Comprehensive Parameter Research Articles

Blood urea nitrogen to albumin ratio is associated with cerebral small vessel diseases

Blood urea nitrogen (BUN) to albumin ratio (BAR) is a comprehensive parameter that reflects renal, inflammatory, nutritional, and endothelial functions. BAR has been shown to be associated with various cancers, pneumonia, sepsis, and pulmonary and cardiovascular diseases; however, few studies have been conducted on its association with cerebrovascular diseases. In this study, we evaluated the association between BAR and cerebral small vessel disease (cSVD) in health check-up participants. We assessed consecutive health check-up participants between January 2006 and December 2013. For the cSVD subtype, we quantitatively measured the volume of white matter hyperintensity (WMH) and qualitatively measured the presence of lacune and cerebral microbleeds (CMBs). The BAR was calculated by dividing BUN by albumin as follows: BAR = BUN (mg/dl)/albumin (g/dl). A total of 3012 participants were evaluated. In multivariable linear regression analysis, BAR showed a statistically significant association with WMH volume after adjusting for confounders [β = 0.076, 95% confidence interval (CI): 0.027–0.125]. In multivariable logistic regression analyses, BAR was significantly associated with lacunes [adjusted odds ratio (aOR) = 1.20, 95% CI: 1.00–1.44] and CMBs (aOR = 1.28, 95% CI: 1.06–1.55). BAR was associated with all types of cSVD in the health check-up participants.

Development of a Machine-Learning-Based Tool for Overnight Orthokeratology Lens Fitting.

Orthokeratology (ortho-K) is widely used to control myopia. Overnight ortho-K lens fitting with the selection of appropriate parameters is an important technique for achieving successful reductions in myopic refractive error. In this study, we developed a machine-learning model that could select ortho-K lens parameters at an expert level. Machine-learning models were established to predict the optimal ortho-K parameters, including toric lens option (toric or non-toric), overall diameter (OAD; 10.5 or 11.0 mm), base curve (BC), return zone depth (RZD), landing zone angle (LZA), and lens sagittal depth (LensSag). The analysis included 547 eyes of 297 Korean adolescents with myopia or astigmatism. The dataset was randomly divided into training (80%, n = 437 eyes) and validation (20%, n = 110 eyes) sets at the patient level. The model was trained based on clinical ortho-K lens fitting performed by highly experienced experts and ophthalmic measurements. The final machine-learning models showed accuracies of 92.7% and 86.4% for predicting the toric lens option and OAD, respectively. The mean absolute errors for the BC, RZD, LZA, and LensSag predictions were 0.052 mm, 2.727 µm, 0.118°, and 5.215 µm, respectively. The machine-learning model outperformed the manufacturer's conventional initial lens selector in predicting BC and RZD. We developed an expert-level machine-learning-based model for determining comprehensive ortho-K lens parameters. We also created a web-based application. This model may provide more accurate fitting parameters for lenses than those of conventional calculations, thus reducing the need to rely on trial and error.

On optimization of Spin-Forming Process Parameters for Magnesium Alloy Wheel Hub Based on Gray Relational Analysis.

To study the influencing factors of process parameters on the wall thickness deviation and internal warpage deviation of the workpiece in magnesium alloy wheel hub spin molding, a two-pass heterogeneous spin molding model is proposed. To ensure the accuracy of the simulation results, the stress-strain data of AZ31 magnesium alloy at different temperatures and different strain rates were obtained through tests. Wall thickness deviation and internal warp deviation after molding were used as evaluation indexes of workpiece molding quality. ABAQUS software facilitated the numerical simulation and analysis of the magnesium alloy wheel hub spinning process. Gray relational degree analysis optimized the first-pass process parameters, elucidating the impact of the axial offset, the thinning ratio, and the feed ratio on forming quality. The application of optimized parameters in the hub spinning simulation resulted in a substantial 28.84% reduction in wall thickness deviation and a 4.88% reduction in inner diameter deviation. This study underscores the efficacy of employing Gray Relational Analysis for comprehensive parameter optimization, ensuring wheel hub quality. Moreover, it provides a theoretical foundation for enterprises to expedite research and development cycles and minimize associated costs.

Metal pollution in the topsoil of lands adjacent to Sahiwal Coal Fired Power Plant (SCFPP) in Sahiwal, Pakistan.

Coal fly ash from a coal fired power plant is a significant anthropogenic source of various heavy metals in surrounding soils. In this study, heavy metal contamination in topsoil around Sahiwal coal fired power plant (SCFPP) was investigated. Within distance of 0-10, 11-20, 21-30 and 31-40 km of SCFPP, total 56 soil samples were taken, 14 replicate from each distance along with a background subsurface soil sample beyond 60 km. Soil samples were subjected to heavy metals analysis including Fe, Cu and Pb by Atomic Absorption Spectrophotometer (AAS). Composite samples for each distance were analyzed for Al, As, Ba, Cd, Co, Cr, Mn, Mo, Ni, Se, Sr, Zn by Inductively Coupled Plasma (ICP). Pollution indices of exposed soil including Enrichment Factor (EF), Contamination Factor (CF), Geoaccumulation Index (Igeo), and Pollution Load Index (PLI) were calculated. Ecological risk index ([Formula: see text]) of individual metals and the Potential Ecological Risk Index (PERI) for all metals were determined. Soil samples within 40 km of SCFPP were significantly polluted with Pb (mean 2.81 ppm), Cu (mean 0.93 ppm), and Fe (mean 7.93 ppm) compared to their background values (Pb 0.45, Cu 0.3, and Fe 4.9 ppm). Some individual replicates were highly contaminated where Pb, Fe, and Cu values were as high as 6.10, 35.4 and 2.51 ppm respectively. PLI, Igeo, CF, and EF for metals classified the soil around CFPP as "moderate to high degree of pollution", "uncontaminated to moderately contaminated", "moderate to very high contamination", and "moderate to significant enrichment" respectively with average values for Cu as 2.75, 0.82, 3.09, 4.01; Pb 4.79, 1.56, 6.16, 7.76, and for Fe as 1.20, 0.40, 1.62, 3.35 respectively. Average Ecological Risk Index ([Formula: see text]) of each metal and Potential Ecological Risk Index (PERI) for all metals classified the soils as "low risk soils" in all distances. However, ([Formula: see text]) of Pb at a number of sites in all distances have shown "moderate risk". The linear correlation of physico-chemical parameter (EC, pH, Saturation %) and metals have recorded several differential correlations, however, their collective impact on Pb in 0-10 km, has recorded statistically significant correlation (p-value 0.01). This mix of correlations indicates complex interplay of many factors influencing metal concentrations at different sampling sites. The concentration of As, Cr, Co, Cd, and Zn was found within satisfactory limits and lower than in many parts of the world. Although the topsoil around SCFPP is largely recorded at low risk, for complete assessment of its ecological health, further research considering comprehensive environmental parameters, all important trace metals and variety of input pathways is suggested.

Quantification of brain-wide vascular resistivity via ultrafast Doppler in human neonates helps early detection of white matter injury

Preterm birth is associated with cerebrovascular development disruption and can induce white matter injuries (WMI). Transfontanellar ultrasound Doppler is the most widely used clinical imaging technique to monitor neonatal cerebral vascularisation and haemodynamics based on vascular indexes such as the resistivity index (RI); however, it has poor predictive value for brain damage. Indeed, these RI measurements are currently limited to large vessels, leading to a very limited probing of the brain’s vascularisation, which may hinder prognosis. Here we show that ultrafast Doppler imaging (UfD) enables simultaneous quantification, in the whole field of view, of the local RI and vessel diameter, even in small vessels. Combining both pieces of information, we defined two new comprehensive resistivity parameters of the vascular trees. First, we showed that our technique is more sensitive in the early characterisation of the RI modifications between term and preterm neonates and for the first time we could show that the RI depends both on the vessel diameter and vascular territory. We then showed that our parameters can be used for early prediction of WMI. Our results demonstrate the potential of UfD to provide new biomarkers and pave the way for continuous monitoring of neonatal brain resistivity.

Study on the impact of Cr content on the microstructure and properties of Cu-Cr alloys

Copper has excellent electrical conductivity, but the disadvantages of low strength and hardness limit its application range. In this paper, Cu-XCr (X=5, 7, and 9, where 5, 7, and 9 are all mass percentages) alloys were treated as the object of study; the impacts of Cr contents on the microstructure, Vickers hardness, and conductivity of Cu-XCr alloys were investigated using an XHC-SV2 upright metallographic microscope, a digital display Vickers hardness tester, and a Sigma2008C digital metal conductivity meter; the optimal Cr content was determined by the comprehensive performance parameter Z. The research results showed that the second phase of Cu-XCr alloys contained dendritic, nearly spherical, and other shapes. The size was relatively small, and the distribution was relatively uniform; the Vickers hardness improved and the electrical conductivity declined with climbing Cr content; when X=7, the comprehensive performance parameter Z of Cu-XCr alloys reached a maximum of 6.7×105 HV2·%IACS.

Short-circuit current-limiting scheme with fast switch fault current limiter on 750kv autotransformer

This paper proposes different short-circuit current limiting schemes using FSFCL on autotransformers, derives the equivalent models of installing the fast switch fault current limiter at different positions of autotransformers, and simulates the current limiting effect of each scheme using PSASP. The simulation results show that the fast switch fault current limiter on the side near the neutral point of the common winding of the autotransformer is the optimal scheme for comprehensive parameters.

Multilevel Evaluation Model of Electric Power Steering System Based on Improved Combination Weighting and Cloud Theory

In order to rapidly evaluate the working performance of an Electric Power Steering System (EPS) and clarify the key indicators that affect its working condition, targeted maintenance measures can be taken to improve the vehicle’s handling, stability, and safety. This paper took an EPS system as the research object and decomposed it into five working indicators: steering lightness, steering returnability, steering wheel mid-zone performance, steering wheel vibration, and power assist characteristics. On this basis, the subjective exponential extension Analytic Hierarchy Process (AHP) was combined with the objective improved entropy weight method, and then the combination weighting method of game theory was employed to ensure that the results were reliable. At the same time, a cloud model was introduced to understand the actual situation of each indicator by using cloud parameters and multi-level feedback results. According to the principle of maximum membership and the comprehensive evaluation method, the EPS performance evaluation result was obtained. The results showed that the comprehensive cloud parameter of EPS was U (74.31, 6.08, 0.50), which indicated that its working performance was in good condition, with some minor damage that did not affect its normal use; however, attention should be paid to the maintenance and repair of vulnerable components to ensure the handling, stability, and safety of the vehicle.

Optimization of parameters for a fringe projection measurement system by use of an improved differential evolution method.

Fringe projection 3D measurement is widely used for object surface reconstruction. While improving measurement accuracy is a crucial task. Measurement accuracy is profoundly affected by various optical structural parameters. However, the current practice of system construction lacks theoretical guidelines and often relies on the experience of the operator, inevitably leading to unpredictable error. This paper investigates a theoretical optimization model and proposes an automatic optimization method for qualitatively determining the multiple optimal optical structural parameters in fringe projection measurement system. The aim is to enhance measurement accuracy conducting a rational comprehensive optimal structural parameters design prior to the system construction. Firstly, the mathematical model of the measurement system is established based on the principle of optical triangulation, and the phase sensitivity criterion is defined as the optimization norm. Within the full measurement range, the optimization merit function is formulated by combing three positions: the center position, the left and right boundary of the CCD. The imaging effectiveness criteria and sensor geometric dimensions are taken into account as the constraint boundaries. Subsequently, a combined improved differential evolution and Levy flight optimization algorithm is applied to search for the optimal parameters. The optimal structural parameters of the system were designed based on the optimization process. Experimental results validated the improvement in measurement accuracy achieved by the optimized structural parameters.

Identification of Crucial Photosensitizing Factors to Promote CO2 -to-CO Conversion.

The sensitizing ability of a catalytic system is closely related to the visible-light absorption ability, excited-state lifetime, redox potential, and electron-transfer rate of photosensitizers (PSs), however it remains a great challenge to concurrently mediate these factors to boost CO2 photoreduction. Herein, a series of Ir(III)-based PSs (Ir-1-Ir-6) were prepared as molecular platforms to understand the interplay of these factors and identify the primary factors for efficient CO2 photoreduction. Among them, less efficient visible-light absorption capacity results in lower CO yields of Ir-1, Ir-2 or Ir-4. Ir-3 shows the most efficient photocatalytic activity among these mononuclear PSs due to some comprehensive parameters. Although the Kobs of Ir-3 is ≈10 times higher than that of Ir-5, the CO yield of Ir-3 is slightly higher than that of Ir-5 due to the compensation of Ir-5's strong visible-light-absorbing ability. Ir-6 exhibits excellent photocatalytic performance due to the strong visible-light absorption ability, comparable thermodynamic driving force, and electron transfer rate among these PSs. Remarkably, the CO2 photoreduction to CO with Ir-6 can achieve 91.5 μmol, over 54 times higher than Ir-1, and the optimized TONC-1 can reach up to 28160. Various photophysical properties of the PSs were concurrently adjusted by fine ligand modification to promote CO2 photoreduction.

Identification of Crucial Photosensitizing Factors to Promote CO2‐to‐CO Conversion

AbstractThe sensitizing ability of a catalytic system is closely related to the visible‐light absorption ability, excited‐state lifetime, redox potential, and electron‐transfer rate of photosensitizers (PSs), however it remains a great challenge to concurrently mediate these factors to boost CO2 photoreduction. Herein, a series of Ir(III)‐based PSs (Ir‐1–Ir‐6) were prepared as molecular platforms to understand the interplay of these factors and identify the primary factors for efficient CO2 photoreduction. Among them, less efficient visible‐light absorption capacity results in lower CO yields of Ir‐1, Ir‐2 or Ir‐4. Ir‐3 shows the most efficient photocatalytic activity among these mononuclear PSs due to some comprehensive parameters. Although the Kobs of Ir‐3 is ≈10 times higher than that of Ir‐5, the CO yield of Ir‐3 is slightly higher than that of Ir‐5 due to the compensation of Ir‐5’s strong visible‐light‐absorbing ability. Ir‐6 exhibits excellent photocatalytic performance due to the strong visible‐light absorption ability, comparable thermodynamic driving force, and electron transfer rate among these PSs. Remarkably, the CO2 photoreduction to CO with Ir‐6 can achieve 91.5 μmol, over 54 times higher than Ir‐1, and the optimized TONC‐1 can reach up to 28160. Various photophysical properties of the PSs were concurrently adjusted by fine ligand modification to promote CO2 photoreduction.

Parameter collaborative analysis and multi-objective optimization of a nanofluid filtered solar membrane distillation system using heat pump

The distributed solar powered membrane distillation (SPMD) system for water/electricity co-production is a valuable solution for relieving energy pressure in remote areas but it suffers from a complicated operation mechanism. This paper performs a comprehensive parameter analysis and user-oriented optimization on a nanofluid filtered solar membrane distillation system using heat pump to enhance its competitiveness. Response surface methodology is employed to examine the interactions of the influencing factors on the system performance, and the plots reveal significant interactions, particularly on equivalent specific thermal energy consumption (eSTEC). An artificial neural network is integrated with genetic algorithm to perform single and multi-objective system performance optimization. The results show that maximum system exergy efficiency, permeate flux and minimum eSTEC are 19.39 %, 35.8 kg·m−2·h−1 and 463.5 kWh·m−3, respectively. Furthermore, the technique for order preference by similarity to an ideal solution is utilized, based on performance indicator weight derived from the analytical hierarchy process, to offer a user-oriented optimal solution. It demonstrates that after optimization, the system exergy efficiency and permeate flux are improved by 9.9 % and 30.2 %, respectively, along with a reduction of eSTEC by 5.7 %. This research contributes to advancing solar-to-water/electricity efficiency and offering valuable insights for the development of SPMD systems.

Exploring the relationship between malnutrition and the systemic immune-inflammation index in older inpatients: a study based on comprehensive geriatric assessment

BackgroundMalnutrition is a prevalent and major challenge among senior citizens, possibly due to the continual low-grade inflammatory state of the body. A novel inflammatory parameter, the systemic immune-inflammation index (SII), is highly valuable in evaluating and predicting the prognosis of a wide range of diseases. This study aims to explore the significance of the SII in assessing malnutrition in older inpatients.MethodsThis retrospective study included 500 senior hospitalized patients who met the inclusion and exclusion criteria from the Comprehensive Geriatric Assessment database of the First Hospital of Jilin University. The Mini-Nutritional Assessment (MNA) questionnaire was used to evaluate the nutritional status of patients. The SII was calculated using complete blood counts, and we performed natural logarithm transformation of the SII [ln(SII)]. Multivariable logistic regression analysis was used to identify the association between ln(SII) and malnutrition. To ensure the stability of the findings, a sensitivity analysis was conducted.ResultsThe 500 patients had a mean age of 77.29 ± 9.85 years, and 68.6% were male. In accordance with the MNA, 30.4% of the patients were malnourished or at risk of malnutrition, and patients in this group had considerably greater levels of ln(SII) than patients with adequate nutrition (P < 0.001). The optimum ln(SII) cutoff value for patients with malnutrition or at risk of malnutrition was 6.46 (SII = 635.87) with 46.7% sensitivity and 80.2% specificity [95% CI: 0.613–0.721, AUC: 0.667, P < 0.001]. Multivariable logistic regression demonstrated that ln(SII) was an independent risk factor for the risk of malnutrition or malnutrition in older individuals (OR 3.984, 95% CI: 2.426–6.543, P < 0.001). Other metrics from the geriatric comprehensive assessment, including body mass index, calf circumference, fat ratio, activities of daily living and instrumental activities of daily living, and geriatric depression scale scores, were also independently correlated with nutritional status.ConclusionsAccording to our research, a high SII is an independent predictor of older inpatient malnutrition, and the SII aids in screening for malnutrition and may be a potential target for intervention. Comprehensive geriatric assessment parameters such as BMI, calf circumference, fat ratio, activities of daily living and depression were also linked to malnutrition.

Correlation between mechanical properties and pore structure deterioration of recycled concrete under sulfate freeze-thaw cycles: an experimental study

Concrete is subject to the combined erosive effects of physical and chemical activities in cold, salty soil regions. In this work, durability tests of recycled concrete (RC) subjected to sulfate freeze-thaw cycles were conducted. The macroscopic performance deterioration law of RC under the influence of the replacement rate (0%, 50%, 100%) and the moisture content of coarse recycled concrete aggregate (CRCA) (0%, 50%, 100%) was investigated by analyzing the change characteristics of apparent damage, mass loss rate, and Relative dynamic modulus of elasticity (RDME) of RC during the erosion process. At the same time, nuclear magnetic resonance (NMR) and microhardness testing equipment were used to examine the multi-parameter evolution features such as porosity, pore distribution, interfacial transition zone (ITZ) width, and strength. The findings indicate that the main cause of the variation in the degree of damage to the RC surface layer is the variance in the effective water-cement (w/c) ratio of the mortar due to replacement rate and moisture content. The strength and area of erosion damage increase when the CRCA replacement rate rises due to the easier inward penetration of the sulfate solution. CRCA with a 50% moisture content could increase the strength of the mortar by decreasing the mortar's effective w/c ratio. The rate and effectiveness of salt solution replenishment inward were simultaneously slowed down by the improved ITZ performance. In erosive situations, the fractal dimension of RC reduces to varying degrees. This is due to the expansion of the pore structure. The porosity/fractal dimension is employed as the comprehensive pore parameter η in this research so as to take into account the integrity of the pore structure and the specificity of the pore distribution. The improved microstructure damage variables can reflect the erosive microstructure deterioration process of RC.

In Silico Exploration of few TriazoloPyrimidine Derivatives as Virtual Inhibitors against SARS-CoV-2 : A Comprehensive Analysis Integrating Molecular Docking and ADME-Toxicity Evaluation

This ground-breaking research delves into the potential of novel triazolopyrimidine derivatives as inhibitors of SARS-CoV, employing a robust in-silico approach that integrates molecular docking studies, molecular dynamics simulations, and comprehensive ADMET parameter assessments. The overarching goal is to identify compounds that exhibit both robust binding affinity and favourable pharmacokinetic profiles, laying the foundation for potential antiviral drug development. The methodology begins with the meticulous selection of triazolo pyrimidine derivatives based on their structural characteristics. Molecular docking studies are then conducted, focusing on a specific binding site on the SARS-CoV target. Subsequent molecular dynamics simulations provide a dynamic perspective on the stability of the binding interactions over time. ADMET parameter assessments are employed to evaluate drug-likeness and safety, crucial factors in determining the compounds' viability as potential drug candidates. The findings of this research underscore promising interactions between the triazolo pyrimidine derivatives and the targeted viral site, suggesting their potential as inhibitors of SARS-CoV. Importantly, the ADMET assessments contribute valuable insights into the pharmacokinetic properties of the compounds, informing their overall safety and suitability for further development. Notably, molecular docking studies include a comparative analysis with the standard antiviral drug remdesivir, revealing that the triazolo pyrimidine derivatives exhibit energy scores surpassing those of remdesivir. This finding signifies a potential advantage in terms of binding affinity and effectiveness against SARS-CoV. The implications of this research are profound, extending beyond the identification of potential inhibitors for SARS-CoV to contributing essential knowledge for the development of therapeutics against coronaviruses in general. This study serves as a crucial stepping stone for subsequent experimental validation and optimization of the identified compounds, propelling future drug discovery endeavours in the fight against coronaviral infections.

Comprehensive parameter study and evaluation of a reinforcement concept for conformable CFRP tanks for hydrogen storage

Comprehensive parameter study and evaluation of a reinforcement concept for conformable CFRP tanks for hydrogen storage

Deep Learning-based Computational Electromagnetics for Deep Exploration of Oil and Gas Reservoirs

Abstract The exploration potential of oil and gas is substantial, with considerable untapped resources available for future exploration and development. This paper investigates the application of time-frequency electromagnetic (EM) methods within the domain of computational electromagnetics, enhanced by deep learning techniques, for the profound exploration of oil and gas reservoirs. To identify zones favorable for hydrocarbon presence, it captures electric and magnetic field responses across time and frequency domains. From these responses, it extracts crucial parameters such as resistivity and excitation polarizability. The methodology initiates with a forward simulation to establish the most productive construction parameters for the time-frequency EM approach. Subsequently, data collected are processed and analyzed to qualitatively evaluate each physical parameter. This is followed by quantitative constraints on resistivity and polarizability, employing the adaptive differential evolution algorithm to execute a comprehensive parameter inversion using the horizontal E-field (Ex) data derived from the time-frequency EM method. This inversion facilitates the determination of resistivity and polarizability distributions at various depths, culminating in the generation of corresponding depth cross-sections. The inversion fitting and example analyses yielded that the apparent resistivity profiles of low-resistance backslope formations and backslope formations with high and low resistance interlayers form relative anomaly magnitudes of about 13% and 7.8%, respectively. In the real measurement from the depression to the uplift zone, the thickness of the giant inclined low-resistance conductive layer gradually decreases. The points of the extreme values and minima gradually move from the low-frequency band to the high-frequency band, which is identical to the simulation law. The anomaly amplitude is between [-0.4 Ω·m, 0.6 Ω·m], which can be accurately imaged. This study innovatively combines deep learning technology and electromagnetism, which provides a basis for the next decision on oil and gas exploration and target evaluation preference.

2013–2022年云南丽江森林生物多样性国家野外科学观测研究站日气象数据集

Meteorological factors are important environmental elements of field positioning observation stations of CNERN, CERN and CFERN. The acquisition of high-quality observational data through long-term positioning observations is beneficial for advancing our scientific understanding of ecosystem processes and tracking dynamic changes in biodiversity. It also provides important support for the development of regional and national ecological, environmental, and resource security construction initiatives. As one of the Forest Biodiversity National Observation and Research Stations approved in 2021, Lijiang Station has been collecting data in accordance with the CERN meteorological monitoring standards for past consecutive years. Up to now, Lijiang Station has consistently maintained comprehensive data parameters, a structured approach, and a high level of data standardization. These data can serve as important foundation for various research endeavors, including dynamic changes in the structure, processes, and functions of regional forest ecosystems, the formation, evolution, and preservation of regional biodiversity mechanisms, forest biodiversity and ecosystem functioning, the monitoring, evaluation, and conservation of biodiversity for key wild protected species, and the assessment and innovative exploitation of distinctive biological resources. This dataset comprises 3,652 entries and 39 fields of daily meteorological data spanning from 2013 to 2022. It covers atmospheric elements (air temperature and humidity, atmospheric pressure, wind speed and direction, precipitation, radiation elements) and soil elements (soil stratified temperature and soil heat flux). The release of the dataset offers comprehensive fundamental meteorological data for scientific research in related fields and provides essential data support for local and large-scale networking research.

Research of The Uludağ National Park in Terms of Resource Economy

The potential of forest-mountain ecosystem services as a source of sustainable development has been widely recognized. This paper aims to provide comprehensive economic value resource parameters of Uludağ National Park (UNP), the region of Bursa, Turkey. In this context, 10 ecosystem parameters were determined under 3 categories. The first of these groups is the supplier parameters. Group of supplier parameters consists of crop production, animal production, industrial and firewood production, grazing, beekeeping and non-wood forest products. Group of regulatory parameters as regulators of the ecosystem consists of biodiversity, carbon sequestration and water resources. Within the scope of socio-economic parameter, there is tourism-recreation value.Valution is based on a wide variety of techniques, drawings on market prices, results of local surveys and other econometric valuation methods called as Faustman and Hartman. It shows that at UNP, water resources provide annual benefits of about 147 046 $ ha-1, secondly biodiversity provides a 15 312 $ ha-1 annualy and carbon sequestration has a 6 806 $ ha-1 annual value. After this regulatory parameters, as a socio-economic parameter tourism and recreation provides 4 362 $ ha-1 economic value annualyand supplier resource parameters following them. The paper shows the degree of importance of the UNP main values for the country and region as a whole. It also discusses some reasons of concern when drawing policy tools for improving UNP income and conservation practices local and regional level.

Appraisal of treated drinking water quality from arsenic removal units in West Bengal, India: Approach on safety, efficiency, sustainability, future health risk and socioeconomics

The present study depicts the true failed scenario of the arsenic (As) removal units (ARU) in West Bengal by evaluating their treated water quality. Annual As removal efficiency of the 12 studied ARUs range between 35.2% and 82.6%. A comprehensive physico-chemical parameters and trace elements analysis find almost 25% and 16.7% of treated drinking water samples with poor water quality index (WQI) and high heavy metal evaluation index (HEI), respectively. The pond-based water treatment plant maintains the production of continuous As-safe water with a range between 60.2% and 66.7% due to its high Fe/As ratio. It’s a discontent concluding the treated drinking water of the groundwater based-ARUs were observed with sufficient As mediated cancer risk (3 ×10−3). The non-cancer risk (HQ) of As is safe for the surface water treatment plant (0.38), whereas it is threatening for the groundwater based-ARUs (7.44). However, the drinking water samples are safe in view of HQ from the other trace elements like Hg, Al, Cd, Cr, Pb, F- and NO3-. Small scale ARU could be a feasible mitigation strategy in reducing the As menace in the long run if the plants are maintained correctly. Nevertheless, surface treated water is the most sustainable solution as withdrawal of groundwater for drinking purpose is not a viable practice.