Miracle-dz/UniFAR-FTUs huggingface dataset:
Livelihood Analysis and a New Inferential Model for Development of Forest-Dependent Rural Communities

The livelihood of many households and communities in the Central Zagros of Iran is strongly dependent on income from forests. While this has led to the widespread over-utilization of forests, poverty levels have remained high and rural development low. The objective of this study was to understand how households utilize forests and to what extent forests contribute to household income and alleviate poverty in order to develop strategies to raise families out of poverty and offer development perspectives to communities that avoid destructive forest utilization. To do so, semi-structured interviews were conducted in five rural communities, community poverty was quantified using several indices (e.g., the Census Ratio Index, Poverty Gap Index), the level of rural development was quantified using socio-economic indicators, and an inferential model was developed that combines household dependence on forests with the level of rural development to provide development perspectives. Local households earned income from nine livelihood strategies that involve forests. Forest-dependent strategies provided the second highest economic share (18.1%) of household income, averaging IRR 27.7 million (USD 657) annually, and moved 12% of households above the poverty line (76% still remained below). Without forest income, most indices of poverty decreased, income inequality increased by 11%, and poverty depth increased 1.54-fold. The low development index of most villages indicates that rural villagers are heavily dependent on forests to meet their livelihood. Our conceptual model indicates that communities should pursue different development strategies that consider whether households depend on forests to meet their livelihood or derive more supplemental income.
Power Quality Disturbances Classification Based on WOA-SVM Algorithm

With the rapid development of new energy technology, all kinds of distributed power grids are connected to the main power grid, generating a large number of complex disturbance signals. In order to ensure the safety of the power grid and solve the difficult problem of power grid disturbance identification, this paper proposes a classification recognition algorithm based on the combination of energy difference (ED) and support vector machine (SVM) optimized by the Whale optimization algorithm (WOA). Firstly, the disturbance signal is decomposed with multi-resolution, and the energy difference of each layer is calculated as the characteristic quantity of the disturbance signal. Then, WOA algorithm was used to optimize SVM, and the optimal solutions of penalty parameters and kernel parameters were obtained. Finally, the feature vector is input into the WOA-SVM model, and the output disturbance signal type is automatically identified. Experimental results show that this method can accurately identify single and complex disturbance signals. In the noise environment, it shows strong robustness. The overall recognition rate of ED-WOA-SVM is over 99%.
Structural damage identification via time domain response and Markov Chain Monte Carlo method

Abstract The present work addresses the problem of structural damage identification built on the statistical inversion approach. Here, the damage state of the structure is continuously described by a cohesion parameter, which is spatially discretized by the finite element method. The inverse problem of damage identification is then posed as the determination of the posterior probability densities of the nodal cohesion parameters. The Markov Chain Monte Carlo method, implemented with the Metropolis–Hastings algorithm, is considered in order to approximate the posterior probabilities by drawing samples from the desired joint posterior probability density function. With this approach, prior information on the sought parameters can be used and the uncertainty concerning the known values of the material properties can be quantified in the estimation of the cohesion parameters. The assessment of the proposed approach has been performed by means of numerical simulations on a simply supported Euler–Bernoulli beam. The damage identification and assessment are performed considering time domain response data. Different damage scenarios and noise levels were addressed, demonstrating the feasibility of the proposed approach.
Development of a Cross-Platform Software Application for Programming CNC Machine Tools

This paper describes an approach to the development of a cross-platform application for creating universal cycles of typical types of machining working on machines with different CNC systems. The problem of the portability of control programs to numerical control systems from different manufacturers is outlined and the need for the creation of this toolkit is justified. The concept and implementation of a cross-platform application for programming CNC machines are presented. An architectural model of a cross-platform toolkit for the preparation of control programs for CNC machines is presented, which is based on the application subsystem and a gallery of macro programs, which includes the possibility of flexible expansion of the system without negative consequences for the interaction of system modules. Typical elementary types of processing are represented by macroprograms. The user interface of the software application is based on the principles of constructing the interface of dialog systems embedded in the CNC system, based on the sequential input of information about the processing being performed. The results of testing a cross-platform application on Fanuc, Siemens CNC systems and on the digital twin of the CNC system "AxiOMA Control" are described.
Threshold Voltage Measurement Protocol “Triple Sense” Applied to GaN HEMTs

The threshold voltage instability in p-GaN gate high electron mobility transistors (HEMTs) has been brought into evidence in recent years. It can lead to reliability issues in switching applications, and it can be followed by other degradation mechanisms. In this paper, a Vth measurement protocol established for SiC MOSFETs is applied to GaN HEMTs: the triple sense protocol, which uses voltage bias to precondition the transistor gate. It has been experimentally verified that the proposed protocol increased the stability of the Vth measurement, even for measurements following degrading voltage bias stress on both drain and gate.
Radiographic Outcomes of Titanium Augment vs Bone Graft in Lateral Column Lengthening for Adult-Acquired Flatfoot Deformity

Background: Lateral column lengthening (LCL) is a surgical procedure used to manage forefoot abduction and, in theory, also increases the longitudinal arch by plantarflexion of the first ray through tensioning the peroneus longus for patients with stage IIB adult acquired flatfoot deformity (AAFD). This procedure utilizes an opening wedge osteotomy of the calcaneus, which is then filled with autograft, allograft, or a porous metal wedge. The primary aim of this study was to compare the radiographic outcomes of these different bone substitutes following LCL for stage IIB AAFD. Methods: We conducted a retrospective review of all patients who underwent LCL from October 2008 until October 2018. Preoperative weightbearing radiographs, initial postoperative radiographs, and 1-year weightbearing radiographs were reviewed. The following radiographic measurements were recorded: incongruency angle, talonavicular coverage angle (TNCA), talar–first metatarsal angle (T-1MT), and calcaneal pitch. Results: A total of 44 patients were included in our study. The mean age of the cohort was 54 (range, 18-74). The study cohort was divided into 2 groups. There were 17 (38.7%) patients who received a titanium metal wedge and 27 (61.5%) that received autograft or allograft. Patients that underwent LCL with the autograft/allograft group were significantly older (59 vs 47 years old, P = .006). Patients who underwent LCL with a titanium wedge had a significantly higher preoperative talonavicular angle (32 vs 27 degrees, P = .013). There were no significant differences in postoperative TNCA, incongruency angle, or calcaneal pitch at 6 months or 1 year. Conclusion: At 6 months and 1 year, no radiographic differences were found between autograft/allograft bone substitutes vs titanium wedge in LCL. Level of Evidence: Level III, retrospective cohort study.
The Role of Financial Inclusion in Moderating the Incidence of Entrepreneurship on Energy Poverty in Ghana

This paper assesses the role of financial inclusion in moderating the incidence of entrepreneurship on energy poverty in Ghana. The assessment is made by using pooled data and two stage least squares. The exposition builds from the 7th (GLSS7) and 6th (GLSS6) rounds focusing on the Ghana Living Standards Survey (GSS, 2014, 2019) that is collected by the Ghana Statistical Service (GSS) from ten principal regions in the country. The findings show that entrepreneurship has an unconditional positive incidence on energy poverty while the interactive incidence between entrepreneurship and financial inclusion on energy poverty is negative. The corresponding financial inclusion policy thresholds that should be exceeded in order for financial inclusion to effectively moderate entrepreneurship for negative outcomes in energy poverty: (i) are between 0.154 and 0.280 index for the full sample; (ii) is between 0.187 index for the rural sub-sample; (iii) are between 0.200 and 0.333 index for the male sample. (iv) Thresholds are not computed for the rural and female sub-samples because at least one estimated coefficient that is needed for the computation of such thresholds is not significant. Policy implications are discussed. This study has complemented the existing literature by assessing how financial inclusion can be employed to influence the nexus between entrepreneurship and poverty in Ghana.
Snow/Cloud Discrimination with Multispectral Satellite Measurements

Abstract An algorithm is developed and evaluated for discriminating between clouds, snow-covered land and snow-free land in satellite image data. The multispectral technique uses daytime images of NOAA AVHRR channels 1 (0.63 μm), 3 (3.7 μm), and 4 (11.0 μm). Reflectance is derived for channel 3 by using the channel 4 emission temperature to estimate and remove the channel 3 thermal emission. Separation of clouds from snow and land is based primarily on the derived channel 3 reflectance. Observed reflectance in channel 3 is 0.02 to 0.04 for snow, 0.03 to 0.10 for land, 0.02 to 0.27 for ice clouds and 0.08 to 0.36 for liquid clouds. These ranges overlap for thin cirrus and snow, so the routine attempts analysis of cirrus based on differences in transmission between channels 3 and 4. Six case were analyzed and the total cloud cover was verified against a total of 110 surface observations in the standard categories of clear, scattered, broken and overcast. One of the cases is presented in detail to illustrate.
Modelling and Simulation of a Redundant Agricultural Manipulator with Virtual Prototyping

The development of autonomous robots for agricultural applications includes motion planning, fruit picking, and collision avoidance with surrounding environments, and these become challenging tasks. For harvesting applications, robust control of the manipulator is needed for the effective motion of the robot. Several combinations of Proportional(P)- Integrative(I)- Derivative(D) controllers are modelled and a simulation study was performed for trajectory tracking of a redundant manipulator in virtual agricultural environments. The article presents a comprehensive study on kinematic modelling and dynamic control of redundant manipulator for fruit-picking applications in virtual environments. The collisions with surrounding environment were eliminated using ‘bounding box technique’. The joint variables are obtained by constructing Inverse Kinematics (IK) problem and are determined using a classical optimization technique. Different controllers are modelled in the ‘Simulink’ environment and are tuned to generate error-free trajectory tracking during harvesting. The task space locations (TSLs) are considered as via-points, and joint variables at each TSLs are obtained by Sequential Quadratic Programming (SQP) technique. Joint-level trajectories are generated using Quintic and B-spline polynomials. For effective trajectory tracking, torque variations are controlled using the PID and Feedforward (FF) controller. The dynamic simulations of the robot manipulator are performed in Simscape Multibody software. Results show that the during the trajectory tracking of the manipulator, the Feed-forward controller performs best with Quintic polynomial trajectory.
SOFTWARE GEOGEBRA SEBAGAI MEDIA PENUNJANG PEMBELAJARAN GEOMETRI DI SMK MUHAMMADIYAH 1 PURBALINGGA

Matematika merupakan ilmu universal yang melingkupi berbagai bidang kehidupan dan menjadi penunjang ilmu pengetahuan lainnya. Salah satu pembahasan dalam matematika di tingkat sekolah menengah adalah geometri. Geometri merupakan salah satu materi yang mengharuskan siswa untuk memiliki bayangan 3 dimensi. Terdapat salah satu aplikasi (software) yang dapat membantu siswa dalam pemahaman materi geometri, yaitu software geogebra. Oleh karena itu, pelatihan software geogebra ini dilakukan dalam rangka mengenalkan dan memberi pengetahuan kepada siswa tentang penggunaan aplikasi dan terapannya pada materi geometri. Pelatihan dilakukan terhadap siswa SMK Muhammadiyah 1 Purbalingga. Metode pelatihan dilakukan dengan memberikan pemahaman tentang sofrware, praktek penggunaan dan memberikan soal studi kasus. Hasil pelatihan menunjukan pemahaman siswa terhadap materi matematika semakin meningkat, terlihat dari antusias dan semangat siswa dalam mengikuti pelatihan.
Classification Model Based on U-Net for Crack Detection from Asphalt Pavement Images

The purpose of our study is to detect cracks accurately from asphalt pavement surface images, which includes unexpected objects, non-uniform illumination, and irregularities in surfaces. We propose a method to construct a classification Convolutional Neural Network (CNN) model based on the pre-trained U-Net, which is a well-known semantic segmentation model. Firstly, we train the U-Net with a limited amount of the asphalt pavement surface dataset which is obtained by a Mobile Mapping System (MMS). Then, we use the encoder of the trained U-Net as a feature extractor to construct a classification model, and train by fine-tuning. We describe comparative evaluations with VGG11, ResNet18, and GoogLeNet as well-known models constructed by transfer learning using ImageNet, which is a large size dataset of natural images. Experimental results show our model has high classification performance, compared to the other models constructed by transfer learning using ImageNet. Our method is effective to construct convolutional neural network model using the limited training dataset.
Modeling and simulation in photoacoustics

This thesis deals with the problem of photoacoustic imaging. In this imaging setting, one heats a medium up with an electromagnetic wave. The medium dilates and emits an ultrasonic wave. The aim is then to reconstruct inner properties of the medium from boundary measurements. It is an inverse problem on the initial condition for the wave equation. In an idealized frame, the reconstruction procedure has been thoroughly studied. The main goal of this thesis is to stray from the standard model by considering less restrictive assumptions. For each of them (boundary condition, partial view, attenuation, inhomogeneous sound speed) the thesis proposes a correction based on adapted mathematical tools (asymptotic analysis, dual approach , correlation.). Reconstruction of the initial condition of the wave equation is however not enough. It depends on the electromagnetique illumination. A second inverse problem has to be solved on the electromagnetic wave propagation to acces the physical coefficients of interest. The thesis presents algorithmic results in the frame of the diffusion equation and theoretic estimates in the frame of the transport equation. The thesis also presents an improving result for a topological derivative based imaging approach.
Self-Paced Ensemble-SHAP Approach for the Classification and Interpretation of Crash Severity in Work Zone Areas

The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict and interpret the severity of work-zone-related crashes. The proposed methodology is an ensemble learning approach that aims to mitigate the issue of imbalanced classification in datasets of significant magnitude. The proposed solution provides an intuitive way to tackle issues related to imbalanced classes, demonstrating remarkable computational efficacy, praiseworthy accuracy, and extensive adaptability to various machine learning models. The study employed work zone crash data from the state of New Jersey spanning a period of two years (2017 and 2018) to train and evaluate the model. The study compared the prediction outcomes of the SPE model with various tree-based machine learning models, such as Light Gradient Boosting Machine, adaptive boosting, and classification and regression tree, along with binary logistic regression. The performance of the SPE model was superior to that of tree-based machine learning models and binary logistic regression. According to the SHAP interpretation, the variables that exhibited the highest degree of influence were crash type, road system, and road median type. According to the model, on highways with barrier-type medians, it is expected that crashes that happen in the same direction and those that happen at a right angle will be the most severe crashes. Additionally, this study found that severe injuries were more likely to result from work zone crashes that happened at night on state highways with localized street lighting.
Building a Forecast Using a Linear Prediction Filter for the Purpose of Detecting Anomalies in the Signals of Automated Process Control Systems

Forecasting technology plays an important role in the construction of systems for detecting anomalies in dynamic data flows of automated process control systems (APCS) resulting from the impact of cyberattacks. To form a forecast of the studied signals, methods for forming a single-component forecast and a multi-component forecast of an information signal using a linear prediction digital filter are considered. It is shown that for the detection of anomalies in the observed signals of APCS, the predictor prediction error signal implemented using a linear prediction filter is informative. The high information content of the use of spectral analysis of the prediction error signal in detecting anomalies in the observed signals of automated process control systems is shown.
Internal Variability of the Troposphere–Stratosphere Coupled System Simulated in a Simple Global Circulation Model

Internal variability of a troposphere‐stratosphere coupled system is investigated in a series of numerical experiments with a simple global circulation model under a perpetual winter condition. In order to examine the relative importance of forced planetary waves in the interaction with the zonal mean zonal flow and baroclinic disturbances, amplitude of a sinusoidal surface topography of zonal wavenumber 1 or 2 is swept as an experimental parameter; 1000-day integrations are performed for 110 combinations of the external parameters. Intraseasonal variability of the extratropical winter stratosphere in the parameter sweep experiment of the zonal wavenumber-1 topography is classified into four regimes, dependent on the topographic amplitude: (I) a nearly radiative equilibrium state; (II) small undulations of the polar vortex; (III) intermittent breakdowns of the polar vortex, or occurrence of stratospheric sudden warming events; and (IV) a usually weak and warm state of the polar vortex. The behavior of planetary waves in the stratosphere is characterized by linear propagation in regime I, and by quasi-linear, or weakly nonlinear, wave‐mean flow interaction in regime II. In regimes III and IV, on the other hand, it is in a highly nonlinear state, reflecting the occurrence of sudden warming events and the usually distorted polar vortex, respectively. The Northern Hemisphere winter stratosphere corresponds to regime III in the intraseasonal variations, while the southern counterpart is close to regime II. A lag-correlation analysis shows that the dynamical linkage between the stratosphere and the troposphere is also dependent on the regimes. The vertical linkage is primarily the upward control in regime I; planetary waves are generated by nonlinear interaction of baroclinic disturbances in the troposphere and propagate into the stratosphere. In regime II, stratospheric variations are largely confined in the stratosphere, although planetary waves are generated in the troposphere. The linkage in regime III, on the other hand, is inevitably two-way. Planetary wave variability has upward influence from the troposphere to the stratosphere as well as the zonal mean zonal wind shows preconditioning in the troposphere before stratospheric sudden warming events. Downward propagation of signals to the upper troposphere are also seen in the zonal mean temperature in high latitudes; it is higher than normal at the final stage of the sequence of sudden warming events. The linkage is also two-way in regime IV.
Neural Network Training for Prediction of Climatological Time Series, Regularized by Minimization of the Generalized Cross-Validation Function

Abstract Neural network (NN) training is the optimization process by which the relation between the NN input and output is established. A new formulation for the NN training is presented where an NN model is reconstructed such that it produces predicted output data optimally fitting the observed ones. The optimal level of fit is determined by minimization of the generalized cross-validation function, which is integrated in the training. The training process is fully automated, does not require the user to set aside data for validation, and enables objective testing and evaluation of the predictions. Results are demonstrated and discussed using synthetic data produced by Lorenz’s low-order circulation model and on real data from the equatorial Pacific.