Miracle-dz/UniFAR-FTUs huggingface dataset:
Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Diabetes mellitus is a hyperglycemia-like chronic condition that is a troublesome disease. It is estimated that, according to the growing morbidity, by 2040, the world will cross 642 million diabetic patients. This means that each one of the ten adults will be diabetes-affected. Diabetes can also lead to other illnesses such as heart attacks, kidney damage, and even blindness. The prediction of diabetes in advance motivates us to develop a machine learning-based model. A dataset was obtained from the online repository for this work. The obtained dataset was imbalanced. An imbalanced dataset presents a challenge that is needed to be balanced for prediction using multiple machine learning like Tomek and SMOTE. These techniques remove necessary outliers that are incomplete in the provided dataset. These outliers are also managed using the IQR method. Additionally, this research employed a two-stage model selection methodology. In the first stage, logistic regression, Support Vector Machine, k-nearest neighbors, gradient boost, Naive Bayes, and Random Forests were applied to determine the efficiency of prediction based on patients’ preconditioning. At this stage, Random Forest was found to be the best with an accuracy of 80.7% after applying SMOTE oversampling technique to balance the dataset. In the second stage, three better-performing models were used by utilizing a voting algorithm. The results were encouraging, and the model obtained 82.0% accuracy with the default dataset and 81.7% accuracy with the balanced dataset. Naive Bayes Theorem, Gradient Boosting Classifier, and Random Forest were used as inputs to the voting algorithm.
Geostress Regression Analysis Method and Engineering Case Application

Considering the gravity and tectonic stress as the main factors of in-situ stress of rock mass,a multivariate regression method,based on 3-D FEM direct modeling,is summarized.The method is applied to Shenzhen Pumped Storage Power Plant,the regression coefficients corresponding to the in-situ stress factors are determined by fitting the stress measured values,and the initial stress field is obtained for the rock mass of whole engineering.
Establishment and Solution of the Complementary Power Generation Model of Wind-Energy,Hydro-Energy and Natural Gas

A complementary clean energy power generation model of wind-energy,hydro-energy and natural gas is proposed. Taking the lowest consumption of natural gas as objective function and the full use of wind-energy and hydro-energy as constraints,an optimal complementary power generation model of various clean energy sources is built. To solve the built model an improved simulated annealing based particle sward optimization(SA-PSO) algorithm is put forward. During the solution process the particle velocity is updated by linear decreasing inertia weight coefficient,then further crossover and mutation operation of particles is performed; the post-crossover filial generations are accepted by simulated annealing algorithm and further optimization and adjustment are implemented,in this process the probability of accepting non-optimal solution decreases with the decreasing of annealing temperature. In the early stage of evolution the SA-PSO algorithm possesses strong global searching ability,so it is favorable to jump out the local optimum,and in the later stage the local searching ability is satisfied and the probability of accepting non-optimal solution reduces gradually,besides,the evolution by SA-PSO algorithm is fast. Results of a calculation example show that the built model and the proposed SA-PSO algorithm are feasible.
Securing the UAV-Aided Non-Orthogonal Downlink in the Face of Colluding Eavesdroppers

Non-orthogonal unmanned aerial vehicle (UAV)-aided secure downlink transmissions are investigated. A single-antenna UAV serves multiple ground users in the face of multiple colluding eavesdroppers (EVs) and only imperfect location information of the EVs is available. Specially, a power splitting based secure non-orthogonal downlink transmission scheme is considered, where the transmit power is divided into two parts for transmitting confidential information and artificial noise. Explicitly, we maximize the minimum average secrecy rate among all the users by optimizing the UAV trajectory, the transmit power and the power splitting ratio. In order to tackle this non-convex optimization problem, we propose an iterative algorithm based on the block coordinate descent and successive convex approximation techniques. Numerical results demonstrate the superiority of our proposed algorithm.
Research on recognizing the radar signal using the bispectrum cascade feature

To deal with the problem of emitter identification caused by parameter complexity and agility of multi-function radars,a new identification method based on the bispectrum feature is proposed.This method takes the bispectrum of emitters as the specific emitter feature and the Bhattacharyya distance measurement as the cascade feature extraction algorithm.Bispectrum analysis can restrain the effect of Gauss White Noise on radar signal,and retain the amplitude and phase information.So this paper utilizes this characteristic to enhance the validity of the radar emitter signal feature.Aimed at the problem that the signal bispectrum contains many redundancy bispectrums,the Bhattacharyya distance is introduced to extract the bispectrum cascade feature with best separation as the radar signal feature.Experimental results show that the proposed method is suitable for extracting radar signal characteristics and has a good recognition performance.The average accuracy is not less than 95% at the signal to noise ratio of a wide range.
Effect of Honey on Pediatric Radio/Chemotherapy-Induced Oral Mucositis (R/CIOM): A Systematic Review and Meta-Analysis

Background As a common side effect of radio/chemotherapy, oral mucositis severely affects patients' quality of life. Honey has been recommended for adults with radio/chemotherapy-induced oral mucositis (R/CIOM), but its effect for pediatric patients has not been systematically evaluated. Therefore, the aim of this systematic review and meta-analysis was to illuminate whether honey is effective in treating pediatric R/CIOM. Methods Two authors searched electronic databases including PubMed, Embase, the Cochrane Library, Web of Science, and Scopus to identify relevant studies, published in English. Then, the outcomes data was extracted from eligible studies and pooled in the meta-analysis. Results Totally, five studies containing 316 patients were included in our systematic review and meta-analysis. The result indicated that honey intervention significantly reduced the recovery time (MD = −5.10, 95% CI [−9.60, −0.61], P < 0.001, I2 = 98%, random-effect model) in pediatric patients. Honey also reduced the occurrence of all grades of R/CIOM (RR = 0.19, 95% CI [0.12, 0.30], P < 0.001, I2 = 0%, fixed-effect model) and the occurrence of grade III and grade IV R/CIOM (RR = 0.18, 95% CI [0.08, 0.41], P < 0.001, I2 = 7%, fixed-effect model), and the sensitivity analysis showed the results were stable and robust. Conclusion Therefore, honey could be a competent candidate for the complementary treatment of pediatric R/CIOM.
Improving Heat Resistance of Nile Tilapia (Oreochromis niloticus) by Dietary Zinc Supplementation

In this study, we investigated the effects of zinc threonine supplementation on growth, immunity-related genes, antioxidative stress, and resistance to heat stress (HS) in Nile tilapia. A total of 300 Nile tilapia ( 76.69 ± 0.11 g) were randomly allocated into five zinc treatments, with three replicates of 20 fish in each treatment. Based on the control group with zinc-free feeds, dietary zinc levels of 20 or 40 mg/kg diet were added in the form of zinc sulfate (ZnSO4) and zinc threonine (Thr-Zn). After 45 days of feeding, part of the fish was exposed to HS. The highest performance of growth and feed intake was found in fish fed with zinc (ZnSO4 or Thr-Zn) at 40 mg/kg, which also significantly elevated the villus length and absorption area. Additionally, significantly higher values of superoxide dismutase, catalase, and glutathione peroxidase activity but lower value of malondialdehyde both before and after the heat stress were documented in fish fed with zinc at 40 mg/kg in diets. After HS, dietary supplementation with zinc threonine significantly reduced the expression of HSP 70 in the liver but increased the expression of IL-10 and TNF-α. Our study demonstrated that zinc threonine supplementation at 40 mg/kg facilitated the coping of tilapia with HS by improving the growth, intestinal morphology, antioxidative resistance, and immune responses.
Closed-Form Evaluation of Mixed Potential Shielded Layered Media Green’s Functions With Spectral Differential Equation Approximation Method

A novel, robust, and computationally efficient approach is proposed for the evaluation of the Michalski–Zheng’s mixed-potential Green’s functions of general shielded layered media. A high-order variant of the spectral differential equation approximation method (SDEAM) is used to cast the spectra of Green’s functions’ components into pole-residue forms. The latter allows for closed-form evaluation of the Sommerfeld integrals (SIs) defining spatial components of the mixed-potential Green’s functions required by Method-of-Moment (MoM) discretization of different types of integral equations. The method produces Green’s functions for all elevations of interest at a fixed cost, making it substantially faster than popular alternative approaches. Numerical results validate the developed high-order SDEAM scheme and demonstrate its extended range of validity compared to the discrete complex image method (DCIM).
Design of Front Signal Conditioning Module Based on Single Channel Vibration Signal Processing

In the diagnosis of mechanical failure,the vibration signal is an indispensable part of the basis for judgment.The vibration signal is extremely weak,and the frequency is easy to be mixed because of the environmental reasons,so it can not be transformed by A/D converter.This paper introduced a kind of design method of the front signal conditioning module,the constant current source was structured by the four operational amplifier integration chip LM348,signal amplification circuit selected programmable amplifier PGA204/205 and the filter circuit was structured by the monolithic integrated filter chip MAX274.The practice test shows,this module has a great value for projects.
Nonlinear mathematical model of regenerative cycle microturbine

Nonlinear dynamic model of regenerative cycle microturbine was established and considering versatility,the static model was built by using analytical generic characteristics of compressor,recuperator and turbine under the off-design conditions.The general mathematical model was instantiated according to the specific design parameters;the determination method of analytical characteristic coefficients was given and the computer simulation iteration order was achieved.Results show that there is small difference between the static characteristic simulation results and the actual reference data of Capstone C30,so better consistency with actual reference data can be gotten according to regulating coefficients of analytical generic characteristic formulae using more detailed specific design parameters. The best operation mode with constant turbine input temperature is adopted,with the dynamic time about 100sfrom empty load to full load;in the same time the metal wall average temperature of recuperator drops from 750Kof empty load to 670Kof full load.
Detection Method for Echolocation Clicks Based on LSTM Networks

Odontocete clicks are a kind of short-time echolocation signal with high frequency. Research on the detection method of clicks is helpful for the accurate detection of marine mammal vocalization, so as to better protection of marine mammals. A method based on image processing is proposed to detect odontocete echolocation clicks. The collected data are divided into fixed-length frames and generate spectrograms. The spectrograms are filtered to remove noise and enhance the line-shape clicks. Considering that the echolocation signals are like lines in time-frequency domain, line detection is subsequently used to obtain the precise position of the lines. Finally, a Long Short-Term Memory network was trained to obtain a detector to distinguish clicks. The performance of the proposed method was evaluated using real audio recordings. The experimental results indicate that comparing with the traditional energy detector method, the proposed algorithm shows higher recall and precise under low signal-to-noise ratio (SNR). The proposed method can provide technical support in odontocete survey to accurately determine the species and better for marine bioacoustics study.
Gap design of solid rocket motor nozzle structures

Gap design of solid rocket motor nozzle was analysized through thermal and structure analysis.In order to determine thermal loading and working pressure of nozzle inner contour,the gas was regarded as one dimensional isentropic flow,and temperature field of nozzle was computed based on three-dimension finite element model.Thermal analysis result was imported into structural model and point-point contact elements were used to simulate contact state of different material, and displacement and stress field of nozzle was calculated under thermal loading and working pressure.Variation tendency of nozzle gap and contact stress was obtained in terms of multi-time computational results,and some modification suggestions,which are available for designers of solid rocket motor nozzle,were presented by the tendency.
Public Awareness and Perception of Family Medicine in Jeddah, Saudi Arabia

Background Family medicine is a specialty that provides care for family members' physical, social, and psychological aspects regardless of age, gender, and health conditions. A family physician can manage a wide range of health conditions and prevent various diseases. However, there are scarce data on the awareness and perceptions of the Saudi population regarding family medicine; therefore, we aim to determine the awareness and perception of family medicine and family physicians in the population of Jeddah, Saudi Arabia. Methodology This cross-sectional study was conducted in Jeddah and randomly enrolled 519 participants aged 18 years and older through an online self-administered survey. Microsoft Excel and SPSS were employed for the data entry and analysis. Result The analysis indicated that 86.7% of the participants had heard about family physicians, 55.1% recognized the family physician's role, 61.7% had never visited one, and 57.2% were unfamiliar with the locations of family medicine clinics. Conclusion The results of this study demonstrated that the general population of Jeddah has moderate positive responses toward the role of a family physician as a vital element in the healthcare system. However, the majority of the participants had never previously visited a family physician and were unfamiliar with the locations of family medicine clinics.
Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics

The improvements of organic photovoltaics (OPVs) are mainly implemented by the design of novel materials and optimizations of experimental conditions through extensive trial‐and‐error experiments based on chemical intuition, which may be tedious and inefficient for exploring a larger chemical space. In the recent five years, data‐driven methods using machine learning (ML) algorithms and the knowledge of known materials/experimental parameters are introduced to OPV studies to help build a quantitative structure‐property relationship model and accelerate the molecular design and parameter optimization. Here, these recent promising progresses based on experimental OPV datasets are summarized. This review introduces the general workflow (e.g., dataset collection, feature engineering, ML model generation, and evaluation) of ML‐OPV projects and discusses the applications of this framework for predicting OPV performance and experimental optimizations in OPVs. Finally, an outlook of future work directions in this exciting and quickly developing field is presented.
Controls on decadal, annual, and seasonal concentration‐discharge relationships in the Sleepers River Research Watershed, Vermont, northeastern United States

Past studies on concentration‐discharge (C‐Q) relationships have focused on short‐term or low‐temporal resolution data. While advancing understanding of catchment processes, these studies provided limited insight on catchment response over time or to climate change. Using 15 solutes from 1992 to 2015 at Sleepers River Research Watershed, Vermont, we compared C‐Q relationships over decades, years, and seasons to elucidate controls on stream chemical variation. We applied end‐member mixing analysis (EMMA) to identify solute sources and flow path routing. EMMA identified three end‐members: near‐surface runoff (NSR), riparian groundwater, and hillslope hollow groundwater. Shifting mixing proportions of these end‐members accounted for the temporal variability of conservative (no chemical reaction en route from source to stream) solutes in streamflow. For example, an increase in NSR fraction, typical of high flow, caused flushing (increased concentrations) of NO3−, DOC, Al, and Fe, which were greatest in NSR, dilution of specific conductance and base cation, SO42−, Si, Sr, Ba, and Mn concentrations, which were greatest in the two groundwater end‐members. This behaviour is reflected in the b‐coefficient of the C‐Q relation (C = aQb), which indicates the strength of dilution (b < −0.1) and flushing (b > 0.1) effects. For conservative solutes, the b‐coefficient decreased significantly (p < 0.01) with an increase in the groundwater to NSR concentration ratio. Solutes that are conservative and have relatively constant concentrations in end‐members over time showed consistent annual C‐Q patterns over years and decades. Furthermore, the strength of dilution or flushing was stronger during the snowmelt period, when the NSR fraction peaked, than during the dormant and growing seasons. With shorter snowmelt periods and snow to rain shifts, the flushing or dilution power of snowmelt runoff will weaken and alter catchment response to climate change. These insights provide more tools for the interpretation of catchment processes and responses to climate change.
Factors Affecting the BIM Acceptance of Construction Managers

Construction Management(CM) and Building Information Management(BIM) have a lot in common that they need to establish a cooperation system among different disciplines and promote smooth communication among project stakeholders throughout the project life-cycle. BIM can be an effective information management tool for CM’s business and improve their service value. However, BIM uses by Construction Managers(CMr) are very limited. The objective of this study is to analyze the intention of CMrs to adopt BIM and establish effective BIM use strategy in the CMr’s point of view. This study utilizes Technology Acceptance Model(TAM) as a research method, and the results show that 1) CMrs think it is more important whether BIM is required to be adopted in their business process at the social or industry level than that BIM can improve their personal competence, recognition, and impressions through their voluntary BIM adoption; 2) that CMrs think it is important to figure out business processes where BIM can be smoothly incorporated; and 3) that CMrs hesitate the use and trial of BIM in their realworld business although they agree that BIM needs to be eventually adopted in their business in the future. Based on these results, directions for establish a strategy for BIM adoption were proposed at the CMr’s point of view.
Mitigation of DDOS and MiTM Attacks using Belief Based Secure Correlation Approach in SDN-Based IoT Networks

In recent years the domain of Internet of Things (IoT) has acquired great interest from the ICT community. Environmental observation and collecting information is one of the key reasons that IoT infrastructure facilitates the creation of many varieties of the latest business methods and applications. There are however still issues about security measures to be resolved to ensure adequate operation of devices. Distributed Denial of Service (DDoS) attacks are currently the most severe virtual threats that are causing serious damage to many IoT devices. With this in mind, numerous research projects were carried out to discover new methods and develop Novel techniques and solutions for DDOS attacks prevention. The use of new technology, such as software-defined networking (SDN) along with IoT devices has proven to be an innovative solution to mitigate DDoS attacks. In this article, we are using a novel data sharing system in IoT units that link IoT units with the SDN controller and encrypt information from IoT unit. We use conventional Redstone cryptographic algorithms to encrypt information from IoT devices in this framework. The Proposed Belief Based Secure Correlation methodology supports the prevention of DDOS attacks and other forms of data attacks. The system proposes new routes for transmission through the controller and communicates with approved switches for the safe transmission of data. To simulate our entire scenario, we proposed the algorithm Belief Based Secure Correlation (BBSC) implemented in SDN–IoT Testbed and verified IoT data is secure during transmission in the network.
A Learning Approach for Efficient Multicast Beamforming Based on Determinantal Point Process

The problem of single-group multicast beamforming (SMBF) is well-known NP-hard. It motivates the pursuit of computationally efficient near-optimal solutions. Due to multicasting, the multicast group is bottlenecked by the user(s) with the minimum received signal-to-noise ratio (SNR). This paper provides an in-depth interpretation of the SMBF problem from the multicasting point of view and proposes to solve it in two steps: i) select the bottlenecking users by a machine learning approach based on determinantal point process (DPP), and ii) design the beamformer for the selected users. The DPP model jointly considers the magnitudes and directions of users’ channel vectors, and thus enables an efficient selection of the bottlenecking users. Moreover, for a specific channel model, the DPP model is only associated with network size and each takes a one-off training cost, thus can be used as a codebook. The proposed DPP-based subset selection is incorporated adaptively into two fast beamforming algorithms, i.e., the QR decomposition algorithm and the successive beamforming (SB) algorithm. They specifically design the beamformers for the selected users by leveraging channel orthogonalization therein. Numerical results demonstrate the superiority of the proposed QR-DPP and SB-DPP algorithms in terms of the performance-complexity compromise and their robustness to different scenarios.