Polynomial Regression Research Articles

Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information

Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method.
Approach: By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on POIs as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo (MC) simulation data for MEICC detectors that were compared with conventional PCDs. 
Main-Results: For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with > 3% for the polynomial fitting method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error (NRMSE). The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data.
Significance: We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and standard deviation in VMAI estimation with the same patch strategy.&#xD.

Congruence and Discrepancy: Matching Effect of Searching and Integration on Green Product Development Performance

The development of green products in manufacturing enterprises is challenged by limited independent innovation capabilities and insufficient green technology. Based on knowledge-based theory, this study investigates the influence of boundary-spanning green technology search (BGTS) and technology integration capability (TIC) on green product development performance (GPDP). Using polynomial regression analysis with response surface methodology (PRA with RSM) on data from 341 companies, the following findings were obtained: (1) Higher levels of BGTS and TIC congruence are associated with higher GPDP compared to lower levels of congruence. (2) The degree of discrepancy between the BGTS and TIC has an inverted U-shaped relationship with GPDP. (3) GPDP is higher in the “low BGTS–high TIC” combination than in the “high BGTS–low TIC” combination. (4) Green technology innovation capability mediates the relationship between BGTS-TIC congruence and GPDP. (5) Strategic foresight positively moderates the relationship between the BGTS-TIC congruence and green technology innovation capability. These findings enrich the research content of organizational search theory, deepen the research of green technology innovation, expand research on the development of green products, and provide a decision-making reference for manufacturing enterprises’ green transition practices.

Effect of healthy dietary intention-behavior (in)consistency on depression and anxiety in the process of behavior change.

Although an increasing number of people are now committed to pursuing a healthy diet, it remains unclear how mental health changes during this process. The present study aimed to examine the combined effect of healthy dietary intention and behavior on depression and anxiety across two sub-studies. This study consisted of two parts: a cross-sectional survey (Study 1) involving 1,433 college students and an 11-day daily diary study (Study 2) with 117 college students. Polynomial regression and response surface analysis indicated that in cases of congruence, individuals' intention and behavior align at a high level and were associated with lower levels of depression and anxiety in the motivational phase but were not found similar association in the volitional phase. In cases of incongruence, individuals with high intention-low behavior tend to experience lower levels of depression and anxiety in the motivational phase. However, individuals with high intention-low behavior tend to experience higherlevels of depression and anxiety in the volitional phase. These findings not only enrich the current understanding of diet and health but also contribute to the development of holistic strategies for promoting a healthy diet.

Unveiling the outcome of multispectral indices in evaluating total suspended solid of water quality

Constant monitoring of TSS in water body remains essential to avoid excessive suspended sediment that can damage water quality. This study examines TSS as a visible indicator of water quality parameter in Hartbeespoort Dam located in the Northwest of South Africa. The study employed multi-spectral spatial bands of Landsat 8–9 (OLI & TIR) and MSI of Sentinel-2A and 2B. Band combination indices such as NDSSI, WRSI was utilized respectively using Landsat 8–9 and MSI Sentinel 2. Nechad TSS algorithm and Automated Water Extraction Index (AWEI) was also utilized with green band and red band of Landsat 8–9 and Sentinel 2 to further understand the concentration of suspended solid. The values derived was correlated with real time in-situ data measurement. NDSSI mapping of TSS showed high values range of 0.895 (suspended solid) while WRSI in Sentinel 2 spatial band showed highest value of 0.551 (suspended solid). The regression analysis utilizes Linear, Exponential and polynomial equation to validate with the in-suit data measurement. The result revealed a strong correlation such as (NDSSI and in-suit =(R2 = 0.54) in polynomial equation, WRSI and in-suit = (R2 = 0.71) in exponential regression, Nechad algorithm and in-suit = (R2 = 0.54) in polynomial regression and AWEI with in-suit = (R2 = 0.71) in exponential regression. The value derived from Nechad TSS algorithm and AWEI confirmed the capacity of NDSSI indices in Landsat 8–9 and WRSI in Sentinel 2 remote sensor.

Regression Analysis and Optimum Values of Austempering Affecting Mechanical Properties of Compacted Graphite Iron.

The study examined the effect of heat treatment parameters of compacted graphite iron (CGI) on the mechanical properties of the material. The microstructure was characterized using optical microscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Three levels of heat treatment parameters were adopted considering the orthogonal test plan 24. The effects of austenitizing temperature and time and austempering on tensile strength, yield strength, and elongation were analyzed. Polynomial regression was chosen because it extends linear regression and allows for modeling more complex, nonlinear relationships between variables. Total regression models were determined for each dataset. The models for tensile strength (Rm) had an approximately 82% coefficient of determination, for yield strength (R0.2) around 50%, and for elongation (A5) around 80%. For optimization, the response surface method (RSM) was used. The results obtained were compared with the proposed mathematical models. The ANOVO results showed that austempering temperature (Tpi) had the greatest effect on each parameter studied. The optimal conditions for the analyzed parameters, assuming tensile strength and yield strength at the maximum level and an elongation of about 0.7%, are obtained for the following heat treatment parameters: Tγ = 890 °C; Tpi = 290 °C; τγ = 120 min; τpi = 150 min.

Novel eco-friendly HPTLC method using dual-wavelength detection for simultaneous quantification of duloxetine and tadalafil with greenness evaluation and application in human plasma

A novel, environmentally friendly, and sensitive HPTLC method has been developed and validated for simultaneous quantification of duloxetine (DLX) and tadalafil (TDL) in their pure state, laboratory-prepared mixtures, and spiked human plasma. This method is particularly important for patients dealing with depression and sexual issues, as it allows for the measurement of these co-administered antidepressant and sexual stimulant drugs in biological fluids. The separation process employed a stationary phase of pre-coated silica gel 60 F254 and a mobile phase consisting of ethyl acetate, acetonitrile, and 33% ammonia (8:1:1, v/v). The optimized mobile phase resulted in well-defined bands for DLX and TDL, with Rf values of 0.3 and 0.8, respectively with dual-wavelength detection at 232 nm for DLX and 222 nm for TDL. Polynomial regression analysis revealed exceptional linearity for both drugs, with correlation coefficients of 0.9999 over concentration ranges of 10–900 ng/band for DLX and 10-1200 ng/band for TDL. The quantitation limits were 8.2 ng/band for DLX and 8.6 ng/band for TDL, while the detection limits (LOD) were 2.7 ng/band for DLX and 2.8 ng/band for TDL. The validation of this method followed the guidelines set by the International Council for Harmonization (ICH). Additionally, the suggested method’s greenness was assessed by means of four up-to-date ecological tools, namely the Eco-Scale, the National Environmental Method Index (NEMI), the Green Analytical Procedure Index (GAPI), and the Analytical Greenness metric approach (AGREE). The proposed method was also assessed using the Blue Applicability Grade Index (BAGI), a recently developed metric for assessing the practicality (blueness) of procedures.

A Machine Learning-Based Algorithm for the Prediction of Eigenfrequencies of Railway Bridges

As part of the development of advanced, data-driven methods for predictive maintenance of railway infrastructure, this paper analyzes and evaluates more realistic predictions of eigenfrequencies of railway bridges, also referred to as natural frequencies, based on a population of already assessed, measured existing bridges using regression techniques. For this purpose, Machine Learning (ML) techniques such as Polynomial Regression (PR), ANN and XGBoost are consistently evaluated and the application of the XGBoost algorithm is identified as the most suitable prediction model for these eigenfrequencies, usable for dynamic train-bridge interactions. The results of the post-processing are incorporated into the safety architecture for bridge verification (risk management). The presented data-based techniques are a steppingstone towards digitalization of structural health monitoring and offer safety and longevity of the railway bridges. Furthermore, the use of these methods can save costs that would be incurred by physical in-situ measurements. The types of bridges analyzed with ML are Filler Beam Bridges (FBE), which outnumber other construction types of bridges in Germany (DB InfraGO AG). This methodology is applicable to any bridge type as long as sufficient data are gathered for training, validation and testing.

Prediction of wall geometry forcold-metal-transfer-based wire-arc additive manufacturing

Wire-arc additive manufacturing (WAAM) is an advanced technique for fabricating large metal components through layer-by-layer material deposition using arc welding methods. This study focused on optimizing the WAAM process by employing machine learning models to predict and control bead geometries, specifically bead height (BH) and bead width (BW), while ensuring consistent height increments in multibead walls. Based on CMT technology in cold metal transfer experiments, linear regression models achieved high accuracy in predicting BH and BW. Analysis of variance results highlighted the considerable influence of voltage (V) and travel speed (TS) on bead geometries. For multibead wall characteristics, polynomial regression models incorporating non-linear terms, such as travel speed (TS²) and dwell time (Dt²), were developed to predict height (H) and waviness (W). Various optimization metrics were employed to balance the trade-offs between H and W for identifying optimal welding conditions that achieved the target H while minimizing W. A notable innovation of this research is the optimization of dwell time (Dt) for each layer to achieve a linear incremental H profile, minimizing W and ensuring consistent layer quality.

Metabolomic profiling reveals the step-wise alteration of bile acid metabolism in patients with diabetic kidney disease

BackgroundDiabetic kidney disease (DKD) is the major complication of diabetes concomitant with gut dysbiosis and glycometabolic disorder, which are strongly associated with bile acid (BA) metabolism. Yet studies investigating the BA metabolism involving in DKD pathogenesis are limited. This study aimed to explore the metabolomic profiling of BAs in DKD and analyze its association with DKD progression.MethodsAn ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method was established to quantify BAs in the plasma, fecal and urine samples of patients with DKD or T2DM and healthy individuals (n = 30 for each group). The key BAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and receiver-operating characteristic (ROC) curve. Polynomial regression and Pearson’s correlation analyses were performed to assess the correlation between the key BAs and the clinical indicators reflecting DKD progression.ResultsMetabolomic profiling of 50 kinds of BAs presented the markedly step-wise alterations of BAs in plasma and feces as well as the little in urine of patients with DKD. Eight kinds of BAs in the plasma, eight kinds in the feces and three kinds in the urine were abnormally expressed, accompanying with the increased conjugated/unconjugated ratios of cholic acid, deoxycholic acid, chenodeoxycholic acid, ursodeoxycholic acid and hyocholic acid in the plasma, and of cholic acid, chenodeoxycholic acid and lithocholic acid in the feces. Moreover, the increased plasma level of glycochenodeoxycholic acid, and the increased fecal levels of glycolithocholic acid, 7-ketodeoxycholic acid and chenodeoxycholic acid-3-β-D-glucuronide are strongly correlated with the clinical indicators reflecting DKD progression, including eGFR, 24 h urinary protein and 24 h urinary microalbumin.ConclusionsOur study for the first time disclosed the specific alterations of BA metabolism reflecting the step-wise progression of DKD, providing the basis for early identification and therapeutical strategies for DKD.

Modeling of the effect of gradation and compaction characteristics on the california bearing ratio of granular materials for subbase and landfill liner construction

The California bearing ratio (CBR) of a granular materials are influence by the soil particle distribution indices such as D10, D30, D50, and D60 and also the compaction properties such as the maximum dry density (MDD) and the optimum moisture content (OMC). For this reason, the particle packing and compactibility of the soil play a big role in the design and construction of subbases and landfills. In this research paper, experimental data entries have been collected reflecting the CBR behavior of granular soil used to construct landfill and subbase. The database was utilized in the ratio of 78–22% to predict the CBR behavior considering the artificial neural network (ANN), the evolutionary polynomial regression (EPR), the genetic programming (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and the response surface methodology (RSM) intelligent learning and symbolic abilities. The relative importance values for each input parameter were carried out, which indicated that the (CBR) value depends mainly on the average particle size (D30, 50 & 60). They showed a combined influence index of 66% of the considered parameters in the model exercise. This further shows the importance and structural influence of the particles within the D50 and D60 range in a granular material consistency in the design and construction purposes. Performance indices were also used to study the ability of the models. The ANN model showed the best performance with accuracy of 88%, then GP, EPR and RF with almost the same accuracies of 85% and lastly the XGBoost with accuracy of 81%. Also, the RSM produced an R2 of 0.9464 with a p-value of less than 0.0001. These values show that the ANN produced the decisive model with the superior performance indices in the forecast of CBR of granular material used as subbase and waste compacted earth liner material. The results further show that optimal performance of the CBR depended on D50 and D60 for the design of subgrade, subbase, and liner purposes and also during the performance monitoring phase of the constructed flexible pavement foundations and compacted earth liners.

UWB-Assisted Bluetooth Localization Using Regression Models and Multi-Scan Processing.

Bluetooth devices have been widely used for pedestrian positioning and navigation in complex indoor scenes. Bluetooth beacons are scattered throughout the entire indoor walkable area containing stairwells, and pedestrian positioning can be obtained by the received Bluetooth packets. However, the positioning performance is sharply deteriorated by the multipath effects originating from indoor clutter and walls. In this work, an ultra-wideband (UWB)-assisted Bluetooth acquisition of signal strength value method is proposed for the construction of a Bluetooth fingerprint library, and a multi-frame fusion particle filtering approach is proposed for indoor pedestrian localization for online matching. First, a polynomial regression model is developed to fit the relationship between signal strength and location. Then, particle filtering is utilized to continuously update the hypothetical location and combine the data from multiple frames before and after to attenuate the interference generated by the multipath. Finally, the position corresponding to the maximum likelihood probability of the multi-frame signal is used to obtain a more accurate position estimation with an average error as low as 70 cm.

Predicting and Evaluating Water Quality using Machine Learning in Maharashtra, India

Water quality is critical for public health and environmental sustainability, necessitating effective monitoring to prevent contamination. In this study, we focus on predicting and evaluating water quality in Maharashtra, India, using machine learning techniques. Groundwater contamination in Maharashtra is a significant issue due to poor waste management, yet research in this area is limited. Traditional water quality monitoring methods involve complex calculations based on fixed parameters, which can lead to errors. This study aims to streamline the monitoring process by identifying the most significant features, thereby saving time, money, and energy. We calculated the Water Quality Index (WQI) using the Weighted Arithmetic Mean method, analyzing data from 2012 to 2022 from the National Water Monitoring Program in India. The analysis identified three key parameters, BOD, pH, and Fecal Coliform, as most correlated with the WQI. Machine learning techniques, including regression and classification, were employed to predict WQI and Water Quality Classification (WQC). The results indicate that Polynomial Regression and Ridge Regression achieved high accuracy in predicting the WQI, while the Decision Tree classifier excelled in WQC classification. This research demonstrates the potential of machine learning to enhance water quality monitoring, offering a cost-effective solution for managing water resources in Maharashtra.

МІЖНАРОДНЕ ІНВЕСТИЦІЙНЕ СПІВРОБІТНИЦТВО ТА ЙОГО РОЛЬ У ПОВОЄННОМУ ВІДНОВЛЕННІ ПРИМОРСЬКИХ ТЕРИТОРІЙ УКРАЇНИ

The article is devoted to the study of the scientific and theoretical foundations of international investment cooperation and the analysis of its impact on the economic development of the coastal areas of Ukraine. It is conducted the critical review of existing definitions of the studied concept. It is proposed the proper definition that considers the socio-economic nature of such cooperation. International investment cooperation should be understood as economic relations formed as a result of international investment and mediating the movement of international investments. It is defined the main purpose of the investigated phenomenon, which consists in making a profit. It is emphasized the key difference between international investment cooperation and other forms of cross-border movement of capital by the presence of a commercial basis that accompanies this process in one way or another. It is considered previous developments in formulating the principles of investment cooperation. It is proposed own list of principles for its implementation: economic expediency, freedom and equality of parties, fair distribution of benefits and risks, regulatory and tax coordination, social, environmental and ethical responsibility, strategic, transparent and contractual nature. By constructing polynomial regression equations of the dependence of the gross product of the coastal regions of Ukraine on direct foreign investment, the regularities of the influence of international investment cooperation on the economic development of the respective territories are investigated. It is determined that such cooperation is characterized by its fundamental importance for the economic growth, its direct impact on the regional economic complex, the marginal possibilities of rational placement of international investments and the unevenness of the positive effect they generate. It is substantiated the practical advantages and limitations of the potential increase in the volume of international investment in the post-war period.

How Channel Integration Benefits Manufactures’ Online Market Entry Agility? The Mediating Role of dealers’ Dependency Behavior

ABSTRACT Purpose The effectiveness of channel integration performed by manufacturers in facilitating their online market entry is still an under-researched topic. This study fills the research gap by investigating and comparing the effects of channel integration strategies and their components implemented by manufacturers. Methodology/approach A questionnaire was designed and distributed to purchasing managers in downstream dealers. The survey yielded 2051 valid responses, and data analysis employed hierarchical and polynomial regressions, along with structural equation modeling and response surface analyses. Findings First, dealers’ dependence plays a crucial role in triggering manufacturers’ online market entry. Second, manufacturers’ channel consistency and transparency are more relevant to dealers’ active dependence, while information and interaction integration lead to dealers’ passive dependence. Third, channel ambidexterity is superior to integration ambidexterity in ensuring dealers’ dependence. Originality/value Significant contributions to omnichannel integration research are made by introducing four distinct types of channel integration strategies. Enhancements to the interfirm dependence literature are achieved through a comparative analysis of the effectiveness of channel integration strategies in shaping dealers’ dependency behavior. Implications Manufacturers should carefully evaluate the effectiveness of these strategies, avoiding a blind pursuit of all forms of channel integration ambidexterity. Strategic leverage of channel feature ambidexterity is recommended as a cornerstone for fostering dealers’ dependence and achieving success in online market entry.

Emotion regulation motives in adolescence shape regulatory strategy use.

Emotion regulation (ER) motives (i.e., the reasons individuals regulate their emotions) are key factors influencing the development of adolescents' ER abilities. However, age- and gender-related trends in adolescents' ER motives, as well as their impact on the use of ER strategies, remain unclear. We recruited 5629 participants from two schools in Southwest China (M = 15.18, SD = 1.73; 45.11% male) to complete the Emotion Regulation Goals Scale and the Emotion Regulation Questionnaire. Linear and polynomial regression analyses were conducted to examine age- and gender-related differences in ER motive patterns. We then tested whether different patterns of ER motives were also associated with participants' habitual use of two common ER strategies. This study revealed nonlinear and diverse age-related differences in adolescents' contra-hedonic and pro-social motives, with notable fluctuations in contra-hedonic motives. Boys exhibited higher levels of contra-hedonic motives, while girls showed higher levels of performance motives. Moreover, ER motives were significantly and broadly associated with individuals' habitual use of reappraisal and suppression. More specifically, pro-hedonic motives were associated with greater use of reappraisal, while contra-hedonic motives were linked to higher levels of suppression. Furthermore, instrumental motives (i.e., performance, pro-social, and impression management) were positively related to both reappraisal and suppression. These findings enhance our understanding of how adolescents' ER motives vary by age and gender, as well as the crucial role different ER motives play in shaping patterns of ER strategy use.

Development of analytical “aroma wheels” for Oolong tea infusions (Shuixian and Rougui) and prediction of dynamic aroma release and colour changes during “Chinese tea ceremony” with machine learning

The flavour of tea as a worldwide popular beverage has been studied extensively. This study aimed to apply established flavour analysis techniques (GC–MS, GC-O-MS and APCI-MS/MS) in innovative ways to characterise the flavour profile of oolong tea infusions for two types of oolong tea (type A- Shuixian, type B- Rougui). GC–MS identified 48 aroma compounds, with type B having a higher abundance of most compounds. GC-O-MS analysis determined the noticeable aroma difference based on 20 key aroma compounds, facilitating the creation of an analytical “Aroma Wheel” with 8 key odour descriptors. APCI-MS/MS assessed real-time aroma release during successive brews linked with the “Chinese tea ceremony” (Gongfu Cha). Multivariate Polynomial Regression (MPR) and Long Short-Term Memory (LSTM) network approaches were applied to aroma and colour data from seven successive brews. The results revealed a progressive decline in both colour and aroma with seven repeated brews, particularly notable after the fourth brew.

How Blockchain-Enabled Drivers Stimulate Consumers’ Organic Food Purchase Intention: An Integrated Framework of Information Systems Success Model Within Stimulus-Organism-Response Theory in the Context of Vietnam

Integrating the Information System Success (ISS) model with the Stimulus-Organism-Response (SOR) paradigm, this study aims to explore how blockchain-enabled stimuli (blockchain-enabled traceability and blockchain-enabled transparency) affect ISS-related organisms (perceived information quality and perceived system quality), which individually, congruently, and incongruently trigger consumers’ behavioral responses (organic food purchase intentions). Using a purposive sampling of 5326 Vietnamese consumers, multiple linear regression and polynomial regression with response surface analysis are employed to examine the hypothesized model. The research indicates that both blockchain-enabled traceability and blockchain-enabled transparency significantly and positively affect perceived information quality and perceived system quality, which both significantly mediate the impacts of blockchain-enabled traceability and blockchain-enabled transparency on organic food purchase intention. The research also finds that higher congruence between perceived information quality and perceived system quality leads to increased organic food purchase intention, while greater disparity between perceived information quality and perceived system quality results in lower organic food purchase intention. This study contributes to the extant literature by integrating the ISS model with the SOR model to provide a comprehensive framework for understanding how blockchain technology affects consumer behavior in the organic food market. It also highlights the crucial role of both information and system quality in leveraging blockchain technology to enhance consumer trust and purchase intentions.

Prediction of Fan Array Performance with Polynomial and Support Vector Regression Models

The increasing utilisation of demand-controlled ventilation strategies leads to the frequent operation of fans under part-load conditions. To accurately predict the energy demand of a ventilation system with a fan array in the early design stages, models that calculate reliable results across the whole operating range are required. We present the comparison of a polynomial and a machine learning approach through support vector regression (SVR) to predict the fan performance over a wide range of typical operating points. For fitting and validation, we use experimental data. We investigate the extrapolation performance of both approaches. The SVR model achieves a slightly better representation of the experimental data with a lower error, especially when only sparse data are available. Both approaches yield similar results when the evaluation is conducted within the experimentally captured domain but deviates outside the domain. At operating points that are far from the experimentally captured domain, the polynomial models yield fan efficiencies that are physically plausible, while the SVR models drastically overpredict the fan efficiency. To rate the influence of such deviations towards modelling the actual energy demand, both approaches are applied to an operation simulation of a simplified office building. Both approaches yield similar results despite differing extrapolation capabilities.

Impacts of dietary Ajwain (Trachyspermum ammi L.) on growth, antioxidative capacity, immune responses, and intestinal histology of grey mullet (Liza ramada)

This study investigated the impacts of dietary supplements with ajwain seed extract (ASE) on the growth, biochemical parameters, digestive enzymes, antioxidant activity, immunity, and intestine histology of Liza ramada. Juveniles (N = 450) with an initial weight of 40.12 ± 0.14 g were fed six experimental diets enriched with ASE at 0 (control), 0.2, 0.4, 0.6, 0.8, and 1.0 g/kg diet for 60 days. Results revealed that fish-fed diets containing ASE at levels of 0.6 g/kg or higher exhibited significantly improved growth parameters including final weight, weight gain percentage, and specific growth rate, along with enhanced feed conversion efficiency compared to lower ASE levels and the control diet. Survival rates remained similar across all experimental groups. Digestive enzyme activity, particularly lipase, was highest in groups fed higher ASE levels (≥ 0.4 g/kg diet). Histomorphological analysis of the intestine showed improved villi structures with increasing ASE supplementation, reaching an optimal appearance at 0.8 g/kg. Blood biochemical analysis indicated higher total protein and globulin concentrations, and lower total cholesterol levels in groups with higher ASE intake (≥ 0.4 g/kg diet). Immune parameters increased with increasing ASE concentrations, including lysozyme activity, bactericidal percentage, and nitroblue tetrazolium levels. Antioxidant status, measured through superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and malondialdehyde (MDA) levels, showed significant improvements with ASE supplementation, with optimal dietary ASE levels for maximizing SOD (0.73 g/kg), CAT (0.74 g/kg), GPx (0.83 g/kg), and minimizing MDA (0.73 g/kg) identified through polynomial regression analysis. These findings suggest that ASE supplementation (0.6–0.8 g/kg) positively influences the growth performance, digestive function, gut health, immunity, and antioxidant status of L. ramada, highlighting its potential as a beneficial dietary additive in aquaculture.

Turkey’s Hydropower Potential in the Near Future and the Possible Impacts of Climate Change—A Case Study of the Euphrates–Tigris Basin

Hydropower is becoming an important renewable energy source in Turkey, but the ever-changing atmospheric and climatic conditions of Turkey make it very difficult to be projected efficiently. Thus, an efficient estimation technique is crucial for it to be adopted as a reliable energy source in the future. This study evaluates Turkey’s hydropower potential in the Euphrates–Tigris Basin under changing climatic conditions. We adapted an empirical equation to model reservoir outflows, considering the site-specific characteristics of 14 major dams. Initial results from employing a model with a constant empirical coefficient, α, yielded moderate predictive accuracy, with R2 values ranging from 0.289 to 0.612. A polynomial regression identified optimal α values tailored to each dam’s surface area, significantly improving model performance. The adjusted α reduced predictive bias and increased R2 values, enhancing forecast reliability. Seasonal analysis revealed distinct hydropower trends: Ataturk Dam showed a notable decrease of 5.5% in hydropower generation up to 2050, while Birecik and Keban Dams exhibited increases of 2.5% and 2.2%, respectively. By putting these discoveries into practice, water resource management may become more robust and sustainable, which is essential for meeting Turkey’s rising energy needs and preparing for future climatic challenges. This study contributes valuable insights for optimizing reservoir operations, ensuring long-term hydropower sustainability, and enhancing the resilience of water resource management systems globally.