Miracle-dz/UniFAR-FTUs huggingface dataset:
Is there any gender difference in environmental concern? Evidence from the smallholder farmers in Oromia regional state of Ethiopia

Abstract Understanding and closing the gender gap in environmental concern is the only way to identify and implement the best policies for the environment and sustainable development. This paper examines the gender difference in environmental concern in the case of Ethiopia, Oromia regional state. For this, 140 individuals are sampled from the study areas. The result reveals that women play triple role in their communities: reproductive role, productive role, and community roles. The result also shows that women are more likely to depend on natural resources and hence more likely vulnerable to climate change effects. However, there is no statistically significant difference between women and men in terms of climate change awareness and choice of climate change adaptation strategies. The estimated logit model predicts that women are 31.25 times more likely to participate in environmental conservation than men. Increase in the daily work load by 1 hour is more likely to reduce the individual participation in environmental conservation by 0.237 times. The result also shows that increase in livestock possession decreases individual participation in environmental conservations. A number of policy implications are drawn. There is a need to increase the incentive and distribution of improved cooking stoves and biogas so as to decrease the women work loads. There is also a need to engage civil society organizations, government institutions, and relevant women’s networks to ensure inclusive approaches to the implementation of gender-responsive forest policies. Finally, the regional government is advised to organize gender-awareness seminars and workshops for forestry officials, including decision-makers and policy committee members.
A Three-Resonator Wireless Power Transfer System With Constant-Output Feature Within a Misalignment Range

The magnetic coupling of a practical wireless power transfer (WPT) system is greatly influenced by the variations in the alignment of the magnetic couplers. For a given coupling range, a WPT system is expected to output the required rated power with a given load. However, this requirement cannot be easily met by the widely adopted series–series or inductor–capacitor–capacitor compensation topologies in a conventional two-resonator WPT system. This article proposes a design methodology for three-resonator WPT systems to achieve a constant output power in a wide coupling range. The basic characteristics of the three-resonator WPT system are reviewed and the proposed design methodology is presented in detail. A 3.3-kW three-resonator WPT prototype is designed and built, achieving nearly constant output in the given misalignment range. The measured dc–dc efficiency of the prototype reaches 94.47$\%$ at the rated power.
Deep Learning for Joint Classification and Segmentation of Histopathology Image

Liver cancer is one of the most prevalent cancer deaths worldwide. Thus, early detection and diagnosis of possible liver cancer help in reducing cancer death. Histopathological Image Analysis (HIA) used to be carried out traditionally, but these are time-consuming and require expert knowledge. We propose a patch-based deep learning method for liver cell classification and segmentation. In this work, a two-step approach for the classification and segmentation of whole-slide image (WSI) is proposed. Since WSIs are too large to be fed into convolutional neural networks (CNN) directly, we first extract patches from them. The patches are fed into a modified version of U-Net with its equivalent mask for precise segmentation. In classification tasks, the WSIs are scaled 4 times, 16 times, and 64 times respectively. Patches extracted from each scale are then fed into the convolutional network with its corresponding label. During inference, we perform majority voting on the result obtained from the convolutional network. The proposed method has demonstrated better results in both classification and segmentation of liver cancer cells.
Analysis and Prediction of Public Concern and Academic Concern in Middle School Physics

Physics in middle school plays an irreplaceable role in cultivating students’ scientific quality and creativity. It occupies an essential position in middle school teaching, which has always been the focus of academia (academic concern) and the public (public concern). The relationship between academic and public concern for physics in middle school has become an important research issue. To solve this problem, this article proposes the Pearson correlation-linear prediction and ARIMA (autoregressive integrated moving average) (PCLA) framework. It consists of four steps. Firstly, PCLA obtains the public concern and academic concern in middle school physics. Secondly, the Pearson correlation between the public concern and academic concern is analysed, and a linear model between the two concerns is established. Thirdly, for better predicting public concern, the evaluation index is used to choose the best model between the ARIMA model and the SARIMA model. Finally, based on the result of the third step and the linear model, academic concern can also be predicted. The results show that the PCLA framework can reflect the relationship between academic concern and public concern for physics in middle school and can accurately predict public concern and academic concern.
The High Risk Factors and Preventive Measures of Percutaneous Nephrolithotomy under the Guidance of B-Ultrasound in the Treatment of Postoperative Renal Calculi

Objective The aim of this study is to explore and analyze the high risk factors and preventive measures of percutaneous nephrolithotomy under the guidance of B-ultrasound in the treatment of postoperative renal calculi. Methods The clinical data of 220 patients with renal calculi admitted to our hospital from 2018 to October 2021 were retrospectively analyzed. All patients were treated with percutaneous nephrolithotomy n = 36) and nonbleeding group (n = 184), comparing the personal data, disease-related data, surgical operation related data of the two groups of patients, single factor and logistic multifactor regression analysis to explore the influence of B-guided percutaneous. Nephrolithotomy is a high-risk factor for postoperative bleeding in patients with kidney stones, and preventive measures are based on high-risk factors. Results There was no significant difference in the proportion of patients with different genders, whether they had renal surgery, whether they had hypertension, and those with postoperative hepatic insufficiency in the hemorrhagic group and the nonbleeding group (p > 0.05). There was no significant difference in age and body mass index between the bleeding group and the nonbleeding group (p > 0.05). The proportion of patients with diabetes in the bleeding group was higher than that in the nonbleeding group, and the difference between the groups was statistically significant (p < 0.05). Compared with the nonbleeding group, the bleeding group had a higher proportion of patients with calculus diameter ≥2 cm. The proportion of patients with staghorn calculi in the bleeding group was higher than that in the nonbleeding group. The difference between the groups was statistically significant (p < 0.05). There was no significant difference in the proportion of patients with hemorrhage, single or multiple renal stones, and ureteral stones in the hemorrhage group compared with the nonbleeding group (p > 0.05). Compared with the nonbleeding group, the proportion of patients with bleeding in the first stage was higher, and the proportion of patients with operation time >90 min was higher. The difference between the groups was statistically significant (p < 0.05). There was no significant difference in the proportion of patients in the bleeding group compared with the nonbleeding group (p > 0.05). Using Logic multifactorial regression analysis, independent risk factors for bleeding after percutaneous nephrolithotomy under ultrasound-guided bovery include diabetes mellitus, stone diameter, staghorn kidney stones, surgical timing, and staging surgery (p < 0.05). Conclusion The independent high-risk factors affecting bleeding after percutaneous nephrolithotomy guided by B-ultrasound include diabetes, stone diameter, staghorn type kidney stones, operation time, and staged surgery. According to this, effective preventive measures can effectively reduce the operation and the occurrence of postbleeding.
A 22nm 3.5TOPS/W Flexible Micro-Robotic Vision SoC with 2MB eMRAM for Fully-on-Chip Intelligence

We present a highly flexible micro-robotic vision SoC featuring a hybrid Processing Element (PE) for efficient processing of both Convolutional Neural Network (CNN) and non-CNN vision tasks with 2MB embedded MRAM for retentive fully-on-chip weight storage. Fabricated in 22nm, the design achieves 0.22nJ/pix for Harris corner detection (a non-CNN vision task) and 3.5TOPS/W (INT16) for CNN, a 60% efficiency improvement over state-of-the-art NVM-based NN ASICs.
Designing Automatic Temperature Measuring Device for Covid-19

The COVID-19 virus pandemic has changed people’s daily habits. It increases public awareness of health protocol, especially in public places. One of the most common methods to detect Covid-19 is to check the body temperature. Currently, visitors are required to do a body check temperature with a thermometer gun or a thermal camera before they can enter public places. However, a thermometer gun measurement needs to be done close to the subject, therefore risking the operator from being exposed to the virus. On the other hand, a thermal camera can solve this problem, but it is quite expensive. This study aims to create an alternative for temperature measuring devices that can be connected to the wi-fi, therefore, allowing the temperature measuring devices to be used in large quantities while only monitored by one user. The product design process in this study used the quality function deployment (QFD) method. The House of Quality and parts deployment matrix were developed as the basis for making the concept. Several product concepts were made with the help of a morphological chart. The selected concept was then created and tested to ensure the product can be operated properly. After the product was tested and worked well, the product accuracy was then compared with the accuracy of a thermometer gun. The data from the test were processed with the paired t-test method using the minitab application. From the paired t-test it was found that there was no significant temperature difference between the temperature data gathered from the thermometer gun and from the product.
Quality of Chemically Processed Flying Fish (Hyrundicthys oxycephalus) Waste Silage

Flying fish are the fish that are widely found in the waters of the Makassar Strait, especially in Majene Regency. During the harvest season, flying fish leave a lot of waste that pollutes the environment. This research aimed to evaluate flying fish waste silage's physical and chemical qualities. The design used was a completely randomized design (CRD) with four treatments and four replications groups. To process the sample, formic acid at concentrations of 0%, 2.5%, 3%, and 3.5% was used. Anova was applied for data analysis, followed by Duncan's test if the treatments differed significantly. Parameters observed were chemical quality, including crude protein and fat content, total bacteria count, and pH, while physical-chemical quality included aroma, color, and texture of flying fish waste silage. The results showed that the addition of formic acid to fly fish silage had a significant effect (P < 0.05) on protein, fat, pH, and total bacteria levels but had no significant effect (P> 0.05) on the aroma, color, and texture of the hay. This research concludes that chemically processing flying fish silage with the addition of 2.5% formic acid can increase the protein content to the optimum level in the P1 treatment resulting in the highest protein content of 48.81% and the fat content of 1.63%, pH 3.5, total bacteria 4 x 104, sour taste, gray color and liquid texture that can be safely used as raw feed.
Modelling Thermoplastic Filaments’ Sintering by Level Set Method

The viscous sintering kinetics of thermoplastic polymers is generally studied by monitoring the evolution of the bonding neck between two particles (spherical, or cylindrical) and using a refined Frenkel-Eshelby’s model. Recently, we showed that the entire contour of sintering filaments could be modelled by lemniscates as figure-eight shape curves to assess bonding abilities of a 3D-printable plasticized biopolymer. Using COMSOL Multiphysics® software, we set up a 2D finite element model of thermoplastic filaments’ viscous sintering with flow front tracking by the level set method. This leads to contrasted images of the two phases, i.e. air and polymer, allowing the prediction of the shape of the interface corresponding to the filaments’ contour. An image analysis procedure is applied to the simulated sequences and the ones acquired during sintering trials of extruded filaments based on zein, a corn protein plasticized by 20w% glycerol. This method is based on the assessment of the coordinates of sintered filaments’ edge pixels and their fitting by lemniscates of Booth. We show that the 2D FEM approach combined with level set method allows simulating the hot melt viscous sintering of a 3D-printable thermoplastic biopolymer as a two-phase flow. Furthermore, the image analysis is successfully applied to simulated and experimental sequences, thanks to the monitoring of the filaments’ contour, to assess their bonding kinetics and check its modelling.
Optimized Deep Brain Stimulation Surgery to Avoid Vascular Damage: A Single-Center Retrospective Analysis of Path Planning for Various Deep Targets by MRI Image Fusion

Co-registration of stereotactic and preoperative magnetic resonance imaging (MRI) images can serve as an alternative for trajectory planning. However, the role of this strategy has not yet been proven by any control studies, and the trajectories of commonly used targets have not been systematically studied. The purpose of this study was to analyze the trajectories for various targets, and to assess the role of trajectories realized on fused images in preventing intracranial hemorrhage (ICH). Data from 1019 patients who underwent electrode placement for deep brain stimulation were acquired. Electrode trajectories were not planned for 396 patients, whereas trajectories were planned for 623 patients. Preoperative various MRI sequences and frame-placed MRI images were fused for trajectory planning. The patients’ clinical characteristics, the stereotactic systems, intracranial hemorrhage cases, and trajectory angles were recorded and analyzed. No statistically significant differences in the proportions of male patients, patients receiving local anesthesia, and diseases or target distributions (p > 0.05) were found between the trajectory planning group and the non-trajectory planning group, but statistically significant differences were observed in the numbers of both patients and leads associated with symptomatic ICH (p < 0.05). Regarding the ring and arc angle values, statistically significant differences were found among various target groups (p < 0.05). The anatomic structures through which leads passed were found to be diverse. Trajectory planning based on MRI fusion is a safe technique for lead placement. The electrode for each given target has its own relatively constant trajectory.
The aerodynamic interference between tanker and receiver aircraft during air-to-air refuelling

Wind tunnel data from a tanker wing and receiver aircraft model at varying vertical separation have been compared with theoretical results. In the aerodynamic model the tanker wing is represented by a horseshoe vortex while the aerodynamic loads on the receiver are determined by the vortex lattice method and lifting-line theory, although an approximate method is used to determine the side force on the fin. In the longitudinal case data were obtained for low, mid and high tailplane positions and, with the exception of the pitching moment results, fairly good agreement is obtained between theory and experiment. The relatively small differences are due mainly to the wind tunnel boundary interference effect which could not be quantified for the pitching moment measurements. The lateral aerodynamic interference was determined by banking the tanker wing and displacing it sideways and by yawing the receiver model. Fairly good agreement is obtained between the theory and experiment for the most significant terms which are the rolling moments due to bank and sideways displacement. The effect of the sidewash due to the tanker wake on the receiver in yaw is found to be relatively insignificant. Over the range of bank, yaw and sideways displacements tested the results are almost linear.
Creating a Functional Biomimetic Cartilage Implant Using Hydrogels Based on Methacrylated Chondroitin Sulfate and Hyaluronic Acid

The load-bearing function of articular cartilage tissue contrasts with the poor load-bearing capacity of most soft hydrogels used for its regeneration. The present study explores whether a hydrogel based on the methacrylated natural polymers chondroitin sulfate (CSMA) and hyaluronic acid (HAMA), injected into warp-knitted spacer fabrics, could be used to create a biomimetic construct with cartilage-like mechanical properties. The swelling ratio of the combined CSMA/HAMA hydrogels in the first 20 days was higher for hydrogels with a higher CSMA concentration, and these hydrogels also degraded quicker, whereas those with a 1.33 wt% of HAMA were stable for more than 120 days. When confined by a polyamide 6 (PA6) spacer fabric, the volumetric swelling of the combined CSMA/HAMA gels (10 wt%, 6.5 × CSMA:HAMA ratio) was reduced by ~53%. Both the apparent peak and the equilibrium modulus significantly increased in the PA6-restricted constructs compared to the free-swelling hydrogels after 28 days of swelling, and no significant differences in the moduli and time constant compared to native bovine cartilage were observed. Moreover, the cell viability in the CSMA/HAMA PA6 constructs was comparable to that in gelatin–methacrylamide (GelMA) PA6 constructs at one day after polymerization. These results suggest that using a HydroSpacer construct with an extracellular matrix (ECM)-like biopolymer-based hydrogel is a promising approach for mimicking the load-bearing properties of native cartilage.
Dissecting role of AAA-ATPase RUVBLs in circadian system via mathematical modeling

The circadian genes in mammals are involved in a transcription-translation feedback loop, whereas in cyanobacteria, there exists a post-translational oscillator (PTO) consisting of KaiA, KaiB, and KaiC. KaiC has both ATPase and kinase activities. ATP binding, hydrolysis and phosphate transfer drive the dynamics of circadian system. In this study, we built a mathematical model for our lat-est found circadian genes Ruvbl 1 / 2 ( Ruvbls ) and added the model to a previously published model for mammalian circadian system. RUVBLs are the ﬁrst ATPase known to directly participate in the mammalian circadian system. We described the entire process whereby an ATPase is involved in the traditional transcription-translation feedback loop. Additionally, a phase-shift adenosine-like molecule, cordycepin, which was discovered from our high-throughput screening and targeted RUVBLs, was also simulated in our mathematical model. Notably, this model gives a more detailed description of circadian system, and considers the participation of ATPase for the ﬁrst time, which can lead to a deeper insight into mammalian circadian clock regulation. Our model aligns well our experimental data. Further, based on wet-lab experiments and dry-lab modeling, we discussed the role of ATP and ATPase in mammalian circadian system. Finally, we compared the similarities and diﬀerences between KaiC and RUVBLs, and discussed the potential of RUVBLs as a component of mammalian post-translational oscillator.
NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach

Objective: Neonatal intensive care unit (NICU) admission among term neonates is associated with significant morbidity and mortality, as well as high healthcare costs. A comprehensive NICU admission risk assessment using an integrated statistical approach for this rare admission event may be used to build a risk calculation algorithm for this group of neonates prior to delivery. Methods: A single-center case–control retrospective study was conducted between August 2005 and December 2019, including in-hospital singleton live born neonates, born at ≥37 weeks’ gestation. Analyses included univariate and multivariable models combined with the machine learning gradient-boosting model (GBM). The primary aim of the study was to identify and quantify risk factors and causes of NICU admission of term neonates. Results: During the study period, 206,509 births were registered at the Shaare Zedek Medical Center. After applying the study exclusion criteria, 192,527 term neonates were included in the study; 5292 (2.75%) were admitted to the NICU. The NICU admission risk was significantly higher (ORs [95%CIs]) for offspring of nulliparous women (1.19 [1.07, 1.33]), those with diabetes mellitus or hypertensive complications of pregnancy (2.52 [2.09, 3.03] and 1.28 [1.02, 1.60] respectively), and for those born during the 37th week of gestation (2.99 [2.63, 3.41]; p < 0.001 for all), adjusted for congenital malformations and genetic syndromes. A GBM to predict NICU admission applied to data prior to delivery showed an area under the receiver operating characteristic curve of 0.750 (95%CI 0.743–0.757) and classified 27% as high risk and 73% as low risk. This risk stratification was significantly associated with adverse maternal and neonatal outcomes. Conclusion: The present study identified NICU admission risk factors for term neonates; along with the machine learning ranking of the risk factors, the highly predictive model may serve as a basis for individual risk calculation algorithm prior to delivery. We suggest that in the future, this type of planning of the delivery will serve different health systems, in both high- and low-resource environments, along with the NICU admission or transfer policy.
Automated Vision Inspection of Critical Steel Components based on Signal Analysis Extracted form Images

This paper presents an automated machine vision system for the quality inspection of blanked metal components by the analysis of product images. The binary classification to identify defective samples among the compliant ones is carried out by means of two different supervised machine learning algorithms, a Sequential Neural Network (SNN) and a Support Vector Machine (SVM). The features used by the algorithms are extracted from the images using a novel approach, that exploits some geometrical characteristics of the product. Indeed, the spatial features of the target object in the images are processed as they represent a time series signal and hence features are extracted and exploited as input data for the classification task.