Abstract

Obesity and headache are conditions associated with a substantial personal and societal impact. This study aims to evaluate the association between body mass index (BMI) and Headache among adults in Gharyan City, Libya. This was a descriptive cross-sectional study, conducted at Gharyan city, Libya, during the period from December 2024 to March 2025. Two hundred (200) participants, including 94 males (47%) and 106 females (53%), were enrolled in this study. Data were collected by using a structured questionnaire. The participants were divided into four categories, based on BMI: underweight (<18.5), normal weight (18.5 to 24.9), overweight (25 to 29.9), and obese (>30). A total of 200 participants, both gender males and females, were enrolled in this study during the study period. The study indicates that high percentage of headache in age was 43 -56 years, and severe obesity BMI > 30 kg/m2 were observed of high percentage in females n=27 (25.47%) compared to males n=17 (18.08%), although the severe obesity and headache found in females n=37 (34.90%), rather than males n=30 (31.91%) Obesity and headache are both, slightly more common in females than males, we recommend that more studies among obesity and headache in large patients should be done, although small sample size and lack of long-term flow-up of patients, were the main limitations of our study and results suggested a need for monitoring by X-ray and MRI diagnosis of patients have obesity

Keywords. Obesity. Headache, Gharyan City, Libya.

شهدت المدن الليبية تحولات متسارعة نتيجة للنمو السريع، والذي عالبا ما حدث في غياب تخطيط عمراني محكم أدى هذا الوضع الى مخاوف بشأن الزحف العمراني وعدم المساواة في الوصول الى الخدمات.تقدم هذه الدراسة منهجية تعتمد على نظم المعلومات الجغرافية (GIS) لتتبع هذه التغيرات في مدينة طرابلس، ليبيا تستخدم بيانات مفتوحة المصدر حول الشكل الحضري، والغطاء الارضي، وأنماط الاستيطان، بالاضافة الى مواقع الخدمات العامة، قمت بقياس كيفية نمو طرابلس وتحديد المناطق التي تفتقر الى الخدمات.

تظهر صور الاقمار الصناعية والمقاييس المكانية نموا قويا في المناطف المبينة على مدى أربعة عقود، حيث ازداد الزحف العمراني حدة في العقد الاول من القرن الحادي والعشرين ارتفع مؤشر "شانون انثروبي من 0.74 في عام 1984 الى 0.90 في عام 2010 مما يشيرالى أن النمو أصبح أكثر تشتتا، وفي الوقت نفسه، شهدت المناطق الطرفية الجديدة زيادة سكانية دون توفر خدمات كافية، مما يضطر الكثير من السكان لقطع مسافات أطول للوصول الى الرعاية الصحية والمدارس.

قمت بدمج نقاط الخدمات من خرائط الشوارع المفتوحة مع الشبكات السكانية، ومن ثم رسمت خرائط لفجوات الخدمة لتحديد المناطق ذات التغطية الضعيفة، تظهر النتائج واضحا لعدم المساواة الخدمية، حيث تحتوي مناطق غير المخطط لها على أقل من المستشفيات والمدارس والمرافق لكل فرد، بينما تتمتع الاحياء المركزية المخططة بمستويات خدمة أفضل. تساعد هذه المنهجية المعتمدة على نظام المعلومات الجغرافية المخططين في رؤية شكل النمو فجوات الخدمة، كما تدعم اتخاذ قرارات تخطيطية أفضل، تختتم الدراسة بخطوات للتحكم في النمو بشكل أفضل واقترح التوسع الموجه والاستثمار الخدمي المركز، وهى خطوات يمكن أن تدعم نموا عادلا ومستداما في طرابلس والمدن المماثلة.

الكلمات الافتتاحية: التوسع الحضري- نظم المعلومات الجغرافيةGIS - عدم المساواة المكانية- المورفولوجيا الحضرية- امكانية الوصول للخدمات.

ABSTRACT This study investigates the effects of self-assessment on students’ motivation and language proficiency among English Department students at the Faculty of Arts, AL-Assaba. A quantitative research design was adopted, using a questionnaire distributed to a sample of 60 students. The findings indicate that self-assessment plays a significant role in enhancing learners’ motivation, self-awareness, and perceived language proficiency. Most students reported positive attitudes toward self-assessment and felt it Improved their English skills. The study concludes that incorporating self-assessment into English language instruction can foster learner autonomy and Improve learning outcomes. It recommends Integrating self-assessment practices into curricula and providing guidance to students on effective self-assessment. Keywords: Self-assessment, Motivation, Language Proficiency, Learner Autonomy, English Department Students, Higher Education.  

Abstract

Stem cell therapy has emerged as a promising approach for craniofacial bone remodeling and dental tissue regeneration. In this study, a mixed-methods design was employed, combining laboratory analyses with clinical data from 100 participants. Results demonstrated that 80% of respondents had received mesenchymal stem cell (MSC) therapy, with 70% reporting improvements in bone density and 65% noting enhanced dental tissue regeneration. Imaging tests confirmed progress in 85% of cases, while overall satisfaction with therapy outcomes was reported by 80–85% of participants. Correlation analysis revealed strong positive relationships between stem cell treatment and improvements in bone and dental tissues, with coefficients ranging from 0.60 to 0.70 (p < 0.01). Side effects were minimal, with only 20% reporting complications such as swelling or pain. These findings underscore the therapeutic potential of stem cell-based interventions, highlighting both their efficacy and safety. The study concludes that stem cell therapy represents a viable and effective strategy for regenerative dentistry and craniofacial surgery, though larger multicenter trials are needed to standardize protocols and validate long-term outcomes.

Abstract

One of the most challenging  skills for English as a Foreign Language (EFL) learners is often the ability to speak, especially in environments where opportunities for authentic English exposure are scarce. This study focuses on prevalent linguistic errors found in the spoken English of third semester EFL students at the Faculty of Arts, Gharian University. . The three primary types of errors that are examined are phonological, grammatical, and lexical. The study also explores  the possible reasons behind these errors, such as first-language (L1) interference, limited English exposure, inadequate speaking practice in the classroom, fossilization, and emotional factors. Students often face difficulties with English phonemes that do not exist in Arabic, as well as with the correct grammatical forms, and suitable vocabulary choices, according to the results. The study emphasizes how important it is to implement focused interventions to improve students' spoken accuracy and fluency. It suggests pedagogical recommendations to enhance the accuracy and fluency of students’ spoken English, including increasing the number of opportunities for communicative practice, targeted pronunciation training, and the incorporation of authentic listening resources.

Keywords, English as a Foreign Language, linguistic errors, speaking skill

This paper presents the design and implementation of a smart robotic vacuum cleaner based on Internet of Things (IoT) technologies, named IO-VAC, specifically developed for smart home environments. The paper covers both the mechanical and electronic aspects of the robot’s development. A 2D layout was created using ArchiCAD to define the positioning of components and sensor angles when designing the robot, followed by a 3D model to visualize the final design. The robot chassis was fabricated using reinforced plastic to ensure durability and lightweight structure. The IO-VAC is controlled using an Arduino Mega board programmed in C/C++. Where, the system includes three ultrasonic sensors for obstacle avoidance, along with an infrared (IR) sensor for edge detection to prevent falls. The embedded system of IO-VAC integrates an ESP8266 Wi-Fi module, allowing for remote control via the Blynk IoT platform to enable users to control the robot’s movement (forward, backward, left, right), adjust suction power, select the desired cleaning algorithm, and monitor the battery level in real-time. In this paper three movement algorithms were developed: random, spiral and zigzag, and their performance was tested on two types of surfaces smooth and rough to ensure reliability and efficiency in various cleaning conditions after testing the validation of IO-VAC. Based on the test results of robot performance, the zigzag algorithm proved to be the most effective in terms of covered area, cleaning mission time, power consumption and minimizing redundancy. This work demonstrates an effective integration of embedded systems with IoT technologies, offering a smart and cost-efficient solution for automating household cleaning tasks.

This study presents a comprehensive empirical comparison between Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for multi-domain Arabic text classification. A curated dataset covering seven categories was used for evaluation. The experimental results demonstrate that LSTM-based RNN models significantly outperform CNN architectures, achieving 98% accuracy. The findings highlight the importance of sequential modeling for handling the morphological and syntactic complexity of the Arabic language.