Miracle-dz/UniFAR-FTUs huggingface dataset:
An end‐to‐end brain tumor segmentation system using multi‐inception‐UNET

Accurate detection and pixel‐wise classification of brain tumors in Magnetic Resonance Imaging (MRI) scans are vital for their diagnosis, prognosis study and treatment planning. Manual segmentation of tumors from MRI is highly subjective and tedious. With recent advances in deep learning, automatic brain tumor segmentation is an emerging research direction in the medical imaging domain. We present a study to improve the automatic segmentation process by introducing size variability in the Convolutional Neural Network (CNN). For pixel‐wise classification of tumorous slices convolutional neural network‐based encoder‐decoder UNET model is referred. A multi‐inception‐UNET model is proposed to improve scalability of the UNET model. Extensive experiments have been performed using the Brain Tumor Segmentation Challenge (BRATS) datasets to establish the validity of our proposed model. Experimental results show that our proposed method achieved the best results on BraTS 2015, 2017 and 2019 datasets for complete tumor, core tumor and enhancing tumor regions respectively.
An ultra-broadband power amplifier by using a simple analysis method

This letter presents a simple approach to design ultra-broadband power amplifiers (PAs). The theories of the hybrid modes class-EFJ PAs are applied. The relationship between the operating frequency and load impedance is analyzed for class-EFJ PAs. By appropriately selecting load impedances at different frequencies, a PA can work well in 1.0-4.0 GHz with high efficiency. In addition, the second harmonic impedance is obtained by combining the selected fundamental impedance and load-pull simulation. For validation, an ultra-broadband high efficiency PA is implemented. Experiments show that output power is from 40.1 dBm to 42.6 dBm and drain efficiency is between 60.6% and 72.7% at the saturation level in 1.0-4.0 GHz. The ACLR is smaller than −27.2 dBc in the same frequency band with an average output power of 35.2 dBm.
The Study of Digital Identity of Mobile Users Based on Android Dataset

A smartphone is said to be an extension of a mobile user‟s oneself. It is the ultimate mobile device today for humans, the one device that humans cannot avoid using, leave behind when on the move and becomes a necessity to own one. It allows humans to communicate and perform many daily personal tasks. Smartphones continue to evolve embedding advanced device technologies, sophisticated processes and applications. This symbiotic combination generates immense data on a temporal and spatial scale for a mobile user. These data represent the behaviors of a mobile user, which is unique. In the digital mobile realm, there exists some digital identity about a mobile user, what can be thought as the mobile user‟s digital pheromone. This data mining study uses Android Device Analyzer dataset consists of Battery Level, Battery Temperature, Battery Volt and App variables to understand the levels of volatility and their correlation measures. The finding shows there are wide dispersions for Battery Level, Battery Temp, Battery Volt and no statistical relationship exists for Battery Volt/App and Battery Temp/App correlation. Using a simple inequality that only a limited number of mobile users are contained within one standard deviation from the population mean, a smaller set of users exists. The results show that „uniqueness‟ percentage of a mobile user increased from 61.7% and 66.7% for single variable to 81.7% in multi-variate condition, to 100% using the simple inequality. Ultimately, this uniqueness indicates that a mobile user can have a unique identity, in the digital mobile world from the daily mobile data generated.
Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning

Malaria can be difficult to detect from thin blood smears. Image recognition methods such as convolutional neural network can be used to detect malaria, but the training process takes a long time. Previous research created a new architecture and compares it to several other architectures such as VGG-16 and ResNet. The effect of preprocessing is analyzed in this research. VGG-16, ResNet, and the custom architecture created by the previous research are being used in this study. The preprocessing methods being analyzed in this research include gray-world normalization and comprehensive normalization. The highest accuracy improvement per epoch (0.5256% using ResNet-50 and 0.0352% using custom architecture) is achieved through gray-world normalization, that also improves final accuracy (90.1% using ResNet-50 and 93.1% using custom architecture) when compared to other methods with the same epochs for ResNet and custom architecture.
Doubly Hybrid Functionals Close to Chemical Accuracy for Both Finite and Extended Systems: Implementation and Test of XYG3 and XYGJ-OS

While being widely used to understand the chemical reactions in heterogeneous catalysis or other multidisciplinary systems, a great challenge that semilocal and hybrid density functional approximations (DFAs) are facing is to deliver a uniformly accurate description for both finite and extended systems. Herein, we perform reliable and well-converged periodic calculations of two doubly hybrid approximations (DHAs), XYG3 and XYGJ-OS, and demonstrate that the good accuracy of DHAs achieved for molecules is transferable to the semiconductors and insulators. Such an accuracy is not only for energetic properties but also for the first- and second-order response properties, which is general for different kinds of chemical environments, including simple cubic bulks, perovskite-type transition metal oxides like TiO2, and heterogeneous systems like CO adsorption on the NaCl(100) surface. The present finding has strengthened the predictive power of DFT, which not only will inspire the future development of the top-rung DFAs but also will boost their applications in multidisciplinary studies with high accuracy and efficiency.
Current characteristics of various ejection modes in electrohydrodynamic printing

The real-time observation of electrohydrodynamic printing (EHDP) process is of importance in practical applications. The electric current is a reflection of charge transfer and might provide a potential method to detect the liquid behavior. In this paper, current during EHDP process is measured and studied to investigate the relationship with liquid behaviors. Experimental results show that the liquid ejection can be accurately reflected by the current signal. Current of various ejection modes in EHDP are then examined, and various characteristic numbers are summarized to identify the present liquid ejection mode. This work proposes a simple and efficient method by combining current detection and visual observation to achieve better real-time monitoring and controlling of EHDP process.
Making Photo-selective TiO2 Materials by Cation–Anion Codoping: From Structure and Electronic Properties to Photoactivity

Photoselective oxidation yielding high-added value chemicals appears as a green novel process with potential to be explored. In this study we combine spectroscopic XPS (N 1s and O 1s) and multiwavelength Raman data with density functional theory calculations to explore the structural and electronic properties of W,N-codoped TiO2 anatase surfaces and interpret the outstanding photocatalytic properties of such a system in partial oxidation reactions. Theoretical calculations allow us to examine several substitutional and N-interstitial configurations at different concentrations of the W,N dopants (similar to those experimentally found), as well as their interaction with structural point defects: Ti cation vacant sites and surface wolframyl species that are required to compensate the extra charge of the W6+ and N-containing anions. The joint use of theoretical and experimental XPS and Raman tools renders key structural information of W,N-codoped microcrystalline TiO2 solids. The incorporation of N at substit.
Acceptance testing based test case prioritization

Abstract Software testing is an important and expensive phase of development. Whenever changes are made in the code, it becomes a time-consuming process to execute all the test cases in regression testing. Therefore, the testing process needs some test case reduction techniques and prioritization techniques to improve the regression testing process. Test case prioritization aims at ordering test cases to increase the fault detection capability. There are many existing techniques for test case reduction as well as for prioritization that use the coverage information which degrades the number of ties uncounted during the prioritization. This paper will take its focus on the multi-level random walk algorithm, which has been used for test case reduction. In this process, test case selection for further reduction is done randomly on every iteration that degrades the performance of a testing process in terms of coverage and will also generate a situation for random test case tie. To overcome this situation of random test case selection and handling test case tie, a solution is being proposed in this paper, which includes a combination of optimized multi-level random walk and genetic algorithm. In regression testing, another important aspect is the test case prioritization that finds fault as early as possible if test cases are prioritized properly. So, this paper introduces new prioritization techniques, which are based on fault prediction in acceptance testing. The performance of the proposed approach in terms of fault detection is evaluated with the help of many programs.
A competitive inexact nonmonotone filter SQP method: convergence analysis and numerical results

We propose an inexact nonmonotone successive quadratic programming (SQP) algorithm for solving nonlinear programming problems with equality constraints and bounded variables. Regarding the value of the current feasibility violation and the minimum value of its linear approximation over a trust region, several scenarios are envisaged. In one scenario, a possible infeasible stationary point is detected. In other scenarios, the search direction is computed using an inexact (truncated) solution of a feasible strictly convex quadratic program (QP). The search direction is shown to be a descent direction for the objective function or the feasibility violation in the feasible or infeasible iterations, respectively. A new penalty parameter updating formula is proposed to turn the search direction into a descent direction for an -penalty function. In certain iterations, an accelerator direction is developed to obtain a superlinear local convergence rate of the algorithm. Using a nonmonotone filter strategy, the global convergence of the algorithm and a superlinear local rate of convergence are guaranteed. The main advantage of the algorithm is that the global convergence of the algorithm is established using inexact solutions of the QPs. Furthermore, the use of inexact solutions instead of exact solutions of the subproblems enhances the robustness and efficiency of the algorithm. The algorithm is implemented using MATLAB and the program is tested on a wide range of test problems from the CUTEst library. Comparison of the obtained numerical results with those obtained by testing some similar SQP algorithms affirms the efficiency and robustness of the proposed algorithm.
Understanding the Diverse Performance Measures of Nonprofits: A Quantitative Study of Performance Measurement Adoption

Abstract Given that nonprofits embrace diverse perspectives when adopting performance measurement, one key area of scholarly attention is understanding what leads nonprofits to embrace different performance measures. In this article, the author combines agency theory, resource dependence theory, and neo-institutional theory to explore how performance measures of nonprofits are associated with prominent organizational contingencies. Based on survey data from California nonprofits (n = 143), the regression results demonstrate that the adoption of performance measurement is not only influenced by external institutional pressures but also is associated with task characteristics and environmental uncertainty of nonprofits. This study helps nonprofits’ stakeholders to better understand why nonprofits choose to adopt certain measurement dimensions.
A Study on the Essential and Parkinson’s Arm Tremor Classification

In this article, the challenge of discriminating between essential and Parkinson’s tremor is addressed. Although a variety of methods have been proposed for diagnosing the severity of these highly occurring tremor types, their rapid and effective identification, especially in their early stages, proves particularly difficult and complicated due to their wide range of causes and similarity of symptoms. To this goal, a clinical analysis was performed, where a number of volunteers including essential and Parkinson’s tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted features from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately discriminate among different tremor types. Overall, SVM related classifiers proved to be the most successful in terms of classifying between Parkinson’s, essential and no tremor diagnosed with percentages reaching up to 100% for a single accelerometer measurement at the metacarpal area. In general and in motion while holding an object position, Coarse Gaussian SVM classifier reached 82.62% accuracy.
The feedback control of the fractional Mackey-Glass system with monotone production rate

This work is devoted to analyze the stability of the feedback control of the fractional Mackey-Glass system. Both the stability at the zero equilibrium point and the positive equilibrium point of the control system was obtained. Given small control gain for system, the stability interval about time delay and the stability region about destruction rate and time delay was obtain. Finally, an example was given to illustrate the result of paper.
Long-term improvement of quality of life in adult ADHD – results of the randomized multimodal COMPAS trial

Abstract Attention Deficit Hyperactivity Disorder (ADHD) negatively impacts Quality of Life (QoL) in adults. Earlier studies on pharmacological treatment have shown short-term beneficial effects on QoL. No data are available on long-term effects on multimodal psychosocial and/or pharmacological interventions. The Comparison of Methylphenidate and Psychotherapy in Adult ADHD Study (COMPAS), a prospective, multicenter randomized clinical trial, compared cognitive behavioral group psychotherapy (GPT) with individual clinical management (CM) and methylphenidate (MPH) with placebo (Plac) (2 × 2 factorial design over a period of 52 weeks). Change of QoL was measured after three months of intensive treatment, after 26 weeks, and after one year of maintenance treatment, using the Quality of life enjoyment and satisfaction questionnaire (Q-LES-Q, eight subscales). A follow-up was performed 1.5 years after treatment completion. Q-LES-Q scores were analyzed descriptively (means and confidence intervals). A linear regression model was used to test for significant differences between the four treatment arms. The full analysis set comprised 419 patients. Q-LES-Q scores increased between baseline and the end of treatment across all Q-LES-Q subscales (with exception of stable scores in the physical health/activities subscale in the CM + Plac group) and remained stable improved at follow-up. Regression analyses did not show any significant differences between the four treatment conditions. COMPAS is the first study to demonstrate stable and long-term improvements of QoL after multimodal treatment regardless of medication with MPH or Placebo, and regardless of structured GPT or individual CM. Trial registration: ISRCTN54096201
A Comparison between W score and Ryan score in diagnosing of Laryngopharyngeal re�ux disease

Objective To assess the diagnostic value of W score which was supposed to identify laryngopharyngeal re�ux disease (LPRD) patients from the normal population by Dx-pH monitoring, comparing with Ryan score. Methods One hundred and eight patients with suspected LPRD and complete follow-up results after more than 8 weeks of anti-re�ux therapy were enrolled from the Department of Otolaryngology-Head and Neck Surgery, Gastroenterology and Respiratory Medicine of seven hospitals. Their Dx-pH monitoring data before treatment were reanalyzed to obtain the W score besides Ryan score and then the diagnostic sensitivity and speci�city were compared according to the result of anti-re�ux therapy. Eighty-seven (80.6%) cases were anti-re�ux therapy effective, and 21 patients (19.4%) were ineffective. Twenty-seven patients (25.0%) had a positive Ryan score. The W score was positive in 79 (73.1%) patients. There were 52 patients who had a negative Ryan score, but a positive W score. The diagnostic sensitivity, speci�city, positive predictive value and negative predictive value of the Ryan score were 28.7%, 90.5%, 92.6% and 23.5%, respectively (kappa = 0.092, p = 0.068), whereas those of the W score for LPRD were 83.9%, 71.4%, 92.4% and 51.7%, respectively (kappa = 0.484, p < 0.001). W score is much more sensitive for the diagnosis of LPRD. Prospective studies with larger patient populations are necessary to validate and improve the new diagnostic criteria. This study was conducted as a retrospective analysis of patients who were suspected with LPRD and with complete 24h pharyngeal pH monitoring. All the patients Adult patients with LPRD related symptoms 1 RSI ≥ Scale
Perceptions of Women’s Gender Shifting Dynamics in Contemporary Moroccan Cinema: The Application of the Social Identity Theory (SIT)

… even unreal images can be perceptually realistic. Unreal images are those which are referentially fictional. ‘ The Terminator ’ is a represented fictional character that lacks reference to any category of being existing outside the fiction. Spielberg ’ s dinosaurs obviously refer to creatures that once existed, but as moving photographic images they are referentially fictional. By contrast, referentially realistic images bear indexical and iconic homologies with their referents. They resemble the referent, which, in turn, stands in a causal, existential relationship to the image. A perceptually realistic image is one which structurally corresponds to the viewer ’ s audiovisual experience of three-dimensional space. Perceptually realistic images correspond to this experience because film-makers build them to do so.
Green Synthesis of Silver Nanoparticles: A Review

The bio-molecules from various plant components and microbial species have been used as potential agents for the synthesis of silver nanoparticles (AgNPs). In spite of a wide range of bio-molecules assisting in the process, synthesizing stable and widely applicable AgNPs by many researchers still poses a considerable challenge to the researchers. The biological agents for synthesizing AgNPs cover compounds produced naturally in microbes and plants. More than 100 different biological sources for synthesizing AgNPs are reported in the past decade by various authors. Reaction parameters under which the AgNPs were being synthesized hold prominent impact on their size, shape and application. Available published information on AgNPs synthesis, effects of various parameters, characterization techniques, properties and their application are summarised and critically discussed in this review.
Opportunities and Challenges of Aging Workforces toward HR Practices in Business Management: A Comparison Between Thailand and China

Article History: Received: 10 November 2020; Revised: 12 January 2021; Accepted: 27January 2021; Published online: 05April 2021 Abstract: As the number of elderly population are getting higher, many countries are now facing with rapidly growth in number of aging citizens due to increase in life expectancy, healthcare advancement, and baby boomers become aged. This leads Human Resource sector to understand more on aging workforces as an individual change process in terms of competencies, motivation, behavior and values. The aim of this study is to analyze and compare the opportunities and challenges in managing aging workforces toward Human Resource practices between Thai and Chinese business sectors. In line with the gathering of secondary data and literature-based analysis found that both Thai and Chinese business sectors recognize the common important challenges of the composition of the workforce and also shrinking young labor forces However, business sector in Thailand presents certain challenges from a human resources perspective. i.e.technology challenges when compared to China. Several companies in China realized that aging workforces tends to have positive contributions; as it will increase competitive edge and intellectual capital especially in technology development. However, several companies in Thailand has recruited of aging workforces by increasing the number of part-time employees and offering flexible work options which are a priority for aging workforces.
Disability and Academic Careers: Using the Social Relational Model to Reveal the Role of Human Resource Management Practices in Creating Disability

Disabled people continue to face a variety of significant barriers to full participation and inclusion in work and employment. However, their experiences remain only sparsely discussed in relation to human resource management (HRM) practices and employment contexts. The current study contributes to this gap in understanding by drawing together relevant work connecting HRM practices, diversity management and disability studies to examine the experiences of a sample of 75 disabled academics in the UK. Through the social relational model of disability, HRM practices socially construct disability in the workplace. Interview and email data from disabled academics in the UK are drawn upon to illustrate how organisational practices and policies, while intended to ‘accommodate’ disabled people, inadvertently construct and shape disability for people with impairments or chronic health conditions.
In Situ Spectroscopic Probes for Structures and Processes at the Surface of Noble Metallic Nanoparticles

Metallic nanoparticles (NPs) have attracted significant attention in research because of their wide applicability in various areas. Because the properties of metallic NPs can be regulated by altering the covered ligand layer, it is crucial to be able to probe the composition, structure, and dynamic processes of molecules on metallic NPs for understanding nanoparticle–molecule interactions so as to enable the exploiting of NPs with desired properties and functionalities. However, a detailed description of the conformations of molecules and the dynamic interactions between molecules and metallic NPs is challenging owing to the requirement of real‐time, in situ, and non‐destructive surface probing technologies. This review presents an overview of probing the structures and dynamic processes of molecules on the surface of metallic NPs by different spectroscopic techniques.