Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of psychologists in managing mental health comorbidities in adults with neurological disorders

ABSTRACT Neurological disorders are associated with very high rates of depression and anxiety, which significantly increase disease burden, but remain an area of considerable unmet need. Psychological therapies, especially those based on the principles of cognitive and behavioural therapy, appear to be efficacious for improving both mental health (e.g., depression and anxiety) and functional outcomes (e.g., sleep, fatigue) in patients with a variety of neurological disorders. However, access to these therapies is limited. Neuropsychologists rarely have the capacity to deliver ongoing psychological therapy and there appears to be a lack of community psychologist that specialise in this area. This commentary argues that these barriers need to be addressed and psychologists should play a greater role in reducing the burden of neurological disorders. One step towards this is building the confidence and competence of psychologists to effectively work with patients with neurological symptoms, especially common cognitive difficulties (e.g., inattention, poor memory, slower processing speed). Thus, this commentary provides a practical guide for modifying psychological therapies for adults with neurological disorders, including principles of compensatory cognitive rehabilitation. It aims to provide principles to help maximise engagement, learning and retention of psychological concepts and ultimately to encourage more psychologists to work with this under-served group. Key Points What is already known about this topic: Depression and anxiety are common in neurological disorders but often go untreated. Psychological therapies can improve mental health and functional outcomes across neurological disorders, but access to these interventions is very limited. Neuropsychologists report a lack of confidence providing ongoing psychological interventions for mental health, and identifying community psychologists specialising in neurology is challenging. What this topic adds: A discussion of the mental health burden of neurological disorders and role of psychological therapy. A guide to modifying psychological therapies for adults with neurological disorders, especially for cognitive difficulties. Tips for maximising engagement, learning and retention within therapy to support effective work with people with neurological disorders.
New natural gradient algorithm for cyclostationary sources

A new natural gradient type algorithm (NGA) for the separation of cyclostationary sources is introduced. Based on the interpretation of blind source separation (BSS) as a two-stage process, including prewhitening and rotation, the cyclostationary NGA (CSNGA) algorithm is constructed such that it also ensures that the recovered sources are decorrelated in the cyclostationary sense. The method is generalised to the case of complex valued source signals, and modified so that adequate algorithm performance is attained even when only one source cycle frequency is known. The properties of the new algorithm are investigated when additive white Gaussian noise is present, and it is found that, in general, the CSNGA approach improves the convergence properties of the natural gradient algorithm. Computer simulations support the validity of the approach.
Adaptive array beamforming with robust capabilities under random sensor position errors

The problem of adaptive array beamforming with multiple-beam constraints in the presence of steering error caused by random sensor position errors is considered. First the statistical relationship between the random sensor position errors and the induced random phase perturbation is derived. Based on the result, a cost function consisting of terms which utilise a posteriori information owing to the received array data and a priori information owing to the probabilistic distribution of the resulting phase perturbation, respectively, is constructed. Then, an appropriate estimate of the actual phase angle vector associated with each of the desired signals can be obtained by performing a nonlinear optimisation problem. An implementation algorithm is further presented to solve iteratively the problem. Theoretical analysis regarding the convergence property of the iterative procedure is also investigated. Finally, several computer simulation examples are provided for demonstrating the effectiveness of the proposed technique.
Immunoregulatory Biomarkers of the Remission Phase in Type 1 Diabetes: miR-30d-5p Modulates PD-1 Expression and Regulatory T Cell Expansion

The partial remission (PR) phase of type 1 diabetes (T1D) is an underexplored period characterized by endogenous insulin production and downmodulated autoimmunity. To comprehend the mechanisms behind this transitory phase and develop precision medicine strategies, biomarker discovery and patient stratification are unmet needs. MicroRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression and modulate several biological processes, functioning as biomarkers for many diseases. Here, we identify and validate a unique miRNA signature during PR in pediatric patients with T1D by employing small RNA sequencing and RT-qPCR. These miRNAs were mainly related to the immune system, metabolism, stress, and apoptosis pathways. The implication in autoimmunity of the most dysregulated miRNA, miR-30d-5p, was evaluated in vivo in the non-obese diabetic mouse. MiR-30d-5p inhibition resulted in increased regulatory T cell percentages in the pancreatic lymph nodes together with a higher expression of CD200. In the spleen, a decrease in PD-1+ T lymphocytes and reduced PDCD1 expression were observed. Moreover, miR-30d-5p inhibition led to an increased islet leukocytic infiltrate and changes in both effector and memory T lymphocytes. In conclusion, the miRNA signature found during PR shows new putative biomarkers and highlights the immunomodulatory role of miR-30d-5p, elucidating the processes driving this phase.
An Auxiliary Friction Method for Miniaturizing the Inertial Impact Piezoelectric Actuators

The inertial impact piezoelectric actuators (IIPAs) are promising to be miniaturized as microactuators due to their simple structure and high positioning resolution. In this article, a novel method is proposed to reduce the dependence on the inertial mass by exerting auxiliary friction on the inertial mass. To verify the validity of the method, a traditional IIPA (TIIPA) and an auxiliary friction IIPA (AFIIPA) are designed. The static analysis is conducted to determine their structural parameters. Then, their size and weight are identified as 9.6 mm × 10 mm × 7 mm and 0.8 g (the lightest IIPA to our knowledge), respectively. The dynamic analysis and experiments are performed, and the results demonstrate that, under the same experimental conditions, the AFIIPA has lower sensitivity to inertial mass reduction and better output performances than those of the TIIPA. The maximum speed, output force, and resolution of the AFIIPA are improved by 81.8%, 67.2%, and 30.9%, respectively, compared with the TIIPA. Besides, the AFIIPA has balanced comprehensive performances with extremely lightweight compared with some previous microactuators. This study provides a new miniaturization method for the IIPAs, which could advance the further development and applications of the micro-IIPAs.
Distribution shape of two-dimensional DCT coefficients of natural images

The univariate distribution of DCT coefficients of natural images is investigated. The probability density function (PDF) of the coefficients is modelled with the generalised Gaussian function (GGF) which includes the Gaussian and the Laplacian PDF as special cases. The shape parameter of the GGF is estimated according to the maximum likelihood principle, χ2 tests of fit showed that GGFs model the distribution of DCT coefficients more accurately than Laplacian PDFs.
On the design of a position feedback control law for a simple mechanical system subject to impacts

The control of mechanical systems subject to smooth impacts is considered in this paper. As the dynamic model of the mechanical system varies in dependence on the condition of contact or non-contact, a family of dynamic compensators from position measurements is first derived, thus guaranteeing the asymptotic stability in the two different conditions. As the system can switch between the two conditions an arbitrary number of times, the stability of the overall system is not guaranteed by the stability in the two separate phases; hence, a Lyapunov approach is proposed in order to guarantee the asymptotic stability of the overall system independently of the number of transitions between the two phases. Second, it is shown how to select among the considered family of compensators the one that reduces as much as possible the effects of the impacts on the trajectory of the system, while considering a regulation problem, with zero steady-state regulation error. Both simulation and experimental results have been carried out showing the effectiveness of the proposed technique.
The Influence of Economic and Technology Factors on Performance Outcomes of Community Pharmacists in Nigeria: A Structural Equation Modeling Study

Community pharmacists, as healthcare providers, operate within local and global business environments. Therefore, they are not immune from the effects of the business environment on practice performance. However, limited empirical research is available to explore these effects. The study proposes an empirical model to investigate the influence of economic and technological factors on the performance domains of community pharmacists in southwestern Nigeria. A cross-sectional study with self-administered questionnaires to 661 randomly selected community pharmacists. Performance measures were operationalized based on theory into 3 domains: operational, economic, and social performance domains. Study hypotheses were tested by applying factor-based structural equation modeling (SEM) using WarpPLS software. Results showed acceptable internal reliability of constructs and fit of the model to the data. Technology, compared to economic factors, had a significant influence on operational performance (β=0.242, p=0.001 vs. β=0.067, p=0.055). At the same time, economic factors had a higher influence on economic performance (β=0.070, p=0.036 vs. β=0.032, p=0.203). Both predictors affected social performance, with economic factors having a relatively stronger impact compared to technological factors. (β=0.095, p=0.007 vs. β=0.069, p=0.037). Community pharmacists should continue to strengthen economic value for their customers while incorporating relevant technology to improve practice outcomes. Macroeconomic policy by governments to enable community pharmacy practice is also recommended. The study recommends that community pharmacists emphasize the relevance of regular performance assessments to identify areas for improvement. This study adds substantial theoretical and methodological value to the existing literature by using SEM to explore the impact of business environmental factors on disaggregated performance measures of community pharmacists. Keywords: Community pharmacists, Economic, Measures, Nigeria, Performance, Technology, Structural equation modeling.
Flow development of Herschel–Bulkley fluids in a sudden three-dimensional square expansion

The flow development of Herschel–Bulkley fluids in a sudden three-dimensional square expansion is studied numerically. The flow is modeled using the mixed-Galerkin finite element formulation to solve the conservation of mass and momentum equations. The Herschel–Bulkley material behavior is described using a regularized model based on the Papanastasiou model. Solutions are obtained for a downstream-to-upstream expansion ratio of 2:1 and for a wide range of pressure gradient values and rheological parameters. The results show that, during the evolution of the flow, two core regions and dead zones at the corners are formed. The extent of the core regions decreases with the pressure gradient and the Reynolds number, and increases with the power-law index. It is also found that the volume flow rate at steady flow increases with the pressure gradient, power-law index, and Reynolds number.
Sparse Array Beampattern Synthesis via Two-Stage Penalty Dual Decomposition Method

In this work, a new two-stage penalty dual decomposition (TSPDD) method is devised for the power mask-constrained sparse array beampattern synthesis problem. Unlike the usual approximation of <inline-formula> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula>-quasi-norm with weighted <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm or <inline-formula> <tex-math notation="LaTeX">$l_{\mathrm {p}}$ </tex-math></inline-formula>-quasi-norm (<inline-formula> <tex-math notation="LaTeX">$0 < p < 1$ </tex-math></inline-formula>), we directly utilize the objective function of <inline-formula> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula>-quasi-norm to solve this problem without any approximation, so it can obtain a sparser solution than some other methods. The TSPDD solve the augmented Lagrangian problem via dual decomposition (DD)-type methods. It is an iteration algorithm. In the first stage, we update the penalty parameters, while in the second stage, we update the dual variables. Furthermore, in order to further enhance the sparsity of the solution, we extended the algorithm TSPDD to extended two-stage penalty dual decomposition (E-TSPDD) by discarding the array elements with smaller weights and replacing two close array elements with one. Finally, we considered the application of the proposed method in the sparse array synthesis problem with mutual coupling. Numerical examples show that the proposed methods have a good convergence and performances. It can achieve satisfactory radiation pattern with fewer antennas than the existing techniques and can handle the mutual coupling effect in the sparse array synthesis task.
Analysis of the light-scattering cross section for surface ripples on solids

A detailed theory of the scattering of light by acoustic surface ripples arising from both bulk and surface phonons in solids is given. The measured Brillouin spectra of Al, Ni, GaAs and GaP are presented. The experimental results are compared with theoretical predictions for the ripple scattering mechanism. This mechanism is dominant for Al and Ni, where there is good agreement between theory and experiment for the spectral distribution and angular dependence of the intensity, but poor agreement for the absolute cross section. The alternative scattering mechanism by way of the elasto-optic effect is shown to be important in GaAs and GaP, although in the latter the scattering from surface phonons is still apparently dominated by the ripple mechanism.
Constraints on the IMF and the brown dwarf population of the young cluster IC 348

We use a deep near-infrared census of the young stellar cluster IC 348 to construct and analyze its luminosity function. Our mosaic image of IC 348 covers the full extent of the cluster with a completeness limit of J 19:5 and is therefore sensitive for 2 Myr old cluster members with masses as low as M 0:005 M for the mean extinction of the known cluster members (AV 3:5 mag). By using information on stellar ages, extinctions, and the binary population in IC 348 from several recent studies, we can derive statistical constraints on the stellar and sub-stellar mass function of the cluster by modeling the observed luminosity function. We find that the stellar part of the mass function in IC 348 is well described by the galactic field star IMF. While several brown dwarfs have recently been identified in IC 348, our data show that the cluster harbors only a relatively small population of sub-stellar objects. We find that brown dwarfs in the mass range 0:02 0:075 M constitute at most10% of the total cluster population, in contrast to recent results suggesting much larger brown dwarf populations in other young clusters and also the galactic field. Our results suggest that IC 348 has 2 fewer brown dwarfs than the Orion Trapezium cluster. A similar brown dwarf "deficit" was recently found in the Taurus star forming region. We speculate about the possible causes for this result, including the presence or absence of nearby massive stars and their influence on the formation of low-mass young stellar objects.
Predicting high-risk cholesterol levels

The pattern of longitudinal changes in cholesterol levels has important implications for screening policies and for understanding the role of cholesterol as a risk factor for coronary heart disease. We explored a variety of longitudinal models to predict changes in cholesterol over several years, emphasizing the probability that an individual will develop a cholesterol level that requires further diagnostic tests or treatment. The first question was whether measured cholesterol is Markovian. A chi-square statistic based on the bootstrap and motivated by the Chapman-Kolmogorov equations established that it is not. Related bootstrap-based tests indicate that the probability structure of measured cholesterol is not that of a low order autoregressive moving average (ARMA) model. We then tested several alternative models to predict future cholesterol levels from the pattern of previous measured values, using receiver-operating characteristic (ROC) curves to summarize the sensitivity and specificity of the resulting rules for predicting high risk values. One method was based on the Gaussian assumption that the logarithms of cholesterol levels are jointly Gaussian; a second was based on ordinary least squares regression; a third was based on logistic regression. We developed a bootstrap technique for finding confidence regions for points on the ROC curves. Bootstrap simulations were used in three different ways in computing the regions: one to bias correct each point on a curve, a second to find the bootstrap distribution of points for each threshold that defines a particular value of sensitivity and specificity, and a third to find the volume of the (ellipsoidal) regions. The results of our analyses suggest that the models can be used to identify subgroups of individuals who are unlikely to develop very high risk levels of cholesterol. The models also can be used to help formulate schedules for screening individuals.
Active adaptive sound control in a duct: A computer simulation

Most active sound cancellation systems reported in the literature use open‐loop control, depend on near‐zero phase delay in control system elements, and require constant acoustic signal transit time from a signal pickup (microphone) to a control sound source (loudspeaker). The applicability of such systems can be significantly enhanced by using closed‐loop control. This study concerns a digital computer simulation of adaptive closed‐loop control for a specific application, sound cancellation in a duct. The key element is an extension of Sondhi’s adaptive echo canceler and Widrow’s adaptive noise canceler from signal processing to control. The adaptive algorithm is thus based on the LMS gradient search method. The simulation shows that one or more pure tones can be canceled down to the computer bit noise level (−120 dB). In the presence of additive white noise, pure tones can be canceled to at least 10 dB below the noise spectrum level for SNR’s down to at least 0 dB. The underlying theory implies that the.
Hyperspectral Image Classification with Deep CNN Using an Enhanced Elephant Herding Optimization for Updating Hyper-Parameters

Deep learning approaches based on convolutional neural networks (CNNs) have recently achieved success in computer vision, demonstrating significant superiority in the domain of image processing. For hyperspectral image (HSI) classification, convolutional neural networks are an efficient option. Hyperspectral image classification approaches are often based on spectral information. Convolutional neural networks are used for image classification in order to achieve greater performance. The complex computation in convolutional neural networks requires hyper-parameters that attain high accuracy outputs, and this process needs more computational time and effort. Following up on the proposed technique, a bio-inspired metaheuristic strategy based on an enhanced form of elephant herding optimization is proposed in this research paper. It allows one to automatically search for and target the suitable values of convolutional neural network hyper-parameters. To design an automatic system for hyperspectral image classification, the enhanced elephant herding optimization (EEHO) with the AdaBound optimizer is implemented for the tuning and updating of the hyper-parameters of convolutional neural networks (CNN–EEHO–AdaBound). The validation of the convolutional network hyper-parameters should produce a highly accurate response of high-accuracy outputs in order to achieve high-level accuracy in HSI classification, and this process takes a significant amount of processing time. The experiments are carried out on benchmark datasets (Indian Pines and Salinas) for evaluation. The proposed methodology outperforms state-of-the-art methods in a performance comparative analysis, with the findings proving its effectiveness. The results show the improved accuracy of HSI classification by optimising and tuning the hyper-parameters.
The COVID-19 pandemic and its effect on the Brief-COPE structure

This study aimed to investigate the dimensional structure of Brief-COPE during the COVID-19 pandemic. Six hundred and sixty-six adults from all regions of Brazil, mostly women (77 %), aged between 18 and 79 years old (M = 36.70; SD = 13.12), took part in this research. They answered sociodemographic questions and the Brief-COPE. Descriptive and confirmatory factor analyzes were performed. The first order dimensional structure was confirmed with the 14 original factors. The second order structure did not converge, and a new second order structure was proposed based on the correlation indexes among the first order factors. The proposed new model presented adequate indexes of adjustment to the data. Such results suggest that the pandemic may have changed the way participants perceive and relate coping strategies.
Generalized Regression Neural Network Based Quantitative Structure-Property Relationship for the Prediction of Absorption Energy

Generalized Regression Neural Network (GRNN) was used to develop a quantitative structure-property relationship (QSPR) model to improve the calculation accuracy of density functional theory (DFT). The model has been applied to evaluate optical absorption energies of 150 organic molecules based on the molecular descriptors. The entire dataset was divided into a training set of 120 molecules and a test set of 30 molecules according to the method, termed SPXY (Sample set Partitioning based on joint x–y distances), extended Kennard and Stones (KS) algorithm according to their differences in both x (instrumental responses) and y (predicted parameter) spaces in the calculation of inter-sample distances. Back-propagation neural network with SPXY partitioning algorithm (BPNN-SPXY) and GRNN with KS algorithm (GRNN-KS) were also utilized to construct model to compare with the results obtained by GRNN with SPXY algorithm (GRNN-SPXY). The root-mean-square errors in absorption energy predictions for the whole data set given by DFT, BPNN-SPXY , GRNN-KS and GRNN-SPXY were 0.47, 0.21, 0.17 and 0.13, respectively. The GRNN-SPXY prediction results are in good agreement with the experimental value of absorption energy. Keywords-Generalized Regression Neural Network; Sample subset partitioning; Kennard and Stones algorithm; Absorption energy; Density functional theory