Search Results (188)

Nahj al-Balāghah is widely recognized as a foundational and authoritative scripture in Shia Islam. One notable aspect of Nahj al-Balāghah is the deliberate selection and structured arrangement of Ḥadiths. According to the book’s introduction, al-Raḍī explains that he chose the Ḥadiths based on literary considerations. An analysis comparing the selected Ḥadiths with their full versions suggests their inclusion was determined not only by eloquence and rhetorical value but also by conceptual significance. Through textual and descriptive analytical methods, this study examines the author’s motives, especially his political and religious aims, in incorporating materials related to ʿĀisha. A comparison of the relevant ḥadīths in Nahj al-Balāghah and other historical sources indicates that Sayyid Raḍī omitted—or at least refrained from including—certain statements attributed to ʿAlī regarding the Prophet Muḥammad’s youngest wife. The omitted parts concern ʿĀisha’s inconsiderate behavior, grudges, sins, following Satan, and ignoring the Prophet’s prediction. Considering sectarian conflicts between Shiites and Sunnis in the 3rd and 4th centuries AH, some arising from criticisms of ʿĀisha’s conduct and sometimes escalating into violence, al-Raḍī, the supreme judge appointed by the ʿAbbāsid Caliphate, was compelled to omit and censor ʿAli’s harsh remarks about ʿĀisha to prevent further sectarian tensions. Full article

As sustainability marketing research advances, conceptual debates persist regarding relevant stakeholders, motivations, and the integration of sustainability into marketing. However, these debates are not always consistently reflected in empirical work. Given the urgency of sustainability, a unifying framework is crucial to synthesize four decades of research. We aim to provide a durable framework to ensure that critical research progresses efficiently by supporting meaningful knowledge generation in sustainability marketing research. We conduct a scoping review of 48 conceptual articles, resulting in the A-VO-S Map. Its three dimensions are: Actors (Consumers, Businesses, Institutions), Value Orientations (Self-, Societally-, and Environmentally-Oriented), and Scope of Sustainability (Peripheral to Central). We then present an evidence map of empirical sustainability marketing research based on a content analysis of 191 empirical articles from 19 top journals. Findings reveal that Consumers are overrepresented, Societal-Orientation lacks supporting evidence, and sustainability is studied with a Moderate Scope. We conclude the paper with a practitioner-informed research agenda and an interactive version of our evidence map. Thus, we offer unique contributions over prior reviews through a meta-framework that functions as an evidence map, with high valorization potential due to its interactive tool and practitioner-informed perspectives. Full article

Shrinkage of injection-molded parts is a major challenge for dimensional accuracy, especially for semi-crystalline polymers where crystallization induces pronounced volume change and heat release during cooling. Because packing pressure is effective only before gate or local solidification, multi-stage packing is commonly used to regulate the overall shrinkage behavior. In practice, however, the solidification/transition temperature taken from standard material tests does not necessarily represent the actual in-cavity state behavior under specific cooling rate and pressure history, which compromises the consistency of P–V–T-based shrinkage prediction. In this study, a modified P–V–T-based framework (Tait equation) is developed for polypropylene (PP) by introducing a Thermal Enthalpy Transformation Method (TETM) to determine a process-relevant solidification time and crystallization-completion temperature (including the corresponding target specific volume) directly from in-cavity melt temperature monitoring using an infrared temperature sensor. The novelty TETM utilizes the crystallization-induced enthalpy release to identify the temperature–time plateau, from which one can identify the effective solidification point. Because the Tait equation adopts a two-domain formulation (molten and solidified states), accurate identification of the domain-switching temperature is critical for reliable shrinkage prediction in practical molding conditions. In the experiment execution, the optimum filling time was defined using the minimum pressure required for melt-filling. Four target specific volumes, three melt temperatures, and two mold temperatures were examined, and a two-stage packing strategy was implemented to achieve comparable shrinkage performance under different target specific volumes. A conventional benchmark based on the solidification temperature reported in the Moldex3D material database was used for comparison only. The results show that the target specific volume determined by the TETM exhibits a more consistent and near-linear relationship with the measured shrinkage rate, demonstrating that the TETM improves the robustness of solidification-time identification and the practical usability of P–V–T information for shrinkage control. Full article

In the face of rapid aging, depression in later life has become a prominent issue in urban public health and environmental research. As potential places for social activities and emotional healing, the social stayability of shaded community spaces is an essential environmental factor influencing the mental well-being of the elderly. In order to overcome the challenge of depression relief in later life, it is important to investigate what attributes of social stayability in shaded spaces influence the mental well-being of the elderly, as well as their gap structures. This study innovatively develops a fuzzy multi-criteria decision-making method and builds an analytical framework combining Fuzzy-BWM and VIKOR to comprehensively evaluate three dimensions of physical accessibility, facilities, and spatial conditions, and environmental comfort and safety of shaded spaces. Using the Pioneer community in Panyu, Guangzhou, and the Yuehan community in Macau as empirical cases, this study integrates expert judgment and residents’ perception data to identify the key attributes and gap structure of shaded space stayability in mitigating depression-related psychological risk and promoting emotional restoration and psychological well-being among older adults. The results show that facilities and spatial conditions have the greatest impact on social stayability. The two attributes of sitting comfort and public service facilities are the dominant factors that affect stay intention and emotional recovery. Environmental comfort and safety have a secondary but stable supporting effect on psychological security. This study reveals the coupling relationship between functional configuration and perceptual experience and advocates for the transformation of urban renewal thinking from spatial optimization to psychological health promotion. This study’s results offer theoretical support and policy implications for building restorative, inclusive, and age-friendly cities. The findings provide a quantitative basis for decision making regarding sustainable community space governance and intervention prioritization. Full article

Folk festivals and other intangible cultural heritage have received widespread attention, and their socio-cultural value can be used to promote tourism, strengthen local identity, and build city brands. However, it remains unclear how these intangible cultural heritage festivals transform their multi-dimensional and multi-configuration material characteristics into economic benefits and image enhancement. This study proposes a practical decision-making framework aimed at understanding how different festival design and governance strategies can work synergistically under different cultural conditions. Based primarily on a literature review and expert questionnaire survey, this study identified six stable materialized practice modules: productization, spatialization, experientialization, digitalization, branding/communication, and co-creation governance. At the same time, this framework also incorporates two other conditional intervention properties: classicism and novelty. The interactions between these modules shape people’s understanding of intangible cultural heritage festivals. Subsequently, this study used a multimodal national dataset that included official statistics, industry reports, e-commerce and social media data, questionnaires, and expert ratings to construct module scores and cultural attributes for 167 festival case studies. Through rough set analysis (RSA), this study simplifies the attributes and extracts clear “if-then” rules, establishing a configurational causal relationship between module configuration and classic/novel conditions to form high economic benefits and enhance local image. The findings of this study reveal a robust core built around spatialization, digitalization, and co-creative governance, with brand promotion/communication yielding benefits depending on the specific context. This further confirms that classicism reinforces the legitimacy and effectiveness of rituals/spaces and governance pathways, while novelty amplifies the impact of digitalization and immersive interaction. In summary, this study constructs an integrated and easy-to-understand process that links indicators, weights, and rules, and provides operational support for screening schemes and resource allocation in festival event combinations and venue brand governance. Full article

This article explores the Rausyan Fikr Institute as a distinctive intellectual and philosophical movement within Indonesia’s Shia Muslim community, focusing on its role in mobilizing dissent narratives and fostering intellectual activism. Within the broader historical trajectory of Shi’ism in Indonesia—from its early cultural impact and political mobilization during the Iranian Revolution to its institutional development in the Reformasi era —the Rausyan Fikr Institute represents a unique approach to implementing Shia philosophical thought through grassroots mobilization. Using the framework of ideologically structured action (ISA), this article highlights how Rausyan Fikr articulates its identity through the transmission of philosophical frameworks, critical discourse on current social-political issues, and inclusive educational initiatives. It explores three elements: (i) the dissemination of Shia Islam-inspired thought through translation, publishing, and education, (ii) the development of dissent narratives on capitalism, feminism, and dominant political structure, and (iii) the engagement with wider communities and mobilization strategies for its members, which involve students, women, and families alike in establishing space for intellectual development. The article concludes by reflecting on the Rausyan Fikr Institute’s resilience in sustaining philosophical activism under sectarian pressures, its contribution to Indonesia’s broader intellectual and religious discourse, and the challenges it encounters in preserving both ideological identity and relevance in a contested socio-political landscape. Full article

Introduction: Long-term heart failure and mortality after catheter ablation for premature ventricular contraction (PVC) remain underexplored. Methods: We retrospectively analyzed 4195 adults who underwent PVC ablation in a nationwide claims database. To address class imbalance, we used synthetic minority over-sampling technique (SMOTE) and random over-sampling examples (ROSE). Five supervised algorithms were compared: logistic regression, decision tree, random forest, XGBoost, and LightGBM. Discrimination was assessed by stratified five-fold cross-validation using the area under the receiver operating characteristic curve (ROC AUC). Because rare events can bias ROC, we also examined precision–recall (PR) curves. Results: For predicting three-year heart failure, LightGBM with ROSE achieved the highest ROC AUC at 0.822. For three-year mortality, logistic regression with ROSE and LightGBM with ROSE showed balanced performance with ROC AUCs of 0.886 and 0.882. Pairwise DeLong tests indicated that these leading models formed a high-performing cluster without significant differences in ROC AUC. Age, prior heart failure, malignancy, and end-stage renal disease were the most influential predictors by model explainability analysis. Discussion: Addressing class imbalance and benchmarking modern learners against a transparent logistic baseline yielded robust, clinically interpretable risk stratification after PVC ablation. These models are suitable for integration into electronic health record dashboards, with external validation and local threshold optimization as next steps. Full article

This study proposes a federated learning-based real-time surface roughness prediction framework for WEDM to address issues of empirical parameter tuning and data privacy. By sharing only the model parameters, cross-machine training was enabled without exposing raw data. A custom data acquisition system collected discharge current and spindle current signals, which were solely used as input features to train the deep learning model. Data balancing techniques improved prediction accuracy, achieving performance comparable to centralized models. After optimizing the training dataset through balancing and augmentation, the federated model achieved a Root Mean Square Error (RMSE) of 0.076, which closely approaches the 0.074 RMSE obtained by the centralized model. The results show that federated learning enhances both data security and model generalization, offering an effective solution for smart manufacturing. Full article

In recent years, an increasing number of women worldwide have been affected by breast cancer. Early detection is crucial, as it is the only way to identify abnormalities at an early stage. However, most deep learning models developed for classifying breast cancer abnormalities tend to be large-scale and computationally intensive, often overlooking the constraints of cost and limited computational resources. This research addresses these challenges by utilizing the CBIS-DDSM dataset and introducing a novel concatenated classification architecture and a two-stage strategy to develop an optimized, lightweight model for breast mass abnormality classification. Through data augmentation and image preprocessing, the proposed model demonstrates a superior performance compared to standalone CNN and DNN models. The two-stage strategy involves first constructing a compact model using knowledge distillation and then refining its structure with a heuristic approach known as Simplified Swarm Optimization (SSO). The experimental results confirm that knowledge distillation significantly enhances the model’s performance. Furthermore, by applying SSO’s full-variable update mechanism, the final model—SSO-Concatenated NASNetMobile (SSO-CNNM)—achieves outstanding performance metrics. It attains a compression rate of 96.17%, along with accuracy, precision, recall, and AUC scores of 96.47%, 97.4%, 94.94%, and 98.23%, respectively, outperforming other existing methods. Full article

Background/Objectives: Patients with end-stage renal disease (ESRD) are at an increased risk of major adverse cardiac events (MACEs), highlighting the need for accurate risk prediction and personalized interventions. This study aims to develop and evaluate machine learning (ML) models to identify key predictive features and enhance clinical decision-making in MACE risk assessment. Methods: A dataset comprising 84 variables, including patient demographics, laboratory findings, and comorbidities, was analyzed using CatBoost, XGBoost, and LightGBM. Feature selection, cross-validation, and SHAP (SHapley Additive exPlanations) analyses were employed to improve model interpretability and clinical relevance. Results: CatBoost exhibited the highest predictive performance among the models tested, achieving an AUC of 0.745 (0.605–0.83) with balanced sensitivity and specificity. Key predictors of MACEs included antiplatelet use, the grade of left ventricular hypertrophy, and serum albumin levels. SHAP analysis enhanced the interpretability of model outputs, supporting clinician-led risk stratification. Conclusions: This study highlights the potential of ML-based predictive modeling to improve MACE risk assessment in patients with ESRD. The findings support the adoption of ML models in clinical workflows by integrating explainable AI methods to enable individualized treatment planning. Future integration with electronic health record systems may facilitate real-time decision-making and enhance patient outcomes. Full article

Despite the growing interest among comparative legal scholars in Islamic law, the application of Shia Islamic law remains an overlooked area within the field of comparative law. This article addresses this gap by offering a classification of contemporary national legal systems according to their incorporation of Shia Islamic law. The analysis begins with secular legal systems in countries with significant Shia populations and progresses to those jurisdictions where Shia Islamic law is officially recognised. Through this examination, I define the historical, cultural, and political contexts influencing the application of Shia Islamic law and assess how and to what extent these states implement Shia Islamic rulings, incorporating case studies to illustrate varying degrees of application. Full article

The microcellular injection molding (MuCell^®) process, which uses supercritical fluid (SCF) as a foaming agent, is considered an important green molding solution to reduce product weight, molding energy, and cycle time and to improve the foam quality. However, maximizing the foaming density while keeping size uniformity in the foaming cell requires further attention. In this study, H₂O and the SCF N₂ were employed as cofoaming agents in the MuCell^® process of polypropylene (PP). Owing to the different critical points of N₂ and H₂O, bubble nucleation was expected to occur in interactive ways. Various process parameters were investigated, including the SCF N₂ content, the moisture content adsorbed within the resin under targeted PP weight reductions of 30% and 40%, the melt and mold temperature conditions, and the gas counter pressure. The resulting foaming morphology was examined to evaluate the foam quality in terms of the foaming density and bubble size distribution. The bubble coalescence, particularly in the skin layer, was examined, and the associated gas permeability flow rate was measured. The results indicated that H₂O-assisted foaming led to bubble coalescence and allowed for gas penetration in the direction of the part thickness direction, resulting in an overall increase in foaming density, particularly in the skin layer. Under high SCF N₂ and H₂O contents, the solid skin layer disappeared, regulating the gas permeability from one surface side to the other. Under the optimized process parameters, the gas permeability flow rate in the filter-like foaming PP material reached 300–450 mL/min. The application of gas counter pressure also helped increase the foam density and bubble coalescence, enhancing the gas permeability in the PP material to about 500 mL/min. These results demonstrate the potential application of microcellular injection molding using water as a cofoaming agent in moisture-release devices. Full article

Suffering is a problem addressed by many world religions. This paper examines the understanding of suffering in three religions: Catholic Christianity, Shia Islam, and Shakta Hinduism. In each of these cases, ordinary suffering is transformed into mystical sorrow, becoming a path to closeness and divine union. This transformation makes use of religious symbolism of the family, emphasizing the role of the mother. Worldly suffering is no longer meaningless; instead, it becomes a spiritual path through which the individual person, and even the world at large, may be redeemed. Full article

Background: Microcalcifications in the breast are often an early warning sign of breast cancer, and their accurate detection is crucial for the early discovery and management of the disease. In recent years, deep learning technology, particularly models based on object detection, has significantly improved the ability to detect microcalcifications. This study aims to use the advanced YOLO-v8 object detection algorithm to identify breast microcalcifications and explore its advantages in terms of performance and clinical application. Methods: This study collected mammograms from 7615 female participants, with a dataset including 10,323 breast images containing microcalcifications. We used the YOLO-v8 model for microcalcification detection and trained and validated the model using five-fold cross-validation. The model’s performance was evaluated through metrics such as accuracy, recall, F1 score, mAP50, and mAP50-95. Additionally, this study explored the potential applications of this technology in clinical practice. Results: The YOLO-v8 model achieved an mAP50 of 0.921, an mAP50-95 of 0.709, an F1 score of 0.82, a detection accuracy of 0.842, and a recall rate of 0.796 in breast microcalcification detection. Compared to previous similar deep learning object detection techniques like Mask R-CNN, YOLO-v8 has shown improvements in both speed and accuracy. Conclusions: YOLO-v8 outperforms traditional detection methods in detecting breast microcalcifications. Its multi-scale detection capability significantly enhances both speed and accuracy, making it more clinically practical for large-scale screenings. Future research should further explore the model’s potential in benign and malignant classification to promote its application in clinical settings, assisting radiologists in diagnosing breast cancer more efficiently. Full article

This article explores the compatibility of liberal citizenship with Twelver Shia jurisprudence, a topic previously analyzed from the perspective of Sunni schools, most notably in the extensive research of Andrew F. March. This study confronts the challenges of reconciling liberal citizenship with Islamic jurisprudence, as highlighted in March’s work, through the lens of Shia legal thought. Rather than aiming to critique or review March’s research, this article considers his work solely as a representative example addressing the topic from the perspective of Sunni jurisprudence. This approach provides readers with a fundamental contrast, illuminating the unique insights that emerge from examining the subject within the framework of Shia jurisprudence. Unlike Sunni jurisprudence, which addresses these issues case-by-case by reviewing relevant Quranic and narrational sources, Twelver Shia jurisprudence offers a more foundational resolution. Owing to the belief in the occultation of the twelfth Imam and its implications for the implementation of Islamic law, Shia scholars have advanced theories such as the theory of obstruction (insidād) and the suspension of the social and political dimensions of Sharia. These theories effectively narrow the scope of Sharia, allowing for the acceptance of laws from non-Islamic states and circumventing potential conflicts with liberal citizenship in the absence of the twelfth Imam. Full article