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Introduction: Depression is a common mental disorder; however, no study has investigated adolescent and late-life depression in Vietnam, and little has been done to compare the clinical characteristics among age groups of patients with this condition in the literature. Therefore, the study aims to examine the differences in symptom presentation among adolescents, adults, and the elderly with major depressive disorder (MDD). Methods: A total of 238 patients diagnosed with MDD according to the DSM-5, including 43 aged 10 to 17 years old, 74 aged 18 to 59 years old, and 121 aged 60 and older, were recruited in a cross-sectional study from March to August 2022 at three psychiatric clinics in Ho Chi Minh City. The eligible participant completed an epidemiological questionnaire. Results: All participants exhibited depressed mood, and 58.8% admitted to having suicidal ideation, with an alarming rate among adolescents (72.1%). Adolescents were less likely to experience the symptoms of interest decrease, appetite problems, insomnia, fatigue, and anxious distress than these other groups (p < 0.05). The pattern of symptom profiles in depressed adults largely remained the same in the older patients, except for psychomotor retardation and concentration problems, in which the latter were less likely to exhibit these symptoms compared to the first one (p

Background/Context: The problems that school district leaders identify and how they frame them have consequences—for both policy implementation and the ways that teachers respond. Although that is likely true in all community contexts (from rural to urban, inside and outside metropolitan centers), the influence of broader discourses associated with accountability reforms centered around state standardized testing may not be uniform across those contexts. Purpose/Objective/Research Question or Focus of Study: In this article, we report the results of an interview study we conducted with leaders in 50 school districts across the U.S. state of Missouri, in which we investigated what they identified as—and how they framed—their districts’ most salient problems related to mathematics. Guiding our analysis were the following questions: (1) What do those who oversee school districts’ mathematics instruction and curriculum identify as mathematics-related problems, and how do they frame those problems? (2) Do leaders’ identification and framing of problems differ with respect to districts’ size and proximity to metropolitan centers? (3) If so, to what extent are institutional factors, including mathematics achievement, economic resources, the presence of a mathematics-specific leader, and the district’s pedagogical commitments, predictive of leaders’ identification and framing of problems? Research Design: In 50 school districts in Missouri, sampled from different categories of size and proximity to metropolitan centers, we interviewed the district leader most responsible for overseeing mathematics curriculum and instruction about their mathematics-related challenges. We also collected contextual information from governmental websites, including population data, student achievement rates, student racial and economic demographics, and district economic resources (i.e., per-pupil expenditures). Through qualitative analysis we identified what leaders identified as their most pressing challenge and how they framed that challenge. Through regression analysis we identified which community and district characteristics were predictive of leaders’ problem identification and framing. Conclusions/Recommendations: Our results point to meaningful differences in leaders’ identification and framing of problems related to whether they work within metropolitan areas. In particular, we argue that leaders in nonmetropolitan districts appear more likely to adopt the frame offered by the “corporate model of schooling,” defining and framing problems around improvement in standardized test scores, whereas leaders in metropolitan areas are more likely to define problems in terms of equity and the ways that students experience school mathematics. And leaders from smaller districts were more likely to employ a strictly “management” framing of outcomes-related problems, describing responses focused exclusively on changing district programs and structures (rather than a “learning” frame that foregrounds supporting and developing staff). Institutional factors predictive of leaders’ identification of problems concerning equity and student experience included per-pupil expenditures, higher rates of test proficiency, commitment to inquiry-based pedagogy, and the presence of a district mathematics leader.

Introduction: Major depressive disorder (MDD) presents a diverse clinical picture, especially with somatic symptoms, which can lead to negative impacts on the course and prognosis of the illness. This study aimed to (1) assess the prevalence of various somatic symptoms in MDD patients and (2) assess their association with demographic factors. Methods: A total of 345 outpatients diagnosed with MDD according to DSM-5 criteria were enrolled in this cross-sectional study over 6 months. Participants completed a questionnaire that included clinical and demographic information as well as the PHQ-9. Results: There were 99.7% of patients who had at least one somatic symptom. The common somatic symptoms were fatigue (89.9%), insomnia (87.8%), palpitations (77.7%), headache (69.6%) and dizziness (61.4%). Total PHQ-9 scores and total number of somatic symptoms were found to have a regression relationship with a regression coefficient of 0.14 (t = 6.001, p < 0.001). In multiple logistic regression analysis, female gender was found to be associated with dizziness (odd ratios [OR] = 2.54, 95% confidence interval [CI] 1.53-4.21, p < 0.01), headaches (OR = 1.94, 95% CI 1.16-3.32, p < 0.05), and bowel problems (OR = 0.59, 95% CI 0.37-0.96, p < 0.05); while headaches (OR = 1.73, 95% CI 1.05-2.85, p < 0.05), and stomach problems (OR = 0.56, 95% CI 0.36-0.88, p < 0.05) were associated with age 40 and below. Conclusions: The study findings provide a resource for clinicians in mental health settings as well as primary care clinics in detecting inexplicable somatic symptoms associated with MDD.

Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis; however, its effects on cancer progression remain poorly understood. Here, to address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed dedicated regression-based methods to analyse ribosome profiling and alternative polyadenylation data, and identified heterogeneous nuclear ribonucleoprotein C (HNRNPC) as a translational controller of a specific mRNA regulon. We found that HNRNPC is downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3′ untranslated region lengthening and, subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. In addition, the reduced expression of HNRNPC and its regulon is associated with the worse prognosis in breast cancer patient cohorts.

Motivation: Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decisions may have (potentially negative) long-lasting effects on the end-user affected by the decision. While deep learning models achieve impressive results, they often function as a black-box. Inspired by linear models, we propose a novel class of structurally constrained deep neural networks, which we call FLAN (Feature-wise Latent Additive Networks). Crucially, FLANs process each input feature separately, computing for each of them a representation in a common latent space. These feature-wise latent representations are then simply summed, and the aggregated representation is used for the prediction. These feature-wise representations allow a user to estimate the effect of each individual feature independently from the others, similarly to the way linear models are interpreted.: Results: We demonstrate FLAN on a series of benchmark datasets in different biological domains. Our experiments show that FLAN achieves good performances even in complex datasets (e.g. TCR-epitope binding prediction), despite the structural constraint we imposed. On the other hand, this constraint enables us to interpret FLAN by deciphering its decision process, as well as obtaining biological insights (e.g. by identifying the marker genes of different cell populations). In supplementary experiments, we show similar performances also on non-biological datasets.Code and data availabilityCode and example data are available at ., Briefings in Bioinformatics, 24 (3), ISSN:1467-5463, ISSN:1477-4054

A new gillnet made from multiple mesh sizes ranging from 125 to 180 mm of stretched mesh (experimental gillnet) was tested under commercial fishing conditions to compare the fishing performance with that of conventional gillnets with a 125 mm mesh opening (control gillnet). Catch efficiency and size selectivity between the two gillnet types were evaluated throughout one year of fishing in three different locations in the waters of Vietnam. Experimental gillnets caught narrow-barred Spanish mackerel (Scomberomorus commerson), spotted mackerel (Scomberomorus guttatus), and wahoo (Acanthocybium solandri) in comparable amounts to the control gillnets, with the moon phase, month, and depth explaining some of the variation in the catch per unit effort (CPUE). An analysis of the size-dependent catch comparison rates and selectivity parameters showed that the experimental gillnets captured a wider range of narrow-barred Spanish mackerel sizes, but with a substantial proportion of individuals larger than those caught by the control gillnets. This is of higher weight per unit effort, and fishing enterprises therefore could improve their economic benefits by using modified gillnets with multiple mesh sizes. Our findings also support the biological and environmental benefits of the modified gillnet size selection, which might also extend to other species.

Most of the phenomena in life, such as heat transfer, wave propagation, thermoelectricity, sheet materials, multifunctional material properties, etc., can be expressed through mathematical equations or partial differential equations (PDEs). However, with complex differential equations, it is difficult or impossible to find solutions, especially differential equations for complex problems such as solid-liquid interactions, mechanical-thermal-electrical environments, and functional material plate environments in the multiphysics environment, etc. Since then, many numerical methods have been researched and developed to find approximate solutions to partial differential equations. Today, the finite element method (FEM) is the most widely used and effective among the commonly used numerical methods. However, with the continuous emergence of new complex problems in science and technology, especially problems in the multiphysics environment in sheet-material structures, the finite element method has revealed new challenges. There are certain limitations related to the element technique and the weak form discretization of the problem of sheet structure with many different degrees of freedom, significantly affecting the accuracy and efficiency of calculations. Therefore, it is always important to propose improvements to the traditional finite element method and the optimal method algorithm to meet the increasing requirements in the behavior analysis of sheet materials. This research direction is always topical and has received the attention of scientists around the world for many decades.

The article proposes using the Levenberg – Marquardt (L – M) method to optimize the fin with the longitudinal profile. The objective in optimizing the fin shape: The fin volume achieved the minimum value, with the optimization variables being the fin's width and length. The research performed two problems about optimal design the fin with the straight profile for triangular and rectangular shapes, obtained a tiny relative error compared to the results of the published studies. Specifically, the problem with the triangular-shaped fin, the relative error of the minimum volume compared to the two published is 0.022% & 0.092%; and the problem with the rectangular-shaped fin, the relative error is 0.68% & approximately 0%, respectively.

Most of the current task-oriented dialogue systems (ToD), despite having interest-ing results, are designed for a handful of languages such as Chinese and English.Therefore, their performance in low-resource languages is still a significant prob-lem due to the absence of a standard dataset and evaluation policy. To addressthis problem, we proposed ViWOZ, a fully-annotated Vietnamese task-orienteddialogue dataset. ViWOZ is the first multi-turn, multi-domain, task-orienteddataset in Vietnamese, a low-resource language. The dataset consists of 5,000dialogues in total, including 60,946 fully annotated utterances. Furthermore, weprovide a comprehensive benchmark for both modular and end-to-end models inlow-resource language scenarios. With those characteristics, the ViWOZ datasetenables future studies on the construction of multilingual task-oriented dialoguesystems.

Summary/AbstractThe neural crest (NC) is highly multipotent and generates diverse lineages in the developing embryo. However, spatiotemporally distinct NC populations display differences in fate potential, such as increased gliogenic and parasympathetic potential from later migrating, nerve-associated Schwann cell precursors (SCPs). Interestingly, while melanogenic potential is shared by both early migrating NC and SCPs, differences in melanocyte identity resulting from differentiation through these temporally distinct progenitors have not been determined. Here, we leverage a human pluripotent stem cell (hPSC) model of NC temporal patterning to comprehensively characterize human NC heterogeneity, fate bias, and lineage development. We captured the transition of NC differentiation between temporally and transcriptionally distinct melanogenic progenitors and identified modules of candidate transcription factor and signaling activity associated with this transition. For the first time, we established a protocol for the directed differentiation of melanocytes from hPSCs through a SCP intermediate, termed trajectory 2 (T2) melanocytes. Leveraging an existing protocol for differentiating early NC-derived melanocytes, termed trajectory 1 (T1), we performed the first comprehensive comparison of transcriptional and functional differences between these distinct melanocyte populations, revealing differences in pigmentation and unique expression of transcription factors, ligands, receptors and surface markers. We found a significant link between the T2 melanocyte transcriptional signature and decreased survival in melanoma patients in the cancer genome atlas (TCGA). We performed anin vivoCRISPRi screen of T1 and T2 melanocyte signature genes in a human melanoma cell line and discovered several T2-specific markers that promote lung metastasis in mice. We further demonstrated that one of these factors, SNRPB, regulates the splicing of transcripts involved in metastasis relevant functions such as migration, cell adhesion and proliferation. Overall, this study identifies distinct developmental trajectories as a source of diversity in melanocytes and implicates the unique molecular signature of SCP-derived melanocytes in metastatic melanoma.

This is a case report of an elderly male patient who was diagnosed with meningococcal pneumonia resulting in respiratory failure. The patient had pre-existing conditions of chronic obstructive pulmonary disease (COPD) and prostate cancer. Upon admission, the patient exhibited symptoms such as a purulent cough producing sputum, dyspnea, decreased oxygen saturation, and chest X-ray images that were consistent with pneumonia. The bacteria responsible for the infection was identified as Neisseria meningitidis through bacterial culture, which was found to be antibiotic-resistant. The patient did not show any signs of septicemia or meningitis. Treatment was initiated with an appropriate antibiotic regimen, and the patient was discharged after demonstrating improvement over 10 days. Close contacts of the patient were given prophylactic antibiotics, and no secondary infections were recorded. In this article, we will present a detailed case report, and provide an overview of the epidemiology of meningococcal infections in Vietnam, as well as summarizing the clinical characteristics of meningococcal pneumonia cases globally.

Motivation Being able to interpret and explain the predictions made by a machine learning model is of fundamental importance. Unfortunately, a trade-off between accuracy and interpretability is often observed. As a result, the interest in developing more transparent yet powerful models has grown considerably over the past few years. Interpretable models are especially needed in high-stake scenarios, such as computational biology and medical informatics, where erroneous or biased models’ predictions can have deleterious consequences for a patient. Furthermore, understanding the inner workings of a model can help increase the trust in the model. Results We introduce a novel structurally constrained neural network, MonoNet, which is more transparent, while still retaining the same learning capabilities of traditional neural models. MonoNet contains monotonically connected layers that ensure monotonic relationships between (high-level) features and outputs. We show how, by leveraging the monotonic constraint in conjunction with other post hoc strategies, we can interpret our model. To demonstrate our model’s capabilities, we train MonoNet to classify cellular populations in a single-cell proteomic dataset. We also demonstrate MonoNet’s performance in other benchmark datasets in different domains, including non-biological applications (in the Supplementary Material). Our experiments show how our model can achieve good performance, while providing at the same time useful biological insights about the most important biomarkers. We finally carry out an information-theoretical analysis to show how the monotonic constraint actively contributes to the learning process of the model. Availability and implementation Code and sample data are available at https://github.com/phineasng/mononet. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Convolutional neural networks (CNNs) have recently proven their excellent ability to segment 2D cardiac ultrasound images. However, the majority of attempts to perform full-sequence segmentation of cardiac ultrasound videos either rely on models trained only on keyframe images or fail to maintain the topology over time. To address these issues, in this work, we consider segmentation of ultrasound video as a registration estimation problem and present a novel method for diffeomorphic image registration using neural ordinary differential equations (Neural ODE). In particular, we consider the registration field vector field between frames as a continuous trajectory ODE. The estimated registration field is then applied to the segmentation mask of the first frame to obtain a segment for the whole cardiac cycle. The proposed method, Echo-ODE, introduces several key improvements compared to the previous state-of-the-art. Firstly, by solving a continuous ODE, the proposed method achieves smoother segmentation, preserving the topology of segmentation maps over the whole sequence (Hausdorff distance: 3.7-4.4). Secondly, it maintains temporal consistency between frames without explicitly optimizing for temporal consistency attributes, achieving temporal consistency in 91% of the videos in the dataset. Lastly, the proposed method is able to maintain the clinical accuracy of the segmentation maps (MAE of the LVEF: 2.7-3.1). The results show that our method surpasses the previous state-of-the-art in multiple aspects, demonstrating the importance of spatial-temporal data processing for the implementation of Neural ODEs in medical imaging applications. These findings open up new research directions for solving echocardiography segmentation tasks., Comment: Submitted to IEEE TMI in June 2023

BACKGROUND: The effects of the environment and genotype in the coffee bean chemical composition were studied using nine trials covering an altitudinal gradient [600–1100 m above sea level (a.s.l.)] with three genotypes of Coffea arabica in the northwest mountainous region of Vietnam. The impacts of the climatic conditions on bean physical characteristics and chemical composition were assessed. RESULTS: We showed that the environment had a significant effect on the bean density and on all bean chemical compounds. The environment effect was stronger than the genotype and genotype-environment interaction effects for cafestol, kahweol, arachidic (C20:0), behenic acid (C22:0), 2,3-butanediol, 2-methyl-2-buten-1-ol, benzaldehyde, benzene ethanol, butyrolactone, decane, dodecane, ethanol, pentanoic acid, and phenylacetaldehyde bean content. A 2 °C increase in temperature had more influence on bean chemical compounds than a 100 mm increase in soil water content. Temperature was positively correlated with lipids and volatile compounds. With an innovative method using iterative moving averages, we showed that correlation of temperature, vapour pressure deficit (VPD) and rainfall with lipids and volatiles was higher between the 10th and 20th weeks after flowering highlighting this period as crucial for the synthesis of these chemicals. Genotype specific responses were evidenced and could be considered in future breeding programmes to maintain coffee beverage quality in the midst of climate change. CONCLUSION: This first study of the effect of the genotype–environment interactions on chemical compounds enhances our understanding of the sensitivity of coffee quality to genotype environment interactions during bean development. This work addresses the growing concern of the effect of climate change on speciality crops and more specifically coffee. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

In this paper, we design a PD-fuzzy controller which is developed from PID method. The model for testing here is acrobot – a SIMO model which imitates the action of barbell athlete. We create a hardware platform for real experiment. The aim of controlling is keeping link 1 at up-position and link 2 at down-position. Through both simulation and experiment, the PD-fuzzy controller shows better control quality than original PID controller.

In recent years, the boom of the quadcopter industry resulted in a broad range of real-world applications which highlighted the urgent need to improve quadcopter control quality. Typically, external disturbances, such as wind, parameter uncertainties caused by payload variations, or the ground effect, can severely degrade the quadcopter’s altitude control performance. Meanwhile, widely used controllers like the proportional-integral-derivative control cannot guarantee control performance when the system is critically affected by factors that exhibit a high degree of variability with time. In this paper, an adaptive control algorithm is proposed to improve quadcopter altitude tracking performance in the presence of both the ground effect and a time-varying payload. First, we derive an adaptive altitude control algorithm using the sliding mode control technique to account for these uncertainties in the quadcopter dynamics model. Second, we apply Lyapunov theory to analyze the stability of the closed-loop system. Finally, we conduct several numerical simulations and experiments to validate the effectiveness of the proposed method.

With the rapid growth of the furniture product line industry and IKEA as the one of the pioneers, manual labour (and its limits of stability and high cost) is increasingly being replaced by automat...

Automating stages for deformable objects in the production line, in which assembling a wire harness into a predefined position is a complex task owing to the specialized characteristics of the objects. Besides a few automatized systems proposed in the other studies to implement this task under simplified setup conditions, a significant portion of this process remains to be completed manually in industrial environments. To construct an automatic wire harness assembly system, the development of a method that can automatically detect the wire harness profile in a 3D environment and, consequently, guide robot arms to implement assembly tasks is indispensable. Therefore, this study presents an approach that satisfies this requirement, which not only proposes a deep learning-based system to detect the wire profile, but also improves the accuracy of the detected results through a correction method according to the depth values of contiguous areas. The verification of the approach in a robot system that highlights its usefulness and practicality demonstrates the potential of the proposed method to replace people and consequently, reduce labour costs in factory environments.

Direct speech-to-speech translation (S2ST) is among the most challenging problems in the translation paradigm due to the significant scarcity of S2ST data. While effort has been made to increase the data size from unlabeled speech by cascading pretrained speech recognition (ASR), machine translation (MT) and text-to-speech (TTS) models; unlabeled text has remained relatively under-utilized to improve S2ST. We propose an effective way to utilize the massive existing unlabeled text from different languages to create a large amount of S2ST data to improve S2ST performance by applying various acoustic effects to the generated synthetic data. Empirically our method outperforms the state of the art in Spanish-English translation by up to 2 BLEU. Significant gains by the proposed method are demonstrated in extremely low-resource settings for both Spanish-English and Russian-English translations.

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 tuning the gains of a sliding mode controller remains a challenging but interesting topic. In this paper, a mathematical method for the sliding mode controller gain tuning is investigated. 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 the gains. In addition, appropriate gain ranges, which ensure that the system performance is met and the actuators work properly, are derived. 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.

Nowadays, quadcopters are used widely in different applications, but their flight time is limited during operation. In this paper, a precision landing method based on a Kalman filter is proposed for an autonomous indoor persistent drone system that aims to increase the flight time of quadcopters. First, a local positioning system is used for tracking performance. Second, instead of using this local positioning system during the landing phase, a multi-ranger sensor is proposed to increase the accuracy of horizontal errors. Next, based on the relative position provided by the multi-ranger sensor, a Kalman filter technique is applied to estimate the relative velocity of the system, which is then applied to control the position of the quadcopter during the landing phase. Finally, a charging state machine law is proposed to charge the battery of three quadcopters sequentially. The experimental results demonstrate that the proposed concept based on a multi-ranger sensor can enhance the accuracy of the landing phase in comparison with the conventional method.

This article presents a formation tracking control method for the operation of multi-agent systems under disturbances. This study aims to ensure that the followers of a quadcopter converge into the desired formation while the center formation of the follower quadcopters tracks the leader’s trajectory within a finite time. The distributed finite-time formation control problem is first investigated using the fast terminal sliding mode control (FTSMC) theory. A disturbance observer is then integrated into the FTSMC to overcome the model uncertainties and bounded disturbances. Subsequently, the Lyapunov function is proposed to ensure the stability of the system. It is shown that formation tracking control can be achieved even in the presence of disturbances. Simulation and experimental results verify the effectiveness of the proposed formation tracking control method compared to existing ones.

This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.

A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article aims at an effective solution to this task. Different from the existing works, we resolve the task in a more practical context. First, the temporal aspects of the task and its data are considered. For the task, historical datasets are used to support the task on current ones. For the data, both labeled and unlabeled data before the midterm break are used. Second, our solution examines assessment data for the task, and thus, requires less data collection cost and effort over time. Third, we propose a semisupervised learning method, ST_OS, to obtain a better prediction model because ST_OS handles data insufficiency when exploiting all the labeled and unlabeled data. Moreover, ST_OS combines Self-Training and Tri-Training to create a resulting ensemble model in an effective semisupervised learning process with local learning for each selected unlabeled instance. Above all, the task is addressed in a general manner for different course types. As a result, our solution outperforms several existing supervised and semisupervised learning ones with higher Accuracy and F-measure. Therefore, it can be used as a forecasting tool before their courses end. More activities can be then improved to help the students complete the courses successfully.

Underwater vehicles are a powerful tool that can assist oceanologists with measuring the state of oceans on a large scale. The heading control is essential for the underwater vehicle to follow a specific path. This study describes the general decoupled dynamics of underwater vehicles, which is a nonlinear second-order differential equation considering linear and quadratic damping hydrodynamics. A novel aspect of this study is the development of a new analytical solution for the second-order nonlinear differential equation, which involves the heading motion of the underwater vehicle. In this study, the time-optimal trajectory is formulated as the closed-form solution for the heading dynamics of the underwater vehicle. The concept of this trajectory is based on the shortest arrival time when the maximum force from the thrusters is applied to the underwater vehicle. Finally, a simulation of the time-optimal trajectory and evaluation of the robustness of the controller were demonstrated in order to verify the effectiveness of the proposed trajectory for controlling underwater vehicles.

This paper introduces a robust adaptive sliding mode control to solve a finite-time stability of the uncertain nonlinear systems with multiple inputs and multiple outputs (MIMO). The proposed algorithm guarantees a strict robustness and fast convergence of the system trajectories to zero in a finite time under the negative effects of uncertainties and/or external disturbances. The fundamental methodology is based on an improved modification of the super-twisting sliding technique to alleviate an undesirable influence of the chattering phenomenon. In addition, a nonlinear adaptive law is constructed to ensure a strict stability of the control system even without prior awareness of the upper bounds of uncertainties and disturbances. A general stability of the closed-loop disturbed MIMO nonlinear system is achieved by the Lyapunov theorem. Lastly, the proposed algorithm is applied to stabilize the typical chaotic behaviors of Duffing – Holmes system and Lorenz system. The advantages and effectiveness of the proposed method are clearly demonstrated through the results of numerical simulations compared with other existent methods.

In this article, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural network algorithm to achieve the time-varying sliding surface; their coefficients in sliding surface are adjusted through backpropagation law. The disturbance observer is also combined with sliding mode controllers to estimate and handle the external disturbances. Finally, the Lyapunov theory is applied to validate the stability of suggested control method. The performance of proposed sliding mode control has been evaluated using a numerical simulation. The results show that the attitude and altitude controller based on suggested algorithm has a better tracking performance and disturbance rejection.

This paper presents a new control algorithm for controlling randomly switching systems under random disturbance. The proposed controller is designed based on a dual prescribed sliding mode control and a dual modified Riccati-like equation. The sliding surface of the proposed controller includes two parts: a classical type and a prescribed performance. The combined sliding surface provides strong disturbance rejection capability for the system. The dual Riccati equation provides two functionalities: (i) a classical type associated with the system parameters, and (ii) a disturbance-type with parameters associated with the boundaries of disturbance. A fuzzy model, which functions as a filter, is applied in the proposed control system for approximating uncertainty. Its fuzzified values are also used in the adaptation laws to improve the system performance. One of the innovative features of the proposed approach is expressed by its adaptation laws, which are formed by the constraint matrices, which are related to both the nominal system and disturbance. In these laws, the parameters of the PID component are updated based on the main matrices of Riccati equations. The composite controller, which combines a sliding mode control, Riccati equations, PID control, and fuzzy model, helps to improve the performance of the classical controls when the system works under severe disturbance conditions. The proposed controller is employed for a suspension system of bus driver and compared with an existing controller under two randomly disturbance conditions. The results of a computer simulation show that the proposed controller obtains good performance in terms of vibration compression.

For the purpose of ensuring sustainable development, one of the important factors is to increase labor productivity and minimize the number of workers directly working in the mine. In the context of increasing requirements for underground coal mining, the number of workers tends to decline due to shifting to other types of economy. Therefore, the mechanization investment in coal mining is the inevitable destination, which guarantees the long - term strategic development of the coal mining industry. Although the mechanized mining has obtained several achievements, the annual coal mining output is still not high, specifically reaching 73.8%; 51.3%; 61.4% according to the plan respectively for the years of 2013, 2014 and 2015. A total of mechanized lines has been invested and applied in Vietnam National Coal - Mineral Industries Holding Corporation Limited, but there are only six lines in operation until at this time. For five recent years, when the mining industry has accumulated a lot of experience in using mechanization technology, it is found that the potential factors causing production interruption have resulted in the effectiveness of these longwalls not achieved as expected. Summary of advantages, difficulties, and unsuccessful implementation of mechanization technology in the condition of Quang Ninh mine area has shown a big gap in production organization that should be specifically studying. In this study, the author wants to build a basic background in determining the impact of potential factors conscious of the longwall mechanized.

This study provides a novel quad-function-link network to adjust the lower and upper weights of a wavelet interval type-2 fuzzy brain emotional structure to increase the response and performance for the synchronization of 3D nonlinear chaotic systems. The proposed control system is a hybrid method that comprises a new wavelet interval type-2 fuzzy quad-function-link brain emotional controller and a robust controller. It contains a fuzzy inference system and three substructures with five layers. The substructures are an amygdala, a prefrontal cortex, and a novel quad-function-link network that can adjust the weights efficiently for the amygdala and prefrontal cortex networks to achieve the synchronization of the master–slave systems well with reduced tracking errors. Then, a Lyapunov stability function is employed to provide the adaptive laws, and they are effectively used online to adjust the system parameters. Finally, simulation studies of two 3D nonlinear chaotic systems are used to verify the superiority and advantage of the proposed algorithm.

The mechanizetion of Ha Lam coal seam 11 was designed with mining output of 600.000 tous per year. In fact, due to the negative impact of geological conditions as well as the irrationality of the initial production organization, the mining output of the longwall is not reached according to the designed capacity. The paper proposes a method to determine the effective working time of the actual longwall based on each component stage. For specific conditions at coal seam 11 in Ha Lam coal mine. The mean flow rate of the mining stream is considered as a directional factor of the linear function describing the relation between the daily output and the effective mining in the longwall face. This relation is also considered as motivation in particular for supervisory personnel, as it shows advantages resulting from elongation of this time, as well as it shows possible losses of the daily output in a case when the effective working time is given longwall face was shortened.

Introduction To investigate effects of Sitagliptin on the enhancement of beta-cell function, reducing insulin resistance, serum glucagon like peptide-1 (GLP-1) concentrations and blood glucose in patients with type 2 diabetes mellitus (T2D) and suggest one of the underlying mechanisms on beta-cell function and insulin resistance. Patients and methods This was a cross-sectional and observational study in comparison to the control group. A study population of 44 newly diagnosed patients with T2D treated with Sitagliptin with a dose of 100 mg/day for 3 months was analyzed to compare 52 healthy participants. Indices for beta-cell function, peripheral insulin sensitivity, and insulin resistance were calculated with homeostasis model assessment 2 (HOMA2) calculator and compared. Serum GLP-1 concentrations were analyzed, and regression analysis was conducted to find the correlations between GLP-1 and beta-cell function and insulin resistance. Results Newly diagnosed patients with T2D witnessed a significant reduction in beta-cell function, serum GLP-1 concentrations at the time of diagnosis. After treatment with Sitagliptin 100 mg/day, they achieved significant improvements in beta-cell function, peripheral insulin sensitivity and insulin resistance. Serum GLP-1 concentrations were increased significantly to those levels in the control group and correlated with peripheral insulin sensitivity and insulin resistance in patients whose beta-cell functions improved. Conclusion Sitagliptin improved beta-cell function, insulin resistance and blood glucose in newly diagnosed patients with T2D. Meanwhile, Sitagliptin ameliorated serum GLP-1 concentrations, which contributed to the enhancement of beta-cell.

Land cover/land use classification using high resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values, structures, shapes, and spatial elements. This paper reveals the object-oriented classification method to establish the land cover map using VNREDSat-1 data, with a spatial resolution of 10 m. Land cover/land use system is classified according to CORINE with level 3 with 14 types of land cover/land use. Extraction of 14 types of land cover/land use using object-oriented classification method based on reflectance spectral characteristics, shape index, location of objects, brightness, NDVI plant index, and density objects. The overall accuracy of classification result is about 0.71%.

FDCA (2,5-furandicarboxylic acid) can be enzymatically converted from HMF (5-hydroxymethylfurfural). Pseudomonas putida S12 is promising for FDCA production, but generating stable P. putida S12 is ...

It is essential to conduct the quality control for gauranteeing sound products after finishing conventional manufacturing processes. Vision-based inpection system has been extensively applied to various industries linked with concept of the smart factory since it does not only enhance the inspecting accuracy, but also decrease the cost for the human inspection, substantially. This paper mainly concerns the development of the inspecting system for the casting products with supported by the convolutional neural network, which makes it possible to detect various types of defects such as blow hole, chipping, crack, and wash automatically. To obtain high accuracy in inspecting system, it does not only require sub-partitioning of the original images, but also apply multiple labeling according to the order of the sub-images and the existence of the defects. Performance of the proposed inspecting algorithm has been validated with the 400 casting products, in which it exhibits substantially high accuracy more than 98%, experimentally.

Nowadays, with the increasing popularity of quadcopter unmanned aerial vehicles in several real-world applications, achieving a fully autonomous quadcopter flight has become an imperative topic investigated in many studies. One of the most pressing issues in such a topic is the precision landing task, which always is devastatingly influenced by the ground effect and external disturbances. In this paper, we present an autonomous quadcopter landing algorithm allowing the vehicle to land robustly and precisely onto a heaving platform. Firstly, a robust control algorithm addressing the altitude flight under the ground effect and external disturbances is derived. We strictly prove the closed-loop system stability by using the Lyapunov theory. Secondly, a landing target state estimator is proposed to provide state estimations of the moving landing target. In addition, we propose a landing procedure to ensure the landing task is achieved safely and reliably. Finally, we use a DJI-F450 drone equipped with an infrared sensor and a laser ranging sensor as the experimental quadcopter platform and conduct experiments to evaluate the performance of our new algorithm in real flight conditions. The experimental results demonstrate the effectiveness of the proposed method.

This paper mainly proposes an artificial neural network (ANN) model for predicting edge stretchability of GPa-grade steels, which is substantially difficult to predict due to the complex nonlinear relation among the numerous sheared edge qualities. We newly suggest the physically characterized parameters, such as material properties, deformed shape, and work hardening of sheared edge, to predict the various materials and punching methods, simultaneously. The proposed parameters are trained with the pre-damage strain which is calculated by inherent fracture strain and experimental results in terms of hole expansion ratio. To prevent the overfitting issues, cross validation method with additional datasets from a different kind of edge stretchability test such as sheared edge tensioning test are utilized. Experimental validations have been conducted with various GPa-grade steels and sheared edge conditions, which are compared with the proposed ANN model and numerical simulation. The proposed ANN model exhibits remarkable performance in the prediction of hole expansion ratio having a mean absolute error of 1.5% when compared to the previous studies such as numerical simulation and ANN model with utilizing the maximum hardness measured at the sheared edge.