Global Context Research Articles

Factors and Business Strategies for Making Local Products Successful: Case Study From the Developing Economy

Purpose: This study provides insights into the factors that determine the success of local products, taking the Hai Phong fish sauce industry as a typical example. Theoretical framework: The research team utilized qualitative techniques, focusing on gathering and analyzing secondary data across various developing countries, with a particular emphasis on local product brands in Hai Phong City, Vietnam. Results and discussion: Meticulous consideration of customer preferences and local needs is core to the success and sustainability of local products in highly competitive markets. It is concerned with preserving local culture and heritage as well as promoting ethical and sustainable values, thereby enhancing the overall impact of these products in the regional and global context. bridge. Policy implications recommend creating an enabling environment that encourages using high-quality raw materials and traditional techniques in local product development, and strategic investments in branding and marketing, while ensuring strict compliance with food safety regulations. This study has limitations, mainly related to the focus on Hai Phong fish sauce products, which may limit the generalizability of the findings. Research Implications: Future research should include a broader range of products and local areas. Traditional production methods and craftsmanship are essential in product differentiation, cultural preservation, and consumer loyalty. Furthermore, appropriate packaging and branding are indispensable to attract consumer attention and communicate the value of the product. Marketing and distribution strategies enhance product visibility and consumer engagement. Originality/value: Appropriate packaging and branding are indispensable to attract consumer attention and communicate the value of the product. Marketing and distribution strategies enhance product visibility and consumer engagement.

The importance of utilizing CT images at early stages for COVID-19 diagnosis – a Scoping Review

Overcoming the challenges posed by the severe acute respiratory syndrome caused by COVID-19 (SARS-CoV-2) has been one of the most recurrent themes when it comes to public health in a global context. Since March 2020, when it reached the stage of a pandemic by the World Health Organization, the world has been unfolding itself to overcome the challenges posed by this disease. As time goes by, new variants are demanding an even greater effort from health services to overcome the impacts caused by the disease. Several diagnostic methods have been developed with the aim of detecting the main signs of the disease as early as possible. The accelerated deterioration of the health status of patients affected by this disease reinforces the need for a conscious and timely action, in order to allow a rapid therapeutic intervention to propitiate a more favorable clinical outcome for the patient. However, each of the different methods developed over the past two years has advantages and disadvantages. Thus, the objective of this article is to present a scoping review of the literature currently available based on the criteria established by Amog et all (2) and published in the “Journal of Clinical Epidemiology” – volume 147, aiming to systematically summarize the existing knowledge and, based on the literature, discuss the importance as well as the advantages and disadvantages of adopting computed tomography images in the diagnosis of patients infected by COVID-19.

Toward Sustainable Global Product Development Performance: Exploring the Criticality of Organizational Factors and the Moderating Influence of Global Innovation Culture

In today’s dynamic and global landscape, innovation and globalization are intricately linked drivers that propel modern businesses forward, serving as indispensable pillars upon which organizations heavily rely to maintain their competitive edge. Leveraging innovation within the context of global product development (GPD) practices is imperative for organizations seeking to survive and effectively compete in the rapidly evolving marketplace. While preceding studies have primarily focused on the importance of individual and organizational capabilities, policies, and cultural factors in driving product development (PD) performance, they often overlook their interconnectedness within a global context. To address this gap, this study delves into the relationship between organizational factors and global product development performance (GPDP) while examining the moderating influence of global innovation culture (GIC). These organizational factors encompass technological innovation capabilities (TICs), team creativity (TC), dynamic capabilities (DCA), and competitive advantage (CA). Our hypothesis suggests a positive impact of these factors on GPD performance. Employing a structural equation modeling (SEM) approach through SMART-PLS 3, we analyzed data collected from 480 questionnaire forms distributed among registered global product development (GPD) organizations operating in China. The empirical findings underscore the significant influence of organizational factors on GPD performance, highlighting the critical role of global innovation culture in shaping these relationships. These insights shed light on the importance of establishing a harmonious balance between organizational culture and various factors to enhance interaction among organizational elements and optimize global product development performance. Ultimately, these efforts can lead to improvements in the overall effectiveness and sustained global product development.

Unlocking Knowledge in the Digital Savanna: Innovative Approaches to Crafting E-Learning Experiences at the University of Botswana

The digital revolution has reshaped the landscape of education, offering unprecedented opportunities for knowledge dissemination and acquisition. This study presents a comprehensive exploration of innovative e-learning approaches implemented at the University of Botswana, which represents a remarkable case study in the evolving global educational context. The research leverages a rigorous mixed-methods approach, including case studies, surveys, and qualitative interviews with educators and students, to investigate the multifaceted strategies adopted by the university to transform traditional pedagogy. The study explores how the University of Botswana has embraced diverse e-learning methodologies, such as blended learning, gamification, and virtual labs, to enhance its educational delivery. Blended learning, combining in-person and online instruction, has not only expanded access but also yielded positive impacts on student engagement and performance. Gamification strategies have reinvigorated learning by incorporating game elements, fostering motivation, and increasing knowledge retention. Virtual labs have enriched the student experience by enabling hands-on, remote experimentation, overcoming geographical constraints. The findings underscore the University of Botswana's dedication to utilizing digital technology to democratize and elevate education in an African context. Moreover, this research delves into the institutional challenges and successes in integrating e-learning, highlighting the significance of faculty development, technological infrastructure, and student support systems in achieving transformative educational outcomes. The experiences and strategies documented in this study offer valuable insights and best practices that can inform higher education institutions globally as they navigate the ever-evolving digital realm. This research contributes to the broader discourse on the global transformation of education in the digital age, with implications for enhancing access, equity, and educational quality. In an era marked by dynamic technological advancements and changing educational paradigms, the University of Botswana's innovative e-learning initiatives serve as a beacon of inspiration and a source of valuable lessons for academic institutions committed to unlocking knowledge in the digital savanna.

ВЛИЯНИЕ КУЛЬТУРНЫХ ФАКТОРОВ НА СИСТЕМУ ХОЗЯЙСТВОВАНИЯ

The article examines the influence of cultural factors on the economic system in the modern world. The study draws attention to how the cultural characteristics of different peoples shape their approaches to economic activity, having a significant impact on such aspects as the organizational structure of enterprises, production methods, attitudes to labor and resources, as well as management practices. The authors analyze the differences in cultural approaches to business and economic activity in different countries, paying attention to the influence of historical, religious, ethnic and social factors on the formation of these approaches. Special attention is paid to cross-cultural differences in business management and decision-making, as well as to the influence of cultural stereotypes on the development of technology and innovation. The article emphasizes the importance of understanding cultural factors when developing business management and development strategies in a global context, as well as the need to adapt strategies to local cultural characteristics in order to achieve success in global markets.

The Active Form of Security: Technology and the Material-aesthetic Script

How are socially and politically controversial security practices materially-technologically scripted into our lives in ever-deeper ways? This essay proposes that acts of aesthetic design are at the heart of that process and are being deployed by technology corporations to “smooth” the diffusion of security practices, discourses, and politics across global space. To substantiate that claim, we make three moves. First, we propose an understanding of the “script” that returns to the roots of the concept in theater and the arts. That understanding stresses that our material-technological enmeshing is governed strongly by aesthetic and affective factors that operate through forms of resonance (rather that reason). In consequence—we argue—much of the power of technology conglomerates is linked to their capacity to harness these aesthetico-affective resonances. Second, we demonstrate this through a case study focused on the material-aesthetic design activities of Google, teasing out how it deploys aesthetic practices to extend its sociopolitical power. Third, we speculatively conclude by introducing the architect Keller Easterling’s concept of “active form” to show how conceptualizing scripts in aesthetic terms also provides insights into how security practices are diffused across global contexts, irrelevant contextual sociopolitical differences, and seemingly without any limit.

InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding.

Multi-task scene understanding aims to design models that can simultaneously predict several scene understanding tasks with one versatile model. Previous studies typically process multi-task features in a more local way, and thus cannot effectively learn spatially global and cross-task interactions, which hampers the models' ability to fully leverage the consistency of various tasks in multi-task learning. To tackle this problem, we propose an Inverted Pyramid multi-task Transformer, capable of modeling cross-task interaction among spatial features of different tasks in a global context. Specifically, we first utilize a transformer encoder to capture task-generic features for all tasks. And then, we design a transformer decoder to establish spatial and cross-task interaction globally, and a novel UP-Transformer block is devised to increase the resolutions of multi-task features gradually and establish cross-task interaction at different scales. Furthermore, two types of Cross-Scale Self-Attention modules, i.e., Fusion Attention and Selective Attention, are proposed to efficiently facilitate cross-task interaction across different feature scales. An Encoder Feature Aggregation strategy is further introduced to better model multi-scale information in the decoder. Comprehensive experiments on several 2D/3D multi-task benchmarks clearly demonstrate our proposal's effectiveness, establishing significant state-of-the-art performances. The codes are publicly available https://github.com/prismformore/Multi-Task-Transformer/tree/main/InvPT.

Global context aware dual channel pyramid model for robust image shadow removal

Deep learning has made significant advancements in shadow removal. However, current methods primarily focus on local operations, leading to artifacts around shadow boundaries and inconsistent lighting between shadow and non-shadow regions. To address this issue, a global context aware dual channel pyramid model is proposed for robust image shadow removal in this paper. The model leverages a multi-scale channel attention framework based on transformers to capture global information. It consists of a shadow detection module and a dual-channel shadow interaction module to utilize non-shadow regions in aiding shadow restoration. Additionally, Winograd-based separable convolution attention shadow interaction attention is proposed to effectively expand the perceptual field and facilitate comprehensive utilization of contextual relevance between shadow and non-shadow regions. Extensive experiments are conducted on several popular public datasets such as ISTD, SRD and ISTD + to evaluate the effectiveness of our model.

Lipids as biomarkers to assess the nutritional and physiological status of two diadromous fish (Anguilla anguilla and Chelon auratus) at early life stages in a temperate macrotidal estuary.

Estuaries are considered as key habitats for the early life stages of fish. However, in the face of massive destruction of many estuarine intertidal areas, management and conservation measures are needed. Fish condition indicators may be used as a proxy of habitat quality and provide valuable information for management of coastal areas. In this study, the larvae of golden mullet (Chelon auratus) and European glass eels (Anguilla anguilla) were sampled in three sites of the Gironde Estuary. Different lipid classes and fatty acids were quantified: phospholipids (globally, phosphatidylethanolamine and phosphatidylcholine), triglycerides, omega-3 (particularly docosahexaenoic and eicosapentaenoic acids), omega-6 and C18:1. These biomarkers provide information on the nutritional status of the larvae as well as on prey availability and larvae diet between sites. One site significantly differed from the others as it seemed to offer abundant and better-quality prey. The very high levels of omega-3 contained in mullet larvae suggested that this site provided a high amount of diatoms. However, the mullet larvae that colonized this site also showed physiological stress that could be explained by exposure to pollutants through their prey. This work constitutes an essential baseline for developing biomarkers to assess the quality of habitats in a global change context.

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation

Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning local vessel features, making it challenging to capture the graph structural information and fail to perceive the global context of vessels. Therefore, we propose a novel graph neural network-guided vision transformer enhanced network (G2ViT) for vessel segmentation. G2ViT skillfully orchestrates the Convolutional Neural Network, Graph Neural Network, and Vision Transformer to enhance comprehension of the entire graphical structure of blood vessels. To achieve deeper insights into the global graph structure and higher-level global context cognizance, we investigate a graph neural network-guided vision transformer module. This module constructs graph-structured representation in an unprecedented manner using the high-level features extracted by CNNs for graph reasoning. To increase the receptive field while ensuring minimal loss of edge information, G2ViT introduces a multi-scale edge feature attention module (MEFA), leveraging dilated convolutions with different dilation rates and the Sobel edge detection algorithm to obtain multi-scale edge information of vessels. To avoid critical information loss during upsampling and downsampling, we design a multi-level feature fusion module (MLF2) to fuse complementary information between coarse and fine features. Experiments on retinal vessel datasets (DRIVE, STARE, CHASE_DB1, and HRF) and coronary angiography datasets (DCA1 and CHUAC) indicate that the G2ViT excels in robustness, generality, and applicability. Furthermore, it has acceptable inference time and computational complexity and presents a new solution for blood vessel segmentation.

Intellectuals, expressions, and transnational networking: a case study on Feng Zikai’s overseas cultural practices and religious productions Protection of Life during the Cold War period (1950s–1970s)

ABSTRACT Feng Zikai and Master Kong Hiap in Singapore not only completed and published the masterpiece Protection of Life at the behest of their mentor, Master Hong Yi but also actively contributed to the growth of religions during the P.R.C.. The focus of this paper will be on the Protection of Life, which was completed secretly in socialist China between the 1950s and 1970s, as well as Feng Zikai's overseas cultural practices, to investigate how these helped Feng Zikai to broaden his cultural expressions and preserve his teacher Master Hong Yi's religious thoughts. Through revisiting Feng’s creations and other cultural practices, this paper reveals a competing and conflicting revolutionary structure of feeling, across the 1949 geographical fault line as well as in the global Cold War context. Besides, this paper intends to investigate intellectuals’ living status and social networking critically in socialist China during the mid-twentieth-century.

Impact of Drip Irrigation on Crop Growth, Yield, Water Productivity, and Weed Dynamics: A Review

The global water scenario indicates efficient water management practices are required to sustain agricultural productivity. The scarcity of groundwater resources is a global issue that is caused by increasing global population and climate change in the form of global warming. To alleviate the issue, the major water-consuming area implemented drip irrigation projects. It is recognized for its water-saving potential and gained prominence in crop cultivation. This study aimed to investigate the effect of drip irrigation on the growth, yield, water productivity, and weed dynamics of various crops. Results revealed that wheat under drip irrigation exhibited enhanced growth parameters, including increased plant height, leaf area index, and dry matter accumulation attributed to optimized soil moisture content. Drip irrigation significantly improved yield and yield attributes compared to traditional irrigation methods. Importantly, drip irrigation increased water productivity while reducing water use per unit of production. These findings highlight the potential of drip irrigation to reduce water shortages and increase crop output in the current global water context.

Conflict sensitivity and activism: insights from Cambodia's resource conflicts

ABSTRACT This paper examines debates relating to international aid, civil society, and natural resource conflicts, through a case study of conflict sensitivity programming in Cambodia. The paper highlights the value of confrontational activism, yet the practice of conflict sensitivity can dissuade such activism. Embedded in the country's history and politics, the paper analyses the conflict sensitivity work of an international non-government organisation, revealing significant community-level success, including women's empowerment, but limitations in addressing systemic drivers of conflict. The case study also reveals how the application of conflict sensitivity contributed towards conflict and risk avoidance. In a global context of prevalent natural resource conflicts and related violence, this paper calls for critical reflection on the practice of conflict sensitivity, which continues to gain popularity globally. For development institutions and practitioners, conflict sensitivity has extensive value, but it must be implemented in ways that do not inadvertently dissuade more emancipatory civil society activism.

Dissident Sounds: Electronic music in Venezuela from the notions of radical education

Based on the theories of radical education, this article discusses the education of electronic music in Venezuela. After a historiographical review of the state of music education in the country, which shows that there is little information on the subject, the institutional life that has promoted electroacoustic music in Venezuela is approached from a critical perspective. This documentary research gathers and analyses data provided by a bibliographic review and unstructured interviews with experts in the field. Among the most salient findings is the discontinuity in the teaching of electroacoustic music, as well as a critical review of the notions of radical education in the case of Venezuela, where the educational system shows stagnation in the face of the global context.

DEDU-Net: Dual-Encoder-Decoder-U-Net for road extraction from high-resolution remote sensing images

ABSTRACT The development of the modern urban economy is closely tied to road construction, with roads being one of the most crucial components in urban development. The use of high-resolution remote sensing images to monitor the road conditions in cities and surrounding towns has become a highly emphasized research focus in recent years. However, due to the complexity of the environment, this task still faces significant challenges. For instance, urban road structures are intricate, which involve multiple lanes, intersections, building obstructions, etc. Rural roads may consist of narrow paths or irregular dirt roads. In addressing these issues, this paper proposes a road extraction algorithm based on a Dual-Encoder-Decoder U- Net (DEDU-Net). The algorithm leverages a dual encoder-decoder network to extract multi-scale information from the image. It then employs a dual decoder network to restore the feature map to the original image, achieving precise road extraction. Additionally, a new module, the Global Fusion Module (GFM), is introduced. This module achieves global context information fusion by weighting features. In the experimental section, two publicly available datasets were used for testing: the CHN6-CUG dataset and the Gansu Mountain Road dataset. For example, for the Gansu Mountain Road and CHN6-CUG mixed dataset, the model achieved an Overall Accuracy (OA) of 94.947% and a mean Intersection over Union (mIoU) of 70.971%. The results indicate that compared to traditional methods, this proposed method exhibits higher accuracy and robustness. It can adapt to both urban and rural roads, delivering outstanding performance even in complex scenarios.

A multilingual analysis of pro Russian misinformation on Twitter during the Russian invasion of Ukraine

The Russian government has long since engaged in an information campaign of propaganda and disinformation as a major part of foreign policy. This has been taken to new heights since the invasion of Ukraine in February 2022. In this study, we investigate pro-Russian misinformation within the opening weeks of the invasion in 6 languages: English, Japanese, Spanish, French, German, and Korean. Using Twitter data, we apply a combination of network and language embedding models to identify popular topics of misinformation amongst users in each language. Despite English users forming the most dominant language base on Twitter, we find that the popularity of misinformation in Japanese regularly outstrips English for certain topics. Misinformation shared by Spanish users is also over-represented in proportion to its much smaller user base. Our results provide insight into the current state of misinformation in each language. While we discuss some of the possible drivers behind the factors such as language over-representation, our study also highlights the need for further cross-lingual misinformation research in order to better understand this phenomena in a truly global context.

Development of Soil Quality Databases and Land Potential Assessment Using GIS: A Case Study of Lao Cai Province, Vietnam

The utilization of Geographic Information Systems (GIS), particularly the Mapinfo software, is indispensable in the establishment of a land quality and land potential database encompassing each land parcel within Lao Cai province. Through a multifaceted approach involving data collection techniques, field surveys, data analysis, and the creation of both spatial and attribute databases, the GIS application in Lao Cai province has successfully compiled a comprehensive dataset. This dataset encompasses a range of layers, including boundaries, administrative demarcations, land use statuses, survey points, soil types, slope characteristics, soil depth, irrigation patterns, climatic conditions, soil fertility data, and transportation and hydrological information. The amalgamation of this spatial data with attribute data yields a comprehensive repository featuring information on 405 units of agricultural land quality and potential, in addition to 65 aggregated non-agricultural land parcels. This combined dataset functions as the foundational land quality and potential database for Lao Cai province. Assessment outcomes for land quality and land potential throughout the entire province reveal that 51.42% of the land exhibits low quality, 35.06% demonstrates moderate quality, and 13.52% boasts high quality. In terms of land potential, 29.15% is categorized as having low potential, 44.88% as having moderate potential, and 25.97% as having high potential. Furthermore, the precise delineation of land quality and potential for each land parcel within the database effectively caters to the information retrieval needs of land administrators and stakeholders. This research also seeks to contribute to the standardization of land databases at both the provincial and national levels, thereby bolstering land management efforts within the context of globalization, development, and climate change adaptation.

Groundwater vulnerability assessment of elevated heavy metal contamination related health hazard in coastal multi-aquifers of Sundarban Biosphere Reserve, India

The concept of groundwater (GW) vulnerability emerged as a response to the escalating contamination of GW globally. Over recent decades, both scholars and policymakers have extensively utilized GW vulnerability assessments to safeguard groundwater from pollution. Within the Sundarban Biosphere Reserve (SBR), inhabitants residing near coastal aquifers face significant challenges, notably the scarcity of fresh water during the hot and dry season. Furthermore, there is a concern regarding the presence of heavy metals (HMs) in this region, adversely impacting GW quality. In both Indian and global contexts, HM contamination, including arsenic (As), fluoride (F‾), and nitrate (NO3–), and its repercussions on human health (HH) in coastal districts, represent critical issues. In light of these challenges, this study aims to assess GW vulnerability and its implications for HH in SBR utilizing the Hazard Quotient (HQ) approach. Fifteen hydro-geochemical parameters have been selected for this research endeavor. Various statistical indices, such as “groundwater quality index (GWQI)”, “degree of contamination (CD)”, “index of ecological risk (ERI)”, and “human health hazard index (HHHI)”, have been employed to accomplish the study objectives. Piper's and Box plot diagrams were utilized to assess hydrogeochemical properties and GW quality. The findings reveal average concentrations of HMs, such as As, NO3–, and F‾ in the study area, to be 0.204 mg/l, 6.28 mg/l, and 0.79 mg/l, respectively. Among all variables, a positive correlation of 0.876 is observed between electrical conductivity (EC) and sodium ions (Na+). Eventually, the outcomes of this study can provide valuable insights for planners and policymakers to make informed decisions regarding the sustainable management of water resources.

Qatar's National Mental Health Study-The World Mental Health Qatar.

We provide an overview of Qatar's first epidemiological study on prevalence, predictors, and treatment contact for mood and anxiety disorders. We highlight the importance of the three-pronged study, its aims, and its key components. The first component comprised a probability-based representative survey of Qatari and non-Qatari (Arab) adult males and females recruited from the general population and interviewed using the International Diagnostic Interview (CIDI version 3.3). The second component, a clinical reappraisal study, assessed concordance between diagnoses based on the CIDI and independent clinical assessments conducted by trained clinical interviewers. The third component comprised a resting-state functional magnetic resonance imaging study of healthy survey respondents who were matched to patients with psychosis. 5000 survey interviews provided data on prevalence and treatment of common mental disorders. Clinical re-interviews (N=485) provided important diagnostic validity data. Finally, state-of-the art structural and functional brain markers for psychosis were also collected (N=100). Descriptive epidemiological data were collected to inform future mental health priorities in Qatar and situates these within a global context. The study fills important gaps in regional and global estimates and establish necessary baseline to develop comprehensive risk estimates for mental health in Qatar's young population.

LGNet: Local and global point dependency network for 3D object detection

3D object detection is a challenging task in autonomous driving industry scenarios. Many pre-existing methods employ the set-abstraction operation for generating key-point representations, which, however, cannot learn the long-range context dependency properly. In addition, the pooling operator, which only focuses on maximum channel response, is adopted to aggregate features of neighbor points without semantic information. To fix these issues, we propose LGNet, a new framework that simultaneously captures local and global point dependencies for enhancing 3D object detection. Specifically, we first introduce a new local point-graph pooling module to compute point-to-point correlations in a local region and aggregate features from neighboring points. To further capture the long-range dependency in a global context, we devised a global point-aware module to integrate local and global features at higher resolution. Experiments on the KITTI 3D detection dataset and Waymo Open Dataset benchmark show that LGNet achieves state-of-the-art performance in multiple classes. We will upload the code on https://github.com/MWPony/LGNet.