Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Sophocarpine on the Pharmacokinetics of Umbralisib in Rat Plasma Using a Novel UPLC-MS/MS Method

Umbralisib is a dual inhibitor of phosphatidylinositol 3-kinase delta (PI3Kδ) and casein kinase 1 epsilon (CK1ε) for treating marginal zone lymphoma (MZL) and follicular lymphoma (FL). This study aimed to develop a fast and stable ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for quantitative analysis of umbralisib in rat plasma and its application for evaluating the effect of sophocarpine on the pharmacokinetics of umbralisib. A direct protein preparation with acetonitrile was used to deal with rat plasma. Umbralisib and duvelisib (internal standard, IS) were isolated on a Waters Acquity UPLC BEH C18 column with mobile phase consisted of acetonitrile and 0.1% formic acid in water. The linear range was from 0.5 to 1,000 ng/ml. Both of the precision (RSD%) and accuracy (RE%) were less than 15% in a permissible range. The mean recovery and matrix effect of umbralisib were 86.3–96.2% and 97.8–112.0%, respectively. When umbralisib was combined with sophocarpine, AUC0→∞ of umbralisib was significantly reduced to 2462.799 ± 535.736 ng/ml•h from 5416.665 ± 1,451.846 ng/ml•h, and Cmax also was markedly diminished. Moreover, CLz/F was increased more than two times. This developed, optimized and technical UPLC-MS/MS method was extremely suitable for detecting the concentrations of umbralisib in rat plasma after an oral administration, and sophocarpine significantly changed the pharmacokinetics of umbralisib in rats. This obvious pharmacokinetic changes indicates that there seems to exist herb-drug interaction between sophocarpine and umbralisib.
Integrating Social Presence With Social Learning to Promote Purchase Intention: Based on Social Cognitive Theory

Consumers mainly acquire information through social learning in online shopping environment, and social presence as a media attribute generated by real-time interactions in live streaming commerce is more conducive for consumers’ social learning. Therefore, it is worth investigating the roles of social presence and social learning on consumers’ purchase intention (PI) in the strong interactive environment. Based on social cognitive theory (SCT) framework and drawing on social presence theory and social learning theory, this study investigates the relationships among social presence, social learning process, and PI. Social presence is operationalized into social presence of others (SPO) and social presence of interactions (SPI), whereas social learning process contains external interaction process [exploitative learning (ETL) and exploratory learning (ERL)] and internal psychological process [cognitive appraisal (CAP) and affective appraisal AAP)]. The results from a survey of 372 consumers of live streaming commerce indicate that SPO and SPI positively affect ETL and ERL and then contribute significantly to the building of CAP and AAP, which can lead to PI. The findings also provide guidance for brand managers and retailers in building more effective interactive atmosphere and promoting consumers’ positive attitude toward brands in live streaming marketing.
Multi-Agent Trajectory Prediction by Combining Egocentric and Allocentric Views

: Trajectory prediction of road participants such as vehicles and pedes-trians is crucial for autonomous driving. Recently, graph neural network (GNN) is widely adopted to capture the social interactions among the agents. Many GNN-based models formulate the prediction task as a single-agent prediction problem where multiple inference is needed for multi-agent prediction (which is common in practice), which leads to fundamental inconsistency in terms of homotopy as well as inefﬁciency for the memory and time. Moreover, even for models that do perform joint prediction, typically one centric agent is selected and all other agents’ information is normalized based on that. Such centric-only normalization leads to asymmetric encoding of different agents in GNN, which might harm its performance. In this work, we propose a efﬁcient multi-agent prediction framework that can predict all agents’ trajectories jointly by normalizing and processing all agents’ information symmetrically and homogeneously with combined egocen-tirc and allocentric views. Experiments are conducted on two interaction-rich behavior datasets: INTERACTION (vehicles) and TrajNet++ (pedestrian). The re-sults show that the proposed framework can signiﬁcantly boost the inference speed of the GNN-based model for multi-agent prediction and achieve better performance. In the INTERACTION dataset’s challenge, the proposed model achieved the 1st place in the regular track and generalization track.
A Comparative Investigation of Interturn Faults in Induction Motors Suggesting a Novel Transient Diagnostic Method Based on the Goerges Phenomenon

Early interturn short circuits are among the most challenging faults to detect in electrical machines. It is not only difficult to identify the fault, but the diagnosis and decision-making time are crucial since the severity of this fault increases very fast, leading to a catastrophic breakdown. Despite the importance of this fault's prompt detection, it has been lately shown that classical methods may be insensitive to early severity conditions leading to false-positive diagnostic conclusions. This article investigates the validity and reliability of current-based approaches to those utilizing the magnetic flux performing a comparative critical assessment. The work is carried out with a finite-element analysis and experimental testing while both the steady-state and transient operations are examined. The article demonstrates the incapability of classical approaches to reliably detect early interturn faults while flux-based methods prove to be sensitive and superior to the current-based ones at the steady state. Moreover, a transient method based on the Goerges phenomenon is proposed in the article for the first time.
Progress in Recommender Systems Research: Crisis? What Crisis?

Scholars in algorithmic recommender systems research have developed a largely standardized scientific method, where progress is claimed by showing that a new algorithm outperforms existing ones on or more accuracy measures. In theory, reproducing and thereby verifying such improvements is easy, as it merely involves the execution of the experiment code on the same data. However, as recent work shows, the reported progress is often only virtual, because of a number of issues related to (i) a lack of reproducibility, (ii) technical and theoretical flaws, and (iii) scholarship practices that are strongly prone to researcher biases. As a result, several recent works could show that the latest published algorithms actually do not outperform existing methods when evaluated independently. Despite these issues, we currently see no signs of a crisis, where researchers re-think their scientific method, but rather a situation of stagnation, where researchers continue to focus on the same topics. In this paper, we discuss these issues, analyze their potential underlying reasons, and outline a set of guidelines to ensure progress in recommender systems research.
Gender Differences in the Production of Sibilant Contrast by Native Chinese Speakers

The current study investigated the gender differences in the production of sibilant contrast by native Chinese speakers. Recordings of words with /s/ and /ȿ/ at word-initial position were collected from 102 native Chinese speakers. A series of acoustic parameters such as Centre of Gravity (COG), Standard Deviation, Skewness, and Kurtosis were measured for the sibilants in the recordings. Significant higher COGs in sibilant production were found for females than for males. Vowel contexts can have different influences on the COG of the preceding sibilants. No significant gender difference was found in spectral distance between /s/-/ȿ/ contrast.
Selection of Avocado Plants Based on Fruit Characters, Fat Content, and Continual Harvest along the year in West Java-Indonesia

Avocado ( Persea americana Mill.) belongs to Lauraceae family, is originated from lowland and highland tropical America region. Avocado plant was introduced to Indonesia in 1750 by Spanish. Fruit of avocado contains high unsaturated ( healthy) fat, protein, fiber, vitamins, minerals, and energy. Fruit of avocado can be eaten freshly, used as an additional ingredient cooking and material for cosmetics. Avocado fruits are available along the year in market cities of Indonesia but their fruits fluctuate in quantity and uncertain quality. It because the plants commonly come from seeds and grown in home-yards or small areas by small farmers. The purposes of this research were to evaluate and select some cultivars for parent plants that produce good fruit characters, high content of fat, and continual yield along the year. The research was started on January 2011 until 2013 for obtaining data, collecting avocado fruits and scions in West Java province, which is the biggest avocado fruit production in Indonesia. From 180 accessions of avocado fruits, there were 31 potential accessions for selecting good cultivars and 6 accessions of them (10, 5, 39, 48, 61, 123) were selected. The selected fruit character had scores 72 – 80 of maximum 100, fat content 9.78% – 20.57%, and harvest periods from January to December. These accessions as grafted plants have grown and propagated in our research station of the Indonesian Institute of Sciences (LIPI) in Cibinong for further study and for releasing to the farmers.
Electrical Resistivity of Cement Based Materials

Electrical resistivity (ER) of cement based materials (CBM) is an important measurement that indirectly relates to the durability of CBM. This study investigates the correlation between ER of CBM namely concrete with water-to-cement (w/c). As one of CBM hydration parameters w/c is an important parameter that contributes to variations of ER. The results of this study showed that different w/c can be used as indication for the setting characteristics as well as the pore structure of the concrete. ER is obtained by applying current into the concrete and measuring the response voltage during concrete hydration period of 28 days. Two aspects were also being considered in this study: (i) test specimen size and (ii) spacing between the probes. Results show the direct correlation between ER and test specimen sizes and spacing between the probes.
Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers

In this paper, we present a new method for forecasting power consumption. Household power consumption prediction is essential to manage and plan energy utilization. This study proposes a new technique using machine learning models based on the stationary wavelet transform (SWT) and transformers to forecast household power consumption in different resolutions. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from household power consumption data. The SWT and its inverse are used to decompose and reconstruct the actual and the forecasted household power consumption data, respectively, and deep transformers are used to forecast the SWT subbands. Experimental findings show that our hybrid approach achieves superior prediction performance compared to the existing power consumption prediction methods.
Experimental Study of Light Wave Propagation for Underwater Optical Wireless Communication (UOWC)

Underwater Optical Wireless Communication (UOWC) is identified as a promising technology because it offers higher bandwidth than acoustics and radio frequency techniques. This paper investigates the performance of light wave propagation for UOWC through experimental approach. An experimental set-up is developed consists of a transmitter, receiver and a glass chamber to emulate the water channel. Three types of water including clear, sea and cloudy are used to investigate their interaction with the light emitted by light emitting diode (LED) and laser diode. The geometrical loss (GL) analysis shows the white LED suffered a severe GL (GL<<1) as the transmission link increases due to the wide viewing angle while green and yellow LED obtained an equal GL due to the same size of viewing angle. However, red laser does not experience any GL. Therefore, the received power by red laser is 35% higher than by green LED. The analysis deduces that the estimated attenuation coefficient c had an increase of 15% and 55% for green LED and red laser respectively when the UOWC medium changed from clear water to sea water. This study contributes to identify the potential and limitations of different parameter design in order to optimize UOWC performance.
On the Capacity of DNA-based Data Storage under Substitution Errors

Advances in biochemical technologies, such as synthesizing and sequencing devices, have fueled manifold recent experiments on archival digital data storage using DNA. In this paper we review and analyze recent results on information-theoretic aspects of such storage systems. The discussion focuses on a channel model that incorporates the main properties of DNA-based data storage. Namely, the user data is synthesized many times onto a large number of short-length DNA strands. The receiver then draws strands from the stored sequences in an uncontrollable manner. Since the synthesis and sequencing are prone to errors, a received sequence can differ from its original strand, and their relationship is described by a probabilistic channel. Recently, the capacity of this channel was derived for the case of substitution errors inside the sequences. We review the main techniques used to prove a coding theorem and its converse, showing the achievability of the capacity and the fact that it cannot be exceeded. We further provide an intuitive interpretation of the capacity formula for relevant channel parameters, compare with sub-optimal decoding methods, and conclude with a discussion on cost-efficiency.
Sensor Fault-Resilient Control of Electronically Coupled Distributed Energy Resources in Islanded Microgrids

In this article, a sensor fault-resilient control approach for electronically interfaced distributed energy resources (DERs) in islanded microgrids is proposed. The proposed control strategy consists of a sliding-mode observer (SMO) and an <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> output feedback controller that ensures resiliency of the DER units in an islanded microgrid against erroneous measurements due to sensor malfunctioning. Errors in the measurements due to malfunctioning of the associated sensors are modeled as sensor faults in this article. The SMO in the proposed approach estimates the sensor faults, and the estimated faults are used to devise the fault-tolerant control strategy so that the <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> output feedback control remains unperturbed by the erroneous measurements. In order to demonstrate the efficacy of the proposed control strategy, rigorous simulation studies are carried out on a test islanded microgrid system with single and multiple DERs. The efficacy of the proposed approach has been further validated through an experimental study carried out on a prototype islanded microgrid system.
Cross-Block Difference Guided Fast CU Partition for VVC Intra Coding

In this paper, we propose a new fast CU partition method for VVC intra coding based on the cross-block difference. This difference is measured by the gradient and the content of sub-blocks obtained from partition and is employed to guide the skipping of unnecessary horizontal and vertical partition modes. With this guidance, a fast determination of block partitions is accordingly achieved. Compared with VVC, our proposed method can save 41.64% (on average) encoding time with only 0.97% (on average) increase of BD-rate.
Fire-resistant capacity of RC structures considering time-dependent creep strain at elevated temperature

This paper introduces a numerical evaluation of the fire-resistant capacity of reinforced concrete structures. On the basis of a time-dependent temperature distribution across a section determined from a transient heat transfer analysis considering heat conduction, convection and radiation, a layered fibre section method is adopted to take into account the non-linear material properties depending on the temperature. Furthermore, non-mechanical strains of concrete and steel such as thermal strain, transient strain and creep strain, which increase with elevated temperature variation induced by fire, are implemented into the formulation. Numerical results for structural members are compared with experimental data obtained from standard fire tests to investigate the influence of non-mechanical strains at elevated temperature. Beyond the member level, frame structures exposed to fire are also analysed to evaluate the influence of fire on statically indeterminate structures exposed to practical fire conditions.
Quaternion-driven CNN for Object Pose Tracking

In this work we propose a new mixed-input neural network for instance level monocular object tracking. The proposed Y-like architecture has two inputs and one output. An object id with a quaternion representing the object rotation in the previous frame are fed to the first input, whereas the object sub-window is fed to the second input. We demonstrate that on the basis of quaternions the neural network learns attention blobs representing the object rotation in the previous frame. A single neural network has been trained for six objects to estimate their fiducial points in sequences of RGB images. A tracking by optimization approach has been leveraged in the experiments. The algorithm has been evaluated on the OPT benchmark dataset for 6DoF object pose tracking as well as a custom dataset including image sequences with both real and rendered objects.
Proliferation of Opportunistic Routing: A Systematic Review

Opportunistic networks (OppNet) a new paradigm emerges after the great advancement of wireless technology and MANETs (Mobile Ad-hoc Networks). Store-Carry-forward phenomena is used to route the messages in OppNets; therefore, it is called delay tolerant network (DTNs). OppNet is such a heterogeneous flexible network that works in hurricane affected regions and volcanoes environment. The objective of this article is to determine, evaluate, elect and synthesize all terrific quality research work on the basis of Research Questions (RQ) which is significant to use for opportunistic routing. Systematic literature review on state-of-the-art researches on routing protocols are presented with comparative analysis. To eliciting relevant work for review, advanced exploration is conducted on contrasting digital libraries. This work gives the opportunity to the readers to understand what has been done in field of Opportunistic Routing (OR), what parameters effect the performance of protocol, name of the organization; who works in this field. At last, issues, challenges, and upcoming future terms of this network are discussed.
Trajectories of Healthcare Utilization Among Children and Adolescents With Autism Spectrum Disorder and/or Attention-Deficit/Hyperactivity Disorder in Japan

Background Early intervention and prevention of psychiatric comorbidities of children with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are urgent issues. However, the differences in the diagnoses of ASD and ADHD and psychiatric comorbidities associated with age, long-term healthcare utilization trajectories, and its associated diagnostic features have not been fully elucidated in Japan. Method We conducted a retrospective observational study using the medical records. Member hospitals of three major consortiums of hospitals providing child and adolescent psychiatric services in Japan were recruited for the study. Children who accessed the psychiatry services of the participating hospitals in April 2015 were followed up for 5 years, and data on their clinical diagnoses, consultation numbers, and hospitalizations were collected. Non-hierarchical clustering was performed using two 10-timepoint longitudinal variables: consultation numbers and hospitalization. Among the major clusters, the differences in the prevalence of ASD, ADHD, comorbid intellectual disability, neurotic disorders, and other psychiatric disorders were assessed. Results A total of 44 facilities participated in the study (59.5%), and 1,003 participants were enrolled. Among them, 591 diagnosed with ASD and/or ADHD (58.9%) and 589 without missing data were assessed. The mean age was 10.1 years, and 363 (70.9%) were boys. Compared with the pre-schoolers, the school-aged children and adolescents had fewer ASD, more ADHD, and fewer comorbid intellectual disability diagnoses, as well as more diagnoses of other psychiatric disorders. A total of 309 participants (54.7%) continued consultation for 2 years, and 207 (35.1%) continued for 5 years. Clustering analysis identified three, two, and three major clusters among pre-schoolers, school-aged children, and adolescents, respectively. The largest cluster was characterized by early termination of the consultation and accounted for 55.4, 70.6, and 73.4% of pre-schoolers, school-aged children, and adolescents, respectively. Among the school-aged children, the diagnosis of ADHD was associated with a cluster that required longer periods of consultations. Among the adolescents, comorbid psychiatric disorders other than intellectual disability and neurotic disorders were associated with clusters that required hospitalization. Conclusion Continuous healthcare needs were common and psychiatric comorbidities were associated with complex trajectory among adolescents. The promotion of early intervention and prevention of comorbidities are important.
Leadership and management in education : developing essential skills and competencies

This book is designed to help educational administrators in developing essential skills and competencies for leading and managing educational institutions. Text covers three inter-related parts: history and foundations, structures and processes, leadership and management. Each part contains comprehensive chapters with discussions on theoretical concepts and best practices in approaching leadership and managerial issues in educational contexts including clear learning objective and a focus for each chapter.
Advanced Intrusion Detection Using Deep Learning-LSTM Network On Cloud Environment

Cloud Computing is a favored choice of any IT organization in the current context since that provides flexibility and pay-per-use service to the users. Moreover, due to its open and inclusive architecture which is accessible to attackers. Security and privacy are a big roadblock to its success. For any IT organization, intrusion detection systems are essential to the detection and endurance of effective detection system against attacker aggressive attacks. To recognize minor occurrences and become significant breaches, a fully managed intrusion detection system is required. The most prevalent approach for intrusion detection on the cloud is the Intrusion Detection System (IDS). This research introduces a cloud-based deep learning-LSTM IDS model and evaluates it to a hybrid Stacked Contractive Auto Encoder (SCAE) + Support Vector Machine (SVM) IDS model. Deep learning algorithms like basic machine learning can be built to conduct attack detection and classification simultaneously. Also examine the detection methodologies used by certain existing intrusion detection systems. On two well-known Intrusion Detection datasets (KDD Cup 99 and NSL-KDD), our strategy outperforms current methods in terms of accurate detection.