Search Results (21)

Engineered wetland has been used as a Best Management Practice (BMP) to remove pollutants and maintain water quality in watersheds. This study is focused on developing models to analyze the impacts of discharges on the efficiency of wetlands to improve water quality downstream. The watershed hydrological Soil & Water Assessment Tool (SWAT) and wetland (Personal Computer Storm Water Management Model—PCSWMM) models were developed to analyze the efficiency of engineered wetlands to remove the pollutants for different basins under three different climatic conditions (i.e., dry, average and wet year). The SWAT was calibrated and validated to simulate discharge and water quality parameters. The wetland model was developed using inflow hydrographs and concentrations of the water quality parameters biochemical oxygen demand (BOD), total suspended solids (TSSs), total nitrogen (TN) and total phosphorous (TP), simulated from a Soil & Water Assessment Tool (SWAT) model. A PCSWMM (wetland) was developed based on the physical and first order decay process within the wetland system for three basins in Prairie du Pont watershed in Illinois, USA. The results showed that pollutant removal efficiencies decreased from low to high discharges (dry to wet climatic conditions) for all watersheds and pollutants (except for BOD) based on trendline analysis. Nevertheless, the efficiencies were highly variable, specifically during low discharges. Furthermore, the sensitivity of the k-parameter (areal rate constant) was pollutant dependent. Overall, this study is helpful to understand the efficacy of wetlands’ pollutant removal as a function of discharge. The approach can be used in watersheds located in other geographic regions for the preliminary design of engineered wetlands to remove non-point source pollution and treat stormwater runoff. Full article

The Oligocene Dongying Formation beach-bar system, widely distributed in the HHK Depression of the Bohai Bay Basin, constitutes a key target for mid-deep hydrocarbon exploration, though its provenance remains controversial due to complex peripheral source terrains. To address this, we developed an integrated methodology combining LA-ICP-MS zircon U-Pb dating with whole-rock rare earth element (REE) analysis, facilitating provenance studies in areas with limited drilling and heavy mineral data. Analysis of 849 high-concordance zircons (concordance >90%) from 12 samples across 5 wells revealed that Geochemical homogeneity is evidenced by strongly consistent moving-average trendlines of detrital zircon U-Pb ages among the southern/northern provenances and the central uplift zone, complemented by uniform REE patterns characterized by HREE (Gd-Lu) enrichment and LREE depletion; geochemical disparities manifest as dual dominant age peaks (500–1000 Ma and 1800–3100 Ma) in the southern provenance and central uplift samples, contrasting with three distinct peaks (65–135 Ma, 500–1000 Ma, and 1800–3100 Ma) in the northern provenance; spatial quantification via multidimensional scaling (MDS) demonstrates closer affinity between the southern provenance and central uplift (d_ij = 4.472) than to the northern provenance (d_ij = 6.708). Collectively, these results confirm a dual (north–south) provenance system for the central uplift beach-bar deposits, with the southern provenance dominant and the northern acting as a subsidiary source. This work establishes a dual-provenance beach-bar model, providing a universal theoretical and technical framework for provenance analysis in hydrocarbon exploration within analogous settings. Full article

Forest ecosystems in the Mediterranean basin are significantly affected by summer wildfires. Drought, extreme temperatures, and strong winds increase the fire risk in Greece. This study explores the potential of NDVI for assessing and forecasting post-fire regeneration in burnt areas of the Peloponnese (2007) and Evros (2011). NDVI data from Landsat 7 and 9 were analyzed to identify the stages of the regeneration process and the dominant vegetation species at each stage. Comparing pre-fire and post-fire values highlighted the recovery rate, while the trendline slope indicated the regeneration rate. This combined analysis forms a methodology that allows drawing conclusions about the vegetation type that prevails after the fire. Validation was conducted using photointerpretation techniques and CORINE land cover data. The findings suggest that sclerophyllous species regenerate faster, while fir forests recover slowly and may be replaced by sclerophylls. To predict vegetation regrowth, two time series models (ARMA, VARIMA) and two machine learning-based ones (random forest, XGBoost) were tested. Their performance was evaluated by comparing the predicted and actual numerical values, calculating error metrics (RMSE, MAPE), and analyzing how the predicted patterns align with the observed ones. The results showed the overperformance of multivariate models and the need to introduce additional variables, such as soil characteristics and the effect of climate change on weather parameters, to improve predictions. Full article

Cellular heterogeneity, an inherent feature of biological systems, plays a critical role in processes such as development, immune response, and disease progression. Human mesenchymal stem cells (hMSCs) exemplify this heterogeneity due to their multi-lineage differentiation potential. However, their inherent variability complicates clinical use, and there is no universally accepted method for detecting and quantifying cell population heterogeneity. Dielectrophoresis (DEP) has emerged as a powerful electrokinetic technique for characterizing and manipulating cells based on their dielectric properties, offering label-free analysis capabilities. Quantitative information from the DEP spectrum, such as transient slope, measure cells’ transition between negative and positive DEP behaviors. In this study, we employed DEP to estimate transient slope of various cell populations, including relatively homogeneous HEK-293 cells, heterogeneous hMSCs, and cancer cells (PC3 and DU145). Our analysis encompassed hMSCs derived from bone marrow, adipose, and umbilical cord tissue, to capture tissue-specific heterogeneity. Transient slope was assessed using two methods, involving linear trendline fitting to different low-frequency regions of the DEP spectrum. We found that transient slope serves as a reliable indicator of cell population heterogeneity, with more heterogeneous populations exhibiting lower transient slopes and higher standard deviations. Validation using cell morphology, size, and stemness further supported the utility of transient slope as a heterogeneity metric. This label-free approach holds promise for advancing cell sorting, biomanufacturing, and personalized medicine. Full article

With decreases in cost accompanying advances in technology, renewable energy is becoming increasingly viable. Much software is available for the techno-economic analysis of energy systems, and HOMER Pro software is frequently applied for micro-grid and industrial analysis. Around the globe, techno-economic analyses of a variety of renewable energy systems have been undertaken using HOMER Pro Version 3.16.0. This study reviews the primary techno-economic findings of past research to investigate recent trends. Based on high-level trendline analysis, it appears that the costs of renewable energy systems have decreased in academic HOMER Pro-based literature. Of the articles analyzed, the LCOEs for 100% renewable energy systems have decreased from $0.91/kWh to $0.70/kWh, while the LCOEs for 0% renewable energy systems have increased from $0.74/kWh to $0.78/kWh. Full article

The first-line management of cervical dystonia (CD) symptoms is intramuscular injection of botulinum toxin type A (BoNTA). However, a comparison of safety among BoNTAs is difficult because, per regulatory authorities, units of BoNTA activity are not interchangeable. Dysphagia and muscle weakness are widely considered two key adverse events to monitor closely in the treatment of CD. This integrated analysis compared the safety of BoNTAs approved for CD in the US by evaluating relationships between the incidence of dysphagia and muscle weakness in prescribing information and the core neurotoxin content. Coefficients The coefficients of determination (R²) and trendlines were estimated via regression-based lines of best fit. Adverse drug reaction (ADR) rates were strongly correlated with core neurotoxin amounts for conventional BoNTAs (slope coefficients: dysphagia = 0.048, R² = 0.74; muscle weakness = 0.096, R² = 0.82). The published ADR rates at approved doses for conventional BoNTAs were higher compared with DaxibotulinumtoxinA (DAXI; DAXXIFY^®, Revance Therapeutics, Inc., Nashville, TN, USA) by core neurotoxin content. The use of a core neurotoxin amount was found to be an effective method for comparing the safety of BoNTA products. Current clinical trials suggest that DAXI, a novel BoNTA formulation, provides a potentially wider safety margin compared with other approved BoNTAs for CD. The lower amount of core neurotoxin administered at approved doses compared with conventional BoNTAs may explain low on-target ADRs like muscle weakness, whereas reduced diffusion from the injection site is thought to be responsible for low off-target ADRs like dysphagia. Full article

The objective of this research was to construct energy intensity forecasting models for key manufacturing industries, with a particular focus on “catching up” European economies. Future energy intensity values serve as the foundation for energy demand forecasts, which are essential inputs for the analysis of countries’ decarbonisation scenarios. The Lithuanian case is analysed in the context of its efforts to reach the economic development level of the most advanced European Union (EU) countries. The scientific literature and energy policy analysis, interdependence (correlation and regression), tendency and case analysis, logical economic reasoning, and graphical representation methods have been applied. The energy intensity forecasts until 2050 were based on historical statistical data of value added and final energy consumption of EU countries from 2000 to 2021. The analysis of historical trends revealed a remarkable decrease in industrial energy intensity in most EU countries, including Lithuania. Given the rapid pace of decline in historical energy intensity, the values observed in individual Lithuanian industries have already reached levels comparable to the most economically advanced EU countries. Four econometric trendlines were employed to construct forecasting models for energy intensity. The results for Lithuania demonstrated that the selected trendlines exhibited a high degree of fit with historical energy intensity data from the EU, as evidenced by their R² values. Furthermore, the forecasts were shown to be highly accurate, with their MAPEs remaining below 10% in most cases. Nevertheless, the logarithmic trendline was found to be the most accurate for forecasting energy intensity in total manufacturing (MAPE = 4.0%), non-metallic minerals (MAPE = 3.5%), and food, beverages, and tobacco (MAPE = 4.1%) industries, with the exponential trendline in the chemical industry (MAPE = 8.7%) and the moving average in the total manufacturing industry (MAPE = 4.0%), food industries (MAPE = 4.0%), and remaining aggregate industries (MAPE = 14.5%). It is forecasted that energy intensity could decline by 8 to 16% to 1.10–1.20 kWh/EUR in Lithuania’s manufacturing industries by 2050. Full article

Malaysian wind maps used in wind analysis and engineering design are dated back to the last 50 years of wind data. Cotemporally, the mean wind speed was used as the basic wind design and due to changes in the weather condition worldwide, wind monitoring assumption and structure design are jeopardized. Therefore, this study aims to map the wind based on the trend of the highest wind speed recorded. The wind speed was analyzed based on a trend basis. The study included 42 Malaysian Metrological Department weather stations and the annual wind speed was acquired based on the monthly highest wind speed (1990–2019). The data were then processed using the 95% confidence interval method to determine the mean of the month, and the annual wind trendline was obtained. The trendline was then clustered using Ward’s method with the assistance of the high-level programming language, PYTHON v3.11.6. The clustering analysis produced two clusters for the Malaysian Peninsula and two for Sabah and Sarawak. The recommendation is to use the highest wind speed recorded on the map as the design wind speed. The recommendation is expected to help experts to compensate for the uncertainties in wind speed during the design stage and avoid incidents in the field. Full article

This study describes the development of two highly sensitive immunosensor platforms for the trace determination of copper ions, Cu(II), in drinking water. These platforms were a microwell-based enzyme-linked immunosorbent assay (ELISA) and a kinetic exclusion assay (KinExA) with a KinExA^TM 3200 immunosensor. Both ELISA and KinExA were developed utilizing the same antibody and coating reagent. The antibody was a mouse monoclonal antibody, designated as 8D66, that specifically recognized Cu(II)-ethylenediamine tetraacetic acid complex (Cu(II)-EDTA) but did not recognize Cu(II)-free EDTA. The 8D66 monoclonal antibody was generated by the fusion of spleen cells of an immunized BALB/c mouse with SP2/0-Ag14 myeloma cells. The immunogen was a protein conjugate of Cu(II)-EDTA with keyhole limpet hemocyanin protein. The coating reagent was Cu(II)-EDTA covalently linked to bovine serum albumin protein (Cu(II)-EDTA-BSA). Both assays involved the competitive binding reaction between Cu(II)-EDTA complexes, formed in the sample solution, and Cu(II)-EDTA-BSA conjugate which has been immobilized onto ELISA plates (in ELISA) or polymethylmethacrylate beads (in KinExA) for a limited quantity of binding sites of the 8D66 antibody. In ELISA, color signals were generated by a peroxidase-labeled secondary antibody and 3,3′,5,5′-tetramethylbenzidine substrate. In KinExA, a fluorescein isothiocyanate-labeled secondary antibody was used to generate KinExAgram (trend-line fluorescence responses vs. time). The conditions of both ELISA and KinExA were investigated, and the optimum procedures were established. Both ELISA and KinExA were validated, and all validation parameters were acceptable. Many different metal ions that are commonly encountered in drinking water did not interfere with the Cu(II) analysis by both ELISA and KinExA. Both assays were applied to the determination of Cu(II) in drinking water with satisfactory accuracy and precision. Both assays were compared favorably with inductively coupled plasma atomic emission spectroscopy in terms of their abilities to accurately and precisely determine Cu(II) in drinking water samples. A comparative evaluation of ELISA and KinExA revealed that KinExA had a higher sensitivity and better precision than ELISA, whereas both assays had comparable accuracy. Both ELISA and KinExA were superior to the existing atomic spectrometric methods for Cu(II) in terms of sensitivity, convenience, and analysis throughputs. The proposed ELISA and KinExA are anticipated to effectively contribute to assessing Cu(II) concentrations and control the exposure of humans to its potential toxicities. Full article

The aims were to compare predicted maximal velocity from load–velocity relationships established with different resisted and assisted loads by different regression analyses to the measured maximal velocity during sprint running, and to compare maximal velocity measured between a robotic pulley system and laser gun. Sixteen experienced male sprinters performed regular 50 m sprints, a 50 m with 5-kilogram-assisted sprint, and 10, 20, 30, and 30 m resisted sprints with, respectively, 65, 50, 25, and 10% calculated reduction in maximal velocity. Maximal velocity obtained by laser gun during the regular sprint was compared with predicted maximal velocity calculated from four trendlines (linear and polynomial based upon four resisted loads, and linear and polynomial based upon four resisted and one assisted load). Main findings demonstrate that the robotic pulley system and laser measure similar maximal velocities at all loads except at the load of 10% velocity reduction. Theoretical maximal velocity based upon calculated predictions were underestimated by 0.62–0.22 m/s (2.2–0.78 km/h; 6.7–2.3%) compared to measured maximal velocity. It was concluded that different regression analyses underestimated measured maximal velocity in regular sprinting and polynomial regression analysis (with resisted and assisted loads) estimation was closest to measured velocity (2.3%). Full article

Climate parameter projections obtained by global and regional models (GCM and RCM, respectively) offer a challenge to many researchers in terms of controlling the quality of the outcome data using several scales. In the literature, the proposed models, namely statistical downscaled and regression-based models, are mostly used to adjust the RCM data series. Contrariwise, in practice, these conceptual models perform poorly in certain cases and at certain scales. In this regard, a new downscaling model is proposed herein for annual rainfall projection, based on fusion models, namely polynomial regression (Poly_R), classification and regression tree (CRT), and principal component regression (PCR). The proposed model downscales the rainfall data projected by the coupled model intercomparison phase five (CMIP5) under different representative concentration pathway (RCP) scenarios (2.6, 4.5, 6.0, and 8.5) using overlapping data between the observation and the CMIP5 historical data. This process aims to define the framework for how to use the output equations and algorithm to correct data forecasting by RCM. Generally, the model can be summarized into three levels of analysis, starting with an iterative downscaling using a trendline model that is obtained by Poly_R fitting. Then, the CRT is used to classify and predict the data in subsets. Finally, multiple regression is given by a PCR model using principal components and standardized variables. The final model is also used to downscale the predicted data obtained by both previous models. The results provide the best performance of the fusion model in all RCP cases, compared to the delta change correction and linear scale models. This performance is proved by R² scores which range between 0.87 and 0.95. Full article

The current study provides a thorough analysis and evaluation of fixed-wing UAV correlations with respect to NATO classification, region of production, entry-into-service (EIS) date and other operational specifications. A set of 202 existing fixed-wing platforms is used to populate an in-house database. A screening of the corresponding data is conducted using a correlations matrix, and a statistical analysis of the key UAV design parameters is, in turn, performed. The results are presented using a wide variety of charts and statistical coefficients, to provide as much information as possible for future UAV design and performance assessment studies. Correlations for each mission type are provided, followed by a detailed evaluation of the key design parameters and design ratios (wingspan, gross takeoff weight, empty weight, payload weight, endurance, and operational speeds) with respect to NATO classification and region of origin. These key parameters are then plotted as a function of EIS date for every NATO category to identify any underlying trends and, finally, the platforms are classified in regard to some qualitative attributes, such as mission type and low observability. The results suggest that the trendlines extracted for each category significantly deviate from the generic trends. Therefore, omitting the classification in terms of region, size and weight can lead to misleading outcomes and should be avoided. Another conclusion lies in the fact that, apart from the average trendline, the design engineers should also have an indication of the data variance, due to the high dispersion observed in the datasets of several design parameters. Full article

Following the first COVID-19 infected cases, Malta rapidly imposed strict lockdown measures, including restrictions on international travel, together with national social distancing measures, such as prohibition of public gatherings and closure of workplaces. The study aimed to elucidate the effect of the intervention and relaxation of the social distancing measures upon the infection rate by means of a trendline analysis of the daily case data. In addition, the study derived a predictive model by fitting historical data of the SARS-CoV-2 positive cases within a two-parameter Weibull distribution, whilst incorporating swab-testing rates, to forecast the infection rate at minute computational expense. The trendline analysis portrayed the wave of infection to fit within a tri-phasic pattern, where the primary phase was imposed with social measure interventions. Following the relaxation of public measures, the two latter phases transpired, where the two peaks resolved without further escalation of national measures. The derived forecasting model attained accurate predictions of the daily infected cases, attaining a high goodness-of-fit, utilising uncensored government-official infection-rate and swabbing-rate data within the first COVID-19 wave in Malta. Full article

The use of reclaimed asphalt pavement (RAP) is necessary for sustainable and cost-effective road infrastructure construction. This research investigates the effect of the area of weak zones (WZ) on the resilient modulus (M_RT) of mixtures of fresh asphalt with 20% RAP. Experimentation on fresh asphalt–RAP mixtures comprising Superpave (SP-A, SP-B) and Asphalt Institute (MS-2) gradations with 20/30, 40/50, 60/70 and 80/100 penetration grade binders was carried out. WZ were determined based on the analysis of magnified digital images of asphalt specimens obtained using optical microscopy. This study demonstrates that the 20/30 grade binder caused an increase in the M_RT at 25 °C up to 1.8, 2.9 and 9.2 times for a 0.1 s load duration, and 2.4, 3.0 and 9.7 times for a 0.3 s load duration. In contrast, improvement at 40 °C was observed to be up to 1.9, 3.1 and 9.7 times for a 0.1 s load duration, and 1.9, 3.0 and 12.4 times for a 0.3 s load duration in comparison with 40/50, 60/70 and 80/100 grade binders, respectively. Experimental data were validated by factorial analysis. Power trendline equations were also developed between M_RT and WZ to explain the effect of gravel particle orientation on the sustainable resilience of surface course mixtures. Full article

A long waiting period for available beds in emergency departments (EDs) is the major obstacle to a smooth process flow in ED services. We developed a new bed assignment information system that incorporates current strategies and resources to ease the bottleneck in the service flow. The study’s purpose was to evaluate the effect of the lean intervention plan. We included 54,541 ED patient visits in the preintervention phase and 52,874 ED patient visits in the postintervention phase. Segmented regression analysis (SRA) was used to estimate the level and trend in the preintervention and postintervention phases and changes in the level and trend after the intervention. After the intervention, the weekly length of stay (LOS) for patient visits, admitted patient visits, and nonadmitted patient visits decreased significantly by 0.75, 2.82, and 0.17 h, respectively. The trendline direction for overall patient visits and nonadmitted patient visits significantly changed after the intervention. However, no significant change was noted for admitted patient visits, although the postintervention trend visually differed from the preintervention trend. The concept of lean intervention can be applied to solve various problems encountered in the medical field, and the most common approach, SRA, can be used to evaluate the effect of intervention plans. Full article