Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of a Collaborative Problem Solving Parent Group on Parent and Child Outcomes

Abstract Twenty-one parents participated in a Collaborative Problem Solving (CPS) parent group targeting children’s challenging behavior; 18 parents were in a waitlist control group. All parents completed pre- and post-treatment measures on CPS learning targets, their child’s behavior, and relationship quality, and parents in the treatment group completed measures six months later. Only parents learning CPS reported significant improvements on learning targets and conflict, and changes were sustained six months later. Children’s behavior improved significantly in the treatment group by six-month follow-up. These findings support CPS parent groups as an economical and effective approach for parents of children with behavioral difficulties.
Stabilization of Switched Fuzzy Systems via Stabilizing Switching-Dependent ADT Method

This article is concerned with the stabilization problem of switched Takagi–Sugeno fuzzy systems via the stabilizing switching behavior. Different from the situations considered in the existing literature, the stabilizing-switching-dependent average dwell-time method is applied to break through the limitation of the noncoexistence of dwell-time-dependent and dwell-time-independent destabilizing switching behavior. Then, the stability criteria are established by balancing the state divergence produced from the unstable mode and destabilizing switching behavior through the stabilizing switching behavior. The obtained results are suitable for the situation where all modes are unstable and applied to the design of the state feedback controller. The designed controller relaxes the stabilization assumption for the mode of the fuzzy system. Finally, the simulation illustrates the feasibility and superiority of the proposed method.
State Dependent Switching Control of Affine Linear Systems With Dwell Time: Application to Power Converters

This paper addresses a state dependent switching law for the stabilization of continuous-time, switched affine linear systems satisfying dwell time constraints. Such a law is based on the solution of Lyapunov-Metzler inequalities from which stability conditions are derived. The key point of this law is that the switching rule calculation depends on the evolution forward by the dwell time of quadratic Lyapunov functions assigned to each subsystem. As such, the proposed law is readily applicable to power converters showing that it is an interesting alternative to other switching control techniques.
Drag phenomena from holographic massive gravity

We consider the motion of point particles in a strongly coupled field theory with broken translation invariance. We obtain the energy and momentum loss rates and drag coefficients for a class of such particles by solving for the motion of classical strings in holographic massive gravity. At low temperatures compared to the graviton mass the behaviour of the string is controlled by the appearance of an exotic ground state with non-zero entropy at zero temperature. Additionally, we find an upper bound on the diffusion constant for a collection of these particles which is saturated when the mass of the graviton goes to zero.
Effect of two different levels of positive end-expiratory pressure (PEEP) on oxygenation and ventilation during pneumoperitoneum for laparoscopic surgery in children: A randomized controlled study

Background: Positive end-expiratory pressure (PEEP) is used to attenuate the changes in respiratory parameters because of pneumoperitoneum during laparoscopic (LAP) surgery. As the ideal level of PEEP during LAP in children is not known, this study compared the effect of 5- and 10–cm H2O of PEEP on oxygenation, ventilator, and hemodynamic parameters during pediatric LAP. Method: After obtaining approval from the Institute Ethics Committee and written informed parental consent, 30 American Society of Anesthesiologists (ASA) I and II children aged 2–10 years, undergoing LAP were randomized to receive PEEP of 5- or 10–cm H2O during pneumoperitoneum. Baseline hemodynamic and ventilatory parameters, PaO2, and PaCO2 were measured 2 min after tracheal intubation, 2 min and 1 h after pneumoperitoneum, and after deflation of pneumoperitoneum. Oxygenation index, dynamic compliance, and alveolar-arterial oxygen gradient (D (A-a) O2) were calculated at the above-mentioned time points. Data were analyzed using Student's t-test and repeated measures ANOVA with Bonferroni correction. Results: The oxygenation index and D(A-a)O2 decreased in PEEP 5 Group and increased in PEEP 10 Group after pneumoperitoneum, the difference between the two groups being statistically significant (P = 0.001). The dynamic compliance decreased in PEEP 5 Group and increased or remained the same in PEEP 10 Group after pneumoperitoneum, the difference between the two groups being significant (P = 0.001). There were no significant changes in the hemodynamic parameters in the two groups. Conclusion: Use of 10-cm H2O PEEP during pneumoperitoneum in children improves ventilation and oxygenation, without significant hemodynamic changes.
Changes in the antioxidant properties of Ocimum africanum leaves aqueous extract from the southeastern region of Nigeria after in vitro gastrointestinal digestion

ABSTRACT The potential for antioxidant compounds to be released from the original food matrix is greatly determined by bioaccessibility. Digestive enzymes have a substantial impact on the antioxidant properties of dietary items after ingestion. The present study aimed to determine the antioxidant properties of Ocimum africanum leaves aqueous extract (OALAE) before and after in vitro digestion. In vitro gastro-pancreatic digestion of extracted samples (steamed, air dried and fried) was done using artificial gastric fluid, and 4 mL of artificial intestinal fluid. Antioxidant analyses were carried out on the OALAE samples (group 1: steamed, group 2: fried, group 3: air dried and group 4: garlic acid standard), before digestion (undigested), post-intestinal digestion with enzymes and post-intestinal digestion without enzymes. The trends of antioxidant properties of the OALAE (in-vitro) were as follows: air dried > steamed > fried. A significant (p < 0.05) decrease in 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS), ferric reducing antioxidant power (FRAP), total antioxidant capacity (TAC), reduced glutathione (GSH), vitamin E, vitamin C, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and non-heme iron content of OALAE in post-intestinal digestion with enzyme and post-intestinal digestion without enzyme was found compared to the undigested extract in group 1, 2, 3, and 4. Significant decrease was observed in antioxidant activity (illustrated using DPPH, ABTS, FRAP, and TAC) of post-intestinal digestion with enzyme compared to post-intestinal without enzymes in group 1, 2, 3 and 4. The results suggested that antioxidant properties of O. africanum leaves are unstable and may show limited activity after stimulated gastrointestinal digestion.
Using facial landmarks to detect driver fatigue

Using state-of-the-art computer vision techniques, a model was designed to be able to detect fatigue in haul truck drivers in South American mines. The proposed solution uses a DNN-based (Deep Neural Networks) face detection model, caffe, to detect the presence of the driver’s face and a facial landmark detection model of MobileNetV2 architecture to identify key features of the face. Afterwards, the eye aspect ratio (EAR) and the mouth aspect ratio (MAR) are calculated, which are then used to calculate the percent eye-closure over a time period (PERCLOS), blink rates and duration, yawns, distraction, and other indicators. The results successfully detect fatigue symptoms over 72% of the times on haul truck drivers in Peruvian mines.
Fast Data Recovery for Improved Mobility Support in Multiradio Dual Connectivity

Data aggregation is one of the crucial features of the 3GPP Multi-Radio Dual Connectivity (MR-DC) technology. However, mobility events and radio link failures, which may occur during the data aggregation, may pose challenges in meeting the latency, reliability, and throughput key performance indicators (KPIs). Unlike single connectivity, the user equipment (UE) in MR-DC operation can experience such events in either of the two base stations (BSs) serving the UE with MR-DC. In typical MR-DC deployments, these events occur more frequently in the BS acting as the secondary node (SN) since the SN operates at a higher frequency band. In this paper, we show that handovers (HOs) and signal blockage events that occur at the SN can create out-of-order data reception or losses at the UE’s Packet Data Control Protocol (PDCP) layer, making the application stop receiving data for up to hundreds of milliseconds. Thus, challenging to meet the KPIs defined for such application. To mitigate this effect, we propose an intelligent and efficient mechanism that operates in the transmitting PDCP layer and significantly minimizes the data interruption periods suffered by the application when the UE aggregates data and HOs or failures of the SN occur. We use LTE/NR testbed experiments to show that the proposed mechanism achieves a high and stable aggregate throughput with near-zero interruption time and data reliability of at least 99.999% without transport layer retransmissions. The experiments are conducted for saturated TCP traffic and under link quality variations based on traces extracted from a Nokia-proprietary system-level simulator.
A Compressed Data Partition and Loop Scheduling Scheme for Neural Networks

Neural networks (NNs) have been widely adopted in various application domains. Deeper NNs greatly enhance the output accuracy, but complex NNs with more parameters incur intensive memory accesses, and the data usually need to be partitioned since it may exceed the on-chip storage. However, there is no research considering the partition and scheduling co-design of the NNs. In this paper, we propose a sparse NN data partition and loop scheduling scheme. We establish the compression efficiency model of the matrix sparse algorithm and design a partition selection method based on sparsity characteristics analyzed by the compression efficiency model. Further, we design a loop scheduling scheme based on the proper partition size. The experiment results show that the average memory access of each layer can be compressed to 68% of the original, and the throughput of the AlexNet, VGG and VGG19 is increased to an average of 1.66 times.
Skeleton of Implementing Voice Control for Building Automation Systems

Building automation is one of the most popular trends in the 21st century, as it provides buildings with modern and luxurious touches. Furthermore, voice-controlled automation systems are very convenient for the current fast-paced life style as people need to quickly, efficiently, and safely control everything around them. The goal of this article is to provide a skeleton for implementing voice control to a building automation system. The article also presents and evaluates a use case of the proposed skeleton integrated with a KNX-based office automation system. Through the integrated system, lights and blinds are controlled and sensors are polled using voice commands from an authorized personal. This article serves as an educational reference for engineering students interested in applying authorized voice control capability to automation systems.
The ABC Analysis of Drug Use and Cost in Cardiology Outpatients -National Health Insurance

Background: Cardiovascular disease is one of the chronic diseases with an increasing prevalence from year to year. The results of Indonesia's basic health research (RISKESDAS 2018) showed that it was experienced by 2.9% of the population. Ensuring the continuity of drug availability is very important in pharmacy services for this patient group. The history of drug use is considered in the planning dan procurement of drug products. Objective: This study aims to analyze the drug use pattern and cost of drugs for cardiology outpatients in the National Health Insurance scheme (JKN). Methods: A retrospective observational study was conducted using the prescription of cardiology outpatients JKN for March-May 2021. The ABC method carried out the analysis of drug use patterns and cost. Results: From 2,986 prescriptions for cardiovascular disease, there were 94 types of drugs from 37 therapeutic classes. In the ABC analysis of drug use for class A, 68.82% contained seven types of drugs, class B, 20.55% contained nine types of drugs, and class C, 10.63% with 78 types of drugs. The results of ABC analysis for investment value (cost) of drugs class A 66.96% contained two types of drugs, class B 19.81% with four types of drugs, and class C 14.23% with 88 types of drugs. Conclusion: ABC analysis of the drug use pattern and cost/investment showed different patterns which had value to consider in procurement planning to maintain service continuity.
Risk Factors for Suboptimal Adherence Identified by Patient-Reported Outcomes Assessments in Routine HIV Care at 2 North American Clinics

Purpose Use of patient-reported outcomes assessments (PROs) can improve patient–provider communication and focus provider attention on current health issues. This analysis examines the association between suboptimal antiretroviral therapy (ART) adherence and factors obtained through PROs among people with HIV (PWH) at 2 North American outpatient clinics. Patients and Methods Immediately before a clinic visit, PWH completed self-administered PROs. Unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated from logistic regression models to identify sociodemographic and health-related factors (satisfaction with ART, difficulty meeting housing costs, depression, intimate partner violence, risk of malnutrition, smoking status, alcohol use, and substance use) associated with suboptimal adherence (defined as self-reporting <95% or <80% adherence). Multiple imputation was performed to account for missing data in the multivariate analyses. Results Of 1632 PWH, 1239 (76%) responded to the adherence assessment; of these, 268 (22%) and 106 (9%) reported <95% and <80% adherence, respectively. Of 1580 PWH who responded, 354 (22%) were dissatisfied with their HIV medication. Of responding PWH, 19% reported moderate-to-severe depression, 23% indicated they were at risk of malnutrition, 34% were current smokers, and 62% reported substance use in the past 3 months. Dissatisfaction with ART was significantly associated with <95% and <80% adherence in the unadjusted analysis (unadjusted OR [95% CI], 3.38 [2.51–4.56] and 4.26 [2.82–6.42], respectively) and adjusted analysis (adjusted OR [95% CI], 2.76 [1.91–4.00] and 3.28 [1.95–5.52], respectively); significance remained after multiple imputation. In adjusted analyses, no risk of malnutrition was significantly associated with reduced odds of <95% adherence after multiple imputation (adjusted OR [95% CI], 0.714 [0.511–0.997]); no other factors were associated with <95% or <80% adherence. Conclusion These results suggest that implementation of PROs evaluating treatment satisfaction may provide value to adherence management in routine HIV care.
Combining Multiple Classifiers using Ensemble Method for Anomaly Detection in Blockchain Networks: A Comprehensive Review

—Blockchain is one of the most anticipated technology revolutions, with immense promise in various applications. It is a distributed and encrypted database that can address a range of challenges connected to online security and trust. While many people identify Blockchain with cryptocurrencies such as Bitcoin, it has a wide range of applications in supply chain management, health, Internet of Things (IoT), education, identity theft prevention, logistics, and the execution of digital smart contracts. Although Blockchain Technology (BT) has numerous advantages for Decentralized Applications (DApps), it is nevertheless vulnerable to abuse, smart contract failures, security, theft, trespassing, and other concerns. As a result, using Machine Learning (ML) models to detect anomalies is an excellent way to detect and safeguard blockchain networks from criminal activity. Adapting ensemble learning methods in ML to create better prediction outcomes is a viable approach for anomaly identification. Ensemble learning, as the name implies, refers to creating a stronger and more accurate classification by combining the prediction results of numerous weak models. As a result, an in-depth evaluation of ensemble learning methodologies for anomaly detection in the blockchain network ecosystem is applied in this paper. It comprises numerous ensemble methods (e.g., averaging, voting, stacking, boosting, bagging). The review collects data from three established databases, which are Scopus, Web of Science (WoS), and Google Scholar. Specific keywords are employed, such as Blockchain, Ethereum, Bitcoin, Anomaly Detection, and Ensemble Learning, employing advanced searching algorithms. The results of the search found 60 primary articles from 2017 to 2022 (30 from Scopus, 20 from the WoS, and 10 from Google Scholar). Based on these findings, we decided to divide our debate into three primary themes: (1) the fundamentals of Blockchain Technology (BT), (2) the overview of ensemble learning, and (3) the integration and analysis of ensemble learning in blockchain networks for anomaly detection. In terms of awareness and knowledge, the results are also discussed in terms of what they mean and where future research should go.
Resilient Average Consensus of Second-Order Multi-Agent Systems

In this paper, we study the problem of resilient average consensus for second-order multi-agent systems with misbehaving agents. General types of misbehaviors are considered, including false data injection attacks and accidental faults. The difficulties of this problem are to detect errors in a distributed way and accurately compensate two-dimension state errors by one-dimension acceleration input. We first provide sufficient conditions for second-order average consensus. Then, we design detection methods via two-hop communication information and propose schemes to compensate errors accurately in a distributed way inspired by sufficient conditions. Hence, we propose a finite input-errors detection-compensation-based consensus algorithm (FIDC). Considering infinite attacks on input, velocity and position, an extension named IADC is proposed with a fault-tolerance mechanism. We prove that the proposed algorithms allow agents to asymptotically achieve second-order average consensus. Finally, extensive simulations are conducted to verify the effectiveness of the algorithms.
Divergence Of Persistent Length Of A Semiflexible Homopolymer Chain In The Stiff Chain Limit

In this brief report, we revisit analytical calculation [Mishra, {\it et al.}, Physica A {\bf 323} (2003) 453 and Mishra, NewYork Sci. J. {\bf{3(1)}} (2010) 32. ] of the persistent length of a semiflexible homopolymer chain in %the extremely stiff chain limit, {\bf $k\to0$ (where, $k$ is stiffness of the chain)} for directed walk lattice model the extremely stiff chain limit, $k\to0$ (where, $k$ is stiffness of the chain) for directed walk lattice model in two and three dimensions. Our study for two dimensional (square and rectangular) and three dimensional (cubic) lattice case clearly indicates that the persistent length diverges according to expression $(1-g_c)^{-1}$, where $g_c$ is the critical value of step fugacity required for polymerization of an infinitely long linear semiflexible homopolymer chain and nature of the divergence is independent of the space dimension. This is obviously true because in the case of extremely stiff chain limit the polymer chain is a one dimensional object and its shape is like a rigid rod.
Psychological Distress and Smartphone Addiction: Does Perceived Social Support Make a Difference

The smartphone is a double-edged sword for university students; it is a necessary tool for communication which also brings along negative consequences if used excessively. This cross-sectional study was conducted to identify the prevalence rate of smartphone addiction among the undergraduates, to determine psychological distress as a significant predictor of smartphone addiction, and to examine perceived social support as a significant mediator in psychological distress and smartphone addiction. A total of 112 (61 females, 51 males) undergraduates in Kuala Lumpur, Malaysia, aged 18 to 24 years were selected through the purposive sampling method. Participants completed a set of questionnaires via Google Form consisting of Kessler Psychological Distress Scale, Smartphone Addiction Scale-Short Version, and Multidimensional Scale of Perceived Social Support. SmartPLS (Partial Least Squares) which is based on the principles of path modeling and bootstrapping was employed to build the causal model. Results revealed that 66.10% of undergraduates were addicted to smartphones. Also, psychological distress significantly predicted smartphone addiction, with t-value = 7.423, p < 0.001. However, perceived social support did not mediate the relationship between psychological distress and smartphone addiction, with t-value = -0.939, p > 0.05. These findings imply that smartphones have become indispensable for undergraduates and the psychological distress of undergraduates should be given priority in reducing smartphone addiction. Suggestions are made for higher learning institutions to design intervention plans to reduce the psychological distress and smartphone addiction amongst university students.
Minimum Norm Coverage Control of AUVs for Underwater Surveillance with Communication Constraints

Coverage control of multiple autonomous underwater vehicles (AUVs) plays a critical role in underwater surveillance. However, the finite energy and harsh underwater communication conditions bring challenges to the coverage control. To this end, an optimization-based energy-efficient min-norm coverage controller considering communication constraints is proposed in this paper. In this method, the control Lyapunov function is leveraged to determine a min-norm control input when achieving coverage. A triggered high-order control barrier function inspired dynamical weighted minimum spanning tree is optimized under the multiple AUV dynamics to enforce minimum effects on the min-norm controller. The attenuation of communication is maintained at an allowable rate by achieving the forward invariance of the state admissible set. Extensive simulation results demonstrate the effectiveness of the proposed method.
An Output Regulation Approach to Distributed Voltage Regulation of Multiple Coupled Distributed Generation Units in DC Microgrids

In this paper, we propose distributed voltage regulation control laws for multiple coupled distributed generation (DG) units in a DC microgrid. Each DG unit is subject to a possibly time-varying current load and the DG units are coupled through transmission lines. Under the proposed control law, each DG unit, utilizing its own state and the information received from its neighboring units, is able to regulate its output voltage to a possibly time-varying reference voltage, regardless of the influence from its load and the coupling with other units. Simulation examples are provided to illustrate the implementation of the proposed control law and verify its effectiveness.