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In this age of fierce competitions, customer retention is one of the most important tasks for many companies. Many previous works proposed models to predict customer churn based on various machine learning techniques. In this study, we proposed an advanced churn prediction model using kernel Support Vector Machines (SVM) algorithm for a telecom company. Baseline SVM models were initially built to find out the most suitable kernel types and will be used to make comparison with other approaches. Dimension reduction strategies such as Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS) were applied to the dataset to find out the most important features. Furthermore, resampling techniques to deal with imbalanced data such as Synthetic Minority Oversampling Technique Tomek Link (SMOTE Tomek) and Synthetic Minority Oversampling Technique ENN (SMOTE ENN) were used on the dataset. Using the above-mentioned techniques, we have obtained better results compared to those obtained from previous works, we achieved an F1-score and accuracy of 99% and 98.9% respectively.

Raising environmental awareness and product development are two separate and costly investments that many small and medium-sized fashion businesses cannot afford to achieve sustainability. Therefore, there is a need to determine which factors exert a more significant impact on consumer loyalty and purchase intention toward eco-friendly fashions. Thus, this study employs a mixed-methods approach with thematic analysis and the SEM-PLS technique to research how Vietnamese Gen Z’s perceptions of product-service quality, environmental awareness, and pro-environmental behavior influence their purchase intention and loyalty toward eco-friendly fashion products. Most interviewees acknowledged that they primarily gained knowledge about eco-friendly fashion through social media platforms. The qualitative results further showed that their knowledge of and attitudes toward eco-friendly fashion practices were insufficient to convince young customers to afford eco-friendly fashion products. The SEM-PLS results of 313 participants show that while customers’ perceived behavioral control plays a more significant role in stimulating purchase intention, only product-service quality factors impact loyalty. Hence, this study suggests that businesses should prioritize improving service and product quality rather than funding green marketing when targeting Vietnamese Gen Z in case of financial constraints. Government should prioritize financial and technological support for fashion firms to develop high-quality eco-friendly fashion to ensure the product availability.

Magnesium oxide nanoparticle (nMgO) is a light metal based antimicrobial nanoparticle that can be metabolized and fully resorbed in the body. To take advantage of the antimicrobial properties of nMgO for medical use, it is necessary to determine the minimal inhibitory, bactericidal and fungicidal concentrations (MIC, MBC and MFC) of nMgO against prevalent infectious bacteria and yeasts. The objective of this study was to use consistent methods and conditions to reveal and directly compare the efficacy of nMgO against nine prevalent pathogenic microorganisms, including two gram-negative bacteria, three gram-positive bacteria with drug-resistant strains, and four yeasts with drug-resistant strains. The MIC of nMgO varied from 0.5 mg/mL to 1.2 mg/mL and the minimal lethal concentration (MLC) of nMgO at 90% killing varied from 0.7 mg/mL to 1.4 mg/mL against different pathogenic bacteria and yeasts. The most potent concentrations (MPC) of nMgO were 1.4 and/or 1.6 mg/mL, depending on the type of bacteria and yeasts tested. As the concentration of nMgO increased, the adhesion of bacteria and yeasts decreased. Moreover, S. epidermidis biofilm was disrupted at 1.6 mg/mL of nMgO. E. coli and some yeasts showed membrane damage after cultured with ≥0.5 mg/mL nMgO. Overall, nMgO killed both planktonic bacteria and disrupted nascent biofilms, suggesting new antimicrobial mechanisms of nMgO. Production of reactive oxygen species (ROS), Ca 2+ ion concentrations, and quorum sensing likely contribute to the action mechanisms of nMgO against planktonic bacteria, but transient alkaline pH of 7 to 10 or increased Mg 2+ ion concentrations from 1 to 50 mM showed no inhibitory or killing effects on bacteria such as S. epidermidis . Further studies are needed to determine if specific concentrations of nMgO at MIC, MLC or MPC level can be integrated into medical devices to evoke desired antimicrobial responses without harming host cells.

Staphylococci are important commensal and opportunistic bacteria found in various animals, including dogs and humans. The emergence of antibiotic-resistant Staphylococci is a growing global concern, including in Vietnam. This study aimed to investigate the prevalence, species distribution, and antibiotic-resistance profiles of Staphylococci isolated from dogs visiting veterinary clinics in Vietnam. A total of 309 Staphylococcus strains were isolated from 410 nasal and skin samples collected from both healthy and diseased dogs between December 2021 and December 2023 in Ho Chi Minh city. The isolation rate of Staphylococcus spp. was 71.2% (95% confidence interval [CI]: 66.6%–75.6%), with 78.9% (95% CI: 73.6%–83.7%) in diseased dogs, 56.9% (95% CI: 48.4%–65.2%) in healthy dogs, 80.1% (95% CI: 74.3%–85.1%) in skin samples, and 60.3% (95% CI: 52.9%–67.5%) in nasal samples. Species identification indicated that S. pseudintermedius was dominant, followed by S. aureus . Other species identified included S. epidermidis and S. schleiferi. Antibiotic susceptibility testing showed complex resistance patterns. Approximately 91.3% of isolates were resistant to at least one antibiotic, and 60.5% were multidrug-resistant (resistant to three or more antibiotics). A total of 215 antibiotic-resistance phenotypes were observed, with 85 phenotypes showing resistance to more than ten different antibiotics. Isolates from diseased dogs exhibited higher antibiotic-resistance rates than those from healthy dogs. Several antibiotic-resistance genes were identified, with aacA-aphD being the most prevalent, followed by tetK , gyrA , mecA , msrA , dfrA , and ermA . These findings highlight the widespread presence of antibiotic-resistant Staphylococci in dogs and emphasize the necessity for ongoing surveillance of antibiotic-resistance evolution in animals and its implications for human health.

Since the discovery of SARS-CoV-2, changes in genotype and reinfection with different variants have been observed in COVID-19-recovered patients, raising questions around the clinical pattern and severity of primary infection and reinfection. In this systematic review, we summarize the results of 23 studies addressing SARS-CoV-2 reinfections. A total of 23,231 reinfected patients were included, with pooled estimated reinfection rates ranging from 0.1 to 6.8%. Reinfections were more prevalent during the Omicron variant period. The mean age of reinfected patients was 38.0 ± 6. years and females were predominant among reinfected patients (M/F = 0.8). The most common symptoms during the first and second infection were fever (41.1%), cough (35.7% and 44.6%), myalgia (34.5% and 33.3%), fatigue (23.8% and 25.6%), and headaches (24.4% and 21.4%). No significant differences of clinical pattern were observed between primary infection and reinfection. No significant differences in the severity of infection were observed between primary infection and reinfection. Being female, being a patient with comorbidities, lacking anti-nucleocapsid IgG after the first infection, being infected during the Delta and Omicron wave, and being unvaccinated were associated with a higher risk of reinfection. Conflicting age-related findings were found in two studies. Reinfection with SARS-CoV-2 suggests that natural immunity is not long-lasting in COVID-19 patients.

Flood prediction is an important task, which helps local decision-makers in taking effective measures to reduce damage to the people and economy. Currently, most studies use machine learning to predict flooding in a given region; however, the extrapolation problem is considered a major challenge when using these techniques and is rarely studied. Therefore, this study will focus on an approach to resolve the extrapolation problem in flood depth prediction by integrating machine learning (XGBoost, Extra-Trees (EXT), CatBoost (CB), and light gradient boost machines (LightGBM)) and hydraulic modeling under MIKE FLOOD. The results show that the hydraulic model worked well in providing the flood depth data needed to build the machine learning model. Among the four proposed machine learning models, XGBoost was found to be the best at solving the extrapolation problem in the estimation of flood depth, followed by EXT, CB, and LightGBM. Quang Binh province was hit by floods with depths ranging from 0 to 3.2 m. Areas with high flood depths are concentrated along and downstream of the two major rivers (Gianh and Nhat Le – Kien Giang).

Enterohemorrhagic Escherichia coli (EHEC) serotype O157:H7 is a human pathogen responsible for outbreaks of bloody diarrhea and hemolytic uremic syndrome (HUS) worldwide. Conventional antimicrobials trigger an SOS response in EHEC that promotes the release of the potent Shiga toxin that is responsible for much of the morbidity and mortality associated with EHEC infection. Cattle are a natural reservoir of EHEC, and approximately 75% of EHEC outbreaks are linked to the consumption of contaminated bovine-derived products. This review will discuss how EHEC causes disease in humans but is asymptomatic in adult ruminants. It will also analyze factors utilized by EHEC as it travels through the bovine gastrointestinal (GI) tract that allow for its survival through the acidic environment of the distal stomachs, and for its ultimate colonization in the recto-anal junction (RAJ). Understanding the factors crucial for EHEC survival and colonization in cattle will aid in the development of alternative strategies to prevent EHEC shedding into the environment and consequent human infection.

Aim Islands provide opportunities for isolation and speciation. Many landmasses in the Indo‐Australian Archipelago (IAA) are oceanic islands, and founder‐event speciation is expected to be the predominant form of speciation of volant taxa on these islands. We studied the biogeographic history of flying foxes, a group with many endemic species and a predilection for islands, to test this hypothesis and infer the biogeographic origin of the group. Location Australasia, Indo‐Australian Archipelago, Madagascar, Pacific Islands. Taxon Pteropus (Pteropodidae). Methods To infer the biogeographic history of Pteropus, we sequenced up to 6,169 bp of genetic data from 10 markers and reconstructed a multilocus species tree of 34 currently recognized Pteropus species and subspecies with three Acerodon outgroups using BEAST and subsequently estimated ancestral areas using models implemented in BioGeoBEARS. Results Species‐level resolution was occasionally low because of slow rates of molecular evolution and/or recent divergences. Older divergences, however, were more strongly supported and allow the evolutionary history of the group to be inferred. The genus diverged in Wallacea from its common ancestor with Acerodon; founder‐event speciation out of Wallacea was a common inference. Pteropus species in Micronesia and the western Indian Ocean were also inferred to result from founder‐event speciation. Main conclusions Dispersal between regions of the IAA and the islands found therein fostered diversification of Pteropus throughout the IAA and beyond. Dispersal in Pteropus is far higher than in most other volant taxa studied to date, highlighting the importance of inter‐island movement in the biogeographic history of this large clade of large bats.

Triple-layered composite plates are created by joining three composite layers using shear connectors. These layers, which are assumed to be always in contact and able to move relatively to each other during deformation, could be the same or different in geometric dimensions and material. They are applied in various engineering fields such as ship-building, aircraft wing manufacturing, etc. However, there are only a few publications regarding the calculation of this kind of plate. This paper proposes novel equations, which utilize Mindlin’s theory and finite element modelling to simulate the forced vibration of triple-layered composite plates with layers connected by shear connectors subjected to a moving load. Moreover, a Matlab computation program is introduced to verify the reliability of the proposed equations, as well as the influence of some parameters, such as boundary conditions, the rigidity of the shear connector, thickness-to-length ratio, and the moving load velocity on the dynamic response of the composite plate.

Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. However, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. In this paper, we propose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with the shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University (Vietnam) to evaluate the performance and practicability of the system’s implementation. We examine data transfer on the LoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit designed with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management of waste collection.