Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Nutritional Status on Sexual Function in Male Kidney Transplant Recipients

Background and Objectives: Sexual function and nutritional status assessment are relevant topics in chronic kidney disease patients. This study was designed to investigate whether bioelectrical impedance analysis-derived nutritional parameters, nutritional biomarkers, and handgrip strength influence sexual function and to analyze the changes in sexual function after kidney transplantation in men. Materials and Methods: Fifty-four men with kidney failure who had undergone replacement therapy entered the study. In addition, sexual function and nutritional status were evaluated before kidney transplantation and 12 months after. We used the International Index of Erectile Function, bioelectrical impedance analysis, three different malnutrition screening tools, handgrip strength, and anthropometric measurements. The demographic profiles and biochemical nutritional markers were collected. Results: Sexual inactivity was associated with a lower phase angle (6 (1) vs. 7 (1), p < 0.05) and a higher fat mass index (7 (5) vs. 3 (4), p < 0.05). Normal erectile function before kidney transplantation was significantly related to higher fat-free mass (67 (11) vs. 74 (7), p < 0.05) and lean mass (64 (10) vs. 70 (7), p < 0.05). The improvement in erectile function after kidney transplantation was nonsignificant (44% vs. 33%, p = 0.57). Only a weak association between muscle mass and sexual desire 12 months after kidney transplantation was found (rS = 0.36, p = 0.02). Further, linear regression revealed that higher muscle mass could predict better sexual desire after kidney transplantation following adjustment for age, estimated glomerular filtration rate, and diabetes mellitus. Conclusions: Kidney transplantation did not significantly improve erectile dysfunction in our study population. Sexual desire and intercourse satisfaction are the most affected domains in patients with chronic kidney disease. Higher muscle mass predicts higher sexual desire after kidney transplantation. Higher levels of fat-free and lean mass are associated with normal erectile function before kidney transplantation.
Bioprocess development and optimization of parameters for enhancing bio-assisted method for sulfur removal from lakhra coal using rhodococcus sp SL-9

ABSTRACT The burning of coal releases toxic gases that disturb the environmental conditions. It is imperative to use this energy as much as possible. Due to its characteristics, it is possible to manipulate unused loss reserves. In this case, it is necessary to continue research emphasizing clean coal technology from an energy perspective, safety, and environmental sustainability problem. It wants to be implemented that 50% of the world’s coal reserves are low grades. In this regard, the current work is related to removing sulfur obtained from coal using newly isolated bacteria sl-9. It was determined that different process parameters might be used to remove Sulfur. There is a reduction of 45% of the total Sulfur in unprocessed coal when several parameters are controlled, such as 20% (w/v) pulp density, 180 rpm, 20 mesh (850 microns), and pH 7. So, Sl-9 has the potential to lower coal’s sulfate content.
Lattice Boltzmann simulation of droplet base electrowetting

In this article, the lattice Boltzmann method has been extended for modelling and simulation of electrowetting operations. Spreading, motion, and splitting of a microsized fluid droplet on a flat substrate due to the electrowetting phenomenon are simulated here. When a voltage is applied to the substrate, liquid-solid surface tension is locally decreased. To estimate the contact angle after applying the voltage, new relations for surface tensions based on the free energy functional minimisation are proposed. The results are compared with the analytical and experimental results reported in the literature, which show good agreement with them. It is concluded that the introduced model is a powerful method for the study of electrowetting operations in three dimensions.
Dynamic data reconciliation to enhance the performance of model free adaptive control

As a novel data-driven control method, model-free adaptive control (MFAC) has a high requirement for the accuracy of the feedback signal. However, the sensor used to obtain the output inevitably contains measurement noise due to its own error or external interference, which may lead to an adverse effect on the control performance. The dynamic data reconciliation (DDR) is proposed and combined with MFAC to improve the control performance in this paper, which uses predicted output and measured data to suppress measurement noise considering Gaussian and non-Gaussian distributed measurement noise. The effectiveness of the DDR combined with MFAC (DDR-MFAC) is illustrated in the single-input single-output and multiple-input multiple-output systems with Gaussian and non-Gaussian distributed measurement noise. DDR-MFAC is also successfully applied to DC–AC converter, which improves its conversion precision.
On the redundancy of universal lossless coding for general piecewise stationary sources

A lower bound on the achievable redundancy for universal lossless coding of parametric sources with abruptly changing statistics is derived. Unlike the previously known bound for a problem that assumes a fixed number of changes in the statistics, the new bound is general and can be used even if the number of changes increases with the data length.
Lightweight Underwater Target Detection Algorithm Based on Dynamic Sampling Transformer and Knowledge-Distillation Optimization

Underwater robot perception is a critical task. Due to the complex underwater environment and low quality of optical images, it is difficult to obtain accurate and stable target position information using traditional methods, making it unable to meet practical use requirements. The relatively low computing power of underwater robots prevents them from supporting real-time detection with complex model algorithms for deep learning. To resolve the above problems, a lightweight underwater target detection and recognition algorithm based on knowledge distillation optimization is proposed based on the YOLOv5-lite model. Firstly, a dynamic sampling Transformer module is proposed. After the feature matrix is sparsely sampled, the query matrix is dynamically shifted to achieve the purpose of targeted attention modeling. Additionally, the shared kernel parameter convolution is used to optimize the matrix encoding and simplify the forward-propagation memory overhead. Then, a distillation method with decoupled localization and recognition is designed in the model-training process. The ability to transfer the effective localization knowledge of the positive sample boxes is enhanced, which ensures that the model maintains the same number of parameters to improve the detection accuracy. Validated by real offshore underwater image data, the experimental results show that our method provides an improvement of 6.6% and 5.0% over both baseline networks with different complexity models under the statistical index of detection accuracy mAP, which also suggests 58.8% better efficiency than models such as the standard YOLOv5. Through a comparison with other mainstream single-stage networks, the effectiveness and sophistication of the proposed algorithm are validated.
Recent advances in magnetoresistance biosensors: a short review

Recent years have seen the development of spintronic devices and their applications in biomedical areas. Spintronic devices rely on detecting or manipulating a magnetic field, a field to which biological matter is relatively transparent. The recent use of spintronic devices in biomedical areas has included diagnosing diseases such as cancer and cirrhosis, genotyping DNA, point-of-care devices, and flexible electronics. Up to date, most of the spintronic devices in biomedical applications fall into three mainstream types: anisotropic, giant, and tunneling magnetoresistance devices. Each of these has its advantages and drawbacks, which are explored and discussed in this article. The advent of spintronics gives us a new low-power, low-cost, ease-of-manufacture alternative to standard CMOS sensors. The sensitivity of spintronic biosensors has been progressing steadily, expected to increase tremendously in the next few years.
Kinetics of microphase separation in block copolymers: A molecular-dynamics study

We study the kinetics of microphase separation in block copolymers (BCPs) via molecular-dynamics (MD) simulations in d = 3. The BCPs consist of AnBm polymer chains. In the early stages, the BCP segregation is analogous to usual spinodal decomposition in fluid or polymer mixtures. At late times, the BCP evolution freezes into a micro-scale morphology dictated by the n : m ratio, e.g., lamellar, cylindrical, droplet, etc. We investigate the crossovers in a) the scaling forms of the correlation function and structure factor; and b) the domain growth law.
Bioactive Compounds Extracted from Saudi Dates Using Green Methods and Utilization of These Extracts in Functional Yogurt

The bioactive compounds of four Saudi date flesh extracts (Ambara (AF), Majdool (MF), Sagai (SF), and Sukkari (SKF)) prepared using different extraction methods—namely, supercritical fluid extraction (SFE), subcritical CO2 extraction (SCE), and Soxhlet extraction (SXE)—were evaluated. A total of 19 bioactive compounds were detected in extracts prepared using SFE and SCE methods, whereas less than 12 compounds were detected in extracts obtained using the SXE method. Both the date variety and extraction method affected the phenolic profile of date flesh extract (p ≤ 0.05). The apparent viscosity, surface color, and bioactive properties of yogurt were affected by both date flesh extracts and storage duration in varied magnitudes (p ≤ 0.05). The incorporation of date flesh extracts into yogurt formulations increased the total phenolic content (TPC), DPPH antiradical activity, viscosity, and redness (a*) and decreased the lightness (L*) and yellowness (b*) of the developed product (p ≤ 0.05). The elongation of storage time progressively (p ≤ 0.05) reduced the pH, TPC, DPPH antiradical activity, bacterial counts, and L* and b* values and increased the acidity, syneresis, viscosity, and a* values with few exceptions. Date flesh extracts can improve the health quality of yogurt without major influence on the sensory attributes while stored at 4 °C.
Support Vector Regression for Bluetooth Ranging in Multipath Environments

Ranging solutions for Internet of Things (IoT) localization applications seek to provide high accuracy with low cost of implementation. Among candidate IoT technologies that may fit this criterion, Bluetooth is a desirable choice as Bluetooth low energy (BLE) support is ubiquitous in modern smartphones, providing low implementation costs and low power consumption. Recent advancements in BLE ranging technology employ the Multicarrier Phase Difference technique which takes two-way channel frequency response (CFR) measurements. However, accurate ranging with these measurements is challenging due to many closely spaced multipath components from squaring the one-way CFR, a single or low number of snapshots, and model imperfections that arise in practical scenarios. To overcome these challenges, we propose a data-driven support vector regression (SVR) approach. Using real-world BLE measurements, our proposed SVR method demonstrates decimeter-level accuracy with single antenna devices, whereas multiple signal classification (MUSIC), a popular model-based method, requires multiple antennas to obtain comparable performance. Moreover, we show robustness in different multipath environments, including indoor, outdoor, and nonline-of-sight conditions, we determine generalization capabilities with training size, and we analytically establish the reduction in computational complexity compared to MUSIC.
Optimum placement of actuators in structural and control design using Stackelberg games

Actuator placement has a significant impact on the dynamic response of actively controlled structures. Misplaced actuators and sensors often lead to controllability and observability problems, and the desired system performance may not be achieved with any choice of control law. This paper addresses the design of actively controlled structures wherein both the actuator placement and controller design aspects are addressed simultaneously. It is assumed that a hierarchical structure exists between the actuator placement and controller design objective functions with the actuator placement problem considered as being more important. The resulting multiobjective design problem is solved as a bi-level Stackelberg game. A computational procedure based on variable updating using response surface methods is developed for exchanging information between the two levels (leader and follower). The optimization problem has mixed discrete-continuous variables with discrete variables corresponding to actuator placement and continuous variables associated with the controller design problem. The solution approach includes a blend of genetic algorithms and sequential quadratic programming techniques and is applied to the design of a flexible truss structure. The proposed approach successfully designed an optimum controller while minimizing the weight of the structure and simultaneously maximizing the energy dissipated by the controller to bring the structure to its equilibrium position when subjected to an external disturbance.
UAV‐based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates

The accuracy of the traditional soil and plant-based techniques for assessing sugar-beet demand for nitrogen (N) and yield prediction is generally low. Refining N and irrigation water management is a key to maximizing return for sugarbeet ( Beta vulgaris L.) growers from agronomic, economic, and environmental perspective. The use of Normalized Difference Vegetative Index (NDVI) in combination with the unmanned aerial vehicle (UAV)-based data collection for in-season estimation of sugarbeet root yield and sugar concentration has potential for precision N management. Sugarbeet field trials were conducted in Idaho in 2019 and 2020 to assess (1) effects of water and N fertilizer rates on yield and estimated recoverable sugar (ERS) and (2) feasibility of predicting root yield and ERS using UAV NDVI. At the lowest N rate, application of water at 100% level resulted in greater yield, compared to
Sliding mode observers for robust detection and reconstruction of actuator and sensor faults

This paper describes a method for designing sliding mode observers for detection and reconstruction of actuator and sensor faults, that is robust against system uncertainty. The method uses ℋ︁∞ concepts to design the sliding motion so that an upper bound on the effect of the uncertainty on the reconstruction of the faults will be minimized. The design method is first applied to the case of actuator faults, and then by some appropriate filtering, the method is extended to the case of sensor faults. A VTOL aircraft example taken from the fault detection literature is used to demonstrate the method and its effectiveness. Copyright © 2003 John Wiley & Sons, Ltd.
Modeling of Unsteady Turbulent Flow in a Buried Co-Axial Exchanger—The Use of Green's Functions Theory

This work presents an analytical and numerical investigation of the heat transfer problem of turbulent flow under forced convection in a buried co-axial exchanger. A hybrid model consisting of a finite element method at the boundary (BEM) for the heat transfer problem on the boundary, and a finite volume method (FVM) to solve the turbulent flow inside solves this complicated problem. The development of the BEM method is based on Green's functions theory. The mathematical model employed makes a scientific contribution to a similar practical situation. The results can be of great interest for industrial processes requiring the estimation of the heating time necessary to obtain steady states. Essentially, the axial evolution of the heat transfer coefficient versus Reynolds number and transfer duration is treated here. The temperature field and the heat flux density at the wall are also investigated.
A systematic review of the co-occurrence of self-harm and aggression: Is dual harm a unique behavioural construct?

Introduction Dual harm is the co-occurrence of self-harm and aggression during an individual’s lifetime. It is unclear whether sufficient evidence exists for dual harm as a unique clinical entity. This systematic review aimed to examine whether there are psychological factors that are uniquely associated with dual harm when compared to those who have engaged in sole harm (self-harm alone, aggression alone) and no harmful behaviours. Our secondary aim was to conduct a critical appraisal of the literature. Methods The review searched PsycINFO, PubMed, CINAHL, and EThOS on September 27, 2022, resulting in 31 eligible papers that represented 15,094 individuals. An adapted version of the Agency for Healthcare Research and Quality was used to assess risk of bias and a narrative synthesis was conducted. Results The included studies assessed differences in mental health problems, personality, and emotion related factors between the different behavioural groups. We found weak evidence that dual harm is an independent construct with unique psychological characteristics. Rather, our review suggests that dual harm results from the interaction of psychological risk factors that are associated with self-harm and aggression. Discussion The critical appraisal identified numerous limitations within the dual harm literature. Clinical implications and recommendations for future research are provided. Systematic review registration https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=197323, identifier CRD42020197323.
The Construction of New Bivariate Exponential Distributions from a Bayesian Perspective

Abstract We use an economic approach of Mendel to derive new bivariate exponential lifetime distributions. Features distinguishing this approach from the existing ones are (1) it makes use of the principle of indifference; (2) our parameter of interest is a measurable function of observable quantities; (3) the assessment of the probability measure for random lifetimes is performed by assessing that for random lifetime costs with a change of variables; and (4) characterization properties other than the bivariate loss-of-memory property are used to construct distributions. For the infinite population case, our distributions correspond to mixtures of existing bivariate exponential distributions such as the Freund distribution, the Marshall-Olkin distribution, and the Friday-Patil distribution. Moreover, a family of natural conjugate priors for Bayesian Freund (-type) bivariate exponential distributions is discussed.
Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things

Industrial Internet of Things (IIoT) applications generate data in varying amounts with diverse quality of service requirements. The adaptive network access approach and distributed resource management in IIoT networks can reduce the communication overheads caused by centralized resource management approaches. In this regard, statistical learning is a promising tool for addressing decision-making problems in a dynamic environment. This article considers uplink dominant IIoT networks in which massive devices generate delay-sensitive and delay-tolerant data and communicate over shared radio resources. We propose a novel grant-free access scheme using a statistical learning approach that enables IIoT entities to perform delay-sensitive and delay-tolerant transmissions over dynamically partitioned resources in a prioritized manner. In order to improve utilization of available radio resources, we design an adaptive network access mechanism operating in a semi-distributed manner. This mechanism enables end devices to use their transmission history to choose between static and dynamic resource allocation-based grant-free schemes in a dynamic environment. Simulation results show that average latency and resource utilization vary in grant-free access schemes employing static and dynamic resource allocations. Thus, compared to a single transmission scheme, the proposed adaptive network access offers better channel utilization while meeting the application-specific latency bound in IIoT networks.
General structures for classification

The problem of classifying signals is of interest in several application areas. Typically, we are given a finite number m of pairwise disjoint sets C/sub 1/,. ,C/sub m/ of signals, and we would like to synthesize a system that maps the elements of each C/sub j/ into a real number a/sub j/, such that the numbers a/sub 1/,. ,a/sub m/ are distinct. The main purpose of this paper is to show that this classification can be performed by certain simple structures. Involving linear functionals and memoryless nonlinear elements, assuming only that the C/sub j/ are compact subsets of a real normed linear space. The results on which this conclusion is based have applications other than to classification problems. For example, one result provides a relatively simple completion of a proof of a well-known proposition concerning approximations in R/sup n/ using sigmoidal functions. >
Numerical Modeling and Computer Simulation of a Meander Line Antenna for Alzheimer’s Disease Treatment, a Feasibility Study

Alzheimer’s disease (AD) is a brain disorder that eventually causes memory loss and the ability to perform simple cognitive functions; research efforts within pharmaceuticals and other medical treatments have minimal impact on the disease. Our preliminary biological studies showed that Repeated Electromagnetic Field Stimulation (REFMS) applying an EM frequency of 64 MHz and a specific absorption rate (SAR) of 0.4 – 0.9 W/kg decrease the level of amyloid-β peptides (Aβ), which is the most likely etiology of AD. This study emphasizes uniform E/H field and SAR distribution with adequate penetration depth penetration through multiple human head layers driven with low input power for safety treatments. In this work, we performed numerical modeling and computer simulations of a portable Meander Line antenna (MLA) to achieve the required EMF parameters to treat AD. The MLA device features a low cost, small size, wide bandwidth, and the ability to integrate into a portable system. This study utilized a High-Frequency Simulation System (HFSS) in the design of the MLA with the desired characteristics suited for AD treatment in humans. The team designed a 24-turn antenna with a 60 cm length and 25 cm width and achieved the required resonant frequency of 64 MHz. Here we used two numerical human head phantoms to test the antenna, the MIDA and spherical head phantom with six and seven tissue layers, respectively. The antenna was fed from a 50-Watt input source to obtain the SAR of 0.6 W/kg requirement in the center of the simulated brain tissue layer. We found that the E/H field and SAR distribution produced was not homogeneous; there were areas of high SAR values close to the antenna transmitter, also areas of low SAR value far away from the antenna. This paper details the antenna parameters, the scattering parameters response, the efficiency response, and the E and H field distribution; we presented the computer simulation results and discussed future work for a practical model.
First VLF detections of ionospheric disturbances due to Soft Gamma Ray Repeater SGR J1550–5418

We report the detection of sudden ionospheric disturbances (SIDs) due to a Soft Gamma Ray Repeater (SGR) SGR J1550–5418. This detection was made with receiving stations of Indian Centre for Space Physics which were monitoring Very Low Frequency signals (VLFs) from the VTX transmitter located near the southern tip of Indian sub-continent. This positive detections add to the list of a handful of similar detections of other GRBs and SGRs throughout the world.