Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of the Probe-Sample Interaction Force at the Piconewton Scale by a Magnetic Microprobe in Aqueous Solutions

This article describes the design and implementation of a control system that controls the probe-sample interaction force at the piconewton scale by manipulating a magnetic microprobe in aqueous solutions under a microscope. The control system has two functions, namely accurate force generation by magnetic flux control and precise control of the probe-sample interaction force. Magnetic flux control uses an improved six-input-six-output digital control law along with a disturbance estimator to achieve two control objectives. First, the magnetic flux at each pole tip of a previously developed hexapole electromagnetic actuator can be individually and precisely controlled at frequencies over 4 kHz, and the experimental results precisely followed the theoretical predictions based on the design. Second, together with the optimized flux allocation, the flux control system generates precise magnetic forces, making the six-input hexapole actuator behave like a decoupled three-axis force generator with a bandwidth of more than 4 kHz. Interaction force control compares the measured deformation of the sample with the expected deformation, updated in real time by a deformation predictor, to generate the magnetic force to control the interaction between the probe and the sample. Digital control laws, the estimator, and the predictor are all implemented using a high-speed Field Programmable Gate Array (FPGA) system. Experiments confirm that through mathematical modeling, digital control technology, high-speed electronics, and real-time computation, accurate three-dimensional force generation at the piconewton scale with high bandwidth and precise interaction force control with zero-mean random error, attributed to random thermal force and measurement noise, are achieved.
Breast density classification using Laws' mask texture features

Characterisation of tissue density is clinically significant as high density is associated with the risk of developing breast cancer and also masks lesions. Accordingly, in the present work, PCA-kNN and PCA-NN based computer aided diagnostic (CAD) systems for breast tissue density classification have been proposed. The work has been carried out on the MIAS dataset. Five statistical texture features mean, standard deviation, entropy, kurtosis and skewness are evaluated from Laws' texture energy images resulting from Laws' masks of lengths 3, 5, 7 and 9. Principal component analysis is then applied to these texture feature vectors for feature space dimensionality reduction. The kNN classifier and the NN classifier are used for the classification task. The highest classification accuracy of 95.6% is achieved by using the first 8 principal components computed from texture features derived from Law's mask of length 5 for k = 8 using the kNN classifier.
Environmental Target and Government Performance in China

Abstract When do environmental targets improve the environmental performance of local governments? Previous studies have reached inconsistent conclusions on the effectiveness of environmental targets. Drawing on the principal-agent theory, this article identifies goal divergence and information asymmetry as challenges that impede the achievement of environmental targets. We analyze two policy instruments put in place to steer implementation, namely the mandatory target system and key cities for emission reduction, as components of the national 11th Five-Year Plan. We apply the difference-in-differences (DID) method to a longitudinal dataset of Chinese cities from 2001 to 2010 to investigate the impact of the two policy instruments on air and water pollution. Combining time variations, inter-provincial differences, and inter-prefectural differences in policy intensity, we find that mandatory pollution reduction targets have a pollution-reducing impact on two targeted pollutants, and key environmental protection cities reduce emissions only for air pollutants. However, we find no evidence of a synergy between the two types of policies. Our findings contribute to the understanding of implementation problems for government agents.
Detecting perfectly insulated obstacles by shape optimization techniques of order two

The paper extends investigations of identification problems by shape optimization methods for perfectly conducting inclusions to the case of perfectly insulating material. The Kohn and Vogelius criteria as well as a tracking type objective are considered for a variational formulation. In case of problems in dimension two, the necessary condition implies immediately a perfectly matching situation for both formulations. Similar to the perfectly conducting case, the compactness of the shape Hessian is shown and the ill-posedness of the identification problem follows. That is, the second order quadratic form is no longer coercive. We illustrate the general results by some explicit examples and we present some numerical results.
pEffect of Synthesized Oxiranes and Pyrrolidines on KMP II and LPG 3 genes in Leishmania infantum Promastigotes

The research includes synthesis of some heterocycles: oxiranes (O1-O3) through the reaction of chalcones with hydrogen peroxide, and pyrrolidines (P1-P3) by the condensation of chalcones with Schiff bases. The structures of these synthesized heterocycles had been supported by spectral data (1HNMR, IR, UV) and observed their effect on KMP II and LPG 3 genes. The results showed that some compounds gave genetic heterogeneity in these genes suggesting that it could be a good target for treatment of L. infantum .
Circulating Vitamin D Level and Its Impact on Mortality and Recurrence in Stage III Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Simple Summary We conducted a systematic review and meta-analysis to investigate the relationship between pre-operative vitamin D (VD) levels and time-to-outcome in stage III colorectal cancer (CRC) patients. Four articles were included in the analysis, with pooled data from 2628 patients for overall survival and 2024 patients for disease-free survival. The results showed that patients with lower levels of VD had a 38% and 13% increased risk of death and recurrence, respectively, according to random-effects models. These findings suggest that a low VD concentration negatively impacts the time-to-outcome in stage III CRC. Abstract Background: Vitamin D (VD) has been implicated in several diseases, including colorectal cancer (CRC). This study aimed to determine whether there is an association between VD levels and time-to-outcome in stage III CRC patients through a systematic review and meta-analysis. Methods: The study adhered to the PRISMA 2020 statement. Articles were searched in PubMed/MEDLINE and Scopus/ELSEVIER. Four articles were selected, with the primary objective of providing a pooled estimate of the risk of death specifically in stage III CRC patients based on pre-operative VD levels. Study heterogeneity and publication bias were analyzed using Tau2 statistics and funnel plots. Results: The selected studies showed significant heterogeneity regarding time-to-outcome, technical assessments, and serum VD concentration measures. The pooled analysis of 2628 and 2024 patients revealed a 38% and 13% increase in the risk of death (HR: 1.38, 95% CI: 0.71–2.71) and recurrence (HR: 1.13; 95% CI: 0.84–1.53), respectively, for random-effects models among patients with lower levels of VD. Conclusions: Our findings suggest that a low concentration of VD has a significant negative impact on time-to-outcome in stage III CRC.
Quasi-hidden Markov model and its applications in change-point problems

ABSTRACT In a hidden Markov model (HMM), the observed data are modelled as a Markov chain plus independent noises, hence loosely speaking, the model has a short memory. In this article, we introduce a broad class of models, quasi-hidden Markov models (QHMMs), which incorporate long memory in the models. We develop the forward–backward algorithm and the Viterbi algorithm associated with a QHMM. We illustrate the applications of the QHMM with the change-point problems. The structure of the QHMM enables a non-Bayesian approach. The input parameters of the model are estimated by the maximum likelihood principle. The exact inferences on change-point problems under a QHMM have a computational cost , which becomes prohibitive for large data sets. Hence, we also propose approximate algorithms, which are of complexity, by keeping a long but selected memory in the computation. We illustrate with step functions with Gaussian noises and Poisson processes with changing intensity. The approach bypasses model selection, and our numerical study shows that its performance is comparable and sometimes superior to the binary segmentation algorithm and the pruned exact linear time method.
Investigating the Inhibition of FTSJ1, a Tryptophan tRNA-Specific 2′-O-Methyltransferase by NV TRIDs, as a Mechanism of Readthrough in Nonsense Mutated CFTR

Cystic Fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the CFTR gene, coding for the CFTR chloride channel. About 10% of the CFTR gene mutations are “stop” mutations that generate a premature termination codon (PTC), thus synthesizing a truncated CFTR protein. A way to bypass PTC relies on ribosome readthrough, which is the ribosome’s capacity to skip a PTC, thus generating a full-length protein. “TRIDs” are molecules exerting ribosome readthrough; for some, the mechanism of action is still under debate. We investigate a possible mechanism of action (MOA) by which our recently synthesized TRIDs, namely NV848, NV914, and NV930, could exert their readthrough activity by in silico analysis and in vitro studies. Our results suggest a likely inhibition of FTSJ1, a tryptophan tRNA-specific 2′-O-methyltransferase.
Effects of Dietary Nitrate Supplementation on Performance during Single and Repeated Bouts of Short-Duration High-Intensity Exercise: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Inorganic nitrate (NO3−) has emerged as a potential ergogenic aid over the last couple of decades. While recent systematic reviews and meta-analyses have suggested some small positive effects of NO3− supplementation on performance across a range of exercise tasks, the effect of NO3− supplementation on performance during single and repeated bouts of short-duration, high-intensity exercise is unclear. This review was conducted following PRISMA guidelines. MEDLINE and SPORTDiscus were searched from inception to January 2023. A paired analysis model for cross-over trials was incorporated to perform a random effects meta-analysis for each performance outcome and to generate standardized mean differences (SMD) between the NO3− and placebo supplementation conditions. The systematic review and meta-analysis included 27 and 23 studies, respectively. Time to reach peak power (SMD: 0.75, p = 0.02), mean power output (SMD: 0.20, p = 0.02), and total distance covered in the Yo-Yo intermittent recovery level 1 test (SMD: 0.17, p < 0.0001) were all improved after NO3− supplementation. Dietary NO3− supplementation had small positive effects on some performance outcomes during single and repeated bouts of high-intensity exercise. Therefore, athletes competing in sports requiring single or repeated bouts of high-intensity exercise may benefit from NO3− supplementation.
Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning

Over time, software has become increasingly important in various fields. If the current software is more dependent than in the past and is broken owing to large and small issues, such as coding and system errors, it is expected to cause significant damage to the entire industry. To address this problem, the field of software reliability is crucial. In the past, efforts in software reliability were made to develop models by assuming a nonhomogeneous Poisson-process model (NHPP); however, as models became more complex, there were many special cases in which models fit well. Hence, this study proposes a software reliability model using deep learning that relies on data rather than mathematical and statistical assumptions. A software reliability model based on recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), which are the most basic deep and recurrent neural networks, was constructed. The dataset was divided into two, Datasets 1 and 2, which both used 80% and 90% of the entire data, respectively. Using 11 criteria, the estimated and learned results based on these datasets proved that the software reliability model using deep learning has excellent capabilities. The software reliability model using GRU showed the most satisfactory results.
Primary Teachers’ Perspectives on Using Artificial Intelligence Technology in English as a Second Language Teaching and Learning: A Systematic Review

Artificial Intelligence in Education (AIEd) is an emerging education field. This paper explicitly investigates the use of AI technology in language learning specifically in English as a Second Language (ESL). As a robust cloud-computing solution, AI-powered technology can be used to aid teaching and learning processes and improve language learning. While students' perspectives on AI-related research are prevalent, teachers' perspectives are not extensively discussed. In addition, studies on the effects of AI technology in ESL primary education, are understudied. Using two databases, namely Google Scholar and the Educational Resources Information Centre (ERIC), 12 articles related to the use of AI technology in ESL teaching and learning in primary schools were extracted out of 672, from 2019 to 2022 by using the PRISMA review methodology . Teachers’ per spectives were discussed based on the effectiveness, convenience, motivation and challenges. It is found that teachers perceived the integration of AI technology positively due to its dynamic characteristics and effectiveness despite facing various challenges resulting in disadvantages associated with its use. This paper explores AI technology's impact on ESL primary education from the perspective of teachers as curriculum stakeholders. Future research should delve further into the challenges that impede the use of AI technology in ESL primary education. This is essential for sustaining the momentum of AI-powered education.
Sex differences on cognitive reflection

The Cognitive Reflection Test or CRT is a popular measure designed to distinguish the intuitive thinking processing from the analytical one. Sex differences in the CRT have been found in multiple studies. In this research, the influence of the mathematical component on sex differences is studied by using two different instruments to measure cognitive reflection: the original CRT and one extended version of the original one, both with a high arithmetic component and, on the other hand, a set of five classic heuristic tasks, called CHT, with a lower numeracy content. The results show that these sex differences disappear when the mathematical nature of the tasks is reduced. Finally, in this work a more elaborate way of scoring the intuitive responses is addressed in relation to the meaning of these differences. La prueba de reflexión cognitiva o CRT (Cognitive Reflection Test) es una medida popular diseñada para distinguir el procesamiento del pensamiento intuitivo del analítico. Se han encontrado diferencias de sexo en el CRT en múltiples estudios. En esta investigación se estudia la influencia del componente matemático en las diferencias de sexo utilizando dos instrumentos diferentes para medir la reflexión cognitiva: el CRT original y una versión ampliada del original, ambos con un alto componente aritmético y, por otro lado, un conjunto de cinco tareas heurísticas clásicas, denominadas CHT, con un contenido numérico más bajo. Los resultados muestran que estas diferencias de sexo desaparecen cuando se reduce el carácter matemático de las tareas. Finalmente, en este trabajo se aborda una forma más elaborada de puntuar las respuestas intuitivas en relación con el significado de estas diferencias.
Infrared image stripe noise removal using wavelet analysis and parameter estimation

Infrared image systems often generate stripe noise because of the non-uniformity of the focal plane array, which significantly reduces the visual quality of the image. To solve this problem, we proposed an effective single-frame denoising method in this paper. First, the wavelet function extracts the approximate and vertical components of the original image containing stripe noise. Then, the approximation component is denoised by parameter estimation, and the vertical component is denoised by guided filtering. Finally, wavelet reconstruction is performed to realize the denoising process of the original image. This method avoids the loss of details of other components of the image and achieves an excellent denoising effect. The experimental results on the public datasets indicate that our proposed method can effectively eliminate the stripe noise of infrared images compared with some advanced methods.
An Analytical Model for Weak Avalanche Induced Kink Effect of PDSOI nMOSFETs

Silicon-on-insulator (SOI) devices and circuits are widely used in semiconductor industry for its superior natures on integration density, isolations degree. Inefficient body contact of partially-depleted SOI (PDSOI) devices cannot evacuate excess carriers which transported from the weak avalanche multiplication of pinch-off region at high drain voltage bias. These accumulated carriers elevate the internal body potential and trigger electrical effects namely the body bias effect of threshold voltage and the turn-on of parasitic bipolar junction transistor (BJT). A comprehensive analytical model which include all these mechanisms are proposed to describe this kink effect. This model is constructed by an equivalent circuit and five strong nonlinearly coupled equations to describe the electrical behaviors. To solve these equations, a C++ Qt-based graphical interface software is developed for the parameter extraction of the model. The fixed point iteration algorithm are adopted for the solution of equations. The effectiveness and accuracy of this model are verified by the T-gate shaped PDSOI nMOSFETs of 130 nm manufacturing process node. It can predict the electrical behaviors of kink effect. This is meaningful for the circuit simulation of PDSOI platform.
MANAGING WORKFORCE DIVERSITY AND THE QUEST FOR ETHICAL LEADERSHIP

This study proposes a framework for analyzing and understanding the relationship between ethical leadership and the management of workforce diversity. Using meta-synthesis as a methodological approach, the study examines the current research stream in workforce and workplace diversity. Second, the concept of ethical leadership is examined, including the conditions for its applicability and possibility in creating leader-follower relationship. It is argued here that our understanding of diversity and all of its ramifications are products of our ethical and cultural conditioning. A review of the ontological nature of ethical leadership indicates how organizational leaders function as role models and how members of the organization identify themselves with the organization ideal as embodied in the leader’s ideal. Further, we explain how the actions of organizational leaders as parental figures and role models are capable of altering followers’ perception of their individualities and their realities. We argue that organizational leaders can and should use their power of influence (as role models) to make their followers recognize the inherent advantages and challenges of a diverse workforce and workplace. We point out that the inability of our leaders to manage the diversity in our society and institutions poses a serious threat to the nation’s security. On this basis, we suggest that ethical leadership seems to be an appropriate form of leadership behavior for managing a diverse workforce capable of achieving the goals of an organization. Finally, five ethical leadership approaches for creating and sustaining effective management of workforce diversity are suggested. Keywords: Workforce Diversity, Ethical Leadership, Ethical Climate, Cultural Orientations.
Generalized discretization of continuous attributes in rough set theory

An approach is proposed to discretize continuous interval-valued attributes in rough set theory. The (approach) is characterized by emphasizing class-separability in discretization. The existing discretization definition based on cut-splitting is extended and the generalized discretization definition based on class-separability criterion is given. Then the detailed algorithm of generalized discretization is described and its time complexity is analyzed. (Finally,) simulation experiment is made on radar emitter signals. Experimental results show that the proposed (approach) discretizes the decision table with continuous interval-valued attributes efficiently and has good (characteristics) of simplifying classifier design and heightening recognition rate. So the (approach) expands the (application) scope of rough set theory.
Modeling and Validation of Total Ionizing Dose Effect on the TSVs in RF Microsystem

Radio frequency (RF) systems utilizing through-silicon vias (TSVs) have been widely used in the aerospace and nuclear industry, which means that studying the total ionizing dose (TID) effect on TSV structures has become necessary. To investigate the TID effect on TSV structures, a 1D TSV capacitance model was established in COMSOL Multiphysics (COMSOL), and the impact of irradiation was simulated. Then, three types of TSV components were designed, and an irradiation experiment based on them was conducted, to validate the simulation results. After irradiation, the S21 degraded for 0.2 dB, 0.6 dB, and 0.8 dB, at the irradiation dose of 30 krad (Si), 90 krad (Si), 150 krad (Si), respectively. The variation trend was consistent with the simulation in the high-frequency structure simulator (HFSS), and the effect of irradiation on the TSV component was nonlinear. With the increase in the irradiation dose, the S21 of TSV components deteriorated, while the variation of S21 decreased. The simulation and irradiation experiment validated a relatively accurate method for assessing the RF systems’ performance under an irradiation environment, and the TID effect on structures similar to TSVs in RF systems, such as through-silicon capacitors.
Statistical Analysis of Methotrexate Degradation by UV-C Photolysis and UV-C/TiO2 Photocatalysis

Methotrexate (MTX) is a folic acid analog and has been used to treat a wide variety of malignant and non-malignant diseases. The wide use of these substances has led to the continuous discharge of the parent compound and its metabolites in wastewater. In conventional wastewater treatment plants, the removal or degradation of drugs is not complete. In order to study the MTX degradation by photolysis and photocatalysis processes, two reactors were used with TiO2 as a catalyst and UV-C lamps as a radiation source. H2O2 addition was also studied (absence and 3 mM/L), and different initial pHs (3.5, 7, and 9.5) were tested to define the best degradation parameters. Results were analyzed by means of ANOVA and the Tukey test. Results show that photolysis in acidic conditions with 3 mM of H2O2 added is the best condition for MTX degradation in these reactors, with a kinetic constant of 0.028 min−1. According to the ANOVA test, all considered factors (process, pH, H2O2 addition, and experimentation time) caused statistically significant differences in the MTX degradation results.