Search Results (5,479)

This article critically examines the Islamic Religious Community (IRC) in North Macedonia, focusing on its significant role in influencing religious practices, socio-political dynamics, and interfaith relations within the country. Since its inception following the dissolution of Yugoslavia, the IRC has been central to redefining the public role of Islam, advocating for religious freedoms, and supporting the development of democratic values. Through a mixed-methods approach, this research traces the historical evolution of the IRC, analyzes its constitutional and legal framework, and assesses its impact on North Macedonia’s multi-ethnic and multi-religious society. While the IRC’s contributions to promoting interfaith understanding and national stability are highlighted, the study also critically engages with the institution’s shortcomings, particularly the controversies related to its leadership and the challenges posed by its political entanglements. These issues have, at times, compromised the IRC’s efforts in fostering social cohesion and interfaith harmony. The findings provide valuable insights into the complex role of religious institutions in enhancing social stability and democratic governance in diverse societies, positioning the IRC as a significant case study with implications for similar contexts globally. Full article

Background: Warehousing operations, crucial to logistics and supply chain management, often seek innovative technologies to boost efficiency and reduce costs. For instance, AR devices have shown the potential to significantly reduce operational costs by up to 20% in similar industries. Therefore, this paper delves into the pivotal role of smart glasses in revolutionising warehouse effectiveness and efficiency, recognising their transformative potential. However, challenges such as employee resistance and health concerns highlight the need for a balanced trade-off between operational effectiveness and human acceptance. Methods: This study uses scenario and regression analyses to examine data from a German logistics service provider (LSP). Additionally, structured interviews with employees from various LSPs provide valuable insights into human acceptance. Results: The findings reveal that smart glasses convert dead time into value-added time, significantly enhancing the efficiency of order picking processes. Despite the economic benefits, including higher profits and competitive advantages, the lack of employee acceptance due to health concerns still needs to be addressed. Conclusions: After weighing the financial advantages against health impairments, the study recommends implementing smart glass technology in picking processes, given the current state of technical development. This study’s practical implications include guiding LSPs in technology adoption strategies, while theoretically, it adds to the body of knowledge on the human-technology interface in logistics. Full article

This essay examines the role of Anglo Protestant missions in the Persian Gulf in racializing “the Moslem world” for the emergent white world order at the beginning of the 20th century. More specifically, I consider the way Protestant missionaries extracted knowledge about Islam, racializing “the Moslem world” as a civilizational “unit” devoid of energetic life—and therefore incompatible with the modern world—even as they simultaneously mediated the rise of oil extraction along the Persian Gulf in that same period. Extraction was not only evident in the material relations of empire, but also in the way Protestant missionary discourse shaped “the Muslim world” into a racial unit in need of management and optimization. I consider two energetic grammars used by Protestant missionaries to signify the changes occurring in “the Moslem World”, namely, Samuel Zwemer’s use of “disintegration” and Basil Mathews use of “ferment”. I argue that it was in these material and discursive entanglements of oil extraction where knowledge about Islam became an important tool of European colonial governance, and where energetic grammars of religion became critical to the biopolitical production and management of racialized Muslim populations. Full article

In the aftermath of the 2003 Casablanca bombings, the Moroccan state emphasized, through official public discourse, the components that constitute “official Moroccan Islam” to combat extremist ideologies. These religious elements include Mālikism in jurisprudence, Ashʿarism in theology, and the Sufism of Imam Al-Junayd (d. 298/910), all balanced by the pledge of allegiance to King Mohammed VI (a descendant of the Sharifian lineage), the constitutionally designated Commander of the Faithful and sole religious leader. Since the reform policy initiated in 2004, the Moroccan state has constructed a narrative on the distinctiveness of Moroccan Islam—moderate and tolerant—and promoted it among its own citizens and beyond its borders. However, while the Moroccan state claims to have a unique form of Islam, controversial arguments have been raised questioning the nature of the state’s purported Islam. Other criticisms include investigating the state’s endorsement of Sufism and its broader policy of institutionalization. Yet, this article argues that the state supports any form of Islam, not necessarily Sufism, that aligns with its religious and political leadership. Like other Arab and Muslim states, Morocco’s religious policy is impacted by the global context, where Salafism is now perceived as a threat to established worldviews. In addition, this article argues that Morocco’s support for Sufi Islam is based not merely on its perceived political passivity, but because it complements the state’s policies and gains advantages from this support. It concludes that the official narrative of Moroccan Islam, which emphasizes a Sufi-oriented approach to counter extremism, is open to question, particularly given that Salafism was the state’s preferred form of Islam in post-colonial Morocco. This highlights the complex and often conflicting relationship between political actors and religious leaders in shaping Morocco’s religious discourse. Full article

Although the Indonesian government has conducted various interventions to escalate the uptake of rooftop PV in Indonesian households, adoption has still been sluggish. Few studies have been conducted to explore the issue, and these studies are scattered. The paper aims to assess generalizations of the previous studies regarding adopters’ and non-adopters’ characteristics of Indonesian households and their perceptions of rooftop PV attributes using meta-analysis. The findings show that statistically significant differences between the two studies in terms of socio-demographic factors, problem awareness, innovativeness, and perceived qualities of rooftop photovoltaics exist. Despite the differences, the adopters of both studies perceived equally that using renewable energy was important, that rooftop photovoltaics were environmentally friendly, and that they were generally aware of environmental problems. It appears that the non-adopters sample drawn from stratified random sampling demonstrates a similar distribution specified by Diffusion of Innovation. Furthermore, the non-adopters in the two research show a comparable belief regarding the significance of putting renewable energy into practice. Due to inconclusive patterns, an empirical investigation that sufficiently represents both the rooftop PV adopters and non-adopters in Indonesian households is suggested. Other potential future research are also discussed. Full article

This article provides a thorough examination of the past evolution and present conditions of Islamic education in Kazakhstan. It commences with an examination of the influence of the Hanafi school within Sunni Islam and traces the evolution of the Islamic educational system throughout various historical periods until the present day. Particular focus is given to blending Islamic teachings with contemporary educational structures. The researchers heuristically analyzed the current state of the field, identified the main problems, and suggested prospects for Islamic education for further development. In this study, the PRISMA methodology is adopted to systematize the literature, enabling a detailed examination of the characteristics and nuances of Islamic education within the historical and cultural traditions of Kazakhstan. The current study utilizes the historical method, which uses primary and secondary data sources. This method prioritizes historical analysis as a means to assess past occurrences. This paper examines the axiological importance of madrasas and Islamic universities, as well as the pedagogical approaches employed in these educational establishments. We have presented infographics that contribute to a detailed understanding of the field. From the heuristic analyses, we formulated practical recommendations for the government designed to enhance the quality and effectiveness of Islamic education. This paper highlights the importance of Islamic education as an important component of the cultural and scientific space of Kazakhstan, offering strategies for its sustainable development in the context of globalization and the transformation of educational systems. The results of this work are of significant interest to academics, madrasas, colleges, and higher education institutions specializing in Islamic education. Full article

The Unani Tibb is a medical system of Greek descent that has undergone substantial dissemination since the 11th century and is currently prevalent in modern South and Central Asia, particularly in primary health care. The ingredients of Unani herbal medicines are primarily derived from plants. Our research aimed to address the pressing issues of antibiotic resistance, multi-drug resistance, and the emergence of superbugs by examining the molecular-level effects of Unani ingredients as potential new natural antibiotic candidates. We utilized a machine learning approach to tackle these challenges, employing decision trees, kernels, neural networks, and probability-based methods. We used 12 machine learning algorithms and several techniques for preprocessing data, such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and Principal Component Analysis (PCA). To ensure that our model was optimal, we conducted grid-search tuning to tune all the hyperparameters of the machine learning models. The application of Multi-Layer Perceptron (MLP) with SMOTE pre-processing techniques resulted in an impressive accuracy precision and recall values. This analysis identified 20 important metabolites as essential components of the formula, which we predicted as natural antibiotics. In the final stage of our investigation, we verified our prediction by conducting a literature search for journal validation or by analyzing the structural similarity with known antibiotics using asymmetric similarity. Full article

Smart environments exploit rising technologies like Internet of Things (IoT) and edge intelligence (EI) to achieve unseen effectiveness and efficiency in every tasks, including air sanitization. The latter represents a key preventative measure–made even more evident by the COVID-19 pandemic–to significantly reduce disease transmission and create healthier and safer indoor spaces, for the sake of its occupants. Therefore, in this paper, we present an IoT-based system aimed at the continuous monitoring of the air quality and, through EI techniques, at the proactively activation of ozone lamps, while ensuring safety in sanitization. Indeed, these devices ensure extreme effectiveness in killing viruses and bacteria but, due to ozone toxicity, they must be properly controlled with advanced technologies for preventing occupants from dangerous exposition as well as for ensuring system reliability, operational efficiency, and regulatory compliance. Full article

There has been a rise in Islamic schools in Australia, a trend similarly seen in other Western countries, and yet limited opportunities for teacher preparation on what it means to impart an Islamically grounded education. This study utilises qualitative research methods, specifically portraiture to shed light on the experiences of in-service Islamic school educators, with varied backgrounds and religious affiliations in a cohort of the Graduate Certificate in Education (Islamic Education) program in Australia. This faith-based teacher education program aims to foster “faithful praxis”, and recenter the Divine in teaching and learning. The research focused on analysing transformative affective shifts as reflected in the program’s final portfolios. Portraits of four participants reflect a reconceptualisation of education as a holistic process that aims to nurture the whole student: mind, body, and soul. The portraits also highlight awakening experiences that signify the role of reflexivity and self-reflection of the educator so that educational renewal is of the whole collective in the school community. Implications of this study signify the role of spirituality in teaching and learning and the importance of moving beyond conventional and secular models of teacher education programs. Full article

The rapid and sensitive detection of Salmonella typhimurium in food matrices is crucial for ensuring food safety. This study presents the development of an ultrasensitive electrochemical biosensor using surface-modified bacterial cellulose (BC) integrated with polypyrrole (Ppy) and reduced graphene oxide (RGO), further functionalized with immobilized S. typhimurium-specific phage particles. The BC substrate, with its ultra-fibrous and porous structure, was modified through in situ oxidative polymerization of Ppy and RGO, resulting in a highly conductive and flexible biointerface. The immobilization of phages onto this composite was facilitated by electrostatic interactions between the polycationic Ppy and the negatively charged phage capsid heads, optimizing phage orientation and enhancing bacterial capture efficiency. Morphological and chemical characterization confirmed the successful fabrication and phage immobilization. The biosensor demonstrated a detection limit of 1 CFU/mL for S. typhimurium in phosphate-buffered saline (PBS), with a linear detection range spanning 10⁰ to 10⁷ CFU/mL. In real samples, the sensor achieved detection limits of 5 CFU/mL in milk and 3 CFU/mL in chicken, with a linear detection range spanning 10⁰ to 10⁶ CFU/mL, maintaining high accuracy and reproducibility. The biosensor also effectively discriminated between live and dead bacterial cells, demonstrating its potential in real-world food safety applications. The biosensor performed excellently over a wide pH range (4–10) and remained stable for up to six weeks. Overall, the developed BC/Ppy/RGO–phage biosensor offers a promising tool for the rapid, sensitive, and selective detection of S. typhimurium, with robust performance across different food matrices. Full article

Mosques, revered as sacred spaces, are distinguished by their sustainable architectural designs. A critical aspect of these designs is the implementation of daylighting strategies, essential for ensuring adequate light levels, particularly for reading the Holy Quran. Traditional daylighting typologies in mosques, such as multi-lateral lighting, clerestories, skylights, and shading devices, are widely recognized. However, the potential application of electrochromic (EC) switchable glazing within mosque architecture remains underexplored. This study delves into the effectiveness of various daylighting typologies, assessing their performance before and after the integration of EC switchable glazing in the Al-Shagroud Mosque located in Saudi Arabia. Employing comprehensive radiance simulations in accordance with the LEEDv4.1 standards, the study evaluates additional metrics including useful daylight illuminance (UDI_{300–3000lux}), workplane illuminance (WPI), uniformity index (Ui), and daylight glare probability (DGP) under multiple scenarios. The results demonstrate that clerestory lighting effectively meets LEEDv4.1 benchmarks but exhibits limitations in achieving uniform light distribution, particularly in the men’s prayer hall. In contrast, the women’s prayer hall benefits from a combination of clerestory lighting and shading devices, which improves light quality and mitigates glare issues. However, the use of skylights without adequate shading leads to excessive WPI and high DGP levels, compromising visual comfort. Moreover, managing daylight quantity and quality through the integration of different typologies, including hybrid models, presents substantial challenges. The integration of EC switchable glazing, while maintaining optimal WPI and reducing DGP, offers enhanced visual comfort in prayer areas. Nevertheless, this technology faces difficulties in ensuring even light distribution, as evidenced by lower uniformity index values. This research highlights the critical need for optimizing daylighting in mosques to balance sustainability and comfort, without detracting from their sacred ambiance. Full article

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model’s performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality. Full article

Raspberry (Rubus idaeus L.) fruit are known for their extremely short shelf life. Decay, leakiness, and loss of firmness are the most common limiting factors contributing to their short storage life. However, storage in elevated CO₂ and reduced O₂ atmospheres can delay senescence in fruit by reducing softening, respiration and ethylene production rates, and pathogen growth. In this study, raspberries were exposed to four different CO₂ atmospheres—15 kPa CO₂ and 6 kPa O₂ (15 kPa); 8 kPa CO₂ and 13 kPa O₂ (8 kPa); 5 kPa CO₂ and 16 kPa O₂ (5 kPa); or 0.03 kPa CO₂ and 21 kPa O₂ (0.03 kPa)—and were evaluated for their postharvest quality periodically during two weeks of storage in 2020 and 2021. Raspberry fruits kept in a 15 kPa CO₂ atmosphere followed by 8 kPa CO₂ had higher firmness, brighter red color, and the least fungal decay or leakiness. In all atmospheres, the total anthocyanin content increased over time, although the rate of increase was slowed by high CO₂. The raspberries’ visual attributes deteriorated over time in all atmospheres, but high CO₂ atmospheres slowed the rate of deterioration. After five days, the quality of air-stored raspberries was significantly degraded, while the raspberries stored in elevated CO₂ maintained good quality for up to ten days. Full article

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply. Full article

Background: Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. Methods: A cross-sectional study was conducted among 1496 adolescents using a structured questionnaire. Data were collected through a two-stage stratified cluster sampling method. Chi-square tests and logistic regression analyses were performed using SPSS. Machine learning models, including Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM), were employed to identify and predict EDS risk factors using Python and Google Colab. Results: The prevalence of EDS in the cohort was 11.6%. SHAP values from the CatBoost model identified self-rated health status, gender, and depression as the most significant predictors of EDS. Among the models, GBM achieved the highest accuracy (90.15%) and precision (88.81%), while CatBoost had comparable accuracy (89.48%) and the lowest log loss (0.25). ROC-AUC analysis showed that CatBoost and GBM performed robustly in distinguishing between EDS and non-EDS cases, with AUC scores of 0.86. Both models demonstrated the superior predictive performance for EDS compared to others. Conclusions: The study emphasizes the role of health and demographic factors in predicting EDS among adolescents in Bangladesh. Machine learning techniques offer valuable insights into the relative contribution of these factors, and can guide targeted interventions. Future research should include longitudinal and interventional studies in diverse settings to improve generalizability and develop effective strategies for managing EDS among adolescents. Full article