglers is a critical element of prediction serving systems, tasked with executing inferences on input data for a pre-trained machine-learning model. In this paper, we propose NeRCC, as a general straggler-resistant framework for approximate coded computing. NeRCC includes three layers: (1) encoding regression and sampling, which generates coded data points, as a combination of original data points, (2) computing, in which a cluster of workers run inference on the coded data points, (3) decoding regression and sampling, which approximately recovers the predictions of the original data points from the available predictions on the coded data points. We argue that the overall objective of the framework reveals an underlying interconnection between two regression models in the encoding and decoding layers. We propose a solution to the nested regressions problem by summarizing their dependence on two regularization terms that are jointly optimized. Our extensive experiments on different datasets and various machine learning models, including LeNet5, RepVGG, and Vision Transformer (ViT), demonstrate that NeRCC accurately approximates the original predictions in a wide range of stragglers, outperforming the state-of-the-art by up to 23%.

Published
2024

12. History of generative Artificial Intelligence (AI) chatbots: past, present, and future development

Author

Al-Amin, Md., Ali, Mohammad Shazed, Salam, Abdus, Khan, Arif, Ali, Ashraf, Ullah, Ahsan, Alam, Md Nur, and Chowdhury, Shamsul Kabir

Subjects
Computer Science - General Literature, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction
Abstract

This research provides an in-depth comprehensive review of the progress of chatbot technology over time, from the initial basic systems relying on rules to today's advanced conversational bots powered by artificial intelligence. Spanning many decades, the paper explores the major milestones, innovations, and paradigm shifts that have driven the evolution of chatbots. Looking back at the very basic statistical model in 1906 via the early chatbots, such as ELIZA and ALICE in the 1960s and 1970s, the study traces key innovations leading to today's advanced conversational agents, such as ChatGPT and Google Bard. The study synthesizes insights from academic literature and industry sources to highlight crucial milestones, including the introduction of Turing tests, influential projects such as CALO, and recent transformer-based models. Tracing the path forward, the paper highlights how natural language processing and machine learning have been integrated into modern chatbots for more sophisticated capabilities. This chronological survey of the chatbot landscape provides a holistic reference to understand the technological and historical factors propelling conversational AI. By synthesizing learnings from this historical analysis, the research offers important context about the developmental trajectory of chatbots and their immense future potential across various field of application which could be the potential take ways for the respective research community and stakeholders.

Published
2024

14. The Effects of Flipped Classroom Strategy Based on 'Addie Model' for Algebraic Skill Development

Author

Alzoebi, Ali Mohammad, Ghunaimat, Mohammad Ali, and Alawneh, Esraa Ahmad

Abstract

The study aimed to investigate the effect of the flipped classroom strategy based on ADDIE Model on Prince Faisal College students' development of algebraic thinking skills. To achieve the goal of the study, the researchers developed a measure of development of algebraic thinking skills. The study used the quasi-experimental approach with a pre-post design, and the study was applied to (53) students, who were divided into two groups: an experimental group of (27) students, and a control group of (26) students. The results showed there is an effect of the strategy on the dimensional measurement of the test of developing all algebraic thinking skill. Flipped Classroom helped develop algebraic thinking skills. The study recommended using the stages of ADDIE model in applying the flipped classroom strategy, and flipped classroom based on ADDIE model enhances student learning, because the strategy integrates student learning at home and in the classroom.

Published
2023

15. Few-Shot Channel-Agnostic Analog Coding: A Near-Optimal Scheme

Author

Maddah-Ali, Mohammad Ali and Mohajer, Soheil

Subjects
Computer Science - Information Theory
Abstract

In this paper, we investigate the problem of transmitting an analog source to a destination over $N$ uses of an additive-white-Gaussian-noise (AWGN) channel, where $N$ is very small (in the order of 10 or even less). The proposed coding scheme is based on representing the source symbol using a novel progressive expansion technique, partitioning the digits of expansion into $N$ ordered sets, and finally mapping the symbols in each set to a real number by applying the reverse progressive expansion. In the last step, we introduce some gaps between the signal levels to prevent the carry-over of the additive noise from propagation to other levels. This shields the most significant levels of the signal from an additive noise, hitting the signal at a less significant level. The parameters of the progressive expansion and the shielding procedure are opportunistically independent of the $\SNR$ so that the proposed scheme achieves a distortion $D$, where $-\log(D)$ is within $O(\log\log(\SNR))$ of the optimal performance for all values of $\SNR$, leading to a channel-agnostic scheme.

Published
2024

16. Game of Coding: Beyond Honest-Majority Assumptions

Author

Nodehi, Hanzaleh Akbari, Cadambe, Viveck R., and Maddah-Ali, Mohammad Ali

Subjects
Computer Science - Information Theory
Abstract

Coding theory revolves around the incorporation of redundancy into transmitted symbols, computation tasks, and stored data to guard against adversarial manipulation. However, error correction in coding theory is contingent upon a strict trust assumption. In the context of computation and storage, it is required that honest nodes outnumber adversarial ones by a certain margin. However, in several emerging real-world cases, particularly, in decentralized blockchain-oriented applications, such assumptions are often unrealistic. Consequently, despite the important role of coding in addressing significant challenges within decentralized systems, its applications become constrained. Still, in decentralized platforms, a distinctive characteristic emerges, offering new avenues for secure coding beyond the constraints of conventional methods. In these scenarios, the adversary benefits when the legitimate decoder recovers the data, and preferably with a high estimation error. This incentive motivates them to act rationally, trying to maximize their gains. In this paper, we propose a game theoretic formulation for coding, called the game of coding, that captures this unique dynamic where each of the adversaries and the data collector (decoder) have respective utility functions to optimize. The utility functions reflect the fact that both the data collector and the adversary are interested in increasing the chance of data being recoverable by the data collector. Moreover, the utility functions express the interest of the data collector to estimate the input with lower estimation error, but the opposite interest of the adversary. As a first, still highly non-trivial step, we characterize the equilibrium of the game for the repetition code with a repetition factor of 2 for a wide class of utility functions with minimal assumptions.

Published
2024

17. A fully decentralized auditing approach for edge computing: A Game-Theoretic Perspective

Author

Seyedi, Zahra, Rahmati, Farhad, Ali, Mohammad, and Liu, Ximeng

Subjects
Computer Science - Cryptography and Security, Computer Science - Computer Science and Game Theory
Abstract

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme's security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods.

Published
2023

18. A Comparative Analysis of Supportive Navigation on Movie Recommenders

Author

Ali, Mohammad Sualeh, Tariq, Muhammed Maaz, Ahmed, Alina, Soomro, Abdul Razaque, and Syed, Danysh

Subjects
Computer Science - Information Retrieval, Computer Science - Human-Computer Interaction
Abstract

This literature review covers the research and thought process that went into making a solution for the infinite scrolling problem faced in streaming services such as Netflix. Using the data collected, we have come to the conclusion that an alternate layout can somewhat alleviate the problems it takes in navigating a list of movies. We also found out by a comparative analysis that some layouts, the circular one in particular, is advantageous in certain settings making it an ideal candidate for a movie recommender system., Comment: This was an extensive survey and prototyping we did to purpose and alternative user interface for movie recommender systems like Netflix

Published
2023

20. Federated Online/Offline Remote Data Inspection for Distributed Edge Computing

Author

Ali, Mohammad and Liu, Ximeng

Subjects
Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particularly vulnerable to several intentional attacks or unintentional events. Given the limited resources of edge servers and prohibitive storage costs incurred by app vendors, designing an efficient approach to inspect and maintain the data over tremendous edge servers is a critical issue. To tackle the problem, we design a novel data inspection approach, named ${\text{O}^2\text{DI}}$, that provides the following services: i) using ${\text{O}^2\text{DI}}$, app vendors can inspect the data cached in edge servers without having the original data, which reduces the incurred I/O and storage overhead significantly; ii) computational operations conducted by both edge servers and app vendors are highly efficient because of a novel online/offline technique; iii) many data files cached in different edge servers can be verified quickly and at once by using a novel batch verification method; iv) corrupted data in edge servers can be localized and repaired efficiently. We analyze the security and performance of ${\text{O}^2\text{DI}}$. We see that it is secure in the random oracle model, much faster, and more cost-effective compared to state-of-the-art approaches.

Published
2023

21. A Novel Hybrid Internal Pipeline Leak Detection and Location System Based on Modified Real-Time Transient Modelling

Author

Seyed Ali Mohammad Tajalli, Mazda Moattari, Seyed Vahid Naghavi, and Mohammad Reza Salehizadeh

Subjects
pipeline leak detection, real-time transient modelling, extended Kalman filter, support vector machine, K-nearest neighbors, Engineering design, TA174
Abstract

A This paper proposes a modified real-time transient modelling (MRTTM) framework to address the critical challenge of leak detection and localization in pipeline transmission systems. Pipelines are essential infrastructure for transporting liquids and gases, but they are susceptible to leaks, with severe environmental and economic impacts. MRTTM tackles this challenge with a three-stage operational process. First, “Data Collection” gathers sensor data from designated observation points. Second, the “Detection” stage identifies leaks. Finally, “Decision-Making” utilizes MRTTM to pinpoint the exact leak magnitude and location. This paper introduces an innovative method designed to significantly enhance pipeline leak detection and localization through the application of artificial intelligence and advanced signal processing techniques. The improved MRTTM framework integrates AI for pattern recognition, state space modelling for leak segment identification, and an extended Kalman filter (EKF) for precise leak location estimation, addressing the limitations of traditional methods. This paper showcases the application of MRTTM through a case study using the K-nearest neighbors (KNN) method on a water transmission pipeline for leak detection. KNN aids in classifying leak patterns and identifying the most likely leak location. Additionally, MRTTM incorporates the EKF, enabling real-time updates during transient events for faster leak identification. Preprocessing sensor data before comparison with the leakage pattern bank (LPB) minimizes false alarms and enhances detection reliability. Overall, the AI-powered MRTTM framework offers a powerful solution for swift and precise leak detection and localization in pipeline systems. The functionality of the framework is examined, and the results effectively approve the effectiveness of this methodology. The experimental results validate the practical utility of the MRTTM framework in real-world applications, demonstrating up to 90% detection accuracy and an F1 score of 0.92.

Published
2024
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22. Magnetic graphene oxide nanosheets with amidoamine dendronized crosslinks for dual pH and redox-sensitive doxorubicin delivery

Author

Amir Reza Sarikhani, Mehdi Abedi, Samira Sadat Abolmaali, Sedigheh Borandeh, and Ali Mohammad Tamaddon

Subjects
Superparamagnetic nanoparticle, Fe3O4, Drug release kinetic, Stimuli-responsive, Redox, Graphene oxide, Chemistry, QD1-999
Abstract

Abstract Delivering anticancer drugs to the appropriate site within the body poses a critical challenge in cancer treatment with chemotherapeutic agents like doxorubicin (DOX). Magnetic graphene oxide (GO) nanosheets with generation 1 (G1) amidoamine-dendronized crosslinks were developed by coupling cystamine-functionalized GO nanosheets with Fe3O4 nanoparticles modified with primary amine and methyl acrylate. These magnetic GO nanosheets were loaded with DOX to create a dual pH- and redox-responsive delivery system for cancer chemotherapy. The prepared magnetic nanosheets underwent characterization using FTIR, XRD, DLS, VSM, FE-SEM, and TEM. Physical DOX adsorption was evaluated using various isotherms, including Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich. The in-vitro release profiles of DOX from the magnetic nanosheets were studied under different pH conditions, with and without glutathione (GSH), and the drug release data were fitted with various kinetic models. Additionally, an MTT assay was employed to assess the compatibility and antitumor activity of DOX-loaded magnetic nanosheets in the HepG2 cell line. The results showed that the maximum drug loading was 13.1% (w/w) at a drug/carrier ratio of 1. Without GSH addition, the maximum drug release after 10 days was only 17.9% and 24.1% at pH 7.4 and 5.3, respectively. However, in the presence of GSH, the maximum drug release reached 51.7% and 64.8% at pH 7.4 and 5.3, respectively. Finally, the research findings suggest that the magnetic nanosheets exhibited pH- and redox-stimuli drug release, high biocompatibility, and superior antitumor activity compared to free DOX.

Published
2024
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23. A Descriptive Study on Paediatric Ocular Trauma at a Tertiary Eye Care Centre, Andhra Pradesh, India

Author

Meher Divyageetha Mandavilli, Ahmad Ali Mohammad, Lakshmi Sativada, and Dinesh Kanth Vudayana

Subjects
best corrected visual acuity, home, hyphema, open globe injury, wooden stick, Medicine
Abstract

Introduction: Ocular trauma is one of the leading causes of treatable visual morbidity and blindness. Paediatric ocular injury accounts for 8 to 14% of total injuries in children. Ocular trauma in children differs from that in adults in terms of the objects involved in causing injury, as well as the evaluation and management protocols. Aim: To understand the causes, locations, contributing factors, and patterns of paediatric ocular trauma, along with its short-term visual outcomes in a tertiary care centre, Andhra Pradesh. Materials and Methods: This descriptive study was conducted at the Department of Ophthalmology, Great Eastern Medical School and Hospital (GEMS), Srikakulam, Andhra Pradesh, India, over a period of six months, from August 2023 to January 2024. A detailed history regarding the cause of injury and any systemic injuries was recorded. The demographic profiles of all patients were noted in terms of age, sex, location of injury, nature of the object causing injury, time of presentation, and treatment. A comprehensive ocular examination was performed, which included visual acuity assessment, slit lamp examination and fundus examination. All patients were managed according to established treatment protocols. Follow-up appointments were scheduled at one week, three weeks, six weeks, and three months. Results: In the present study, a total of 31 children under the age of 15 years were included. The mean age of presentation was 9.94±3.91 years. Most ocular injuries occurred in the age group of 11-15 years, accounting for 17 (54.83%) of cases. Regarding the location of injury, the most common site was the home, representing 15 (48.38%) of incidents. The object most frequently causing injury was a wooden stick, which constituted 7 (22.58%) of cases. Out of the 31 children, 25 (80.64%) presented with closed globe injuries, while 6 (19.35%) had open globe injuries, indicating that closed globe injuries were more common. The visual acuity was converted to log Minimum Angle of Resolution (MAR) units, with the mean preoperative Best-corrected Visual Acuity (BCVA) being 1.0726±0.7038 log MAR units. The mean postoperative BCVA after three months was 0.5720±0.6275 log MAR units. Linear regression analysis conducted between preoperative visual acuity and postoperative BCVA showed an r-value of 0.7681 and a p-value of

Published
2024
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24. Comparison of professional competency and anxiety of nursing students trained based on two internship models: a comparative study

Author

Roya Dokoohaki, Masoume Rambod, Nilofar Pasyar, Ali Mohammad Parviniannasab, Maryam Shaygan, Majid Najafi Kalyani, Zinat Mohebbi, and Azita Jaberi

Subjects
Anxiety, Clinical competence, Education, Internship, Students, Nursing, Special aspects of education, LC8-6691, Medicine
Abstract

Abstract Background Improving the professional competency of nursing students during the internship is critical. This study aimed to compare the professional competency and anxiety of nursing students trained based on two internship models. Methods This is a two-group posttest-only quasi-experimental design study. One hundred nursing students who passed internship models A (a previous internship model) and B (an intervention with more educator support and a more planned and programmed process) were randomly enrolled in this study. Internship model groups A and B were conducted for the students in semesters 7 and 8. The outcomes assessed in both groups were “The Competency Inventory for Registered Nurses” and Spielberger “State-Trait Anxiety Inventory”. T-test and MANOVA were used to analyze the data. Results The mean scores of competency were 134.56 (SD = 43.23) and 160.19 (SD = 35.81) for the nursing students in the internship model groups A and B, respectively. The mean scores of nursing students’ anxiety were 92.14 (SD = 15.36) and 80.44 (SD = 18.16) in the internship model groups A and B, respectively. MANOVA test showed a significant difference between the groups regarding professional competency (F = 10.34, p = 0.002) and anxiety (F = 11.31, p = 0.001). Conclusions The internship model group B could improve the professional competency of nursing students to a great extent and they experienced mild anxiety; it is suggested that this intervention should be done for nursing students. Conducting more studies to evaluate the effect of this model on the nursing students’ competency and anxiety after graduation and as a novice nurse is suggested.

Published
2024
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25. Spermatic cord liposarcoma presented as scrotal swelling: a case report

Author

Mohamed Salah Ayyad, Ibrahim Elgaml, Alrawy Ali Mohammad, Amr Mohamed Rizq, and Samira Ebrahim

Subjects
Cancer, Imaging, Liposarcoma, Spermatic cord, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Abstract Background Liposarcoma is considered the most common among spermatic cord sarcomas. It usually presents as a progressively enlarging inguinoscrotal mass. The resemblance between the spermatic cord liposarcoma and the inguinal hernia allows the swelling to be tolerated for a long duration giving time for hematogenous dissemination. In this article, we share the experience of a unique case of metastatic liposarcoma of the spermatic cord that showed a stationary course of the lower abdominal satellite lesions on follow-up following palliative resection. Case presentation We present a case of a 53-year-old male with progressively enlarging painless scrotal swelling over 1-year duration. A heterogeneous lesion of the right hemiscrotum and spermatic cord with non-specific radiological features was described by imaging. Two lower abdominal skip lesions were also discovered synchronously in addition to the primary one and were designated inoperable as they were adherent to the nearby bowel loops. The patient underwent cytoreductive surgery of the primary tumour which was proven to be an undifferentiated liposarcoma with free safety margins based on histopathology. Finally, the patient was referred for adjuvant chemotherapy and sequential follow-up. Conclusions Despite being a rare tumour, the radiologist should always consider the liposarcoma of the spermatic cord in the differential diagnosis of inguinoscrotal lesions even in the absence of typical imaging features. The diagnosis and management of spermatic cord liposarcoma remain a challenge with no clear accepted guidelines.

Published
2024
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26. Growth Mindset EFL Teachers’ Oral Feedback Practices and Their Beliefs

Author

Samane HosseiniZade, Ali Mohammad Fazilatfar, and Ali Akbar Jabbari

Subjects
teacher’s beliefs, oral corrective feedback, corrective feedback practices, growth mindset, teacher mindset, qualitative data analysis, English language, PE1-3729
Abstract

Despite the abundance of research on the link between teachers’ beliefs and practices, little research has been conducted investigating the relationship between the beliefs of teachers with specific individual attributes and corrective feedback practices. Thus, this study aims to investigate whether growth mindset teachers’ oral corrective feedback (OCF, henceforth) practices are aligned with their beliefs. Eight in-service growth mindset EFL teachers participated in the study. Having collected the data via two questionnaires and a set of classroom observations, MAXQDA software was used to code and quantify the data. The findings showed that growth mindset teachers’ OCF beliefs and their actual OCF practices were aligned in terms of OCF timing. Regarding OCF types, female teachers’ practices were aligned with their beliefs, but male teachers’ beliefs were more incongruent with their practices. Teachers’ beliefs and practices, however, were inconsistent regarding OCF amount and frequency, which can be due to contextual factors, such as occasional time limit. The implications and suggestions for further research are suggested.

Published
2024
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27. Impact of the COVID-19 Pandemic on Receiving Dental Care Services From Community Health Centers in Iran

Author

Roya Zamani, Reza Esmaeili, Elahe Moghimi Shahri, and Ali Mohammad Mokhtari

Subjects
covid-19, dental care, dental service, check-up, tooth extraction, dental restoration, dental scaling, Public aspects of medicine, RA1-1270
Abstract

Background: The fear of contracting coronavirus has influenced people’s decisions regarding visiting crowded places, such as facilities providing medical and dental services. We aimed to investigate the impact of the COVID-19 pandemic on dental care utilization patterns in 16 community health centers in Gonabad. Methods: This cross-sectional study utilized data from 16 community health centers in Gonabad from 2019 to 2021 (before and during the COVID-19 pandemic). Data pertaining to daily dental services, including check-ups, tooth extractions, tooth restorations, and scaling, were extracted and subsequently subjected to statistical analysis using SPSS software, version 22 at a significance level of

Published
2024

28. Uncontrolled blood pressure among the established hypertensive elderly people in Jashore, Bangladesh: A cross‐sectional type of observational study

Author

Goutam Kumar Acherjya, Ali Mohammad, Tarafder Keya, Mohammad Touhidul Islam, Md. Selim Reza, Md. Shakur Ahmed, Huq Md. Zahirul, Biswas Debashis, Shamsuzzaman Sheikh, and Alam Nur

Subjects
hypertension of elderly people, risk factors, treatment and uncontrol BP, Geriatrics, RC952-954.6
Abstract

Abstract Objectives Hypertension is one of the major modifiable risk factors for cardiovascular mortality and morbidity throughout the world. Increased life expectancy leads to increase prevalence of non‐communicable diseases among the elderly people including Bangladesh. However, different studies reported high prevalence of uncontrolled hypertension ranging from 52.6% to 67.9% among the elderly people in different countries. With this view, we aimed to assess the frequency of uncontrolled blood pressure (BP) among the elderly hypertensive people and its associated risk factors and treatment pattern in Bangladesh. Methodology This cross‐sectional type of observational study recruited 246 eligible hypertensive elderly patients attending in 250 Bedded General Hospital, Jashore, Bangladesh dated from 1st July to 31st December 2022. A structured questionnaire was developed and data on associated risk factors, treatment pattern and current blood pressure (BP) measurement were collected by face‐to‐face interview for the purposive sampling technique. Results The mean age of our study patients was 72 ± 7 years with a male and female ratio nearly 1:1. Of the total hypertensive patients aged over 65 years or more, 56.5% remained with uncontrolled hypertension even on their prescribed antihypertensive medications. The mean systolic (SBP) and diastolic (DBP) blood pressure were significantly high (P

Published
2024
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29. Quantitative Electroencephalography and Balance Test Correlations in Patients with Chronic Patellofemoral Pain

Author

Naghmeh Ebrahimi, Amin Kordi Yoosefinejad, Zahra Rojhani-Shirazi, Mohammad Nami, and Ali-Mohammad Kamali

Subjects
electroencephalography, postural balance, correlation, patellofemoral pain, brain waves, cerebral cortex, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Background: Quantitative Electroencephalography (QEEG) is a tool helping better understand the electrical activity of the brain and a non-invasive method to assess cortical activity. To date, the brain activity of patients with chronic patellofemoral pain (PFP) has not been investigated.Objective: The current study aimed to investigate the effect of PFP on higher levels of the central nervous system by assessing the correlation between QEEG and modified excursion balance test (mSEBT) in patients with PFP.Material and Methods: Twenty-two patients with chronic PFP participated in this observational study. Their cortical electrical activity was recorded in a resting state with their eyes open, via a 32-channel QEEG. C3, C4, and Cz were considered as regions of interest. In addition to QEEG, the balance performance of the participants was evaluated via mSEBT.Results: The obtained findings revealed a negative and moderate to high correlation between theta absolute power and posteromedial direction of mSEBT in C4 (P: 0.000, r: -0.68), Cz (P: 0.001, r: -0.66), and C3 (P 0.000, r: -0.70). Additionally, a significantly close correlation is between alpha absolute power in C3 (P: 0.001, r: -0.70), C4 (P: 0.000, r: -0.71), and Cz (P: 0.000, r: -0.74) and the posteromedial direction of mSEBT. No significant correlations were between the other two directions of mSEBT, alpha, and theta. Conclusion: According to our results, balance impairment in patients with chronic PFP correlated with their QEEG neurodynamics. Moreover, our findings demonstrated the efficiency of QEEG as a neuromodulation method for patients with PFP.

Published
2024
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31. Exploration of Online Discussion Forum on Reading EST Texts for ESL Learners

Author

Azmuddin, Ruhil Amal, Jamali, Mazura, Rahim, Azwin Arif Abdul, Ali, Mohammad Musab Azmat, and Khamis, Nor Yazi

Abstract

Purpose: This study explored ESL learners' utilization of Online Discussion Forums (ODFs) in Interactive Reading for Academic Disciplines (iREAD) to assist reading of online scientific texts within an English for Specific Purposes course at a public university. Method: This qualitative research employed two data collection methods during lessons of a 14-week semester. The first was analysis of ODF threads in iREAD from 30 ESL learners of various engineering programs at a public university in the East coast of Malaysia. Twelve from 30 ESL students were purposively selected to participate in focus group interviews. Interactive Analysis Model was used to analyse the threads, whilst the interviews transcripts were analysed thematically. NVivo Pro 12 software was used to manage the data and the generated themes were later analysed using inter-rater reliability analysis. The Kappa values of the themes indicated near perfect scores, to verify the reliability of the identified themes. Findings: The findings revealed the positive capability of ODF as a tool in facilitating learners' reading comprehension of English for Science and Technical (EST) online materials. Analysis of the data on ODF threads confirm that students have the capabilities to engage in higher level of discussions and debate to aid their understanding. The themes that emerged from the interviews reflect positive impacts to facilitate reading of EST online materials, where learners were able to work collaboratively and increase knowledge and understand better. In addition, significance of EST materials was important in providing exposure to students' background knowledge of their content courses. The notion of social constructivism and connectivism were reflected as learners constructed knowledge, negotiated, and synthesized ideas using the language across networked learning communities. Significance: An implication from this study suggests that exposure to EST online materials through communication and collaboration via ODF has strengthened students' knowledge of the subject content. Additionally, learners' comprehension of reading online EST materials has been enhanced through ODF because of the tool's interactive heuristic approach to learning. This becomes significant for program developers and lecturers to combine both elements of ODF and material selection in lessons considering its positive impacts toward learning.

Published
2022

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