Statistical Regression Models Research Articles

Climate Change Risks for the Mediterranean Agri-Food Sector: The Case of Greece

The study assesses the direct effects of climate change by 2060, including extreme events, on the productivity of regional crop farming and livestock in Greece, and the broader socio-economic effects on the agri-food and other sectors. Different approaches (i.e., agronomic models, statistical regression models, and equations linking thermal stress to livestock output) were combined to estimate the effects on productivity from changes in the average values of climatic parameters, and subsequently the direct economic effects from this long-term climate change. Recorded damages from extreme events together with climatic thresholds per event and crop were combined to estimate the direct economic effects of these extremes. The broader socio-economic effects were then estimated through input–output analysis. Under average levels of future extreme events, the total direct economic losses for Greek agriculture due to climate change will be significant, from EUR 437 million/year to EUR 1 billion/year. These losses approximately double when indirect effects on other sectors using agricultural products as inputs (e.g., food and beverage, hotels, and restaurants) are considered, and escalate further under a tenfold impact of extreme events. Losses in the GDP and employment are moderate at the national level, but significant in regions where the contribution of agriculture is high.

Streets classification models by urban features for road traffic noise estimation

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

Determination of Mouth Width for Facial Reconstruction Based on Statistical Regression Model.

It is important to generate predictable statistical models by increasing the number of variables on the human skeletal and soft tissue structures on the face to increase the accuracy of human facial reconstructions. The purpose of this study was to determine mouth width 3-dimensionally based on statistical regression model. Cone-beam computed tomography scan data from 130 individuals were used to measure the horizontal and vertical dimensions of orbital and nasal structures and intercanine width. The correlation between these hard tissue variables and the mouth width was evaluated using the statistical regression model. Orbital width, nasal width, and intercanine width were found to be strong predictors of the mouth width determination and were used to generate the regression formulae to find the most approximate position of the mouth. These specific variables may contribute to improving the accuracy of mouth width determination for oral and maxillofacial reconstructions.

Statistical analysis, regression, and neural network modeling of the tensile strength of thermoplastic unidirectional carbon fiber-polysulfone composites

Statistical analysis, regression, and neural network modeling of the tensile strength of thermoplastic unidirectional carbon fiber-polysulfone composites

Post-COVID Trajectory of Pentraxin 3 Plasma Levels Over 6 Months and Their Association with the Risk of Developing Post-Acute Depression and Anxiety.

Clinical manifestations of coronavirus disease 2019 (COVID-19) often persist after acute disease resolution. Underlying molecular mechanisms are unclear. The objective of this original article was to longitudinally measure plasma levels of markers of the innate immune response to investigate whether they associate with and predict post-COVID symptomatology. Adult patients with previous severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection during the first pandemic wave who underwent the 6-month multidisciplinary follow-up were included. Plasma levels of pentraxin 3 (PTX3), the complement components C3a and C5a, and chitinase-3 like-protein-1 (CHI3L1) were measured at hospital admission during acute disease (baseline) and at 1 and 6 months after hospital discharge. Associations with post-COVID-19 sequelae at 6 months were investigated using descriptive statistic and multiple regression models. Ninety-four COVID-19 patients were included. Baseline PTX3, C5a, C3a, and CHI3L1 did not predict post-COVID-19 sequelae. The extent of the reduction of PTX3 over time (delta PTX3) was associated with lower depressive and anxiety symptoms at 6 months (both p < 0.05). When entering sex, age, need for intensive care unit or non-invasive ventilation during hospital stay, psychiatric history, and baseline PTX3 as nuisance covariates into a generalized linear model (GLM), the difference between baseline and 6-month PTX3 levels (delta PTX3) significantly predicted depression (χ2 = 4.66, p = 0.031) and anxiety (χ2 = 4.68, p = 0.031) at 6 months. No differences in PTX3 levels or PTX3 delta were found in patients with or without persistent or new-onset other COVID-19 symptoms or signs at 6 months. Plasma levels of C3a, C5a, and CHI3L1 did not correlate with PTX3 levels at either time point and failed to associate with residual or de novo respiratory or systemic clinical manifestations of the disease at 6 months. A lower reduction of plasma PTX3 after acute COVID-19 associates with the presence of depression and anxiety, suggesting an involvement of inflammation in post-COVID-19 psychopathology and a potential role of PTX3 as a biomarker.

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

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.

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.

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.

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.