Miracle-dz/UniFAR-FTUs huggingface dataset:
Clonazepam-Related Paradoxical Behavioral Disinhibition: An Uncommon But Grave Adverse Effect.

To the Editors: L iterature suggests behavioral disinhibition includes motor, instinctual, emotional, cognitive, and perceptual aspects. The clinical signs and symptoms of this pathology are indistinguishable from the signs and symptoms that comprise the criteria for the diagnosis of mania in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. These include symptoms such as irritability, hyperactivity, pressured speech, decreased need for sleep, distractibility, hypersexuality, hyperphagia, aggressive outbursts, euphoria or elation, grandiosity, racing thoughts, and perceptual abnormalities such as hallucinations and paranoid ideation. The incidence of drug-associated behavioral disinhibition varies significantly and cannot be quantified precisely, as most descriptions arise predominately from case reports rather than clinical trials. Adverse effects are seldom the target of clinical studies, although they are nowmonitoredmoremeticulously in controlled trials. The incidence is contingent upon the sample studied but tends to be elevated in patients with preexisting poor impulse control. Alcohol amplifies the disinhibiting effect of benzodiazepines. Disinhibition has been reported after treatment with tricyclic antidepressants, and reports are now appearing that describe disinhibition in patientswhohave been treatedwith selective serotonin (5-hydroxytryptamine [5-HT]) reuptake inhibitors. Disinhibition is uncommon with antipsychotics and benzodiazepine anticonvulsants but few case reports have emerged. The prior case reports of behavioral disinhibition illustrate patients treated with high doses of high-potency benzodiazepines, such as alprazolam, flunitrazepam, and triazolam, especially when they are administered intravenously or intranasally. To the best of our knowledge, this is the first case of disinhibition syndrome that occurred in the context of low-dose clonazepam therapy for treatment of rapid eye movement (REM) sleep behavior disorder (RBD).
A Semi-Supervised Approach for User Reviews Topic Modeling and Classification

Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews, i.e., comments, before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, in order to make a decision. Topic modeling is a common approach to categorize reviews so that the customers only read the reviews in a certain topic of interest, i.e., category. In literature, most related studies applies unsupervised learning for topic modeling and reviews classification. This paper proposes a combination of both unsupervised and supervised learning, i.e., semi-supervised learning, for classifying the reviews. The proposed approach was applied on a set of Amazon reviews and results showed that this approach is more efficient as compared to the unsupervised learning approach solely.
Machining Learning for 2D-SLAM Object Classification and Recognition

Artificial intelligence has being widely used in the last decade, and the relevant machine learning (ML) topics are also attracted more and more attention. Therefore, in this note, certain ML related algorithms are integrated into two dimensional simultaneous localization and mapping (2D-SLAM) technology in order to give meaningful labels to certain specific objects in the environment. For the 2D-SLAM technology, the main objective is to reconstruct a binary map of the unknown environment. However, 2D-SLAM itself does not have the capability of recognizing scanning objects. To extend the application of 2D-SLAM, in this study, point cloud clustering in conjunction with image preprocessing are used in the ML methods. The proposed method can predict the label of the clustered point clouds. Consequently, the 2D-SLAM could achieve environment awareness applications, for example, forest tree counting use. Based on a given bunch of training data, this research show that the model training accuracy is 99.89%, the validation accuracy is 97.96%, and the testing accuracy could achieve 95.96%; Finally, the predicted accuracy for the given SLAM map can be up to 80%.
Learning Control for Voltage and Frequency Regulation of an Infinite Bus System*

A new learning methodology in terms of a discretization of a so-called Chen-Fliess series of a control affine nonlinear system was recently proposed, in part, for the purpose of systematically including system structure and expert knowledge into control strategies. The main objective of this paper is to appropriately embed this learning unit as a supporting predictive controller for power dynamical systems. In particular, an infinite bus system is used for the prototype design of a smart and active control policy to regulate voltage and frequency. It is demonstrated by simulation how a controller employing a Chen-Fliess learning unit can recover from a fault and address modeling mismatch.
Treating the Lips and Its Anatomical Correlate in Respect to Vascular Compromise

Abstract Treating the lips to increase facial attractiveness and youthfulness is challenging when trying to consider ethnic differences in an increasingly more diverse society. Multiple injection techniques are currently available for treating lip contour and volume, but a validation in the cadaveric model under the aspects of safety has not been performed yet. The injection techniques presented in this study are based on the experience and personal selection of the authors. The authors have assessed, treated, and evaluated for more than 20 years patients from the Middle East and Central Europe. Cadaveric verification was performed for each of the presented techniques to identify the positioning of the injected product inside the lips and its relation to the superior/inferior labial arteries. The results of the anatomic analyses revealed that in 58.3% of the performed injections, the product was placed close to the superior/inferior labial arteries. In 60.0% of the cases, applications using a needle placed the injected product in endangered locations, whereas 57.1% of the cases using cannulas placed the product in endangered locations (i.e., in the vicinity of the superior/inferior labial arteries). This anatomic study revealed that injected material into the lips is frequently placed in close proximity to labial arteries representing a high risk for intra-arterial applications, leading to tissue loss (necrosis) and potential end-arterial embolism (potential blindness). Nevertheless, treatment of the lips should be a multistep approach focusing first on the far (upper and middle face) and close (labiomandibular and labiomental) perioral regions.
Convolutional LSTM Networks for Vibration-Based Defect Identification of the Composite Structure

The increasing need for reliable and sensitive detection methods have been brought about by the design of lighter, more flexible or complicated composite structures. In the field of vibration-based testing, many studies about the assessment of composite structures have been proposed. Artificial neural networks(ANNs) have offered a novel route to explain the relationship between vibration feature and defect status, and an increasing number of ANN frameworks have been designed for adhesive defect identification in the composite structure. This paper proposed a novel ANN framework based on Convolutional Long Short-Term Memory (ConvLSTM) in order to identify composite structure with adhesive defect. The proposed framework allowed pattern classification of the defect accordingly. To verify the feasibility of the proposed approach, two specimens with different levels of debonding were tested. The obtained results indicate that the classification accuracy strongly depended on the proper adaption of ConvLSTM
IoT Based Communication Technologies to Integrate and Maximize the Efficiency of Renewable Energy Resources with Smart Grid

One of the future goal in smart grid that maximize the performance of the system up to 100% and in this production large amount should be obtained from renewable energy resources. To make a reduction in the cost of energy, geothermal sources and fossil fuels are also used with smart grid to increase the efficiency of the grid. Today due to high cost of energy, very high losses in the actual power system, risks occur due to nuclear and oil and gas energy, and globally environment changes are the way of motivating to convert these sources into conventional energy sources. So today is the era of SG with integrating of renewable energy resources to maximize the efficiency and reduce the cost and make the system more stable. Traditional power systems are converted into smart grids due high efficiency, more securable, more reliable, and especially highly protection of the user data and bi directional energy flow. Due to this in this paper we discuss the IOT (Internet of things) based communication technologies to make the system more stable with high efficiency, minimum cost and with high protection of the user data in smart grid. IOT aided smart grid systems that consist of existing applications, prototypes and architectures. This research paper also highlights the challenges, open issues and a lot of research directions for the internet of things (IOT)-aided smart grid systems.
Changes in Serotype of Streptococcus pneumoniae After the Introduction of the 13-Valent Pneumococcal Vaccine in a Homogenous Population on Jeju Island

We compared the serotypes of Streptococcus pneumoniae between the pre-pneumococcal conjugate vaccine (PCV)13 era and post-PCV13 era among homogenous inhabitants of an isolated South Korean island. A total of 325 S. pneumoniae strains were isolated. In the pre-PCV13 era, 19A/F, 15A/F, 19B, and 23A serotypes were identified. In the post-PCV13 era, 15 serotypes were identified. The 19F and 23A serotypes showed the highest prevalence in the pre- and post-PCV13 era, respectively. After PCV13 introduction, the PCV 13 serotype coverage rate was decreased (80.0% and 30.5% in the pre- and post-PCV13 eras, respectively), while the proportion of non-PCV 13 serotypes increased.
Android Malware Detection Using Machine Learning

The usage of mobile devices is increasing exponentially. There were lots of critical applications such as banking to health applications are available on mobile devices through mobile applications. This penetration and spread of mobile applications brings some threats. Malicious software(Malware) is one of these dangers. Malware has the potential to cause damage to various scales such as theft of sensitive data, identity and credit card. To reduce the effects of these threats, antiviruses have been developed and malware analysis teams have been established, but human effort may be insufficient in the rapidly growing malware market. For this reason, automated malware scanning solutions should be developed by making use of machine learning algorithms. In this study, machine learning models were created by using the n-gram features of the smali files, which are the decompiled Android packages. The trained models are combined with different feature extraction and feature selection methods and as a result their performances are reported.
Analysis of an Active-Clamp Flyback Converter

This study explored the operation principles of active clamp flyback converters. A control strategy was adopted to achieve strong noise immunity, and a voltage mode was configured to attain advantages such as simple circuit design. Experiments were performed to verify the feasibility of the proposed active clamp circuit. The experiment results facilitated the completion of an active clamp flyback converter with a switching frequency of 300 kHz, an output power of 65 W, and an output voltage of 19 V.
Forensic Malware Identification Using Naive Bayes Method

Malware is a kind of software that, if installed on a malware victim’s device, might carry malicious actions. The malicious actions might be data theft, system failure, or denial of service. Malware analysis is a process to identify whether a piece of software is a malware or not. However, with the advancement of malware technologies, there are several evasion techniques that could be implemented by malware developers to prevent analysis, such as polymorphic and oligomorphic. Therefore, this research proposes an automatic malware detection system. In the system, the malware characteristics data were obtained through both static and dynamic analysis processes. Data from the analysis process were classified using Naive Bayes algorithm to identify whether the software is a malware or not. The process of identifying malware and benign files using the Naive Bayes machine learning method has an accuracy value of 93 percent for the detection process using static characteristics and 85 percent for detection through dynamic characteristics.
Attacks on Self-Driving Cars and Their Countermeasures: A Survey

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle’s operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.
A New Functional Observer Design of Delayed Singular Systems in Discret-time and Frequency domains

This paper presents both discrete-time and frequency domain design of functional unknown inputs observer for discrete-time singular delayed systems. The main interest is that the proposed observer estimates a functional state and an unknown input vector. The discrete-time procedure design is based on the unbiasedness of the estimation error of the observer using Lyapunov functional. The problem is solved by means of Linear Matrix Inequalities (LMIs) to find the optimal gain implemented in the functional observer design. The frequency domain procedure design is derived from discrete-time domain results, where we propose a suitable coprime Matrix Fractions Descriptions (MFDs). A numerical example is given to prove the effectiveness of the proposed approach.
Mindfulness-based interventions for adolescent health.

PURPOSE OF REVIEW Mindfulness, the practice of paying attention to the present moment, purposefully and nonjudgmentally, has been gaining popularity as adjunct treatment for adolescents with a range of physical and mental health problems. Research conducted in adults and emerging research conducted in adolescents has shown that mindfulness-based interventions can improve outcomes in several areas. The purpose of this review is to discuss recent research on the effects of mindfulness and suggest exercises that primary care clinicians can offer to their adolescent patients. RECENT FINDINGS Research has shown positive effects of mindfulness across several health conditions commonly encountered during adolescence. Mindfulness-based Interventions can reduce symptoms of anxiety and depression. The application of mindfulness can help with the prevention and treatment of binge eating, over-eating, and restrictive eating disorders. In the treatment of substance use disorders, mindfulness can improve emotion regulation and reduce symptoms of withdrawal and craving. Mindfulness can also lead to improvement in overall quality of life for patients suffering from chronic pain. Lastly, mindfulness can be useful for adolescents with ADHD, sleep problems, chronic illness, and stress related to performance sports. SUMMARY Although research in adolescents remains limited, mindfulness holds promise in the treatment of a range of health conditions in adolescents.
A Bayesian Fusion Model for Heart Rate Annotations

Lack of gold standard of truth is a critical problem in medical field. Consensus extraction from noisy labels to estimate ground truth is a common approach. However, significant inter-observer variability and various biases limit the accuracy. For this challenge, we proposed an unsupervised Bayesian framework to aggregate multiple heart rate (HR) annotations from the electrocardiogram (ECG) signal and infer the underlying ground truth and precision of annotators when the ground truth is not available. We further introduced the bias and precision adjustment factor of annotator to model the annotator’s performance in each instance. Test on the 2014 PhysioNet/ Computing in Cardiology challenge database showed that the proposed model had an improvement not only over the existing aggregation model (EM-R method), mean and median strategies (the highest of 10.36%，20.26% and 12.43%) but also to the best single annotator (13.75%) used in this paper.
Changes in timing of seasonal peak photosynthetic activity in northern ecosystems

Seasonality in photosynthetic activity is a critical component of seasonal carbon, water, and energy cycles in the Earth system. This characteristic is a consequence of plant's adaptive evolutionary processes to a given set of environmental conditions. Changing climate in northern lands (>30°N) alters the state of climatic constraints on plant growth, and therefore, changes in the seasonality and carbon accumulation are anticipated. However, how photosynthetic seasonality evolved to its current state, and what role climatic constraints and their variability played in this process and ultimately in carbon cycle is still poorly understood due to its complexity. Here, we take the “laws of minimum” as a basis and introduce a new framework where the timing (day of year) of peak photosynthetic activity (DOYPmax) acts as a proxy for plant's adaptive state to climatic constraints on its growth. Our analyses confirm that spatial variations in DOYPmax reflect spatial gradients in climatic constraints as well as seasonal maximum and total productivity. We find a widespread warming‐induced advance in DOYPmax (−1.66 ± 0.30 days/decade, p < 0.001) across northern lands, indicating a spatiotemporal dynamism of climatic constraints to plant growth. We show that the observed changes in DOYPmax are associated with an increase in total gross primary productivity through enhanced carbon assimilation early in the growing season, which leads to an earlier phase shift in land‐atmosphere carbon fluxes and an increase in their amplitude. Such changes are expected to continue in the future based on our analysis of earth system model projections. Our study provides a simplified, yet realistic framework based on first principles for the complex mechanisms by which various climatic factors constrain plant growth in northern ecosystems.
Enhancing the Robot PD-Type Feedback Control Performance by Means of Inertial Effects Compensation

The paper describes a modification of the PD-type algorithm with gravity effects compensation for control of robot manipulators, which tries to partially compensate the influence of inertial terms in the robot motion equations to achieve uniform effect of control on individual axes. In this way it is possible to reduce the overall settling time and to enhance the tracking precision for given magnitude of the control signal, which is demonstrated on a 5-DOF robot example. In comparison to the inverse dynamics method, the influence of non-linear terms in the robot motion equations on total control input is reduced, which helps to increase the control performance and to prevent from reaching the saturation limits. For the basic version of the method asymptotical stability of the control error is proved.
Evaluating Traffic Flow Effects of Cooperative Adaptive Cruise Control based on Enhanced Microscopic Simulation

Traffic flow effects of cooperative adaptive cruise control (CACC) have not been fully understood, though such understanding is essential for the forthcoming research and applications concerning connected and automated vehicles as well as the implication for future traffic management and control. This paper aims to explore the impacts of CACC on traffic flow efficiency based on in-depth microscopic simulation studies using an enhanced version of AIMSUN. First, the Gipps car-following and lane-changing models used by default in AIMSUN were replaced or enhanced with more advanced car-following and lane-changing models to improve the capability of the simulation system in realistically reproducing capacity drop phenomena and capturing traffic merging processes. Second, a simulation platform was established using the enhanced AIMSUN with respect to a 10-km Dutch freeway stretch that includes multiple bottlenecks involving recurrent congestion. Third, we explored on this simulation platform the impacts of CACC on traffic flow mixed with manually driven vehicles and CACC equipped vehicles, in consideration of a variety of CACC market penetration rates. The study results suggest that the CACC impacts are quite positive.
The Z Specification for Exam Scheduling System (ESS) thru Genetic Algorithm

Formal methods are widely used by using mathematical notations to precisely express requirements specification. In this paper, we discuss the formal specification using a case study of Examination Scheduling System (ESS). ESS is a system that generates exam schedule by using genetic algorithm. ESS is developed to solve the exam scheduling problem in order to reduce time-consuming for conventional manually managing the exam venue. By using genetic algorithm, it helps to generate exam schedule very fast and reduce time taken on processing the exam schedule manually. The formal specification of ESS using Z language is also presented in order to show the mapping of the implementation of ESS with its design by using the formal specification. The Z schema can effectively remove ambiguity presents in natural language specification. Hence, this will help to reduce defect in developing the system during implementation phase.
Prediction of Time Series Data Based on Transformer with Soft Dynamic Time Wrapping

It is a challenge to predict the long-term future data from time series data. This paper proposes to use a Transformer with soft dynamic time wrapping for early stopping criteria, called a soft-DTW Transformer. Our experiment in an open-source dataset HouseTwenty shows that the average prediction error rate with soft-DTW Transformer is 27.79%, greatly reduced from 45.70% for using SVR, a common time series method.