تأتي هذه الدراسة في ظل تصاعد دعوات كثيرة من أجل إصدار قانون جديد لتنظيم الإدارة المحلية في مصر، نظرًا لأن القانون الحالي ( قانون رقم 43 لسنة 1979) لم يعد مواكباً لما جاء بالدستور الجديد ( دستور 2014 ) من صلاحيات جديدة ودعم صريح للامركزية و ترسيخ لديمقراطيتها و تعزيز لصلاحياتها .و تستعرض هذه الدراسة تاريخ الإدارة المحلية في مصر، مع التركيز على انتخابات المجالس المحلية خلال فترة حكم الرئيس الأسبق حسني مبارك ، وهي فترة غنية بالتفاعلات السياسية وشهدت عدة استحقاقات انتخابية محلية وقرارات بشأنها لم تتناولها دراسات سابقة بشكل كافٍ ، وقد استخدمت الدراسة المدخل التاريخي وذلك سعياً لحصر السلبيات والإستفادة منها وكذا الوقوف على مواضع القوة فى تجربة " إدارة المحليات " فى مصر بشكلها الحديث ومفهومها المعاصر عبر قرابة قرن كامل من الزمان ، سعيا للوصول إلى نتائج علمية يمكن أن نستقرأ منها توصيات فاعلة فى حالات مماثلة للدول النامية أو حديثة العهد بالتعددية السياسية والحريات والديمقراطية فى إدارة الأقاليم ..
To improve the counting accuracy in dense rice seed scenarios, this study proposes a YOLOv5-based dense rice seed counting method that integrates C3CBAM and Soft-NMS. This method integrates the CBAM attention module into the shallow C3 modules of the backbone network to enhance image features. Additionally, it removes the original large and medium-sized object detection heads of YOLOv5 and adds a dedicated detection head for tiny rice seeds. For post-processing of model prediction data, the Soft-NMS algorithm is employed to replace standard Non-Maximum Suppression (NMS) and reduce missed detections. Finally, image acquisition, seed counting, and a user interface are integrated into a single system, enabling rice breeders to conduct seed counting tasks more intuitively and efficiently. Compared with the baseline YOLOv5 model, the recall and mAP@[0.5:0.95] of the improved model increase by 6.4 % and 5.7 %, respectively. Furthermore, this study designs experiments with three levels of seed density. In the intermediate-type rice seed samples, the detection accuracy reaches 100 % under light and moderate density conditions, while it maintains stable counting performance under heavy density conditions with an accuracy above 99.7 %. This work significantly enhances rice seed counting efficiency for researchers and facilitates rice variety improvement studies.
Mandlenkosi Maphosa, Kablan Antoine Effossou, Philani Moyo
Conservation areas are increasingly seen as crucial for addressing biodiversity loss and climate change, yet their expansion often produces socio-economic tensions with adjacent communities, particularly where governance is exclusionary. This study examines perceptions of equity, resource access, and governance among rural communities and private game reserve officials in the Makana Local Municipality, Eastern Cape, South Africa. It also investigates how climate variability intensifies these challenges by deepening local vulnerability. Guided by vulnerability and participatory governance theory, the study adopted a qualitative, interpretivist approach. Data were collected between July and August 2024 through 58 in-depth interviews with community members, 2 focus group discussions and 5 key informant interviews with conservation managers and community leaders. The study focused on communities surrounding Amakhala and Lalibela Game Reserves, including Alicedale, Seven Fountains, and Kraabos. Findings reveal that while some benefits from conservation exist—such as employment and limited outreach, these are perceived as symbolic, precarious, and inequitably distributed. Communities report restricted access to land, water, and sacred sites, with governance processes experienced as opaque and exclusionary. Climate variability, particularly erratic rainfall and drought, exacerbates these vulnerabilities by undermining agricultural livelihoods and intensifying resource scarcity. The study concludes that socially just and climate-resilient conservation in South Africa requires a shift from rhetorical inclusion to meaningful participation, transparent governance, and equitable benefit-sharing. Aligning conservation with local rights, needs, and adaptive capacities is essential for enhancing both biodiversity protection and community resilience.
Pouya Shaeri, Yasaman Mohammadpour, Alimohammad Beigi
et al.
Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment expressed in these online discussions can offer valuable insights into public perception, inform policy decisions, and enhance emergency responses. Although sentiment analysis has been widely studied in various fields, its specific application to climate-induced events, particularly in real-time, high-impact situations like the 2025 Los Angeles forest fires, remains underexplored. In this survey, we thoroughly examine the methods, datasets, challenges, and ethical considerations related to sentiment analysis of social media content concerning weather and climate change events. We present a detailed taxonomy of approaches, ranging from lexicon-based and machine learning models to the latest strategies driven by large language models (LLMs). Additionally, we discuss data collection and annotation techniques, including weak supervision and real-time event tracking. Finally, we highlight several open problems, such as misinformation detection, multimodal sentiment extraction, and model alignment with human values. Our goal is to guide researchers and practitioners in effectively understanding sentiment during the climate crisis era.
Understanding the intrinsic mechanisms of social platforms is an urgent demand to maintain social stability. The rise of large language models provides significant potential for social network simulations to capture attitude dynamics and reproduce collective behaviors. However, existing studies mainly focus on scaling up agent populations, neglecting the dynamic evolution of social relationships. To address this gap, we introduce DynamiX, a novel large-scale social network simulator dedicated to dynamic social network modeling. DynamiX uses a dynamic hierarchy module for selecting core agents with key characteristics at each timestep, enabling accurate alignment of real-world adaptive switching of user roles. Furthermore, we design distinct dynamic social relationship modeling strategies for different user types. For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances, simulating homogeneous connections, and autonomous behavior decisions. For ordinary users, we construct an inequality-oriented behavior decision-making module, effectively addressing unequal social interactions and capturing the patterns of relationship adjustments driven by multi-dimensional factors. Experimental results demonstrate that DynamiX exhibits marked improvements in attitude evolution simulation and collective behavior analysis compared to static networks. Besides, DynamiX opens a new theoretical perspective on follower growth prediction, providing empirical evidence for opinion leaders cultivation.
Valeria Widler, Barbara Kaminska, Andre C. R. Martins
et al.
The increasing polarization in democratic societies is an emergent outcome of political opinion dynamics. Yet, the fundamental mechanisms behind the formation of political opinions, from individual beliefs to collective consensus, remain unknown. Understanding that a causal mechanism must account for both bottom-up and top-down influences, we conceptualize political opinion dynamics as hierarchical coarse-graining, where microscale opinions integrate into a macro-scale state variable. Using the CODA (Continuous Opinions Discrete Actions) model, we simulate Bayesian opinion updating, social identity-based information integration, and migration between social identity groups to represent higher-level connectivity. This results in coarse-graining across micro, meso, and macro levels. Our findings show that higher-level connectivity shapes information integration, yielding three regimes: independent (disconnected, local convergence), parallel (fast, global convergence), and iterative (slow, stepwise convergence). In the iterative regime, low connectivity fosters transient diversity, indicating an informed consensus. In all regimes, time-scale separation leads to downward causation, where agents converge on the aggregate majority choice, driving consensus. Critically, any degree of coherent higher-level information integration can overcome misalignment via global downward causation. The results highlight how emergent properties of the causal mechanism, such as downward causation, are essential for consensus and may inform more precise investigations into polarized political discourse.
We have in recent years seen growing calls for pedagogies for social change amongst communication and development scholars, identifying resistances, critiques, and emerging practices in the field. This review article addresses this ‘pedagogical turn’, suggesting that it is in these pedagogies we can see the pathways to unlearn and relearn communication for social change. Offering a decolonial analytical lens, this article asks two questions: What characterizes these critical pedagogies? And how can the various pedagogies contribute to unlearning and relearning the field of communication and social change? This article is structured in five parts, first offering a review of key critiques articulated within the field of communication and social development in the past two decades, arguing that, in practice, what we are seeing is the organic development of a pluriverse of knowledges, values, and visions of society. Secondly, it proposes the decolonial term of ‘unlearning’ as a pedagogical pathway and epistemological ambition for the production and recognition of a pluriverse of knowledges, thereby challenging dominant perceptions of society and social change. Thirdly, it introduces a model of analysis which structures ways whereby we can think about monocultures and ecologies in relation to a range of dimensions of the pluriverse. Fourthly, it reviews key critical pedagogies, discussing how they address epistemic injustice both in broader societal contexts as well as in the university space. This article concludes by discussing how the process of unlearning through critical pedagogies has implications for the configuration and definition of the field of communication and social change, suggesting three areas for further research: ways of seeing (positionality), new subject positions (relationality), and new design processes (transition).
سید جلال الدین حسینی, آزیتا رجبی, افشین سفاهن
et al.
توسعه حملونقل عمومی محور، یکی از راهکارهای مهم و مصادیق توسعه پایدار شهری است که بهمنظور حل معضلات ترافیکی و بهبود شرایط حملونقل در شهرها مورداستفاده قرار میگیرد. در این رویکرد، برنامهریزی و گسترش حملونقل عمومی، بهعنوان جایگزینی برای استفاده از خودروهای شخصی و حملونقل خصوصی مطرح شده است. منطقه 11 شهرداری تهران یکی از مناطق مرکزی شهر تهران است که دارای بار ترافیکی بالایی است و بهتبع آن با مشکلاتی در سیستم حملونقل شهری خود مواجه است. در تحقیق مذکور، باهدف انطباق شاخصهای توسعه حملونقل عمومی محور با وضعیت فعلی منطقه 11 شهر تهران و بازپسگیری شهر از فضای خودرو محور به فضای انسانمحور، شاخصهای استاندارد توسعه حملونقل عمومی محور بهعنوان مبنای تحلیل و رتبهبندی بررسیشدهاند. در این رویکرد، با توجه به شاخصهای مختلف، میزان قابلیت منطقه برای تحقق توسعه حملونقل عمومی محور ارزیابی گردیده است. بهاینترتیب، این تحقیق به مسئولین و برنامهریزان شهری کمک میکند تا بر اساس نتایج بهدستآمده، راهکارهای مناسبی برای بهبود حملونقل در منطقه 11 شهر تهران ارائه دهند. روش تحقیق پژوهش حاضر ازنظر هدف؛ کاربردی و ازنظر متدولوژی توصیفی و ازنظر روش جمعآوری اطلاعات مبتنی بر روشهای کتابخانهای – اسنادی و مطالعات میدانی بوده و در تحلیل اطلاعات نیز از نرمافزار سیستم اطلاعات جغرافیایی و روشهای رتبهبندی و در مقایسه تطبیقی، از ضریب ناموزونی موریس و روش بیمقیاس خطی و از نرمافزار Choice Expert برای تحلیل سلسلهمراتبی AHP استفاده شده است. با توجه به نتایج حاصله، میتوان گفت که منطقه 11 شهرداری تهران، بهرغم داشتن پتانسیل بالا برای تحقق توسعه حملونقل عمومی محور، در شرایط فعلی ظرفیت تبدیلشدن به یک مرکز توسعه حملونقل عمومی محور را ندارد و جهت تبدیلشدن باید تغییرات وسیعی در ساختارهای فضایی آن ایجاد شود.
Andrew Moses Obeti, Lawrence Muhwezi, John Muhumuza Kakitahi
et al.
Force Account Mechanism (FAM) is the predominant road maintenance system in Uganda’s local government setup and a similar, though slightly different approach, is used in some large private sector agriculture plantations. With the Uganda Road Fund (URF) 2021/2022 annual report and previous research citing challenges in cost management and efficiency of the FAM method of road maintenance, it becomes paramount to analyse how FAM is implemented in government-led operations, in comparison to similar private sector approaches, while proposing possible solutions to these challenges. This research offered to analyse unpaved road maintenance cost drivers alongside providing a cost model solution to improve on cost prediction of the FAM system. Gulu District Local Government (DLG) and Kakira Sugar Limited (KSL) were selected as case study areas. Two descriptive research methods were used: observations and case study approach. The selected case study areas were accessible and reachable in terms of data. Control parameters affecting unpaved mechanized road maintenance were identified as machine repair costs, tool costs, labour costs, material costs, fuel costs and machine fuel costs. Unpaved mechanized road maintenance costs at KSL and Gulu DLG were computed as a cost/km ratio of 26,442,032Ugx/km (6,958.4USD/km) and 32,674,895Ugx/km (8,598.65USD/km) respectively. The Uganda National Roads Authority (UNRA) unpaved road maintenance costs were calculated as an average of 34,987,122.9Ugx/km (9,165USD/km) while the World Bank ROCKS database provided a comparable figure of 7,971USD/km (30,553,440.83Ugx/km). A USD to Ugx conversion rate of 3,800 was used. Two linear regression cost models with a 0.679 and 0.687 R2 value were computed, and these can be used in preliminary road maintenance cost prediction. The study recommends the need for an effective, digital road maintenance cost database system for mechanized unpaved road maintenance works, cost driver analytics and management, alongside improvement in aspects of maintenance processes at both the DLG and KSL. Further research can be conducted on equipment condition level prediction and analytics in the private sector and at the DLG.
Sai Puppala, Ismail Hossain, Md Jahangir Alam
et al.
Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The system is designed to seamlessly aggregate and curate diverse social media data sources, including user posts, multimedia content, and trending news. Leveraging Federated Learning techniques, the GPT model is trained on decentralized data sources to ensure privacy and security while providing personalized insights and recommendations. Users interact with the chatbot through an intuitive interface, accessing tailored information and real-time updates on social media trends and user-generated content. The system's innovative architecture enables efficient processing of input files, parsing and enriching text data with metadata, and generating relevant questions and answers using advanced language models. By facilitating interactive access to a wealth of social network information, this personalized chatbot system represents a significant advancement in social media communication and knowledge dissemination.
Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "public sentiment towards vaccination", "political discourse surrounding climate policies," etc. In this paper, we introduce a novel approach to uncovering latent themes in social media messaging. Recognizing the limitations of the traditional topic-level analysis, which tends to capture only overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Traditional theme discovery methods typically involve manual processes and a human-in-the-loop approach. While valuable, these methods face challenges in scalability, consistency, and resource intensity in terms of time and cost. To address these challenges, we propose a machine-in-the-loop approach that leverages the advanced capabilities of Large Language Models (LLMs). To demonstrate our approach, we apply our framework to contentious topics, such as climate debate and vaccine debate. We use two publicly available datasets: (1) the climate campaigns dataset of 21k Facebook ads and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads. Our quantitative and qualitative analysis shows that our methodology yields more accurate and interpretable results compared to the baselines. Our results not only demonstrate the effectiveness of our approach in uncovering latent themes but also illuminate how these themes are tailored for demographic targeting in social media contexts. Additionally, our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.