Miracle-dz/UniFAR-FTUs huggingface dataset:
Air quality modeling for effective environmental management in Uttarakhand, India: A comparison of logistic regression and naive bayes

Introduction: Air pollution increases the load of hospitalization cases, especially for those who have respiratory problems. For effective environmental management, this study aims to compare the performance of two classification algorithms in machine learning (logistic regression and naive bayes) and to evaluate the selection of the best algorithm for predicting the air quality class. Materials and methods: Pollutants data (PM10, SO2, NO2) have been collected from the Haldwani, Kashipur and Rudrapur regions in Uttarakhand (India). In part I of the study, the Air Quality Index (AQI) is calculated and assigned a class accordingly. In part II, the performance of algorithms is compared, and the air quality class is predicted through the best algorithm. In part III, accuracy is calculated after comparing the predicted class with the actual class. Then, it is compared with the accuracy of our selected algorithm. Results: The study finds a positive correlation between PM10 and SO2 pollutants. The result shows that the highest accuracy is achieved through logistic regression to predict the air quality class. Further, logistic regression has achieved the same accuracy i.e., 98.70% after comparing predicted values with the actual values. Conclusion: Logistic regression is the best algorithm to predict the air quality class in the regions of Uttarakhand, where pollutants are being measured in the Government’s hospital. The research also indicates that asthma patients in the Kashipur and Rudrapur regions may experience more health effects due to moderately polluted air quality; however, the situation is improving during the monsoon season.
Anti-Inflammatory Effect of Sparstolonin B through Inhibiting Expression of NF-κB and STAT-1

Sparstolonin B (SsnB), which is found in Sparganium stoloniferum, prevents the synthesis of inflammatory mediators and is related to functional pathways of survival. In this study, we assessed the possible protective functions of SsnB on lipopolysaccharide (LPS)-induced inflammatory responses. We determined the functions of SsnB on controlling heme oxygenase (HO)-1, cyclooxygenase (COX-)2, and inducible nitric oxide synthase (iNOS) in LPS-activated human umbilical vein endothelial cells (HUVECs). Furthermore, the distinct function of SsnB on the expression of iNOS and well-known pro-inflammatory mediators, such as tumor necrosis factor (TNF)-α and interleukin (IL)-1β, were assessed in the pulmonary histological status of LPS-injected mice. SsnB upregulated the HO-1 production, inhibited luciferase-NF-κB interaction, and lowered COX-2/PGE2 and iNOS/NO, which lead to the reduction of STAT-1 phosphorylation. Moreover, SsnB enhanced the nuclear translocation of Nrf2, elevated the binding activity between Nrf2 and antioxidant response elements (AREs), and weakened IL-1β expression on LPS-treated HUVECs. SsnB-suppressed iNOS/NO synthesis was restored by the process of the RNAi inhibition of HO-1. In experiment with an LPS-injected animal model, SsnB remarkably decreased the iNOS expression in the pulmonary biostructure and TNF-α level in the bronchoalveolar lavage fluid (BALF). Therefore, these results demonstrate that SsnB is responsible for inflammation ameliorative activity by controlling iNOS through inhibition of both NF-κB expression and p-STAT-1. Therefore, SsnB could be a candidate for promoting novel clinical substances to remedy pathologic inflammation.
Real-Time Strawberry Plant Classification and Efficiency Increase with Hybrid System Deep Learning: Microcontroller and Mobile Application

The strawberry plant has three life stages: seedling, blooming, and crop. It needs different acclimatization conditions in these life stages. A dataset consisting of 10,000 photographs of the strawberry plant was prepared. Using this dataset, classification in convolutional neural networks was performed in Matrix Laboratory (MATLAB). Nine different algorithms were used in this process. They were realized in ResNet101 architecture, and the highest accuracy rate was 99.8%. A low-resolution camera was used while growing strawberry plants in the application greenhouse. Every day at 10:00, a picture of the strawberry plant was taken. The captured image was processed in ResNet101 architecture. The result of the detection process appeared on the computer screen and was sent to the microcontroller via a USB connection. The microcontroller adjusted air-conditioning in the greenhouse according to the state of the strawberry plant. For this, it decided based on the data received from the temperature, humidity, wind direction, and wind speed sensors outside the greenhouse and the temperature, humidity, and soil moisture sensors inside the greenhouse. In addition, all data from the sensors and the life stage of the plant were displayed with a mobile application. The mobile application also provided the possibility for manual control. In the study, the greenhouse was divided into two. Strawberries were grown with the hybrid system on one side of the greenhouse and a normal system on the other side of the greenhouse. This study achieved 9.75% more crop, had a 4.75% earlier crop yield, and required 8.59% less irrigation in strawberry plants grown using the hybrid system.
Surface Layer Protein Pattern of Levilactobacillus brevis Strains Investigated by Proteomics

The outermost constituent of many bacterial cells is represented by an S-layer, i.e., a semiporous lattice-like layer composed of self-assembling protein subunits called S-layer proteins (Slps). These proteins are involved in several processes, such as protecting against environmental stresses, mediating bacterial adhesion to host cells, and modulating gut immune response. Slps may also act as a scaffold for the external display of additional cell surface proteins also named S-layer associated proteins (SLAPs). Levilactobacillus brevis is an S-layer forming lactic acid bacterium present in many different environments, such as sourdough, milk, cheese, and the intestinal tract of humans and animals. This microorganism exhibits probiotic features including the inhibition of bacterial infection and the improvement of human immune function. The potential role of Slps in its probiotic and biotechnological features was documented. A shotgun proteomic approach was applied to identify in a single experiment both the Slps and the SLAPs pattern of five different L. brevis strains isolated from traditional sourdoughs of the Southern Italian region. This study reveals that these closely related strains expressed a specific pattern of surface proteins, possibly affecting their peculiar properties.
Numerical analysis of the problem of flow past a cylindrical body applying the r-functions method and the galerkin method

The article considers the stationary problem of viscous incompressible fluid flow past a cylindrical body. For solving the problem it is proposed a numerical method, based on the joint use of R-functions method and the Galerkin method. The computational experiment has been conducted for the task of flow past square cylinder for different Reynolds numbers.
Supersymmetric D-branes and calibrations on general Script N = 1 backgrounds

We study the conditions to have supersymmetric D-branes on general = 1 backgrounds with Ramond-Ramond fluxes. These conditions can be written in terms of the two pure spinors associated to the SU(3) × SU(3) structure on TM⊕TM, and can be split into two parts each involving a different pure spinor. The first involves the integrable pure spinor and requires the D-brane to wrap a generalised complex submanifold with respect to the generalised complex structure associated to it. The second contains the non-integrable pure spinor and is related to the stability of the brane. The two conditions can be rephrased as a generalised calibration condition for the brane. The results preserve the generalised mirror symmetry relating the type IIA and IIB backgrounds considered, giving further evidence for this duality.
Analysis of the root position and angulation of maxillary premolars in alveolar bone using cone-beam computed tomography

Purpose This study investigated whether the relationship between the maxillary sinus and the root of the maxillary premolar is correlated with the root position and whether there is a difference in the long axis angle of premolars and the buccal bone thickness according to the sinus-root relationship and root position. Materials and Methods Cone-beam computed tomographic images of 587 maxillary first premolars and 580 second premolars from 303 patients were retrospectively reviewed. The maxillary sinus floor-root relationship was classified into 4 types, and the root position in the alveolar bone was evaluated as buccal, middle, or palatal. The long axis angle of the maxillary premolars in the alveolar bone and the buccal bone thickness were measured. The correlation between these parameters was analyzed. Results The maxillary sinus floor-root relationship showed a statistically significant correlation with the root position in the alveolar bone. Most maxillary first premolars were buccally located, and more than half of the second premolars had their roots in the middle. The long axis angle of the premolars was significantly larger in buccal-positioned teeth than in middle-positioned teeth, and the buccal bone was thinner. Conclusion When the root of the maxillary premolar was separated from the sinus floor, the premolar was often located on the buccal side. Most of the maxillary first premolars had a thinner buccal bone and larger inclination than the second premolars. It is recommended to evaluate the root position, sagittal angle and buccal bone thickness using CBCT for implant treatment planning.
Ferroelectric Devices for Intelligent Computing

Recently, transistor scaling is approaching its physical limit, hindering the further development of the computing capability. In the post-Moore era, emerging logic and storage devices have been the fundamental hardware for expanding the capability of intelligent computing. In this article, the recent progress of ferroelectric devices for intelligent computing is reviewed. The material properties and electrical characteristics of ferroelectric devices are elucidated, followed by a discussion of novel ferroelectric materials and devices that can be used for intelligent computing. Ferroelectric capacitors, transistors, and tunneling junction devices used for low-power logic, high-performance memory, and neuromorphic applications are comprehensively reviewed and compared. In addition, to provide useful guidance for developing high-performance ferroelectric-based intelligent computing systems, the key challenges for realizing ultrascaled ferroelectric devices for high-efficiency computing are discussed.
Accuracy of Endodontic Access Cavities Performed Using an Augmented Reality Appliance: An In Vitro Study

Introduction: The purpose of this study was to compare and contrast the accuracy of endodontic access cavities created using an augmented reality appliance to those performed using the conventional technique. Materials and Methods: 60 single-rooted anterior teeth were chosen for study and randomly divided between two study groups: Group A—endodontic access cavities created using an augmented reality appliance as a guide (n = 30) (AR); and Group B—endodontic access cavities performed with the manual (freehand) technique (n = 30) (MN). A 3D implant planning software was used to plan the endodontic access cavities for the AR group, with a cone-beam computed tomography (CBCT) and 3D intraoral surface scan taken preoperatively and subsequently transferred to the augmented reality device. A second CBCT scan was taken after performing the endodontic access cavities to compare the planned and performed endodontic access for accuracy. Therapeutic planning software and Student’s t-test were used to analyze the cavities at the apical, coronal, and angular levels. The repeatability and reproducibility of the digital measurement technique were analyzed using Gage R&R statistical analysis. Results: The paired t-test found statistically significant differences between the study groups at the coronal (p = 0.0029) and apical (p = 0.0063) levels; no statistically significant differences were found between the AR and MN groups at the angular (p = 0.6596) level. Conclusions: Augmented reality devices enable the safer and more accurate performance of endodontic access cavities when compared with the conventional freehand technique.
Order-Sorted Rigid E-Unification

Rigid E-Unification is a special type of unification which arises naturally whenextending Andrew's method of matings to logic with equality. We study the rigid E-Unificationproblem in the presence of subsorts. We present an order sorted methodfor the computation of order sorted rigid-E-unifiers. The method is based on an unsortedone which we refine and extend to handle sort information. Our approach is toincorporate the sort information within the method so as to leverage it. We show viaexamples how the order sorted method is able to detect failures due to sort conflictsat an early stage in the construction of potential rigid E Unifiers. The algorithm presentedhere is NP-complete, as is the unsorted one. This is significant, specially due tothe complications presented by the sort information.
Secondary Organic Aerosol Formation Regulates Cloud Condensation Nuclei in the Global Remote Troposphere

Formation of secondary organic aerosols (SOA) through the atmospheric oxidation of organic vapors has potential to enable particle growth to cloud condensation nuclei (CCN)‐relevant sizes. In this work, we constrain a global aerosol model by using aircraft measurements to reveal the global importance of SOA formation in CCN production. Our improved model, with explicit size‐resolved aerosol microphysics and parametrizations of semivolatile organic oxidation products, presents a state‐of‐the‐art performance in simulating both particle number concentrations and organic aerosol concentrations dominated (80–95%) by SOA in the remote atmosphere, which have been challenges in previous modeling studies. The SOA formation in concert with aerosol nucleation contributes to more than 50% of CCN concentrations in those pristine environments featuring low background aerosol concentrations. We estimate that the SOA‐derived CCN alters the magnitude of cloud radiative forcing by ∼0.1 W m−2. Our findings underscore the necessity for aerosol‐climate models to represent controls on CCN concentrations by SOA production.
Spin-sprayed Ni–Zn–Co ferrite films with high μr″>100 in extremely wide frequency range 100 MHz–1 GHz

Ni–Zn–Co ferrite films were deposited by the spin-spray ferrite plating from an aqueous solution. Film with optimized Zn and Co contents (i.e., Ni0.22Zn0.52Co0.03Fe2.23O4) exhibited the real permeability μr′ higher than 260 at frequencies up to 130 MHz, and the imaginary permeability μr″ higher than 100 in the extremely wide frequency range from 100 MHz to 1 GHz. Prepared at the very low temperature of 90 °C without postdeposition annealing, these films are promising to be actually applied to electromagnetic noise suppressors which directly cover the noise source elements on printed circuit boards.
Incretins as a Potential Treatment Option for Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a metabolic disease affecting an increasing number of pregnant women around the world. It is not only associated with numerous perinatal complications but also has long-term consequences impacting maternal health and fetal development. To prevent them, it is important to keep glucose levels under control. As much as 15–30% of GDM patients will require treatment with insulin, metformin, or glyburide. With that in mind, it is crucial to keep searching for novel and improved pharmacotherapies. Nowadays, there are ongoing studies investigating the use of other groups of drugs that have proven successful in the treatment of T2DM. Glucagon-like peptide-1 (GLP-1) receptor agonist and dipeptidyl peptidase-4 (DPP-4) inhibitor are among the drugs targeting the incretin system and are currently receiving significant attention. The aim of our review is to demonstrate the potential of these medications in treating GDM and preventing its later complications. It seems that both groups may be successful in the GDM management used alone or as an addition to better-known drugs, including metformin and glyburide. However, more clinical trials are needed to confirm their importance in GDM treatment and to demonstrate effective therapeutic strategies.
Sliding periodic boundary conditions for lattice Boltzmann and lattice kinetic equations

We present a method to impose linear shear flow in discrete-velocity kinetic models of hydrodynamics through the use of sliding periodic boundary conditions. Our method is derived by an explicit coarse-graining of the Lees-Edwards boundary conditions for Couette flow in molecular dynamics, followed by a projection of the resulting equations onto the subspace spanned by the discrete velocities of the lattice Boltzmann method. The boundary conditions are obtained without resort to perturbative expansions or modifications of the discrete velocity equilibria, allowing our method to be applied to a wide class of lattice Boltzmann models. Our numerical results for the sheared hydrodynamics of a one-component isothermal fluid show excellent agreement with analytical results, while for a two-component fluid the results show a clear improvement over previous methods for introducing Lees-Edwards boundary conditions into lattice Boltzmann. Using our method, we obtain a dynamical steady state in a sheared spinodally decomposing two-dimensional fluid, under conditions where previous methods give spurious finite-size artifacts.
Autonomous Demand Side Management with Instantaneous Load Billing: An Aggregative Game Approach

In this paper, we investigate a practical demand side management scenario where the selfish consumers compete to minimize their individual energy cost through scheduling their future energy consumption profiles. We propose an instantaneous load billing scheme to effectively convince the consumers to shift their peak-time consumption and to fairly charge the consumers for their energy consumption. For the considered DSM scenario, an aggregative game is first formulated to model the strategic behaviors of the selfish consumers. By resorting to the variational inequality theory, we analyze the conditions for the existence and uniqueness of the Nash equilibrium (NE) of the formulated game. Subsequently, for the scenario where there is a central unit calculating and sending the real-time aggregated load to all consumers, we develop a one timescale distributed iterative proximal-point algorithm with provable convergence to achieve the NE of the formulated game. Finally, considering the alternative situation where the central unit does not exist, but the consumers are connected and they would like to share their estimated information with others, we present a distributed agreement-based algorithm, by which the consumers can achieve the NE of the formulated game through exchanging information with their immediate neighbors.
The Ineffectiveness of Capital Asset Pricing Model and Its Possible Solutions

Capital Asset Pricing Model (CAPM) is one of the most widely used models for estimating returns in the investment world. Many relevant studies have shown that CAPM has different validity in different markets. The ineffectiveness of CAPM does exist according in previous studies. Therefore, this article summarized possible reasons for the ineffectiveness of CAPM, which includes the defects of beta measurement, market portfolio measurement, and three model assumptions. In addition, this paper provided several solutions that may help to ameliorate the negative influence of the defects and increase the accuracy of return estimation, specifically, the robust Least Trimmed Squares (LTS) and Maximum Likelihood type M-estimator (MM-estimator) methods can bring to a more accurate beta, while the Fama-French Model may lead to a more appropriate expected return. It is hoped that this article will help investors to build better investment strategies and provide some reference for the researchers who focus on the study of CAPM.
Development of a Clinical Prognostic Model for Metabolism-Related Genes in Squamous Lung Cancer and Correlation Analysis of Immune Microenvironment

Background The incidence of squamous lung cancer (LUSC) has substantially increased. Systematic studies of metabolic genomic patterns are fundamental for the treatment and prediction of LUSC. Because cancer metabolism and immune cell metabolism have been studied in depth, metabolism and the state and function of immune cells have become key factors in tumor development. This also indicates that metabolic genes and the tumor immune microenvironment (TME) are crucial in tumor treatment. This study is aimed at dissecting the connection between TME and LUSC digestion-related qualities. Methods The information used in this study was obtained from The Cancer Genome Atlas dataset. Metabolism-related genes in patients with LUSC were screened, and relevant clinical data were collated. Next, genes associated with prognosis were screened using univariate COX regression and LASSO regression analyses. Finally, a timer database study was conducted to analyze the molecular mechanisms of immune cell infiltration of LUSC prognosis-related metabolic genes at the immune cell level. Results Nine metabolism-related genes were identified: ADCY7, ALDH3B1, CHIA, CYP2C18, ENTPD6, GGCT, HPRT1, PLA2G1B, and PTGIS. A clinical prediction model for LUSC based on metabolism-related genes was constructed. In addition, 22 subpopulations of tumor-infiltrating immune cells (TIIC) in the TME were analyzed using the CIBERSORT algorithm. Finally, we used the TIMER database to analyze the immune infiltration of LUSC and the relationship between metabolism-related genes and immune cells. Conclusion Our study identified metabolic genes associated with the prognosis of LUSC, which are important markers for its diagnosis, clinically relevant assessments, and prognosis. The relationship between metabolic genes with prognostic impact and immune infiltration was also analyzed, and a metabolic gene-based clinical prediction model was identified, providing a new perspective for LUSC treatment.