Miracle-dz/UniFAR-FTUs huggingface dataset:
Stress distribution in endodontically treated single-rooted premolars restored with everstick post and sharonlay: A finite element analysis

ABSTRACT Background: The objective of the study is to compare stress distribution in a tooth restored with everstick post and sharonlay by means of three-dimensional finite element analysis (3D-FEA). Materials and Methods: An experimental original study was carried out in which two 3D-FEA models were constructed: (1) tooth restored with everstick post and metal ceramic crown and (2) tooth restored with sharonlay. The material properties were assigned and a force of 100N, 200N, 300N, and 400N was applied to the centric stop of the occlusal surface in centric occlusion at a 45° inclination in a linguolabial direction to the long axis of the tooth. Analysis was run and the stress distribution pattern was studied. As all stress distribution analysis was performed with the Ansys 11.0 software (Inventor AutoCAD 2010; Autodesk) program, the significance of P value or tests for statistical analysis was considered. Results: Sharonlay showed more total deformation, larger stress, and strain concentration than that of everstick post. Conclusion: Tooth restored with sharonlay showed greater chances of deformation than everstick post. It also showed maximum strain concentration near the apical portion of the remaining tooth structure and more stress in the cervical third of the postsystem than everstick post.
Combining noninvasive brain stimulation with behavioral pharmacology methods to study mechanisms of substance use disorder

Psychotropic drugs and transcranial magnetic stimulation (TMS) are effective for treating certain psychiatric conditions. Drugs and TMS have also been used as tools to explore the relationship between brain function and behavior in humans. Combining centrally acting drugs and TMS has proven useful for characterizing the neural basis of movement. This combined intervention approach also holds promise for improving our understanding of the mechanisms underlying disordered behavior associated with psychiatric conditions, including addiction, though challenges exist. For example, altered neocortical function has been implicated in substance use disorder, but the relationship between acute neuromodulation of neocortex with TMS and direct effects on addiction-related behaviors is not well established. We propose that the combination of human behavioral pharmacology methods with TMS can be leveraged to help establish these links. This perspective article describes an ongoing study that combines the administration of delta-9-tetrahydrocannabinol (THC), the main psychoactive compound in cannabis, with neuroimaging-guided TMS in individuals with problematic cannabis use. The study examines the impact of the left dorsolateral prefrontal cortex (DLPFC) stimulation on cognitive outcomes impacted by THC intoxication, including the subjective response to THC and the impairing effects of THC on behavioral performance. A framework for integrating TMS with human behavioral pharmacology methods, along with key details of the study design, are presented. We also discuss challenges, alternatives, and future directions.
A New Classification Model Based on Transfer Learning of DCNN and Stacknet for Fast Classification of Pneumonia Through X-Ray Images

Coronavirus has spread worldwide, with over 688 million confirmed cases and 6.8 million deaths. The results could be important as containment restrictions begin to be relaxed and we are not immune to new strains. They underscore the need to introduce increasingly effective techniques to deal with such a spread and help identify new infections more quickly, at a reasonable cost and with a minimum error rate. Machine learning models constitute a new approach, used increasingly in this field. In this proposed work, the authors built a classification model named CovStacknet based on StackNet metamodeling methodology combined with the deep convolutional neural network as the basis for feature extraction from x-ray images. Firstly, the proposed model used VGG16 as a transfer learning of deep convolutional neural networks and achieved an accuracy score of 98%. Secondly, the proposed model is extended to evaluate four other deep convolutional neural networks, ResNet-50, Inception-V3, MobileNet-V2 and DenseNet, and ResNet-50, has achieved the best performance.
Critic Learning-Based Safe Optimal Control for Nonlinear Systems with Asymmetric Input Constraints and Unmatched Disturbances

In this paper, the safe optimal control method for continuous-time (CT) nonlinear safety-critical systems with asymmetric input constraints and unmatched disturbances based on the adaptive dynamic programming (ADP) is investigated. Initially, a new non-quadratic form function is implemented to effectively handle the asymmetric input constraints. Subsequently, the safe optimal control problem is transformed into a two-player zero-sum game (ZSG) problem to suppress the influence of unmatched disturbances, and a new Hamilton–Jacobi–Isaacs (HJI) equation is introduced by integrating the control barrier function (CBF) with the cost function to penalize unsafe behavior. Moreover, a damping factor is embedded in the CBF to balance safety and optimality. To obtain a safe optimal controller, only one critic neural network (CNN) is utilized to tackle the complex HJI equation, leading to a decreased computational load in contrast to the utilization of the conventional actor–critic network. Then, the system state and the parameters of the CNN are uniformly ultimately bounded (UUB) through the application of the Lyapunov stability method. Lastly, two examples are presented to confirm the efficacy of the presented approach.
The Knowledge-Sharing Implications of Social Responsibility of Firms: The Importance of Ethical Climate

Using a variety of theoretical foundations, this paper examines the association between corporate social responsibility (CSR) and employees’ knowledge-sharing behavior, investigating the sequential mediation role of perceived organizational support and organizational commitment as well as the moderating role of ethical climate. Hypotheses were formulated to address both the sequential mediation effect of perceived organizational support and organizational commitment on the link between CSR and knowledge-sharing and the moderation effect of ethical climate on the relationship between CSR and perceived organizational support. In order to mitigate the problems of potential common method bias, data were gathered from a sample of 204 South Korean employees at three distinct time points. The results of the study offer compelling evidence that CSR activities exert a positive influence on employees’ knowledge-sharing behavior, an effect that is sequentially mediated by both perceived organizational support and organizational commitment. Furthermore, the research uncovers the positive moderating role played by the ethical climate in the relationship between CSR and perceived organizational support. By integrating multiple theoretical frameworks, this study not only advances the extant literature but also offers invaluable insights for organizations striving to bolster knowledge-sharing through the strategic implementation of CSR initiatives.
Positive Psychology Interventions in Patients with Depression: A Review

Positive Psychology Interventions are a new branch of Psychology. Such interventions employ various techniques to improve psychological well-being and build positive feelings, behaviors, and cognitions rather than focusing on reducing negative emotions and negative cognitions. The broadening of positive emotions has been found to help in the development of long-term resources such as psychological resilience and flourishing. In this view, the review paper is aimed at exploring the impact of positive psychology interventions on patients with depression. The related literature was searched on PubMed and Google Scholar for all the available studies associated with Positive Psychology Interventions (PPI), Positive Psychotherapy, and PPI in depression. The search was restricted to 20-year-old studies, i.e., between 1998 and 2022. The review of studies shows that PPI is better or equally effective than Cognitive Behavior Therapy (CBT) or treatment as usual. PPI results not only in the reduction of depressive symptoms but also in enhancing wellbeing, happiness, satisfaction in life, and attention. It was seen that there is a dearth of Indian studies with a clinical population. Future research needs to be done with a larger sample size among diverse populations and conducted by trained therapists; standard protocols should be followed; and long follow-up assessments need to be done.
Diffusion Tensor Imaging Summary Statistics of White Matter Regions of Interest

Diffusion tensor imaging (DTI) allows for study of the microstructural properties of white matter tracts. Regional summary measures were calculated from DTI to include measures of diffusion and anisotropy of various fiber tracts within the brain. A standard DTItemplate, with a corresponding white matter tract atlas, was registered to each individual subject. Registrations were subsequently applied to the segmented atlas. Visual inspection of the images ensured adequate registration. The mean of all voxels from each of the regions of interest from the atlas were obtained from maps of fraction (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD).
Utilizing Class Information for DNN Representation Shaping

Statistical characteristics of DNN (Deep Neural Network) representations, such as sparsity and correlation, are known to be relevant to the performance and interpretability of deep learning. When a statistical characteristic is desired, often an adequate regularizer can be designed and applied during the training phase. Typically, such a regularizer aims to manipulate a statistical characteristic over all classes together. For classification tasks, however, it might be advantageous to enforce the desired characteristic per class such that different classes can be better distinguished. Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and class-wise Variance Regularizer (cw-VR). cw-CR targets to reduce the covariance of representations calculated from the same class samples. cw-VR is similar, but variance instead of covariance is targeted. For the sake of completeness, their counterparts without using class information, Covariance Regularizer (CR) and Variance Regularizer (VR), are considered together. The four regularizers are conceptually simple and computationally very efficient, and the visualization shows that the regularizers indeed perform distinct representation shaping. In terms of classification performance, significant improvements over the baseline and L1/L2 weight decay methods were found for 20 out of 21 tasks over popular benchmark datasets. In particular, cw-VR achieved the best performance for 12 tasks.
An Autonomous Parking System using the Hybridization of the Rapidly-Exploring Random Trees Star and Ant Colony System

One key autonomous driving car application that can be utilized to address the problem of parking spaces is autonomous parking. The goal of this study is to develop a path planning algorithm that can swiftly create a path for autonomous parking in a variety of car parking settings. The method employed is a mix of Rapidly Exploring Random Tree Star and Ant Colony Systems (RRT-ACS). The RRT-ACS method can quickly provide an ideal path. The reed sheep planner technique is also used to provide a smooth curving path that non-holonomic vehicles can follow. The performance of the RRT-ACS algorithm-based autonomous parking system is compared to that of the RRT*-connect and informed RRT*-connect algorithms. The simulation tests were performed in common parking settings such as parallel parking and vertical parking. The test results reveal that the suggested autonomous parking system outperforms other comparative algorithms. It is possible to infer that a route planning algorithm capable of rapidly designing a path for automatic parking in a variety of car parking circumstances has been successfully built. These findings may have ramifications for self-driving cars equipped with advanced driver support systems.
Soil and Phytomicrobiome for Plant Disease Suppression and Management under Climate Change: A Review

The phytomicrobiome plays a crucial role in soil and ecosystem health, encompassing both beneficial members providing critical ecosystem goods and services and pathogens threatening food safety and security. The potential benefits of harnessing the power of the phytomicrobiome for plant disease suppression and management are indisputable and of interest in agriculture but also in forestry and landscaping. Indeed, plant diseases can be mitigated by in situ manipulations of resident microorganisms through agronomic practices (such as minimum tillage, crop rotation, cover cropping, organic mulching, etc.) as well as by applying microbial inoculants. However, numerous challenges, such as the lack of standardized methods for microbiome analysis and the difficulty in translating research findings into practical applications are at stake. Moreover, climate change is affecting the distribution, abundance, and virulence of many plant pathogens, while also altering the phytomicrobiome functioning, further compounding disease management strategies. Here, we will first review literature demonstrating how agricultural practices have been found effective in promoting soil health and enhancing disease suppressiveness and mitigation through a shift of the phytomicrobiome. Challenges and barriers to the identification and use of the phytomicrobiome for plant disease management will then be discussed before focusing on the potential impacts of climate change on the phytomicrobiome functioning and disease outcome.
Additions to the Genus Helicosporium (Tubeufiaceae, Tubeufiales) from China with an Identification Key for Helicosporium Taxa

Helicosporous hyphomycetes is a group of filamentous fungi that shows promising application prospects in metabolizing bioactive natural compounds. During a study of helicosporous fungi in China, six new helicosporous taxa were collected and isolated from decaying wood in Guangxi Zhuang Autonomous Region, China. Morphological comparisons with multi-gene phylogenetic analyses revealed that these six taxa belong to Helicosporium (Tubeufiaceae, Tubeufiales), and they were recognized as three novel species and were named Helicosporium liuzhouense, H. multidentatum, and H. nanningense. Detailed descriptions and illustrations of the newly discovered taxa and comparisons with similar fungi are provided. In addition, a list and a key to accepted Helicosporium species are provided.
Modeling and Identification of Discrete Second-Order Systems via Stochastic Mechanics

For a class of second-order mechanical or structural systems subjected to random disturbances, their discrete-time models are derived. Since the output of stochastic system is random and has no longer its velocity in the ordinary sense, the notion of the Nelson process which is a stochastic process having forward and backward mean velocities is introduced to describe the action integral. Then the stochastic version of the Euler-Lagrange equation is derived from the Hamilton’s principle. The discrete-time second-order model is obtained by discretizing the stochastic Euler-Lagrange equation and/or Hamilton’s principle. The structural matrices are identified using the discrete second-order model.
Allocating Cost of Uncertainties From Renewable Generation in Stochastic Electricity Market: General Mechanism and Analytical Solution

Uncertainties of the volatile renewable generations require more flexible reserve capacity and incur additional operational cost named as “cost of uncertainties”. The cost of uncertainties should be charged among responsible participants in electricity market appropriately. This article proposes a general mechanism to allocate the cost of uncertainties among multiple renewable generation sources in a chance-constrained electricity market framework. The proposed mechanism ensures coherency and fairness of allocation rigorously based on cost allocation theory and is applicable to arbitrary probability model of uncertainties. Furthermore, we develop an analytical algorithm to implement the cost allocation mechanism efficiently by leveraging different probability models of renewable generations. Numerical tests demonstrate that the proposed cost allocation mechanism formulates effective price signals to penalize renewable generation sources with more variability and conforms to cost causation principle.
Mountain railway alignment optimization based on landform recognition and presetting of dominating structures

Mountain railway alignment optimization has always been a challenge for designers and researchers in this field. It is extremely difficult for existing methods that optimize alignments before major structures to generate a better alignment than the best one provided by human designers when the terrain is drastically undulating between the start and endpoints. To fill this gap, a “structures before alignments” design process is proposed in this paper. Primarily, a landform recognition method is devised for recognizing dominating landforms. Then, a bi‐level alignment optimization model is proposed, with the upper level dedicated to characterizing dominating structures and the lower level focusing on optimizing the entire alignments. To solve this bi‐level model, a three‐stage optimization method is designed. At the first stage, a scanning process and screening operators are devised for generating all the possible locations of dominating structures. At the second stage, a hierarchical multi‐criteria decision‐making procedure is applied for selecting the optimized dominating structure layouts. At the third stage, alignments are optimized based on the determined structure layouts using a bi‐objective optimization method, which minimizes construction cost and geo‐hazard risk simultaneously. The proposed model and solution method are applied to two real‐world cases whose results verify their capabilities in producing alignment alternatives with better combinations of construction cost and geo‐hazard risk than manually designed alternatives.
Mathematical Modeling and Georeferenced Forecasting for the COVID-19 at the State of RS, Brazil

In this contribution, we present a predictive tool developed to help in the management of the evolution of the COVID-19 pandemic situation in Rio Grande do Sul (RS) - Brazil. This tool is the result of georeferenced data analysis, mathematical modeling, and parameter calibration for the dynamics of a SIR-type model defined on a spatial structure that allows distinct subpopulations to interact, similar to the controlled distancing (A_l for l = 1, ···, 21) groupings proposed by the RS government and public health authorities. The predictive analysis, updated biweekly, provides three distinct scenarios per month (milder, average, and severe) and is made available as WebSIGs (Geographic Information System - GIS). The forecast of the average scenario for each Al group is the result of a simulation of the proposed SIRtype dynamics with calibrated parameters derived from an augmented Lagrangian maximum a posteriori estimation and data on the number of infected cases made available by the RS Health Secretariat. The milder and severe scenarios are obtained from the average scenario, with changes in the contagion rates of each Al group. When compared to the number of infections reported in each Al group, the modeling predictions for a biweekly time window (the first two weeks) were quite satisfactory, with errors ranging from 0% to 5.13%, gradually increasing over time. Therefore, we suggest a biweekly re-calibration of the parameters and corresponding forecasts as a wise strategy.
Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning

Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.
NANO- HYDROXYAPATITE (NANO-HAP) REINFORCED GLASS IONOMER CEMENT:- AN ALTERNATIVE RESTORATIVE MATERIAL.

water for 24hrs before shear bond strength testing. For micro hardness evaluation, ten blocks of each restorative material were made according to the groups assigned. All these specimens were stored in distilled water for 24hrs before microhardness testing. Results: Nano- HAp GIC shows the highest Microhardness (112.79 VHN) followed by Composite & Zirconomer whereas the shear bond strength of Composite (53.37 MPa) was found to be the highest when compared to HAp & Zirconomer Conclusion: Highest shear bond strength values were seen in composites followed by Zirconomer and Nano-hydroxyapitite (GIC). Nano-HAp showed the highest mean micro hardness values when compared to Zirconomer and composites.
RASSA: Resistive Accelerator for Approximate Long Read DNA Mapping

Constructing human DNA sequence in real time is paramount to development of precision medicine and on-site pathogen detection of disease outbreaks. Single-molecule, real-time sequencing from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are new technologies that can produce long reads within minutes, potentially enabling real time genomic analysis. However, long read DNA sequencing poses new challenges. First, long reads contain many thousands of base pairs (bps). Second, long reads tend to exhibit about 15-20% insertion, deletion (indel) and substitution errors.
Carrier-envelope phase stability of a polarization-encoded chirped pulse Ti:Sapphire amplifier

The scheme of polarization-encoded chirped pulse amplification (PE-CPA) reduces the gain narrowing effect in Ti:Sapphire (Ti:Sa) amplifiers by utilizing both crystal axes of Ti:Sa. Hence, the carrier-envelope phase (CEP) of a PE-CPA pulse originates from two othogonal polarization directions. The CEP stability of PE-CPA pulses was investigated for various amplification conditions by varying the pump pulse energy. The CEP stability is directly compared to the conventional CPA scheme under the same laser parameters. A quasi-common-path interferometer was realized inside a four-pass amplifier stage, ensuring exceptional geometrical path length stability and high spectral phase sensitivity. The spectral phase noise of PE-CPA pulses showed a minor increase of 4 mrad compared to the conventional scheme, while at unsaturated amplification of a net gain of 13, it revealed a CEP stability better than 63 mrad.