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Purpose The purpose of this paper is to explore how Palestinian refugee families in Malaysia cope with the challenges they face during their transitional destination. These families have recently fled from conflict zones in various Arab countries and moved to Malaysia where they registered in the office of the United Nations High Commissioner for Refugees (UNHCR) in Kuala Lumpur, waiting for resettlement in a third country. Design/methodology/approach The paper utilizes the qualitative research approach, drawing descriptive analysis (thematic analysis technique) of data collected by semi-structured, in-depth interviews with 30 Palestinian families. For sample selection, the author used purposive sampling and employed the Snowball technique to select the families who have experienced refuge life for the second time. Then the sample was equally divided into three groups to include ten families moving from Iraq, Syria and the Gaza Strip. The interviews were conducted with the head of the families who varied in terms of gender and age composition. The study involved 9 female participants and 21 males. They are all Muslims and aged between 24 and 80 years old. In terms of educational background, all of the participants are literate with formal school education, diploma or tertiary education. Findings The study shows that refugee families adopt four main ways of coping commonly used to improve the quality of their life during their transition in Malaysia. They turn to religion and spirituality, keep in touch with relatives and friends in the diaspora and conflict zones, develop a positive image about their present situation by comparing it to worse living conditions of others living in conflict zones and plan to migrate to Europe to hold a European passport which will give them the opportunity to visit their original country, Palestine. Originality/value The paper tackles the Palestinian refugee family life and explores their real-life stories and experiences during transition in Malaysia. It provides them with an opportunity to speak up their suffering and reveal the various strategies they adopt to cope with life challenges. Besides, the result of the study will be a fruitful addition to the corpus of sociological knowledge as well as an important contribution to the families that stay in transition in different societies around the world.

Laparoscopic adjustable gastric banding (LAGB) is considered to be a safe and effective method of weight loss and reduction of comorbidities associated with obesity. Despite its improved early safety profile compared with Roux-en-Y gastric bypass, patients with LAGB can manifest unique complications that must be recognized and managed appropriately to achieve good outcomes. This review will prepare the general surgeon to identify, diagnose and manage the common complications encountered in patients presenting following LAGB.

Millions of children in conflict-affected countries are deprived of their fundamental rights to education. Using the qualitative exploratory research method, this study aims to explore ways of providing education to such children, and to identify the challenges facing their implementation. It also presents two short case studies conducted on Palestinian and Syrian refugees residing in Malaysia to explore their perceptions towards their education in their current situation and future orientation. The results show that despite the educational programmes initiated by various organizations, the affected community continue to face numerous political, financial, psychological, economic, administrative, or institutional challenges. The analysis of the interviews data revealed several categories and themes, among them related to the participants' current situation, educational needs, roles of different members of the community involved, and the challenges. The study recommends increasing efforts to meet the educational demands of the huge number of children out of schools.

This paper addresses the barriers that prevent Palestinian refugee children from obtaining education during their transition in the host country, Malaysia. It creates awareness among the Palestinian refugee community about this critical issue which threatens the future of their children who might spend many years without receiving adequate learning opportunities. The present study is based on a qualitative approach which is commonly used to explore recent phenomenon and provide an in-depth understanding of human behaviours and beliefs based on their points of view. It collected data employing semi-structured interviews with 30 Palestinian refugee families moving from Iraq, Syria and the Gaza Strip, and currently residing in Malaysia, waiting to be resettled in a third country through the United Nations High Commissioner for Refugees (UNHCR) office in Kuala Lumpur. Using thematic analysis technique, the research shows that there are economic, institutional and parental challenges preventing the Palestinian refugee children from receiving education in Malaysia. Also, it reveals how such challenges lead to negative psychological impacts among the parents and their children. Moreover, it is found that the parents did not play an adequate role in utilising potential alternative pathways to education. Finally, it offers a realistic solution to return these children to a viable education.

Family and marriage in Sri Lanka have endured far-reaching changes in recent decades. Starting with the migration from Sri Lanka, particularly after the 1970s, is one of the contributing factors in such family changes. Given that Sri Lanka has been giving greater importance to family and marriage, the present paper attempts to investigate changes in marriage choices, traditions, and marital relationships due to migration. The qualitative research method was utilized, compiling data through semi-structured interviews with thirty Sri Lankan migrant families using a purposive sampling technique, which was analyzed on a thematic basis. Based on the findings, migration was the cause of change in marital choice and traditions since migrants become economically independent and cherish new cultures. The marriage contracts and couple matching among migrant workers had not been spelt out their ancient traditions of Sri Lanka, which leaned more toward love matches. The study has identified the women who migrated as housemaids had formed families with Pakistani and Indian migrant workers, even accepting religious exogamy after arriving in Bahrain based on self-desire. These migrants concealed their personal choice and married without their parent's concerns and presence. The present study identified that wedlock without proper documents caused their children to be undocumented. Furthermore, the current study revealed that migrant workers who married other nationalities failed to establish fruitful families as their lives ended in divorce due to the lack of knowledge about marital partners and cohesive relationships with them. These findings are significant because of the dearth of literature on marriage migration among Middle Eastern migrants and the range of post-marriage challenges that migrants face in that particular destination. The information explored enriches the literature with a new sociological and empirical study.

Traditional methods for predicting sow reproductive cycles are expensive, labor-intensive, and expose workers to occupational hazards, including respiratory toxins, repetitive stress injuries, and mental health issues. On large farms with limited staff, managing individual sow health becomes increasingly difficult, underscoring the need for automated solutions. This is where computer vision offers a promising, non-contact method to detect estrus by analyzing vulva features like size and using thermal imagery to monitor temperature changes, which are key indicators of reproductive readiness. Automating this process enhances breeding efficiency by ensuring insemination occurs at the optimal time while continuously monitoring sows and alerting staff to anomalies for early intervention. A critical aspect of automating estrus detection is the accurate analysis of vulva size and temperature variations, as these subtle changes signal ovulation. Maintaining consistent camera distance during data collection is essential for achieving reliable results, as variations in distance can distort measurements, leading to inaccurate estimations of estrus and resulting in missed breeding opportunities or false positives. This inconsistency is particularly problematic in thermal image analysis, where differences in resolution can cause pixel intensity values to misrepresent actual temperature variations. To improve the accuracy and generalizability of computer vision models, researchers must address these challenges by standardizing image capture protocols and calibrating equipment to account for external factors such as atmospheric conditions. Overcoming these limitations will ensure that automated estrus detection remains a reliable and scalable solution that improves sow reproductive health, optimizes farm productivity, and safeguards worker well-being. This dissertation details the comprehensive process of automating estrus detection in sows, starting with the accurate detection of vulva shape and size, which is essential for reliable estrus identification. To achieve this, various methods were proposed to determine the most effective approach for vulva segmentation. The initial chapter explores multiple object segmentation techniques, beginning with the manual annotation of thermal images to enhance the visibility of the Region of Interest (ROI) and ensure precise segmentation. To optimize vulva segmentation, the system was trained using a series of models, evolving from U-Net for semantic segmentation to YOLOv8 and ultimately YOLOv9, as part of a continuous effort to enhance performance and accuracy. The second chapter refines the detection framework by integrating YOLO for vulva segmentation and keypoint detection, allowing for geometric assessments of vulva size and shape. Building on the precise vulva segmentation in the previous chapter, we gained critical insights into its shape, enabling accurate perimeter measurements. Keypoint identification facilitated precise Euclidean distance calculations—horizontally between the labia and vertically from the clitoris to the perineum. Camera calibration through monocular depth estimation linked image-based measurements to real-world distances, ensuring accurate scaling and analysis. Finally, we introduce a classification method to distinguish estrus from non-estrus states by comparing pixel-based dimensions and perimeter measurements. This approach applies a nearest-neighbor algorithm, aggregating distance calculations to classify new data points based on similarity to reference datasets, ensuring reliable estrus detection. Building upon the foundation established in the previous chapters, the final chapter enhances the detection model by further refining YOLO-based segmentation and expanding the analysis to incorporate pixel intensity values from thermal images. This additional layer of analysis leverages the subtle thermal variations in the vulva region, providing crucial insights into estrus detection that were not captured through geometric measurements alone. By integrating pixel intensity as a complementary factor, the model achieves greater sensitivity and accuracy in distinguishing between estrus and non-estrus states. This holistic approach enhances the robustness of the detection framework, ensuring more precise and reliable results across varying environmental conditions and improving the overall effectiveness of automated estrus monitoring systems. To validate our approach, we implemented a three-stage evaluation process. In the first stage, we computed the Mean Squared Error (MSE) to compare the ground truth keypoints of labia distance with the predicted keypoints, applying the same method to measure the distance between the clitoris and perineum. Next, we conducted a quantitative analysis to benchmark the performance of our YOLOv9 segmentation model against the previous U-Net and YOLOv8 models. Finally, we evaluated the classification process by constructing a confusion matrix and comparing the outcomes across different methods. This comprehensive evaluation highlights the advancements in accuracy and performance achieved through our updated model, underscoring its potential for more reliable and automated estrus detection in sows. Through rigorous experimentation and quantitative evaluation, this dissertation demonstrates the transformative potential of computer vision in livestock management. The proposed system offers an automated, non-contact solution for estrus detection, contributing to more efficient reproductive health monitoring and sustainable agricultural practices.

The objective of computer vision research is to endow computers with human-like perception to enable the capability to detect their surroundings, interpret the data they sense, take appropriate actions, and learn from their experiences to improve future performance. The area has progressed from using traditional pattern recognition and image processing technologies to advanced techniques in image understanding such as model-based and knowledge-based vision. In the past few years, there has been a surge of interest in machine learning algorithms for computer vision-based applications. Machine learning technology has the potential to significantly contribute to the development of flexible and robust vision algorithms that will improve the performance of practical vision systems with a higher level of competence and greater generality. Additionally, the development of machine learning-based architectures has the potential to reduce system development time while simultaneously achieving the above-stated performance improvements. This work proposes utilizing a computer vision-based approach that leverages machine and deep learning systems to aid the detection and identification of sow reproduction cycles by segmentation and object detection techniques. A lightweight machine learning system is proposed for object detection to address dataset collection issues in one of the most crucial and potentially lucrative farming applications. This technique was designed to detect the vulvae region in pre-estrous sows using a single thermal image. In the first experiment, the support vector machine (SVM) classifier was used after extracting features determined by 12 Gabor filters. The features are then concatenated with the features obtained from the Histogram of oriented gradients (HOG) to produce the results of the first experiment. In the second experiment, the number of distinct Gabor filters used was increased from 12 to 96. The system is trained on cropped image windows and uses the Gaussian pyramid technique to look for the vulva in the input image. The resulting process is shown to be lightweight, simple, and robust when applied to and evaluated on a large number of images. The results from extensive qualitative and quantitative testing experiments are included. The experimental results include false detection, missing detection, and favorable detection rates. The results indicate state-of-the-art accuracy. Additionally, the project was expanded by utilizing the ”You Only Look Once” (YOLO) deep learning Object Detection models for fast object detection. The results from object detection have been used to label images for segmentation. The bounding box from the detected area was systematically colored to achieve the segmented and labeled images. Then these segmented images are used as custom data to train U-Net segmentation.The first step involves building a machine-learning model using Gabor filters and HOG for feature extraction and SVM for classification. The results discovered the deficiency of the model, therefore a second stage was suggested in which the dataset was trained using YOLOv3-dependent deep learning object detection. The resulting segmentation model is found to be the best choice to aid the process of vulva localization. Since the model depends on the original gray-scale image and the mask of the region of interest (ROI), a custom dataset containing these features was obtained, augmented, and used to train a U-Net segmentation model. The results of the final approach show that the proposed system can segment the sow’s vulva region even in low-rank images and has excellent performance efficiency. Furthermore, the resulting algorithm can be used to improve the automation of estrous detection by providing reliable ROI identification and segmentation and enabling beneficial temporal change detection and tracking in future efforts.

This paper addresses the perceptions and practices of doctorate candidates at the International Islamic University Malaysia (IIUM) toward its vision of devoting educational efforts for restoring the dynamic role of the Muslim Ummah in all fields of knowledge and intellectual discourse. It investigates to what extent they possess competent research skills and foster core values such as cooperation and trust. It utilizes the qualitative research approach, using thematic analysis of data collected by semi-structured interviews. The sample is composed of ten Ph.D candidates, coming from the Middle East and Asian countries who currently study at IIUM. They are all Muslims; four females and six males. Eight candidates are the international students and two are Malaysians. Purposive sampling was implemented to select the candidates who were still pursuing their Ph.D degree at the time of conducting the study. They all came from five different kulliyyah (faculties), with two from each kulliyyah. The study showed that the Ph.D candidates' practices did not reflect the vision of IIUM. They lacked of competent research knowledge and skills as well as the core values represented by cooperation and mutual trust, which were clearly manifested in the IIUM's vision. The implication of this current qualitative study may bring into the realization of the existing critical situation of the Muslim Ummah. The Maqasid al- Shariah may be seen as the practical formula for IIUM to restore the core educational system for Islamic revivalism and glory.

Laparoscopic adjustable gastric banding (LAGB) is considered to be a safe and effective method of weight loss and reduction of comorbidities associated with obesity. Despite its improved early safety profile compared with Roux-en-Y gastric bypass, patients with LAGB can manifest unique complications that must be recognized and managed appropriately to achieve good outcomes. This review will prepare the general surgeon to identify, diagnose and manage the common complications encountered in patients presenting following LAGB.

Laparoscopic adjustable gastric banding (LAGB) is considered to be a safe and effective method of weight loss and reduction of comorbidities associated with obesity. Despite its improved early safety profile compared with Roux-en-Y gastric bypass, patients with LAGB can manifest unique complications that must be recognized and managed appropriately to achieve good outcomes. This review will prepare the general surgeon to identify, diagnose and manage the common complications encountered in patients presenting following LAGB. On considère que le cerclage gastrique laparoscopique par anneau modulable constitue une méthode sûre et efficace pour la perte de poids et la réduction des comorbidités associées à l’obésité. Malgré son profil d’innocuité précoce amélioré comparativement à la dérivation gastrique de Roux-en-Y, les patients soumis à un cerclage gastrique peuvent présenter des complications particulières qu’il faut savoir reconnaître et corriger de manière appropriée pour obtenir de bons résultats. La présente synthèse préparera le chirurgien général à reconnaître, diagnostiquer et traiter les complications courantes chez les patients qui consultent après un cerclage gastrique laparoscopique.