Purpose
This study aims to examine the impact of inbound logistics on dynamic supply chain capabilities and, subsequently, on supply chain resilience in the Vietnamese textile industry.
...Design/methodology/approach
A conceptual framework based on a resource-based view was empirically tested using partial least squares structural equation modeling and data collected from 215 Vietnamese textile enterprises from December 2021 to March 2022.
Findings
The research shows that inbound logistics capability positively affects dynamic supply chain capabilities. In particular, the study has ratified reengineering as the chief factor that textile firms should consider when building a resilient supply chain.
Originality/value
This study considers the Vietnamese textile industry to assess the indirect effect of inbound logistics on supply chain resilience through dynamic supply chain capabilities in a theoretical sense while assisting managers in comprehending the functions of supply chain collaboration, agility and reengineering as the foundation for supply chain resilience in a managerial sense.
We have developed the Southern Integrated Prescribed Fire Information System (SIPFIS) to disseminate prescribed fire information, including daily forecasts of potential air quality impacts for ...southeastern USA. SIPFIS is a Web-based Geographic Information Systems (WebGIS) assisted online analysis tool that provides easy access to air quality and fire-related data products, and it facilitates visual analysis of exposure to smoke from prescribed fires. We have demonstrated that the information that SIPFIS provides can help users to accomplish several fire management activities, especially those related to assessing environmental and health impacts associated with prescribed burning. SIPFIS can easily and conveniently assist tasks such as checking residential community-level smoke exposures for personal use, pre-screening for fire-related exceptional events that could lead to air quality exceedances, supporting analysis for air quality forecasts, and the evaluation of prescribed burning operations, among others. The SIPFIS database is currently expanding to include social vulnerability and human health information, and this will evolve to bring more enhanced interactive functions in the future.
Intraperitoneal ascites is a consequence or combination of many different underlying diseases. Laparoscopy with peritoneal biopsy is a tool for rapid and accurate diagnosis.
We retrospectively ...identified patients who could not be diagnosed by clinical examination, laboratory investigations, and imaging tests.
A total of 103 (55 male and 48 female) patients were selected. The median age of the study group was 54 years (range 38-64 years). Typical clinical symptoms included fever (58.2%), abdominal pain (56.3%), and digestive disorders (62.1%). Fever and digestive disorders were higher in the peritoneal tuberculosis (TB) group than in the metastatic cancer group (62.1% vs. 12.5%,
=0.009) and (66.3% vs. 12.5%,
=0.004). Abdominal pain was more common in the metastatic cancer group than in the other groups (100% vs. 55.8%,
=0.020). Patients in the TB and chronic inflammation groups had lower red blood cell counts and blood albumin (41 vs. 42,
=0.039) than those in the metastatic cancer group, respectively. The rate of intestinal wall thickening on ultrasound and peritoneal thickening on computed tomography was higher in the cancer group than in the benign group (87.5% vs. 7.4%,
=0.000) (75% vs. 23.2%,
=0.005), respectively. There was no difference in the median peritoneal fluid volume between the two groups (390 vs. 340,
=0.058). Pathological results showed 88.3%, 7.8%, and 3.9% of peritoneal TB, metastatic cancer, and chronic inflammatory lesions, respectively. The median hospital stay did not differ between the two groups (4 vs. 3 days,
=0.051). Both groups of patients had no morbidity or mortality.
Unidentified ascites and peritonitis must be difficult for making diagnose by conventional methods. Laparoscopy might be supportive of making a rapid diagnosis and starting early treatment.
In this study, a new approach of machine learning (ML) models integrated with the analytic hierarchy process (AHP) method was proposed to develop a holistic flood risk assessment map. Flood ...susceptibility maps were created using ML techniques. AHP was utilized to combine flood vulnerability and exposure criteria. We selected Quang Binh province of Vietnam as a case study and collected available data, including 696 flooding locations of historical flooding events in 2007, 2010, 2016, and 2020; and flood influencing factors of elevation, slope, curvature, flow direction, flow accumulation, distance from river, river density, land cover, geology, and rainfall. These data were used to construct training and testing datasets. The susceptibility models were validated and compared using statistical techniques. An integrated flood risk assessment framework was proposed to incorporate flood hazard (flood susceptibility), flood exposure (distance from river, land use, population density, and rainfall), and flood vulnerability (poverty rate, number of freshwater stations, road density, number of schools, and healthcare facilities). Model validation suggested that deep learning has the best performance of AUC = 0.984 compared with other ensemble models of MultiBoostAB Ensemble (0.958), Random SubSpace Ensemble (0.962), and credal decision tree (AUC = 0.918). The final flood risk map shows 5075 ha (0.63%) in extremely high risk, 47,955 ha (5.95%) in high‐risk, 40,460 ha (5.02%) in medium risk, 431,908 ha (53.55%) in low risk areas, and 281,127 ha (34.86%) in very low risk. The present study highlights that the integration of ML models and AHP is a promising framework for mapping flood risks in flood‐prone areas.
Flash floods and landslides are dangerous natural hazards in hilly areas. They often occur extensively and potentially cause widespread destruction to agriculture, infrastructure, roads, houses, and ...human beings. This research aimed to analyze the hazard susceptibility on a mountainous roadway using advanced Machine Learning (ML) models. We conducted field surveys to collect data on flash flood and landslide locations in 2017, 2018, and 2019 on a particular roadway in Vietnam, National Highway 6, consisting of 88 flash flood sites and 235 landslide sites. The state-of-art ML models were utilized for the predictive modeling, including AdaBoost-RBF, Bagging-RBF, MultiBoostAB-RBF, and Random Sub-spaceRBF, with Radial Basis Function (RBF) serving as the primary classifier. The AdaBoost-RBF model outperformed all others in predicting landslide and flash flood vulnerability. The resulting map showed that 44.89% or 14,183 ha is in very high susceptibility zones, 15.55% or 4914 ha is in high susceptibility zones, 10.37% or 3.275 ha is in moderate susceptibility zones, 13.69% or 4324 ha is in low susceptibility zones, and 15.50% or 4899 ha is in very low susceptibility zones. A detailed map of the areas where landslides and flash floods are most likely to occur on the roadway might provide local authorities with crucial information for disaster management.
Display omitted
•Modeling landslide and flash flood susceptibility for a mountainous roadway.•Advanced machine learning algorithms were developed for susceptibility modeling.•Hazard assessment maps may aid disaster risk mitigation and management.
This study uses the Mehrabian-Russell model to assess place attachment in the themed-hotel context. We explored the theoretical framework using four constructs related to the ...stimulus-organism-response paradigm. A total of 282 usable survey questionnaires were collected from travelers to test our hypotheses. We conducted multiple stages of data analysis, using structural equation modeling (SEM) and generative probabilistic modeling to verify the findings. Latent Dirichlet Allocation (LDA) was adopted to further verify key attributes in servicescapes. According to the SEM results, social cues are positively associated with pleasant arousal, and ambience cues are positively associated with social cues. We also verified that pleasant arousal is positively associated with place attachment. Furthermore, we confirmed that emotional factors can trigger the formation of place attachment to a themed hotel. The LDA results allowed us to further classify servicescapes by design cues, social cues, and ambience cues. Theoretical and practical implications are discussed.
•The impact of multi-criteria ratings on recommendation agents performance is investigated.•LDA for feature extraction and EM and SOM for data clustering are used.•Online customers’ reviews from ...TripAdviosr are analysed.•Sparsity issue was alleviated by the clustering techniques.•The accuracy of CF recommender systems was improved by multi-criteria ratings.
Recommender Systems (RSs) have played an important role in online retailing portals and customers’ decision-making processes. Recommender systems that are based on the conventional Collaborative Filtering (CF) approach rely on single customers’ ratings on retailing websites. Multi-criteria CF (MCCF) approaches that rely on multi-aspects of the products have provided more reliable and effective recommendations on retailing websites. However, these approaches should be improved in terms of accuracy by solving sparsity issues and incorporating criteria ratings. In addition, most of the recommendation agents that are based on MCCF cannot learn automatically from the features of the products to model customers’ preferences and generate accurate recommendations on retailing websites. Besides, although previous studies have utilized single and multi-criteria ratings in recommendation agents of tourism websites, still, if there is a lack of ratings of items, most of these systems will fail to generate accurate recommendations to users. In this research, we develop a new recommendation agent based on a MCCF approach to effectively improve the performance of previous recommendation systems for tourism websites. The results demonstrated that the method can predict the most relevant products to users, particularly when the dataset is sparse.
•A SWOT analysis of COVID-19 and Sustainable Development Goals (SDGs) is performed.•A bibliometric analysis of previous works related to SDGs and the covid-19 crisis is performed.•COVID-19 crisis has ...caused an unprecedented “income shock” that is assumed to prompt food insecurity.
The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments’ aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.
PDF
