Case Study Research Articles

Ubuntu, African Ancestrality and Its Resignifications in the African Diaspora

ABSTRACT In this text, I reflect on the African cosmovivencias of Ubuntu and the place of the ancestors within those cosmovivencias. I also approach the resignifications that Ubuntu and ancestrality have had in the African Diaspora. I show how ancestrality is present both in Ubuntu and in the cultural, religious, and traditional manifestations, which express the resignifications of the African cosmovivencias of Ubuntu in Latin America. To support my argument, I bring examples of cases that I consider to be experiences of these cosmovivencias. For Africa, I mention Ubuntu (South Africa) and the instances of the figure of “Kimbilikiti,” a common spirit of the Lega ethnic group (eastern DRC); the ancestral ritual for good fishing, practiced jointly in Uvira (also in east DRC); and the figure of “Abashingantahe” (wise men, judges), in Burundi. For Latin America, I bring up the experiences understood as “suficiencias íntimas” and “vivir sabroso” in Colombia and the African rooted religions and the quilombola communities. All these cases have the African ancestry in common.

Dynamic modelling of improved diagnostic testing for drug-resistant tuberculosis in high burden settings.

Limited diagnostic testing for drug-resistant TB (DR-TB) may lead to high rates of misdiagnosis and undertreatment. Current diagnostic tests focus only on detection of rifampicin-resistant TB (RR-TB). This study aims to determine the impact of improved diagnostic testing for a wider range of drug resistance on DR-TB outcomes in high-burden TB settings, using the Philippines and Thailand as case studies. A dynamic compartmental model was designed to simulate population level TB transmission, accounting for acquired drug resistance from treatment failure of drug susceptible TB. Three scenarios were analyzed: (1) Use of GeneXpert MTB/RIF on all presumptive TB cases (Status Quo); (2) GeneXpert MTB/RIF + GeneXpert XDR, (3) GeneXpert MTB/RIF + targeted Next Generation Sequencing (tNGS). Scenarios were modelled over a 10-year period, from 2025 to 2034. Compared to the status quo, Scenario 2 results in a fourfold increase in annual DR-TB cases diagnosed in the Philippines and a fivefold increase in Thailand. DR-TB treatment failure decreases by 20% in the Philippines and 23% in Thailand. Scenario 3 further increases DR-TB case detection, reducing DR-TB treatment failure by 26% in the Philippines and 29% in Thailand. Reductions in DR-TB incidence and mortality ranged from 3 to 6%. The use of GeneXpert XDR or tNGS as an additional diagnostic test for DR-TB significantly improves DR-TB case detection and reduces treatment failure, supporting their consideration for use in high burden settings. These findings highlight the importance of detecting a wider range of TB resistance in addition to RR-TB, the potential impact these improved diagnostic tests can have on DR-TB outcomes, and the need for additional research on cost-effectiveness of these interventions.

Mobility Aid Design for the Elderly (MADE): a design thinking approach using a smart walker as a case study

Mobility Aid Design for the Elderly (MADE): a design thinking approach using a smart walker as a case study

Exploring the Dynamics of Communication in the Handling of Child Victims of Intrafamilial Child Sexual Abuse: A Case Study in the Women's and Children's Protection Technical Implementation Unit, Pekanbaru City Government in Indonesia

This study aims to examine the dynamics of communication in handling intrafamilial child sexual abuse, with a case study of RR, a child victim of sexual abuse by her stepfather and cousin, reported to the Technical Implementation Unit for the Protection of Women and Children (Unit Pelaksana Teknis Perlindungan Perempuan dan Anak/UPT PPA) of the Pekanbaru City Government. Data collection techniques include in-depth interviews, observations, and document studies. Using the constructivist paradigm and the social construction theory by Peter L. Berger and Thomas Luckmann, the collected data were analyzed qualitatively to explore how communication patterns and interactions among the involved parties, including the UPT PPA team, the victim, the victim's family, the Social Services, the Police, Legal Aid, orphanages, and other informal parties, build the social reality of child protection for the victim RR. Data sources were selected using purposive sampling based on criteria until saturation was reached. The findings indicate that despite communication barriers and non-cooperation from the family, effective communication and good coordination among all involved parties are crucial to ensuring the recovery and ongoing legal process for the child victim of sexual abuse. An empathetic, sensitive, and collaborative approach can help the victim feel supported and gradually recover from the traumatic experience. Collaboration between institutions plays an essential role in providing protection and social rehabilitation for RR. The dynamics of communication in handling intrafamilial child sexual abuse involve the dialectical processes of externalization, objectification, and internalization among the various parties involved. Received: 30 July 2024 / Accepted: 28 October 2024 / Published: 05 November 2024

Assessment of wetland loss through industrialization and community expansion: a case study of the greater amanzule wetland in the Ellembelle district of Ghana

Wetland ecosystems provide numerous services to humanity and the biosphere at large. However, these services are constantly dwindling due to the massive wetland loss caused by urbanization. Urbanization-induced wetland loss is a global problem, with most developing countries experiencing it currently. Several studies on wetland loss have been conducted. However, there is a dearth of studies integrating GIS and other ecological models to estimate the periodic deterioration of wetlands in rural settings in underdeveloped nations. As a result, this study aims to determine wetland loss in a developing country’s countryside, using the Greater Amanzule Wetland in the Ellembelle District of Ghana as a case study. The study used the GIS and Remote Sensing dataset, the Normalization Difference Vegetation Index, and the InVEST-Habitat Quality model to analyze the spatial pattern of wetland loss and habitat quality variations caused by industrialization and community expansions between 2000 and 2020. Our results indicate that, from 2000 to 2015, community built-up and industrial land areas grew from 25.19 ha to 84.21 ha and 0–4.71 ha respectively, causing a wetland loss of −39.64 ha (−31.10%). Between 2015 and 2020, community built-up land grew to 78.65 ha while industrial land slightly reduced to 2.54 ha, leading to the wetland degradation of approximately −69.06 ha (−42.53%). Wetland habitat quality steadily declined, from 0.97 in 2000 to 0.93 in 2015 and 0.93 in 2020. The study findings led to policy recommendations to reduce wetland encroachment and enhance sustainability. These recommendations included conducting effective land-use plan inspections before issuing permits, coordinating effective management, and promoting the recovery of natural and artificial vegetation to sustain the wetland.

Navigating the Global Dietary Transition and Its Impact on Planetary and Human Health: The Role of Middle-Income Countries

Abstract This case study examines global dietary transitions and their impact on environmental sustainability, nutritional well-being, and socio-economic inequalities, focusing on middle-income countries (MICs) like China, India, Brazil, Turkey, and Mexico. As economic expansion, urbanization, and cultural shifts drive changes in eating habits, MICs encounter both advantages and disadvantages. The study explores how socio-economic transformation influences dietary preferences, resulting in increased consumption of processed foods and animal-based products, linked to rising non-communicable diseases and environmental impacts such as greenhouse gas emissions and resource depletion. It delves into the influence of cultural heritage and social norms on dietary practices, alongside the impact of Western dietary patterns and media. Additionally, the case study underscores the role of governmental strategies in fostering sustainable and nutritious diets through policies that support local agriculture and public health. It also investigates contributions from the private sector and emphasizes the significance of community-driven and grassroots movements in promoting sustainable dietary habits and reinforcing food security. This study aims to provide insights into the interconnection between dietary transitions, their environmental and nutritional impacts, and the socio-economic drivers and disparities within and across MICs, offering guidance for sustainable development and public health improvement. Information © The Author 2024

Politicizing belonging: Community formation at the Late Preclassic Maya hamlet of Dos Ceibas

Abstract People are often assumed to expand existing mechanisms—kinship in particular—to include others when they form communities. These models (con)fuse similarity with sameness, as we argue based on Husserl's concept of empathy. People recognize others without overlooking differences. They form community by negotiating belonging. We ask how individuals materialize community, how they create unity in a political process, and how they employ bordering and bonding social interactions. Our case study is Dos Ceibas, a Late Preclassic (350 b.c. to a.d. 250) Maya hamlet in the Petexbatun region. The North Plaza originated as a residential group—possibly of the hamlet's founder—and was transformed over multiple construction episodes into a public and ceremonial place. By a.d. 250, Dos Ceibas consisted of a small pyramid overlooking a plaza and two likely residential buildings. Its growth pattern sets the North Plaza apart from nearby Group MP16 and magnified internal differences. At the same time, Dos Ceibas's pyramid and plaza were likely communal constructions that project a shared community identity. The comparison with contemporary settlements nearby identifies distinct settlement layouts and suggests localized community identities.

Evaluating landslide hazard, vulnerability, and risk using machine learning; A case study from the Alaknanda Valley, NW Himalaya

Evaluating landslide hazard, vulnerability, and risk using machine learning; A case study from the Alaknanda Valley, NW Himalaya

The Internal Control Management of Chinese Real Estate Industry: A Case Study of Yinfeng Group, Shandong

With the development of China's economy, the number of policy supports provided by SMEs to private enterprises has also increased yearly. Their status and contribution to the national economy cannot be ignored. However, most private enterprises lack an understanding of small and medium-sized enterprises (SMEs) or do not comprehend the internal control mechanisms necessary to formulate and implement an effective internal control management system. This lack of understanding is the main bottleneck preventing enterprises from becoming larger and stronger. Enhancing internal management is particularly beneficial for improving the risk-prevention capabilities of small and medium-sized private enterprises, thereby facilitating their growth. This paper analyzes the problems faced by small and medium-sized private enterprises, using the internal supervision of the internal control system of Yinfeng Group as a case study. Based on internal control's basic and practical meanings, the paper provides suggestions to improve internal control management.

Direct CKM determination from W decays at future lepton colliders

Abstract We project the reach of future lepton colliders for measuring CKM elements from direct observations of W decays. We focus our attention to |Vcs| and |Vcb| determinations, using FCC-ee as case study. We employ state-of-the-art jet flavor taggers to obtain the projected sensitivity, and scan over tagger performances to show their effect. We conclude that future lepton collider can sizeably improve the sensitivity on |Vcs| and |Vcb|, albeit the achievable reach will strongly depend on the level of systematic uncertainties on tagger parameters.

The Effect of Turnover and E-SPTPD Services on Restaurant Taxpayer Compliance with Taxpayer Awareness as a Moderating Variable (Case Study on Restaurant Taxpayers in Sidoarjo Regency)

The Covid-19 pandemic that has occurred over the past four years has had an impact on several aspects, one of which is tax revenue. More specifically, restaurant tax revenue in Sidoarjo Regency also decreased during the period 2020 to 2022. This study examines the influence of turnover, and e-SPTPD services, on the level of taxpayer compliance with tax awareness as a moderation variable. The data used in this study is primary data through the purposive sampling method on certain criteria. The research population is restaurant taxpayers in Sidoarjo Regency. Data collection uses a questionnaire that is distributed door-to-door to the respondents. Based on the results of the path coefficient test, the results of the hypothesis test, namely turnover, and e-SPTPD services have a positive effect on taxpayer compliance. Meanwhile, tax awareness cannot strengthen the influence of turnover, as well as e-SPTPD services on taxpayer compliance. This research can be an evaluation of government policies on tax compliance which has implications for optimizing tax revenue, especially in restaurant businesses in Sidoarjo Regency. Namely by considering turnover, and e-SPTPD services.

Detecting, Interpreting and Modifying the Heterogeneous Causal Network in Multi‐Source Event Sequences

AbstractUncovering causal relations from event sequences to guide decision‐making has become an essential task across various domains. Unfortunately, this task remains a challenge because real‐world event sequences are usually collected from multiple sources. Most existing works are specifically designed for homogeneous causal analysis between events from a single source, without considering cross‐source causality. In this work, we propose a heterogeneous causal analysis algorithm to detect the heterogeneous causal network between high‐level events in multi‐source event sequences while preserving the causal semantic relationships between diverse data sources. Additionally, the flexibility of our algorithm allows to incorporate high‐level event similarity into learning model and provides a fuzzy modification mechanism. Based on the algorithm, we further propose a visual analytics framework that supports interpreting the causal network at three granularities and offers a multi‐granularity modification mechanism to incorporate user feedback efficiently. We evaluate the accuracy of our algorithm through an experimental study, illustrate the usefulness of our system through a case study, and demonstrate the efficiency of our modification mechanisms through a user study.

Socio-ecological metabolism and rural livelihood conditions: Two case studies on forest litter uses in France and Poland (1875–1910)

Socio-ecological metabolism and rural livelihood conditions: Two case studies on forest litter uses in France and Poland (1875–1910)

Solitary Bone Plasmacytoma: The Role of Imaging in a Case Study

Solitary Bone Plasmacytoma: The Role of Imaging in a Case Study

Metaverse Applications in Education 4.0: A Decade of Systematic Literature Review

The advent of Education 4.0, which aligns with the fourth industrial revolution, has been significantly influenced by advancements in digital technologies. Central to this evolution is the metaverse, a virtual shared space that merges augmented reality, virtual reality, and physical reality. This paper delves into the metaverse's applications within Education 4.0, highlighting its potential to revolutionize learning experiences, enhance collaboration, and improve access to quality education. By reviewing current literature and case studies, we identify the primary benefits, such as increased engagement, personalized learning, and broader accessibility. Additionally, we address the challenges associated with metaverse integration, including technical limitations, privacy concerns, and the need for new pedagogical approaches. Through a mixed-methods research approach, combining qualitative and quantitative data along with expert interviews, this paper provides a comprehensive overview of the metaverse's role in future education. The findings suggest that while significant hurdles remain, the metaverse offers a promising avenue for transforming educational practices to meet the demands of the digital age, ultimately fostering more dynamic, inclusive, and effective learning environments. Future research should focus on evaluating long-term impacts and developing standards for metaverse applications in education.

Visualisation Design Ideation with AI: A New Framework, Vocabulary, and Tool

This paper introduces an innovative framework for visualisation design ideation, which includes a collection of terms for creative visualisation design, the five-step process, and an implementation called VisAlchemy. Throughout the visualisation ideation process, individuals engage in exploring various concepts, brainstorming, sketching ideas, prototyping, and experimenting with different methods to visually represent data or information. Sometimes, designers feel incapable of sketching, and the ideation process can be quite lengthy. In such cases, generative AI can provide assistance. However, even with AI, it can be difficult to know which vocabulary to use and how to strategically approach the design process. Our strategy prompts imaginative and structured narratives for generative AI use, facilitating the generation and refinement of visualisation design ideas. We aim to inspire fresh and innovative ideas, encouraging creativity and exploring unconventional concepts. VisAlchemy is a five-step framework: a methodical approach to defining, exploring, and refining prompts to enhance the generative AI process. The framework blends design elements and aesthetics with context and application. In addition, we present a vocabulary set of 300 words, underpinned from a corpus of visualisation design and art papers, along with a demonstration tool called VisAlchemy. The interactive interface of the VisAlchemy tool allows users to adhere to the framework and generate innovative visualisation design concepts. It is built using the SDXL Turbo language model. Finally, we demonstrate its use through case studies and examples and show the transformative power of the framework to create inspired and exciting design ideas through refinement, re-ordering, weighting of words and word rephrasing.

Is environmental risk assessment possible with the alternatives to acute fish toxicity test? Case study with pharmaceuticals

BackgroundAcute fish toxicity test (AFT) is one of the cornerstones of environmental risk assessment (ERA) of chemicals for the aquatic environment. Despite many efforts to find an alternative able to fully replace the test, there is still lasting pressure from stakeholders for AFT results. ResultsHere, we present the results of a case study with eight pharmaceuticals from various pharmaceutical groups with different levels of expected toxicity to fish. Selected compounds were tested in two validated alternative tests—fish embryo toxicity test with zebrafish (Danio rerio) (zFET) and in vitro RTgill-W1 assay according to their corresponding OECD guidelines TG 236 and TG 249, respectively. Data for AFT were collected from PubMed and ECOTOX knowledgebase databases, and acute toxicity to fish was further predicted in silico by the ECOSAR program. Predicted environmental risks (risk quotients, RQ, calculated using the exposure data from NORMAN) from both zFET and RTgill-W1 well correlated with the average RQs based on AFT LC50s. The strongest and most significant correlation was observed while comparing the AFT results with the median of combined alternative methods (zFET, RTgill-W1, ECOSAR).ConclusionsThis proposed approach combining experimental data with modeling could serve as a reliable tool for predictions of environmental risks promoting the 3R alternatives to acute fish toxicity testing.

Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.

SMART DENGUE CONTROL

This research presents a novel approach to combating dengue by integrating Biological Control methods with Artificial Intelligence (AI). The objective of the study is to reduce the Aedes aegypti mosquito population and decrease dengue transmission through sustainable strategies. A comprehensive research methodology was used, involving extensive literature review, case studies, and data analysis to identify the main factors driving the high incidence of dengue in Brazil. The research applied AI tools to analyze epidemiological, meteorological, and environmental data, allowing for early outbreak predictions and targeted interventions. AI-enhanced surveillance systems were also employed to monitor mosquito populations and identify breeding sites more accurately. These strategies were combined with biological control methods, such as using natural predators and genetically modified mosquitoes, to manage mosquito populations effectively. The study demonstrate that the integration of AI and biological control can significantly improve the efficiency, accuracy, and sustainability of dengue control measures. The study concludes that this data-driven approach, when adapted to local contexts, can reduce reliance on chemical pesticides and offer a more resilient framework for long-term dengue management.