Statistical Regression Models Research Articles

Service quality: perspective of people with type 2 diabetes mellitus and hypertension in rural and urban public primary healthcare centers in Iran

ObjectiveThis study aimed to assess the service quality (SQ) for Type 2 diabetes mellitus (T2DM) and hypertension in primary healthcare settings from the perspective of service users in Iran.MethodsThe Cross-sectional study was conducted from January to March 2020 in urban and rural public health centers in the East Azerbaijan province of Iran. A total of 561 individuals aged 18 or above with either or both conditions of T2DM and hypertension were eligible to participate in the study. The study employed a two-step stratified sampling method in East Azerbaijan province, Iran. A validated questionnaire assessed SQ. Data were analyzed using One-way ANOVA and multiple linear regression statistical models in STATA-17.ResultsAmong the 561 individuals who participated in the study 176 (31.3%) were individuals with hypertension, 165 (29.4%) with T2DM, and 220 (39.2%) with both hypertension and T2DM mutually. The participants’ anthropometric indicators and biochemical characteristics showed that the mean Fasting Blood Glucose (FBG) in individuals with T2DM was 174.4 (Standard deviation (SD) = 73.57) in patients with T2DM without hypertension and 159.4 (SD = 65.46) in patients with both T2DM and hypertension. The total SQ scores were 82.37 (SD = 12.19), 82.48 (SD = 12.45), and 81.69 (SD = 11.75) for hypertension, T2DM, and both conditions, respectively. Among people with hypertension and without diabetes, those who had specific service providers had higher SQ scores (b = 7.03; p = 0.001) compared to their peers who did not have specific service providers. Those who resided in rural areas had lower SQ scores (b = -6.07; p = 0.020) compared to their counterparts in urban areas. In the group of patients with T2DM and without hypertension, those who were living in non-metropolitan cities reported greater SQ scores compared to patients in metropolitan areas (b = 5.09; p = 0.038). Additionally, a one-point increase in self-management total score was related with a 0.13-point decrease in SQ score (P = 0.018). In the group of people with both hypertension and T2DM, those who had specific service providers had higher SQ scores (b = 8.32; p < 0.001) compared to the group without specific service providers.ConclusionStudy reveals gaps in T2DM and hypertension care quality despite routine check-ups. Higher SQ correlates with better self-care. Improving service quality in primary healthcare settings necessitates a comprehensive approach that prioritizes patient empowerment, continuity of care, and equitable access to services, particularly for vulnerable populations in rural areas.

ROTI-based statistical regression models for GNSS precise point positioning errors associated with ionospheric plasma irregularities

ROTI-based statistical regression models for GNSS precise point positioning errors associated with ionospheric plasma irregularities

Comparative analysis of thermal indices for modeling cold and heat stress in US dairy systems

Quantifying the impact of thermal stress on milk yields is essential to effectively manage present and future risks in dairy systems. Despite the existence of numerous heat indices designed to communicate stress thresholds, little information is available regarding the accuracy of different indices in estimating milk yield losses from both cold and heat stress at large spatio-temporal scales. To address this gap, we comparatively analyzed the performance of existing thermal indices in capturing US milk yield response to both cold and heat stress at the national scale. We selected four commonly used thermal indices: the Temperature and Humidity Index (THI), Black Globe Humidity Index (BGHI), Adjusted Temperature and Humidity Index (THIadj), and Comprehensive Climate Index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981-2020, we systematically compared the patterns of yield sensitivities and statistical performance of the four indices. We found that the US state-level milk yield variability was better explained by the THIadj and CCI, which combine the effects of temperature, humidity, wind, and solar radiation. Our analysis also reveals a continuous and nonlinear responses of milk yields to a range of cold to heat stress across all four indices. This implies that solely relying on fixed thresholds of these indices to model milk yield changes may be insufficient to capture cumulative thermal stress. Cold extremes reduced milk yields comparably to those impacted by heat extremes on the national scale. Additionally, we found large spatial variability in milk yield sensitivities, implying further limitations to the use of fixed thresholds across locations. Moreover, we found decreased yield sensitivity to thermal stress in the most recent two decades, suggesting adaptive changes in management to reduce weather-related risks.

Structured porous 17-PH stainless steel layer fabrication through laser powder bed fusion

ABSTRACT This study aimed to fabricate thin, porous layers of 17-4 PH stainless steel with a defined porosity using laser powder bed fusion (LPBF). The central composite design (CCD) approach was utilized to examine the effect of process parameters on porosity. Three different methods were employed to measure the porosity of 17-4 PH stainless steel samples, i.e. theoretical analysis, buoyancy, and X-ray computed tomography (X-CT). A statistical quadratic regression model is generated that correlates with LPBF parameters to forecast porosity with high prediction accuracy. The maximum obtained porosity is 51.25% ± 0.33% with a laser power of 60 W, a scanning speed of 1800 mm/s, and a hatch spacing of 0.115 mm, which resulted in an average pore size of 24.8 ± 0.38 µm. Permeability was also analysed, as the volume energy density decrease ranges from 19.30 to 14.22 J/mm3, the permeability coefficient increases from 1.39 to 9.41 × 10−11 m2. In addition, it is observed that the minimum energy density to fabricate the 17-4 PH SS with the highest porosity and free of defects and fragmentation is 14.2 J/mm3.

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

The Impact of Agricultural Productivity on Economic Growth in Africa

This study investigates the relationship between agricultural productivity and economic growth in five African countries: Tanzania, Ghana, Kenya, Morocco, and South Africa. Secondary data from reputable sources are analyzed using statistical tests and regression models. The findings indicate that agriculture significantly contributes to Africa's economic growth. South Africa exhibits the highest agricultural productivity, followed by Morocco, Kenya, Ghana, and Tanzania. However, despite their agricultural strengths, South Africa and Morocco have relatively lower contributions to economic growth due to other factors like low employment rates. Ghana and Kenya show comparable performance, while Tanzania lags behind despite experiencing economic growth and having a high employment rate. It is emphasized that economic growth is influenced by various factors beyond agriculture, and countries with high agricultural productivity should diversify their economies and address other determinants of growth. Policy measures to strengthen the agricultural sector and promote diversification are crucial for sustainable economic development in Africa.

Code-Free Machine Learning Approach for EVO-ICL Vault Prediction: A Retrospective Two-Center Study.

Establishing a development environment for machine learning is difficult for medical researchers because learning to code is a major barrier. This study aimed to improve the accuracy of a postoperative vault value prediction model for implantable collamer lens (ICL) sizing using machine learning without coding experience. We used Orange data mining, a recently developed open-source, code-free machine learning tool. This study included eye-pair data from 294 patients from the B&VIIT Eye Center and 26 patients from Kim's Eye Hospital. The model was developed using OCULUS Pentacam data from the B&VIIT Eye Center and was internally evaluated through 10-fold cross-validation. External validation was performed using data from Kim's Eye Hospital. The machine learning model was successfully trained using the data collected without coding. The random forest showed mean absolute errors of 124.8 µm and 152.4 µm for the internal 10-fold cross-validation and the external validation, respectively. For high vault prediction (>750 µm), the random forest showed areas under the curve of 0.725 and 0.760 for the internal and external validation datasets, respectively. The developed model performed better than the classic statistical regression models and the Google no-code platform. Applying a no-code machine learning tool to our ICL implantation datasets showed a more accurate prediction of the postoperative vault than the classic regression and Google no-code models. Because of significant bias in measurements and surgery between clinics, the no-code development of a customized machine learning nomogram will improve the accuracy of ICL implantation.

Associations of glyphosate exposure and serum sex steroid hormones among 6–19-year-old children and adolescents

Glyphosate, ranked as one of the most widely used herbicides in the world, has raised concerns about its potential disruptive effects on sex hormones. However, limited human evidence was available, especially for children and adolescents. The present study aimed to examine the associations between exposure to glyphosate and sex hormones among participants aged 6–19 years, utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2013 and 2016. Children and adolescents who had available data on urinary glyphosate, serum sex steroid hormones, including testosterone (TT), estradiol (E2) and sex hormone binding globulin (SHBG), and covariates were selected. Additionally, the ratio of TT to E2 (TT/E2) and the free androgen index (FAI), which was calculated using TT/SHBG, were also included as sex hormone indicators. Survey regression statistical modeling was used to examine the associations between urinary glyphosate concentration and sex hormone indicators by age and sex group. Among the 964 participants, 83.71% had been exposed to glyphosate (>lower limit of detection). The survey regression revealed a marginally negative association between urinary glyphosate and E2 in the overall population, while this association was more pronounced in adolescents with a significant trend. In further sex-stratified analyses among adolescents, a significant decrease in E2, FAI, and TT (p trend <0.05) was observed in female adolescents for the highest quartile of urinary glyphosate compared to the lowest quartile. However, no similar association was observed among male adolescents. Our findings suggest that exposure to glyphosate at the current level may decrease the levels of sex steroids in adolescents, particularly female adolescents. Considering the cross-sectional study design, further research is needed to confirm our findings.

Prediction and decision-making based on nonlinear risks model in stomach cancer treatment

Objectives. The goals are to develop a nonlinear risk model and examine its prediction applicability for clinical use.Methods. Methods of survival analysis and regression statistical models were used.Results. A practical approach to assessing nonlinear risks of adverse events using the example of gastric cancer treatment is proposed. A model for predicting the metachronous peritoneal dissemination in patients undergoing radical surgery for gastric cancer was proposed and studied. Assessment of risks for various periods of observation was performed, and the clinical suitability of developed approach was assessed.Conclusion. In clinical oncological practice, not only timely treatment plays an important role, but also the prevention of adverse outcomes after treatment. Individualization of patient monitoring after treatment reduces the risks of fatal outcomes and the costs of additional research and treatment in the event of cancer progression. Based on the results of this study, we propose solutions that should lead to more effective and high-quality treatment tactics and follow-up after treatment for gastric cancer, also to the selection of optimal approaches and to obtaining clinically favorable outcomes of the disease. The proposed risk prediction method will ultimately lead to individualized patient management based on the results of personal data.

Premonition-driven deep learning model for short-term ship violent roll motion prediction based on the hull attitude premonition mechanism

Ship transit forecasting can provide advanced movement information to a multidimensional wave motion compensation system. However, traditional deep learning models based on data-driven or physical data have difficulty in accurately predicting the rapid variations in ship motion attitudes that frequently occur in complex ocean environments. Therefore, a premonition-driven deep learning model is proposed in this paper to significantly improve the prediction accuracy of ship violent roll motion. This model applies precursor information to predict premonitory values of ship prospective motion attitudes based on an integrated statistical regression model and then employs these premonitory values to revise the outputs of the deep learning time series model. First, a hull attitude premonition mechanism is presented based on biological premonition cognitive behaviour, which captures the correlation relationships between extreme values by downgrading the globally continuous time series prediction problem to a time-independent regression problem. The corresponding methods for predicting local extreme values and deep feedforward neural network cluster are established based on these correlation relationships between the extreme values to achieve precise premonitory values. Second, the time and amplitude variables of the extreme angle values are transformed to countdown time variables and sequential sets of premonitory angle values. Two types of premonition units and their respective premonitory long short-term memory neural networks are constructed to upgrade the regression problem to a globally continuous time series prediction problem. Finally, a single-step output recurrent strategy is applied to forecast the short-term ship motion attitude for fifteen seconds by considering eleven initially predicted points across the two datasets. In sixty-six simulation experiments, the mean absolute error, root mean squared error, and mean absolute percentage error of the premonition-driven long short-term memory neural networks are considerably lower than those of traditional deep learning models. These simulation experiments verify that the premonition-driven deep learning model can effectively forecast short-term violent ship roll motion and provide safeguards for cargo transfer.

Experimental and statistical study on the effect of process parameters on the quality of continuous fiber composites made via additive manufacturing

Ongoing research in additive manufacturing towards structural and industrial application has led to the use of commingled roving as a manufacturing feedstock for printing high fiber volume fraction composites. The prospects of using this technology for high performance applications necessitates the need for a comprehensive experimental investigation into the effects of processing parameters on the quality of an additively manufactured composite printed from commingled roving feedstock. In this work, transverse flexure and void fraction matrix pyrolysis testing are both performed to evaluate composite quality. The transverse flexure test is a testing approach that evaluates the quality of the interfacial fiber-matrix bond while the void fraction test estimates the void content in the printed composite. A full observational study consisting of 27 different test combinations is done to investigate the effects of three different process parameters namely, temperature, pressure, and print speed across three different levels. Composite samples were made from commingled roving of E-glass and amorphous PET using an in-house built continuous fiber composite digital manufacturing system. Least squares regression analysis is performed to study the main, interaction and quadratic effects of process parameters. A statistical regression model having an R2 adjusted value of 80.1% is generated from the transverse flexure study, which is used to explain main and interaction effects and also predict performance. Response surface plots are also generated and are used to optimize process parameters which can subsequently be of help in scaling up composite manufacturing. Results show that all three process parameters are highly statistically significant at the 0.01 level of significance. Pressure * Temperature and Pressure * Printspeed are significant interaction terms. Pressure plays a weightier role when print speed is increased or temperature is decreased as it closes more voids that would ordinarily have been introduced because of drop in polymer melt viscosity. Micrographic analysis is also performed.

EXPLORING THE IMPACT OF PIM 3 SCORE ON CLINICAL DECISION-MAKING: A SINGLE-CENTER STUDY

Objective: In the dynamic landscape of healthcare, the quest for precision in clinical decision-making is pivotal. The Pediatric Index of Mortality 3 (PIM 3) score has emerged as a crucial metric in predicting mortality risk among critically ill children, shaping medical choices in pediatric intensive care units.&#x0D; Methods: This single-center study, conducted at [Your Institution's Name], employed a [research design] to analyze the impact of the PIM 3 score on clinical decision-making within the pediatric intensive care unit. The study included [number] critically ill pediatric patients aged 1 mo to 18 y, with data extracted from electronic health records. Statistical analyses, including correlation and regression models, were applied to explore relationships and identify factors influencing clinical decision-making.&#x0D; Results: Among the 581 patients, gender distribution and age demographics varied, with notable associations between diagnostic categories and outcomes. Survivors exhibited lower PIM 3 scores compared to nonsurvivors. Probability scores related to outcomes revealed distinct patterns, emphasizing the predictive utility of the PIM 3 score.&#x0D; Conclusion: The study demonstrated a correlation between higher PIM 3 scores and increased mortality risk, guiding clinical decision-making in critically ill pediatric patients. Bridging theory and practice, the findings provide valuable insights for enhancing bedside decision-making and improving the quality of care. Acknowledging contextual factors is crucial for a comprehensive understanding of decision-making processes.

Antenatal care service utilization disparities between urban and rural communities in Ethiopia: A negative binomial Poisson regression of 2019 Ethiopian Demography Health Survey.

Although there have been consistent improvements in maternal mortality, it remains high in developing countries due to unequal access to healthcare services during pregnancy and childbirth. Thus, this study aimed to further analyze the variations in the number of antenatal care utilizations and associated factors among pregnant women in urban and rural Ethiopia. A total of 3962 pregnant women were included in the analysis of 2019 Ethiopian Demographic and Health Survey data. A negative binomial Poisson regression statistical model was used to analyze the data using STATA version 14.0. An incident rate ratio with a 95% confidence interval was used to show the significantly associated variables. Of the 3962 (weighted 3916.67) pregnant women, about 155 (15.21%) lived in urban and 848 (29.29%) rural residences and did not use antenatal care services in 2019. Women age group 20-24 (IRR = 1.30, 95%CI:1.05-1.61), 25-29 (IRR = 1.56, 95%CI:1.27-1.92), 30-34 (IRR = 1.65, 95%CI:1.33-2.05), and 35-39 years old (IRR = 1.55, 95%CI:1.18-2.03), attending primary, secondary, and higher education (IRR = 1.18, 95%CI:1.07-1.30), (IRR = 1.26, 95%CI:1.13-1.42) and (IRR = 1.25, 95%CI:1.11-1.41) respectively, reside in middle household wealth (IRR = 1.31, 95%CI:1.13-1.52), richer (IRR = 1.45, 95%CI:1.26-1.66) and richest (IRR = 1.68, 95%CI:1.46-1.93) increases the number of antenatal care utilization among urban residences. While attending primary (IRR = 1.34, 95%CI:1.24-1.45), secondary (IRR = 1.54, 95%CI:1.34-1.76) and higher education (IRR = 1.58, 95%CI:1.28-1.95), following Protestant (IRR = 0.76, 95%CI:0.69-0.83), Muslim (IRR = 0.79, 95%CI:0.73-0.85) and Others (IRR = 0.56, 95%CI:0.43-0.71) religions, reside in poorer, middle, richer, and richest household wealth (IRR = 1.51, 95%CI:1.37-1.67), (IRR = 1.66, 95%CI:1.50-1.83), (IRR = 1.71, 95%CI:1.55-1.91) and (IRR = 1.89, 95%CI:1.72-2.09) respectively, being married and widowed/separated (IRR = 1.85, 95%CI:1.19-2.86), and (IRR = 1.95, 95%CI:1.24-3.07) respectively were significantly associated with the number of antenatal care utilization among rural residences. The utilization of antenatal care is low among rural residents than among urban residents. To increase the frequency of antenatal care utilization, health extension workers and supporting actors should give special attention to pregnant women with low socioeconomic and educational levels through a safety-net lens.

Bioinspired Stevia rebaudiana Green Zinc Oxide Nanoparticles for the Adsorptive Removal of Antibiotics from Water.

Green zinc oxide nanoparticles (ZnO NPs) synthesized using Stevia rebaudiana as a reducing agent were investigated as ecofriendly adsorbents for the removal of the antibiotics ciprofloxacin (CIP) and tetracycline (TET) from water. Green ZnO NPs were synthesized using a rapid novel approach that did not require annealing or calcination at high temperatures to produce mesoporous NPs with a size range of 37.36-71.33 nm, a specific surface area of 15.28 m2/g, and a negative surface charge of -15 mV at pH 5. The green ZnO NPs exhibited an antioxidant activity of 85.57% at 250 μg/mL and an antibacterial activity with MIC and MBC of 50 and 100 mg/mL, respectively, against both Escherichia coli and Staphylococcus aureus. The best adsorption performance was achieved using a 4 g/L dose and pH 5, yielding, respectively, 86.77 ± 0.82% removal and 27.07 ± 0.26 mg/g adsorption capacity for CIP at 10 mg/L and 67.86 ± 3.41% and 15.88 ± 0.37 mg/g for TET at 25 mg/L. The green ZnO NPs achieved 79.71% ± 0.28 and 61.55% ± 0.53 removal of 10 mg/L CIP and 25 mg/L TET, respectively, in a spiked tap water binary system of the two contaminants. Adsorption of CIP and TET occurred mainly via electrostatic interactions, whereby CIP was bound more strongly than TET by virtue of its charge and size. The synthesis and adsorption processes were evaluated by a stepwise regression statistical model to optimize their parameters. Lastly, the green ZnO NPs were regenerated and reused for 5 cycles, indicating their functionality as simple, reusable, and low-cost adsorbents for the removal of CIP and TET from wastewater, in accordance with SDGs #6 and 12 for the sustainable management of water.

Greater Frequency of Cooking Dinner at Home and More Time Spent Cooking Are Inversely Associated With Ultra-Processed Food Consumption Among US Adults

BackgroundCooking at home has been promoted as a strategy to improve diet quality; however, the association between cooking behavior and ultra-processed food intake is unknown. ObjectiveThe objective of this study was to examine associations between frequency of cooking dinner at home and time spent cooking dinner with ultra-processed food intake. DesignCross-sectional, nationally representative data from the 2007-2010 National Health and Nutrition Examination Survey were analyzed. Participants/settingParticipants were 9,491 adults (20 years and older) in the United States. Main outcome measuresThe main outcome measure was the proportion of energy intake (averaged from two 24-hour dietary recalls) from the following 4 Nova food-processing groups: (1) unprocessed or minimally processed foods, (2) processed culinary ingredients, (3) processed foods, and (4) ultra-processed foods. Statistical analyses performedSeparate linear regression models examined associations between cooking frequency and time spent cooking dinner and proportion of energy intake from the 4 Nova food-processing groups, adjusting for sociodemographic characteristics and total energy intake. ResultsUltra-processed foods comprised >50% of energy consumed independent of cooking frequency or time spent cooking. Higher household frequency of cooking dinner and greater time spent cooking dinner were both associated with lower intake of ultra-processed foods (P trends < .001) and higher intake of unprocessed or minimally processed foods (P trends < .001) in a dose–response manner. Compared with cooking 0 to 2 times/wk, adults who cooked dinner 7 times/wk consumed a mean of 6.30% (95% CI –7.96% to –4.64%; P < .001) less energy from ultra-processed foods. Adults who spent more than 90 minutes cooking dinner consumed 4.28% less energy from ultra-processed foods (95% CI –6.08% to –2.49%; P < .001) compared with those who spent 0 to 45 minutes cooking dinner. ConclusionsCooking at home is associated with lower consumption of ultra-processed foods and higher consumption of unprocessed or minimally processed foods. However, ultra-processed food intake is high among US adults regardless of cooking frequency.

Evaluación estadística del circuito de clasificación-molienda de la planta de carbón en pulpa de Antapite de la compañía Sierra Sun Group (Perú) empleando modeol s estadísticos de regresión y ANOVA

El presente trabajo pudo mostrar una evaluación estadística del circuito de molienda – clasificación de una planta aurífera de la planta Antapite de la Compañía Minera Sierra Sun Group (Perú). El circuito de molienda empleaba un molino de bolas, cuyo mineral descargado tenía una la ley de oro de 29.50 g/t, el cual alimentaba a un hidrociclón convencional Krebs (de 10 pulgadas de tamaño, 254 mm), cuyo rebose tenía una distribución de oro de 71.30 % (para tamaños de partículas de menos 74 μm). Sin embargo, el objetivo fue aumentar esta distribución a un valor equivalente a 77.39% (para partículas menores a 74 μm), es decir, al menos 5 puntos por encima de la actual. Este rebose alimentó al circuito metalúrgico formado por concentración gravimétrica y/o cianuración – adsorción dependiendo de la mineralogía con la que se encontraba asociada el oro. Se realizó un análisis estadístico de regresión múltiple para las variables de operación del hidrociclón Krebs de 10 pulgadas (254 mm) que permitió obtener la distribución de oro (objetivo mencionado anteriormente). De esta manera se plantearon tres escenarios diferentes, en los cuales se eliminaron las variables no significativas. El primer escenario evaluó la dependencia del tamaño de corte corregido d50C en función de la presión y caudal de la pulpa de alimentación al hidrociclón, el segundo escenario analizó la variable independiente: distribución de oro en el rebose del hidrociclón con respecto a las variables dependientes: caudal alimentado, presión, d50 y d50C para tamaños de partículas menores a 74 μm. Por último, el tercer escenario permitió evaluar la dependencia de la distribución de oro objetivo frente al d50C. Finalmente, se logró definir el parámetro que influye en la dependencia de la distribución de oro objetivo en el rebose, pudiendo encontrar así el valor de d50 (tamaño de corte sin corregir en micras) en función del caudal y presión de trabajo para llegar a la distribución de oro objetivo de 77.39% en el rebose (para partículas menores a 74 μm).

Operating costs and profitability to company value

Increasing economic growth makes competition tighter in the business world, especially in the food and beverage business. This study investigates the interaction between operational costs, profitability, and company value in Indonesia's food and beverage sector. The aim is to gain insight into how these factors interact and affect the overall value of companies in the Indonesian region. This analysis uses quantitative methods based on financial data and relevant metrics for a sample of Food and Beverages companies operating in Indonesia, as many as 24 companies, with purposive sampling techniques of 17 companies sampled for 2020 – 2022. This study examines the relationship between firm cost, profitability, and value through statistical analysis and linear regression modelling techniques with SPSS version 26. The results showed that operational expenses had a significant effect on the value of the company. Profitability has a substantial impact on the value of the company. Then, operating costs and profitability have a considerable effect on the value of the company. The findings contribute to the existing literature on company valuations by providing empirical evidence specific to Indonesia's Food and Beverages sector. The research has implications for policymakers, investors, and managers, as it offers valuable insights into the factors that drive corporate value in the industry, helping stakeholders make decisions and strategies to improve performance and competitiveness. Provide benefits in the context of financial management.

Cash turnover and receivables turnover to the profitability of food and beverages sector companies for the 2018-2022 period

Increasingly complex market dynamics and intense business competition are challenges for companies to maintain and improve their financial performance, especially in the food and beverages subsector. This study investigates the interaction between cash turnover, accounts receivables, and company profitability in Indonesia's food and beverages sector. The goal is to gain insight into how these factors interact and affect the overall profitability of a particular food and beverage company. This analysis uses quantitative methods based on financial data and relevant metrics for a sample of 17 food and beverage companies operating in Indonesia according to the variables studied. This study examines the relationship between cash turnover, receivables turnover, and profitability through statistical analysis and linear regression modeling techniques with SPSS version 26. The results showed that cash turnover significantly negatively affected profitability in food and beverages. Receivables turnover does not exert a significant effect on profitability. Cash turnover and receivables turnover also simultaneously do not have a substantial impact on profitability. The findings contribute to the existing literature on company valuations by providing empirical evidence specific to the food and beverages sector. The research has implications for policymakers, investors, and managers, as it offers valuable insights into the factors that drive a company's profitability, helping stakeholders make decisions and strategies to improve performance and competitiveness. Provide benefits in the context of financial management.

Development of a Predictive Model for the Indirect Tensile Strength Load–Displacement Curve for Asphalt Mixtures

The indirect tensile (IDT) strength test is one of the common laboratory tests conducted to characterize asphalt mixtures. The IDT load–displacement curve is used to calculate various indicators to evaluate the cracking performance of asphalt mixtures. This study evaluated the effect of the mix composition on the IDT load–displacement curve. Asphalt mixtures with different characteristics, including binder content, binder grade, and mix gradation, were prepared and tested. The study developed statistical regression models to predict the entire IDT load–displacement curve using parameters that describe the mix composition and properties, including the binder complex shear modulus and phase angle, aggregate gradation, and binder content. The results demonstrated that the predicted model parameters were found to correlate well with those calculated from the experimental IDT load–displacement curves. All predicted cracking performance indicators calculated from the predicted IDT load–displacement curve had a strong correlation with those calculated from the measured IDT load–displacement curve. The proposed model can be used as a tool to check the preliminary cracking performance of the asphalt mixtures during the mix design stage. In addition, the mix composition can be adjusted to improve the cracking performance of the designed mix (e.g., adjust binder grade, binder content, gradation, etc.).

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.