Indexing Techniques Research Articles

Analysis of the carbon emissions trend in the Indian manufacturing sector: a decomposition and decoupling approach.

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector.

Using Selection Index Technique For Improvement of Yield and Its Components in Maize (Zea may L.)

Using Selection Index Technique For Improvement of Yield and Its Components in Maize (Zea may L.)

Adoption of post-harvest practices and their influencing factors: A study of ginger growers in Syangja, Nepal

Proper ginger post-harvest practices enhance quality, extend shelf life, and ameliorate market access. Prioritizing post-harvest practices in ginger can tremendously increase the market value of ginger in Nepal. Nevertheless, this cannot be done without thorough understanding about the current adoption of post-harvest practices among farmers. So, this study was carried out to know the status of adoption of post-harvest practices in ginger, examine factors associated with adoption, identify and rank major constraints of adoption. The collection of primary data involved 80 ginger growers chosen randomly from altogether 6 wards in Galyang, Waling, and Chapakot municipalities. Data entry and analysis were performed using two software: MS Excel 2021 and SPSS Version 27. The adoption status of ginger post-harvest practices was assessed and quantified using frequency distribution. The chi-square test revealed a significant association between adoption and membership (5%), extension (1%) and training (1%). However, the association between demographic factors like age, gender, education level, experiences in ginger cultivation, and the adoption of post-harvest practices in ginger was statistically non-significant. Moreover, an independent sample t-test and indexing technique were employed. Average adoption index was computed based on which high and low adopters were categorized. Notable advantages emerged for high adopters as they cultivated ginger in larger areas (p < 0.01) and gained higher economic returns (p < 0.05). Five constraints regarding the adoption were ranked through indexing, where a poor marketing system had the highest index value of 0.83, followed by the unavailability of improved processing technologies. In contrast, labor crisis was the least ranked constraint. This study concluded that farmers still follow the traditional methods of ginger post-harvest practices, denoting significant potential for improvement. Thus, offering impactful trainings and support to farmers in line with their recommendations can be the pivotal step towards enhancing the post-harvest practices in ginger sector in Syangja district.

Direct simulations of external flow and noise radiation using the generalized interpolation-supplemented cascaded lattice Boltzmann method

Direct simulations of external flow and the associated noise radiation are studied by an improved lattice Boltzmann method, i.e., the generalized interpolation-supplemented cascaded lattice Boltzmann method (GICLBM). In this method, the cascaded collision scheme is used to improve the numerical stability of the conventional collision schemes, and the generalized interpolation approach is used in the particle streaming process so as to allow a non-uniform and body-fitted mesh partition. With that, both near- and far-field flow dynamics and noise radiation are resolved simultaneously. In order to capture sound waves, the perfectly matched layer is also implemented so as to avoid waves reflecting to and polluting the inner acoustic field. Moreover, a novel index technique is developed for the GICLBM to enable implicit streaming, which brings an efficient memory reduction. Three cases are then performed to showcase the feasibility, accuracy, extensibility, and efficiency of the present framework, including flow past a square cylinder, flow past an elliptic cylinder, and flow past a NACA 0012 airfoil, each implemented with a type of body-fitted mesh. Both the fluid dynamic and noise radiation are found to be in good agreement with results using the Navier–Stokes solvers. This study demonstrates the potential of the GICLBM for accurately and efficiently simulating external problems as well as sound generation and propagation.

USP’s characterization of commercial poly(lactic-co-glycolic acid) utilizing SEC-Multi-Angle Light Scattering and Refractive Index techniques via Absolute Method approach

This research focuses on evaluating the data quality and reliability of Size Exclusion Chromatography with Multi-Angle Light Scattering and Refractive Index (SEC-MALS/RI) chromatography in determining molecular weight-related parameters in poly(lactic-co-glycolic) acid (PLGA) polymers. The study utilizes key metrics, particularly percent relative standard deviation (%RSD), to evaluate the precision across different data processing conditions. Exploring baseline selection of the chromatograms reveals that selection of the baseline based on the intrinsic shape of the chromatogram, rather than relying on elution peaks, improves consistency. The study emphasizes the critical role of result fitting in addressing systematic errors and sample heterogeneity, recommending the minima of the elution peak selection range for realistic molecular weight distribution representation. Analysis of light-scattering (LS) detector chromatograms highlights varying effects on different PLGA polymers, emphasizing the need for careful assessment and potential exclusion for precision improvement, especially in the presence of experimental artifacts. Across diverse PLGA polymers, the utilization of different column types, along with considerations such as baseline determination and peak selection, shows minimal variations in Mn, Mw, and polydispersity index (PDI), affirming the consistency of the methodology. The %RSD analysis further supports the methodology's precision, with values well within acceptable limits. In summary, this comprehensive evaluation of methodological conditions, including baseline selection, result fitting, LS detector data management, and column types, establishes a reliable SEC-MALS/RI chromatographic approach by Absolute Method for determining molecular weight parameters in PLGA polymers.

Spatiotemporal characteristics and potential pollution factors of water quality in the eastern route of the South-to-North Water Diversion Project in China

The eastern route of the South-to-North Water Diversion Project (SNWDP-ER) in China, an open-channel cross-basin project, is exposed to multiple potential pollution sources and has changing hydrodynamic conditions between diversion and non-diversion periods. After a decade of pollution control efforts, it is imperative to perceive the water quality conditions and identify potential pollution factors for further maintenance. Combining the water quality identification index (WQII) and multivariate statistical techniques (MSTs), this study conducted an exploratory analysis in SNWDP-ER, based on the monitoring data in 2020–2021. The monitoring and WQII results show that the overall water quality in SNWDP-ER is satisfactory and meets the Class III requirements in China Environmental Quality Standard for Surface Water, except for total nitrogen (TN) which falls into Class V or even worse. The periodic alternations of flow direction between diversion and non-diversion periods tend to deteriorate water quality, particularly in the initial stage of alternations. Water quality appears more stable during the diversion period, and impounding lakes along the project are crucial to water environment restoration in this period. Due to the positive influence brought by the self-purification and environmental carrying in the Luoma Lake, Nansi Lake, and Dongping Lake, water quality exhibits an enhancing trend from upstream to downstream in the Lianghu and Jiaodong reaches. Besides, the monitoring stations in Shandong reach exhibit significant divisional heterogeneity under hierarchical cluster analysis (HCA) with the Nansi Lake and Dongping Lake as the demarcation points, and the results of factor analysis/principal component analysis (FA/PCA) further identified the contributions of mining, shipping, and land use to each section. Based on the findings, the study recommends implementing wastewater discharge control, abandoned mines remediation, and continued water quality monitoring for further maintenance and improvement in SNWDP-ER.

Block based motion estimation model using CNN with representative point matching algorithm for object tracking in videos

Motion estimation is considered significant for tracking the movement of an object in video sequences, and it is widely used in various video processing applications. Traditionally, many researchers focus on pixel-based motion estimation for object tracking, but it experienced increased computation time and cost. To reduce computation time, the utilization of a block-based motion estimation approach for object tracking is a recent trend. The existing block-based approach faces difficulty in finding representative points within the intensity domain. Therefore, this current research merged the deep learning approach with a block-matching algorithm for achieving efficient object tracking. In this proposed work, initially, video sequences are collected from a benchmark video dataset. Then, the acquired video sequences are segmented into frames. From the segmented frames, current and previous frames are considered for motion estimation. Frames are sent for the data augmentation process in which the process of flipping, cropping, and rotation is carried out. Then, the augmented frames are sent into Convolutional Neural Network (CNN) for feature extraction. Representative Point Matching (RPM) is used to estimate the motion vector based on the extracted features. After estimating the motion vector, the similarity between two consecutive frames is found using Structural Similarity Index (SSIM) technique. Finally, based on the similarity score, the movement of an object in the video is tracked effectively. Simulation analysis of the proposed block-based motion estimation model is done by evaluating some performance metrics. RMSE, PSNR, Execution Time, SSIM, and accuracy obtained for the proposed model are 27.5, 26.5 db, 31 sec, 0.91, and 94 %. This analysis suggested that the proposed CNN-RPM motion estimation model performs better in tracking the movement of the object.

Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques

<div class="translate-tooltip-mtz green sm-root translate hidden_translate"><div class="header-wrapper"><div class="header-controls"><span>The use of JavaScript Object Notation (JSON) format as a Not only Structured Query Language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.</span></div></div></div>

Can performance-based financing improve efficiency of health centers in Ethiopia? A Malmquist Productivity Index analysis

IntroductionThe Ethiopian government has introduced several healthcare financing reforms intending to improve efficiency. Piloting implementation of performance-based financing is one of these actions. The purpose of this research is to assess the efficiency of healthcare facilities that have implemented performance-based financing compared to those that have not.MethodsEfficiency was measured using a nonparametric data envelopment analysis and the Malmquist Productivity Index technique. Total factor productivity change, technical change, and technological change are compared across eight sampled healthcare facilities that are implementing performance-based financing and eight that are not in Ethiopia.ResultsHealth facilities implementing performance-based financing have a mean technical efficiency score of 64%, allowing for a potential 36% reduction in inputs without affecting outputs. Their scale efficiency is 88%, indicating a potential 12% increase in total outputs without expanding facilities. In contrast, facilities not implementing performance-based financing have a mean technical efficiency score of 62%, with a potential for 38% input reduction without affecting outputs. Their scale efficiency is 87%, suggesting a potential 13% increase in total outputs without scaling up facilities. Among the 16 healthcare facilities observed, seven experienced a decline in the mean total productivity, while one remained stagnant. The remaining eight facilities witnessed an increase in productivity. The healthcare facilities implementing performance-based financing showed a 1.3% decrease in mean total productivity during the observed period. Among them, five showed an increase and three showed a decrease in the total factor of productivity. The mean total factor of productivity of all healthcare facilities not implementing performance-based financing remained stagnant over the three-year period (2019–2021), with four showing an increase and four showing a decrease in total productivity.ConclusionsThe study concludes that implementing performance-based financing did not improve productivity levels among healthcare facilities over three years. In fact, productivity decreased among the facilities implementing performance-based financing, while those not implementing it remained stagnant. This shows health facilities that implement performance-based financing tend to utilize more resources for similar outputs, contradicting the anticipated efficiency improvement.

Investigating the effects of surface water recharge on groundwater quality using hydrochemistry and ANFIS model: A case study Minia Governorate, Egypt

Monitoring and assessing groundwater quality and quantity lays the basis for sustainable management. Therefore, this research aims to investigate various factors that affect groundwater quality, emphasizing its distance to the primary source of recharge, the Nile River. To this end, two separate study areas have been considered, including the West and West-West of Minia, Egypt, located around 30 and 80 km from the Nile River. The chosen areas rely on the same aquifer as groundwater source (Eocene aquifer). Groundwater quality has been assessed in the two studied regions to investigate the difference in quality parameters due to the river's distance. The power of machine learning to associate different variables and generate beneficial relationships has been utilized to mitigate the cost consumed in chemical analysis and alleviate the calculation complexity. Two adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the water quality index (WQI) and the irrigation water quality index (IWQI) using EC and the distance to the river. The findings of the assessment of groundwater quality revealed that the groundwater in the west of Minia exhibits suitability for agricultural utilization and partially meets the criteria for potable drinking water. Conversely, the findings strongly recommend the implementation of treatment processes for groundwater sourced from the West-West of Minia before its usage for various purposes. These outcomes underscore the significant influence of surface water recharge on the overall quality of groundwater. Also, the results revealed the uncertainty of using sodium adsorption ratio (SAR), Sodium Percentage (Na%), and Permeability Index (PI) techniques in assessing groundwater for irrigation and recommended using IWQI. The developed ANFIS models depicted perfect accuracy during the training and validation stages, reporting a coefficient of correlation (R) equal to 0.97 and 0.99 in the case of WQI and 0.96 and 0.98 in the case of IWQI. The research findings could incentivize decision-makers to monitor, manage, and sustain groundwater.

Efficient and Provable Effective Resistance Computation on Large Graphs: An Index-based Approach

Effective resistance (ER) is a fundamental metric for measuring node similarities in a graph, and it finds applications in various domains including graph clustering, recommendation systems, link prediction, and graph neural networks. The state-of-the-art algorithm for computing effective resistance relies on a landmark technique, which involves selecting a node that is easy to reach by all the other nodes as a landmark. The performance of this technique heavily depends on the chosen landmark node. However, in many real-life graphs, it is not always possible to find an easily reachable landmark node, which can significantly hinder the algorithm's efficiency. To overcome this problem, we propose a novel multiple landmarks technique which involves selecting a set of landmark nodes V l such that the other nodes in the graph can easily reach any one of a landmark node in V l . Specifically, we first propose several new formulas to compute ER with multiple landmarks, utilizing the concept of Schur complement. These new formulas allow us to pre-compute and maintain several small-sized matrices related to V l as a compact index. With this powerful index technique, we demonstrate that both single-pair and single-source ER queries can be efficiently answered using a newly-developed V l -absorbed random walk sampling or V l -absorbed push technique. Comprehensive theoretical analysis shows that all proposed index-based algorithms achieve provable performance guarantees for both single-pair and single-source ER queries. Extensive experiments on 5 real-life datasets demonstrate the high efficiency of our multiple landmarks-based index techniques. For instance, our algorithms, with a 1.5 GB index size, can be up to 4 orders of magnitude faster than the state-of-the-art algorithms while achieving the same accuracy on a large road network.

An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data.

DNA-based data storage is a new technology in computational and synthetic biology, that offers a solution for long-term, high-density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns. Therefore, this study proposes an Effective DNA Storage (EDS) approach for archiving medical MRI data. The EDS approach incorporates three key components (i) a novel fraction strategy to address the critical issue of rotating encoding, which often leads to data loss due to single base error propagation; (ii) a novel rule-based quaternary transcoding method that satisfies bio-constraints and ensure reliable mapping; and (iii) an indexing technique designed to simplify random search and access. The effectiveness of this approach is validated through computer simulations and biological experiments, confirming its practicality. The EDS approach outperforms existing methods, providing superior control over bio-constraints and reducing computational time. The results and code provided in this study open new avenues for practical DNA storage of medical MRI data, offering promising prospects for the future of medical data archiving and retrieval.

Application of water quality index and statistical-hydrochemical techniques in groundwater assessment of the Quaternary aquifer, southwest Nile Delta of Egypt

The objective of the current study is to investigate the hydrogeochemistry of the Quaternary groundwater and evaluating its suitability for drinking and irrigation needs using statistical analysis, water quality indicators and physicochemical parameters, in west of the Nile Delta, Egypt. The study area has high population growth and agricultural activities, which require groundwater protection, and predicting probable environmental problems. For these reasons, 54 groundwater samples were collected during April 2021. Hierarchical cluster analysis, Pearson, and factor analysis used for statistical analysis. Biplots, Gibbs and Piper diagrams were used to infer the geochemical processes controlling groundwater chemistry. The groundwater is affected by silicate weathering, reveres ion exchange, dissolution of CO2, and recharge from the Nile. According to the WQICCME drinking value, the groundwater ranged between fair to marginal water quality. The distribution of integrated-weight water quality index of the samples, showed that it is excellent for irrigation (< 25). The nine physicochemical parameters sodium percent (Na%), permeability index, sodium adsorption ratio, Kelley Index, residual sodium carbonate, magnesium hazard, Potential Salinity, Corrosive Ratio and Chloro-alkaline Indices revealed that most of the groundwater are of good quality and can be safely used for agricultural activities, albeit few samples due the west needs some treatment. It is recommended to minimize the fertilizers and nutrients use to decrease the anthropogenic impact on the groundwater. The reuse of irrigation water without treatment should be limited. As well, the pumping rates should be controlled to avoid aquifer salinization.

Chirped Integrated Bragg Grating Design

We analyze the two classic methods for chirped Integrated Bragg Gratings (IBGs) in Silicon-on-Insulator technology using the transfer matrix method based on the effective refractive index (neff) technique, which translates the geometry of an IBG into a matrix of neff depending on the wavelength. We also implement a procedure that allows engineering of the chirped IBG parameters, given a required bandwidth (BW) and group delay (GD). Finally, a complementary method for designing chirped IBG is proposed, showing a significant improvement in the bandwidth of the device or a moderation in the variation of the geometrical parameters of the grating.

An antibiotic efficacy comparative study between market-prescribed and solid-lipid fluoroquinolone ophthalmic nanoformulation against conjunctivitis

Besifloxacin hydrochloride (BSF) drug substance (DS), is effective against gram-positive and gram-negative microorganisms. BSF (liquid suspension form) is already available in two different market brands: Besivance and Besix. Both of these commercialized drug products (DP) have lower mucoadhesive characteristics, therefore they spend less time in contact with various ocular tissues (cornea and conjunctiva). This research work emphasized the design, development, and optimization of BSF-loaded solid-lipid nanoparticles (BSF@SLN) to increase antibacterial activity by enhancing drug absorption through passive diffusion techniques in the ocular organ compared to conventional dosage forms. The SLN was prepared through a very simple process hot homogenization process using glyceryl monostearate and polysorbate-80 along with poloxamer-188 and water for injection. Various characterization techniques like FTIR, DSC, and XRD were performed to check the compatibility between DS and excipients. The preliminary formulation of SLN was characterized through zeta potential, polydispersity index, and particle size techniques. The developed SLN was optimized following 32 factorial designs. Surface morphology and size of optimized BSF@SLN ophthalmic DP were confirmed by using advanced microscopic techniques such as SEM. In-vitro release of optimized BSF@SLN ophthalmic DP was performed by using two chambers of Franz diffusion cell. Stability studies of optimized BSF@SLN ophthalmic nanoformulations were tested for up to 6 months at 25°C ± 2°C/60% ± 5% RH. The antimicrobial activity of optimized ophthalmic nanoformulation was found 2.76 times more effective compared to the Besix eye drop. In-vivo study of BSF@SLN ophthalmic nanoformulations was carried out with an eye irritancy test and compared with marketed DP (Besix). and found significant pharmacological.

Cascaded maximum median-margin discriminant projection with its application to face recognition

This paper introduces a novel method called cascaded maximum median-margin discriminant projection (CMMDP) for robust face recognition. Initially, we introduce the maximum median-margin discriminant projection (MMDP) method, which aims to maximize the distances between class medians and their inter-class neighbors, while simultaneously minimizing the distances between class medians and their intra-class samples. To extract features at multiple levels, we extend the MMDP method to its deep version, known as CMMDP. The CMMDP algorithm incorporates the MMDP technique to learn multistage filter banks specifically designed for facial images. Afterward, we utilize straightforward binary hashing techniques for efficient indexing, along with block histograms for effective feature pooling. The CMMDP model is simple and easy to train, while taking full account of the global and local structure of the samples by maximizing the distances between samples that are dissimilar and minimizing the distances between samples that are similar after projection. This results in a low-dimensional data representation with better discriminative performance. We evaluate the performance of CMMDP on face datasets including AR, ORL, ExtYaleB and CMU PIE databases. The results indicate that CMMDP achieves superior performance than most advanced methods.

Analyzing the Impact of Financial Stability Using the General Aggregate Index Technique for A Sample of Iraqi Private Banks

Studying the state of general financial stability from the perspective of commercial banks and the extent of their application of its indicators is one of the important topics that has received great attention internationally. The problem of the study, which bears the title (Analysis of the impact of financial stability using the general aggregate index technique for a sample of Iraqi banks for the period from 2015 to 2021) is focused on the question: Next: Do commercial banks in Iraq have an impact in achieving financial stability in Iraq if the financial stability rates applied by the Central Bank are applied? The study came in two main parts, the first of which dealt with the theoretical framework of financial stability in terms of the concept, challenges, risks, characteristics and components, while the second dealt with the theoretical framework of financial stability in terms of the concept, challenges, risks, characteristics and components. He touched on showing the extent to which the study sample of commercial banks in Iraq applied financial stability rates through financial analysis of indicators. Thus, the study showed a number of conclusions. One of the most important conclusions was that the state of financial stability in Iraq depends to a large extent in achieving it on commercial banks, which must be developed. And keeping pace with the external environment, other global indicators must be applied, and the position of the commercial banks in the research sample was concluded on the basis of financial analysis, which shows an impact in achieving financial stability.

GIS-based groundwater potential zonation and assessment of groundwater quality and suitability for drinking and irrigation purposes in the Shanmughanadhi river basin, south India

To develop a specific watershed scenario, the quantity and quality of groundwater resources must be assessed and monitored locally. In the present study, an attempt has been made to identify possible groundwater potential zones, determine the quality of groundwater resources in this region, and evaluate their suitability for drinking and irrigation purposes. This goal has been achieved with the combined use of the weighted index overlay analysis (WIOA), groundwater quality index (GWQI), Remote Sensing (RS), and Geographic Information System (GIS) techniques. The proposed study area, Shanmuganadhi, is marked with superior rainfall, oscillating temperature, and runoff with litho-units encompassing charnockite (CHK) and hornblende biotite gneiss (HBG). Over-abstraction of groundwater and intensive agricultural practices have resulted in declining and degrading water quantity and quality in this area. The resulting integrated thematic map has been classified into five groundwater potential zones, namely poor, low, moderate, high, and very high, covering 3.2, 45.49, 15.3, 27.9, and 8.1% area, respectively. Sixty groundwater samples were collected during the pre-monsoon (PRM) and analyzed for major Physico-chemical parameters. From the water quality index assessment, over 28.0% of the samples fall within the “excellent,” 8.0% “good” and 4.0% “poor” in CHK, followed by 5.71% “excellent,” 48.57% “good,” 37.14% “poor,” 5.71% “very poor” and 2.87% “unsuitable” for drinking purposes in HBG terrain of this region. Irrigation indices also demonstrated that most of the groundwater samples in the CHK and HBG exposed zones exhibited good classes for irrigation purposes, as evidenced by the SAR and %Na+. The outcome, which shows the research area's groundwater potential zones, is beneficial for improved groundwater resource planning and management. It is concluded that for larger-scale groundwater-quality investigation and assessment, the groundwater quality index-making approach (GWQI) is more practical and dependable. Water planners and decision-makers may find it helpful in effectively controlling and monitoring groundwater quality at the basin or watershed scale. Reducing urban land use and natural runoff from farms can help prevent water contamination in some regions. To ensure the safe use of groundwater, long-term monitoring of these groundwater was suggested.

Does cleaning of venipuncture site with alcohol affect blood ethanol concentration?

BackgroundIt is important to accurately determine the blood ethanol concentration (BEC) to ensure appropriate diagnosis and treatment of patients in the emergency department (ED) and protect their legal rights. This study aimed to determine whether sterilization of venipuncture site with ethanol, which is frequently used in practice in the ED would affect BEC. MethodsVenous blood samples were collected by two consecutive techniques from 94 individuals who were admitted to the ED, had an indication for BEC measurement, and volunteered to participate in the study. The reference technique involved applying 3 cc of 10 % povidone-iodine solution to a gauze pad, cleaning the right arm antecubital region, and performing phlebotomy. The index technique used 3 cc of alcohol-based antiseptic (89 % ethanol) on another gauze for cleaning the left arm antecubital region. Both techniques allowed the antiseptic to air-dry for 30 s before phlebotomy. Two blood sample tubes per patient were sent to the laboratory, and BEC were measured using the alcohol dehydrogenase enzymatic method. Results94 patients were included in the study. The mean age was 37.8 years (±15.7), with 77 % (n = 72) of them were male. The median BEC levels measured by both the reference and index techniques were 2 mg/dL (IQR: 0.97–16.25) and 2 mg/dL (IQR: 0.90–15.22), respectively, with no significant statistical difference (p = 0.536). 72 (77 %) of the patients had a BEC level below the legal driving limit of 20 mg/dL. Bland-Altman analysis, performed on these patients, revealed a small negative bias, −0.116 mg/dL with a standard deviation of 1.13 mg/dL. The upper and lower limit of the agreement was 2.092 and −2.323 respectively. ConclusionIn patients with a BEC level of less than 20 mg/dL, using ethanol-containing antiseptics before blood sampling does not lead to erroneously elevated BEC levels.

Field diagnosis of the criteria for selecting affordable housing in less developed countries: evidence from homeowners and renters in Ghana

Purpose In the Global North, affordable housing has evolved and thrived, and it is now gaining traction in the Global South, where governments have been vocal supporters of the concept. Therefore, this paper aims to investigate the important criteria for selecting affordable housing units in Ghana. Design/methodology/approach A quantitative research approach was used, and a survey was administered to the residents. The data was analysed using both descriptive and inferential statistics. The relative importance index technique was used to rank the important criteria, and the EFA technique was used to create a taxonomy system for the criteria. Findings The hierarchical ranking of the most significant criteria for selecting affordable housing includes community safety, waste management and access to good-quality education. Furthermore, the important criteria for selecting affordable housing are classified into two groups, namely, “sustainability criteria” and “housing demand and supply and social service provision”. Research limitations/implications This study has implications for the real estate industry and construction stakeholders, as this will inform decision-making in terms of the design of affordable housing and the suitability of the location for the development. Originality/value These findings provide a baseline to support potential homeowners and tenants in their quest to select affordable housing. Furthermore, these findings will aid future longitudinal research into the indicators or criteria for selecting suitable locations for the development of low- and middle-income housing.