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Purpose: This study, based on self-determination theory (SDT), investigates the effect of business students' future time perspective (FTP), directly and indirectly (mediated by deep learning approaches), on quality of university life. Design/methodology/approach: A sample of 547 business students in Ho Chi Minh City, Vietnam, was surveyed via a two-wave process to collect data to validate the measures and to test the hypotheses using structural equation modeling (SEM). Findings: The results produced by SEM demonstrated that FTP had no direct effect on quality of university life and that deep learning approaches fully mediated the impact of FTP on quality of university life. Practical implications: The study findings provide business educators with a better understanding of the role that FTP can play for business students. Increased awareness of this issue may help nurture the FTP of business students, which in turn directs them to pursue deep learning approaches to achieve a higher level of quality of university life. Originality/value: This study is among the first to empirically investigate the overarching role that FTP plays in both deep learning approaches and quality of university life.

This study characterized biofilm formation of various Salmonella strains on common processing plant surface materials (stainless steel, concrete, rubber, polyethylene) under static and fluidic shear stress conditions. Surface-coupons were immersed in well-plates containing 1 mL of Salmonella (6 log CFU/mL) and incubated aerobically for 48 h at 37 °C in static or shear stress conditions. Biofilm density was determined using crystal violet assay, and biofilm cells were enumerated by plating on tryptic soy agar plates. Biofilms were visualized using scanning electron microscopy. Data were analyzed by SAS 9.4 at a significance level of 0.05. A surface–incubation condition interaction was observed for biofilm density (p < 0.001). On stainless steel, the OD600 was higher under shear stress than static incubation; whereas, on polyethylene, the OD600 was higher under static condition. Enumeration revealed surface–incubation condition (p = 0.024) and surface–strain (p < 0.001) interactions. Among all surface–incubation condition combinations, the biofilm cells were highest on polyethylene under fluidic shear stress (6.4 log/coupon; p < 0.001). Biofilms of S. Kentucky on polyethylene had the highest number of cells (7.80 log/coupon) compared to all other strain–surface combinations (p < 0.001). Electron microscopy revealed morphological and extracellular matrix differences between surfaces. Results indicate that Salmonella biofilm formation is influenced by serotype, surface, and fluidic shear stress.

Considering smartphone-based indoor localization and tracking, combination of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) becomes prevalent due to wide deployment of indoor Wi-Fi access points and easily access of inertial measurement units (IMUs) on smartphones. Since Wi-Fi fingerprinting depends on the similarities between online and offline received signal strengths, it suffers from the fluctuation of radio signal measurements. Meanwhile, the PDR relies on the IMUs, which undergo accumulated errors in long walking distance. In this paper, we aim to improve the indoor tracking accuracy, while filling up the gap between two popular Android and iOS platforms. In particular, we propose a hybrid tracking algorithm that integrates the PDR with Wi-Fi/iBeacon signal features. There are three key points in our paper. First, we process Wi-Fi/iBeacon signals to build conversion functions to convert signals to related proximity distances. Besides, in order to support mobile tracking, we introduce a placement methodology for iBeacon installation for a specific region of interest. Second, a mobile smartphone uses an improved PDR to localize itself, while incorporating Wi-Fi/iBeacon data for estimating a starting point and correcting its location along the walking path. Finally, we build an iOS app to implement the proposed scheme and display visual tracking results. Experiment results show that our proposed scheme is more robust and accurate compared to the conventional schemes.

With the rapid development of location-based services (LBSs), efficient and mobile-friendly localization algorithms should be designed for users to deliver a reliable LBS. In this paper, we present an algorithm with the corresponding smartphone app that enables users to calculate their locations based on representative infrastructures, such as nearby Wi-Fi access points and Bluetooth low energy (BLE) beacons subject to low-cost, rapid system deployment, and competitive location accuracy. Working under indoor multiple-floor scenarios, our app has three prominent features for estimating user locations. First, we establish a feature identifier to detect the current floor and the feasible area in which the user may walk. Second, owing to the structures of the indoor environment and the presence of different obstacles, the unpredictable variation of the received signal strength (RSS) in indoor environments is considered in the RSS-distance relationship to provide accurate location estimates. Third, with the prevalence of smartphones, we extract smartphone-inertial measurement units to learn users’ behavior preferences, while collecting reference signals (e.g., Wi-Fi/BLE readings) along the pathway and input to the tracking algorithm. Then, the user’s current location is displayed on the app. With this solution, we can provide an accurate location estimate with relatively low computational complexity regarding mobile device capability, while reducing labor costs from traditional fingerprint deployments. Finally, we test our tracking app in real-time multiple floor scenarios and evaluate the collected tracking data. Experimental results show that our proposed scheme achieves an average localization accuracy of more than 80% within a 2-m error bound in multiple-floor scenarios, while all areas (i.e., corridors, rooms, and stairs) were successfully identified.

Hung Manh Than,1,* Vuong Minh Nong,2,* Cap Trung Nguyen,1 Khiem Phu Dong,3 Hoa Thi Ngo,4 Tra Thu Doan,2 Nga Thu Do,2 Trang Huyen Thi Nguyen,5 Thanh Van Do,2 Co Xuan Dao,6 Tuan Quang Nguyen,6 Thach Ngoc Pham,3 Cuong Duy Do2 1Emergency Department, National Hospital for Tropical Diseases, Hanoi, Vietnam; 2Center for Tropical Diseases, Bach Mai Hospital, Hanoi, Vietnam; 3National Hospital for Tropical Diseases, Hanoi, Vietnam; 4Oxford University Clinical Research Unit, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam; 5Department of Public Health, Thang Long University, Hanoi, Vietnam; 6Bach Mai Hospital, Hanoi, Vietnam*These authors contributed equally to this workCorrespondence: Cuong Duy DoCenter for Tropical Diseases, Bach Mai Hospital, No. 78, Giai Phong Street, Dong Da District, Hanoi 100000, VietnamTel +84 983 264 868Email doduy.cuong@bachmai.edu.vnPurpose: Mental health is an important component of the protection strategy for healthcare workers (HCWs). However, it has not been well described in Vietnam during the COVID-19 outbreak. This study aims to measure the psychological distress and health-related quality-of-life among frontline healthcare workers during the peak of the outbreak in Vietnam.Patients and Methods: We conducted a cross-sectional survey on 173 health workers at two national tertiary hospitals in Hanoi, Vietnam from March to April 2020. The psychological distress was measured by the Depression, Anxiety, and Stress Scale – 21 Items (DASS-21), Impact of Event Scale – Revised (IES-R), and the Insomnia Severity Index (ISI). EQ-5D-5L was used to determine the health-related quality-of-life (HRQoL) outcomes.Results: Among 173 HCWs, the proportion of reported depression symptoms, anxiety symptoms, and stress was 20.2%, 33.5%, and 12.7%, respectively. The median EQ-5D-5L index score was 0.93 (IQR=0.85– 0.94), and the anxiety/depression aspect had the highest reported problems. The most COVID-19-specific concerns among frontline HCWs were the reduction of income (59%) and the increase of living costs (54.3%). HCWs working in the COVID-19-designated hospital had a significantly higher rate of mental health problems and had a lower HRQoL outcome than those working in non-COVID-19-designated hospitals. Other factors associated with psychological distress and sleep problems include age, job title, income, chronic diseases status, and years of working in healthcare settings. HCWs who were ≥ 30 years old, had higher working years, had higher incomes, and had mental health and sleep problems were more likely to have lower HRQoL scores.Conclusion: We reported a moderate rate of psychological distress and lower HRQoL outcomes among frontline HCWs during the COVID-19 outbreak in Vietnam. Various factors were found to be associated with mental health and HRQoL that might be useful for implementing appropriate interventions for HCWs in low-resource settings.Keywords: COVID-19, SARS-CoV-2, mental health, psychological distress, health-related quality-of-life, health workers

ABSTRACT Avian pathogenic Escherichia coli (APEC) causes significant economic and welfare concerns to the broiler industry. For several decades, prophylactic supplementation of antimicrobial growth promoters was the primary method to control APEC; however, the recent shift to no antibiotics ever (NAE) production has increased colibacillosis incidence. The objectives of this study were to determine the influence of season, flock age, and sample type on the prevalence and virulence of E. coli and to identify the serogroups and antimicrobial susceptibility of virulent and nonvirulent E. coli in NAE broiler farms. Litter, feces, cloacal swabs, and tracheal swabs were collected from 4 NAE farms during spring and summer seasons, and E. coli was isolated and confirmed by PCR. Confirmed E. coli isolates were tested for 5 APEC-virulence-associated genes (VAGs) using quantitative PCR (qPCR). Further, E. coli isolates with all five VAGs (100 isolates) and E. coli isolates without any VAGs (87 isolates) were screened against 11 antimicrobials through Kirby-Bauer disk diffusion assay, and their serogroups were tested using PCR. Data were analyzed using the GLIMMIX procedure of SAS 9.4, and statistical significance was determined at a P value of ≤0.05. Overall, the prevalence of E. coli was not affected by season, flock age, or sample type. However, the prevalence of all tested VAGs decreased from spring to summer (P ≤ 0.002). The frequency of resistance was highest for tetracycline, and serogroups O8 (31%) and O78 (11%) were most frequent in virulent E. coli. In conclusion, there is a high prevalence of virulent E. coli in NAE farms, especially in the spring season. IMPORTANCE Avian pathogenic Escherichia coli causes one of the most detrimental bacterial diseases to the United States poultry industry, colibacillosis. Colibacillosis leads to decreased performance, early mortality, and subsequent production loss. Previously, colibacillosis was largely mitigated by the use of antimicrobial growth promoters. Due to concerns about antimicrobial resistance, the use of these promoters has been largely removed from the broiler industry. With recent shifts in the poultry industry to NAE broiler production, there is an increase in bacterial disease and mortality. We do not know how this shift to NAE affects APEC prevalence within broiler farms. Therefore, in the current study, we attempted to assess the prevalence and virulence of E. coli within an antibiotic-free broiler environment, assessed antimicrobial susceptibility, and identified the serogroups of virulent and nonvirulent E. coli.

Campylobacter jejuni is one of the most common causes of foodborne human gastroenteritis in the developed world. This bacterium colonizes in the ceca of chickens, spreads throughout the poultry production chain, and contaminates poultry products. Despite numerous on farm intervention strategies and developments in post-harvest antimicrobial treatments, C. jejuni is frequently detected on broiler meat products. This indicates that C. jejuni is evolving over time to overcome the stresses/interventions that are present throughout poultry production and processing. The development of aerotolerance has been reported to be a major survival strategy used by C. jejuni in high oxygen environments. Recent studies have indicated that C. jejuni can enter a viable but non-culturable (VBNC) state or develop biofilm in response to environmental stressors such as refrigeration and freezing stress and aerobic stress. This review provides an overview of different stressors that C. jejuni are exposed to throughout the poultry production chain and the genotypic and phenotypic survival mechanisms, with special attention to aerotolerance, biofilm formation, and development of the VBNC state.

In poultry processing, Salmonella and Campylobacter contaminations are major food safety concerns. Peracetic acid (PAA) is an antimicrobial commonly used in commercial poultry processing to reduce pathogen prevalence so as to meet the USDA-FSIS performance standards. The objective of this study was to determine the prevalence of Salmonella and Campylobacter on broiler meat in various steps of commercial poultry processing in plants that use PAA. Post-pick, pre-chill, post-chill, and drumstick chicken samples were collected from three processing plants and mechanically deboned meat (MDM) was collected from two of the three plants. Each plant was sampled thrice, and 10 samples were collected from each processing step during each visit. Among the 420 samples, 79 were contaminated with Salmonella and 155 were contaminated with Campylobacter. Salmonella and Campylobacter contamination on the post-pick samples averaged 32.2%. Significant reductions in Salmonella and Campylobacter were observed in pre-chill to post-chill samples, where the prevalence was reduced from 34% and 64.4% to nondetectable limits and 1.1%, respectively (p < 0.001). Salmonella and Campylobacter remained undetectable on the drumstick samples in all three processing plants. However, the prevalence of Salmonella and Campylobacter on MDM was similar to the post-pick prevalence, which suggests substantial cross-contamination from post-chill to MDM.

Magnetic induction (MI) communication is a promising technology for next-generation low-power underwater wireless sensor networks (UWSNs). Clustering algorithm design becomes an important and challenging issue in today’s MI-based UWSNs. In contrast to the conventional approaches which suffer from continuous movement of ocean current and traffic loads in different areas of the network, we consider a clustering algorithm based on the Voronoi diagram and node density distribution to improve the energy efficiency and to prolong the network lifetime. In particular, we propose a jellyfish breathing process for cluster head selection and an automatic adjustment algorithm for sensor nodes. The simulation results show that the proposed clustering algorithm achieves a high network capacity rate and a good equalization for the remaining energy.

In poultry processing, spoilage microbes are persistent microorganisms, which affect the quality of broiler meat. Peracetic acid (PAA) is the most common antimicrobial used by commercial processing plants, which can reduce the prevalence of these microbes. The goal of this study was to determine the concentrations of aerobic bacteria, coliforms, lactic acid bacteria, and Pseudomonas on broiler meat in processing plants that use peracetic acid in various concentrations as the primary antimicrobial. Samples were collected from three processing plants at five processing steps: post-pick (defeathering), pre-chill, post-chill, mechanically deboned meat (MDM), and drumsticks. Samples were rinsed in buffered peptone water for bacteria isolation. Over six log CFU/sample of aerobic plate counts (APC), lactic acid bacteria, and coliforms were detected on post-pick samples. All spoilage bacteria were reduced to nondetectable levels on post-chill samples (p < 0.001). However, the presence of all bacteria on mechanically deboned meat (MDM) samples indicated varying degrees of cross contamination from post-chill and MDM samples. These results suggest PAA effectively reduces spoilage microbes in chilling applications irrespective of differences in PAA concentrations. However, due to the levels of spoilage microbes detected in MDM, it may be worth investigating the potential interventions for this stage of processing.

Developing a deeper understanding of biological components of sperm is essential to improving cryopreservation techniques and reproductive technologies. To fully ascertain the functional determinants of fertility, lipidomic methods have come to the forefront. Lipidomics is the study of the lipid profile (lipidome) within a cell, tissue, or organism and provides a quantitative analysis of the lipid content in that sample. Sperm cells are composed of various lipids, each with their unique contribution to the overall function of the cell. Lipidomics has already been used to find new and exciting information regarding the fatty acid content of sperm cells from different species. While the applications of lipidomics are rapidly evolving, gaps in the knowledge base remain unresolved. Current limitations of lipidomics studies include the number of available samples to analyze and the total amount of cells within those samples needed to detect changes in the lipid profiles across different subjects. The information obtained through lipidomics research is essential to systems and cellular biology. This review provides a concise analysis of the most recent developments in lipidomic research. This scientific resource is important because these developments can be used to not only combat the reproductive challenges faced when using cryopreserved semen and artificial reproductive technologies in livestock such as cattle, but also other mammals, such as humans or endangered species.

Underwater wireless sensor networks (UWSNs) based on magnetic induction (MI) have been recently proposed as a promising candidate for underwater networking due to its benefits, such as small transmission delay, low vulnerability to environment changes, multipath fading negligibility, and high bandwidth. Most of the UWSN applications are location dependent and, thus, localization plays an important functionality for obtaining sensor positions. In this paper, we first study an MI-based monitoring network in shallow sea, then focus on how to design an optimal node deployment strategy and a clustering algorithm to prolong network lifetime for a 3D-UWSN by reducing the network energy consumption. Using the Voronoi diagram, we propose a high-energy node priority clustering algorithm, in which a cluster head would be selected according to the remaining energy of sensor nodes and the geometry distance among them. Moreover, in order to improve the efficiency of data collection, we use the ant colony optimization to find the shortest path for autonomous underwater vehicle. The simulation results show that the proposed approach outperforms other conventional protocols in some certain scenarios.

Abstract Background The prevalence of chronic kidney disease (CKD) in rural Vietnam is unknown. We wished to determine the prevalence of CKD and determine whether a simple questionnaire was able to detect individuals at high risk of CKD before expensive confirmatory laboratory testing. Methods A cross sectional study was performed. We recruited 2037 participants from 13 communes of Long An province, Vietnam, for CKD screening with urine albumin/creatinine ratio (ACR) measured by immunoturbidimetric method and serum creatinine to estimate glomerular filtration rate (eGFR). CKD was defined as either ACR ≥ 30 mg/g or eGFR MDRD

Since radio frequency (RF) signals can be used for both information transmission and energy harvesting, RF-based energy harvesting is capable of integrating with other existing communication techniques for providing better rate–energy tradeoff and quality-of-service. Within the context of an RF-based energy harvesting relaying network, a relay node not only acts as an intermediate node to help the delivery from source to destination, but also harvests energy from an RF dedicated source to prolong its lifetime. Thus, it brings diversity gain and coverage extension as well as provides extra energy for data transmission. This paper investigates a system that enables ambient backscattering communication-assisted simultaneous wireless information and power transfer at the relay. In the proposed system, a backscatter device plays a role as a relay to meet sustainable network coverage and to harvest ambient energy as well. With a power splitting (PS) scheme, we first investigate a nonlinear energy harvesting model at the relay node. In order to adapt to the channel gains, a dynamic PS ratio is required to perform well in changing environments. Moreover, we derive mathematical expressions for the outage probability and the achievable system throughput. Numerical results show the effects of various system parameters on the outage probability and the system throughput performance.

Localization is a key-enabling technology for many applications in underwater wireless sensor networks. Traditional approaches for received signal strength (RSS)-based localization often require uniform distribution for anchor nodes and suffer from poor estimates according to unpredictable and uncontrollable noise conditions. In this paper, we establish an RSS-based localization scheme to determine the location of an unknown normal sensor from a certain measurement set of potential anchor nodes. First, we present a practical path loss model for wireless communication in underwater acoustic environments, where anchor nodes are deployed in a random circumstance. For a given area of interest, the RSS data collection is performed dynamically, where the measurement noises and the correlation among them are taken into account. For a pair of transmitter and receiver, we approximate the geometry distance between them according to a linear regression model. Thus, we can obtain a quick access for the range information, while keeping the error, the communication head and the response time low. We also present a method to correct noises in the distance estimate. Simulation results demonstrate that our localization scheme achieves a better performance for certain scenario settings. The successful localization probability can be up to 90%, where the anchor rate is fixed at 10%.

Wireless sensor networks (WSNs) enable many applications such as intelligent control, prediction, tracking, and other communication network services, which are integrated into many technologies of the Internet-of-Things. The conventional localization frameworks may not function well in practical environments since they were designed either for two-dimensional space only, or have high computational costs, or are sensitive to measurement errors. In order to build an accurate and efficient localization scheme, we consider in this paper a hybrid received signal strength and angle-of-arrival localization in three-dimensional WSNs, where sensors are randomly deployed with the transmit power and the path loss exponent unknown. Moreover, in order to avoid the difficulty of solving the conventional maximum-likelihood estimator due to its non-convex and highly complex natures, we derive a weighted least squares estimate to estimate jointly the location of the unknown node and the two aforementioned channel components through some suitable approximations. Simulation results confirm the effectiveness of the proposed method.

Ambient backscatter communication (AmBC) is considered as a promising future emerging technology. Several works on AmBC have been proposed thanks to its convenience and low cost property. This paper focuses on finding the optimal energy detector at the receiver side and estimating the corresponding bit error rate for the communication system utilizing the AmBC. Through theoretical and numerical analyses, we present two important results. First, we improve the existing energy detector by calculating the optimal averaging power orders. Second, we take advantage of the early work on orthogonal frequency division multiplexing (OFDM), where the repeating structure of ambient OFDM signals is exploited to cancel out the direct-link interference by using a cyclic prefix, then provide a test statistic in which optimal detection threshold and optimal power order are derived accordingly. The study reveals the inherent limitation of AmBC energy detectors and provides a guidance for achieving optimal power order for a given significance level.

Accurate and low-cost localization of multiple targets or nodes is one of fundamental and challenging technical issues in wireless sensor networks (WSNs). Furthermore, compressive sensing allows that a sparse signal can be reconstructed from few measurements, and choosing a suitable sensing matrix is also important. For this purpose, random sensing matrices have been studied, while a few researches on deterministic sensing matrices have been considered. In this paper, we use compressive sensing for multiple target localization in WSNs. We formulate multiple target locations as a sparse matrix in the discrete time domain. Then, we exploit received signal strength information to recover noisy measurements, while utilizing deterministic sensing matrices and greedy algorithm to locate each target. The proposal approach reduces the number of measurements in localization process, takes low-cost, and maintains the accuracy as compared to the conventional approach which is noncompressive sensing. Further simulation shows that the proposed approach is practical in use, while being favorably comparable to the existing random sensing matrices in reconstruction performance. This cooperation between the compressive sensing using deterministic sensing matrices and multiple target localization provides a new point of view in WSN localization.

This paper proposes an approach toward solving an issue pertaining to measuring compressible data in large-scale energy-harvesting wireless sensor networks with channel fading. We consider a scenario in which N sensors observe hidden phenomenon values, transmit their observations using amplify-and-forward protocol over fading channels to a fusion center (FC), and the FC needs to choose a number of sensors to collect data and recover them according to the desired approximation error using the compressive sensing. In order to reduce the communication cost, sparse random matrices are exploited in the pre-processing procedure. We first investigate the sparse representation for sensors with regard to recovery accuracy. Then, we present the construction of sparse random projection matrices based on the fact that the energy consumption can vary across the energy harvesting sensor nodes. The key ingredient is the sparsity level of the random projection, which can greatly reduce the communication costs. The corresponding number of measurements is chosen according to the desired approximation error. Analysis and simulation results validate the potential of the proposed approach.

There are two sources that help to explain the R K , R K ∗ anomalies in the MF331 model. The first is non-LFUV couplings of the new neutral gauge boson Z ′ with leptons, g Z ′ (e) ≠ g Z ′ (μ , τ) , which causes the R K , R K ∗ anomalies via Z ′ -penguin diagrams involving newly charged gauge bosons X μ ± , and exotic U-quarks. The second is the contribution from the box diagram only for the first generation of leptons. We show that the penguin diagrams can not explain R K , R K ∗ anomalies, and that the box diagram is required. The experimental constraints for R K and R K ∗ result in new particle mass degeneracy. The contributions of NP to the branching ratios Br (B s → μ + μ -) , Br (b → s γ) predict results that agree with the experimental limits in the allowed region of the NP scale. [ABSTRACT FROM AUTHOR]

Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered. These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this paper, a survey of deterministic sensing matrices for compressive sensing is presented. We introduce a basic problem in compressive sensing and some disadvantage of the random sensing matrices. Some recent results on construction of the deterministic sensing matrices are discussed.

Localization in wireless sensor networks (WSNs) is one of the primary functions of the intelligent Internet of Things (IoT) that offers automatically discoverable services, while the localization accuracy is a key issue to evaluate the quality of those services. In this paper, we develop a framework to solve the Euclidean distance matrix completion problem, which is an important technical problem for distance-based localization in WSNs. The sensor network localization problem is described as a low-rank dimensional Euclidean distance completion problem with known nodes. The task is to find the sensor locations through recovery of missing entries of a squared distance matrix when the dimension of the data is small compared to the number of data points. We solve a relaxation optimization problem using a modification of Newton’s method, where the cost function depends on the squared distance matrix. The solution obtained in our scheme achieves a lower complexity and can perform better if we use it as an initial guess for an interactive local search of other higher precision localization scheme. Simulation results show the effectiveness of our approach.