Local Perspective Research Articles

RELEVANCE OF DATA ANALYTICS IN SUSTAINABLE FISHERIES MANAGEMENT: AN EVIDENCE-BASED STUDY

Aim of the study: The value of fish resources to socioeconomic development is immense. However, there is growing evidence that loss of quality in the marine environments e.g. marine waters at sea and coastal freshwater zones is threatening the availability of livelihood resources e.g. fish resources evidenced by the dwindling fisheries resources that promote sustainable livelihood. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to boost marine water environment management and promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable practices e.g. marine water pollution that leads to loss of wild catches and leads to unsustainable fishing practices. Material and methods: Owing to this we use a mixed research methods approach involving a systematic literature review and field survey in Alappad to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems e.g. marine water quality loss e.g. due to pollution. Results and conclusions: Findings documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change, and marine water quality loss and implement new policies and regulations thus promoting sustainable fisheries management. Local perspectives of coastal communities could also guide the best data analytics tool or method to be used.

The Museum of London ‘Diversity Matters Programme’ and Redbridge Museum: Creating Equality, Exhibiting Ethnicity from a Local Perspective

The Museum of London ‘Diversity Matters Programme’ was launched in 2018 and closed in 2021. The programme encouraged London’s small non-national museums to embrace the Arts Council England’s directions to stimulate participation across socio-economic barriers such as the Equality Act (2010) and the Equality Duty (2011) recommended. However, the project results indicate a top-down approach to the involvement of minorities that seems to clash with the idea of inclusiveness itself. The local museums aimed to establish a bottom-up local history experience where visitors were content creators. These two opposite perspectives share the same scope but use different methods to achieve inclusion. By discussing survey data, the article investigates the Diversity Matters Programme as realised by Redbridge Museum, London, revealing competing and conflicting power relations that underpin the engineering of diversity and inclusion.

The notion and practice of emergency arbitration

The topic of this paper is the concept of emergency arbitration in theory and practice, its nature, development, and procedural intricacies. The author lays out a comprehensive comparable analysis of various institutional rules on emergency interim measures, followed by a discussion on the status of emergency arbitrators, due process concerns, and enforceability of decisions rendered in this process. Finally, the author touches upon the local perspective as well, evaluating existing rules on interim measures and contemplating the potential introduction of the emergency arbitration mechanism in Serbia.

Multiphysical evolution and dynamic competition involved in natural gas hydrate dissociation in porous media and its implications for engineering

Hydrate (shorted for natural gas hydrate) is one of the most promising future energies, and dissociating hydrate in situ is commonly recognized as the best strategy to realize commercial exploitation. So it is significant to understand the fundamental mechanism in the process of hydrate dissociation involving complicated thermal-hydraulic-mechanical-chemical phenomena. To date, the multiphysical evolution patterns are not revealed by the overall comparison among different phenomena, and we still have little knowledge on the contributions of different influence factors to hydrate dissociation rate. More importantly, the fundamental study has a considerable gap to the real engineering. Therefore, in this paper, a fully-coupled thermal-hydraulic-mechanical-chemical model is developed to further analyze the evolution of all phenomena from both global and local perspectives and find the coupled relationships among physical/chemical phenomena from a dynamic competition perspective. Results show that the global spatial distributions of pressure, hydrate saturation and strain show one simple pattern during hydrate dissociation，while those of temperature and hydrate dissociation rate manifest four and three complex patterns respectively. The effective hydrate reaction specific area, reaction coefficient and pressure difference play different roles in the domination of the trend and value of hydrate dissociation rate. Additionally, two examples are given to demonstrate the implications of the fundamental mechanism to the engineering. A negative-power-law relation is found between the finish time of hydrate dissociation and the heat transfer coefficient of outside heat source (geothermal heat in real engineering). A good linear relation between hydrate dissociation front and pressure transfer front is found, which provides a possible easy way to predict the range of dissociated hydrate by pressure in the real engineering.

Insights into landslide susceptibility in different karst erosion landforms based on interpretable machine learning

AbstractThe aim of the present study was to assess differences in the conditioning factors and the performance of landslide susceptibility mapping (LSM), employing the SHapley Additive exPlanations (SHAP) model to gain profound insights into the intrinsic decision‐making mechanism of LSM in diverse landforms. Two typical karst erosion landforms were selected as the research areas. Based on 15 conditioning factors, LSMs for the two areas were developed using the Bayesian optimization random forest (RF) and eXtreme Gradient Boosting (XGBoost). The SHAP model was used to explore the landslide formation mechanisms from both global and local perspectives. The results show that the area under the curve (AUC) values of the XGBoost models were 0.791 and 0.761, and the AUC values of the RF models were 0.844 and 0.817, in the two different landform areas, respectively. The RF model's accuracy was higher than that of the XGBoost model in both regions. In the low‐elevation hills area, the primary three conditioning factors were identified as slope, topographic relief and distance from the river. Conversely, in the microrelief and mesorelief low mountain area, the predominant conditioning factors were elevation, distance from the river and distance from the road. Both karst landform areas exhibited a high sensitivity to the distance from the river, indicating its significant interaction with other factors contributing to landslide occurrences. Notably, the RF model demonstrated superior performance compared to the XGBoost model, rendering it a more suitable choice for conducting landslide susceptibility mapping research in karst erosion landform areas. In the present study, a comprehensive explanatory framework based on the RF‐SHAP model was proposed, which enables both global and local interpretation of landslides in various karst landscapes. Such an approach explores the intrinsic decision‐making mechanism of the model, enhancing the transparency and realism of landslide susceptibility prediction results.

Nature is ours! – Psychological ownership and preferences for wind energy

Psychological ownership (PO) is a phenomenon whereby individuals feel ownership of goods they do not necessarily formally own. A substantial body of literature in marketing, consumer psychology, and organizational sciences conceptualizes PO as value-enhancing and an underlying factor of the endowment effect. Recent psychological research has documented that people can also experience PO of environmental public goods and suggested that PO could generate land use conflicts and territorial behavior, which is particularly relevant for renewable energy development. Renewable energy represents a critical social issue with competing interests and policy objectives, often faced with severe public opposition. More research is needed on the underlying mechanisms of opposition to mitigate conflicts and increase efficiency in policy implementation. In this paper, we assess how PO influences people's economic choices and valuation of environmental effects from wind energy, illuminating psychological processes underlying decision-making. First, we provide a novel theoretical framework suggesting that PO increases people's valuation of environmental public goods and leads to resistance against their transformation due to weak substitutability between environmental protection and money income. We test these predictions in two discrete choice experiments on preferences for wind energy, where one examination is conducted from a local perspective and the other from a national perspective. The national experiment permits the analysis of spatial dimensions of PO and willingness to pay to avoid wind energy externalities. Using a hybrid mixed logit approach, we find consistent support for hypothesized effects in both experiments. Our scientific findings suggest that the PO phenomenon should be given more attention in public management of renewable energy development to overcome land use conflicts and territorial behavior.

Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.

Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45-79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome.

Hopes and Realities: Local Perspectives on Participation in Community-Based Tourism

Tourism development has been promoted as an alternative major source of income for countries and local communities. This perspective has further underscored local communities as critical stakeholders in tourism development. As such, community-based tourism gained momentum as a viable community participation approach for sustainable tourism development. However, even though community participation is said to be critical in sustainable tourism development, relatively little is known about how locals perceive community-based tourism as an alternative for sustainable livelihood, let alone the kind of local participation expected. The purpose of this article is to determine how the Mmadinare community in Botswana perceives community-based tourism and their own participation. The radical empowerment theory provides a theoretical framework. A qualitative multiple-case study approach using semi-structured interviews and focus groups was applied to gather data. The findings suggest that, in reality, community participation in tourism initiatives remains a challenge attributed to the lack of funding, a lack of capacity building, the limited involvement in decision-making platforms, the low awareness level of tourism development, and an unsuitable policy framework.

Education in the History of State and Power: Transnational, National and Local Perspectives

Education in the History of State and Power: Transnational, National and Local Perspectives

Global semantic-guided network for saliency prediction

The human visual system effectively analyzes scenes based on local, global and semantic properties. Deep learning-based saliency prediction models adopted two-stream networks, leveraged prior knowledge of global semantics, or added long-range dependency modeling structures like transformers to incorporate global saliency information. However, they either brought high complexity to learning local and global features or neglected the design for enhancing local features. In this paper, we propose a Global Semantic-Guided Network (GSGNet), which first enriches global semantics through a modified transformer block and then incorporates semantic information into visual features from local and global perspectives in an efficient way. Multi-head self-attention in transformers captures global features, but lacks information communication within and between feature subspaces (heads) when computing the similarity matrix. To learn global representations and enhance interactions of the subspaces, we propose a Channel-Squeeze Spatial Attention (CSSA) module to emphasize channel-relevant information in a compression manner and learn global spatial relationships. To better fuse local and global contextual information, we propose a hybrid CNN-Transformer block called local–global fusion block (LGFB) for aggregating semantic features simply and efficiently. Experimental results on four public datasets demonstrate that our model achieves compelling performance compared with the state-of-the-art saliency prediction models on various evaluation metrics.

The impacts of extreme temperature on mortality and emergency hospital admissions within East Sussex in comparison with pre-existing national trends.

The impacts of heatwaves are a rapidly growing area of study; however, much of the existing research focusses on national data analysis. This article aims to add a local perspective using data from only one county, East Sussex, and comparing these with the pre-existing national data. Population data were obtained from publicly available sources such as the Office of National Statistics, in addition to anonymized data from patients. Statistical analysis calculated excess mortality and emergency hospital admissions associated with both winter and heatwaves. Further analyses into factors associated with worse health outcomes in pre-existing data, such as the extremes of age (under 1s and over 75s), dementia and respiratory conditions, were conducted and their effect on excess mortality and emergency admissions was compared with national data. Excess winter mortality within East Sussex averaged 22.5%. Excess heatwave mortality averaged 17%, measuring higher than national data. The relative significance of these data is expected to increase over the next 30years in line with the UK Health Security Agency projection of heat-related mortality tripling by 2050 in the context of global warming and increasing temperatures. Although the number of residents dying or requiring emergency admission due to cold weather is larger than that of heatwaves, trends show a worsening impact of heatwaves. The results of this report are significant findings which show more action is required to mitigate the effects of extreme heat.

Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms

There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.

Association between total ischaemic time and cardiac mortality outcomes among STEMI patients in Sarawak Heart Centre, Malaysia: A local geographical perspective

Association between total ischaemic time and cardiac mortality outcomes among STEMI patients in Sarawak Heart Centre, Malaysia: A local geographical perspective

EPH101 Assessing the Relative Benefits Associated With Developing Vaccines for Nipah Virus Infection and Rift Valley Fever From Local, National, and Global Perspectives

EPH101 Assessing the Relative Benefits Associated With Developing Vaccines for Nipah Virus Infection and Rift Valley Fever From Local, National, and Global Perspectives

Community Participation in the Fight against Corruption in Nigeria: The Local Language Policy Perspective

Abstract:
 Decades after Nigeria’s independence, the country is still among the poorest in the world despite the availability of both human and material resources. The image of the country has been damaged by corruption. The level of corruption in Nigeria is beyond imagination. This article explores the role of community participation in the fight against corruption in Nigeria using the local language. The elite theory was used to operate the fight against corruption through community participation via the use of local language and twenty-five scholarly journal articles got from Google scholar, web of science, Sage, and other scholarly search engines were sourced and used. The content analysis tool used for this paper is systematically positioned to review words and themes, within some given qualitative data. The paper opined that local language can serve as a functional vehicle of interaction and an instrument of advocacy in the fight against corruption in Nigeria. The paper further established that local languages are an embodiment of culture, reflecting the depth of the people’s beliefs and knowledge. Hence, using it as a tool in the fight against corruption would be highly effective. The paper concludes that strengthening democratic institutions, including those created for fighting this menace, and the possibility of having a creditable community-sponsored candidate for elective position in the government would create a sense of belonging and indeed, bring a sense of optimism in governance and in the fight against corruption.

Beauty Standard in Malaysian Advertisements: An Analysis from Local Advertisers’ Perspective

The impact of Westernization in Malaysia in recent years has brought a decrease in public identity and an increase in adapting Western beauty standards. The literature directed Malaysians in demonstrating slim and fair idealness has predominant in well-off, urban settings. This study examines the beauty standard in local beauty product advertisements on Instagram. An in-depth interview was conducted with advertisers to gain insights into the local advertising perspective. The result concludes that advertisers still keep up with the traditional beauty standard since it fits society’s preference which results from the hike in revenue. However, diversity of beauty is equally accepted among millennials.

Social Acceptance and Social, Economic, and Environmental Impact of Renewable Energy - A Case Study from Hanimaadhoo Island, Maldives

Social acceptance particularly public opposition on renewable energy have been key obstacles for renewable energy deployment in many developed nations particularly in Europe. The level of awareness of the situation in the global south, particularly in Small Island Developing States (SIDS), was rather limited. This research utilizes Cultural Theory as a framework, incorporating data obtained from household questionnaire surveys, focus group discussion and individual interviews conducted on Hanimaadhoo island in the Maldives. The objective is to gain a more comprehensive understanding of how individuals’ worldviews influence their opinions and behaviors regarding renewable energy. This study also aims to comprehend the anticipated social, economic, and environmental impacts of renewable energy. The authors highlight key findings from a local perspective that they deem crucial to consider for the future deployment and diffusion of renewable energy in the Maldives

Social Acceptance and Social, Economic, and Environmental Impact of Renewable Energy - A Case Study from Hanimaadhoo Island, Maldives

Social acceptance particularly public opposition on renewable energy have been key obstacles for renewable energy deployment in many developed nations particularly in Europe. The level of awareness of the situation in the global south, particularly in Small Island Developing States (SIDS), was rather limited. This research utilizes Cultural Theory as a framework, incorporating data obtained from household questionnaire surveys, focus group discussion and individual interviews conducted on Hanimaadhoo island in the Maldives. The objective is to gain a more comprehensive understanding of how individuals’ worldviews influence their opinions and behaviors regarding renewable energy. This study also aims to comprehend the anticipated social, economic, and environmental impacts of renewable energy. The authors highlight key findings from a local perspective that they deem crucial to consider for the future deployment and diffusion of renewable energy in the Maldives

AHP를 활용한 경기 북부 로컬 창업 활성화 방안 연구

The purpose of this study was to determine the priorities of experts, local entrepreneurs, and support organizations to revitalize local entrepreneurship and set policy directions. The study was conducted in northern Gyeonggi-do, which has a lower Gross Regional Domestic Product (GRDP) than southern Gyeonggi-do, and a higher proportion of small businesses and self-employed entrepreneurs than ICT and manufacturing industries. Previous studies and surveys have shown that northern Gyeonggi-do lacks infrastructure to support entrepreneurship, and the revitalization of local entrepreneurship is helpful in terms of revitalizing the local economy and linking with ICT and manufacturing industries. In this study, in order to structurally analyze the factors for revitalizing local entrepreneurship, sub-factors were subdivided into local cluster perspectives of economic activity localization, industry linkages, and establishment of support systems, and AHP analysis was conducted by experts. The results showed that social factors, physical factors, and institutional factors are the most important factors for local entrepreneurship in the northern part of Gyeonggi-do. In particular, it is necessary to discover specialized brands through local councils and to activate consumption through policy.

Explainable Boosting Machine: A Contemporary Glass-Box Model to Analyze Work Zone-Related Road Traffic Crashes

Examining the factors contributing to work zone crashes and implementing measures to reduce their occurrence can significantly improve road safety. In this research, we utilized the explainable boosting machine (EBM), a modern glass-box machine learning (ML) model, to categorize and predict work zone-related crashes and to interpret the various contributing factors. The issue of data imbalance was also addressed by utilizing work zone crash data from the state of New Jersey, comprising data collected over the course of two years (2017 and 2018) and applying data augmentation strategies such synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, and SVM-SMOTE. The EBM model was trained using augmented data and Bayesian optimization for hyperparameter tuning. The performance of the EBM model was evaluated and compared to black-box ML models such as combined kernel and tree boosting (KTBoost, python 3.7.1 and KTboost package version 0.2.2), light gradient boosting machine (LightGBM version 3.2.1), and extreme gradient boosting (XGBoost version 1.7.6). The EBM model, using borderline-SMOTE-treated data, demonstrated greater efficacy with respect to precision (81.37%), recall (82.53%), geometric mean (75.39%), and Matthews correlation coefficient (0.43). The EBM model also allows for an in-depth evaluation of single and pairwise factor interactions in predicting work zone-related crash severity. It examines both global and local perspectives, and assists in assessing the influence of various factors.