Case Study Of Region Research Articles

Risk Factors for Vaginal Discharge in Adolescent Girls

Purpose: This study aims to examine the risk factors contributing to abnormal vaginal discharge in adolescent girls by integrating biological, hygienic, socio-cultural, economic, and psychological determinants. Unlike previous research on hormonal influences, this study provides a holistic perspective on how various factors impact adolescent reproductive health. Research Design and Methodology: This research employs a systematic literature review (SLR) approach to synthesize findings from scholarly articles published after 2018. Relevant studies were retrieved from Elsevier, Emerald, Wiley, and Springer databases. The analysis focuses on identifying dominant biological, behavioral, and environmental factors contributing to vaginal discharge among adolescent girls, along with potential interventions to mitigate health risks. Findings and Discussion: The results indicate that hormonal fluctuations during puberty influence vaginal microbiota and secretion levels, while poor hygiene practices, lack of reproductive health education, and socio-economic constraints exacerbate infection risks. Cultural stigmas surrounding menstruation and vaginal health further hinder adolescents from seeking timely medical attention. Additionally, stress and mental health issues contribute to hormonal imbalances, increasing susceptibility to abnormal discharge. These findings emphasize the interconnectedness of physiological, behavioral, and environmental factors in adolescent reproductive health. Implications: This study underscores the need for comprehensive school reproductive health education, improved access to sanitary products, and reduced social stigmas through public health campaigns. Policymakers should prioritize affordable sanitary products and ensure proper sanitation facilities, particularly in low-income communities. Future research should explore empirical approaches, including qualitative interviews and regional case studies, to further assess the lived experiences of adolescent girls in diverse cultural contexts.

Temporality and justice in mining impacted regions

Abstract In response to the accelerating impacts of climate change, urgent action is required to decarbonise our socio-technical systems based on coal and oil. Frequently, these shifts involve decisions made in distal capital cities, or distant countries, that have significant impacts on resource-based communities In this paper we focus on temporal justice; the disjuncture between the timeframes of urgent decisions about transition options to meet policy objectives, and the timeframes experienced by residents of regions affected by the impacts of those decisions, and how those impacts fit within trajectories of change in resource-extracting regions. Here we draw on six case studies of regions located in Australasia (Hunter Valley, Latrobe Valley, West Coast New Zealand) Canada (Alberta) and Africa, (Ghana, Zimbabwe). Each of these is at a different point in the mine lifecycle, from places where residents mourn the past glory days, regions in where closure is imminent and regions wary of the rush to start new mines for ‘energy transition minerals’. Collectively, our case studies demonstrate the complexity of transitions and how multiple forms of justice (environmental, procedural, recognition, restorative, distributive and intergenerational) shape the key issues and required responses to transition challenges. The capacity for local communities to respond to these changes is impacted by factors of embedded identity, agency in the transition process and legacy of past injustices. Each of these factors take time to work through. Thus, there is a disconnect between the urgency of transition and the time required for host communities to become effective participants in their own transition.

Caring, Coping and Rebuilding—The Role of Social Infrastructure During and After the 2021 Flood Event in Germany

ABSTRACTPrevious research has indicated the important role of social infrastructures during and after flood events. While struggling to uphold their caring responsibilities, they are also deemed relevant for coping and rebuilding after a disaster. We revisit this line of argument for the 2021 flood event in western Germany to deepen the understanding of the societal dimension of caring, coping and rebuilding (CCR) in and after flood events. Based on 21 semi‐structured interviews in three case study regions, we analyse how social infrastructures were affected during the flood, their contribution to resilience in the acute and rebuilding phases, and factors influencing their response to extreme events. Moving beyond the conventional focus on technical solutions for flood management, our study examines the significant societal aspects of responding to and recovering from flood events. Our research empirically underscores the critical role of social infrastructure during and after flood events. Recognising the assistance provided by these infrastructures, our findings offer a basis for policy recommendations. Ensuring sufficient financial and political support for social infrastructures is crucial, as is actively involving them in rebuilding initiatives. These measures are vital for facilitating the expansion of social infrastructure and enhancing its resilience potential during flood events.

Plausibility, Acceptability, and Trustworthiness: The Resonance of Shale Gas Frames in the United Kingdom

ABSTRACT We employ a mixed-method research design to assess the resonance of key frames used in the UK policy debate over shale gas development amongst two distinct public audiences. We elaborate a conceptual framework drawn from argumentative discourse analysis and social movement studies. The framework conceptualises resonance as the result of frame plausibility, acceptability and trustworthiness. We utilise semi-structured interviews to analyse how frames resonate with local “campaigning publics” in the case study region of the Fylde, Lancashire, UK. The plausibility of the frames and trustworthiness of key organisations amongst the general public are also assessed through a nationally representative UK survey. We find that, amongst the general public, the frames of the anti-shale development discourse coalition are viewed as more plausible than those of the pro-shale coalition. This finding offers a partial explanation for the failure of UK shale policy. Our local community interviews identify contrasting views on frame resonance amongst local “campaigning publics”. We illustrate these contrasting views on frame resonance by exploring how pro – and anti-shale development interviewees generally held different perceptions about the trustworthiness of key actors and institutions, the promise of economic benefits, and the framing contests over climate change and risk and regulation.

An Evaluation of the Rural Tourism Industry’s Competitiveness in the Yangtze River Economic Belt Based on the “Diamond Model”—Exampled by Wenjiang District, Huangpi District, and Jiangning District

Rural tourism has emerged as a significant option to meet the people’s growing needs for a better life in the new era. Evaluating the competitiveness of the rural tourism industry plays a crucial role in promoting rural revitalization. This article is based on a modified Porter’s diamond model. This study focuses on representative rural tourism demonstration areas in the upper, middle, and lower reaches of the Yangtze River Economic Belt, specifically Wenjiang District in Chengdu, Huangpi District in Wuhan, and Jiangning District in Nanjing. The Delphi method and the Analytic Hierarchy Process were employed to construct a competitiveness evaluation index system for rural tourism and to assign weights. This article evaluates and compares the competitiveness of the rural tourism industry in the case study regions. This research reveals that the competitiveness of the rural tourism industry in the Yangtze River Economic Belt exhibits a pattern of strong competitiveness in the middle reaches, weaker competitiveness in the upper reaches, and moderate competitiveness in the lower reaches. These differences are significantly influenced by the region’s tourism resource endowments, tourism infrastructure, and market scale. Huangpi District demonstrates strong competitiveness in its rural tourism industry. This strength is attributed to its well-developed rural tourism supply system. Jiangning District has moderate competitiveness in its rural tourism industry. This is supported by a strong market demand and a relatively complete tourism support system. Wenjiang District shows weaker competitiveness in its rural tourism industry. This weakness results from a fragile rural tourism supply system and a sluggish market demand. This article concludes with recommendations for enhancing the competitiveness of rural tourism in the Yangtze River Economic Belt.

Carbon peaking capacity of Chinese cities not overemphasizing the role of non-fossil energy utilization- A case study of the Yangtze River Delta Region and northeast China

Carbon peaking capacity of Chinese cities not overemphasizing the role of non-fossil energy utilization- A case study of the Yangtze River Delta Region and northeast China

Relationship between accountability and public service delivery quality in Somalia: A case study of the Banadir region

Relationship between accountability and public service delivery quality in Somalia: A case study of the Banadir region

Assessment of surface water quality in mining area: A case study of the southwest region of Burkina Faso

Mining activities, whether industrial or artisanal, are often responsible for the pollution of water resources, leading to significant degradation of water quality. This study aims to assess the quality of surface waters in three out of the four provinces of the South-Western region of Burkina Faso, an area known for its numerous mining sites. To evaluate the water quality in the study area and to assess the importance of each parameter in influencing this quality, we applied Weighted Arithmetic Water Quality Indices (WAWQI) and Hierarchical Cluster Analysis. These analyses were based on data obtained from the measurement of various physicochemical parameters (pH, electrical conductivity, total hardness, calcium, magnesium, sodium, potassium, bicarbonate, chloride, sulphate, nitrate, phosphate) and heavy metals (total iron, manganese, lead, cadmium, chromium, copper, zinc, arsenic) collected at the end of 2020. Samples were taken from a variety of sources: four rivers and five reservoirs. This approach provided a comprehensive assessment of the surface water quality in the study area. The results indicated that, of the twenty parameters analysed, only total iron consistently exceeded the World Health Organization (WHO) standards across all samples, while bicarbonate exceeded the standard in three instances and calcium-phosphate in one. The water quality index values ranged from 8.18 to 165.85, with an average of 46.84. Only one river sampling point, situated at the Poni province (SW4 at Poniro River), exhibited "poor" water quality, while all other water bodies exhibited water quality ranging from "good" to "excellent." The dendrogram analysis confirmed this finding by showing that total iron is separated from the other parameters, highlighting its negative role in the deterioration of water quality, whereas the other parameters play a positive role.

Comparative study on landslide susceptibility assessment of different models: a case study of alpine mountainous region in Xinjiang

Comparative study on landslide susceptibility assessment of different models: a case study of alpine mountainous region in Xinjiang

Integrated Planning Model for Optimizing Investments in Hydrogen Infrastructure Deployment: A Case Study of Yangtze River Delta Region, China

ABSTRACTThe hydrogen industry is of great significance for global energy system transition and decarbonization; thus, the holistic planning of hydrogen infrastructure from a supply chain level is necessary. To this end, first, the framework and methods in the hydrogen infrastructure deployment research were investigated systematically. Second, a hydrogen supply chain design (HSCD) model and a hydrogen refueling station location (HRSL) model were constructed. In view of the limitations of the two models, this paper reformulated the HRSL model through alternating the objective function and constraint, then proposed an integrated planning model using the ‐constraint method, which relaxed the assumption condition and expanded the decision‐making boundary of the original model, realizing the reasonable equilibrium of multiple segments. Finally, a case study was conducted using the Yangtze River Delta region as an example, and the main findings were as follows: (1) To meet the hydrogen demand of 25494t/d, the Yangtze River Delta region needs to build 94 coal gasification hydrogen plants, storage facilities with capacity of 12747t, transmission modes with capacity of 2333t/d, and 150 hydrogen refueling stations. (2) Railways have more advantages in large capacity and long‐distance hydrogen transmission. (3) Road segments with higher traffic flow often have a higher capture proportion. (4) The investment cost accounts for the majority of hydrogen production plant construction, which is 43.80%.

A novel hybrid ViT-LSTM model with explainable AI for brain stroke detection and classification in CT images: A case study of Rajshahi region.

Computed tomography (CT) scans play a key role in the diagnosis of stroke, a leading cause of morbidity and mortality worldwide. However, interpreting these scans is often challenging, necessitating automated solutions for timely and accurate diagnosis. This research proposed a novel hybrid model that integrates a Vision Transformer (ViT) and a Long Short Term Memory (LSTM) to accurately detect and classify stroke characteristics using CT images. The ViT identifies essential features from CT images, while LSTM processes sequential information generated by the ViT, adept at capturing crucial temporal dependencies for understanding patterns and context in sequential data. Moreover, our approach addresses class imbalance issues in stroke datasets by utilizing advanced strategies to improve model robustness. To ensure clinical relevance, Explainable Artificial Intelligence (XAI) methods, including attention maps, SHAP, and LIME, were incorporated to provide reliable and interpretable predictions. The proposed model was evaluated using the primary BrSCTHD-2023 dataset, collected from Rajshahi Medical College Hospital, achieving top accuracies of 73.80%, 91.61%, 93.50%, and 94.55% with the SGD, RMSProp, Adam, and AdamW optimizers, respectively. To further validate and generalize the model, it was also tested on the Kaggle brain stroke dataset, where it achieved an impressive accuracy of 96.61%. The proposed ViT-LSTM model significantly outperformed traditional CNNs and ViT models, demonstrating superior diagnostic performance and generalizability. This study advances automated stroke diagnosis by combining deep learning innovations, domain expertise, and enhanced interpretability to support clinical decision-making, providing reliable diagnostic solutions.

Exploring the interactive coercing relationship between tourism and ecological environment: a case study in Kanto region, Japan

Exploring the interactive coercing relationship between tourism and ecological environment: a case study in Kanto region, Japan

Developing a palaeoceanographic proxy based on the dimensions of Adercotryma glomeratum: a case study in Drake Passage region (Antarctic Peninsula)

Developing a palaeoceanographic proxy based on the dimensions of Adercotryma glomeratum: a case study in Drake Passage region (Antarctic Peninsula)

Characteristics of thermal comfort for the elderly in urban parks during autumn: a case study in cold regions

Characteristics of thermal comfort for the elderly in urban parks during autumn: a case study in cold regions

Implementasi Prinsip Good Governance dalam Peningkatan Kualitas Pelayanan Publik: Studi Kasus pada Pemerintah Daerah

The implementation of good governance principles is crucial in efforts to enhance the quality of public services at the local government level. In this context, the principles of transparency, accountability, participation, and responsiveness serve as important foundations for creating better public services. This research aims to explore how the application of these principles can drive improvements in the quality of public services, as well as to identify the challenges faced in their implementation. Case studies drawn from several local governments in Indonesia provide a clear picture of good governance practices and their impact on public services. Through case studies in various regions, this research reveals how the application of these principles can increase public satisfaction with public services. Data obtained from surveys and field research will be analyzed to provide a clear overview of the relationship between good governance and the quality of public services.

Factors affecting the fitting of a global geopotential model to local geodetic datasets over local areas in Egypt using multiple linear regression approach

Abstract The increasing accuracy of the recently released Global Geopotential Models (GGMs) make them a reasonable geoid models, particularly in developing countries. Incorporating local geodetic datasets into a GGM could enhance its performance significantly. However, such integration requires appropriate mathematical modelling. The current research investigates the factors influencing the fitting of a GGM to heterogeneous geodetic data over local areas. The Multiple Linear Regression (MLR) approach is performed with variable independent factors to model the GGM discrepancies over two study areas in Egypt. Observed Global Navigation Satellite Systems (GNSS)/levelling and measured terrestrial gravity anomalies are investigated, among other independent variables, in the regression modelling. Based on the available data and attained findings, it has been demonstrated that MLR approach could produce a good fitting of a specific GGM’s geoid undulations, namely the XGM2019e_2159 model, locally with a coefficient of determination of more than 0.99. The regression equation has decreased the standard deviation of the investigated GGM-based undulations from ±0.130 m to ±0.046 m. Accordingly, the accuracy of a particular GGM has been enhanced considerably with improvements achieved 99 % and 64 % over the investigated two case study regions in Egypt.

Empowerment and Literacy Initiatives to Enhance Ultra Micro Debtor Classes: Strategies for Sustainable Regional Economic Growth

This article explores empowerment and literacy initiatives aimed at enhancing the capacity of ultra-micro debtor classes as a strategy for fostering sustainable regional economic growth. The study focuses on developing entrepreneurial competencies, financial literacy, and access to relevant technologies for ultra-micro debtors, who often face challenges such as limited education, financial knowledge, and access to resources. Through case studies in regions with significant ultra-micro economic activity, the research identifies community-based empowerment methods, financial literacy training, and digital platforms to support small business management. The findings reveal that these programs not only improve the productivity and income of debtors but also strengthen local economic resilience, promote financial inclusion, and create new job opportunities. The article concludes that collaboration between local governments, financial institutions, and local communities is crucial to ensuring the sustainability of these programs in supporting regional economic growth.Keywords: Ultra-micro debtors, empowerment initiatives, financial literacy, entrepreneurial development.

A new approach to sustainable environmental assessment for wastewater treatment plants: A case study in the central region of Iraq

A new approach to sustainable environmental assessment for wastewater treatment plants: A case study in the central region of Iraq

Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region

Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region

Quantifying sustainable urbanization by predictive modeling for better agricultural management: A case study in the South Asiatic Region

Global population growth and uncontrolled are creating threats to agricultural land. To address urbanization, proactive planning is required. Land use and land cover (LULC) classification maps for 2002-2022 were analyzed using remote sensing (RS) and geographic information systems (GIS) in Sahiwal, Punjab, Pakistan. Idrisi's Cellular Automata (CA)–Markov model was used to predict future scenarios. The results showed that urbanization was rapidly accelerated in large LULC changes that were unpredictable. In particular, the urbanized area increased by 234.7 km2 (91%) from 22.83 km2 in 2002 to 257.53 km2 in 2022, with a reduction of 656.05 km2 (52%), from 1252.52 km2 in 2002 to 596.47 km2 in 2022, of agriculture land. About 17.05 km2 of land was lost to urbanization; however a large portion of CA 251.75 km2 was absorbed due to careless urban growth. The CA-Markov projection revealed that from 2022 to 2042, agriculture will experience the largest net change, losing about -226.09 km2 of land. However, the projected results showed that the urban class will be expanded up to 450.23 km2 and will gain approximately 192.7 km2 in 2042. The overall findings show that it is possible to manage outcomes in a quantitative way and control haphazard and unplanned urban sprawl by putting forward a comprehensive master plan.