ALBERT

Description: Today, business organizations are facing increasing pressure from a variety of sources to operate using sustainable processes. Thus, most companies need to focus on their supply chains to enhance sustainability to meet customer demands and comply with environmental legislation. To achieve these goals, companies must focus on criteria that include CO2 (carbon footprint) and toxic emissions, energy use and efficiency, wastage generations, and worker health and safety. As in other industries, the food processing industry requires large inputs of resources, which results in several negative environmental effects; thus, decision-makers have to evaluate qualitative and quantitative factors. This work identifies the best supplier for edible oil production in the small and medium enterprise (SME) food processing industry in Vietnam. This study also processes a hybrid multicriteria decision-making (MCDM) model using a fuzzy analytical hierarchy process (FAHP) and green data envelopment analysis (GDEA) model to identify the weight of all criteria of a supplier’s selection process based on opinions from company procurement experts. Subsequently, GDEA is applied to rank all potential supplier lists. The primary objective of this work is to present a novel approach which integrates FAHP and DEA for supplier selection and also consider the green issue in edible oil production in uncertain environments. The aim of this research is also to provide a useful guideline for supplier selection based on qualitative and quantitative factors to improve the efficiency of supplier selection in the food industry and other industries. The results reveal that Decision-Making Unit 1 (DMU 1), DMU 3, DMU 7, and DMU 9 are identified as extremely efficient for five DEA models, which are the optimal suppliers for edible oil production. The contributions of this research include a proposed MCDM model using a hybrid FAHP and GDEA model for supplier selection in the SME food processing industry under a fuzzy environment conditions in Vietnam. This research also is part of an evolution of a new hybrid model that is flexible and practical for decision-makers. In addition, the research also provides a useful guideline in supplier selection in the food processing industry and a guideline for supplier selection in other industries.

Description: Recently, many methods have been developed to efficiently eliminate oil spills due to its long-term harmful effects on marine life and human health. Expanded graphite (EG) has been considered as an excellent platform to remove contaminated oil from aqueous solution through a facile adsorption route. As an innovative approach, the decoration of magnetic components, namely, MnFe2O4, into graphite layers was taken into account for facilitating phase separation under magnetic field which resulted into an easy collection of the used adsorbents in a large scale. The expanded graphite/manganese ferrite composites were prepared from Vietnamese graphite flakes via a two-stage process. Characterization was performed using Scanning Electron Microscope (SEM), Fourier-Transform Infrared Spectroscopy (FTIR), X-Ray Powder Diffraction (XRD), Vibrating Sample Magnetometer (VSM), Energy-Dispersive X-ray (EDS), and nitrogen adsorption/desorption analysis. The adsorption behavior of EG-MnFe2O4 for widespread used heavy oils, including diesel oil and crude oil, was investigated under the effects of adsorption conditions, i.e., contact time, loaded oil dosage, and salinity of mixing oil and water. The obtained results showed successful incorporation of MnFe2O4 into graphite sheets and no considerable change on the worm-like structure of EG. The results also showed that incorporated manganese ferrites enhanced the magnetism EG up to 16 emu/g, which made the recovery of used adsorbent conveniently. The EG-MnFe2O4 adsorbents exhibited the strong adsorption ability toward diesel oil (32.20 ± 0.46 g DO/g EG) and crude oil (33.07 ± 0.33 g CO/g EG). In brief, EG-MnFe2O4 material provides a potential and promising platform with high performance for oil spill removal.

Description: Climate change will likely affect the effectiveness of future management of coastal ecosystems, impacting communities that reside within the coastal area. In order to formulate appropriate adaptation counter-measures it is important to understand the actual vulnerability of the communities that depend on these ecosystems. The present research proposes a tool for assessing the vulnerability of coastal communities to climate change by combining survey results with secondary and observed data available from national and local governments. The study focused on fisheries, given that they constitute the source of livelihood for many communities in developing countries such as Vietnam. The results showed that two coastal wards in Binh Thuan province, Vietnam, are highly vulnerable to the impacts of climate change, mainly because of their dependence on fisheries and the topography of the area. The seasonality of their source of livelihood affects the adaptive capacity of residents, making it less likely that they will be able to successfully adapt to changes in fishery resources that could be brought about by climate change. The results also showed that the communities are particularly vulnerable to sea level rise, given that they are both located in the immediate vicinity of the sea and are particularly low in elevation.

Description: The Cat Ba National Park area (Vietnam) with its tropical forest is recognized as being part of the world biodiversity conservation by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and is a well-known destination for tourists, with around 500,000 travelers per year. This area has been the site for many research projects; however, no project has been carried out for forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authorities. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then, a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the support vector machine (SVM). The results show that the Kernel logistic regression model has a high level of performance in both the training and validation dataset, with a prediction capability of 92.2%. Since the Kernel logistic regression model outperforms the benchmark model, we conclude that the proposed model is a promising alternative tool that should also be considered for forest fire susceptibility mapping in other areas. The results of this study are useful for the local authorities in forest planning and management.

Description: Sustainability, Vol. 10, Pages 1151: Determinants of Customer Satisfaction and Loyalty in Vietnamese Life-Insurance Setting Sustainability doi: 10.3390/su10041151 Authors: Ha Nguyen Hoang Nguyen Nhan Nguyen Anh Phan Sustainable services are often regarded as sustainable strategies and operations producing goods and services that satisfy customer needs and significantly improve social and environmental performance. To be sustainable, service providers must satisfy consumers’ needs or otherwise they will become redundant and economically irrelevant. This paper presents the results of an empirical study on the determinants of customer satisfaction and loyalty in life-insurance services in Vietnam based on a database collected through a questionnaire survey of 1476 customers during 2017. A path analysis technique is applied to test the proposed framework on the direct and indirect relationship between variables. The results of statistical analysis indicate that customer satisfaction in life-insurance services is significantly explained by such factors as corporate image, service quality and perceived value. Our findings suggest that a life-insurance service provider should focus on enhancing service quality and corporate image in order to obtain customer satisfaction that leads to customer loyalty.

Description: Energies, Vol. 11, Pages 1504: Multi-Criteria Decision Making (MCDM) Approaches for Solar Power Plant Location Selection in Viet Nam Energies doi: 10.3390/en11061504 Authors: Chia-Nan Wang Van Thanh Nguyen Hoang Tuyet Nhi Thai Duy Hung Duong The ongoing industrialization and modernization period has increased the demand for energy in Viet Nam. This has led to over-exploitation and exhausts fossil fuel sources. Nowadays, Viet Nam’s energy mix is primarily based on thermal and hydro power. The Vietnamese government is trying to increase the proportion of renewable energy. The plan will raise the total solar power capacity from nearly 0 to 12,000 MW, equivalent to about 12 nuclear reactors, by 2030. Therefore, the construction of solar power plants is needed in Viet Nam. In this study, the authors present a multi-criteria decision making (MCDM) model by combining three methodologies, including fuzzy analytical hierarchy process (FAHP), data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS) to find the best location for building a solar power plant based on both quantitative and qualitative criteria. Initially, the potential locations from 46 sites in Viet Nam were selected by several DEA models. Then, AHP with fuzzy logic is employed to determine the weight of the factors. The TOPSIS approach is then applied to rank the locations in the final step. The results show that Binh Thuan is the optimal location to build a solar power plant because it has the highest ranking score in the final phase of this study. The contribution of this study is the proposal of a MCDM model for solar plant location selection in Viet Nam under fuzzy environment conditions. This paper also is part of the evolution of a new approach that is flexible and practical for decision makers. Furthermore, this research provides useful guidelines for solar power plant location selection in many countries as well as a guideline for location selection of other industries.

Description: This research aims to provide new insights into various determinants affecting household recycling. By focusing on Vietnam, this research also extends knowledge about sustainable behavior in emerging markets, which are the major culprits in terms of greenhouse gas emissions. Hypotheses were developed as a result of the critical review of relevant studies in the fields of marketing, psychology, and economics, and then tested using a quantitative survey data. Structured questionnaires were administered to Vietnamese respondents which yielded 486 usable responses. Multivariate statistics reveal that all the determinants influenced their recycling behavior except for moral norms. Attitude towards the importance of recycling exerted the strongest influence, followed by subjective norms and warm glow respectively. On the other hand, attitude towards the inconvenience of recycling significantly reduced recycling behavior. The research findings have important implications for strategies aimed at promoting recycling behavior. Communication and education programs should emphasize how household recycling contributes to environmental protection, as well as stress intrinsic rewards when recycling. Public media campaigns should feature opinion leaders and attractive communicators, who can effectively apply social pressure to perform recycling behavior. Organizations should also make every effort to make recycling more convenient.

Description: This paper proposes applications of a modified stochastic fractal search algorithm (MSFS) to solve the economic load dispatch problem (ELD) in which valve-point effects, prohibited operating zones, power losses in all conductors, multi-fuel sources and other constraints of power system are taken into consideration. The proposed method is first developed in the study by performing two modifications on two procedures of new solution generation from conventional stochastic fractal search (SFS). The first modification is used to change the strategy of producing new solutions of the first and the second update procedures while the second one is to newly update the worst solutions in the first update process and the best solutions in the second update process. These modifications have major influence on the solution search performance of the proposed method. All improvements of the proposed method can be illustrated by solving and analyzing results from various test systems with different system scales including 3-unit, 6-unit, 10-unit, and 20-unit systems. Comparison of results obtained by MSFS, SFS, and other existing methods indicates that the proposed MSFS method is more effective and robust than compared methods in terms of solution quality, high-quality solution search stability and convergence process. Consequently, the proposed method should be used as a very favorable optimization method for the ELD problem and it should be tried for other optimization problems in electrical engineering.

Description: In this article, we consider an inverse problem to determine an unknown source term in a space-time-fractional diffusion equation. The inverse problems are often ill-posed. By an example, we show that this problem is NOT well-posed in the Hadamard sense, i.e., this problem does not satisfy the last condition-the solution’s behavior changes continuously with the input data. It leads to having a regularization model for this problem. We use the Tikhonov method to solve the problem. In the theoretical results, we also propose a priori and a posteriori parameter choice rules and analyze them.

Description: In this paper, a low-loss, spiral lattice photonic crystal fiber (PCF)-based plasmonic biosensor is proposed for its application in detecting various biomolecules (i.e., sugar, protein, DNA, and mRNA) and biochemicals (i.e., serum and urine). Plasmonic material gold (Au) is employed externally to efficiently generate surface plasmon resonance (SPR) in the outer surface of the PCF. A thin layer of titanium oxide (TiO2) is also introduced, which assists in adhering the Au layer to the silica fiber. The sensing performance is investigated using a mode solver based on the finite element method (FEM). Simulation results show a maximum wavelength sensitivity of 23,000 nm/RIU for a bio-samples refractive index (RI) detection range of 1.32–1.40. This sensor also exhibits a very low confinement loss of 0.22 and 2.87 dB/cm for the analyte at 1.32 and 1.40 RI, respectively. Because of the ultra-low propagation loss, the proposed sensor can be fabricated within several centimeters, which reduces the complexity related to splicing, and so on.