Coefficient Of Determination R2 Research Articles

Faktor - Faktor yang Mempengaruhi Kepatuhan Wajib Pajak UMKM dalam Melaksanakan Kewajiban Perpajakan: Studi Kasus pada Wajib Pajak UMKM di KPP Pratama Seberang Ulu

This study aims to examine the effect of tax knowledge, tax sanctions, service quality, tax rates and application of e-filing on taxpayer compliance of UMKMs at KPP Pratama Seberang Ulu. The independent variables in this study are tax knowledge, tax sanctions, service quality, tax rates and application of e-filing while the dependent variable is taxpayer compliance. There are 2 theories in this study, namely attribution and the theory of Planned Behavior. This study uses a quantitative method. The population in this study are UMKM actors registered at KPP Pratama Seberang Ulu. The sample was determined using purposive sampling. Data were collected by distributing questionnaires to 47 UMKMs registered at KPP Pratama Seberang Ulu and in accordance with the specified characteristics. Primary data collection techniques and data analysis using multiple linear regression analysis SPSS software version 25. The methods used in this study are Descriptive Analysis, Validity Test, Reliability Test, Classical Assumption Test, Multiple Linear Regression Analysis Test, F Test, R2 Determination Coefficient Test and t Test. The results of the study indicate that tax knowledge, tax sanctions and the application of e-filing have an effect on taxpayer compliance, while the quality of service and tax rates have no effect on the compliance of UMKM taxpayers at the Seberang Ulu Pratama Tax Office..

Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years

The risk of soil salinization is prevalent in arid and semi-arid regions, posing a critical challenge to sustainable agriculture. This study addresses the need for accurate assessment of regional root-zone soil salt content (SSC) and understanding of underlying driving mechanisms, which are essential for developing effective salinization mitigation and water management strategies. A remote sensing inversion technique, initially proposed to estimate root-zone SSC in cotton fields, was adapted and validated more widely to non-cotton farmlands. Validation results (with a coefficient of determination R2 > 0.53) were obtained using data from a three-year (2020–2022) regional survey conducted in the arid Manas River Basin (MRB), Xinjiang, China. Based on this adapted technique, we analyzed the spatiotemporal distributions of root-zone SSC across all farmlands in MRB from 2001 to 2022. Findings showed that root-zone SSC decreased significantly from 5.47 to 3.77 g kg−1 over the past 20 years but experienced a slight increase of 0.15 g kg−1 in recent five years (2017–2022), attributed to cultivated area expansion and reduced irrigation quotas due to local water shortages. The driving mechanisms behind root-zone SSC distributions were analyzed using an approach combined with two machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP), to identify influential factors and quantify their impacts. The approach demonstrated high predictive accuracy (R2 = 0.96 ± 0.01, root mean squared error RMSE = 0.19 ± 0.03 g kg−1, maximum absolute error MAE = 0.14 ± 0.02 g kg−1) in evaluating SSC drivers. Factors such as initial SSC, crop type distribution, duration of film mulched drip irrigation implementation, normalized difference vegetation index (NDVI), irrigation amount, and actual evapotranspiration (ETa), with mean (SHAP value) ≥ 0.02 g kg−1, were found to be more closely correlated with root-zone SSC variations than other factors. Decreased irrigation amount appeared as the primary driver for recent increased root-zone SSC, especially in the mid- and down-stream sections of MRB. Recommendations for secondary soil salinization risk reduction include regulation of the planting structure (crop choice and extent of planting area) and maintenance of a sufficient irrigation amount.

Impact of External Shocks on Peru's Economic Growth, 2000-2021

Objective: To analyze the impact of China's GDP growth and an international oil price shock (external shocks) on Peru's growth rate, terms of trade, private investment, exports and unemployment for the period analyzed. Originality/Value: This study contributes to the literature on the dependence of a small economy on external factors, verifying through an econometric analysis the implications of an external shock that will be relevant for economic policy decision makers, academia and globalized economic culture. The relevance and value of this research are evidenced by quantifying the impacts of a large economy like China on variables such as terms of trade, private investment and Peru's GDP growth rate. Method: It is quantitative and exploratory with 4 variables, China's GDP growth rate, terms of trade, private investment and Peru's GDP growth rate; It is exploratory because it will give us an overview or knowledge of the behavior of China's GDP growth rate, terms of trade and private investment and their impact on Peru's growth rate from 2000 to 2021. The variables explained are: China's GDP growth rate (tCreChin), terms of trade (tInt), private investment (inv Priva). Explanatory variable: Peru's GDP growth rate (tCrePerú). The design is non-experimental of a correlational type, the population includes data from the 04 variables for the years 2000-2021, each with 22 observations that were extracted from the 21 years analyzed, extracted from institutional secondary sources BCRP and World Bank (WB). Subsequently, tests will be carried out to verify correlation, stationarity and heteroscedasticity in the variables and the estimation of the model parameters for several alternatives or possible combinations of factors. In each case, the “t” test, the “F” test will be performed and the coefficient of determination R2 will be calculated, to determine the best fit. And finally, the best model will be selected for forecasting purposes. Results: The response of GDP to a shock of the GDP variable of China has a positive impact from period 1-3, on the other hand the IT variable has a positive impact from period 1. Another relevant aspect achieved is that in 10 years, 51% of the variability of Peru's economic growth depends on the variability of China's growth, 2.20% of the variability of Peru's economic growth depends on the variability of the IT variance and 1.48% of the variability of Peru's economic growth depends on the variability of private investment. Discussion: Our results find similarity with studies by Guevara and Yamaca (2021) and Chávez (2020) et.al., since an external shock is positive and explains a high percentage of historical variability. A shock to China's GDP growth has a greater effect on Peru's GDP growth; and financial shocks reduce domestic GDP growth. On the other hand, Peruvian monetary policy is synchronized with the behavior of the American FED to mitigate the adverse effects of external factors. In our study we use a VAR demonstrating the influence of China's economic growth, the terms of trade, Peruvian GDP and private investment. Finally, the findings indicate that the impacts of external shocks on economic growth are more long-term than short-term, and also reveal the existence of fragility in the international economy that can be opportunities on the side of raw material prices.

Use of bore-epoxy anchorage system with CFRP laminates for shear strengthening of RC beams: An experimental and analytical investigation

Shear strengthening of reinforced concrete (RC) beams with externally bonded reinforcement (EBR) using carbon fiber reinforced polymer (CFRP) plates and sheets have become a widely accepted practice. To avoid the associated premature debonding failure, this study presents an experimental and analytical investigation on the use of bore-epoxy as an anchorage system for CFRP plates and sheets bonded on both sides of shear deficient RC beams of rectangular cross-sections. Nine (9) RC beams were prepared and strengthened with CFRP plates and sheets as EBR with bore-epoxy anchorage system considering different bore diameters and tested under four point bending. The results indicate that RC beams strengthened with bore-epoxy anchorage system have high shear strength as compared with the unstrengthened control beam and those strenghtended with the conventional EBR approach (without boring). The increase in the shear strength over the control beam was found to be up to 67 % and 121 % for cases of CFRP plates and sheets, respectively. Moreover, the increase of shear strength in comparison with the conventional EBR method ranged from 9.33 % to 20.36 % for CFRP plates and from 12.97 % to 36.10 % for CFRP sheets. Consistently, the contribution of bore-epoxy on the shear strength increased with the increase of bore diameter. Consequently, the existing formulas in the ACI440.2 R for predicting the shear strength of FRP strengthened RC beams were modified to incorporate the improvement made by the proposed bore-epoxy approach. The revised models predicted the experimental shear strength of RC beams, strengthened with bore-epoxy, with a good level of accuracy with average mean absolute percentage error (MAPE) = 6.07 %, 14.71 %, normalized mean square error (NMSE) = 0.097, 0.16, and coefficient of determination R2 = 0.912, 0.974 for plates and sheets, respectively.

Mass transfer process in the adsorption of Cibacron Blue (CB) on untreated pea pods (UPPs): kinetic and thermodynamic studies

ABSTRACT In recent decades, there has been extensive use of synthetic dyes in the dye-based industries, particularly the textile sector. Therefore, the aim of this study was to evaluate the adsorption of Cibacron Blue (CB) onto untreated pea pods (UPPs) in a batch system. The effects of the initial CB concentration (10–20 mg/L), solution pH (2–12), adsorbent dose (0–4 g/L), particle size (50–500 μm), and temperature (295–318 K) on the CB adsorption were investigated in batch configuration to determine the optimum conditions. Analyses of UPPs were characterized by Fourier Transform Infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller (BET), and point-of-zero charge (pHpzc = 5.6). Under optimized conditions (pH: 2.5, particle size: 50 μm, time: 40 min, adsorbent dose: 2.5 g/L, and agitation speed: 250 rpm), up to 30.30 mg/g at 25 °C is removed from the solution. The adsorptions kinetics obey rather a pseudo-second-order kinetic model with a determination coefficient of R2 = 0.999. The adsorption isotherms have been used for the determination of thermodynamic parameters, i.e. the negative free energy ΔG° (−4.33 to 0.783 kJ/mol), negative enthalpy change ΔH° (−54.63 kJ/mol), and entropy (ΔS° = −0.1705 kJ/mol.K) indicate that the CB adsorption onto UPPs is spontaneous and exothermic in nature.

Introducing Angle B: A Novel Quantitative Approach for Sagittal Alignment Reduction in Atlanto-Axial Dislocation Surgery.

This study aims to establish a quantitative relationship between cervical lordosis (CL) and the rotation angle of the axis for the surgical reduction of atlantoaxial dislocation (AAD) and to explore the potential applications of this relationship in preoperative planning and selection of surgical techniques. To construct the correlation between horizontal gaze and location of axis, we introduced the angle B, defined as the angle between the Frankfort horizontal line and the extension line of the posterior edge of the axis, and explored its application in surgical planning. We retrospectively analyzed imaging data from computed tomography of 23 AAD patients who had undergone posterior reduction and fixation. Theoretical equations were deduced through a series of angular transformations, and linear regression analysis was used to validate our findings. Our results showed a strong linear relationship between the change in CL (ΔCL) and the change in B(ΔB) (y=-1.0402x, coefficient of determination R²=0.978, P<0.001), supporting our deduction that ΔCL=ΔB. Furthermore, we found that the rotation angle of the axis (angle D) was equal to ΔCL. By resolving the atlantoaxial interval into the vertical dimension (h) and horizontal dimension (d), we could calculate the maximum vertical and horizontal distance that the axis could move theoretically according to a patient's maximum ΔCL. This finding supports our theory that the introduction of angle B can provide more precise preoperative planning and surgical technique selection for patients with AAD. By introducing angle B and deducing the equation ΔB=ΔCL=D, we have provided an innovative tool for preoperative planning and surgical technique selection for patients with AAD. This equation not only helps surgeons achieve more precise and effective surgical reductions but also emphasizes the important role of angle B in surgical planning.

Automatic Apple Detection and Counting with AD-YOLO and MR-SORT.

In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model's ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit.

Evaluation of Clustering Techniques for Solar Irradiance Prediction for Optimal Design of Microgrids in Rural Communities

Abstract The electrification of rural communities is crucial from both social and economic perspectives, aligned with Sustainable Development Goal 7: “Affordable and Clean Energy”. This study presents a comprehensive comparison of clustering techniques, including k-means, Gaussian Mixture Models (GMM), Hierarchical Clustering, Density-based Spatial Clustering of Applications with Noise (DBSCAN), and Agglomerative Clustering, aimed at enhancing solar irradiance prediction. Leveraging historical climate data from a rural community in the coastal region of Ecuador, each technique is evaluated using error metrics such as Mean Absolute Error (MAE) and Coefficient of Determination R2. This assessment identifies the most effective clustering technique in this specific context. In order to delve deeper into these comparisons, simulations are conducted in AMPL to validate and refine the selection of techniques. In this process, it is considered the sizing and design of a microgrid within the Barcelona community, Ecuador, which integrates various energy sources, including solar. Additionally, a penalty system is introduced for unmet energy demands during less critical periods, thereby optimizing efficiency and enhancing energy availability within the community. In conclusion, this paper introduces a scalable methodology to improve solar irradiance prediction, emphasizing the significance of comparing clustering techniques as its main contribution. This advancement in prediction accuracy has the potential to enhance the feasibility and efficiency of renewable energy systems for rural communities, thereby fostering sustainable economic growth and bolstering efforts in climate change mitigation and adaptation.

Residuos sólidos y gestión ambiental en una institución educativa de secundaria

The purpose of this study was to investigate the impact of solid waste on the environmental management of an educational institution of secondary education in the district of Victor Larco Herrera in the year 2023. It was quantitative and applied. The design was non-experimental, correlational, cross-sectional. The hypothetical-inductive method was used. The population consisted of 280 students, males and females, from 1st, 2nd and 3rd year of high school; the sample consisted of 72 students. A survey was used as a technique and two questionnaires as data collection instruments. The results revealed that the var iable "solid waste" was perceived at a medium level by 40,28%, and the variable "environmental management" was also perceived at a medium level by 36,11%. Likewise, in Kendall's Tau-b the value was 0,109 with a consistency level = 0,352. The straight line was Y = 86,15 + (0,018)X, with coefficient of determination R2 = 0,018. It was concluded that the impact of solid waste is very low, which is explained in 1,8% by environmental management; therefore, it is necessary to carry out awareness workshops to cons erve the environment.

SPATIAL VARIABILITY OF THE HEAT EMITTED FROM THE LAND SURFACE IN DETERMINING THE CHARACTERISTICS OF DESERT SOILS IN AL-SAMAWAH DESERT USING “GIS”

This study was aimed to reveal the variation land surface temperature "LST" with some characteristics of desert soils in the Samawa desert at Al-Muthanna province, and to adopt solutions and treatments by identifying factors and indicators that have a direct impact to desertification, by using geographic information systems mapping for spatial distribution Some physical and chemical properties were selected. . the result showed that there was a statistical relationship between LST and the physical characteristics, as it was revealed that the comparison of sand and silt content with the land surface temperature "LST" . There is a significant relationship with the determination coefficient of R2=0.6. The chemical properties, the results showed that there was a significant connection between the electrical conductivity with the land surface temperature with a coefficient of determination R2=0.5 and an It was also observed that there is a significant relationship between the land surface temperature and the temperature measured in the field with a determination coefficient R2=0.6.

A wearable real-time particulate monitor demonstrates that soaking hay reduces dust exposure.

Affordable particulate matter (PM) monitors suitable for use on horses will facilitate the evaluation of PM mitigation methods and improve the management of equine asthma. Calibrate a real-time wearable PM monitor (Black Beauty [BB]) and compare the PM exposures of horses fed dry or soaked hay. Laboratory calibration; complete cross-over feed trial. Side-by-side sampling with BB monitors and tapered element oscillating microbalances (TEOMs) was performed under varying concentrations of PM from alfalfa hay. Linear regression was used to derive a calibration formula for each unit based on TEOM PM measurements. Precision was evaluated by calculating the coefficient of variation and pairwise correlation coefficients between three BB monitors. PM exposure was measured at the breathing zone of 10 horses for 8 h after they were fed dry or soaked hay. Repeated measures generalised linear models were constructed to determine the effect of hay treatment and measurement duration (initial 20-min vs. 8-h) upon exposure to PM with diameters smaller than or equal to 10 μm (PM10) and 2.5 μm (PM2.5). BB monitor PM2.5 and PM10 measurements were linearly correlated with TEOM data (coefficient of determination r2 > 0.85 and r2 > 0.90 respectively), but underestimated PM2.5 mass concentrations by a factor of 4 and PM10 concentrations by a factor of 44. Measures from the three BB monitors had a coefficient of variation <15% and pairwise r > 0.98. Feeding soaked hay significantly reduced average PM2.5 exposures (20-min: dry: 160 μg/m3, soaked: 53 μg/m3, p < 0.0001; 8-h: dry: 76 μg/m3, soaked: 31 μg/m3, p = 0.0008) and PM10 exposures (20-min: dry: 2829 μg/m3, soaked: 970 μg/m3, p < 0.0001; 8-h: dry: 1581 μg/m3, soaked: 488 μg/m3, p = 0.008). No health outcome measures were collected. With appropriate corrections, the BB monitor can be used to estimate horse PM exposure. While 20-min measurements yielded higher estimates of exposure than 8-h measurements, both intervals demonstrate that soaking hay reduces PM exposures by more than 50%.

Characterizing the hysteretic effects of water and salinity stresses on root-water-uptake

Characterizing the effects of previous water and salinity stresses is critical for the evaluation of plant water status, which, in turn, is essential for understanding soil-plant water relations and optimizing irrigation schemes. Recent research has found that hysteresis of plant response following water stress alone can be described by an exponential function of the stress degree on the previous day. To explore and quantify the effects of hysteresis concerning salinity stress and combined water-salinity stress, a hydroponic experiment and a soil column experiment on winter wheat, and a field experiment on cotton were conducted. Like water stress, previous salinity stress and combined water-salinity stress also resulted in hysteretic effects on root-water-uptake. Leaf stomatal conductance and plant transpiration rate of stressed crops could only recover gradually from a previous stressed status after re-watering. When stress was mild, compensatory recovery was found, while incomplete recovery occurred when stress was severe. Although the recovery process was closely related to stress history and type, a recovery coefficient was quantified universally with an exponential function of the stress extent on the previous day (with a coefficient of determination R2 ≥ 0.60). Consideration of hysteresis for water and salinity stresses with a mathematical model led to significant improvement in the simulation of both relative transpiration rate (R2 = 0.94, root mean squared error RMSE = 0.04, maximal absolute error MAE = 0.12) and soil water content (R2 = 0.90, RMSE = 0.01 cm3 cm–3, MAE = 0.03 cm3 cm–3), especially during the recovery periods severely affected by historical stress. Consideration of hysteresis is expected to benefit regulation of soil water and salinity and thus enhance water use efficiency. However, the mechanisms underlying hysteresis, especially the compensatory recovery mechanisms, still need to be further investigated.

Composition and Injection Rate Co-Optimization Method of Supercritical Multicomponent Thermal Fluid Used for Offshore Heavy Oil Thermal Recovery

Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids.

Prediction of Whole Liver Graft Weight Based on Biometric Variables in Paediatric and Adult Liver Donors.

In paediatric liver transplantation, donor-recipient compatibility depends on graft size. We explored whether the graft weight can be predicted using the donor's biometric parameters. We used seven easily available biometric variables in 142 anonymised paediatric and adult donors, with data collected between 2016 and 2022. The whole or partial liver was transplanted in our hospital from these donors. We identified the variables that had the strongest correlation to our response variable: whole liver graft weight. In child donors, we determined two linear models: using donor weight and height on the one hand and using donor weight and right liver span on the other hand. Both models had a coefficient of determination R2 = 0.86 and p-value < 10-5. We also determined two models in adult donors using donor weight and height (R2 = 0.33, p < 10-4) and donor weight and sternal height (R2 = 0.38, p < 10-4). The models proved valid based on our external dataset of 245 patients from two institutions. In clinical practise, our models could provide rapidly accessible estimates to determine whole graft dimension compatibility in liver transplantation in children and adults. Determining similar models predicting the left lobe and lateral segment weight could prove invaluable in paediatric transplantation.

Study of Neural Network Architectures for Determining Gas Concentrations by Spectra

A study of a number of neural networks of different architectures for determining gas concentrations from spectra obtained using an optical emission gas analyzer, which measures the spectrum of electromagnetic radiation emitted by gases when excited by an electric discharge, is presented. The neural network is trained on data from the optical spectroscopy laboratory and is able to predict gas concentrations from spectra at high speed. The research concerned the deep neural network architectures with convolutional and recurrent layers. Convolutional layers highlight the features of the spectra, while recurrent layers take into account the consistent structure of the data. The quality of the neural network is evaluated by the R2 coefficient of determination, and the comparison between networks by the RMSE indicator between the predicted and real gas concentrations.

Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values

The fundamental period is one of the most important parameters for the design of new structures as well as for estimating the capacity of existing ones. Thus, to estimate it, various design codes and researchers have adopted several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for deriving the analytical equations for the fundamental period of reinforced concrete structures. The methodology is based on machine learning explainability techniques, specifically the so-called SHapley Additive exPlanations values. These values are commonly employed as an explainability tool. However, in the proposed novel approach they are employed as a basis to fit analytical curves, which allows the resulting equations to be constructed sequentially and in an informed manner while controlling the balance between accuracy and complexity. An extended dataset consisting of 4026 data points is employed, on which a Gradient Boosting Machine model is fitted. The model achieves excellent accuracy, with a coefficient of determination R2≈0.99, while the equations derived from the proposed formulation achieve an R2≈0.95 and Mean Absolute Error ≈0.12. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges.

Model for Estimating Soil Chemical Properties with RGB Drone Images

Precision agriculture optimizes crop management by providing accurate data on soil chemical properties, thereby improving agricultural productivity and sustainability. This study aims to develop models to estimate soil chemical properties, such as pH, electrical conductivity (EC), and organic matter (OM), by analyzing drone-captured RGB images. The methodology included photogrammetric flights with a DJI Phantom 4 Pro drone equipped with a 20 Mpx camera and simultaneous sampling, laboratory analysis and on-site measurements, with Royal Eijkelkamp EC meter set voor grond multiparameter sensors and pH meter set for soil and water. The aerial images were processed with the PIX4Dmapper software, to generate the orthophoto and spectral bands. With the resulting orthophoto of 1.6 cm/pixel, eight spectral indices were calculated, using the spatial analysis tools of ArcGIS software. The in situ results showed an average pH value of 5.83, indicating a slightly acidic soil, and an EC of 1.09 dS/m, suggesting a soil with a low concentration of dissolved salts. Laboratory analyses showed a medium-high content of OM, with an average of 5.19 %. A strong correlation was found between OM and pH_index with coefficients of determination R2=0.55, while moderate correlations were also observed between pH with pH_index and EC with sal_index6 with coefficients of determination R2=-0.39 and R2=0.42 respectively. The aforementioned results allowed the generation of two models for the estimation of these variables from RGB images.

Characteristics of gusts with different velocity profiles and control parameters

The characteristics of gust flow are essential for gust response and alleviation. To investigate the influence of control parameters on gusts with different velocity profiles, four vertical gust profiles were designed. Methods were proposed to generate them with two pitching airfoils in a low-speed water tunnel. The velocity field was measured via phase-locked particle image velocimetry. The coefficient of determination R2 was proposed to evaluate the generated gust profile quality, which referred to the quality of the vertical velocity profile. The influence of control parameters on different gust profiles was investigated, and the cause of the profile distortion was explored. For continuous sine gusts, the gust ratio GR increased approximately linearly with the pitching amplitude, while the gust ratio initially increased and then decreased with increasing frequency. As the two control parameters increased, the flow uniformity decreased because the airfoil wakes disturbed the measured flow field. In terms of continuous 1-cosine gusts, the gust ratio increased nonlinearly with pitching amplitude. Compared with those of the sine gusts, the GR values of the 1-cosine gusts were higher, whereas the R2 values were lower. In addition, the discrete and continuous gust profiles had similar distortion near the peaks. However, discrete gusts had lower R2 values than continuous gusts because the starting and stopping vortices of the pitching airfoils disturbed the gust flow. Based on these findings, a method to improve the profile quality and field uniformity by increasing the spacing of the pitching airfoils was proposed. This work can support further studies of gust response and alleviation during complex gust encounters.

Advancing Carbon Stock Estimation and 3D Tree Modeling: Harnessing the Potential of Low-Cost Backpack LiDAR Technology

Abstract The maintenance of carbon stocks in the forest is fundamental to the global carbon cycle and is essential for informing climate change policy. Traditional methods for measuring carbon stocks have often been labor-intensive and expensive, involving measurements that include DBH, and tree height. However, LiDAR technology provides an efficient alternative for swiftly assessing forest vegetation structure. In this study, a low-cost backpack equipped with LiDAR technology was utilized to create three-dimensional tree models, enabling precise estimations of stem volume and carbon stock. By collecting data on DBH and vertical plant structure, the researchers could produce highly accurate three-dimensional models. The results indicated that the low-cost backpack LiDAR yielded DBH estimates very close to manual measurements, characterized by a linear regression equation of y = 1.0106x - 0.2911 and a coefficient of determination R2 = 0.99822. The results confirmed the high accuracy of DBH measurement using LiDAR, with RMSE in a value of 0.53 cm. This study unequivocally establishes that low-cost backpack LiDAR is an effective tool for measuring biomass and carbon stocks in forests, offering superior monitoring capabilities compared to conventional methods.

IoT and AI: a panacea for climate change-resilient smart agriculture

The application of Internet of Things (IoT) and Artificial Intelligence (AI) for disaster preparedness and sustainable agriculture has been a topic of great interest lately. In the last few years, extreme weather swings due to climate change caused by global warming have caught the farming community off guard, especially in the developing world. One of the key objectives of smart agriculture is optimal use of freshwater, which has become an increasingly scarce resource around the world. Reference Evapotranspiration (ETo), an estimation of total flux of water evaporating from a reference surface is an important parameter for irrigation management. IoT & AI-based location-specific estimation of ETo for crop water requirements augments the decision-making process. In this work, we utilize the Hargeaves and Samani (H–S) model and six regression algorithms for the estimation of ETo. We create a location-specific dataset with locally sensed IoT data from a flood warning system and remotely sensed meteorological data, spanning over 5 years. We train and test Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR), Bagging and Random Forest (RF) algorithms on the locally curated dataset with 20 basic, extracted, and derived attributes. We gradually reduce number of attributes in the dataset from 20 to 3 and compare performance of the six algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Coefficient of Determination R2, Kendall Tau and Spearman Rho metrics. SVR shows superior performance with an MAE of 0.03 and an RMSE of 0.05, followed closely by MLP with an MAE of 0.04 and RMSE of 0.06 with a dataset of 12 attributes. The performance of Bagging and RF algorithms remains relatively unchanged with feature reduction whereas RBF shows slight improvement in performance when number of attributes is reduced to 3. Finally, we develop a novel ensemble hybrid model using the Stacked Generalization technique, which outperforms all individual models in prediction accuracy when using reduced-feature datasets. This work clearly delineates the performances of a diverse set of ML algorithms for feature-rich and feature-scarce scenarios and demonstrates the efficacy of our hybrid ensemble ML algorithm for estimating ETo under limited availability of data in resource-constrained environments.