Miracle-dz/UniFAR-FTUs huggingface dataset:
Unbounded random operators and Feynman formulae

We introduce and study probabilistic interpolations of various quantization methods. To do this, we develop a method for finding the expectations of unbounded random operators on a Hilbert space by averaging (with the help of Feynman formulae) the random one-parameter semigroups generated by these operators (the usual method for finding the expectations of bounded random operators is generally inapplicable to unbounded ones). Although the averaging of families of semigroups generates a function that need not possess the semigroup property, the Chernoff iterates of this function approximate a certain semigroup, whose generator is taken for the expectation of the original random operator. In the case of bounded random operators, this expectation coincides with the ordinary one.
Optimal Output Tracking for Switched Systems Under DoS Attacks: A Model-Free Adaptive Predictive Control Method

This brief studies a new resilient event-triggered model-free adaptive predictive control (MFAPC) method with anti-attacks for disturbed switched nonlinear systems in non-ideal network. The switched nonlinear systems are transformed into equivalent dynamic data models by dynamic linearization. Considering the denial of service (DoS) attacks in non-ideal network environment, an anti-attacks method based on a hold mechanism and a resilient event-triggering strategy (RETS) is considered, which reduces attacks impact on system performance. A parameter estimator is given to estimate the external disturbance and further obtain accurate system models. In addition, a new tracking error boundedness analysis method is given by using the average dwell time (ADT) technique and Lyapunov function. Finally, motor simulation results are given to verify the applicability of the proposed method.
A theory of filter banks which represent signals as holomorphic functions on the time-frequency domain

Holomorphic functions have fine mathematical properties such as the Cauchy-Rieman relation, infinite differentiability, conformality, and Laurent expandability. For treating these functions, we can apply various useful theorems such as Cauchy's integral theorem, residue theorem, etc. In this paper, in order to introduce such a powerful mathematical foundation into the time-frequency analysis, we consider a filter bank and a complex logarithmic mapping which transform an input signal into a holomorphic function in the time-frequency domain. As an application of this framework, we show that zeros of the input signal are transformed into poles in the time-frequency domain, hence they can be used as salient and acculate features to describe the input signal. We show several experimental results of this principle.
Linkage Disequilibrium between LDLR rs688 and AvaII Genes and its Significant Association with Ischemic Stroke.

BACKGROUND To analyze the polymorphism distribution of low density lipoprotein receptor rs688, AvaII, NcoI gene in ischemic stroke, and explore the linkage disequilibrium among them. The correlation between the linkage disequilibrium and ischemic stroke was further analyzed. METHODS The levels of serum lipid (triglyceride, cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B) and rs688, AvaII, NcoI polymorphism of low density lipoprotein receptor gene were tested in patients with ischemic stroke (n = 140), healthy control (n = 129) and patients with other cerebrovascular diseases (n = 122). Chi-square test was used to compare the gene frequency and allele frequency of each group. Both the linkage disequilibrium of the three genes and the alleles correlated with ischemic stroke were analyzed. The correlation of linkage disequilibrium gene and ischemic stroke was analyzed with logistic binary regression. RESULTS In the ischemic stroke group, significant difference was observed in frequencies and allelic frequencies of low density lipoprotein receptor (LDLR) rs688 and AvaII. No difference of NcoI was found. Linkage disequilibrium was found for rs688 and AvaII (D' = 0.927, R2 = 0.509). Allelic genes correlate with ischemic stroke included T of rs688 (X2 = 46.105, p < 0.001) and C of AvaII (X2 = 20.436, p < 0.001). CONCLUSIONS Linkage disequilibrium existed between LDLR rs688 and AvaII genes. With the wild type gene (WT) (rs688/AvaII: CC/TT) as reference, rs688/AvaII: CT/TC, CT/CC and TT/CC increased the risk of ischemic stroke, which might be a genetic marker used for the screen of high-risk population contributing to the prevention of the disease.
RELATION BETWEEN LOW SERUM TESTOSTERONE AND PROSTATE CANCER BEHAVIOR

Objectives: Prostate cancer is intensifying globally, including Asian countries also. There are a numerous arguments is still going on in concerning the connection among endogenous testosterone levels and prostate ailments. The present research was performed to recognize the assessment of serum testosterone in identifying the threat and prostate cancer incidences. Methods: Hundred cases were registered in the research, among that, the cases showing low testosterone value (<250 ng/dL) was considered A Group and cases with normal testosterone value (>250 ng/dL) was considered B Group. All cases those went radical prostatectomy were followed for post-operatively with histopathological analysis and variables, namely, Post-operative Gleason grade, pathological tumor status, pathological node status, surgical margin status, extracapsular extension of tumor, seminal vesicle invasion, and matched among groups. Variables were completed with the Student’s t-test; p<0.05 was considered statistically significant. Results: Serum testosterone levels as found to be reduced among 74% and regular among 34% prostate cancer cases. Patients in Group A showed greater complete tumor stage, advanced nodal stage, and widespread metastases on scientific assessment associated with Group B. Conclusion: All the elderly men aged 60 and more should be screened of for serum testosterone levels for timely prostate carcinoma diagnosis and for better prognosis in the management.
Hymenoplasty in India: A Comprehensive Review of Contemporary Trends and Impact on Young Adults

Hymenoplasty is a reconstructive surgical procedure aimed at repairing the hymen, and it has gained significant attention in the Indian context due to cultural and societal factors associated with virginity. This review article aims to investigate contemporary patterns of hymenoplasty in India and assess their influence on young adults. The review employed a systematic approach to gather and analyze relevant literature from various academic databases. Selection criteria were established to ensure the inclusion of high-quality studies focusing on hymenoplasty in India. The review provides an overview of the historical context of hymenoplasty, highlighting cultural and societal factors that influence the practice. It also examines the evolution of attitudes and perceptions regarding virginity and the hymen in India. Contemporary trends in hymenoplasty are analyzed, including the prevalence and frequency of procedures, demographic factors, patient profiles, popular motivations, and variations in surgical techniques. The socio-cultural impact on young adults is explored, emphasizing the psychological and emotional implications, the influence of societal norms and family expectations, gender dynamics, and the stigma and societal judgment that individuals face. The article concludes with recommendations for future research, including evaluating long-term outcomes and psychological well-being, exploring cultural and regional variations, assessing counseling services, and further examining ethical considerations and professional guidelines. Furthermore, the importance of comprehensive sex education, open dialogue, and discussions regarding virginity and sexual health are highlighted as crucial steps toward creating a more informed and empathetic society.
Multimode non‐Gaussian process monitoring based on local entropy independent component analysis

Traditionally, independent component analysis (ICA) as a multivariate statistical process monitoring (MSPM) method has attracted considerable attention due to its excellent ability in analysis of non-Gaussian data sets. However, it may degrade fault detection performance for multimode operating process because of its assumption of one single steady mode. In order to supervise the non-Gaussian process with multiple steady modes more effectively, this paper proposes a process monitoring method based on local entropy independent component analysis (LEICA). This method applies local probability density estimation to remove the effects of multimode characteristics. Furthermore, information entropy theory is used to extract the feature information of process data by calculating their local information entropies. Based on these local entropy data, ICA is applied to establish the local entropy component model for fault detection. Lastly, a numerical example and the Tennessee Eastman (TE) process are used to verify the proposed method and the results demonstrate the superiority of LEICA method. This article is protected by copyright. All rights reserved
Mealybug vectors: A review of their transmission of plant viruses and their management strategies

Mealybugs cause mechanical damage and diseases to plants. Through their feeding activities, they reduce the yield, quality and productivity of crops. This review discusses mealybug vectors of plant viruses, the economic losses they cause, mealybug species and their hosts. Among the numerous mealybug species, Planococcus species are the most effective vector of plant viruses, transmitting many Ampeloviruses. Diverse methods for the control and regulation of mealybugs are also discussed. Physical, cultural and biological control methods are labor-intensive but environmentally friendly compared to chemical methods. However, chlorpyrifos are one the active ingredients of insecticides effective against several mealybug species. Using plant products such as neem oil as a biocontrol method has been effective, similar to other insecticides. Notwithstanding, the biological method of controlling mealybugs is effectively slow but safe and highly recommended. The Anagyrus species have the highest success rate amongst other natural parasites of mealybugs. Also, farm sanitation and pruning as cultural methods help reduce mealybug populations.
Heat kernel estimates and L p-spectral theory of locally symmetric spaces

In this work we derive upper Gaussian bounds for the heat kernel on locally symmetric spaces of non-compact type. Furthermore, we determine explicitly the Lp-spectrum of locally symmetric spaces M whose universal covering is a rank one symmetric space of non-compact type if either the fundamental group of M is small (in a certain sense) or if the fundamental group is arithmetic and M is non-compact.
Power allocation strategies for 6G communication in VL-NOMA systems: an overview

ABSTRACT This paper discusses an overview of power allocation (PA) strategy for enabled sixth-generation (6 G) communication in visible light non-orthogonal multiple access (VL-NOMA) scenario. Light emitting diode (LED) deployed in the advancement of VL-NOMA 6 G limit-less efficiencies when complemented with radio frequency (RF)/millimeter-wave ( ), terahertz (THz), free-space optical (FSO) and VL. The encountered challenges in the currently deployed fifth generation (5 G) technology such as signal failures, low power, data loss, latency from (5 ), and loss of signal strength were solved with 6 G providing disruptive technologies, latency from ( ), massive connectivity, cell-less communications, modified machine learning algorithms, new security measures, more energy-efficient, infrastructure smart networking management, new spectrum, artificial intelligence (AI), disaggregation and virtualization supporting enough bandwidth, and increase data rate. The PA uses available power to distribute entire signals assigning power levels to multi-devices connected to VL-NOMA applications for envisioned virtual reality, unlocking all the possibilities of an indoor and outdoor transmission positioned to achieve superior accuracy, reliability and unlimited access. The 6 G 2030 roadmap positioning LED as a carrier assigning VL-NOMA PA techniques for a green solution improving high quality of services, higher data rate, reduced power consumption (using metasurface schemes), high capacity, energy efficiencies, low cost, illumination, communication and indication as detailed herein. Graphical abstract
Comparative study between PI and SMC controllers for DFIG using fuzzy wind power estimator

In this paper, we are interested in the control of the rotor current of the doubly feed induction generator (DFIG), that is used in wind turbines. In this context, we present a comparative study between two types of controllers. The first is based on a classic PI proportional-integral control, while the second is based on the SMC sliding control. The command chain uses a fuzzy estimator of the wind power, which makes it possible to determine the reference speed of DFIG. The system includes essentially a turbine, two converters, one on the rotor side and the other on the grid side, and a filter. The grid-side converter is used to maintain a constant DC bus voltage and to control rotor reactive power. The rotor side converter is used to control the speed of the DFIG to guarantee maximum power extracted from the turbine. In addition, this converter allows controlling the reactive power through the control of the rotor current according to the axis "d" of the frame linked to the rotating field. The simulation results show the effectiveness of sliding mode control (SMC) for the considered system.
A GPR Segregation Detection Method on Visual Textures and Dielectric Properties

Pavement segregation reduces pavement strength and causes early pavement damage. As a convenient and nondestructive subsurface detection technology, ground penetrating radar (GPR) is widely applied to detect segregation by manual interpretation. This induces labor-intensive and time-consuming. And then the segregation is interpreted automatically by image processing technology based on GPR B-scan images. However, this ignores the intrinsic physical properties of segregation. To address the problem, a new method for automatically detecting segregation is proposed to interpret segregation by combining visual features with intrinsic physical properties of segregation. In the method, GPR data preprocessing is carried out to remove the subgrade layer disturbance and enhance the GPR signal. And then the candidate segregation locating is put forward by the segregation representation index (SRI) and graph cuts algorithm according to segregation visual and statistical characteristics. Segregation visual features and intrinsic physical properties are represented for recognition according to gray-level co-occurrence matrix (GLCM) and dielectric properties. Extreme gradient boosting (XGBoost) is employed as a classifier to recognize segregation. In experiments, experiment samples are collected by 3-D GPR testing vehicle and coring experiments, and comparison experiments are carried out to validate the proposed method by analysis on feature selection, segregation localization, and segregation recognition. The results show the proposed method has a better performance than the other methods, such as multigrained cascade forest (gcForest), deepLab v3+, and SegNet. As a result, the proposed method will provide a new reference for automatic interpretation GPR application, especially, on the combining visual features with intrinsic physical properties.
Distribution of Sediment Measurements in Lake Michigan as a Case Study: Implications for Estimating Sediment and Water Interactions in Eutrophication and Bioaccumulation Models

Lake Michigan, the sixth largest freshwater lake in the world by surface area, was utilized as a water body for assessment. Field data collected at sampling sites throughout the lake in an intensive monitoring effort were utilized for evaluation of the distribution of sediment measurements. An assessment of sediment nutrient and carbon measurements within Lake Michigan was completed to recognize strata resulting from the hydrodynamics of the system. Nonparametric comparison tests revealed that significant differences exist between measurements of sediment nutrients and organic carbon in the lake using strata based upon water column depth (all results demon-strated a p < 0.05, α = 0.05). Cross-validation analysis was applied to the field-collected samples, revealing that large errors occur when estimating sediment flux of carbon or nutrients at a given location in the lake without considering stratification of the distributions of these measurements. Errors in estimating sediment concentrations of nutrients and carbon specific to a location in the lake demonstrated a statistically significant increase when stratification of sediment measurements wasn’t employed among sites. For example, distributions of errors in estimating all nutrients and organic carbon concentrations, whereby distance squared inverse interpolation methods were applied, demonstrated a statistically significant increase in absence of stratification (all p < 0.001, α = 0.05). These results have implications for characterization, monitoring, and modeling sediment and water interaction as related to eutrophication, as well as to contaminant exposure and bioaccumulation for chemicals within Lake Michigan and large water bodies where stratification of the sediment based upon physics of the system exists.
Distortion-Energy Tradeo for Zero Delay Transmission in a Coding Scheme with High Resolution Quantization

This thesis focuses on the communication of a low bandwidth Gaussian source with specific energy restrictions through an AWGN channel. Since the source has low bandwidth, it is assumed that the transmitter is capable of delivering many dimensions per symbol without any loss of information. The offered schemes use high resolution quantization which requires an optimization of the MSE distortion to reach optimality. Several practical schemes with ML estimator and orthogonal modulation are used with different types of (high resolution) quantizers so that the distortion expression can be derived analytically. Due to the cumbersome in analytically expressing the distortion for MMSE estimation, MMSE estimator is offered under suboptimal conditions. To increase the performance of the MMSE estimator, an iterative design algorithm that transitions between receiver and transmitter is introduced. The algorithm assumes that the decoder is applying ML rule and it optimizes the quantizer's point density function accordingly. Then it fixes the transmitter side and runs Monte Carlo Simulations using MMSE estimator in a bounded set of points in the variables' space in search of the minimum distortion.
An Enhanced Photosensitive Sensor Based on ITO/MWCNTs@Polymer Composite@BiVO4 for Quercetin Detection

The fact that antioxidants scavenge free radicals in the human body and naturally treat many health problems that will occur in this way has increased the consumption of antioxidant-containing foods. However, consumption of artificially prepared antioxidants could cause cancer. Therefore, antioxidants from natural sources are preferred. Quercetin is an antioxidant present in natural samples. In this article, multi-walled carbon nanotubes (MWCNTs), a polymer composite (PC) consisting of a mixture of 15% (by mass) polystyrene (PST), 15% (by mass) polyacrylonitrile (PAN) and 70% (by mass) polyindole (PIN), and semiconducting BiVO4 were used to prepare electrodes, and then a photosensitive ITO/MWCNTs@PC@BiVO4-based sensor was fabricated for quercetin detection. Quercetin was analyzed via the photosensitive ITO/MWCNTs@PC@BiVO4 sensor in 0.1 M phosphate buffered saline (pH 7.4) solutions including various quercetin concentrations. The constructed quercetin sensor displayed a wide linear response between 10 and 200 μM and a limit of detection of 0.133 μM. The developed photosensitive ITO/MWCNTs@PC@BiVO4 demonstrated a high sensitivity (442 µA mM−1 cm−2), good reproducibility (relative standard deviation 3.6%), high selectivity and long-term stability (>49 days) towards quercetin sensing. The photoelectrochemical sensor was then applied to detection of quercetin in black tea as a real-life sample. Our study could lead to the development of novel photosensitive PC polyphenol sensors.
Asymptotic Optimality of One-Group Shrinkage Priors in Sparse High-dimensional Problems

We study asymptotic optimality of inference in a high-dimensional sparse normal means model using a broad class of one-group shrinkage priors. Assuming that the proportion of non-zero means is known, we show that the corresponding Bayes estimates asymptotically attain the minimax risk (up to a multiplicative constant) for estimation with squared error loss. The constant is shown to be 1 for the important sub-class of “horseshoe-type” priors proving exact asymptotic minimaxity property for these priors, a result hitherto unknown in the literature. An empirical Bayes version of the estimator is shown to achieve the minimax rate in case the level of sparsity is unknown. We prove that the resulting posterior distributions contract around the true mean vector at the minimax optimal rate and provide important insight about the possible rate of posterior contraction around the corresponding Bayes estimator. Our work shows that for rate optimality, a heavy tailed prior with suﬃcient mass around zero is enough, a pole at zero like the horseshoe prior is not necessary. This part of the work is inspired by van der Pas et al. (2014). We come up with novel unifying arguments to extend their results over the general class of priors. Next we focus on simultaneous hypothesis testing for the means under the additive 0 − 1 loss where the means are modeled through a two-groups mixture distribution. We study asymptotic risk properties of certain multiple testing procedures induced by the class of one-group priors under study, when applied in this set-up. Our key results show that the tests based on the “horseshoe-type” priors asymptotically achieve the risk of the optimal solution in this two-groups framework up to the correct constant and are thus asymptotically Bayes optimal under sparsity (ABOS). This is the ﬁrst result showing that in a sparse problem a class of one-group priors can exactly mimic the performance of an optimal two-groups solution asymptotically. Our work shows an intrinsic technical connection between the theories of minimax estimation and simultaneous hypothesis testing for such one-group priors.