Guidelines For Development Research Articles

A New Approach to Rural Classification Based on the Filter-Method System: An Empirical Study in Nanning, South China

Rural revitalization is a strategic plan to address sustainable rural development in China and is an important revitalization task for Chinese villages. Rural classification is a key strategy for clarifying the direction and positioning of rural development and providing scientific policy decision-making Based on the findings of previous research on rural classification, we propose a new method for rural classification—the filter-method classification system. The operational steps of the classification are as follows: determining the type of villages, implementing the qualitative filter-method, implementing the quantitative filter-method, testing and feedback, and formulating the development guidelines. A total of 1425 villages in Nanning, the capital city of Guangxi, were classified. The classification results show that villages in Nanning city can be divided into four primary categories and eight secondary categories. The accuracy of the primary categories in the classification results was tested to verify the scientific objectivity and applicability of the classification idea and mode. Finally, development suggestions are presented based on the results of the classification of villages. The study results provide a reference for further rural classification work to help rural revitalization and improve the rural classification methodology and the scientific objectivity of classification. It also provides the basis for sustainable monitoring of rural development.

Plant‐based mince texture: A review of the sensory literature with view to informing new product development

AbstractThe texture of plant‐based meat alternatives is a sensory attribute that holds a central value in consumer acceptability. The texture dimensions and drivers of liking for plant‐based mince, a popular product within plant‐based meats, are not yet fully established and literature in this area is limited. Consequently, the successful development and positioning of such products in the market next to traditional proteins is compromised, and improved product development guidelines are needed for the industry. This review aims to inform product development of what is currently known about plant‐based mince texture and where the research gaps are, particularly with respect to sensory dimensions and drivers of liking, by reviewing the present landscape of relevant sensory literature. Potential texture sensory dimensions for plant‐based mince, based on limited studies and seemingly aligning with conventional mince, are posited as juiciness, tenderness, firmness, and/or softness with a key driver of liking being juiciness, but this requires further validation utilizing robust sensory studies. A significant need exists to expand on the currently known texture dimensions and drivers alongside new ways that texture can be improved upon to more closely align to, or exceed, consumer expectations. Once this has been achieved, a robust sensory framework for developing plant‐based mince products can be used to better position plant‐based mince to effectively compete with traditional mince, and not merely cannibalize upon other brands, in the current market.

Investigating Adoption Determinants, Obstacles, and Interventions for AI Implementation in Emirati Media Organizations

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

Development of a habit-based intervention to support healthy eating and physical activity behaviours for pregnant women with overweight or obesity: Healthy Habits in Pregnancy and Beyond (HHIPBe).

The number of women entering pregnancy with overweight or obesity is increasing. This can increase the risk for excessive gestational weight gain (GWG) which is associated with health complications for mother and baby. There are limited evidence-based interventions within antenatal care settings to encourage healthy eating and physical activity behaviours and support women with managing GWG. A previous habit-based intervention 'Ten Top Tips for a Healthy Weight' (10TT) was adapted and made suitable for pregnancy in line with the Medical Research Council's (MRC) complex intervention development guidelines. It involved three key activities: (1) identifying the evidence base; (2) identifying appropriate theory; and, (3) modelling processes. A core element was integrating lived experience via personal and public involvement (PPI). The original 10TTs were adapted with PPI in line with current advice on nutrition and physical activity in pregnancy. New intervention materials were devised, including a leaflet and a logbook and app for self-monitoring to be delivered alongside a brief 1:1 conversation. Behaviour change techniques (BCTs) included in the new materials were coded using a number of behavioural taxonomies. An E-learning resource was created to help standardise the approach to delivery of the intervention and avoid stigmatising conversations. Following MRC guidance for the development of complex interventions alongside significant PPI allowed for the adaption of 10TT habit-based weight management intervention into the 'Healthy Habits in Pregnancy and Beyond' (HHIPBe) intervention. The feasibility and acceptability of implementing this intervention in the antenatal setting will be explored in a feasibility randomised controlled trial. This study was registered on Clinical Trials as 'Healthy Habits in Pregnancy and Beyond (HHIPBe)' ClinicalTrials.gov Identifier: NCT04336878. The study was registered on 07/04/2020.

Maximizing Oxygen Evolution Performance of NiFeOx Semitransparent Electrocatalysts Applicable to Photoelectrochemical Water Splitting Device

In photoelectrochemical (PEC) water splitting, semiconductor‐based photoelectrodes can improve reaction rates and durability by incorporating cocatalysts that serve as active sites for the water splitting process. However, achieving both high light transmittance and efficient catalytic activity is essential for these cocatalysts. This study aimed to optimize the surface loading of semitransparent NiFeOx thin‐film electrocatalysts to enhance the oxygen evolution reaction (OER) rates while maintaining high light transmittance. NiFeOx thin films were deposited on fluorine‐doped SnO2 (FTO) transparent conductive substrates, and the relationship between the NiFeOx loading amount (Γ) and the OER rate was examined using electrochemical techniques. The OER rate of NiFeOx on FTO (NiFeOx/FTO) was the highest at a Γ value of 0.20 μmol cm‐2. To further explore the connection between this optimized Γ and PEC activity, the impact of Γ on the PEC OER performance of visible‐light‐absorbing α‐Fe2O3 semitransparent photoanodes was evaluated as a model system. Applying the optimized Γ of NiFeOx to modify the α‐Fe2O3 surface also led to enhanced PEC OER performance. These findings highlight the critical role of surface design, specifically the optimization of cocatalyst loading and electrocatalytic activity, in improving PEC water splitting efficiency, providing valuable guidelines for future semitransparent photoelectrode development.

PhysioCAFUN: A competency-based curriculum development guideline to strengthen physiology education in Africa.

Physiology education in Africa faces challenges due to gaps in curricula across many of its universities, such as divergent content, a lack of standardized competencies, and suitable benchmarking. Here, we describe the development of Physiology Curriculum for African Universities (PhysioCAFUN), a competency-based curriculum development guideline, as a first step to address such shortcomings. A committee of 15 physiologists from different African regions, Europe, and USA was constituted to draft the PhysioCAFUN, which was introduced and revised during the joint East African Society of Physiological Sciences (EASPS) and African Association of Physiological Sciences (AAPS) conference held in Tanzania late 2023. The PhysioCAFUN consists of 23 modules. Modules 1-15 cover the organ systems, including principles and concepts of physiology, molecular biology and cell physiology. Modules 16-23 contain optional content, including environmental physiology, pharmacology, and topics related to skill development. PhysioCAFUN serves as a freely available resource document for African stakeholders regarding the desired undergraduate physiology training and competencies. It will help universities in Africa, and elsewhere, to draft a curriculum suitable for their local needs where there is a dearth of physiologists, or to benchmark and revise their curricula where physiology programs are already in place.

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

Novel application of metabolic imaging of early embryos using a light-sheet on-a-chip device: a proof-of-concept study.

Is it feasible to safely determine metabolic imaging signatures of nicotinamide adenine dinucleotide [NAD(P)H] associated auto-fluorescence in early embryos using a light-sheet on-a-chip approach? We developed an optofluidic device capable of obtaining high-resolution 3D images of the NAD(P)H autofluorescence of live mouse embryos using a light-sheet on-a-chip device as a proof-of-concept. Selecting the most suitable embryos for implantation and subsequent healthy live birth is crucial to the success rate of assisted reproduction and offspring health. Besides morphological evaluation using optical microscopy, a promising alternative is the non-invasive imaging of live embryos to establish metabolic activity performance. Indeed, in recent years, metabolic imaging has been investigated using highly advanced microscopy technologies such as fluorescence-lifetime imaging and hyperspectral microscopy. The potential safety of the system was investigated by assessing the development and viability of live embryos after embryo culture for 67 h post metabolic imaging at the two-cell embryo stage (n = 115), including a control for culture conditions and sham controls (system non-illuminated). Embryo quality of developed blastocysts was assessed by immunocytochemistry to quantify trophectoderm and inner mass cells (n = 75). Furthermore, inhibition of metabolic activity (FK866 inhibitor) during embryo culture was also assessed (n = 18). The microstructures were fabricated following a standard UV-photolithography process integrating light-sheet fluorescence microscopy into a microfluidic system, including on-chip micro-lenses to generate a light-sheet at the centre of a microchannel. Super-ovulated F1 (CBA/C57Bl6) mice were used to produce two-cell embryos and embryo culture experiments. Blastocyst formation rates and embryo quality (immunocytochemistry) were compared between the study groups. A convolutional neural network (ResNet 34) model using metabolic images was also trained. The optofluidic device was capable of obtaining high-resolution 3D images of live mouse embryos that can be linked to their metabolic activity. The system's design allowed continuous tracking of the embryo location, including high control displacement through the light-sheet and fast imaging of the embryos (<2 s), while keeping a low dose of light exposure (16 J · cm-2 and 8 J · cm-2). Optimum settings for keeping sample viability showed that a modest light dosage was capable of obtaining 30 times higher signal-noise-ratio images than images obtained with a confocal system (P < 0.00001; t-test). The results showed no significant differences between the control, illuminated and non-illuminated embryos (sham control) for embryo development as well as embryo quality at the blastocyst stage (P > 0.05; Yate's chi-squared test). Additionally, embryos with inhibited metabolic activity showed a decreased blastocyst formation rate of 22.2% compared to controls, as well as a 47% reduction in metabolic activity measured by metabolic imaging (P < 0.0001; t-test). This indicates that the optofluidic device was capable of producing metabolic images of live embryos by measuring NAD(P)H autofluorescence, allowing a novel and affordable approach. The obtained metabolic images of two-cell embryos predicted blastocyst formation with an AUC of 0.974. N/A. The study was conducted using a mouse model focused on early embryo development assessing illumination at the two-cell stage. Further safety studies are required to assess the safety and use of 405 nm light at the blastocyst stage by investigating any potential negative impact on live birth rates, offspring health, aneuploidy rates, mutational load, changes in gene expression, and/or effects on epigenome stability in newborns. This light-sheet on-a-chip approach is novel and after rigorous safety studies and a roadmap for technology development, potential future applications could be developed for ART. The overall cost-efficient fabrication of the device will facilitate scalability and integration into future devices if full-safety application is demonstrated. This work was partially supported by an Ideas Grant (no 2004126) from the National Health and Medical Research Council (NHMRC), by the Education Program in Reproduction and Development (EPRD), Department Obstetrics and Gynaecology, Monash University, and by the Department of Mechanical and Aerospace Engineering, Faculty of Engineering, Monash University. The authors E.V-O, R.N., V.J.C., A.N., and F.H. have applied for a patent on the topic of this technology (PCT/AU2023/051132). The remaining authors have nothing to disclose.

Artificial Intelligence in SAP S/4HANA: Transforming Enterprise Resource Planning through Intelligent Automation

This comprehensive article investigates the transformative impact of AI-enhanced SAP S/4HANA Finance across healthcare, manufacturing, scientific research, auto, food &amp; Oil &amp;Gas sectors, focusing on human-AI collaboration patterns and implementation outcomes. Through a mixed-methods approach analyzing 15 organizations over 18 months, the research examines how AI integration transforms traditional ERP functionalities into intelligent financial management systems. The article collected data from 450 end-users and 45 key stakeholders, employing both quantitative metrics and qualitative assessments to evaluate implementation patterns, challenges, and success factors. The findings reveal significant improvements across all sectors: healthcare organizations achieved 40% reduction in billing processing time and 15% improvement in collection rates; manufacturing entities realized 35% reduction in unplanned downtime and 22% decrease in working capital requirements; while research institutions demonstrated 45% faster grant processing and 35% improved budget forecasting accuracy. The article introduces the Adaptive Financial Intelligence Framework (AFIF) for conceptualizing human-AI collaboration in financial management, contributing to both theoretical understanding and practical implementation strategies. The article concludes that successful AI integration depends on industry-specific adaptations, comprehensive training programs, and robust governance frameworks while highlighting the critical role of human expertise in maximizing system benefits. These findings provide valuable insights for organizations pursuing AI-enhanced financial management solutions while offering a roadmap for future developments in human-AI collaboration within enterprise systems.

Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges

The ``Right to be Forgotten," as outlined in regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), allows individuals to request the deletion of their personal data from deployed machine learning models. This provision ensures that individuals can maintain control over their personal information. In the digital health era, this right has become a critical concern for both patients and healthcare providers. To facilitate the effective removal of personal data from machine learning models, the concept of ``machine unlearning" has been introduced. This paper highlights the technical and ethical challenges associated with machine unlearning in digital healthcare. By examining current unlearning methodologies and their limitations, we propose a roadmap for future research and development in this field.

Practical implications for design professionals: integrating emerging patterns into new media interface design

ABSTRACT In the new media era, visual communication design (VCD) in interface design (ID) relies on critical design patterns—layout, color, typography, and interactive elements—that are essential for user engagement. This study investigates these design patterns in ID, drawing on responses from 760 professionals, including UI and UX designers, researchers, and industry experts. Data analysis with SPSS revealed key trends, common practices, and areas for innovation, informing strategies for improved user experiences in new media interfaces. By highlighting emerging patterns and addressing literature gaps, the study offers valuable insights into design preferences and challenges faced by professionals, with implications for educational curricula, tool development, cross-disciplinary collaboration, and user-centric guidelines.

Low-carbon transformation and ecological safeguarding in the Yellow River Basin: Integrating biomechanical and biological insights

This research, titled “Low-carbon transformation and ecological safeguarding in the Yellow River Basin: Integrating biomechanical and biological insights” explores the interplay between economic activities, land use changes, and environmental impact. Through regression analyses and assessments of land use alterations, the study identifies significant provincial variations in factors influencing carbon emissions. In addition to the socio-economic factors, the research incorporates insights from biomechanics and biology, drawing parallels between the ecological systems of the Yellow River Basin and biological processes such as energy efficiency and resource allocation in living organisms. For instance, just as organisms optimize energy usage and adapt to external stressors, the proposed low-carbon strategies aim to optimize resource use and improve the resilience of the basin’s ecosystem. Proposed strategies for low-carbon transformation provide a practical roadmap for sustainable development, informed by biological principles like ecological balance, regeneration, and the importance of maintaining biodiversity. These principles reflect how biomechanical systems, such as musculoskeletal structures, balance energy expenditure and repair to maintain functionality under strain, similar to how ecosystems must manage resource cycles to withstand environmental stressors. The integration of socio-economic indicators, alongside biological and biomechanical insights, underscores the need for region-specific policies that consider not only economic factors but also the natural regenerative capacities of the ecosystem. The study suggests that, like biological systems that repair and adapt to maintain homeostasis, the Yellow River Basin’s ecological processes can be guided by sustainable management practices to ensure long-term resilience and stability. In conclusion, the research contributes valuable insights to the global discourse on balancing economic growth with ecological preservation in the ecologically vital Yellow River Basin, highlighting how the integration of biomechanical and biological principles can enhance both ecological safeguarding and low-carbon transformation strategies.

Robotics and Automation Roadmap: Thailand Perspectives

Robotics and automation are the key industries supporting Thailand’s strategic initiatives toward Thailand 4.0. This paper presents the development of an industry roadmap guiding the future research and development of related technologies in Thailand. The roadmap project was recently conducted during 2021–2022. The strategic targets for the future development of robotics and automation in Thailand were set to increase productivity and reduce adoption cost for the short term (2022–2023); create market opportunities for AI and industrial IoT technologies with data platform for the medium term (2024–2026); and prepare the Industry 5.0 system supporting emerging applications of digital twins and metaverse for the long term (2027–2030).

An IPA Analysis of Tourist Perception and Satisfaction with Nisville Jazz Festival Service Quality

This paper applies Importance-Performance Analysis (IPA) to investigate potential satisfaction or dissatisfaction with service quality at the internationally recognized Nisville Jazz Festival. The research was conducted using the Statistical Package for Social Sciences (SPSS) software version 20, including the Kolmogorov–Smirnov test, factor analysis, one-way analysis of variance (ANOVA), and Principal Component Analysis (PCA) with Varimax rotation for data analysis. The study involved 250 festival attendees, focusing on their motivation and identification of both tangible and intangible attributes of the festival, with 227 providing valid responses to the survey questionnaire. The Service Quality Model (SERVQUAL) was used to identify quality factors. The results indicate that cultural content and hospitality are the most important factors significantly contributing to visitor satisfaction and positively influencing recommendations and repeat visits to the festival. Another important factor in the study is accessibility, while comfort, amenities, safety, and cleanliness require improvement to enhance the overall visitor experience. The findings provide concrete guidelines for the further development of the festival and the enhancement of the tourism offerings in Nis.

Techno-Economic Assessment of Hydrogen Production in Ghana through PV Electrolysis and Biomass Gasification

Abstract The potential to develop a green hydrogen market in Ghana is assessed in this paper. The focus is on biomass gasification and photovoltaic-driven water electrolysis. Using the H2A-Lite model, the technical, economic, and environmental aspects of these technologies are assessed for both current (2023) and future (2040) scenarios. In the current scenario, distributed and centralized Polymer Electrolyte Membrane (PEM) electrolysis gave levelized costs of $5.56/kg and $4.35/kg for hydrogen respectively, while centralized biomass gasification yields the cheapest hydrogen cost at $2.68/kg. The high cost of solar PEM electrolysis is linked to expensive electricity and solar PV, but also to compression, storage, and dispensing costs for distributed systems. By year 2040, a general cost reduction is expected due to cheaper renewable energy and increased efficiency. However, these production methods still face competition from more economical conventional steam methane reforming (SMR) processes having a levelized cost of less than $2/kg H2. The study concludes that a hydrogen development strategy and roadmap for Ghana will be crucial to proceed with hydrogen, setting deployment targets, and engaging stakeholders in promoting the use of hydrogen as an energy carrier while creating new economic opportunities and achieving climate objectives.

AIOps and DevOps : Catalysts of Digital Transformation in the Age of Automated Operations

This article examines the pivotal roles of Artificial Intelligence for IT Operations (AIOps) and Development Operations (DevOps) as key drivers of digital transformation in contemporary organizations. As businesses increasingly rely on complex, distributed IT systems, the integration of AIOps and DevOps emerges as a critical strategy for maintaining operational efficiency, scalability, and innovation. Through a comprehensive analysis of current literature and industry case studies, we explore how these technologies are reshaping the landscape of IT operations, accelerating software delivery cycles, and enabling more resilient and adaptive infrastructures. The article delves into the evolving nature of release engineering, where automation and machine learning are becoming central to ensuring agile and robust IT systems. Furthermore, we critically assess the ethical implications of automated decision-making in operations, including issues of transparency, bias, and job displacement. Our findings suggest that while AIOps and DevOps offer significant benefits in terms of operational optimization and cost reduction, their successful implementation requires careful consideration of organizational culture, skill development, and ethical guidelines. This article contributes to the growing body of knowledge on digital transformation strategies and provides insights for practitioners navigating the complex intersection of artificial intelligence, automation, and IT operations.

The state-of-the-art of N-of-1 therapies and the IRDiRC N-of-1 development roadmap.

In recent years, a small number of people with rare diseases caused by unique genetic variants have been treated with therapies developed specifically for them. This pioneering field of genetic N-of-1 therapies is evolving rapidly, giving hope for the individualized treatment of people living with very rare diseases. In this Review, we outline the concept of N-of-1 individualized therapies, focusing on genetic therapies, and illustrate advances and challenges in the field using cases for which therapies have been successfully developed. We discuss why the traditional drug development and reimbursement pathway is not fit for purpose in this field, and outline the pragmatic, regulatory and ethical challenges this poses for future access to N-of-1 therapies. Finally, we provide a roadmap for N-of-1 individualized therapy development.

Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations

This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

Analisis Peran Bawaslu Provinsi Jawa Timur dalam Pengawasan Netralitas ASN pada Pemilihan Umum

General elections are a form of a country's democratic system. The General Election Supervisory Body is one of the agencies in organizing general elections which is responsible for supervising general elections so that they run honestly and fairly. This research aims to determine the role of the East Java Province Bawaslu in monitoring the neutrality of the State Civil Apparatus in the Election. The method used in this research is descriptive qualitative with data collection techniques through observation, interviews and documentation. Analysis of this research data uses the theory put forward by Sururama and Amalia (2020:44) explaining the stages of supervision which consist of setting implementation standards, determining measurement of activity implementation, comparing activity implementation with standards and analyzing deviations, and taking corrective action. The research results show that there is SKB Number 2 of 2022 concerning Guidelines for the Neutrality Development and Supervision of State Civil Service Employees in the Implementation of General Elections and Elections. Apart from that, Bawaslu also held declaration activities to increase the neutrality of ASN during the election and take action against violations that occurred according to the rules.

Anticolonial co-design: a methodology for including agricultural workers in the development of new AgTech

ABSTRACT Collaborative design (co-design) is becoming a popular method for including end-users in the design process of technologies which may affect them. The research in this paper emerges from an agricultural technology (AgTech) co-design project located in settler colonial state of Aotearoa New Zealand. Inspired by methodological insights from anticolonial science and technology studies (STS), the paper details the authors’ experiences with attempting to include local and migrant agricultural workers within the AgTech co-design project in a way that did not perpetuate colonialism (i.e. was anticolonial). Based on these experiences, the authors identify four practices that can support other researchers wanting to engage in anticolonial co-design: (1) understand the historical context of your research; (2) create a place –and community-based guidelines for technology development; (3) build and maintain strong relationships with collaborators; and (4) be prepared to slow-down or to stop innovation. These practices offer methodological insights into how to do anticolonial co-design within the uneven power relations of settler colonialism and within projects not necessarily designed to engage in anticolonial co-design.