Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Different Scaling Methods on the Surface Topography of Different CAD/CAM Ceramic Compositions

This study evaluated the effect of ultrasonic and manual scaling using different scaler materials on the surface topography of computer-aided designing and computer-aided manufacturing (CAD/CAM) ceramic compositions. After scaling with manual and ultrasonic scalers, the surface properties of four classes of CAD/CAM ceramic discs: lithium disilicate (IPE), leucite-reinforced (IPS), advanced lithium disilicate (CT), and zirconia-reinforced lithium silicate (CD) of 1.5 mm thickness were evaluated. Surface roughness was measured before and after treatment, and scanning electron microscopy was used to evaluate the surface topography following the performed scaling procedures. Two-way ANOVA was conducted to assess the association of the ceramic material and scaling method with the surface roughness. There was a significant difference in the surface roughness between the ceramic materials subjected to different scaling methods (p < 0.001). Post-hoc analyses revealed significant differences between all groups except for IPE and IPS where no significant differences were detected between them. CD showed the highest surface roughness values, while CT showed the lowest surface roughness values for the control specimens and after exposure to different scaling methods. Moreover, the specimens subjected to ultrasonic scaling displayed the highest roughness values, while the least surface roughness was noted with the plastic scaling method.
Effect of Early Peptide Diets on Zebrafish Skeletal Development

Incorporation of dietary peptides has been correlated with decreased presence of skeletal abnormalities in marine larvae. In an attempt to clarify the effect of smaller protein fractions on fish larval and post-larval skeleton, we designed three isoenergetic diets with partial substitution of their protein content with 0% (C), 6% (P6) and 12% (P12) shrimp di- and tripeptides. Experimental diets were tested in zebrafish under two regimes, with inclusion (ADF-Artemia and dry feed) or lack (DF-dry feed only) of live food. Results at the end of metamorphosis highlight the beneficial effect of P12 on growth, survival and early skeletal quality when dry diets are provided from first feeding (DF). Exclusive feeding with P12 also increased the musculoskeletal resistance of the post-larval skeleton against the swimming challenge test (SCT). On the contrary, Artemia inclusion (ADF) overruled any peptide effect in total fish performance. Given the unknown species’ larval nutrient requirements, a 12% dietary peptide incorporation is proposed for successful rearing without live food. A potential nutritional control of the larval and post-larval skeletal development even in aquaculture species is suggested. Limitations of the current molecular analysis are discussed to enable the future identification of the peptide-driven regulatory pathways.
Investigating Electromechanical Buckling Response of FG-GPL-Reinforced Piezoelectric Doubly Curved Shallow Shells Embedded in an Elastic Substrate

This work reports the investigations of the electric potential impacts on the mechanical buckling of the piezoelectric nanocomposite doubly curved shallow shells reinforced by functionally gradient graphene platelets (FGGPLs). A four-variable shear deformation shell theory is utilized to describe the components of displacement. The present nanocomposite shells are presumed to be rested on an elastic foundation and subject to electric potential and in-plane compressive loads. These shells are composed of several bonded layers. Each layer is composed of piezoelectric materials strengthened by uniformly distributed GPLs. The Halpin–Tsai model is employed to calculate the Young’s modulus of each layer, whereas Poisson’s ratio, mass density, and piezoelectric coefficients are evaluated based on the mixture rule. The graphene components are graded from one layer to another according to four different piecewise laws. The stability differential equations are deduced based on the principle of virtual work. To test the validity of this work, the current mechanical buckling load is analogized with that available in the literature. Several parametric investigations have been performed to demonstrate the effects of the shell geometry elastic foundation stiffness, GPL volume fraction, and external electric voltage on the mechanical buckling load of the GPLs/piezoelectric nanocomposite doubly curved shallow shells. It is found that the buckling load of GPLs/piezoelectric nanocomposite doubly curved shallow shells without elastic foundations is reduced by increasing the external electric voltage. Moreover, by increasing the elastic foundation stiffness, the shell strength is enhanced, leading to an increase in the critical buckling load.
EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver’s distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver’s behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.
Autoimmunity and Carcinogenesis: Their Relationship under the Umbrella of Autophagy

The immune system and autophagy share a functional relationship. Both innate and adaptive immune responses involve autophagy and, depending on the disease’s origin and pathophysiology, it may have a detrimental or positive role on autoimmune disorders. As a “double-edged sword” in tumors, autophagy can either facilitate or impede tumor growth. The autophagy regulatory network that influences tumor progression and treatment resistance is dependent on cell and tissue types and tumor stages. The connection between autoimmunity and carcinogenesis has not been sufficiently explored in past studies. As a crucial mechanism between the two phenomena, autophagy may play a substantial role, though the specifics remain unclear. Several autophagy modifiers have demonstrated beneficial effects in models of autoimmune disease, emphasizing their therapeutic potential as treatments for autoimmune disorders. The function of autophagy in the tumor microenvironment and immune cells is the subject of intensive study. The objective of this review is to investigate the role of autophagy in the simultaneous genesis of autoimmunity and malignancy, shedding light on both sides of the issue. We believe our work will assist in the organization of current understanding in the field and promote additional research on this urgent and crucial topic.
Simplified axiom systems of R_0 algebras and independence

The R 0 algebras are further studied because they are a kind of important algebra systems in fuzzy logic, and a series of basic properties of R 0 algebras are given out. Two characterization theorems of R 0 algebras are obtained, and the independence of the conditions in two characterization theorems are discussed, so two independent axiom systems of R 0 algebras are obtained. According to these results, both R 0 algebras and weak R 0 algebras form algebraic varieties, i.e., equation algebraic classes. Hence, these classes of algebras are closed under sub_algebras , homomorphic images and direct products. In addition, the relationships among R 0 algebras and some other algebra systems in fuzzy logic are also discussed.
Inhibitory Potential of the Ocimum sanctum Phytochemicals on Bruton’s Tyrosine Kinase, a Well-Known Drug Target for Treatment of Chronic Lymphocytic Leukemia: An In Silico Investigation

Chronic lymphocytic leukemia (CLL) is an incurable neoplasm of B-lymphocytes, which accounts for about one-third of all leukemias. Ocimum sanctum, an herbaceous perennial, is considered as one of the important sources of drugs for the treatment of various diseases, including cancers and autoimmune diseases. The present study was designed to screen various phytochemicals of O. sanctum for discovering their potential to inhibit Bruton’s tyrosine kinase (BTK), a well-known drug target of CLL. Various phytochemicals of O. sanctum were screened for their potential to inhibit BTK using several in silico protocols. First, the molecular docking approach was used to calculate the docking scores of the selected phytochemicals. Then, the selected top-ranked phytochemicals were screened for their physicochemical characteristics using ADME analysis. Finally, the stability of the selected compounds in their corresponding docking complexes with BTK was analysed using molecular dynamics simulations. Primarily, our observations revealed that, out of the 46 phytochemicals of O. sanctum, six compounds possessed significantly better docking scores (ranging from −9.2 kcal/mol to −10 kcal/mol). Their docking scores were comparable to those of the control inhibitors, acalabrutinib (−10.3 kcal/mol), and ibrutinib (−11.3 kcal/mol). However, after ADME analysis of these top-ranked six compounds, only three compounds (Molludistin, Rosmarinic acid, and Vitexin) possessed drug likeliness characteristics. During the MD analysis, the three compounds Molludistin, Rosmarinic acid, and Vitexin were found to remain stable in the binding pocket in their corresponding docking complexes with BTK. Therefore, among the 46 phytochemicals of O. sanctum tested in this study, the three compounds, Molludistin, Rosmarinic acid, and Vitexin are the best inhibitors of BTK. However, these findings need to be confirmed by biological experiments in the laboratory.
Accurate Face Recognition on Highly Compressed Samples

Compressive sensing is an emerging field for low-dimensional data acquisition. Samples are acquired in the compressed domain and utilized for signal reconstruction or as input features for a classifier. In this work, hardware-aware face recognition using compressed samples was investigated. A linear support vector machine (SVM) classifier was exploited with compressed samples as input features; Faces can be reliably recognized with high average accuracy (up to 99%). To assess the robustness of the proposed scheme, three image datasets covering different facial and illumination conditions were analyzed. Random (binary) and structured (Haar-transform-based) measurement matrices were employed for generating compressed samples. For one of the datasets, Extended Yale B, and using a random binary measurement matrix, the proposed scheme achieved 82% accuracy from as few as 15 compressed samples, which means a 1/20480 sensing ratio. Accuracy and compression are also remarkably high with respect to the state-of-the-art for the other two datasets.
Transformational Leadership and Followers’ Innovative Behavior: Roles of Commitment to Change and Organizational Support for Creativity

This study aims to investigate the relationships among transformational leadership, followers’ innovative behavior, commitment to change, and organizational support for creativity. First, we examine the mediating role of commitment to change in the relationship between transformational leadership and followers’ innovative behavior, using both objective and subjective measures. Our results reveal that commitment to change indeed mediates this relationship. Second, we analyze whether the link between commitment to change and followers’ innovative behavior is moderated by organizational support for creativity. We find that this relationship is stronger for individuals with high organizational support for creativity compared to those with low support. Empirical analysis was conducted using data collected from 535 managers in 11 subsidiaries of a financial institution in South Korea. This study contributes to the management discipline by resolving mixed outcomes between transformational leadership and followers’ innovation and highlighting the influence of commitment to change and perceived organizational support for creativity on innovative behavior.
Persistent Peri-Ablation Blood-Brain Barrier Opening After Laser Interstitial Thermal Therapy for Brain Tumors

Purpose Laser interstitial thermal therapy (LITT) is a minimally invasive, image-guided, cytoreductive procedure to treat recurrent glioblastoma. This study implemented dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods and employed a model selection paradigm to localize and quantify post-LITT blood-brain barrier (BBB) permeability in the ablation vicinity. Serum levels of neuron-specific enolase (NSE), a peripheral marker of increased BBB permeability, were measured. Methods Seventeen patients were enrolled in the study. Using an enzyme-linked immunosorbent assay, serum NSE was measured preoperatively, 24 hours postoperatively, and at two, eight, 12, and 16 weeks postoperatively, depending on postoperative adjuvant treatment. Of the 17 patients, four had longitudinal DCE-MRI data available, from which blood-to-brain forward volumetric transfer constant (Ktrans) data were assessed. Imaging was performed preoperatively, 24 hours postoperatively, and between two and eight weeks postoperatively. Results Serum NSE increased at 24 hours following ablation (p=0.04), peaked at two weeks, and returned to baseline by eight weeks postoperatively. Ktrans was found to be elevated in the peri-ablation periphery 24 hours after the procedure. This increase persisted for two weeks. Conclusion Following the LITT procedure, serum NSE levels and peri-ablation Ktrans estimated from DCE-MRI demonstrated increases during the first two weeks after ablation, suggesting transiently increased BBB permeability.
Experimental Law and Economics

This chapter provides a framework for assessing the contributions of experiments in Law and Economics. We identify criteria for determining the validity of an experiment and find that these criteria depend upon both the purpose of the experiment and the theory of behavior implicated by the experiment. While all experiments must satisfy the standard experimental desiderata of control, falsifiability of theory, internal consistency, external consistency and replicability, the question of whether an experiment also must be contextually attentive - in the sense of matching the real world choice being studied - depends on the underlying theory of decision-making being tested or implicated by the experiment. We find that the importance of contextual attentiveness depends on whether the experiment tests or implicates a "nitary-process" theory of decision-making or a multiple-process theory. Unitary-process theories posit that people employ a single operational approach to make decisions across a broad (or universal) domain of activity. Rational Choice Theory is a unitary-process theory. Because unitary-process theories posit that people employ the same decision-making program in all contexts, experimenters can falsify a unitary-process theory using an experimental choice which bears little resemblance to any real-world choice. Faith in a unitary process account also permits legal policymakers to draw broad normative implications from experiments involving quite artificial choices. By contrast, multiple-process theories hold that people employ multiple decision-making programs when they make choices. Moreover, the relative impact of these programs can depend on the context of the decision. This posited interaction between context and decision-making implies that experimentalists seeking to examine legal decision-making must be sensitive to contextual factors likely to affect deliberative and non-conscious programs in the real world. In addition, policymakers must proceed cautiously before using experimental evidence to draw normative policy conclusions because experimental results may not be robust across contexts.
Why Do We Still Have an Impoverished Understanding of the Item Wording Effect? An Empirical Examination

The item wording (or keying) effect is respondents’ differential response style to positively and negatively worded items. Despite decades of research, the nature of the effect is still unclear. This article proposes a potential reason; namely, that the item wording effect is scale-specific, and thus findings are applicable only to a particular measure involved in an investigation. Using multiple scales and several methods, the present study provides strong and converging support for this hypothesis. In light of the results, we reinterpret major findings of the item wording effect and propose possible future directions for research.
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations

The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge. Previous approaches to this problem have not prioritized the reduction of feature map redundancy, but the introduction of Ghost modules represents a major innovation in this area. Ghost modules use a series of inexpensive linear transformations to extract additional feature maps from a set of intrinsic features, allowing for a more comprehensive representation of the underlying information. GhostNetV1 and GhostNetV2, both of which are based on Ghost modules, serve as the foundation for a group of lightweight face recognition models called GhostFaceNets. GhostNetV2 expands upon the original GhostNetV1 by adding an attention mechanism to capture long-range dependencies. Evaluation of GhostFaceNets using various benchmarks reveals that these models offer superior performance while requiring a computational complexity of approximately 60–275 MFLOPs. This is significantly lower than that of State-Of-The-Art (SOTA) big convolutional neural network (CNN) models, which can require hundreds of millions of FLOPs. GhostFaceNets trained with the ArcFace loss on the refined MS-Celeb-1M dataset demonstrate SOTA performance on all benchmarks. In comparison to previous SOTA mobile CNNs, GhostFaceNets greatly improve efficiency for face verification tasks. The GhostFaceNets code is available at: https://github.com/HamadYA/GhostFaceNets.
Efflux, Signaling and Warfare in a Polymicrobial World

The discovery void of antimicrobial development has occurred at a time when the world has seen a rapid emergence and spread of antimicrobial resistance, the ‘perfect storm’ as it has often been described. While the discovery and development of new antibiotics has continued in the research sphere, the pipeline to clinic has largely been fed by derivatives of existing classes of antibiotics, each prone to pre-existing resistance mechanisms. A novel approach to infection management has come from the ecological perspective whereby microbial networks and evolved communities already possess small molecular capabilities for pathogen control. The spatiotemporal nature of microbial interactions is such that mutualism and parasitism are often two ends of the same stick. Small molecule efflux inhibitors can directly target antibiotic efflux, a primary resistance mechanism adopted by many species of bacteria and fungi. However, a much broader anti-infective capability resides within the action of these inhibitors, borne from the role of efflux in key physiological and virulence processes, including biofilm formation, toxin efflux, and stress management. Understanding how these behaviors manifest within complex polymicrobial communities is key to unlocking the full potential of the advanced repertoires of efflux inhibitors.
Thyroid pathology in canine parvoviral infection

The hypothalamic-pituitary-thyroid axis is suppressed by pro-inflammatory mediators in systemic disease, which affects the thyroid hormone level. Canine parvovirus is one of the important infectious diseases which causes profound morbidity as well as mortality in dogs. It is found that canine parvovirus infection can result in non-thyroidal sickness syndrome. The relation between thyroid pathology and canine parvovirus infection will pave the way for therapeutic and preventive measures for this infectious disease. This study was conducted on 61 dog carcasses brought to the Department of Veterinary Pathology, College of Veterinary and Animal Sciences, Pookode during one year period. Among 61 cases, nine were found to be positive for canine parvovirus which constitutes an incidence of 14.75 per cent. Analysis of gross and histopathological lesions in canine parvovirus positive cases was the goal of this investigation. The gross lesions observed were paleness and nodular growth and histopathological lesions encountered were lymphocytic thyroiditis, colloidal goiter, c cell hyperplasia, severe fibrotic changes and neoplastic lesions. Further research is required to understand the pathophysiology of the euthyroid sick syndrome in canine parvoviral infection.
Electromodulation spectroscopy of direct optical transitions in Ge1−xSnx layers under hydrostatic pressure and built-in strain

Unstrained Ge1−xSnx layers of various Sn concentration (1.5%, 3%, 6% Sn) and Ge0.97Sn0. 03 layers with built-in compressive (e = −0.5%) and tensile (e = 0.3%) strain are grown by molecular beam epitaxy and studied by electromodulation spectroscopy (i.e., contactless electroreflectance and photoreflectance (PR)). In order to obtain unstrained GeSn layers and layers with different built-in in-plane strains, virtual InGaAs substrates of different compositions are grown prior to the deposition of GeSn layers. For unstrained Ge1−xSnx layers, the pressure coefficient for the direct band gap transition is determined from PR measurements at various hydrostatic pressures to be 12.2 ± 0.2 meV/kbar, which is very close to the pressure coefficient for the direct band gap transition in Ge (12.9 meV/kbar). This suggests that the hydrostatic deformation potentials typical of Ge can be applied to describe the pressure-induced changes in the electronic band structure of Ge1−xSnx alloys with low Sn concentrations. The same .