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Abstract This research presents a dataset consisting of electroencephalogram and eye tracking recordings obtained from six patients with amyotrophic lateral sclerosis (ALS) in a locked-in state and one hundred seventy healthy individuals. The ALS patients exhibited varying degrees of disease progression, ranging from partial mobility and weakened speech to complete paralysis and loss of speech. Despite these physical impairments, the ALS patients retained good eye function, which allowed them to use a virtual keyboard for communication. Data from ALS patients was recorded multiple times at their homes, while data from healthy individuals was recorded once in a laboratory setting. For each data recording, the experimental design involved nine recording sessions per participant, each corresponding to a common human action or demand. This dataset can serve as a valuable benchmark for several applications, such as improving spelling systems with brain-computer interfaces, investigating motor imagination, exploring motor cortex function, monitoring motor impairment progress in patients undergoing rehabilitation, and studying the effects of ALS on cognitive and motor processes.

Background: Supplementing probiotics in livestock feed is increasing due to concerns over the potential harm caused by antibiotics and other chemical growth promoters. Several Bacillus sp. have been used as probiotic supplements for livestock. In this study, Bacillus amyloliquefaciens S2.5 was isolated from freshwater and its potential probiotic characteristics were evaluated in vitro. The whole genome of strain S2.5 was sequenced, and its probiotic traits were annotated using bioinformatic tools. Results: Both vegetative cells and spores of strain S2.5 remained stable throughout the 1.5 h of gastric juice and 48 h of intestine simulation. The strain S2.5 harbored the ability to produce glucoamylase, carboxymethyl cellulase, protease, and chitinase. It is also susceptible to all six tested antibiotics. The complete genome sequence shows genes related to acid-bile tolerance, environmental stress resistance, hydrolases, and adhesion to gut mucosa, confirming probiotic traits in the in vitro experiments. Conclusions: B. amyloliquefaciens S2.5 demonstrated potential probiotic characteristics and its genetic profile in the in vitro experiments. Further in vivo assessments of B. amyloliquefaciens S2.5 on livestock and poultry should be performed to assess its practical application.

Abstract The sliding mode control is well-known as a useful control technique that can be applied in several real-world applications. However, a straightforward and efficient process of selecting the sliding mode control gains remains a challenging but interesting topic. This paper investigates a novel gain tuning method for the sliding mode control of second-order mechanical systems. Firstly, we obtain relations between the gains and the natural and damping ratio of the closed-loop system. Secondly, the time constant of the system’s actuators and the system response performance criteria, including settling time and delay time, are taken into consideration to determine appropriate ranges of the gains. These gain ranges allow control designers to select the controller gains in a time-saving manner and ensure that the desired system performance is met and the actuators work properly. Finally, the proposed method is applied to the gain tuning process of a sliding mode altitude controller for an actual quadcopter unmanned aerial vehicle. Simulation and experimental results demonstrate the applicability and effectiveness of this method.

Forecasting the domestic coffee consumption demand is important for policy planning and making the right decisions. Thus, in this study, we try to find out the most suitable model among three proposed models (GM (1,1), DGM (1,1) and Grey Verhulst model (GVM)) for predicting the amount of domestic coffee consumption in Vietnam in the future. Yearly data of coffee consumption from 2010–2020 are used in this research. The experimental results indicated that the GM (1,1) is the most accurate model selected in this study with the lowest average value of [Formula: see text]%. So, the GM (1,1) model is strongly suggested in the analysis of coffee consumption demand in Vietnam. Finding the right tool will help managers make right decisions easily for sustainable development of the coffee industry in Vietnam in the future.

Purpose – This paper analyzes variations in the effects of monetary and fiscal shocks on responses of macroeconomic variables, determinacy region, and welfare costs due to changes in trend inflation. Design/methodology/approach – The authors develop the New-Keynesian model, in which the central banks can employ either nominal interest rate (IR rule) or money supply (MS rule) to conduct monetary policies. They also use their capital and recurrent spending budgets to conduct fiscal policies. By using the simulated method of moment (SMM) for parameter estimation, the authors characterize Vietnam's economy during 1996Q1–2015Q1. Findings – The results report that consequences of monetary policy and fiscal policy shocks become more serious if there is a rise in trend inflation. Furthermore, the money supply might not be an effective instrument, and using the government budget for recurrent spending produces severe consequences in the high-trend inflation economy. Practical implications – This paper's findings are critical for economists and monetary and fiscal authorities in effectively designing both the monetary and fiscal policies in confronting the shift in the inflation targets. Originality/value – This is the first paper that examines the effects of trend inflation on the monetary and fiscal policy implementation in the case of Vietnam.

Arsenic in drinking water is a severe public health risk in Vietnam especially in the Red River Delta (RRD), northern Vietnam. Located in the RRD, Hanam is the most affected province by arsenic contamination of groundwater. Several programs have been initiated and implemented in Vietnam especially in the rural areas to address the problems of arsenic in drinking water, among those piped water from centralized water plants. However, it is the fact that like many developing countries, in Vietnam efforts have been focused mainly on engineering aspect of technologies, while the nature of water users’ needs is neglected. This study assessed the determinants of piped water connection in arsenic contaminated areas of the RRD based on a field survey of 443 households in Hanam province. We found that, most of the respondents in RRD were aware of arsenic contaminated water but did not have deep knowledge on the issue and long-term health effect of consuming arsenic contaminated water. Connection fee, arsenic awareness index and household expenditure presented the most influential factors affecting the decision of getting connected. We discussed the implication of connection subsidies, awareness raising and collective action policy for providing safe water for the welfare of all society.

In the context of video background–foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-ℓ1 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background–foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.

In the field of biomedical engineering, the issue of drug delivery constitutes a multifaceted and demanding endeavor for healthcare professionals. The intravenous administration of pharmacological agents to patients and the normalization of average arterial blood pressure (AABP) to desired thresholds represents a prevalent approach employed within clinical settings. The automated closed-loop infusion of vasoactive drugs for the purpose of modulating blood pressure (BP) in patients suffering from acute hypertension has been the focus of rigorous investigation in recent years. In previous works where model-based and fuzzy controllers are used to control AABP, model-based controllers rely on the precise mathematical model, while fuzzy controllers entail complexity due to rule sets. To overcome these challenges, this paper presents an adaptive closed-loop drug delivery system to control AABP by adjusting the infusion rate, as well as a communication time delay (CTD) for analyzing the wireless connectivity and interruption in transferring feedback data as a new insight. Firstly, a nonlinear backstepping controller (NBC) is developed to control AABP by continuously adjusting vasoactive drugs using real-time feedback. Secondly, a model-free deep reinforcement learning (MF-DRL) algorithm is integrated into the NBC to adjust dynamically the coefficients of the controller. Besides the various analyses such as normal condition (without CTD strategy), stability, and hybrid noise, a CTD analysis is implemented to illustrate the functionality of the system in a wireless manner and interruption in real-time feedback data. [ABSTRACT FROM AUTHOR]