Miracle-dz/UniFAR-FTUs huggingface dataset:
Desirability functions as response in a d‐optimal design for evaluating the extraction and purification steps of six tranquillizers and an anti‐adrenergic by liquid chromatography‐tandem mass spectrometry

Internal standards can be added at different stages of an analytical procedure. When they are added at the beginning of a multiresidue method and their behavior is not exactly the same as that of the analytes, the intended correction for small variations within the analytical process could not be achieved. Because of this, in the present work, the use of d‐optimal designs together with desirability functions is proposed to state the experimental response under study. The overall desirability function used relates two analytical criteria: to assess a similar chemical behavior of each analyte in relation to its internal standard and to avoid a significant reduction of the absolute peak area of the internal standards. This strategy has been applied to the analysis of the effect of four factors related to the extraction and purification steps of six tranquillizers and a β‐blocker from pig muscle analyzed by liquid chromatography–tandem mass spectrometry. The effect of those factors has been evaluated by means of an ad hoc d‐optimal design consisting of only 11 experiments. The resulting levels of the four factors that enable to achieve the greatest overall desirability have also been compared with those obtained when either the standardized or absolute peak area has been considered as response. Differences in both the significant factors and their optimum levels have been observed. It is noticeable that the experimental effort necessary to study the effect of the factors has been reduced by more than 50% thanks to the d‐optimal design. Copyright © 2015 John Wiley & Sons, Ltd.
PET Imaging for Alzheimer's Neuroanatomical Disease Diagnosis

The standard for local assessment of nuclear brain scans information still involves manually determining the volumes of interest (VOIs). A range of program solutions for rapid recognition of neuroanatomical features were created because this method is laborious & operator based. We tested 4 automated VOI identification techniques (Free Surfer, HERMES Brass & two PMOD approaches) throughout this study compare to traditional approach because the performance & quality of those technologies have not been thoroughly studied in the analysis of amyloidosis PET information. In a recent medical trial, we obtained florbetaben cerebral PET & MRI information from 10 sufferers with suspected Alzheimer's dementia (AD) & ten age similarly healthy controls (HCs). The 4 automatic processes and the human definition of VOIs on the information were both used. For every VOI, standardised uptake value ratios (SUVRs) were computed using the cerebellum cortex as a reference region. Utilizing Mann-Whitney-U testing, SUVR assessments among ADs & HCs were made, & significance value (Cohen's d) were determined. According to Pearson's testing, the SUVRs of automated VOIs and traditionally calculated VOIs were associated.A substantial change between ADs and HCs was seen in the composites frontal cortex SUVRs produced by arbitrarily specified VOIs (p=00.0100, d=01.530). The same was true for the 4 computational methods that were examined, which had correlation coefficients ranging from 01.380 to 01.620. In a variety of brain regions, the SUVRs of the machine produced VOIs as well as the drawn VOIs significantly correlated, with local variations in the strength of these relationships. For each of the development applications that were assessed, the lateral temporal VOI demonstrated the highest obtaining accurate (r=0.82 to r=0.95, p0.001). The software packages under evaluation have a potentiality to replace the existing conventional practise of physically defining VOIs in the processing of -amyloid PET information.
A new topology of double-stator permanent magnet machine equipped with AC windings on both stators

: A new double stator permanent magnet machine having two sets of alternating current (AC) windings in separate stators is proposed in this study. The proposed machine is appropriate for low-speed direct-drive applications. 2D-and 3D-ﬁnite element analysis (FEA) is adopted in the result predictions. The considered machine elements are: coil and phase ﬂux linkage, coil and phase induced-electromotive force (EMF), copper loss, current density and torque characteristics. The analysis shows that the studied permanent magnet (PM) machine has better electromagnetic performance than its single-stator equivalent. Moreover, the proposed machine has potential for higher reliability if the separate stators are used independently. The eﬀect of design parameters on open-circuit ﬂux linkage and induced-electromotive force, as well as on the average electromagnetic torque of the proposed double stator machine is also presented. It is observed that for each of the investigated design variables, there is a need to select the optimal value in order to achieve the best average torque. The investigated design parameters are: the split ratio, magnet thickness, rotor radial thickness, inner stator tooth-width, rotor inner and outer iron-width/pitch ratio, and stator yoke size.
Learning High-Order Multi-View Representation by New Tensor Canonical Correlation Analysis

Canonical correlation analysis (CCA) has attracted great interest in multi-view representation. However, most of the CCA methods heavily rely on the matrix structure, which may neglect the prior geometric information in high-order data. To deal with the above issue, we first propose a novel tensor CCA formulation with orthogonality, called TCCA-O, based on the Tucker decomposition to preserve the orthogonality. Then, we incorporate a structured sparse regularization term into the TCCA-O, called TCCA-OS, to improve feature representation. In addition, we develop an efficient alternating direction method of multipliers (ADMM)-based algorithm to solve TCCA-OS and conduct numerical comparisons on four public datasets. The results validate the advantages of the proposed methods in terms of classification accuracy, parameter sensitivity, noise robustness, and model stability. In particular, TCCA-O and TCCA-OS improve the classification accuracy by at least 10.03% and 10.36%, respectively, over the state-of-the-art CCA methods on the Caltech101-7 dataset.
Interpersonal Competition in Elderly Couples: A Functional Near-Infrared Spectroscopy Hyperscanning Study

Elderly people tend not to compete with others, and if they do, the mechanism behind the competition is not clear. In this study, groups of elderly couples and matched cross-sex controls were recruited to perform a competitive button-pressing task, while their brain signals were simultaneously collected using functional near-infrared spectroscopy (fNIRS) hyperscanning. Several fundamental observations were made. First, controls showed attenuated interpersonal competition across task processes, but couples held the competition with each other. Second, couples demonstrated increased inter-brain synchronization (IBS) between the middle temporal cortex and the temporoparietal junction across task processes. Third, Granger causality analysis in couples revealed significant differences between the directions (i.e., from men to women, and from women to men) in the first half of the competitive task, whereas there was no significant difference in the second half. Finally, the groups of couples and controls could be successfully discriminated against based on IBS by using a machine-learning approach. In sum, these findings indicate that elderly couples can maintain interpersonal competition, and such maintenance might be associated with changes in the IBS of the mentalizing system. It suggests the possible positive impact of long-term spouse relationships on interpersonal interactions, both behaviorally and neurally, in terms of competition.
A simple approach to handle the nonlinearity of a fuzzy controller due to the max-min method

Fuzzy controllers are widely applied on various systems. One reason is because even strong nonlinearities can be easily introduced to these controllers. Although nonlinearities can be easily introduced, it is very hard to determine the degree of nonlinearity. This paper suggests a simple approach to control the nonlinearity of the controller due to Mamdani's max-min method (1974) by changing the defuzzification method. We show, that under certain assumptions, even an almost linear controller can be obtained. Finally, a simulation study of an inverted pendulum applying the new defuzzification method is presented.
Looking behind Granger causality

Granger causality as a popular concept in time series analysis is widely applied in empirical research. The interpretation of Granger causality tests in a cause-effect context is, however, often unclear or even controversial, so that the causality label has faded away. Textbooks carefully warn that Granger causality does not imply true causality and preferably refer the Granger causality test to a forecasting technique. Applying theory of inferred causation, we develop in this paper a method to uncover causal structures behind Granger causality. In this way we re-substantialize the causal attribution in Granger causality through providing an causal explanation to the conditional dependence manifested in Granger causality.
A Novel Deep Reinforcement Learning-based Automatic Stock Trading Method and a Case Study

As the most important capital market, how formulating a reasonable stock trading strategy to improve capital return and reduce trading risk has always been the focus of people’s attention. In recent years, with the development of data-driven AI technology, people have begun to try to apply it to stock market data analysis to minimize the trading risk caused by the uncertainty of price fluctuations in the stock market. Accordingly, in this paper, we solve the complex stock trading decision-making problem by exploiting the deep reinforcement learning techniques. Briefly, in this paper, we consider two trading models: 1) for trading a specific single blue chip stock, we design a new trading agent based on Double Deep Q-Network (DDQN) to maximize the return for such specific blue chip stock purchase and sale; 2) To further reduce the risk of stock trading, for the more common multi-stock trading scenarios, we utilize twin-delayed deep deterministic policy gradient (TD3) technique to design a multi-stock collaborative trading agent for achieving the goals of risk hedging and maximizing returns. We further evaluated the efficiency of the proposed trading agents in stock price prediction accuracy and returns based on the actual U.S. Stock Market data.
Operational modal analysis using variational Bayes

Operational modal analysis is the primary tool for modal parameters identification in civil engineering. Bayesian statistics offers an ideal framework for analyzing uncertainties associated with the identified modal parameters. However, the exact Bayesian analysis is usually intractable due to the high computation demanding in obtaining the posterior distributions of modal parameters. In this paper, the variational Bayes is employed to provide an approximated solution. Working with the state space representation of a dynamic system, the joint distribution of the state transition matrix and observation matrix as well as the joint distribution of the process noise and measurement error are firstly obtained analytically using conjugate priors, then the distributions of modal parameters are extracted from these obtained joint distributions based on sampling because no closed form solution exists. A numerical simulation example demonstrates the performance of the proposed approach. The variational Bayes yields a consistent estimation of modal parameters although the variability is slightly under-estimated. Moreover, the variational Bayes is more flexible than the Laplace approximation and much more efficient than Monte Carlo sampling.
Progress and Future Potential of All-Small-Molecule Organic Solar Cells Based on the Benzodithiophene Donor Material

Organic solar cells have obtained a prodigious amount of attention in photovoltaic research due to their unique features of light weight, low cost, eco-friendliness, and semitransparency. A rising trend in this field is the development of all-small-molecules organic solar cells (ASM-OSCs) due to their merits of excellent batch-to-batch reproducibility, well-defined structures, and simple purification. Among the numerous organic photovoltaic (OPV) materials, benzodithiophene (BDT)-based small molecules have come to the fore in achieving outstanding power conversion efficiency (PCE) and breaking the 17% efficiency barrier in single-junction OPV devices, indicating the significant potential of this class of materials in commercial photovoltaic applications. This review specially focuses on up-to-date information about improvements in BDT-based ASM-OSCs since 2011 and provides an outlook on the most significant challenges that remain in the field. We believe there will be more exciting BDT-based photovoltaic materials and devices developed in the near future.
A case study in developing person-centred approaches to evaluating participation in Community Music

Disciplinary background A. Music Psychology informed mixed methods and a person-centred approach as the most suitable means to capture and understand the multiple and varied aspects of a complex intervention. Disciplinary background B. Community Music’s principles of facilitating creative and musical potential of all participants provided the area of interest for investigation. Abstract Aims of this research were exploratory, seeking to identify and understand key psychological and communicative processes taking place during Community Music workshops. Community music (CM) can have powerful impacts on the lives and musicality of people with additional support needs (ASN). A previous study found that music workshops for a group of young adults with diverse ASN led to an ongoing enthusiasm to engage in music; wider recognition of musicality; increased self-confidence; being happier and/or more relaxed; and better ability to interact with unfamiliar situations and people (Wilson & MacDonald 2019). A second study with the same CM organisation demonstrated improvements in individuals’ self-expression, confidence, mood, and social skills across three groups of varied ages and abilities in different areas (MacGlone et al., 2020). Therefore, further investigation is required to identify which aspects of the interventions were effective in achieving these outcomes, from both practitioners’ and participants’ perspectives. For this reason, this paper’s focus explores communicative processes between workshop participants and practitioners during the workshops. In keeping with person-centred research, capturing and understanding participants’ experience may present particular practical challenges, for example, when researching a group comprised of people with different ASN. Both qualitative and quantitative data were gathered from the second study mentioned previously (MacGlone et al., 2020) to address the aims. Interviews were held with 5 community musicians which investigated their approaches to and experiences of delivering the workshops. Interviews were recorded and transcribed. Thematic analysis, which seeks to identify, analyse, and report patterns (themes) within data (Braun & Clarke, 2014) was applied. Quantitative 360-degree video data were gathered and analysed from 1st, and 10th workshops of a 10-week programme from one resource centre. Twelve (4m
Investment-consumption models with transaction fees and Markov-chain parameters

This paper considers an infinite horizon investment-consumption model in which a single agent consumes and distributes his wealth in two assets, a bond and a stock. The problem of maximization of the total utility from consumption is treated. State (amount allocated in assets) and control (consumption, rates of trading) constraints are present. It is shown that the value function is the unique viscosity solution of a system of variational inequalities with gradient constraints.
R&D of on-board metal-insulation REBCO superconducting magnet for electrodynamic suspension system

Metal-insulation (MI) REBCO high-temperature superconducting (HTS) magnet has the advantages of short charging delay, low contact losses, and self-protection abilities, which shows important application prospects in many fields. Recently, Institute of Electrical Engineering, Chinese Academy of Sciences developed a prototype on-board MI HTS coil used for electrodynamic suspension (EDS) system working with an acceleration of beyond 8 g. To design a reliable engineering on-board magnet, specific Ic angle dependence data of short sample under different fields were fully considered to estimate the operating margin and losses accurately of the on-board magnet. A semi-analytical method, combination of the dynamic circuit method and finite element method, was used to analyze the dynamic electromagnetic force distribution inside the on-board magnet under motion state, in which the effect of the propulsion coils and the figure-eight-shaped coils were taken into considerations. Finally, a full-size MI double pancake racetrack coil was fabricated and tested in a liquid nitrogen bath. The test results meet the design requirements and verify the feasibility of the design, which provides an important reference for the experiment of on-board EDS with high acceleration in the future.
The Node-to-Node Graph Matching Algorithm Schema

Many graph matching algorithms follow the approach of node-similarity measurement, that is, matching graphs by means of comparing the corresponding pairs of nodes of the graphs. Based on this idea, the authors propose a high-level schema for node-to-node graph matching, namely N2N graph matching algorithm schema. The chapter shows that such a N2N graph matching algorithm schema is versatile enough to subsume most of the representative node-to-node based graph matching algorithms. It is also shown that improved algorithms can be derived from this N2N graph matching schema, compared with various corresponding algorithms. In addition, the authors point out the limitation and constraints of the propose algorithm schema and suggest some possible treatments
A variable tolling scheme for highway tolls based on traffic forecasting

Highway variable toll collection is an important means of traffic governance in modern society. A reasonable variable tolling scheme can adjust the traffic flow through price, and further develop the bearing capacity of highway on the basis of existing infrastructure to improve its traffic efficiency and service level. In the past, the formulation of variable tolling schemes is usually based on various assumed models, which belongs to the knowledge-driven method. In this paper, a data-driven variable tolling scheme is proposed, which uses the traffic flow prediction model integrating attention mechanism, recurrent neural network and graph neural network to mine the historical traffic information data on the road. In this paper, the traffic flow information on the future road is forecasted, and the information is used to replace some assumptions of bottleneck theory which are not completely in line with the actual situation. Taking the forecast information of traffic flow as the new constraint condition of dynamic congestion pricing in bottleneck theory can help us develop a more scientific and reasonable variable tolling scheme.
On Density Forecast Evaluation

Traditionally, probability integral transforms (PITs) have been popular means for evaluating density forecasts. For an ideal density forecast, the PITs should be uniformly distributed on the unit interval and independent. However, this is only a necessary condition, and not a sufficient one, as shown by some simple examples. I discuss an alternative approach to density forecast evaluation, via the Kullback-Leibler information criterion (KLIC), and illustrate it with a small simulation study.
A Comprehensive Analysis of 5G Security Core Technologies and Services: Conceptual Frameworks, Challenges, and Solutions

Fifth-generation (5G) networks are expected to link very large numbers of devices at high speeds with low latency. Incorporating advancements in radio technology, service-based design, and cloud infrastructure will help bring about the fourth generation (4G) of mobile networking. However, 5G communications provide new challenges and needs in terms of data security as a result of incorporating new technology and better functionality. This article provides a thorough analysis of the problems that arise when trying to maintain the confidentiality and safety of 5G networks, along with some suggestions on how to address those difficulties. This article discusses the current state and future prospects of 5G wireless security based on the essential security services, including authentication, availability, data secrecy, integrity, and non- repudiation. Under addition, we will talk about the many different 5G technologies that are currently in development, such as the Blockchain, SDN, AI, CPS, MECC, D2D, and 4.0 for the manufacturing sector. As a result of the security-focused R&D that has gone into developing today's technology, we're able to provide a wide variety of 5G apps and services with an eye on meeting the needs of users while also addressing possible threats. Finally, the difficulties and opportunities of 5G wireless security in the future are examined.
Weed Detection Using Deep Learning: A Systematic Literature Review

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.