Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Reading and Localization of Additively Manufactured Galinstan-Based Sensor Using a Polarimetric Millimeter-Wave Radar Imaging Technique

A polarimetric millimeter-wave radar imaging technique is proposed to remotely detect, localize, and wirelessly read a novel additively manufactured passive sensor composed of a microfluidic channel filled with liquid metal Galinstan. Very high variation of the radar-cross section of the sensor to small variations of the level of Galinstan in the channel is obtained. Indeed, at the radar-to-sensor distance of 2.4 m, the measured radar echo level of the sensor varies by 4.5 dB when the level of Galinstan changes by 1 mm. This sensitivity is higher than those previously reported in the literature for wireless and passive sensors of the same class. Moreover, the localization technique of the sensor is successfully achieved for only three false detections on 75 measurements in cluttered environments from radar-to-sensor distances up to 15 m.
Integrated Analysis of Transcriptome and Metabolome Provides Insight into Camellia oleifera Oil Alleviating Fat Accumulation in High-Fat Caenorhabditis elegans

Camellia oil (CO) is a high medicinal and nutritional value edible oil. However, its ability to alleviate fat accumulation in high-fat Caenorhabditis elegans has not been well elucidated. Therefore, this study aimed to investigate the effect of CO on fat accumulation in high-fat C. elegans via transcriptome and metabolome analysis. The results showed that CO significantly reduced fat accumulation in high-fat C. elegans by 10.34% (Oil Red O method) and 11.54% (TG content method), respectively. Furthermore, CO primarily altered the transcription levels of genes involved in longevity regulating pathway. Specifically, CO decreased lipid storage in high-fat C. elegans by inhibiting fat synthesis. In addition, CO supplementation modulated the abundance of metabolic biomarkers related to pyrimidine metabolism and riboflavin metabolism. The integrated transcriptome and metabolome analyses indicated that CO supplementation could alleviate fat accumulation in high-fat C. elegans by regulating retinol metabolism, drug metabolism—cytochrome P450, metabolism of xenobiotics by cytochrome P450, ascorbate and aldarate metabolism, and pentose and glucuronate interconversions. Overall, these findings highlight the potential health benefits of CO that could potentially be used as a functional edible oil.
Public Procurement and Mediation to Facilitate Procedures for the preservation of World's Cultural Heritage

All levels of legislation from national rules to international Treaties include provisions for heritage procurement and alternative dispute resolution. Each law has got its own scope and field of application: some of those are obligatory, with direct effect and supreme. Practitioners thus need to apply the right texts for the conclusion of public contracts and for the resolution of their disputes. Mediation is being proposed as a very modern and flexible tool for disputes’ settlements arising in a contest. Some examples of successful public procurement may be found here and a superficial approach to the Hagias-Sofia project.
Optimization Of The Vertical Axis Wind Turbine For Localization In Low Wind Speed Areas

The vertical axis wind turbines (VAWTs) have proved to show how they are well suited for the urban landscape in production of renewable energy. Thus, the major objective of this study was to develop an optimal model for the vertical axis wind turbine using Computational Fluid Dynamics (CFD). VAWT blades are designed in such a way that they exhibit good aerodynamic performance throughout an entire rotation at various angles of attack they experience leading to optimal performance. In this study, an average unsteady wind flow speed of 10 ms-1 was considered to flow towards the VAWT at angles of attack ranging from 0ͦo to 18o. The NACA0012 airfoil blade was used to study the aerodynamic factors on the VAWT and optimize its performance using the CFD software ANSYS Fluent 7.2. A CFD analysis method was used to evaluate both unsteady wind inflow performance and the flow hydraulics that affects the performance of a VAWT. Mathematical model formulations and numerical simulations using Reynolds Averaged Navier-Stokes (RANS) are employed. The k-ω turbulence model was foundto perform well for unsteady wind flow simulations for optimal performance of the VAWT. The numerical simulations of velocity and pressure contours at different angles of attack were analysed in consideration of the lift and drag forces. The NACA0012 airfoil blade was found to perform optimally at angles of attack in the range 0o 16o. Keywords: Vertical Axis Wind Turbine, Computational Fluid Dynamics, Model Equations, angle of attack.
Low Allergenicity in Processed Wheat Flour Products Using Tannins from Agri-Food Wastes

The present study aimed to investigate the effect of the addition of tannins from unutilized resources on wheat allergen reduction, antioxidant properties, and quality by substituting 3%, 5%, and 10% of the flour with chestnut inner skin (CIS) and young persimmon fruit (YPF) powders to produce cookies. The enzyme-linked immunosorbent assay and Western blotting showed significantly lower wheat allergen content in CIS- or YPF-substituted cookies than in control cookies, and this effect was pronounced for CIS-substituted cookies. In addition, the tannin content and antioxidant properties of the CIS- or YPF-substituted cookies were markedly higher than those of the control cookies. Quality analysis of the CIS- and YPF-substituted cookies showed that the specific volume and spread factor, which are quality indicators for cookies, were slightly lower in the CIS- and YPF-substituted cookies than in the control cookies. Compared to the control, CIS substitution did not affect the breaking stress and total energy values of the cookies; however, YPF substitution at 10% increased these values. Color was also affected by the addition of CIS and YPF. The results suggest that the addition of CIS and YPF can reduce wheat allergens in cookies and improve tannin content and antioxidant properties.
Multi-View Graph Convolution Network Reinforcement Learning for CAVs Cooperative Control in Highway Mixed Traffic

The control of connected autonomous vehicles (CAVs) for cooperative sensing and driving in mixed traffic flows is critical for the development of intelligent transportation systems. However, the challenge lies in effectively representing and learning from the interactions between heterogeneous traffic agents, as well as the complex traffic states they produce. To address this, we propose the Multi-View Graph Convolution Network Reinforcement Learning (MVGRL) algorithm for the decision-making and control of CAVs in mixed traffic scenarios. First, multi-view graphs are constructed based on multiple logical correlations and the perception-communication relationship of heterogeneous vehicles in mixed traffic. This enables us to represent the dynamic vehicle networks and gain insights into vehicle behavior. Then, the features of multi-view vehicle network graphs are derived by multi-view graph convolutional network reinforcement learning. Finally, the lane change decisions of CAVs are obtained based on complex traffic state learning. In the highway scenario, the model is able to control the CAVs to complete the ramp exit task and maintain an overall efficient traffic flow. Various test scenarios were formed by adjusting the traffic conditions under a highway off-ramp scenario for comparison experiments. The results show that the proposed MVGRL algorithm model outperforms in decision-making and robustness than the baseline models.
A Study on the Application of Data Mining Techniques in the Management of Sustainable Education for Employment

With the gradual advancement of education management towards data and informationisation, how to establish a perfect employment education management system has become an important element of current student work. Data mining technology can extract valuable implicit information from the database, and is widely used in the practical processing of massive data. Based on the analysis of the characteristics of employment education management in universities, the study first improved the K-means algorithm by adding splitting and aggregation operations to it, used the improved K-means algorithm to cluster and analyse the employment education data, and then combined it with the optimised Apriori algorithm to further mine the useful information in the employment education data. The experimental outcomes demonstrate that the error rate of the improved K-means algorithm is stable at around 10%, with a high accuracy rate and strong stability; combined with the optimised Apriori algorithm applied to the employment education management system, the accuracy rate is basically maintained at over 96%, and the scores of students’ employment knowledge and employment practice are all over 90, indicating that the method can provide effective guidance for students’ employment and give a guideline for the sustainable education management of employment.
Mechanical upside of PAO mainstream fixations: co-simulation based on early postoperative gait characteristics of DDH patients

Purpose: To investigate the early postoperative gait characteristics of patients who underwent periacetabular osteotomy (PAO) and predict the biomechanical performance of two commonly used PAO fixation methods: iliac screw (IS) and transverse screw (TS). Methods: A total of 12 patients with unilateral developmental dysplasia of the hip (DDH) (mean age 27.81 ± 4.64 years, 42% male) that were scheduled to undergo PAO surgery were included in this study. Their preoperative CT images and pre- and postoperative gait data were used to create subject-specific musculoskeletal models and complete the inverse dynamics analysis (IDA). Two patients with typical gait characteristics were selected using clustering analysis, and their IDA data were incorporated into finite element (FE) models of IS and TS fixations. Failure simulation was performed by applying iterative steps with increasing gait load to predict yield load. Stress results and yield loads were calculated for each FE model at different phases of the gait cycle. Results: Postoperative gait showed improvement compared to preoperative gait but remained inferior to that of healthy individuals. Postoperative gait was characterized by a lower hip range of motion, lower peri-ilium muscle forces, particularly in the abductors, and a sharper initial peak and flatter second peak of hip joint reaction force (HRF). Finite element analysis (FEA) showed a trend of increasing stress during the second–fourth phases of the gait cycle, with lower stress levels in other phases. At high-stress gait phases, the mean stress of maximum p¯100 differed significantly between IS and TS (p < 0.05) and between coupled and uncoupled muscle forces (p < 0.05). Failure analysis predicted a slightly larger yield load for TS configurations (6.21*BW) than that for IS (6.16*BW), but both were well above the gait load. Coupled and uncoupled groups showed similar results, but uncoupled groups had lower yield loads (5.9*BW). Conclusion: PAO early postoperative gait shows a normalized trend, but abnormalities persist. IS and TS are both capable of resisting mechanical strain failure, with no significant mechanical advantage found for transverse screw fixation during PAO early postoperative gait. Additionally, it is important to note that the TS may have a higher risk of cyclic fatigue failure due to the localized greater stress concentration. Furthermore, the most medial screw is crucial for pelvic stability.
Source, pattern and flux of dissolved carbon in an alpine headwater catchment on the eastern Tibetan Plateau

Dissolved carbon (DC) transported from headwater streams on the eastern Tibetan Plateau is a crucial component of the carbon cycles in the regional river. However, the spatiotemporal variability and sources of DC in these remote headwater streams remains unclear. An investigation into dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) was conducted with high temporal resolution from a glacier terminus, a forest catchment, and the catchment outlet located on the eastern flank of Mt. Gongga, Southwest China. The results showed that (1) the mean DOC concentration at the glacier terminus (2.38 ± 0.71 mg L−1) was significantly higher than at the forest catchment (2.13 ± 0.49 mg L−1) and at the catchment outlet (2.17 ± 0.58 mg L−1), while the DIC concentration increased by 21.6% at the forest catchment and by 18.7% at the catchment outlet relative to that in the glacier terminus. (2) Compared to the monsoon season, DOC concentrations in the forest catchment decreased by 24.6% postmonsoon, and the seasonal variation of DOC concentrations was minor either at the glacier terminus or at the catchment outlet; relative to monsoon values, postmonsoon DIC concentrations increased by 14.9% and 28.8% at the forest catchment and catchment outlet, respectively. Precipitation and stream pH were identified as important factors influencing the seasonal variability of DC. (3) Silicate and carbonate weathering contributed 52.9 ± 12% and 44.4 ± 9%, respectively, to DIC generation in the forest catchment according to 13C analysis. This study provides a valuable dataset on the dynamics of DC in a region where data is sparse, which improves our understanding of DC transport in alpine regions. The unexpected spatiotemporal stability of DC suggests that less frequent rates of sampling might be adequate to estimate carbon export.
Impact energy assessment of sandwich composites using an ensemble approach boosted by deep learning and electromechanical impedance

Sandwich composites are prone to delamination and fracture during service when exposed to external low-velocity impact. One hindrance to overcome before a broader deployment of sandwich composites is the issue of impact energy assessment (IEA). To promote the solution to this issue, an ensemble deep learning approach is proposed in this study. The approach comprises data expansion, series-to-image conversion, and convolutional neural networks (CNN). The data expansion is implemented using vertical average interpolation. The enhanced data are transformed into images via the Gramian angular summation field to build an image dataset for the CNN model. To validate the developed ensemble approach, hammer-dropping impact experiments on the honeycomb sandwich composites are carried out based on the piezoelectric wafer active sensor network and electromechanical impedance measurement. Accuracy, precision, recall, and F1-score indicators are introduced to evaluate the ensemble approach performance. The above indicator values are all above 0.9600, demonstrating the effectiveness of the proposed ensemble approach in settling the IEA issue.
Distributed Consensus of Multi‐Agent Systems with Input Faults and Time‐Varying Delays

This work studies the consensus problem of multi‐agent systems with input faults and time‐varying delays. The assumed faults in the system are loss of effectiveness of actuator and nonlinear additive term mixed with nominal input. For system faults that are nonlinear, constraints of constant norm‐bounded, sector nonlinearity, and unbounded nonlinearity with known basis functions are considered. Employing a Lyapunov–Krasovskii functional method, delay dependent consensus criteria are established to show the exponential behavior of the system. Finally, one simulation example is solved to demonstrate the advantage of the obtained results.
Characterization of (weakly/properly/robust) efficient solutions in nonsmooth semi-infinite multiobjective optimization using convexificators

Abstract The main aim of this paper is to investigate weakly/properly/robust efficient solutions of a nonsmooth semi-infinite multiobjective programming problem, in terms of convexificators. In some of the results, we assume the feasible set to be locally star-shaped. The appearing functions are not necessarily smooth/locally Lipschitz/convex. First, constraint qualifications and the normal cone to the feasible set are studied. Then, as a major part of the paper, various necessary and sufficient optimality conditions for solutions of the problem under consideration are presented. The paper is closed by a linear approximation problem to detect the solutions and by studying a gap function.
Hot and Cold Spot Analysis Using Stata

Spatial analysis is attracting more attention from Stata users because of the increasing availability of regional data. In this article, I present an implementation of hot and cold spot analysis using Stata. I introduce the new command getisord, which calculates the Getis–Ord G*i (d) statistic. To implement this command, one only needs the latitude and longitude of regions as the additional required information. In combination with shape files, the results obtained from the getisord command can be visually displayed in Stata. In this article, I also offer an interesting illustration to explain how the getisord command works.
Human-in-The-Loop Fuzzy Iterative Learning Control of Consensus for Unknown Mixed-Order Nonlinear Multi-Agent Systems

This article studies the human-in-the-loop fuzzy iterative learning control of leader-following consensus for unknown mixed-order nonlinear multi-agent systems. The human operator participates in the cooperative control of multi-agent systems, which indirectly affects the followers by directly controlling the leader. Moreover, the leader's input is unknown to all followers. The mixed-order multi-agent systems contain both first- and second-order agents, which include the special case of the second-order multi-agent systems. By using fuzzy logic systems to approximate unknown nonlinear dynamics, a fully distributed fuzzy iterative learning controller with time-varying coupling gain is designed. In the estimation parameters, a $\sigma$-modification related to the number of iterations is designed to ensure the convergence of the closed-loop systems. Based on the new composite energy function, the exact consensus of the closed-loop systems is proved. Finally, the simulation results verify the effectiveness of the designed control algorithm.
Transcriptomic Response to Drought Stress in Populus davidiana Dode

Plants are often exposed to drought stress, and decreases in the soil water content can prevent plants from reaching their full genetic potential. Populus davidiana Dode belongs to the genus Populus, and it is a temperate deciduous tree that is cold and drought tolerant. To investigate the mechanism of P. davidiana response to drought stress, transcriptome analysis was performed on drought and control treatments of P. davidiana. We identified 10230 differentially expressed genes (DEGs). Most DEGs were enriched in pathways related to transcriptional regulation and hormone signal transduction, ROS metabolism, lignin synthesis, and the sugar metabolism process in two contrasting groups. Compared with the control condition, soluble sugars, proline, and POD activity were all increased under drought stress. In addition, Na+, K+, and Ca2+ were all higher under drought stress than in the control. These results not only revealed the mechanism of tolerance to drought stress in P. davidiana, but also promoted the development and application of drought-tolerant genetic resources in P. davidiana.
Analysis of the Associated Factors of Doctor Job Satisfaction at Covid-19 Pandemic

The Covid-19 pandemic has caused doctors to have lower job satisfaction compared to the previous period. Preliminary survey conducted at Unand Hospital on health workers, doctor job satisfaction is lower than other health workers. Low job satisfaction among doctors can cause a decrease in hospital service quality. The purpose of this research is to analyze the factors related to the job satisfaction of doctors during the Covid-19 pandemic. The research method used is the quantitative method. Dependent variable in this study is overall job satisfaction, the independent variables are salary, promotion, fringe benefit, supervision, contingent reward, operating conditions, co-workers, nature of work, and communication. The sampling using census conducted on all doctors at Unand Hospital, where 71 doctors met the inclusion criteria. Data was collected by distributing job satisfaction survey (JSS) questionnaires. Univariate and bivariate analysis was used in this study. The result of this study is salary, fringe benefits, and operating procedure has the lowest average of the nine factors of job satisfaction, hospital management should pay attention for this aspect to maintain doctor job satisfaction.
An Output-Capacitor-Free NMOS Digital LDO Using Gate Driving Strength Modulation and Droop Detector

An output-capacitor-free NMOS digital LDO (DLDO) using gate driving strength modulation (GDSM) is described. The proposed DLDO is mainly based on the time-driven topology while the GDSM changes the gate driving level adaptively according to the load current condition. The proposed GDSM lowers the gate driving level in the light load condition which improves the load transient response, widens the load current dynamic range, and reduces the output voltage ripple. A droop detector combined with the modulation scheme is also proposed to further improve the undershoot and recovery time. The proposed DLDO has been implemented in 28 nm CMOS and the 20,<inline-formula> <tex-math notation="LaTeX">$000\times $ </tex-math></inline-formula> load range is obtained even with 256 unity power switch arrays. When the load current changes from 5 mA to 85 mA, the <inline-formula> <tex-math notation="LaTeX">$V_{OUT}$ </tex-math></inline-formula> droop and recovery time are 130 mV and <inline-formula> <tex-math notation="LaTeX">$2.5 \mu \text{s}$ </tex-math></inline-formula>, which are <inline-formula> <tex-math notation="LaTeX">$4.92\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$70\times $ </tex-math></inline-formula> improvements compared to the baseline time-driven DLDO, respectively.
Effects of Carbon Dioxide Concentration on the Growth and Physiology of Albizia saman (Jacq.) Merr

This study was conducted to determine the effects of CO 2 concentration on the growth and physiology of rain tree ( Albizia saman (Jacq.) Merr), by increasing the CO 2 concentration in a greenhouse automated system. The objec-tive of this study was to evaluate the response of rain tree to CO 2 in terms of growth and physiology. CO 2 at an average concentration of 800 μmol mol -1 was injected daily for 2 h from 9 am to 11 am. The seedlings were placed in a greenhouse during the control trial with a mean ambient CO 2 concentration of 400 μmol mol -1 . In this study, the entire randomised block design has been applied, and growth was observed every 30 days for 120 days. Almost all seedling growth parameters were significant under elevated and ambient concentrations. The leaf area in the con - trol samples (400 ppm of CO 2 ) was 243.37 cm 2 , and this value increased to 277.30 cm 2 in the sample treated with 800 ppm of CO 2 . The biomass increased, and the original wet weight ratio and root dry weight of the canopy and the principal (9.06 and 10.12 g, respectively) increased to 9.7 and 16.06 g, respectively, after treatment. Physiology was analysed in terms of relative levels of photosynthesis, stomatal conductance and water use efficiency (WUE). Such parameters increased in the principal treatment of CO 2 (800 ppm), whilst the CO 2 content and transpiration levels declined. As the CO 2 concentration increased, the value of the levels of photosynthesis and stomatal conductance in both samples increased. As the photosynthesis levels increased, the WUE activity increased. However, as photosynthesis levels decreased, the WUE activity also decreased. Transpiration levels but also rely on a certain age if the increased photosynthesis WUE has decreased.