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: 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.