Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Fungal Infections in Gloriosa Superba Plant Using the Convolution Neural Network Model

Herbal treatments’ efficacy, safety, and mild side effects are also high priorities in primary care. Furthermore, as the world’s population expands, food production becomes more difficult. We need to use innovative biotechnology-based fertilization technologies to boost food production output. Gloriosa superba is one of the most well-known plants for its antibacterial and medicinal capabilities. The money plant is also known as the Gloriosa superba. We used a deep learning-based convolution neural network (CNN) classifier model to optimize the CNN algorithm parameter for better prediction. The enhanced particle swarm optimization (PSO) technique was used for optimization. Scale-invariant feature transform (SIFT) was used to extract the fungal spotted area. Digital camera with a high resolution acquires 300 dataset photographs from different villages in India for this investigation. Using a real-time fungal-affected image to train and test the model, different parametric measures are used to assess the model’s performance. The categorization accuracy obtained in this experiment was 99.32 percent.
Optimal Offering Strategy of GenCo With Joint Participation in FTR Auction and Day-Ahead Market Considering Virtual Bidding

Nodal price separations in Day-Ahead (DA) market caused by transmission congestion create congestion charges/surplus that are reallocated to the market participants through the financial transmission right (FTR) auction. From a market participant's viewpoint, these two markets are interrelated because the revenue of market participant in FTR auction is determined based on the day-ahead locational marginal prices. Furthermore, virtual transactions which are designed to improve price convergence between the day-ahead and real-time markets can directly impact day-ahead prices. This impact through virtual transactions may be utilized by a market participant to increase its FTR value and improve its overall strategy in participating in both FTR auction and day-ahead market. To this end, this paper is the first attempt to reveal this tactic in literature and proposes an offer strategy framework for a price-maker generating company participating in both FTR auction and day-ahead market with the consideration of virtual bidding. First, the possibility of FTR value manipulation is conceptually demonstrated by placing virtual bids in the day-ahead market. Second, a two-stage bi-level offering strategy model is formulated for strategic GenCos, which is further converted to a single-level optimization problem by using Karush-Kuhn-Tucker conditions and strong duality theory. Numerical tests on a 5-bus system and IEEE reliability test system (RTS) demonstrate the effectiveness and applicability of the proposed approach.
Safety Impact of Automated Speed Camera Enforcement: Empirical Findings Based on Chicago’s Speed Cameras

Speeding was a factor in over a quarter of crash fatalities annually in the U.S. from 2009 to 2018. In some cities, automated speed camera enforcement is being used to curb speeding and improve roadway safety in instrumented areas. This paper reviews the effectiveness of automated speed cameras and some of the considerations informing the public debate around them in the United States. It then employs data from the City of Chicago, Illinois and the empirical Bayes approach to examine how effective speed cameras have been at reducing injury crashes and fatalities. Chicago installed most of its currently operating speed cameras in the 2013 to 2014 period. From 2015 to 2017, we estimate a 12% reduction in fatal and injury crashes across treated locations included in our analysis. Fatality and severe injury crashes declined by 15%. Some treated sites did not see the expected safety benefits, however. Recommendations to improve efficacy are made.
Chalcones as Potential Ligands for the Treatment of Parkinson’s Disease

Along with the increase in life expectancy, a significant increase of people suffering from neurodegenerative diseases (ND) has been noticed. The second most common ND, after Alzheimer’s disease, is Parkinson’s disease (PD), which manifests itself with a number of motor and non-motor symptoms that hinder the patient’s life. Current therapies can only alleviate those symptoms and slow down the progression of the disease, but not effectively cure it. So now, in addition to understanding the mechanism and causes of PD, it is also important to find a powerful way of treatment. It has been proved that in the etiology and course of PD, the essential roles are played by dopamine (DA) (an important neurotransmitter), enzymes regulating its level (e.g., COMT, MAO), and oxidative stress leading to neuroinflammation. Chalcones, due to their “simple” structure and valuable biological properties are considered as promising candidates for treatment of ND, also including PD. Here, we provide a comprehensive review of chalcones and related structures as potential new therapeutics for cure and prevention of PD. For this purpose, three databases (Pubmed, Scopus and Web of Science) were searched to collect articles published during the last 5 years (January 2018–February 2022). Chalcones have been described as promising enzyme inhibitors (MAO B, COMT, AChE), α-synuclein imaging probes, showing anti-neuroinflammatory activity (inhibition of iNOS or activation of Nrf2 signaling), as well as antagonists of adenosine A1 and/or A2A receptors. This review focused on the structure–activity relationships of these compounds to determine how a particular substituent or its position in the chalcone ring(s) (ring A and/or B) affects biological activity.
A Safety Evaluation-based Image Quality Method for Roads and Bridges

: A computer-aided automatic safety evaluation method is proposed based on quality evaluation on digital images of roads or bridges and other image information collected by highway monitoring devices. Images of qualified roads or bridges are selected to form a reference image database, and reference image sequence and evaluation image sequence are established separately. Then combined with the peak signal to noise ratio (PSNR) and the human visual characteristic information entropy, a safety evaluation function with dynamic weights is obtained. At last, the evaluating algorithm is used to compare similarities between evaluation images and reference images to judge the quality of roads or bridges and get a sequence of evaluation parameters sequence. If the value of the evaluation parameter is greater than the threshold, the road or bridge quality changes greatly, and therefore artificial inspection is required. The experimental results show that the evaluation is consistent with the subjective perception of human vision, and the method proposed in this paper has high degree of automation.
Thermal Decomposition Behavior of Polyimide Containing Flame Retardant SiO2 and Mg(OH)2

The effects of flame retardant silica (SiO2) and magnesium hydroxide (Mg(OH)2) on the thermal decomposition process of polyimide (PI) are discussed in this paper. Firstly, the decomposition process of PI in a nitrogen and oxygen atmosphere was studied by thermogravimetric analysis and differential scanning calorimetry methods, and the kinetic parameters were calculated by the nonlinear fitting method. In an inert atmosphere, PI decomposition consists of a three-step endothermic reaction, whereas in an oxygen atmosphere, PI decomposition consists of two steps, in which the first step does not change, and the second step changes to a violent exothermic peak. The effects of 3 wt % SiO2 (SPI) and 3 wt % Mg(OH)2 (MPI) on the degradation kinetics of PI are discussed. The results show that under an oxygen atmosphere, SiO2 and Mg(OH)2 hydroxide mainly delayed the second-step oxidation exothermic peak temperature of PI by 5 °C. In summary, the first step of the PI degradation is not affected by oxygen, and the flame retardant mainly acts in the second step, which can delay the oxidation heat release. In addition, the addition of SiO2 could prevent PI from aging whereas Mg(OH)2 has barely effect on the aging of PI.
Infrared time-correlated single-photon counting

This Thesis investigates near infrared ( ~ 1550 nm) time-correlated singlephoton counting, studying the single-photon detectors and some of the potential application areas. Custom designed and fabricated InGaAs/InP single-photon avalanche diode detectors were characterised. Our devices yielded single-photon detection efficiencies of ~10 %, timing jitter of 200 ps, and noise equivalent power comparable to the best commercially available avalanche photodiodes operated in Geiger-mode. The afterpulsing phenomenon which limits the maximum count rate of InGaAs/InP single-photon avalanche diodes has been investigated in detail and activation energies calculated for the traps that cause this problem. This was found to be ~250 meV for all the devices tested, despite their differing structures and growth conditions, and points to the InP multiplication region as the likely location of the traps. Ways of reducing the effects caused by the afterpulsing phenomenon were investigated and sub-Geiger mode operation was studied in detail. This approach enabled freerunning, afterpulsing-free operation at room temperature of an InGaAs/InP singlephoton avalanche diode detector for the first time. Finally, time-of-flight photon counting laser ranging was performed using both singlephoton avalanche diodes and superconducting nanowire single-photon detectors. The use of the latter resulted in a surface to surface depth resolution of 4 mm being achieved at low average laser power at an eye-safe wavelength of 1550 nm.
Combining Mobile Technologies, Healthcare & Service Quality. A Bibliometric Analysis

The purpose of the paper is to identify the links and connections among main themes of the search i.e., Mobile Technologies, Service Quality and Healthcare. It also aims at identifying the valuable contributions from authors, journals, countries, and institutions in the searched area. Data has been extracted from Scopus database and it covers the timeline from 2005 to 2021. The final documents (n=245) have been analysed through VOSviewer software and MS Excel. Results show that Guo, X. , International Journal of Medical Informatics, Harbin Institute of Technology, China and United States are top performing author, journal, institution and countries respectively. According to the results for collaborations and associations, Bhuiya A. , United States and Mobile Healthcare are the most prominent author, country and keyword with highest score of total link strength (TLS). Findings suggested important and valuable avenues for future research directions in terms of underperforming countries, under-utilized subject areas and under-explored themes (keywords). This study, to best of our knowledge, is the first of its kind in mapping the literature of combined themes (Mobile Technologies, Service Quality and Healthcare).
Total Knee Arthroplasty, All-in-One versus Four-in-One Femoral Cutting Jig System: A Comparison Study

Background Total knee arthroplasty (TKA) is often indicated for end-stage knee osteoarthritis management. The posterior-stabilized (PS) implant is one of the TKA implants with various component designs, including femoral component cutting jigs. However, little is known about how the differences in cutting jig designs affect the outcomes. This study aims to compare the radiographic and functional outcomes of the patients who underwent cemented TKA using all-in-one and four-in-one femoral component PS implants. Methods A retrospective comparative study assessed patients who underwent cemented TKA using PS implants from 2018 to 2019. The patients were divided into all-in-one and four-in-one groups. Demographic data, surgery duration, postoperative radiological findings after one week, and functional outcomes after two years were collected and compared. Results A total of 96 patients were included in the study, 55 patients were in all-in-one sample, and 41 patients were in four-in-one sample. The majority of the patients in both groups were female, aged >60 years old, overweight (BMI ≥ 25), and presented with an ASA score of II. We found significantly shorter surgery duration in the all-in-one group compared to the four-in-one group (128.00 ± 36.24 vs. 210.61 ± 57.54, p=0.000). The four-in-one group and the all-in-one group showed the insignificant difference in α, β, δ, and γ angles (p=0.476, 0.273, 0.594, and 0.818). The functional outcomes (SF-12, KSS, and KOOS) showed insignificant differences. Conclusion There is no differentiation for the postsurgery functional and radiological outcomes between all-in-one and four-in-one implants.
Five-Dimensional Switching-Table-Based Direct Torque Control of Six-Phase Drives

This article proposes a novel 5-D switching-table-based direct torque control (5D-DTC) strategy for asymmetrical six-phase induction machines (6PIMs). As is well-known, classical DTC of 6PIM is penalized by significant stator current harmonics, which are mapped into the nonenergy subspace (<inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> subspace). The concept of virtual voltage vectors (VVs) has been frequently perceived as able to tackle the problem of <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents. Such a concept maintains zero average volt-second in the <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> subspace, which in turn suppresses current harmonics due to discrete pulsewidth modulation implementation to a great extent compared to the classical DTC. However, it still cannot effectively compensate for <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents, mainly arising from dead band effect and machine/converter asymmetry, due to lack of dedicated regulators for <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents. Indeed, the <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents can be fully suppressed only in the presence of active control over them. This article incorporates additional hysteresis regulators of <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents into the direct torque control strategy. In the proposed 5D-DTC scheme, there are in total five indexes for optimal selection of VVs as torque, stator flux, <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents, and stator flux position indexes. In this way, a clustering method is developed for synthesizing VVs to cover all possible cases of the switching table. A beneficial feature in comparison with 3-D switching-table-based DTC is effective suppression of <inline-formula><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> currents with a rather simple structure, without increasing the switching frequency, and without decreasing dc-link utilization. Experimental results confirm the effectiveness of the proposed technique.
GENERALIZED ADDITIVE MIXED MODELS WITH P-SPLINES APPLIED TO SUGARCANE PRODUCTION IN THE STATE OF SÃO PAULO

In this paper, we apply the generalized additive mixed models with the use of the P-\textit{splines} technique as mixed models, which will be adopted in a problem of the agro-environmental area, in this case on the average levels of sugarcane production, which is influenced by changes in climatic variables such as temperature and precipitation which were measured over 10 years in each mesoregion of the state of São Paulo. The reason for using this approach as a smoothing method is that the tendency of these climate covariates is not known on its most, but it is known that they directly influence the response variable. Besides allowing for the inclusion of fixed and random effects in the models to be proposed, these models allow for the inclusion of an autoregressive process AR(1) as a correlation structure in the residuals.
An Extension of the Mixed Effects Model that Includes Correlation within Group Errors

Mixed effect models allow for a great deal of flexibility in defining random outcomes, but they limit within-group errors to independent disbursed random variables with a zero mean and constant variance. In addition to its random outcomes for the implied structure, this model is expanded in this paper by including within-group correlated errors. We demonstrate how to accurately predict the model's parameters using a marginal maximum probability (ML) method. The model's accuracy is demonstrated by a real-world example. Additionally, we provide several instructions for correlation systems to represent serial and spatial correlation. Finally, we define how to combine variance features and correlation systems to flexibly model the within-group variance-covariance shape. We also discuss how the lme function can be used to maintain the prolonged linear mixed effects model. And, when compared to other models, the exponential spatial correlation version has the smallest AIC and BIC, making it appear to be the best within-group correlation model.
Development of the technology of stowing the developed space during mining

An analysis of the world experience in the development of potash deposits shows that the main problems arising during their development are a high level of mineral losses, an increased risk of flooding of mine workings as a result of water-proof layer discontinuance and the development of emergency water inflows in the mined-out spaces. Reduction of potash ore losses can be achieved by using a long-pillar mining system, but this method is limited by the peculiarities of the geological structure of the potash deposits and the need to preserve the continuity of the water-proof layer during its underworking. The safety of underworking of the water-proof layer can be improved by using the stowing of the developed longwall space. However, the question of the influence of the stowing on the height of the zone of water supply cracks development remains little-studied. The world experience of stowing the developed spaces in the development of layers with long pillars is analyzed and the technology of placing the stowing masses, which can solve these problems, is proposed. The considered technology and the proposed solutions are supported by laboratory tests of stowing materials and mathematical modeling of deformation zones in the overlying rocks.
A set of time‐series classification datasets based on the average price of concrete in major Chinese cities

Time series classification (TSC) is an important and challenging problem in data mining. Time series data sets are an important basis for this research and are widely used in baseline verification of various algorithm models. Aiming at the problem that there are few domestic data sets and the current TSC data set is relatively old, a new set of time‐series classification datasets are established based on the average price data of concrete in major cities in China, which provides new data support for the research of TSC algorithm. We made use of the data center of Oriental Fortune to disclose the sample data of the average price of concrete from October 23, 2013 to January 20, 2021, created 1093 autoregression‐based series data sets by using sliding windows of different lengths, and then through the experimental verification of linear regression model, decision tree model, and random forest model, a set of data sets with lengths of 170, 842, and 1052 were selected. We use three convolutional neural network (CNN) models and three long short‐term memory (LSTM) network models to verify the validity of the data, the CNN's obtained the best accuracy rate of 93.20%, and the LSTM networks obtained the best accuracy rate of 92.99%. The establishment of the new data set has certain significance for the research of TSC and provides some references for other researchers to create datasets. The datasets are freely available at https://gitee.com/lq2012/tsc‐dataset.
App-Based Mindfulness Training Predicts Reductions in Smoking Behavior by Engaging Reinforcement Learning Mechanisms: A Preliminary Naturalistic Single-Arm Study

Mindfulness training (MT) has been shown to influence smoking behavior, yet the involvement of reinforcement learning processes as underlying mechanisms remains unclear. This naturalistic, single-arm study aimed to examine slope trajectories of smoking behavior across uses of our app-based MT craving tool for smoking cessation, and whether this relationship would be mediated by the attenuating impact of MT on expected reward values of smoking. Our craving tool embedded in our MT app-based smoking cessation program was used by 108 participants upon the experience of cigarette cravings in real-world contexts. Each use of the tool involved mindful awareness to the experience of cigarette craving, a decision as to whether the participant wanted to smoke or ride out their craving with a mindfulness exercise, and paying mindful attention to the choice behavior and its outcome (contentment levels felt from engaging in the behavior). Expected reward values were computed using contentment levels experienced from the choice behavior as the reward signal in a Rescorla–Wagner reinforcement learning model. Multi-level mediation analysis revealed a significant decreasing trajectory of smoking frequency across MT craving tool uses and that this relationship was mediated by the negative relationship between MT and expected reward values (all ps < 0.001). After controlling for the mediator, the predictive relationship between MT and smoking was no longer significant (p < 0.001 before and p = 0.357 after controlling for the mediator). Results indicate that the use of our app-based MT craving tool is associated with negative slope trajectories of smoking behavior across uses, mediated by reward learning mechanisms. This single-arm naturalistic study provides preliminary support for further RCT studies examining the involvement of reward learning mechanisms underlying app-based mindfulness training for smoking cessation.
Proton Conduction in Acceptor-Doped BaSnO3: The Impact of the Interaction between Ionic Defects and Acceptor Impurities

Barium stannate is known as a promising proton-conducting material for clean energy applications. In this work, we elucidate the effect of the interaction of protons and oxygen vacancies with acceptor impurities on proton conduction in acceptor-doped BaSnO3. The analysis relies on our theoretical developments in hydration and proton hopping in proton-conducting perovskites. The transport theory, based on the master equation and effective medium approximation, provides the analytical description of hopping conduction considering the effects of disorder and changes in the potential energy landscape for protons caused by acceptor impurities. Using the proposed approach, we establish the dependence of the proton mobility and conductivity on the energies of the acceptor-bound states of ionic defects and external conditions. It is shown that the considered interactions can substantially affect the effective activation energies and prefactors of these transport coefficients. We also demonstrate that the correlation between the ionic radius rA of an acceptor impurity and the energies of its interaction with ionic defects leads to a non-monotonic dependence of the proton conductivity on rA. The obtained results are in reasonable agreement with the experimental data on the bulk conductivity of BaSnO3 doped with different acceptors.