To solve the problem of energy constraint and spectrum scarcity for cognitive radio wireless sensor networks (CR-WSNs), an underlay decode-and-forward relaying scheme is considered, where the energy constrained secondary source and relay nodes are capable of harvesting energy from a multi-antenna power beacon (PB) and using that harvested energy to forward the source information to the destination. Based on the time switching receiver architecture, three relaying protocols, namely, hybrid partial relay selection (H-PRS), conventional opportunistic relay selection (C-ORS), and best opportunistic relay selection (B-ORS) protocols are considered to enhance the end-to-end performance under the joint impact of maximal interference constraint and transceiver hardware impairments. For performance evaluation and comparison, we derive exact and asymptotic closed-form expressions of outage probability (OP) and throughput (TP) to provide significant insights into the impact of our proposed protocols on the system performance over Rayleigh fading channel. Finally, simulation results validate the theoretical results.

Nowadays, wearable devices for human health monitoring are increasingly become popular and widely used. Typically, the wearable device is small size and operates with batteries. Therefore, the wearable device acquires bio-signals and transfers to smartphones or personal computers (PC) via WiFi/ Bluetooth for processing data. To reduce power consumption is one of the most important challenges of designing wearable devices. To solve this problem, the proposed signal quality valuation (SQV) method can be select the high-quality signal and reduce the transfer time to other devices. In this paper, the proposed SQV and data compression method rely on real-time PPG signals analysis to retain important information of PPG signal, improve performance and power consumption of PPG devices. Besides, we also proposed Heuristic rules for heart rate (HR) estimation with compression data. The experimental results show that the highest compression ratio (CR) is 387.8 with BIDMC Physionet database (sampling frequencies of 125 Hz) and HR error as 1.43 bpm for averaging absolute error (avAE), the standard deviation absolute error (sdAE as 0.4) and relative error mean (avRE as 0.019). The proposed real-time PPG measurement system (sampling frequency as 100/ 200/ 400 Hz) reduce power consumption and open the new structure for healthcare application systems

The COVID-19 pandemic and associated restrictive measures implemented may considerably affect people’s lives. This study aimed to assess the well-being of Vietnamese people after COVID-19 lockdown measures were lifted and life gradually returned to normal. An online survey was organized from 21st to 25th April 2020 among Vietnamese residents aged 18 and over. Besides collecting socio-demographic and COVID-19-related data, the WHO-5 Well-Being Index (scored 0–25) was used to score participants’ well-being. A multivariate logistic regression model was used to determine the predictors of well-being. A total of 1922 responses were analyzed (mean age: 31 years; 30.5% male). Mean well-being score was 17.35±4.97. Determinants of high well-being score (≥13) included older age, eating healthy food, practising physical exercise, working from home, and adhering to the COVID-19 preventive measures. Female participants, persons worried about their relatives’ health, and smokers were more likely to have a low well-being score. In conclusion, after the lockdown measures were lifted, the Vietnamese people continued to follow COVID-19 preventive measures and most of them scored high on the well-being scale. Waiting to achieve large scale COVID-19 vaccine coverage, promoting preventive COVID-19 measures remains important, together with strategies to guarantee the well-being of the Vietnamese people.

Edge computing has emerged as a significant computing trend alongside the rapid expansion of the Internet of Things (IoT). Computing operations at the network’s edge offer a solution to the high latency and service overload challenges often associated with cloud computing. Additionally, the Secure Hash Algorithm-3 (SHA-3) plays a crucial role in ensuring data integrity and is implemented for numerous applications in the IoT field. Therefore, integrating the SHA-3 accelerator into edge computing is essential. Moreover, recent studies about SHA-3 have primarily focused on achieving high performance and optimizing resource utilization for SHA-3. However, these studies have overlooked the crucial aspect of data transfer between the external memory and the hash function block, as transfer time plays a significant role. This paper proposes an efficient SHA-3 architecture designed for System-on-Chip (SoC) Field Programmable Gate Array (FPGA) to address the aforementioned challenges and be suitable for real edge computing. The architecture contains three key techniques. First, the harmony of padding and Direct Memory Access (DMA) for managing the data transfer process and enhancing performance efficiency based on Serial Input to Parallel Output (SIPO) and a barrel shifter. Secondly, implementing internal pipelining within the Round Function (RF) to reduce critical path delays and optimize resource utilization. Finally, designing four modes (SHA3-224, SHA3-256, SHA3-384, and SHA3-512) to cater to various applications. Our architecture is implemented and tested on the DE10-Standard Development Kit (Cyclone V SX SoC-5CSXFC6D6F31C6N), which is integrated into the FPGA and is controlled by a Dual-Core ARM Cortex-A9 processor. The result is up to 38.34 Gbps in throughput and 5.61 Mbps/ALM in efficiency for the RF computation, 28.02 Gbps in throughput, and 3.63 Mbps/ALM for the full proposed architecture.

Miscarriage due to blastocyst implantation failure occurs in up to two-thirds of all miscarriage cases in human. The calcium ion has been shown to be involved in many cellular signal transduction pathways as well as in the regulation of cell adhesion, which is necessary for the embryo implantation process. Exposure to endocrine-disrupting chemicals (EDs) during early gestation results in disruption of intrauterine implantation and uterine reception, leading to implantation failure. In this study, ovarian estrogen (E2), bisphenol A (BPA), or 4-tert-octylphenol (OP), with or without ICI 182,780 (ICI) were injected subcutaneously from gestation day 1 to gestation day 3 post-coitus. The expression levels of the calcium transport genes were assessed in maternal uteri and implantation sites. The number of implantation sites was significantly low in the OP group, and implantation sites were absent in the E2 and EDs+ICI groups. There were different calcium transient transport channel expression levels in uterus and implantation site samples. The levels of TRPV5 and TRPV6 gene expression were significantly increased by EDs with/without ICI treatment in uterus. Whereas, TRPV5 and TRPV6 gene expression were significantly lower in implantation sites samples. NCX1 and PMCA1 mRNA levels were significantly decreased by OP and BPA in the implantation site samples. Compared to vehicle treatment in uterus, both the MUC1 mRNA and protein levels were markedly high in all but the BPA group. Taken together, these results suggest that both BPA and OP can impair embryo implantation through alteration of calcium transport gene expressions and by affecting uterine receptivity.

In the era of the post-quantum Internet of Things (IoT), the implementation of anti-quantum cryptographic algorithms in numerous terminals can successfully defend against prospective quantum computing assaults. Lattice-based cryptography can withstand quantum computing attacks, making it a viable substitute for the currently prevalent classical public-key cryptographic technique. Nevertheless, the algorithm’s significant time complexity will result in a substantial computational burden on the edge computing chip in the IoT terminal. The computation of polynomial multiplication is the most demanding task in lattice-based cryptographic algorithms. Therefore, the investigation into efficient methods for calculating polynomial multiplication is highly important. Quick number theory transformations (NTT) are a widely employed technique to accelerate polynomial multiplication. This study presents a hardware implementation of an efficient number theory transformation. We utilize a multi-level pipeline architecture in the design to accomplish parallel calculations and execute it on a low-profile Artix7-XC7A100T FPGA device. The performance evaluation results demonstrate that our implementation significantly enhances performance and reduces resource usage compared to other existing proposals on the same platform. We suggested NTT core can be implemented in edge computing chips to enhance computational speed due to its small and low-latency design. The experimental results show that the proposed design, which supports both NTT and inverse NTT, achieves 417-Megahertz and consumes only 541-LUTs on Artix-7 XC7A100T.

Recent prolonged dry periods and lack of irrigation water have severely affected the productivity of coffee farms’ in the Central Highlands of Vietnam. This paper analyzes the efficiency of irrigation water use for Robusta coffee (Coffea canephora) in Lam Dong province, Highlands, Vietnam. A Cobb-Douglas production function was used to determine coffee productivity’s response to the application of irrigation water and other production factors using data collected from 194 farmers while the Technical Efficiency (TE) and Irrigation Water Use Efficiency (IWUE) were analyzed using a Data Envelopment Analysis (DEA) model. The correlation of different factors to IWUE was determined using the Tobit model. The production function analysis using Cobb-Douglas shows that the volume of irrigation water, amount of working capital, labor and farm size significantly influence coffee productivity. It also shows that indigenous farmers are more efficient in utilizing irrigation water than the (mostly Kinh) migrant farmers. The Tobit result, on the other hand. indicates that farmers’ experience, education level, distance of farm to water source, security of access to water source, extension contact and credit access significantly affect IWUE. The study findings further suggest that mitigating water shortages in coffee farms require sub-regional and national policy support such as better access to credit and extension services, training, land management and household-level effort to improve farming practices, through the application of appropriate technologies and traditional knowledge.

Mangrove ecosystems play an important role in local livelihoods in coastal regions of tropical and subtropical countries. However, in recent years urbanisation has changed the income structure of residents near mangroves. Different landscapes provide different job opportunities; thus, analysis of regional landscape patterns is important for understanding income structures. In this study, surveys on the income structure and landscape patterns of the surrounding areas of three mangrove sites were conducted in the Hau and Hoang Mai River estuaries in Nghe An Province, North-Central Vietnam. The results revealed that both natural and socio-economic landscape components affected income structure. The major occupations in the study area were agriculture, including husbandry, sea fishing, and trading. Land morphology and river type were the major factors influencing the income from agriculture, while coastline morphology primarily affected income from sea fishing. Community-based trading was carried out in the study area; thus, the population inside each administrative unit was a significant factor increasing income, while the retail market size in an area had significant negative effects, potentially due to the increasing number of competitors. Our study aimed to evaluate mangrove ecosystem importance for local livelihoods in relation to landscape patterns, and the results contribute to urban planning based on the conservation and sustainable use of mangrove ecosystems.

Tidal inlets with attached sand spits are a very common coastal morphology. Since the evolution of the sand spits along the coastline have greatly affected on the social-economic development of local coastal areas. However, previous studies mainly focused on the sand spits which are usually in the scales of hundred meters in width. Therefore, in this study, morphological change of a smaller and unexplored sand spit located in the west coast of Phu Quoc Island, will be investigated. It was found that there is a seasonal variation in the evolution of the sand spit at Song Tranh Inlet. The Longshore sediment transport rates (LSTR) along the spit are in the order from 104 to 105 m3/year. LSTRs of fourteen inlets in the literature were reviewed and the LSTRs at Song Tranh Inlet are higher than half of the LSTRs along the fourteen reviewed inlets. This study aims at contrib-uting to the growing literature on sand spit morphological changes as well as the sustainable coastal management for Phu Quoc Island which is well known as the Pearl Island of Vietnam.

In all forms of folk beliefs, ancestor worship is a universal traditional belief form of the Vietnamese people. As a Vietnamese people, “everyone worships their ancestors, everyone worships their parents and grandparent”. Ancestor worship is a common belief in the whole country. It is a belief that expresses the deeply humanistic spirit of the Vietnamese people and has great values in human life. So, what is the nature of ancestor worship? What is the values of ancestor worship in life? And in the context of globalization, how has this the belief changed? This study focuses on analyzing the above contents, thereby highlighting the value of this belief in the spiritual life of Vietnamese people; to point out the positive and negative changes of this belief in the current period; from that, take the right measures to bring into play the positive and limit the negative side of those changes in the spiritual life of Vietnamese people.

Coastal erosion is threatening the infrastructures of coastal community at the Ma River mouth in Thanh Hoa province, Vietnam. When coastal erosion happens, people try to prevent it by emergency solutions like hard structures. However, this approach usually leads to more severe problem since the mechanism for coastal erosion is not investigated and understood properly. In this study the long-term configuration of Ma River mouth in Thanh Hoa Province, Central Vietnam has been investigated using Landsat imagery from 1987 to 2023. An analytical solution of one-line model for shoreline change was also employed to calculate the sand discharge from Ma River as well as the diffusion coefficient for sand transported along-shore by breaking waves. There was an asymmetric configuration of the Ma River mouth during the last 37 years. The sand supply from the Ma River is around 350,000 m3/year. This amount of sand is going mainly to the northern beach of the Ma River delta (from 55% to 75%). The asymmetric morphology of the Ma River mouth delta is the trigger for severe coastal erosion reported recently. This study provides a quick and easy method to investigate the morphological change of cuspate deltas where measured data is limited.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

The COVID-19 pandemic has expanded globally and caused serious consequence in both health system and the economy. MPV is one of two oral antivirals for COVID-19 with mild-to-moderate symptoms that have received FDA approval so far. This study aims to evaluate the cost-utility of using MPV in COVID-19 treatment. To compare the utility costs of MPV and non-MPV treatments for COVID-19 patients, we used a decision tree model. Data on clinical efficacy, cost, and quality of life were gathered from previous studies. The incremental effective cost ratio (ICER) calculated based on costs incurred and quality-adjusted life years (QALY), was considered as primary out-come. The total cost of MPV treatment and not using MPV was 15,362,516 VND (618 USD) and 15,771,262 VND (634 USD), respectively. The estimated QALY with using-MPV was 28.33, while without MPV was 19.72. The ICER was dominant, resulting in savings of 47,453 VND/QALY (1.9 USD). MPV is a cost-effective medication used to treat COVID-19. In mild-to-moderate COVID-19 patients, MPV considerably lowers costs and improves quality of life. However, several clinical factors of the drug still need to be taken into account.

In order to meet the national energy saving goals set in the Vietnam National Energy Efficiency Program in the period of 2019 – 2030 (VNEEP), the Vietnamese government has adopted a series solutions and policies to improve energy efficiency. The Vietnam’s 63 provinces will be as main actor for the national achievement in energy efficiency. Thus, understanding the province’s potentiality of energy efficiency is useful for the harmonious and sustainable development between the economy and energy systems. In this study, provincial and national data from General Statistic Office are analyzed in terms of the energy efficiency levels. With the trends of economic development and energy consumption in both national and regional levels, the Lorenz curve between Vietnamese energy consumption and GDP is investigated. The Lorenz coefficient shows the energy allocation is nether reasonable nor balanced. By using clustering method, the 63 provinces of Vietnam clustered into 7 groups that the provinces in the cluster has the similar indexes of energy efficiency i.e. ability, responsibility, potential and difficulty. The energy consumption and GDP are predicted in the period of 2019 – 2025. Based on the difference of GDP development and energy consumption levels, the target of energy efficiency for each province through clustering is set. The results show that 33 provinces included in the cluster 1, 2, 3, 4 and 6 are heavy contribution. Among them, the provinces in the cluster 2 and 3 need to focus on the industry sector in their energy saving policy. The cluster 7 included the under-developed provinces can learn development’s experiences of the provinces in the cluster 1, 2, 3 and 4 to find the best way of their future development.

The research is designed for developing the pilot small-scale clean development mechanism bundled project activities in Vietnam electricity/ energy sector. Its overall purpose is to assess the potential of rice husk - fuelled bio-power development projects in Mekong delta. Based on estimating the electricity potential of a bundle of rice husk-fuelled bio-power development projects in Mekong delta with the capacity of 11 MW per project, assessing their CO₂ emission reductions (CERs) and CER credits, calculating and comparing their financial indices (NPV, B/C, IRR) in two cases: W/O CDM and W/CDM, the research expects to establish a rice husk energy balance flowchart for the whole Mekong delta in the year 2021 and recommend policies to use for bio-power generation the unused rice husk that is dumped and discharged from local paddy milling centers into rivers and canals, as well as, to put forward a safe and environmentally friendly solution to minimize thoroughly the current serious pollution of rivers and canals in Mekong delta with the increasing unused rice husk quantity in the context is where the sea level rise phenomenon is the strongest in the world .

Nowadays, the uses of laser and optics in the medical areas are extremely vivid, especially low-level laser therapy. The light with the wavelength of 633 nm to 1200 nm could penetrate and propagate deep in biological tissue. To develop the low-level laser therapy device, optimizing light delivery is critical to accurately stimulate the biological effects inside the biological tissue. Nevertheless, each form of the tissues at each zone on the body had various refractive optic, absorption, scattering, and anisotropy coefficients. This paper describes the simulation results of low-level laser propagation from skin surface at the lower spine, the knee, the femur and the prostate gland with four wavelengths (633 nm, 780 nm, 850 nm, and 940 nm) by the Monte Carlo method. These simulation results are the base for developing the low-level laser therapy device, that could be used in clinical for treating the fracture, knee osteoarthritis, spinal degeneration, and benign prostatic hypertrophy.

The pandemic SARS-CoV-2 continues to evolve to give rise to variants of concern that can escape vaccine-induced immunity. As such, more effective vaccines are urgently needed. In this study, we evaluated virus-like particle (VLP) as a vaccine platform for SAS-CoV-2. The spike, envelope, and membrane proteins of the SARS-CoV-2 Wuhan strain were expressed by a single recombinant baculovirus BacMam and assembled into VLPs in cell culture. The morphology and size of the SARS-CoV-2 VLP as shown by transmission electron microscopy was found to be similar to the authentic SARS-CoV-2 virus particle. In a mouse trial, two intramuscular immunizations of the VLP BacMam with no adjuvant elicited spike-specific binding antibodies in both sera and bronchoalveolar lavage fluids. Importantly, BacMam VLP -vaccinated mouse sera showed neutralization activity against SARS-CoV-2 spike pseudotyped lentivirus. Our results indicated that the SARS-CoV-2 VLP BacMam stimulated spike-specific immune responses with neutralization activity.

Curcuma longa is only a rich source of curcumin (1) and its major analogues demethoxycurcumin (2) and bisdemethoxycurcumin (3) among the Curcuma species. The content ratio of these three curcumins in turmeric is depended on the varieties and growing environment. Recently, curcumin (1) has been reported as potential inhibitor of hepatic and enzymatic protein tyrosine phosphatase 1B (PTP1B) and its related disorders such as hypertriglyceridemia, hyperlipidemia and liver steatosis. Thus, we further purified curcumins (1–3) from C. longa and investigated their inhibitory effects against PTP1B and α-glucosidase enzymes. As the result, curcumins (1–3) exhibited potential PTP1B inhibition with IC50 values of 37.8 ± 1.4, 45.3 ± 0.7, and 72.6 ± 1.1 μM, respectively, and α-glucosidase inhibition with similar manner (IC50 values of 78.2 ± 0.2, 82.4 ± 0.6, and 90.6 ± 1.0 μM, respectively). These results reveal a key role of methoxylation in the variation of PTP1B and α-glucosidase inhibitory activity for these curcumins. In addition, density functional theory (DFT) was used accompanying with molecular docking (MD) to analyze the ligand stability and the interaction of curcumins (1–3) with PTP1B and glucoside hydrolase proteins. Assay-based results and the MD data obtained are highly correlation suggesting that the deterioration of the enzyme activity caused by the distortion of structural conformation of PTP1B and glucoside hydrolase may be related to the arrangement of amino acids in protein structure. This reported for the first time that inhibitory effects of curcumins (1–3) against PTP1B and glucoside hydrolase have been examined in vitro and in silico as well.

We have repeated the experiment performed recently by Krasznahorkay et al., (Phys. Rev. Lett. 116, 042501 (2016)), which may indicate a new particle called X17 in the literature. In order to get a reliable, and independent result, we used a different type of electron-positron pair spectrometer which have a more simple acceptance/efficiency as a function of the correlation angle, but the other conditions of the experiment were very similar to the published ones. We could confirm the presence of the anomaly measured at the Ex=18.15 MeV resonance, and also confirm their absence at the Ex=17.6 MeV resonance, and at Ep= 800 keV off resonance energies.

Modern agriculture demands for comprehensive information about the plant itself. Conventional chemistry-based analytical methods - due to their low throughput and high associated cost - are no longer capable of providing these data. In recent years, remote reflectance-based characterization has developed as one of the most promising solutions for rapid assessments for plant attributes. However, in many cases, expensive equipment is required because accurate quantifications need assessment of the full reflectance spectrum. We examined the versatility of visible colour sensors as reflectance measuring devices for biological / biochemical quantifications on sweet basil (Ocimum basilicum). Our results indicate for the wide potential of spectral colour sensors for quantitative determination of leaf phenolic compounds, flavonoids in particular, and non-invasive plant phenotyping in agricultural applications by low-cost sensors.

In this paper, the classical least square (CLS) method with molecular absorption spectrophotometric measurement was used to determine simultaneously of paracetamol (PAR), ibuprofen (IBU), and caffeine (CAF) in tablets. The absorbance spectra of the standard solutions and samples were measured over a wavelength from 220 to 300 nm with a 0.5 nm step. The concentration of PAR, IBU and CAF in the sample solutions were calculated by using a program called CLS-Excel written in Microsoft Excel 2016 and Visual Basic for Applications (VBA). The method and CLS-Excel program were tested on mixed standard laboratory samples with different PAR, IBU, and CAF concentration ratios, and they showed small errors and satisfying repeatability. An analytical procedure for tablets containing PAR, IBU, and CAF was developed. The reliability of the procedure was proved via the recovery and repeatability of the analysis results with an actual tablet sample and comparing the mean contents of active substances in the tablets obtained from the analytical procedure with the from the HPLC method. The procedure is simple with a reduced cost compared with the HPLC standard method.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by the lack of estrogen receptors (ER), progesterone receptors, and HER-2 receptors. Thus, TNBC tumours do not benefit from the current therapies targeting ER or HER-2. Therefore, there is an urgent need to develop novel treatment for this subtype of breast cancer. Marijuana is a common name given to Cannabis plants, a group of plants in the Cannabis genus of the Cannabaceae family. Cannabis plants are among the oldest cultivated crops, traced back at least 12,000 years and are well known for their multi-purpose usage, including medicinal purposes. The main active compounds extracted from Cannabis plants are 21-carbon-containing terpenophenolics, which are referred to as phytocannabinoids. Of these, the tetrahydrocannabinol (THC) group contains highly potent cannabinoids, including delta-9-tetrahydrocannabinol (∆9-THC) and delta-8-tetrahydrocannabinol (∆8-THC), which are the most abundant THCs and are largely responsible for psychological and physiological effects of marijuana. The use of Cannabis plants for medicinal purposes was first recorded in 2337 BC in China, where Cannabis plants were used to treat pains, rheumatism, and gout. Recently, several cannabinoids have been approved for a number of treatments, one of which is the treatment of nausea and vomiting caused by chemotherapy in cancer patients. Furthermore, increasing evidence shows that cannabinoids not only attenuate side effects due to cancer treatment, but might also potentially possess direct antitumor effects in several cancer types, including breast cancer. However, anti-tumour activity of marijuana has been variable in different studies and even promoted tumour growth in some cases. In addition, the mechanisms of cannabinoid action in cancer remain unclear. This review summarizes evidence about the mixed actions of cannabinoids in cancer in general and triple-negative breast cancer in particular.

Emergency control load-shedding is a key solution to prevent blackouts in the power system. This paper proposed a new model of emergency controls load shedding based on the fast identification of the unstable state of the power system. K-means clustering algorithm divided the instability mode into the clusters. The results of analysis of this cluster were used as the basis for classification control. Building load shedding strategies is consisted of the pre-designed rules based on AHP algorithm. When the recognition of the power system “instability” is detected, the signal of load shedding control is triggered immediately, therefore the decision time is greatly shortened comparing to the traditional methods. The effectiveness of the proposed method was tested on the IEEE 39-bus to overcome the limitations of the last traditional methods.

This study examines the Willingness-To-Pay (WTP) of consumers and the determinants of eco-labeling for the organic cocoa powder produced in the Dong Nai UNESCO Biosphere Reserve (DNBR), Southern Vietnam. Eco-labels are designed according to Tiers of eco-labeling for biosphere reserves (BR) introduced by UNESCO include BR Destination (Tier 1), BR Quality Label (Tier 2), and Professional Certification Label (Tier 3). Questionnaires are delivered to 203 customers in the DNBR and nearby places, such as Dong Nai and HCMC. This study employs a hybrid approach using descriptive statistics, ANOVA test, and Partial Least Squares Structural Equation Model (PLS-SEM). The results indicate that gender and educational level have a positive effect on consumers' preferences. Customers are willing to pay more for cocoa powder with an eco-label than one with an organic label. Perceived food safety and product knowledge lower customers’ WTP, whereas agricultural environment and pricing concerns increase it. Tier 2 is suggested for labeling cocoa powder in the DNBR. The DNBR Management Board, together with the federal and provincial governments, should all follow a similar certification process. Increased eco-label awareness is crucial for the future of environmentally responsible shopping and responsible business practices.

This paper investigates experiences of how local cultural norms and social capital influenced the disaster relief process following the 2011 GEJET disaster. To underline social capital’s multiple‐influence aspects, this paper draws from field research that focuses on collective and individual social capital in disaster relief to systematise the findings according to the social capital framework. The paper uncovers new effects of individual social capital and collective capital in a mega-disaster context, thus contributing to pushing the research agenda toward a more critical appraisal of individual social capital and collective capital. We contribute to the nascent but growing literature that clarifies the relationship between social capital and disaster resilience. A qualitative study was conducted to capture the essence of their shared experiences. Findings revealed three main themes capturing the essence of the research participants’ disaster experiences. First, disaster response relied mostly on locally driven relief due to a clear understanding of the local culture. Second, there was an urgency to establish a sense of normalcy by providing quality supplies that would help survivors. Third, Japanese gender roles and expectations were reflected in the disaster relief process. The themes establish a wide array of lived experiences that are important to reflect on the role social capital plays in the policy-making and needs assessment processes in post-disaster relief and response efforts. The findings provide insights for integrating social dimensions into a humanitarian aid culture as citizens work towards a sense of normalcy.

The Cauto River Basin (CRB), the heartland of Cuban agriculture, has been hit hard by drought and water shortage. In response to this pressing issue, this study provides a comprehensive as-sessment of water supply, demand and balance within the Cauto River Basin, considering the baseline and projected socio-economic and climatic conditions by coupling of SWAT and WEAP models. The obtained results reveal that the annual flow in the CRB is projected to slightly de-crease (2.5%) in which the reduction in the rainy season (3.1%) is higher than that in the dry sea-son (1.3%). Total water demand in the baseline scenario is around 1.194 billion m3, dominated by agriculture (96%), with rice crop requiring nearly half. For the future scenario of 2050, the study shows a 16.6% surge in demand to 1.394 billion m3, driven by climate change and agricultural expansion. However, domestic use decreases by 10% due to population reduction. Water deficit in the future is projected to increase by 52% from 172.4 to 262.7 million m3 due to rising water demand and declining water supply. This study shows that integrating a hydrological model into a water allocation model is a promising approach to estimate the water supply, demand and balance, which is a crucial component of water resources management.

This study aimed to evaluate the effect of incorporating linseed oil and fish oil in the diet on intake, ruminal fermentation milk yield and milk fatty acid profile in dairy goats. Four crossbred Saanen lactating goats in mid-lactation and milking 1.30±0.28 g/day were used in a 4 × 4 Latin square design. The basal diet consisted of concentrate and Para grass (C:F 40:60). Treatments included the basal diet without oil supplement (Ctrl) or with 2.5% linseed oil (LO2.5), 2.5% linseed oil and fish oil (3:2, w/w, LFO2.5), and 4.16% linseed oil and fish oil (3:2, w/w, LFO4.16). Diets had no effect on intake, milk yield, milk composition and ruminal fermentation patterns (P > 0.05). The lower (P < 0.05) proportions of C10:0-C14:0 in milk fat were observed with LFO4.16 compared with Ctrl. Compared with the Ctrl and linseed oil added alone, feeding LFO4.16 resulted in a 589% and 303% increase in C18:1 t11, respectively. Milk c9,t11 CLA was markedly increased with the LFO4.16 diet, accounting for 4.53 and 2.94 times greater concentrations than those in Ctrl and LO2.5 diets, respectively (P < 0.01). Goats fed LFO2.5 and LFO4.16 had greater levels of C22:6n-3 (0.63% and 0.87%; P < 0.001) compared with those fed Ctrl and LO2.5 diets (0.06% and 0.08%). Overall, the combined data suggested that including 4.16% linseed oil and fish oil in the diet of dairy goats is effective for improving the concentrations of healthy fatty acids in milk, without affecting milk production.

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the appropriate combination of technologies which will enable the required reductions in such a way that they are appealing to investors. Around the globe, solar and wind power lead in terms of renewable energy deployment, while carbon capture and storage (CCS) is scaling up toward making a significant contribution to deep carbon cuts. Using Japan as a case study nation, this research proposes a linear optimization modeling approach to identify the potential contributions of renewables and CCS toward maximizing carbon reduction and identifying their economic merits over time. Results identify that the combination of these three technologies could enable a carbon dioxide emission reduction of between 55 and 67 percent in the energy sector by 2050 depending on resilience levels and CCS deployment regimes. Further reductions are likely to emerge with increased carbon pricing over time. The findings provide insights for energy system design, energy policy making and investment in carbon reducing technologies which underpin significant carbon reductions, while identifying potential regional social co-benefits.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

In the Vietnamese Mekong delta, soil quality and crop yield are steadily declining under rice monocultures with three crops per year. The objective of this study was to evaluate the medium-term effects of rotating rice with upland crops and adding organic amendments on rice yield and relate it to soil quality. A field trial with split-plot design including two factors and three replicates was carried out from 2017 to 2020, over the course of nine consecutive cropping seasons. Crop rotations and organic amendments were applied as main-plot and subplot factors, respectively. The rotations were: 1) rice–rice–rice (R–R–R), 2) soybean–rice–rice (So–R–R), and 3) sesame–rice–rice (Se–R–R), while organic amendment treatments included: i) no amendment (NO-AM); ii) compost of rice straw and cow manure (RS+CM), and iii) sugarcane compost (SGC); the composts were applied at a rate of 2.0 t ha-1. The rotation cycle started with the so-called spring–summer (SS), followed by summer–autumn (SA) and ended with the winter–spring (WS) season. Rice yield significantly (P &lt; 0.05) increased by organic amendments after nine growing seasons (2019–2020 WS), with an increment of 5.1% for RS+CM (7.07 ton/ha) and 6.1% for SGC (7.14 ton/ha). Contrary to our expectation, rotations with upland crops did not significantly increase rice yield. Rice yield significantly and positively correlated with an integrated soil quality index SQI (r = 0.86) for the topsoil (0–15 cm), but not for the subsoil (15–30 cm). The increased availability of soil nutrients (Si and marginally also P) and improved soil physical properties probably induced by organic amendments, along with other soil properties under study, cumulatively attributed to enhanced rice yield. Use of a SQI involving several soil quality indicators enables to overall quantify the importance of soil fertility for rice yield versus other factors and it provides an effective means of quantifying the integrated effect of improved management. Moreover, integrating a wide range of soil quality indicators in a SQI ensures its applicability across diverse settings, including different crop rotations and various soil types.

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods like LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: a) disease to normal sample classification, b) multiclass classification of different disease samples, and c) disease subtypes detection. Considering classification ROC-AUC, False-positive, and False-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over the state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

Wireless Sensor Networks are critical for modern applications ranging from environmental monitoring to smart city infrastructures. This research proposes a novel optimization learning and deployment strategy for Wireless Sensor Networks (WSNs) by 3D WUSN model is uniformly distributed M sensor nodes across diverse terrains, with clusters representing 20% of sensors, showcasing its ability to balance power consumption and extend network longevity, grounded in fuzzy clustering based protocol using the Fuzzy C-Means (FCM) method, called FCM-3DWUSN. Comparative analysis against established models including LEACH-C, K-Means and LEACH emphasized FCM-3DWUSN's outperformed performance, with average power consumption such as 134.990, 341.790, 143.895, and 192.984; respectively. The experimental result shows that FCM protocol in the 3D WUSN model can reduce energy consumption and improve the network lifetime to gather data and send the information.

Background: Acute hepatopancreatic necrosis disease (AHPND), is a bacterial disease of whiteleg shrimp, which has a high mortality rate (100%) and incurs economic losses. Our objective was to identify the genes which lead to cell and organ damage and investigate bioproducts to prevent and treat. Methods: Litopenaeus vannamei shrimp in Thua Thien Hue province, Vietnam were collected from an infected pond and analysed at the Institute of Biotechnology, Hue University. The PirA gene of Vibrio parahaemolyticus strain K5 was isolated and analyzed for nucleotide sequence and paired with the expression vector pQE30. The expression vector was transformed into E. coli strain M15, the PirA recombinant protein was expressed in the form of 6xHis-PirA fusion protein of about 15 kDa. PirA recombinant protein was purified and determined the PirAvp binding ratio, cloning and sequencing of PirA gene from Vibrio parahaemolyticus strain K5 causing AHPND by PCR method with specific primers and molecular weights of PirAvp and the PirAvp complex. Results: PirA gene from Vibrio parahaemolyticus strain K5 was cloned into pGEM-T easy vector (Promega, USA) and screened E. coli TOP10 colonies containing pGEM T easy/PirA recombinant plasmid on LB agar/ampicillin/IPTG/X-Gal medium. PCR showing a band of about 347 bp, matching the size of PirA gene and two nucleotide sequences (BamHI and HindIII). The results showed that PirA gene has a length of 336 bp and similar to PirA gene on GenBank (Code: KU556825.1). The results of protein extracted from E. coli M15 recombinant cells and 6xHis-PirA target protein was collected in elution fractions from EF2 to EF6, showed that the concentration of 6xHis-PirA protein and EF3 elution fraction collected a highest protein concentration (1,586.54 µg/ml). Conclusions: The purified PirA recombinant protein will provide materials for development research to create biological products to prevent and treat AHPND.

This article conducts a bibliometric analysis focusing on the influence of globalization and technology in the academic context. Previous studies have focused on analyzing the relationship between the number of research citations and international collaboration, but no study has explored it in the scope of consumer behavior and retail. Leveraging the Scopus database, the research analyzes a significant corpus of 2,111 documents from 1971 to 2022. The findings reveal a distinct surge in academic output related to the topic of technology, reflecting a growing interest in understanding the interaction between technological factors and consumer behavior. The study also explores trends in international collaboration in publications as well as its impact on citation counts.

Artificial Intelligence innovations, such as chatbots and specialized education suggestion tools, provide potential interactive and on-demand pedagogical engagement between non-Christians and Christians with Christianity. However, there is little empirical research on the readiness, acceptance, and adoption of A.I.'s involvement in religious education in a secular state, such as Vietnam. This research addresses the literature gap by providing an entrepreneurial analysis and customer perspectives on the ideas of A.I.'s involvement in religious education. Specifically, the study explores whether the Vietnamese across different ages and religious orientations accept and have enough skills to adopt A.I.'s religious education innovation. The interview sample is 32 participants, selected based on their religious orientation (Christians & Non-Christians) and age (Generation X, Generation Y & Generation Z). Most respondents are open to A.I. application in religious education except for the Church's personnel. However, only gen Z generations are fully prepared to adopt this innovation. Theoretically, the research customizes the Unified Theory of Acceptance and Use of Technology model into religious innovation context. Practically, this research acts as market research on the demand for A.I.'s religious innovation in Vietnam, an insight for future religious tech entrepreneurs.

Osteoclasts (OCs) are the multinucleated, bone-resorbing giant cells originally differentiated from a monocyte/macrophage lineage. OC differentiation and maturation, also called osteoclastogenesis, are strictly regulated by a variety of signaling pathways primarily induced by the interactions of two prerequisite cytokines, macrophage colony-stimulating factor (M-CSF) and the receptor activator of nuclear factor-κB ligand (RANKL) to their respective surface receptors, c-fms and RANK, in OC precursors (pre-OCs). Rab11 has emerged as the spatiotemporal regulators of intracellular vesicular transport in the endosomal recycling system; however, how it regulates osteoclastogenesis is incompletely understood. OC-triggered bone resorption is best characterized to be immensely dependent upon lysosomal function, the intracellular acidic organelles containing more than 50 acid hydrolases secreted into bone matrix microenvironment (BME). On the contrary, it is little known about the lysosomal function on modulating the turnover of c-fms and RANK surface receptors via Rab GTPase-mediated vesicular transport, thereby dictating osteoclastogenesis. In this review, I briefly describe the mechanism underlying lysosome-induced osteoclastogenesis via the Rab11-mediated modulation of the surface receptors in OCs.

Initial periods of adsorption kinetics play an important role in estimating the initial adsorption rate and rate constant of adsorption process. Several adsorption processes rapidly occur, and the experimental data of adsorption kinetics under the initial periods can contain potential errors. The pseudo-second-order (PSO) kinetic model has been popularly applied in the field of adsorption. The use of the nonlinear optimization method to obtain the parameters of the PSO model can minimize error functions during modelling compared to the linear method. However, the nonlinear method has limitations in that it cannot directly recognize potential errors in the experimental points of time-dependent adsorption, especially under the initial periods. In this study, for the first time, the different linear types (Types 1–6) of the PSO model are applied to discover the error points under the initial periods. The result indicated that the fitting method using its linear equations (Types 2–5) is really helpful for identifying the error (doubtful) experimental points from the initial periods of adsorption kinetics. The imprecise points lead to low adjusted R2 (adj-R2), high reduced χ2 (red-χ2), and high Bayesian information criterion (BIC) values. After removing those points, the experimental data were adequately fitted with the PSO model. statistical analyses demonstrated that the nonlinear method must be used for modelling the PSO model because its red-χ2 and BIC were lower than the linear method. Type 1 has been extensively applied in the literature because of its very high adj-R2 (0.9999) and its excellent fitting to experimental points. However, its application should be limited because the potential errors from experimental points were not identified by this type. For comparison, the other kinetic models (i.e., pseudo-first-order, pseudo-nth-order, Avrami, and Elovich) are applied. The modelling result using the nonlinear forms of those models indicated that the fault experimental points from the initial periods were not detected.

Global agriculture has been forced to increase food production to feed the growing human population, while confronting various environmental obstacles such as global warming, resistance to pathogens, and constraints on arable land caused by soil salinity, drought, rising sea level, saltwater intrusion, and urbanization. Regarding abiotic stresses, salinity is a worldwide problem for agricultural production. Many efforts, therefore, have been made to cope with the environmental challenges, however, the progress of salinization, which is mainly caused and accelerated by anthropogenic activities, is likely faster than our progress in finding ways to deal with this problem. In addition, drought represents a global threat to the production of major crops. In addition, pests and pathogens cause significant crop losses and diminish global food security. Among the various strategies that have been investigated and applied in plant science, antimicrobial peptides derived from plants have caught widespread attention from scientists since these peptides exhibit beneficial biological activities. In agricultural science, there have been reports on the roles of antimicrobial peptides with active properties against biotic and abiotic stresses. Non-specific plant lipid transfer proteins, thionins, systemins, defensins, cyclotides, and heveins-like antimicrobial peptides are common antimicrobial peptides that have been found to be involved in the defense system against fungi and insect pests. Based on their potential ability to protect crops from pests, bacteria, and pathogenic fungi, the use of antimicrobial peptide genes in creating transgenic plants has been largely conducted during the last decades, and these studies have obtained positive results against the growth of fungi and bacteria. This review will focus on the latest progress in studies of antimicrobial peptides related to biotic and abiotic stress tolerance in plants. We will also update the current progress in the development of antimicrobial peptide-based transgenic crops.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

The Ca Mau Peninsula situated in the Mekong Delta of Vietnam, features a low-lying terrain. Over recent years, the region has encountered the adverse impacts of climate change, leading to both land subsidence and rising sea levels. In this study, we assessed the land subsidence susceptibility in the Ca Mau peninsula utilizing three Boosting machine learning models: AdaBoost, Gradient Boosting, and Extremely Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), Groundwater levels, distance to roads, Digital Terrain Model (DTM), normalized vegetation index (NDVI), geology, soil composition, and proximity to rivers and streams. A dataset comprising 1950 subsidence sample points was employed for the model, with 1910 points obtained from the PSInSAR Radar method, and the remaining points derived from leveling measurements. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the ROC curve, Area under the curve (AUC), Specificity, Sensitivity, and Overall Accuracy. The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC above 0.9 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau peninsula.

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement; the aim being to optimise the efficacy of robotic movements in a defined operational environment. For example, robots have been employed in many domains including: cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then, was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.

This study was conducted to investigate the status of regional linkage in tourism development in in the Midlands and Northern Mountains of Vietnam. The data was collected from a survey of 755 people, including officials from State management bodies in charge of tourism, officials and staffs at tourism resorts, tourism firms, tourism scientists and tourists. In addition, we conducted 10 group discussions, interviewed 30 State tourism agency officials and tourism firms in the Midland and Mountainous provinces of Vietnam. The results show that tourism development in Vietnam in general and the Northwest region in particular is extremely fragmented, not yet forming a regional linkage; Regional and national tourism development programs are just formalistic. The main cause of the situation is the limited regional integration policy in Vietnam, the lack of appropriate regional governance mechanisms and inactive participation of the private sector in regional integration. Based on the findings, we propose a tourism sector linkage model; besides, policy implications are given for fulfilling the linkage policy in the Midlands and Northern Mountains area.

The emergence of the Covid-19 pandemic has created both negative and positive changes, including implementing the circular economy across the globe. This Systematic Review follows the PRISMA statement and employs the Text Mining (Voyant Tools) technique to visualize and analyze the impacts of the Covid-19 on three aspects of the circular economy: economic, social, and environmental. The research employs Latent Dirichlet Allocation (LDA) to identify five major topics: (1) Shortage of medical equipment but high medical waste during Covid-19 due to the high demand in healthcare; (2) The long term negative impacts of lockdown on economic and social activities because of Covid-19 pandemic; (3) The reports on impacts of Covid-19 pandemic on the manufacturing globally, and their coping strategies and new opportunities; (4) The impacts of international restriction on the tourism, trade, shipping, and aviation industry, causing billion-dollar losses; (5) The reduction of pollution with health environment improvements with example cases from China and EU. The research identifies current literature gaps in the circular economy and Covid- 19 topics and encourages the application of text mining tools into researching to stimulate the research process and assist in communicating with the public.

In this study, two heterogeneous fluorescence immunoassays using CdSe/ZnS quantum dot (QD) to label anti-progesterone antibody (P4Ab) for the determination of progesterone (P4) were performed in the wells of a 96-well microtiter plate. First, P4Ab was conjugated to hydrophilic CdSe/ZnS QDs via ethyl-3-(dimethylaminopropyl) carbodiimide(EDC)-N-hydroxysuccinimide) chemistry(NHS) (QDs-P4Ab conjugates). The QDs-P4Ab conjugate was employed as a second antibody in a sandwich assay, where the P4Ab was immobilized onto the 3-aminopropyltrimethoxysilane (APTMS) sol-gel membrane of the wells of a 96-well microtiter plate, and P4 was bound between the immobilized P4Ab and the QDs-P4Ab conjugate. In this assay, the fluorescence intensity of the QDs increased with increasing P4 concentrations. This assay had a detection limit of 553.9 pg/ml and a sensitivity of 18,251.96 pg/ml with a linear range of 2,184.6 – 117,082 pg/ml. In the direct binding assay, P4 was directly bound to the QDs-P4Ab conjugates immobilized onto the APTMS sol-gel membrane of the wells of a 96-well microtiter plate. In this direct binding assay the fluorescence intensity of the QDs decreased with increasing P4 concentrations, and this assay had a linear range of 28.95 – 26,607.7 pg/ml with a detection limit of 3.32 pg/ml and a sensitivity of 987.24 pg/ml. These fluorescence immunoassays have been successfully applied for the determination of P4 in real human serum, and the results were well correlated with those of a certified radioimmunoassay (RIA) method.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

This paper introduces an advanced method that integrates contingent valuation and machine learning (CVML) to estimate residents’ demand for reducing or mitigating environmental pollutions and climate change. To be precise, CVML is an innovative hybrid machine-learning model, and it can leverage a limited amount of survey data for prediction and data enrichment purposes. The model comprises of two interconnected modules: Module I, an unsupervised learning algorithm, and Module II, a supervised learning algorithm. Module I is responsible for grouping the data into groups based on common characteristics, thereby grouping the corresponding dependent variable, whereas Module II is in charge of demonstrating the ability to predict and the capacity to appropriately assign new samples to their respective category based on input attributes. Take a survey on the topic of air pollution in Hanoi in 2019 as an example, we found that CVML can predict households’ willingness– to– pay for polluted air mitigation at a high degree of accuracy (i.e., 98%). We found that CVML can help users reduce costs or save resources because it makes use of secondary data that is available on many open data sources. These findings suggest that CVML is a sound and practical method that could be widely applied in a wide range of fields, particularly environmental economics and sustainability science. In practice, CVML could be used to support decision-makers in improving the financial resources to maintain and/or further support many environmental programs in years to come.

INTRODUCTION: The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients. METHODS: The machine learning study population consisted of 2173 patients (1587 mild and non symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022. RESULTS: The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their important cut off values (from regression analysis) respectively are: 77.56 years old (the mild-moderate group), 5.53 (the mild-moderate group) and 10.51 (the moderate-severe group), 84.80% (the mild-moderate group) and 87.74%(the moderate-severe group), 11.77G/L (the moderate-severe group), 29.73g/L (the moderate-severe group), 7.46mg/dL (the mild-moderate group), 6.32% (the moderate-severe group). Their significant (p<0.0001) R score correlation with the severity of Covid19, are: 0.44, 0.52 and 0.52, 0.33 and 0.44, 0.42, -0.43, 0.40, -0.41. Their significant risk ratio (p<0.00001) from the meta-analysis, respectively are: 4.19 [3.58-4.95], 3.29 [2.76-3.92] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (p<0.00001), 0.34 [0.2743;0.4210]. The pair ALT – Leucocytes and Transferrin – Anion Chloride get the most important correlation shift. ALT – Leucocytes show the important negative link (R=-1, p<0.00001) in the mild group to the significant positive correlation in the moderate group (R=1, p<0.00001). Transferrin–anion Chloride has an important positive association (R=1, p<0.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, p<0.00001). The network map and HCA show that in the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils. CONCLUSIONS: Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the whole picture of all the preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management

Burn injuries have profound implications for aesthetics, functionality, and patient survival. Umbilical cord lining mesenchymal stem cells (SCL-MSCs) have emerged as a potential therapeutic approach for burn wounds due to their easy isolation, favorable differentiation capabilities, and low immunogenicity. This study aimed to evaluate the growth characteristics of SCL-MSCs and their effectiveness in treating burn wounds. Mechanical isolation was employed to extract mesenchymal stem cells from the umbilical cord lining membrane. The isolated cells underwent characterization, including analysis of HLA-E, G, and DR expression, and assessment of immunogenicity against rabbit antigens using ELISA. A rabbit model was utilized to administer burn treatment. SCL-MSCs exhibited typical morphology of mesenchymal stem cells and demonstrated robust proliferation. These cells expressed CD90, HLA-E, and HLA-G markers while lacking HLA-DR expression. Intact cells and supercrushed cell fluid displayed minimal and unstable immunogenicity. SCL-MSCs exhibited remarkable differentiation potential toward fibroblast lineages, with differentiated fibroblasts showing elevated collagen production and efficient sheet formation. In the experimental rabbit model, burn wounds covered with SCL-MSC tegaderm material plates exhibited superior healing compared to controls. The stem cell material plate area demonstrated reduced inflammatory cell counts (P &lt; 0.01) and increased fibroblast count, neovascularization, and mitotic index (P &lt; 0.01). These findings highlight the potential of SCL-MSCs as a valuable resource for burn wound research and treatment.

In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mEDI), melanopic daylight (D65) efficacy ratio (mDER) and 4 other parameters. Due to the importance of mEDI and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mEDI calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model has reached ± 3.3% after an appropriate matrixing process and proper illumination characterization combined with the successful mDER calculation model. This result opens the potential of low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate the non-visual effective parameter mEDI using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other researches.

In recent years, drones have been used in a wide range of fields such as agriculture, transportation of goods, and security. Drones equipped with communication facilities are expected to play an active role as base stations in areas where ground base stations are unavailable, such as disaster areas. In addition, asynchronous operation is being considered for local 5G in order to support all kinds of use cases. In asynchronous operation, cross-link interference between base stations is an issue. This paper attempts to reduce the interference caused by the drone network by introducing circularly polarized antennas. Numerical analyses are conducted to validate the effectiveness of the proposed system, where SIRs (Signal-to-Interference Ratio) are shown to be improved significantly as the numerical evaluation results.

Non-Orthogonal Multiple Access (NOMA) is the key technology promised to be applied in next-generation networks in the near future. In this study, we propose a multi-points cooperative relaying (MPCR) NOMA model instead of just using a relay as the previous studies. Based on the channel state information (CSI), the base station (BS) selects a closest user equipment (UE) and sends a superposed signal to this UE as a first relay node. We have assumed that there are N UEs in the network and Nth UE, which is farthest from BS, has the poorest quality signal transmitted from the BS compared other UEs. Nth UE received the forwarded signal from N-1 relaying nodes that are UEs with better signal quality. At the ith relaying node, it detect its own symbol by using successive interference cancellation (SIC) and will forward the composite signal to the next closest user, namely i+1th UE, and include an excess power which will use for energy harvesting (EH) intention at the next UE. By these, the farthest UE in network can be significantly improved. In addition, closed-form expressions of outage probability for users over both the Rayleigh and Nakagami-m fading channels are also presented. Analysis and simulation results performed by Matlab software which are presented accurately and clearly show that the effectiveness of our proposed model and this model consistents with the multi-access wireless network in future.

United States is the largest producer and the second largest exporter of broiler meat in the world. In the U.S, broiler production is largely converting to antibiotic-free programs which has caused an increase in morbidity and mortality within broiler farms. Escherichia coli and Clostridium perfringens are two important pathogenic bacteria readily found in the broiler environment and result in annual billion-dollar losses from colibacillosis, gangrenous dermatitis, and necrotic enteritis. Broiler industry is in search of non-antibiotic alternatives including novel vaccines, prebiotics, probiotics, and housing management strategies to mitigate production losses due to these diseases. This review provides an overview of the broiler industry and antibiotic free production, current challenges, and emerging research on antibiotic alternatives to reduce pathogenic microbial presence and improve bird health.

AISI 1050 steel circular pocket milling under minimum quantity lubrication (MQL) surroundings including nozzle elevation angle, lubricant flow rate, air pressure and cutting speed was studied. The machined surface quality (Ra) was evaluated for Up-milling side and Down-milling side. In this study, to obtain an manifest explanation on the pivotal effective parameters of MQL surroundings and cutting speed on the machined surface quality (Ra) through analyzing Taguchi L9 (34-2) approach and ANOVA calculation to identify those parameters, by utilizing the experimental outcomes to interpret three-dimensional (3D) surface plots and the diagrams of Signal-to-Noise ratios and major impact plots of Mean. The obtained outcomes of optimal operating parameters indicated that the pivotal effective parameters of MQL surroundings are nozzle elevation angle and lubricant flow rate on the machined surface quality. Simultaneously, a quadratic mathematical paradigm generated by using response surface design (RSD) and desirableness function evaluation were utilized to forecast the optimal values of the machined surface quality compared to Taguchi method. The results unveiled that the optimal values of the machined surface quality are adjacent to experimental attained values. The machined surface quality has been significantly improved using MQL surroundings.

The objective of this study was to describe the overall pattern of morbidity and mortality of children seen at the Thai Binh Pediatric Hospital in Vietnam, with a focus on infectious diseases. A retrospective review of hospitalisation records was conducted from 1 January 2015 to 31 December 2019. Data were obtained from a total of 113,999 records. The median age of patients was 18 months, with 83.98% of patients aged <5 years. Infectious diseases accounted for 61.0% of all cases. The most prevalent diseases were lower respiratory tract infections (32.8%), followed by gastrointestinal infections (13.3%) and confirmed influenza (5.4%). Most infections were not microbiologically documented. A total of 81.4% patients received at least one antibiotic. Most patients (97.0%) were hospitalised for less than 15 days. Regarding outcomes, 87.8% patients were discharged home with a favourable outcome. 12.0% were transferred to the Vietnam National Children’s Hospital because their condition had worsened and 0.1% died. In total, infectious diseases accounted for 40.4% of deaths, followed by neonatal disorders (34.6%). Our data serves a basis for the identification of needs for diagnostic tools and for future evaluation of the effect of the targeted implementation of such facilities. Point-of-care tests, including real-time PCR assays to identify common pathogens should be implemented for more accurate diagnosis and more appropriate antibiotic use.

Facing with errors of a product after machining was a big title in this field. That will affect to product’s quality with unpredictable situation when products is in use for some particular application such as in medical facilities. Improving the precision of machine tools by exploring temperature on multiple points and measuring differential locations of the spindle is going to be executed in this study. A temperature measurement tool and a software running on Windows platform have been developed and combining with Non-bar system in order to support analyzing the temperature rising with the changes of length of the spindle to find the compensation solution based on eccentricity and length distortion. In this study, the whole tests have done on UX300 five – axis CNC machine tools for this investigation and the errors after applying compensation function have reduced 70% at least, with respect to the errors after machining without using compensation function.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 215 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 14 April 2020, a cumulative total of 1,853,265 (118,854) infected (dead) COVID-19 cases were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify their applications in fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Vector geographic data play an important role in the natural resources and environment sector and other location information services. This is also one of the types of data where the cost to create it is relatively large because of the difficulty in surveying, collecting, and authorizing. The rapid development of the Internet has created many advantages in the distribution, exploitation, and use of vector geographic data, but it also gives rise to many problems such as duplication, redistribution, forgery, and illegal data use. The theft on the Internet is becoming more and more sophisticated and the number of violations is increasing, showing the urgent need to research and develop an effective solution to protect the copyright of vector geographic data and prevent them from being illegally collected and used. Among the major studies and solutions, digital watermarking emerges as an effective method and is an active research area for copyright protection. Towards a good solution for copyright protection of vector geographic data, our study proposes a new algorithm with three main contributions, including: (1) generating short, pseudo-random meaningful watermarks to increase robustness and to enable automated as well as visual manual verifying; (2) building a uniformly distributed mapping between the vertex coordinates and the watermark bit indexes to increase the robustness of the watermarks; and (3) integrate two types of watermarks, namely, spatial domain-based watermarking and zero-watermarking to be resistant to most common attacks on geographic vector data. The algorithm also allows working on all types of vector geographic data, including points, polylines, and polygons.

Agricultural land fires have been linked to various and adverse impacts on ecosystems, food security and the agriculture sector. Understanding the patterns and drivers of agricultural land fires is essential for effective agricultural land fire management. The key objectives of this study were to (1) analyze the temporal and spatial patterns of agricultural land fires using satellite remote sensed data, (2) assess a range of environmental conditions that could drive the occurrence of agricultural land fires, (3) determine the best model for predicting agricultural land fires and (4) determine the relative contribution of each environmental condition variable on the best predictive model. We used both univariate and multivariate regressions for the fire prediction capability of four independent environmental conditions (fuel, weather, topographic and anthropogenic). Analysis of historical satellite data revealed that agricultural land fires were more frequent than forested land fires. Our analyses also revealed that fuel condition was the most important variable for predicting agricultural land fires followed by weather, topographic and anthropogenic conditions. This study provides a novel multivariate model for predicting agricultural land fires that harbors the potential to improve agricultural land fire management and reduce fire risk within the agricultural sector.

This study aimed to evaluate the proliferation and migration of fibroblast cells derived from chronic wounds and investigate the impact of low-level laser therapy (LLLT) on their growth and migration. A total of 60 dermal samples were obtained from three sites of chronic pressure ulcers and diabetic ulcers in 20 patients. Following collection in the operating room, the dermal samples were isolated using the procedure described by Freshney RI. Fibroblasts from the wound base (Site 1) exhibited a slow proliferation rate and a decrease in proliferative capacity. Fibroblasts from wound margins (Site 2) and adjacent healing skin (Site 3) could be successfully isolated and maintained for multiple generations without any morphological changes. Fibroblasts from chronic wounds at Sites 2 and 3 showed slower proliferation rates and reduced migration capabilities compared to normal dermal fibroblasts (control samples). The fibroblast cells were exposed to LLLT at different energy levels (2.5, 3, and 3.5 J/cm2). The laser-treated groups demonstrated a significantly higher increase in cell count compared to the control sample, with the highest increase observed at an energy level of 3 J/cm2. These findings suggest that LLLT at an energy level of 3 J/cm2 could effectively enhance fibroblast migration, indicating its potential as a therapeutic intervention for chronic wounds.

Recurrent infections after root canal treatments often involve Enterococcus faecalis, a microorganism closely associated with therapy failures due to its biofilm production, survival in nutrient-deprived conditions, and antibiotic tolerance. Essential oils (EOs), which display anti-inflammatory, antimicrobial, and antibacterial properties, exhibit inhibitory effects on the growth of many microorganisms including E. faecalis. This study assessed the in vitro efficacy of combining antibiotics (Gentamicin 1.5mg/ml, Streptomycin 2.5 mg/ml, Ampicillin 5 mg/ml, and Kanamycin 2.5 mg/ml) with Cinnamon (1.25% to 5%) or Clove (25% to 50%) EOs using disk diffusion tests. Disks were treated with EOs-only, antibiotics-only, or EO-antibiotic combinations, placed on BEA agar plates, incubated for 24 hours, and zones of inhibition were measured against E. faecalis. Results showed robust growth inhibition by cinnamon and clove EOs across all tested concentrations. Furthermore, there were synergistic antimicrobial effects when gentamicin, streptomycin and kanamycin were combined with 2.5% and 50% concentrations of cinnamon and clove EOs, respectively, leading to significant growth inhibition of E. faecalis by 139% to 193% compared to using antibiotics or EOs alone. Clove EO showed positive synergism with three antibiotics (gentamicin, streptomycin, and kanamycin) whereas cinnamon EO showed synergism with two antibiotics (streptomycin and kanamycin). These findings suggest that combining cinnamon and clove EOs with aminoglycoside antibiotics can significantly reduce the expansion of E. faecalis compared to antibiotics alone. Further in vivo studies should determine the safety, efficacy, and treatment duration, with the potential to reduce antibiotic dosages and associated toxicity while preventing recurrent infections.

Bioethanol has been considered as an excellent alternative to fossil fuels since it importantly contributes to the reduced consumption of the crude oil and to the alleviation of environmental pollution [1]. Up to now, the baker yeast Saccharomyces cerevisiae is the most common eukaryotic microorganism used in ethanol production. The inability of S. cerevisiae to grow on pentoses, however, hinders its effective growth on plant biomass hydrolysates, which contain large amounts of C5 and C12 sugars. The industrial-scale bioprocessing requires high temperature bioreactors, diverse carbon sources, and the high titer production of volatile compounds [2]. These criteria indicate that the search for alternative microbes possessing useful traits that meet the required standards of bioethanol production is necessary. Compared to other yeasts, Kluyveromyces marxianus has several advantages over the others, e.g. it could grow on a broad spectrum of substrates (C5, C6 and C12 sugars) [3], tolerate to high temperature, toxin [4,5] and a wide range of pH values [6], and produce volatile short-chain ester [2]. K. marxianus also shows a high ethanol production rate at high temperature and is a Crabtree-negative species [7]. These attributes make K. marxianus a promise as an industrial host for the biosynthesis of biofuels and other valuable chemicals.

Chromophobe renal cell carcinoma (chRCC) and oncocytoma (RO) are renal tumor types originating from alpha intercalated cells of the collecting ducts of the kidney. Both tumor types have similar gross histological morphology and increased mitochondria, which leads to difficulties differentiating between these tumors, especially with core biopsy samples. This study aims to apply a machine learning approach to develop a molecular classifier based on transcriptomics data. Here we generated a meta-data set containing 62 chRCC and 45 RO gene expression arrays. Arrays were subjected to quality control steps, and genes were selected based on differential expression and ROC analysis. The final gene list was evaluated with UMAP based dimension reduction followed by density-based clustering with 95.5% accuracy. Molecular profiling by KEGG pathway analysis identified enrichment of fatty acid oxidation pathway in RO. We finally identified and validated the 30-gene signature, with an accuracy of 94.4% to distinguish chRCC from RO on UMAP analysis. Our results show that chRCC and RO have a distinct gene signature that can differentiate these tumors and complement histology for routine diagnosis of these two tumors.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging infectious disease with currently a pandemic state. Cardiac function can be involved, affecting prognosis, in addition with lung feature severity, particularly in patients with comorbidities. Since the renin angiotensin aldosterone (RAA) system may interact with SARS-Cov-2, researches are still ongoing to assess the prognostic value of RAA blockers in cardiology.

Background. To describe the characteristics of anterior mediastinal masses on conventional magnetic resonance imaging (MRI) and to assess the role of the Apparent Diffusion Coefficient (ADC) value in distinguishing benign from malignant mediastinal lesions. Methods. We conducted a retrospective cross-sectional study on 55 patients with anterior mediastinal mass who performed MRI before treatment. Biopsy and histopathological assessments were done after that. A radiologist evaluates the changes of signal intensity on these sequences: T1- weighted VIBE DIXON pre and post-contrast with Gadolinium, T2 HASTE, T2 TIRM, DWI/ADC, to determine the size, margin of the lesion, the presence of fat, cystic in it. ADCs values were calculated from the ADC maps which were constructed from b = 0 and b = 2000 Results. The study was composed of 55 patients, with 5 benign lesions and 50 malignant lesions. The ADCmean, ADCmedian, ADC10, and ADC90 in the histogram-based approach and hot-spot-ROI-based mean ADC for the malignant lesions was significantly lower than those found in benign lesions (P-value <0.05). The hot-spot-ROI-based mean ADC had the highest value in differentiation between benign and malignant mediastinal lesions, between group A (benign lesions, thymoma A, AB, B1) and group B (thymoma B2, B3 and other malignant lesions). The cut-off point of the ADC value differentiating malignant from benign mediastinal lesions was 1,17x10-3mm2/s with sensitivity of 80%, and specificity of 80%. The cut off point of the ADC value differentiating group A from group B was 0.99 x10-3mm2/s with sensitivity of 78,4%, and specificity of 88.9%. The cut-off point of the ADC value differentiating lymphoma from other malignant lesions was 0,91 x10-3mm2/s with sensitivity of 100%, and specificity of 60.5%. Conclusion. Diffusion-weighted MRI and measurement of ADC value in histogram-based approach and hot-spot-ROI-based mean ADC are very helpful in the differentiation between benign and malignant anterior mediastinal lesions.

In April 2019, a team of Keio University and Bucknell University students was assembled to participate in Ericsson Innovation Awards with a novel concept for generating renewable energy. This conceptual system consists of a vertical axis wind turbine, a crossflow marine hydrokinetic turbine, a floating platform integrated with a quadcopter system, and three to four temporary mooring lines with ship-type anchors. The proposed designed aims to offer solutions to two current problems of floating offshore wind energy: high construction cost of floating platforms and difficulties in maintenance of mooring lines. The combination of two vertical-axis turbines into a single floating platform would enable the system, namely ESwift, to extract energy from both wind and current resources. Additionally, due to the utilization of vertical axis turbines, the center of gravity of the proposed concept is significantly lower with respect to water level, compared to that of existing floating horizontal axis wind turbines, which would potentially reduce the floater's size and construction cost. Lastly, the integrated quadcopter mechanism would assist the floater in terms of stability and mobility, and enables an array of ESwifts to automatically rearrange for maximal energy generation. The authors hope that readers would find the idea described in this open access letter worth pursuing and would further develop and commercialize the ESwift concept.

Introduction: The goals of this retrospective cohort study of 129,443 persons admitted to Calgary acute care hospitals 2013-2021 were to ascertain correlations of “potentially inappropriate medi-cations” (PIMs), “potential prescribing omissions” (PPOs) and other risk factors with readmissions and mortality. Methods: Processing and analysis codes were built in Oracle Database 19c (PL/SQL), R and Excel. Results: The percentage dying during their hospital stay rose from 3.03% during the first to 7.2% during the 6th admission. The percentage dying within 6 months of discharge rose from 9.4% after the first to 24.9 after the sixth admission. Odds ratios (adjusted for age, gender and comorbidities) for readmission were the post-admission number of medications (1.16; 1.12-1.12), STOPP PIMs (1.16; 1.15-1.16); AGS Beers PIMs (1.11; 1.11-1.11) and START omissions not corrected with a prescription (1.39 (1.35-1.42). Odds ratios for mortality were post-admission number of medications (1.04; 1.04-1.05), STOPP PIMs (0.99; 0.96-1.00); AGS Beers PIMs (1.08; 1.07-1.08) and START omissions not corrected with a prescription (1.56 (1.50-1.63). START omissions corrected with a prescription correlated with a dramatic reduction in mortality (0.51; 0.49-0.53). Odds ratios for readmissions for the second through 39th admission were consistently higher if START PPOs were not corrected for the second admission (1.41; 1.36-1.46), third (1.41;1.35-1.48); fourth 1.35;1.28-1.44); fifth 1.38; 1.28-1.49); sixth (1.47;1.34-1.62) and 7th through 39th admission (1.23; 1.14-1.34). For all admissions when a pre-scription was given to correct START PPOs ORs for mortality within six months of discharge were dramatically improved (0.51; 0.49-0.53). This was also true for the second (0.52; 0.50-0.55; fourth (0.56; 0.52-0.61; fifth (0.63; 0.57-0.68); sixth (0.68; 0.61-0.76); and 7th through 39th admissions (0.71; 0.65-0.78). Conclusions: PPOs should be corrected by prescriptions and teams of family physicians, pharmacists and nurses should focus on patients’ understanding of their illnesses, medications and ability for self-care.

The fishmeal production industry is essential for providing protein for animal feed in the aquaculture sector. However, the industry faces challenges related to energy consumption and environmental sustainability. This study evaluates the energy efficiency and environmental benefits of waste heat recovery (WHR) technologies in a fishmeal production plant in Vietnam. Data were collected from the plant between 2016 and 2022, and a specific energy consumption (SEC) indicator and a comprehensive methodology were utilized. Implementing an economizer as a WHR technology resulted in a 55.5% decrease in SEC compared to the state before installation. The enhanced energy efficiency also translated to reduced energy consumption per output unit. Moreover, the economizer contributed to annual energy savings of 4,537.57 GJ/year and cost savings of $26,474.49. Additionally, carbon dioxide (CO2) emissions associated with producing one ton of fishmeal decreased by 58.37%. These findings highlight the potential for WHR technologies to improve energy efficiency and reduce the environmental footprint of fishmeal production. The study's results provide valuable insights for practitioners and policymakers in promoting energy efficiency practices and reducing environmental impact in the industry.

Neuroinflammatory responses are implicated in the pathogenesis of neurodegenerative diseases. In neurodegenerative diseases, neuroinflammatory reactions to neuronal injury are modulated by microglial cells, which are vital innate immune cells in the central nervous system. Activated microglial cells release proinflammatory cytokines, mediators, and neurotoxic factors that induce fatal neuronal injury. The present study investigated the anti-neuroinflammatory effects of cudratricusxanthone L (1), which was isolated from Cudrania tricuspidata. This compound reduced the levels of lipopolysaccharide-stimulated inflammatory mediators and cytokines, including nitric oxide, prostaglandin E2, interleukin (IL)-1β, tumor necrosis factor-α, IL-6, and IL-12. These effects suggested that cudratricusxanthone L (1) suppressed the nuclear factor-kappa B (NF-κB) signaling pathway. Specifically, cudratricusxanthone L (1) also attenuated the phosphorylation of Jun kinase and inhibited p38 mitogen-activated protein kinase (MAPK) signaling in BV2 and rat primary microglial cells. These results indicated that cudratricusxanthone L (1) effectively repressed neuroinflammatory processes in BV2 and rat primary microglial cells by inhibiting NF-κB and the MAPK signaling pathway.

The Soil and Water Assessment Tool (SWAT) is one of the most widely used hydrologic models. SWAT has been undergoing constant changes since its development. However, compartment review and testing of SWAT are comparably limited, especially the flood routing functions. In this study, the daily flood routing subroutines of different SWAT versions were reviewed and tested using a well observed segment of the Weser river located in Germany. Results show several problems with the flood routing subroutines of SWAT. The variable storage subroutine of SWAT (revision 664) does not transform the flood wave. Unphysical results could be obtained with the variable storage routing of SWAT (revision 528). The Muskingum subroutine of SWAT (revisions 664 and 528) overestimates channel evaporation (resulting in a bias of 14\% to 19% in streamflow) and underestimates transmission losses. Simulated results show that the timing and shape of the flood wave could be improved with a corrected Muskingum subroutine. Based on the results of this study, we suggest the SWAT user community to review their existing SWAT models to see how the aforementioned issues will affect their methods, findings and conclusions.

Current instrumental technology in evaluating performance of skin care creams is still rather limited and there are no industrial standard testing equipment, method and performance specifications for it. In this paper, we report our attempt in using an instrument called PhabrOmeter for this purpose. PhabrOmeter is a commercialized instrument for sensory performance evaluation of textiles, leather, paper and nonwoven and has been designated by American Association of Textile Chemists and Colorists (AATCC) as a standard test method for textiles, AATCC TM 202 in 2013. By adopting artificial skin samples treated with skin care creams, we have developed a procedure to apply this instrument for evaluation of skin care creams from sample preparation, measurement to data analysis and interpretation. The results using commercial skin care creams to demonstrate the feasibility and advantages of this instrument are provided.

The Covid-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this study, we propose a new forecasting model called the Multivariate Multilayered LSTM with Covid-19 case injection ($\proposedModel$) for improved energy forecast during the next occurrence of a similar pandemic. We utilize data from commercial buildings in Melbourne, Australia during the Covid-19 pandemic to predict energy consumption and evaluate the model's performance against commonly used methods such as LSTM, Bi-LSTM, Linear Regression, Support Vector Machine and the previously published work of Multilayered LSTM (M-LSTM). The proposed forecasting model was analyzed using the following metrics of mean percent absolute error (MPAE), normalized root mean square error (NRMSE), and $R^2$ score values. The model $\proposedModel$ demonstrates superior performance, achieving the lowest MPAE values of 0.061, 0.093, and 0.158 for data sets from 3 different buildings, respectively. Our results highlight the improved precision and accuracy of the model, providing valuable information for energy management and decision-making during the challenges posed by the occurrence of a pandemic like Covid-19 in the future.

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE by 30%, RMSE by 25%, and MAPE by 20% compared to the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE by 10%, RMSE by 20%, and MAPE by 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%. These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

We estimated the risk of blood clots after COVID-19 vaccination accounting for the roles of vaccination in reducing the infection rate, and indirectly lowering the risk of blood clots caused by infection. A self-controlled case series method was used to examine the association between blood clots and COVID-19 vaccination. To deal with the bias due to under-reported infection among non-hospitalized subjects, a case-control study was used to compare the risk of blood clots in infected subjects to control subjects who were hospitalized due to physical injury. We found increased risks of blood clots after vaccination (incidence rate ratio is 1.13, 95% confidence interval (CI): [1.03, 1.24] after the first dose and 1.23, 95% CI: [1.13,1.34] after the second dose). Furthermore, vaccination attenuated the increased risk of blood clots associated with infection (odds ratio (OR) = 2.16, 95% CI: [1.93, 2.42] in unvaccinated versus OR=1.46, 95% CI: [1.25, 1.70] in vaccinated). A causal model was used to explain and weigh up possible risk factors for blood clots after COVID-19 vaccination. After accounting for vaccine efficacy against infection and the protection against infection-associated blood clots, receiving the COVID-19 vaccines decreases the risk of blood clots, especially during high infection rate period.

Land use/land cover (LULC) and climate changes are two main factors directly affecting hydrologic conditions. However, very few studies in Vietnam have investigated changes in hydrological process under the impact of climate and land use changes on a basin scale. The objective of this study is to assess the individual and combined impacts of land use and climate changes on hydrological processes for the Nam Rom river basin, Northwestern Viet Nam using Remote Sensing (RS) and Soil and Water Assessment Tools (SWAT) model. SWAT model was used for hydrological process simulation. Results indicated that SWAT proved to be a powerful tool in simulating the impacts of land use and climate change on catchment hydrology. The change in historical land use between 1992 and 2015 strongly contributed to increasing hydrological processes (ET, percolation, ground water, and water yield), whereas, climate change led to significant decrease of all hydrological components. The combination of land use and climate changes significantly reduced surface runoff (-16.9%), ground water (-5.7%), water yield (-9.2%), and sediment load (-4.9%). Overall climatic changes had more significant effect on hydrological components than land use changes in the Nam Rom river basin during the 1992–2015. Under impacts of projected land use and climate change scenarios in 2030 on hydrological process of the upper Nam Rom river basin indicate that ET and surface flow are more sensitive to the changes in land use and climate in the future. In conclusion, the findings of this study will basic knowledge of the effects of climate and land-use changes on the hydrology for future development of integrated land use and water management practices in Nam Rom river basin.

Allanite minerals are the principal host of REEs in the Sin Quyen, Iron Oxide Copper Gold (IOCG) type deposit. The studied allanites have concentrations of: REE (14-27 wt%), Ca (9-16 wt%), Al (8-19 wt%), Si (26-34 wt%) and Fe (12-21 wt%). Two populations of allanite are documented, the first is texturally older probably related to the Ca-K alteration (second stage of crystallization). This population has higher REE concentrations ranging from 20 to 27 wt%, and the second population texturally younger has lower total REE concentration ranging from 14 to 19.9 wt%, which occur as a rim surrounding the older and likely arose during the K alteration with Cu-Au mineralization (third crystallization). Differences between the two allanite populations are documented by both optical properties and analysis of their chemical composition. The last parameter indicate that the studied allanites belong to the Ce-La-ferriallanite family, with low HREE with an average of 0.21 wt.%. Temperature 355ºC which was calculated using value of δ34S isotopes is interpreted as a temperature of the second crystallization stage of allanite. The pressure of crystallization solution was calculated and is ranging from 0.98 to 5.88 MPa.

This study assesses the biomethane (CH4) generation and greenhouse gas (GHG) emissions resulting from municipal solid waste landfilling in Phnom Penh, Cambodia, with a focus on the impact of fugitive CH4 emissions and operation processes in four landfilling scenarios: simple dumping (S1), improved management with leachate treatment (S2), engineered landfill with flaring (S3), and engineered landfill with energy recovery (S4). The study also considered the environmental benefits of carbon sequestration and landfill gas utilization. The LandGEM and IPCC FOD models were used to calculate CH4 generation over the period of 2009-2022, and it was found that approximately 18 and 21 M kg/year of CH4 are released, respectively. The energy potential from CH4 recovery was 51–61 GWh/year. Overall GHG emissions in S2 were the highest, amounting to 409–509 M kg CO2-eq/year, while S1 had lower emissions at 397–496 M kg CO2-eq/year. Flaring captured CH4 in S3 could reduce GHG emissions by at least 55%, and using captured CH4 for electricity production in S4 could mitigate at least 83% of GHG emissions. Electricity recovery (S4) could avoid significant amounts of GHG emissions (˗104.5 kg CO2-eq/tMSW). The study suggests that landfill gas-to-energy could significantly reduce GHG emissions.

Flow sensing in hostile environment is of increasing interest for applications in automotive, aerospace, and chemical and resource industries. Compared to their counterparts, thermal flow sensors are attractive candidates due to the ease of fabrication, lack of moving parts and higher sensitivity. Recently, a number of thermal flow sensor prototypes have been reported in the literature demonstrating the measurement of fluid flows under hostile conditions. This paper summarizes the concept of thermal flow sensing, operational modes and transduction mechanisms. Then, the choice of materials and their corresponding properties are presented in details. The paper also reports recent progress in the development of thermal flow sensors for harsh environment. In addition, the issues and considerations in packaging are reviewed. Finally, we conclude the review with the future prospects.

A robust explicit model predictive control (EMPC)-based frequency adaptive grid voltage sensorless control is developed for a grid-connected inverter (GCI) via a linear matrix inequality (LMI) approach under the model parameter uncertainties and non-ideal grid environment. The control scheme is achieved by an enhanced prediction model rather than the conventional prediction model provided by fixed parameters to maintain the quality of grid currents injected into the grid when the model parameters are varied. Furthermore, an LMI-based observer is integrated with the disturbance observer to improve the reference tracking performance and reject disturbances. The proposed observer is employed for the grid frequency adaptive control without need of grid voltage sensors. The proposed current controller and observer employ the LMI scheme to maintain a stable and robust operation of the GCI. The discrete-time frequency response and pole-zero map analyses are used to investigate system stability and robustness against parameter uncertainties. Comprehensive simulation, experimental results and analyses clearly demonstrate the robustness of the proposed control scheme under various adverse test conditions with unexpected grid and system uncertainties.

Despite the recognized benefits of fecal occult blood test (FOBT) and mammography screenings, participation in breast (BC) and colorectal cancer (CRC) screening programs is still suboptimal. This study investigates municipal characteristics associated with their BC/CRC screening uptake profiles among women aged 55–69 years. Using data from 308 municipalities of Flanders from 2014 to 2017, a profile for each municipality based on its BC/CRC screening uptake compared with the median screening uptake was created. Logistic regression with generalized estimating equations was used to assess the associations between municipal characteristics and BC/CRC screening uptake profiles. The overall median uptake of cancer screening was higher for CRC (57.4%) than for BC (54.6%). The following municipal characteristics were associated with worse performance in terms of only CRC, only BC, or both CRC and BC screening uptake, respectively: foreign nationality, self-employment rate, (early) retirement rate, diabetes, disabilities; (early) retirement rate; age group 65–69, foreign nationality, self-employment rate, (early) retirement rate, wage-earners, diabetes. The following municipal characteristics were associated with better performance in terms of only CRC, only BC, or both CRC and BC screening uptake respectively: residential stability, having a partner, having children, jobseeker rate, GP visits, preventive dental visits; having children, GP visits; age group 55–59, residential stability, having a partner, having children, jobseeker rate, higher education, GP visits, preventive dental visits. This study’s results regarding the interrelation between the BC and CRC screening could be used to tailor interventions to improve the participation of the target population in both programs.

This paper aims to measure the effects of delisting on stock returns for Vietnam stock market. The study employs a sample of 118 stocks that were compulsorily delisted from the market between January 2011 and December 2021. Using an event study methodology, the empirical findings confirm that the delisting has negative effects on stock returns in the Vietnam stock market. Specifically, results derived from tests show that the average abnormal return of delisted stocks continuously declines during three trading days following the announcement of delisting. Moreover, it is found that the differences in cumulative abnormal returns between post-delisting and pre-delisting periods are significantly negative for all tracking periods. Apart from the negative effect of delisting on stock abnormal returns, we also find that the impact of delisting on stock returns for smaller companies is greater than bigger companies.

Enteric protozoan pathogenic infections significantly contribute to the global burden of gastrointestinal illnesses. Their occurrence is considerable within remote and indigenous communities and regions due to reduced access to clean water and adequate sanitation. The robustness of these pathogens means requirement of harsh treatment methods such as medicinal drugs or antibiotics. However, such treatments impact the gut microbiome, and create dysbiosis, often leading to opportunistic pathogens, anti-microbial resistance, or functional gastrointestinal disorders (FGIDs) such as irritable bowel syndrome (IBS). Recent studies have shown that these impacts do not remain confined to gut, and are reflected across the gut-brain, gut-liver, and gut-lung axes, among others. Therefore, apart from the medicinal treatment, nutritional supplementation is also a key aspect of providing the recovery from this dysbiosis. Future proteins, prebiotics, probiotics, synbiotics, and food formulations offer a good solution to remedy this dysbiosis. Furthermore, the nutritional supplementation also helps to build a resilience against the opportunistic pathogens and potential future infections and disorders that may arise due to the dysbiosis. Systems biology techniques have shown to be highly effective tools to understand the biochemistry of these processes. Systems biology techniques characterises the fundamental host-pathogen interaction biochemical pathways, at various infection and recovery stages. This same mechanism also allows to track the impact of abovementioned treatment methods of gut microbiome remediation. This manuscript is organised in sections delving into system biology approaches and upcoming developments to understand (1) Infection mechanism and current global status; (2) Cross-organ impacts of dysbiosis, particularly within gut-liver and gut-lung axes; (3) Nutritional interventions. It highlights the impact of antimicrobial resistance (AMR) and Multi-drug resistance (MDR) from a perspective of protozoal infections. It also highlights the role of nutritional interventions to add resilience against the chronic problems caused by these phenomena.

In this study, a motion control problem for the vessels towed by tugboats or towing ships on the sea is considered. The towed vessels including barge ships are need to have assistance of tugboats. Combining two vessels, some work purposes in the sea or harbor area can be completed. In this study, the authors give newly developed mathematical model and control system strategy. Especially, the system model fully presenting the physical characteristics of two vessels are derived. For controlling the system effectively, it is considered that the towed vessel has no power propulsion system but the rudder is activated to improve the maneuverability. Considering the strong nonlinearities included in the vessel dynamics, the modelled system is presented by nonlinear system without linearization of nonlinear parameters. Thus, the control system for the towed vessel is designed based on the nonlinear control scheme. Exactly, the back-stepping control method is applied to its motion control. Also, the PID control method is applied for comparing with the proposed control strategy.

In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generates a magnetic field with high frequency surrounding a coil of sensor positioned at the filament's output end. The sensor's capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide.

Atomic force microscopy (AFM) has been extensively used for the nanoscale characterization of polymeric materials. The coupling of AFM with infrared spectroscope (AFM-IR) provides another advantage to the chemical analyses and thus helps to shed light upon the study of polymers. In this perspective paper, we review recent progress in the use of AFM-IR in polymer science. We describe first the principle of AFM-IR and the recent improvements to enhance its resolution. We discuss then the last progress in the use of AFM-IR as a super-resolution correlated scanned-probe IR spectroscopy for chemical characterization of polymer materials dealing with polymer composites, polymer blends, multilayers and biopolymers. To highlight the advantages of AFM-IR, we report here several results in studying crystallization of both miscible and immiscible blends as well as polymer aging. Then, we demonstrate how this novel technique can be used to determine phase separation, spherulitic structure and crystallization mechanisms at the nanoscale, which have never been achieved before. The review also discusses future trends in the use of AFM-IR in polymer materials, especially in polymer thin film investigation.

Invasive fungal infections by Candida albicans frequently cause mortality in immunocompromised patients. Neutrophils are particularly important for fungal clearance at the early phase of infections, yet little has been known regarding which surface receptor controls neutrophil phagocytic activities during systemic C. albicans infection. CD137, which is encoded by Tnfrsf9, belongs to the tumor necrosis receptor superfamily and has been shown to regulate neutrophils in Gram-positive bacterial infection. Here, we used genetic and immunological tools to probe the involvement of CD137 signaling in innate defense mechanisms against systemic C. albicans infection. We first found that Tnfrsf9-/- mice were susceptible to C. albicans infection, whereas injection of anti-CD137 agonistic antibody protected the host from infection, suggesting that CD137 signaling is indispensable for innate immunity against C. albicans infection. Priming of isolated neutrophils with anti-CD137 antibody promoted their phagocytic and fungicidal activities through phospholipase C. In addition, injection of anti-CD137 antibody significantly augmented restriction of fungal growth in Tnfrsf9-/- mice that received WT neutrophils. In conclusion, our results demonstrate that CD137 signaling contributes to defense mechanisms against systemic C. albicans infection by promoting rapid fungal clearance whereby harmful immunopathology-induced tissue injuries are minimalized.

Background: The robustness of sero-surveillence has delineated the high burden of SARS-CoV-2 infection in children; however, these existing data showed wide variation. This study aimed to identify the serostatus of antibodies against SARS-CoV-2 and associated factors among children following the fourth pandemic wave in Vietnam. Methods: A cross-sectional study was conducted at Vietnam National Children’s Hospital (VNCH) between March 13 and April 3, 2022. 4,032 eligible children seeking medical care for any medical condition not related to acute Covid-19 infections was tested for IgG SARS-CoV-2 Antibodies by ADVIA Centaur® SARS-CoV-2 IgG (sCOVG) assay using the residuals of routine blood samples. Results: The median age of enrolled children was 39 (IQR=14-82) months. The overall seropositive prevalence was 59.2%, and the median antibody titer was 4.78 [IQR 2.38-9.57] UI/mL. The risk of seropositivity and the median antibody titer was not related to gender (58.6% versus 60.1%, 4.9 versus 4.6 UI/mL, all p&gt;0.05). Among age groups, the highest seroprevalence was reported in the children aged 13 to &lt;36 months old. Children aged ≤12 months were likely to be seropositive compared to children aged 36 to &lt;60 months (59.2% versus 57.5%, p=0.49) and those aged ≥144 months (59.2% versus 65.5%, p=0.16). Children aged ≥144 months exhibited a significantly higher titer of protective COVID-19 antibodies than other age groups (p &lt;0.001). In multivariate logistic regression, we observed independent factors associated with SARS-CoV-2 seropositivity, including the age 13 to &lt;36 months (OR=1.29, 95%CI=1.06-1.56, p=0.01), 60 to &lt;144 months (OR=79, 95%CI=0.67-0.95, p=0.01), ≥144 months (OR=1.84, 95%CI=1.21-2.8, p=0.005), the presence of infected household members (OR=2.36, 95%CI=2.06–2.70, p&lt;0.001), participants from Hanoi (OR=1.54, 95%CI=1.34-1.77, p&lt;0.001), underlying conditions (OR=0.71, 95%CI=0.60-0.85, p&lt;=0.001), and using corticosteroids or immunosuppressants (OR=0.64, 95%CI=0.48-0.86, p=0.003). Conclusions: This study highlights a high seroprevalence of antibodies against SARS-CoV-2 among children seeking medical care for non-COVID-19-related conditions in a tertiary children’s hospital in Hanoi, Vietnam. In the context of reopening in-person schools and future emerged COVID-19 variants, this point will also be a key message about the necessity of “rush-out” immunization coverage for children, especially those under the age of three years.

With the introduction of HTTP/3, whose transport is no longer the traditional TCP protocol but the novel QUIC protocol, research for solutions to the unfairness of Adaptive Streaming over HTTP (HAS) has become more challenging. That is, because of different transport layers, the HTTP/3 may not be available for some networks and the clients have to use HTTP/2 for their HAS applications instead. Therefore, the scenario that HAS over HTTP/3 (HAS/3) compete against HTTP/2 (HAS/2) must be considered seriously. However, there have been a shortage of investigations on the performance and the origin of the unfairness in such a cross-protocol scenario in order to produce proper solutions. Therefore, this paper provides a performance evaluation and root-cause analysis of the cross-protocol unfairness between HAS/3 and HAS/2. It is concluded that, due to differences in the congestion control mechanisms of QUIC and TCP, HAS/3 clients obtain larger congestion windows, thus requesting higher video bitrates than HAS/2. As the problem lies in the transport layer, existing client-side ABR-based solutions for the unfairness from the application layer may perform suboptimally for the cross-protocol case.

Various methods of polymer surface tailoring have been studied to control the changes in wetting behavior. Polymers having precisely controlled wetting behavior in a specific environment are blessed with a wealth of opportunities and potential applications exploitable in biomaterial engineering. The controlled wetting behavior can be obtained by combining surface chemistry and morphology. Plasma assisted polymer surface modification technique have played a significant part to control surface chemistry and morphology. This review focuses on plasma polymerization and investigations regarding surface chemistry, surface wettability, coating kinetics, as well as coating stability. We begin with brief overview of plasma polymerization; these include growth mechanisms of plasma polymerization and influence of plasma parameters. Next, surface wettability and theoretical background structures and chemistry of superhydrophobic and superhydrophilic surfaces. In this review, overview of recent work on tunable wettability by tailoring surface chemistry and physical appearance (i.e. substrate texture) is also described. The formation of smart polymer coatings, which adjust their surface wettability by according to outside environment, including, pH, light, electric field and temperature. Finally, the applications of tunable wettability and pH responsiveness of polymer coatings in real life are addressed. This review should be of interest to plasma surface science communality specifically focused controlled wettability of smart polymer surfaces.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

Intrinsic and extrinsic sensory elements influence our food experiences. However, most research in extrinsic multisensory aspects of food has centered on WEIRD (White, Educated, Industrialized, Rich, and Democratic) urban participants. This study breaks from this by investigating multisensory eating experiences in the context of Northern Norway, a region characterized by distinct seasonal shifts, harsh arctic weather, unique atmospheric phenomena (e.g., the midnight sun and northern lights), limited food growth opportunities, and a rich Sámi cultural heritage. Our exploratory research used participant observation and interviews, involving four researchers from diverse backgrounds who closely examined multisensory food experiences within four Northern Norwegian food-related tourism businesses, all infused with Sámi cultural elements. Four themes emerged: 1) Experience elements, involving elements associated with plants, animals, and non-animate objects. 2) Bipolar concepts, which refer to contradicting dimensions where experience elements varied, notably in the interplay between Sámi and Norwegian traditions. 3) Sensory stories, which highlight the narratives, enriching the eating experience with context, such as tales of dining under the captivating northern lights. 4) Values which indicate guiding principles shaping these experiences on a broader scale, emphasizing support for local traditions and culture. Our main contribution is the presentation of a new contextual framework of multisensory eating experiences which can be applicable to studying eating experiences in other contexts.

This investigation aims to discuss the formation process of eddies and the heat transportation in plasma keyhole arc welding. In order to clarify this issue, the measurement of the convection inside the weld pool, the convection on the weld pool surface, also the temperature distribution on the weld pool surface were carried out. The results showed that two eddies were found in the weld pool, which is controlled mainly through the shear force by the plasma flow acting on the weld pool surface. The magnitude, extent and direction of the shear force are thought to be determined primarily by the variation of keyhole profile. The relative shape and strength of each eddy is largely changed depending on the change of the keyhole profile when nozzle diameter changed. These relative strengths of each eddy are considered to decisively govern the heat transport in the weld pool coinciding with the direction of eddies. A larger eddy near the lower part of the keyhole inside the weld pool was found out in the case of 1.6 mm, meanwhile a upward larger eddy was found out near the upper part of the keyhole inside the weld pool in the case of 2.4 mm.

While the container crane is an important part of daily port operations, it has received little attention compared with other infrastructures, such as buildings and bridges. Crane collapse due to earthquake affects the operation of the port, and indirectly impacts the economy. This study proposes fragility analyses for various damage levels of the container crane that allow the port owner and partners to better understand the seismic vulnerability presented by container cranes. A large quantity of nonlinear time history analyses was applied for a three-dimensional (3D) finite element model to quantify the vulnerability of the container crane in considering the uplift and derailment behavior. The uncertainty of demand and capacity of the crane structures were also considered through random variables, i.e. elastic modulus of members, ground motion profile, and intensity. The results analyzed in the case of a Korean container crane showed that the probability of exceeding the first uplift with or without derailment is shown before the crane reaches the structure’s limit states. This means that under low seismic excitation, the crane might be derailed without any structural damage. But when the crane reaches the minor damage state, it is always coupled with a certain probability of uplift with or without derailment. This study also proposes the fragility curves developed for different structural periods to enable port stakeholders to assess the risk of their container crane.

In an age of market liberalization, globalization, and expanding agribusiness, there is a danger that small-scale farmers will find difficulty in fully participating in the market economy. One of the solutions to solve the above situation is to strengthen linkages between farmers and firms through contract farming. The purpose of this study is to assess the factors affecting the level of linkages between businesses and farmers in crop development in Thai Binh province, Vietnam. The two main research methods used were exploratory factor analysis (EFA) and regression analysis with survey data which was conducted from 300 farmers, 10 firms, and 15 factors related to contract-farming. The results indicated that there are 5 factors that significantly affect the level of participation of farmers in the linking process, the order of impacts from strong to weak is as follows: (1) Commitment and sharing of benefits and risks between farmers and firms in the linking process, (2) Issues related to management skills and enterprises capacity of link firms, (3) Price issues that farmers are interested in in the process of implementing links with firms, (4) Policy environment - Institutions related to farmer contracts between firms and farmers, (5) Household awareness about the benefits brought about by farmer contracts with firms. Based on results, a number of specific solutions have been proposed to enhance the efficiency of farming contracts between firms and farmers in cultivation production.

S-adenosylmethionine (SAM) is considered to be a useful therapeutic agent for degenerative cartilage diseases, although its mechanism is not clear. We previously found that polyamines stimulate expression of differentiated phenotype of chondrocytes. We also found that cellular communication network factor 2 (CCN2) played a huge role in the proliferation and differentiation of chondrocytes. Therefore, we hypothesized that polyamines and CCN2 could be involved in the chondroprotective action of SAM. In this study, we initially found that exogenous SAM enhanced proteoglycan production but not cell proliferation in human chondrocyte-like cell line-2/8 (HCS-2/8) cells. Moreover, SAM enhanced gene expression of cartilage-specific matrix (aggrecan and type II collagen), Sry-Box transcription factor 9 (SOX9), CCN2 and chondroitin sulfate biosynthetic enzymes. Blockade of the methionine adenosyltransferase 2A (MAT2A) enzyme catalyzing intracellular SAM biosynthesis restrained the effect of SAM on chondrocytes. The polyamine level in chondrocytes was higher in SAM-treated culture than control culture. Additionally, alcian blue staining and RT-qPCR indicated that the effects of SAM on the production and gene expression of aggrecan were reduced by the inhibition of polyamine synthesis. These results suggest that the stimulation of polyamine synthesis and gene expression of chondrogenic differentiation factors, such as CCN2, account for the mechanism underlying the action of SAM on chondrocytes.

The high mutation rate of human immunodeficiency virus type 1 (HIV-1) plays a major role in treatment resistance from the development of vaccines to long-lasting drugs. In addressing the crux of the issue, various attempts to estimate the mutation rate of HIV-1 resulted in a large range of 10-5 - 10-3 errors/bp/cycle due to the use of different types of investigation methods. In this review, we discuss the different assay methods, their findings on the mutation rates of HIV-1 and how the location of these mutations can be further analyzed for their potential allosteric effects to reveal potentially new inhibitors with different pharmacodynamics that can be used to circumvent fast occurring HIV drug resistance. Given that HIV is one of the fastest mutating viruses, it is a good model for comprehensive study of its mutations that can give rise to much horizontal understanding towards overall viral drug resistance as well as emerging viral diseases.

Therapeutics antibodies have increasingly shifted the paradigm of disease treatments, from small molecules to biologics, especially in cancer therapy. Despite the increasing number of antibody candidates, much remains unknown about the antibody and how its various regions interact. In fact, the constant region can govern effects that might be useful in reducing the unwanted consequences resulted from systemic circulation. For this reason, apart from the commonly used IgG isotypes, IgA antibodies are promising therapeutics drugs, given its localized mucosal effects. While the antibody Fc effector cell activity has been well explored, recent research has shown evidences that the constant region of the antibody can also influence antigen binding, challenging the conventional idea of region-specific antibody functions. To further investigate this, we analyzed the IgA antibody constant and its allosteric effects onto the antigen binding regions, using recombinant Pertuzumab IgA1 and IgA2 variants. We found mutations in the C-region to reduce Her2 binding, and our computational structural analysis showed that such allosteric communications were highly dependent on the antibody hinge, providing the evidence to consider antibodies as a whole protein rather than a sum of functional regions.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

To obtain an accurate and reproducible experimental results in microbial fuel cell (MFC), it is important to know ‘anode maturation biofilm’ to produce a stable and maximum performance. For this purpose, four single chamber MFCs were tested in this study. The linear sweep voltammetry (LSV) polarization tests illustrated that maximum power densities of three MFCs became stable after 9 weeks. Although there were variations afterwards, such variations were negligible. Average maximum power densities from the 9th to the 17th week were 2,990 mW/m2 (MFC-4), 2,983 mW/m2 (MFC-2), 2,368 mW/m2 (MFC-3) and 837 mW/m2 (MFC-1). Polarization resistance shows that MFC-1 had much larger anode resistance (36.6-85.4 Ω) than the other MFCs (1.7-11.6 Ω). Anodic cyclic voltammetry (CV) shows that current production increased over time and MFC-1 had much smaller current production (24.4 mA) than the other MFCs (31.0-34.9 mA) at 17th week. The increased current production indicates anode biofilm became more mature over time, but overall cell performance did not increased accordingly. Possibly due to the bad inoculation, MFC-1 showed the lowest performance. However, its performance was restored to the initial performance and anode resistance was reduced by 47% at 17th week. This study shows that the optimum anode maturation time is 9 weeks and that bioanode performance is a key factor for MFC performance. This study also shows than LSV polarization and CV tests are accurate and non-destructive measurement methods for diagnosing anode performance.

An explosive growth in vehicular wireless services and applications gives rise to spectrum resource starvation. Cognitive radio has been used to vehicular networks to mitigate the impending spectrum starvation problem by allowing vehicles to fully exploit spectrum opportunities unoccupied by licensed users. Efficient and effective detection of licensed user is a critical issue to realize cognitive radio applications. However, spectrum sensing in vehicular environments is a very challenging task due to vehicles mobility. For instance, vehicle mobility has a large effect on the wireless channel, thereby impacting the detection performance of spectrum sensing. Thus, gargantuan efforts have been made in order to analyze the fading properties of mobile radio channel in vehicular environments. Indeed, numerous studies have demonstrated that the wireless channel in vehicular environments can be characterized by a temporally correlated Rayleigh fading. In this paper, we focus on energy detection for spectrum sensing and a counting rule for cooperative sensing based on Neyman-Pearson criteria. Further, we go into the effect of the sensing and reporting channels condition on spectrum sensing performance under temporally correlated Rayleigh sensing channel. For local and cooperative sensing, we derive some alternative expressions for average probability of miss detection. The pertinent numerical and simulating results are provided to further validate our theoretical analyses under a variety of scenarios.