Local Issues Research Articles

Paradoxes in Action: Lived Perspectives of Irregular, Nigerian Sex Workers in Spain

ABSTRACT Despite highly vocal concerns regarding modern slavery, there remains a need for fine-grain research about irregular migratory phenomena, particularly the voices of those trafficked for sex work. This ethnographic study sought to address this lacuna by investigating the lived experiences of 19 Nigerian Edo women working as streetwalkers and madams in Spain. We discuss the women’s perspectives on irregular migration and resettlement, which span wide-ranging psychosocial, sexual, cultural, and spiritual phenomena. Eight seeming paradoxes run through the women’s accounts, including how some distinguish migrant sex work from sex trafficking, and perceive it as taking social transformation into their own hands. Here, we focus on the women’s insights drawn from contextualized, lived experiences, and their strong desire to ensure future generations of Edo women do not follow in their footsteps. We argue that it is vital to consider marginalized migratory populations as “the experts” if we are to understand the local issues and root causes of trafficking, and help inform realistic, reliable, and achievable response strategies.

Spatial and social relevance perceptions by pre-service teachers of learning about oil palm management as a local or nonlocal socioscientific issue

IntroductionPre-service teachers (PST)' perceived relevance of learning about environmental socioscientific issues (SSI) can be an indicator for their motivation to act as change agents. Until now, science education (research) has often addressed the relevance for learning about SSI insufficiently differentiated regarding spatial and social dimensions. However, theoretical frameworks suggest that such differentiation enhances meaningful teaching and learning. This study investigated how local, national, and global subdimensions of spatial relevance as well as individual, societal, and professional subdimensions of social relevance influence PST' relevance perceptions of learning about SSI. Additionally, we examined how relevance perceptions vary depending on whether the SSI is local or nonlocal to PST. We specifically investigated Indonesian PST' relevance perceptions of learning about oil palm management (OPM), a local SSI for PST of one university and a nonlocal SSI for PST of two other universities.MethodsThe PST participated in a 5-week socioscientific inquiry-based educational unit on OPM in curricular courses (N = 111). We followed a mixed-method approach, employing measurements of utility value. Utility value is a specific construct of perceived relevance, which refers to the usefulness of learning about objects for a person's life, profession, and society. Quantitatively, we conducted pretest-posttest-follow-up surveys on PST' perceived utility value for learning about OPM over time. Qualitatively, we analyzed responses to a utility value reflection task that was integrated into the unit.ResultsOverall, the unit increased PST' utility value over time. Local PST perceived lower utility value for learning about OPM than nonlocal PST. In the task responses, local PST referred more to the local subdimension, whereas nonlocal PST referred more to the national subdimension. Nonlocal PST' societal and professional utility value increased stronger over time compared to local PST.DiscussionWe discuss potential reasons for local PST' lower relevance perceptions, e.g., personal experiences and skepticism through local embeddedness. Our findings on relevance perceptions among local and nonlocal PST underscore the importance of spatial- and social-sensitive SSI education. We point out practical implications for promoting relevance perceptions considering local and nonlocal PST. Moreover, we suggest research directions for more differentiated relevance research in science education.

Newcastle disease awareness and vaccination practices among indigenous chicken farmers in the Biharamulo district of Tanzania

BackgroundIndigenous chicken production is a significant economic activity in rural sub-Saharan Africa. These chickens are naturally resilient to unfavourable environmental conditions such as poor-quality feed and infections, making them ideal for village conditions. However, Newcastle disease is a major challenge to village chicken production. Since the chickens are kept in a free-range system, they are more likely to catch the disease from the environment and spread it throughout the whole village. Vaccination is one of the main strategies for controlling Newcastle disease. However, various socio-economic and local environmental issues impede vaccination efforts. The study examined the awareness of and vaccination of indigenous chickens against Newcastle disease in rural areas of the Biharamulo district in northern Tanzania. It sought to understand the extent of knowledge among farmers about the disease, the availability of vaccines, and the factors influencing vaccination uptake. Data collection methods included household interviews, focus group talks, and key informant interviews.ResultsThe findings reveal both challenges and opportunities for improving Newcastle disease control and promoting sustainable poultry farming in the region. While most farmers reported being aware of Newcastle disease, many did not vaccinate their chickens. Those who vaccinated their chickens did so irregularly or used herbal medicines as vaccines. Various socio-demographic aspects, including the age and education level of the chicken owner, awareness of the disease, production experience, and previous experience with Newcastle disease outbreaks, determined the probability of vaccination. The farmers identified limited information about Newcastle disease vaccination as a major constraint on vaccinating their flocks.ConclusionThe study underscores the need for targeted interventions to improve Newcastle disease control among indigenous chicken farmers in rural areas of Biharamulo district. Addressing these challenges requires strengthening farmer education, expanding access to vaccines, and enhancing extension services. Promoting community-based vaccination programs and awareness campaigns can help bridge these gaps, contributing to sustainable poultry farming and improved livelihoods in the region.

Future development of peace education in Bali: Lessons from a critical analysis of the peace education curricula of Hiroshima

This study explores the necessity of promoting peace education in Bali through a critical analysis of the peace education curricula in Hiroshima, identifying lessons that can enhance Bali's efforts in fostering harmony and conflict resolution. By investigating the ideologies, power structures, and socio-political settings influencing Hiroshima's framework, the research employs qualitative methods, including expert interviews and detailed curriculum analysis, to examine its effectiveness and inclusiveness. The findings reveal that Hiroshima's peace education is deeply rooted in its historical trauma, emphasizing anti-war narratives and collective harmony, but it faces challenges in integrating multicultural perspectives and addressing environmental peace. For Bali, the study highlights the potential of incorporating local philosophies like Tri Hita Karana to create a culturally resonant framework that balances global and local issues, fostering inclusivity, sustainability, and social justice. The implications suggest that a localized, participatory, and environmentally integrated approach to peace education can empower students and communities in Bali to actively contribute to a more peaceful and equitable society.

Postulating perfect prompts: Leveraging chatbot technologies to engage math students in sociopolitical issues

ABSTRACT Engaging math students in discussions of sociopolitical issues helps to contextualize mathematics and deepens students’ understanding of local and global issues that may potentially have a lasting impact on their future. However, such discussions are rarely, if not ever, present in mathematics classrooms due to a perceived neutrality of the discipline. In this article, I adopt a critical pedagogical perspective and argue that mathematics as a discipline and the teaching of mathematics is political and elaborate on how teaching mathematics can be used for social justice. I also propose the use of generative artificial intelligence chatbot technologies, such as ChatGPT, as powerful tools within math classrooms to initiate and foster meaningful discussions among students. Using a structured prompting framework consisting of six categories — task, context, examples, persona, format, and tone — I model a sample prompting structure that math teachers can use as a guide in their classrooms.

Incorporating local calibrations into M-E rigid design tables based on the AASHTO M-E design procedure

The paper describes the development of an agency-specific rigid design procedure using the AASHTO M-E design procedure. Furthermore, this local design procedure can include local calibrations of the mechanistic-empirical models used in the AASHTO M-E procedure. The local procedure restricts the user to pre-determined design input parameters, but in these restrictions provides the agency with the opportunity to closely investigate and design for local issues in pavement design. These parameters can include special climate files (or considerations), design cross-sections unique to an agency, traffic load spectra based on weigh-in-motion data, and special inputs related to local concretes. Furthermore, the localization of the AASHTO M-E procedure allows the agency the opportunity to develop an alternative to the AASHTO reliability analysis. The final local procedure can be implemented in whatever software the agency chooses. The final procedure is then beneficial in many aspects: 1) It provides a simplified design scheme that provides pavement engineers with near instantaneous design results, 2) The design results take advantage of the AASHTO M-E models yet include local calibrations and agency design considerations, and 3) The agency design procedure is under the control of the agency and can be modified, localized, upgraded, etc. at the discretion of the agency.

Experiencing and Learning in the Community: A Qualitative Study of the "1.5km Experiential Learning Circle" in Hong Kong

This study explores how the "1.5km Experiential Learning Circle" (ELC) in Hong Kong enhances student engagement and fosters stronger connections with the local community. The ELC encourages students to actively learn by experiencing real-world locations within a 1.5km radius of their schools, including nearby businesses, cultural landmarks, historical sites, government premises, and natural sites. Teachers support this process by identifying local issues and leveraging community resources to enhance learning, including encouraging students to address social challenges and collaborate with external organizations to assist those in need. Through interviews and observations with students, teachers, and community members, this qualitative research investigates how the ELC enhances students’ learning performance, motivation, and attitudes while fostering social responsibility and community engagement. Teachers observed that students became more eager to apply classroom knowledge to real-life contexts through activities like analyzing local businesses, engaging with elders, and solving community issues. Students noted these experiences deepened their understanding, boosted motivation, and enhanced critical thinking. This study highlights the potential of the ELC as a model for experiential learning in urban communities, offering valuable insights for educators looking to create more practical and community-centered learning experiences.

DCFE-YOLO: A novel fabric defect detection method.

Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. First, Dynamic Snake Convolution is introduced into the backbone network to enhance sensitivity to elongated and subtle defects, improving the extraction of edge and texture details. Second, a Channel Priority Convolutional Attention mechanism is incorporated after the Spatial Pyramid Pooling layer to enable more precise defect localization by leveraging multi-scale structures and channel priors. Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. Experimental results demonstrate a significant improvement in detection performance. Specifically, mAP@0.5 increased by 2.9%, precision improved by 3.5%, and mAP@0.5:0.95 rose by 2.3%, highlighting the model's superior capability in detecting complex defects. The project is available at https://github.com/lilian998/fabric.

What do the bloody spiritualists say? Exploring menstruation advocacy and feminist frictions in Sweden

While globally menstruators are increasingly receiving support through solidarity campaigns, the menstruating body remains an ambivalent subject in Swedish politics and feminist scholarship. Menstruation activists emphasise that periods are both a local and global political issue, urging menstruators to become more aware of how their bodily cycles impact them. This article aims to explore a selection of narratives on menstruation as a political and spiritual phenomenon and investigate the still residual ambivalence in the relationship between feminism and menstrual advocacy in a Swedish context. The study is empirically anchored in a campaign for menstruation awareness combined with interviews with feminist women about menstruation as an experience and as a cultural phenomenon. The campaign, here called PeriodPride, addressed different topics, such as body literacy and menstrual solidarity. Drawing on an ethnographic study combined with a narrative approach, three narratives have been identified: (1) the menstruating body as a ‘woman’s issue’, (2) menstruators in need, remembering period poverty, and (3) the forceful cycle, reclaim the value of bodies. These narratives elucidate the discursive complexity of menstruation advocacy, underscoring its entanglement with multiple frameworks of meaning and revealing some of the productive tensions inherent within Swedish feminist traditions.

Exploring Changing People's Perceptions on Local Elections

The article explores changing people perceptions of political parties during local elections in Nepal, highlighting the different factors of recent socio-political changes. Following the restructuring of local governance in the 2022 elections, political parties have increased direct involvement with their communities, changing public perceptions. Using a constructivist interpretivist paradigm with multiple realities ontology, the study employs both structured and unstructured questionnaires and interviews to gather narrative experiences from the public. The research utilizes a convergent design, integrating exploratory and descriptive methods, and employs both qualitative and quantitative techniques, including interviews and focus groups, to capture diverse people sentiments across various respondents. The sampling of 120 respondent in convenience sampling reveals that educational changes, political disillusionment, and grassroots engagement are key drivers of these changing perceptions. With all these changes the people are increasingly evaluating political parties based on local issues such as infrastructure, education, health, and social equity, rather than national political manifesto. The findings indicated a growing trend toward electing leaders who are less affiliated with traditional parties, often younger and perceived as neutral. This shift reflects declining trust in established political parties and highlights the need for greater accountability and transparency. The study underscores a pivotal moment in Nepal’s democratic institutionalization, urging political entities to adapt strategically to maintain their relevance. By exploring these changing voter perceptions, the article contributes to a deeper understanding of political accountability and voter behavior in developing democracies, offering insights for policymakers and political strategists.

Gabor-wavelet-activation implicit neural learning for full waveform inversion

Seismic full waveform inversion (FWI) is an efficient imaging method for estimating subsurface physical parameters. However, when the initial models are inaccurate or seismic data lack low frequencies, most traditional discrete grid-based FWI algorithms often encounter local minimum problems. Additionally, the inversion performance and computational cost are closely related to spatial resolution. Reparameterizing velocity models using neural networks (NNs) effectively mitigates local minimum issues. However, most NN-FWI approaches perform well when overparameterized and are limited to fixed grid-like representations. In contrast, implicit neural learning (INL) enables the representation of models at any resolution using a multilayer perception (MLP) network that learns a continuous function from discrete coordinates. To enhance the capability of INL, we developed a general Gabor-wavelet-activation INL approach and applied it to FWI, referred to as wavelet implicit neural learning FWI (WinFWI), using only the vertical particle-velocity. The constructed MLP was lightweight, which reduced the computational overhead. Numerical experiments on a synthetic block model and Marmousi2 model demonstrate the robustness of the proposed method to challenges such as parameter crosstalk. This was further validated using the Chevron 2014 blind test data. All comparisons show that our method is more general and robust than traditional and INL-based FWI algorithms. Moreover, additional feature visualization and numerical analysis illustrate the potential advantages of WinFWI in balancing computational cost and inversion accuracy.

New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms

This paper introduces a novel approach to blind adaptive equalization for digital communication systems using genetic algorithms (GAs). Unlike traditional methods that rely on linear programming and suffer from local minima issues, this technique utilizes a stochastic linear programming cost function with GAs for robust optimization. The proposed method termed Blind Linear Equalizer based on genetic algorithm (BLE-GA) enhances performance by leveraging a GA’s ability to handle stochastic variables, offering rapid convergence and resilience against signal noise and inter-symbol interference. Extensive simulations demonstrate the effectiveness of BLE-GA across different QAM systems, outperforming conventional techniques like the Constant Modulus Algorithm in scenarios with high modulation levels. This study validates the potential of using GAs in adaptive blind equalization to achieve reliable and efficient communication, even in complex and noisy channel conditions.

Development of Interactive Mathematics E-Modules Based on Local Issues with a Personalized Approach to Facilitate Junior High School Students' Computational Thinking Skills

Development of Interactive Mathematics E-Modules Based on Local Issues with a Personalized Approach to Facilitate Junior High School Students' Computational Thinking Skills

Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection.

The prevalence of age-related brain issues has risen in developed countries because of changes in lifestyle. Alzheimer's disease leads to a rapid and irreversible decline in cognitive abilities by damaging memory cells. A ResNet-18-based system is proposed, integrating Depth Convolution with a Squeeze and Excitation (SE) block to minimize tuning parameters. This design is based on analyses of existing deep learning architectures and feature extraction techniques. Additionally, pre-trained ResNet-18 models were created with and without the SE block to compare ROC and accuracy values across different hyperparameters. The proposed model achieved ROC values of 95% for Alzheimer's Disease (AD), 95% for Cognitively Normal (CN), and 93% for Mild Cognitive Impairment (MCI), with a maximum test accuracy of 88.51%. However, the pre-trained model with SE had 93.26% accuracy and ROC values of 98%, 99%, and 98%, while the model without SE had 94%, 97%, and 94% ROC values and 92.41% accuracy. Collecting medical data can be expensive and raises ethical concerns. Small data sets are also prone to local minima issues in the cost function. A scratch model that experiences extensive hyperparameter tuning may end up being either overfitted or underfitted. Class imbalance also reduces performance. Transfer learning is most effective with small, imbalanced datasets, and pre-trained models with SE blocks perform better than others. The proposed model introduced a method to reduce training parameters and prevent overfitting from imbalanced medical data. Overall performance findings show that the suggested approach performs better than the state-of-the-art techniques.

Factors Affecting Green Growth Policy in Vietnam

Green growth is becoming a global priority as countries increasingly recognize the importance of combining economic development with environmental protection. Green growth policies and strategies are designed to reduce greenhouse gas emissions, promote the use of renewable energy, and improve resource efficiency, contributing to the goal of sustainable development and combating climate change. Government green growth policies are influenced by many economic, social, environmental, and political factors that determine the effectiveness and sustainability of these measures. Essential factors include public awareness, international pressure, economic and financial policies, technology and innovation, local environmental issues, politics and leadership, and public-private partnerships (PPPs). Vietnam has implemented the "National Strategy on Green Growth" for 2021-2030 with clear goals and has achieved many significant results, such as reducing greenhouse gas emissions and increasing forest coverage. However, there are still many limitations in terms of capital, technology, and supporting policy mechanisms. By proposing a qualitative research model on influencing factors and quantitative methods, specifically multivariate linear regression analysis, the author has shown the model results of these influencing factors on green growth policy, contributing to proposing recommendations to improve green growth policy in Vietnam.

VIRTUAL COMPARATIVE GLOBAL AGING CURRICULUM/COURSE – STUDENT GLOBAL COMPETENCES QUALITATIVE THEMATIC ANALYSIS

Abstract Presents global competences acquired by students of the international virtual course Comparative Global Aging implemented in the years 2018-2023. The course is the result of American-Polish cooperation between the University of Nebraska in Omaha and two Polish universities - the University of Wrocaw and the University School of Physical Education in Wrocław. The content of thirty-seven Learning Journals of students from 10 European countries studying in Poland in the ERASMUS student exchange program and UNO students was subjected to qualitative analysis using MAXQDA software. Discuss the results in the three distinguished dimensions of global competences - cognitive, socio-emotional and ethical, and five thematic clusters: Critical and Constructive Thinking, Self-Awareness and Self-Direction in Education, Effective Cross-Cultural Cooperation, Open Intergenerational Communication, Innovation in Action and Problem Solving. Learning outcomes achieved by students, i.e. the ability to examine local, global and cross-cultural issues in the topic of ageing, understand and appreciate the perspectives of people living in other countries and cultures, engage in open and effective interactions, and work to solve the problems of older people using a broad global perspective of ageing countries and societies

Thoughts on the Trends in 21st-Century Liberal Arts Education : Seeking Possibility of Collaboration between Universities and Industry

As society becomes increasingly complex and globalized, the demands on higher education to foster versatile, socially responsible graduates have grown. This study examines Japan’s efforts to modernize liberal arts education to better align with societal needs, particularly emphasizing data science, STEAM, digital transformation (DX), and the Sustainable Development Goals (SDGs). Research by the Japan University Accreditation Association highlights a disconnect between university curricula and the skill expectations of industries, which prioritize abilities like logical thinking, problem-solving, and data analysis. Despite reforms, communication between higher education institutions and industries remains insufficient, limiting collaboration on curriculum development and obstructing a cohesive societal impact. The concept of “21st-century liberal arts education” in Japan includes interdisciplinary approaches to address complex issues, fostering communication skills for global engagement, integrating digital literacy for a tech-centered world, and cultivating critical thinking. These principles reflect an educational response to a “VUCA” (volatility, uncertainty, complexity, ambiguity) world, stressing literacies that prepare students to tackle global challenges. Case studies across Japan, the U.S., and the U.K. reveal parallel trends in collaborative education reform and active learning techniques. Japan’s universities have initiated project-based learning (PBL) courses, supported by partnerships with local businesses, that offer students real-world problem-solving experiences and promote “living education”.</br>The study underscores the need for continuous dialogue and mutual understanding between academia and society to bridge gaps between university-led reforms and societal needs. Round-table discussions involving universities, industries, and governmental agencies reveal shared values but also emphasize the necessity for enhanced communication, especially regarding graduates’ competencies. Japan's “Innovation Commons” spaces represent an innovative model for community-university partnerships aimed at solving local issues, with broad implications for sustainable development and regional revitalization.</br>In conclusion, the study advocates for strengthening public engagement in education through information dissemination, deeper societal integration, and collaborative curricular reforms that align with the SDGs and ethical challenges posed by digital advancements. The study recommends expanding ethical, legal, and social issue (ELSI) subjects and advancing financial literacy as critical aspects of civic education. Ultimately, this approach to liberal arts education aims to equip students with practical and ethical tools to contribute positively to society amidst evolving global demands.

A Spatial-Temporal Exploration of Coordination Failures Preceding Coal Mine Explosion Accidents in China

Coal remains a crucial component of China’s energy supply, with production exceeding half of the global output in 2023. Despite safety improvements, the fatality rate in coal mining rose significantly, underscoring ongoing safety challenges. A total of 174 coal mine explosion investigation reports from China between 2000 and 2024 were analyzed, extracting and mining text related to coordination failures. The texts were categorized by time and region, creating two temporal datasets (2000–2018 and 2019–2024) and six regional datasets (Northeast, East, Central South, Southwest, Northwest, and North China). Using frequent itemset mining and social network construction, the concept of risk propagation was applied to identify the critical paths that lead to coal mine explosions. Over time, coordination failures in China’s coal mine explosions have evolved from localized issues among a few stakeholders to complex, multi-layered challenges involving broader governmental oversight and systemic management issues. Based on regional findings, balancing the frequency and severity of penalties, ensuring meaningful safety inspections, and alleviating the policy pressure on small coal mines are key points for addressing coordination failures.

A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold

Deep Learning (DL) has achieved remarkable success in tackling complex Artificial Intelligence tasks. The standard training of neural networks employs backpropagation to compute gradients and utilizes various optimization algorithms in the Euclidean space \(\mathbb {R}^n \) . However, this optimization process faces challenges, such as the local optimal issues and the problem of gradient vanishing and exploding. To address these problems, Riemannian optimization offers a powerful extension to solve optimization problems in deep learning. By incorporating the prior constraint structure and the metric information of the underlying geometric information, Riemannian optimization-based DL offers a more stable and reliable optimization process, as well as enhanced adaptability to complex data structures. This article presents a comprehensive survey of applying geometric optimization in DL, including the basic procedure of geometric optimization, various geometric optimizers, and some concepts of the Riemannian manifold. In addition, it investigates various applications of geometric optimization in different DL networks for diverse tasks and discusses typical public toolboxes that implement optimization on the manifold. This article also includes a performance comparison among different deep geometric optimization methods in image recognition scenarios. Finally, this article elaborates on future opportunities and challenges in this field.

Urban heat island relation with land use land cover change in Hetauda sub-metropolitan city of Nepal

Urban areas are expanding globally at the expense of natural productive land which affects the quality of life of urban residents. Hetauda sub-metropolitan city of Nepal has been undergoing rapid urban growth for the last few decades causing local climatic effects such as land surface temperature (LST) variation. Thus, exploring spatio-temporal changes in land use, land cover (LULC), and urban heat island (UHI) analysis could be an effective means of exposing local environmental issues caused by anthropogenic activities. Development in thermal Remote Sensing and Geographic Information System (GIS) has enabled the monitoring of spatial LST, UHI, and its correlation to LULC. We used Landsat 8 OLI/TIRS satellite data and a supervised classification algorithm for land use land classification for the years 1995, 2008, and 2018 in Arc map software. The spatial pattern of LST was obtained through mathematical calculation of the thermal band of Landsat images. Correlation analysis was applied to explore the relationship between LST, LULC types, and LUCL indices. The LST was higher for urban/built-up and cultivated land use types. There was approximately 4°C mean LST variation for all three years of study. The regression analysis showed a positive correlation of urban/built-up with the Normal Difference Built-Up Index (NDBI) however a negative correlation with the Normal Difference Vegetation Index (NDVI) which implies that green structure weakens the UHI effects while urban/built-up areas strengthen the UHI. Overall, the study can be useful for urban planners in sustainable urban planning and management as well as to raise public awareness of climate change and the warming effect.