Variation In Quality Research Articles

Revolutionising High Resolution HLA Genotyping for Transplant Assessment: Validation, Implementation and Challenges of Oxford Nanopore Technologies' Q20+ Sequencing.

The advent of third-generation sequencing (TGS) represents a significant shift in the field of genetic sequencing, enabling single-molecule sequencing to overcome limitations of short-read NGS platforms. Several studies have assessed the utilisation of TGS in HLA genotyping, though many of these studies have described the high error rate as an obstacle to achieving a robust and highly accurate HLA typing assay. In 2021, Oxford Nanopore Technologies (ONT) released the high-accuracy sequencing Kit 14 and the MinION flow cell model R10.4.1, which were reported to achieve sequencing accuracies up to 99%. The aim of this study was to validate this novel high-accuracy sequencing kit for HLA genotyping coupled with a full-gene HLA PCR assay. Comparison with historical data obtained using legacy flow cell models such as R9.4, R10.3 and R10.4 was also done to assess progressive improvement in sequencing performance with each sequential release. The workflow was validated based on data throughput, sequence quality and accuracy, and HLA genotyping resolution. An initial validation was performed using an internal reference panel of 42 samples representing common HLA allele groups, followed by an analysis of data obtained from 111 sequencing batch runs since the implementation, to assess assay performance and define quality control metrics to assess inter-run variability and monitor quality. Furthermore, challenges arising from MinION flow cell stability and use, and assessment of barcode contamination are discussed. The findings of this study highlight advantages of ONT sequencing kit 14/R10.4.1 for HLA genotyping and the implementation considerations for the routine diagnostic HLA laboratory.

Cognitive Function Variability and Health-related Quality of Life in Multiple Sclerosis: A Comprehensive Analysis Across Different Multiple Sclerosis Types

Cognitive Function Variability and Health-related Quality of Life in Multiple Sclerosis: A Comprehensive Analysis Across Different Multiple Sclerosis Types

Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season

The temporal dynamics of solute export from catchments are challenging to quantify and model due to confounding hydrological and biogeochemical processes and sparse measurements. Conventionally, the concentration-discharge relationship (C-Q) and statistical approaches to describe it, such as the Weighted Regressions on Time, Discharge and Seasons (WRTDS), have been widely used. Recently, deep learning (DL) approaches, especially Long-Short-Term-Memory (LSTM) models, have shown predictive capability for discharge, temperature, and dissolved oxygen. However, it is not clear if such advances can be expanded to water quality variables driven by complex subsurface biogeochemical processes. This work evaluates the performance of LSTM and WRTDS for 20 water quality variables across ~500 catchments in the continental US. We find that LSTM does not markedly outperform WRTDS in our dataset, potentially limited by the current measurement capabilities of water quality across CONUS. Both models present similar performance patterns across water quality variables, with the LSTM displaying better performance for nutrients compared to weathering-derived solutes. Additionally, the LSTM does not benefit from flexibility in the inputs. For example, incorporation of climate data that constrains streamflow generation, does not significantly improve the LSTM performance. We also find that data availability is not a straightforward predictor of LSTM model performance, although higher availability tends to stabilize performance. To fully assess the potential of the LSTM model, it may be necessary to use a higher frequency dataset across the CONUS, which does not exist today. To evaluate the dynamics of C-Q patterns relative to model performance, we introduce a “simplicity index” considering both the seasonality in the concentration pattern and the linearity in the C-Q relationship, or the C-Q-t pattern. The simplicity index is strongly correlated with model performance and differentiates the underlying controls on water quality dynamics. Further DL experiments and model-intercomparison highlight the strengths and deficiencies of existing frameworks, pointing to the need for further hydrogeochemical theories that are amenable to complex basins and solutes.

The Influence of Public Service Quality, Perceived Ease of Use of J-Mbako Application on Community Commitment in Paying Taxes Through Community Satisfaction as an Intervening Variable

This study aims to determine the effect of service quality, perceived ease of use, and satisfaction on the commitment to use the J-Mbako tax payment application. The number of samples in this study was 176 respondents, namely people who use the J-Mbako application. The analysis technique used was the Structural Equation Model (SEM) using the WarpPLS 7.0 application. The results of the data analysis showed (1) the variables of service quality and perceived ease of use directly have a positive effect on satisfaction. (2) the variables of service quality, perceived ease of use, and satisfaction directly have a positive effect on commitment. (3) the variables of service quality and perceived ease of use indirectly have a significant positive effect on ongoing commitment through satisfaction.

Mediation of customer satisfaction: E-service quality, brand trust, and reuse intention

The purpose of this study is to explain the role of customer satisfaction in mediating e-service quality and brand trust on reuse intention in the Traveloka application. The theories used are the Techlonogy Acceptance Model (TAM) and Expectancy Disconfirmation Theory (EDT). This research is a type of quantitative research that is associative in nature. The population in this study are all Traveloka application users who have transacted at least three (3) times in one year. The sampling technique used purposive sampling with a total sample member of 205 respondents. Respondent data collection was carried out by distributing questionnaires. The data analysis technique in this study used PLS-based SEM. The results showed that all variables had a positive and significant effect. Customer satisfaction acts as a partial mediation in mediating the effect of e-service quality and brand trust on reuse intention on the Traveloka application. The results of this study can provide an empirical contribution about the relationship between the variables of e-service quality, brand trust, customer satisfaction, and reuse intention for the development of the Technology Acceptance Model (TAM) and Expectancy Disconfirmation Theory (EDT). Traveloka management can implement a marketing strategy that emphasizes service quality and information accuracy to increase customer intention to reuse the Traveloka application.

Avocado peel by-product in cattle diets and supplementation with oregano oil and effects on production, carcass, and meat quality

Abstract The objective of this work was to evaluate the effects of including a commercial avocado peel meal (Avomel) by-product as a feed source supplemented with Lippia berlandieri Schauer oregano essential oil (OEO) in cattle diets. Eighteen 22-month-old male Charolais x Angus crossbred cattle (334.96 ± 27.48 kg) were randomly distributed in three experimental groups (n = 6): Control, cattle fed with the control diet without Avomel and without OEO; Avomel, cattle fed 10% of Avomel in the diet; and Avomel + OEO, cattle fed 10% Avomel and 600 mg/kg of OEO in the diet. The evaluated variables were bulls’ weight (BW), feed intake (FI), slaughter variables, carcass quality and the pH, water holding capacity (WHC), color and chemical composition of raw rib eye meat, as well as cooking loss (CL), shear force (SF), texture analysis and sensory evaluation of cooked meat. After 120 days of feeding, BW was highest (P < 0.10) for Avomel and lowest (P < 0.10) for Avomel + OEO. The Avomel + OEO group FI was low. Cattle slaughter variables, carcass quality variables, pH, and WHC did not show differences (P > 0.05). Lightness was highest (P < 0.05) for Avomel meat. Meat chemical composition was not affected (P > 0.05). SF of meat was different (P < 0.05) being highest for Avomel + OEO. On meat texture, springiness was different (P < 0.05), i.e., meat treated with Avomel was highest, but no differences (P > 0.05) were detected for hardness, adhesiveness, cohesiveness, gumminess, chewiness, and resilience. Odor, taste, softness, juiciness, and overall acceptability of cooked meat were not different (P > 0.05), data not shown. The commercial by-product avocado peel meal supplemented with OEO could be used in cattle diets without adversely affecting productive performance, carcass traits, and meat quality.

THE INFLUENCE OF BRAND, PRICE AND PRODUCT QUALITY ON SHOPEE E-COMMERCE PURCHASING DECISIONS

The development of information technology in the 4.0 era has changed people's lifestyles, including product purchasing behavior through e-commerce. This research aims to determine the influence of brand, price and product quality variables on Shopee e-commerce purchasing decisions. The research object was carried out in Semarang district with a generation Z population aged 18-27 years. The number of respondents was 95 people with the sampling technique using a non-probability sampling technique with a purposive sampling method. Data collection used a questionnaire with Likert scale parameters. The data analysis technique uses multiple linear regression and is processed using SPSS version 29 software. The results of data analysis show that brand and price variables have a positive and significant effect on Shopee e-commerce purchasing decisions. Meanwhile, the product quality variable does not significantly influence Shopee e-commerce purchasing decisions. The coefficient of determination value shows a value of 32.1% with a significance level of 0.05.

The Effect of Trust, Promotion, and Service Quality on the Decision to Use the Ollin Application

This research was conducted at the Bank Nagari Koto Agung Branch Office, Block B, Sitiung 1, Sitiung District, Dharmasraya Regency. The purpose of this research was to determine the effect of Trust, Promotion, and Service Quality on the Decision to Use the Online Application at the Bank Nagari Koto Agung Branch Office. A quantitative research method was used, with data collected through a questionnaire distributed from February to March, resulting in a sample of 173 respondents selected using Accidental Sampling. The data analysis techniques applied in this study included validity tests, reliability tests, descriptive analysis, correlation analysis, multiple linear regression analysis, classical assumption tests, t-tests, F-tests, and determination tests (R²). The results of this study indicate that Trust, Promotion, and Service Quality variables have a positive and significant effect, both partially and simultaneously, on the Decision to Use the Online Application at the Bank Nagari Koto Agung Branch Office. The R² value is 0.739, which implies that 73.9% of the variation in the dependent variable can be explained by the independent variables, while the remaining 26.1% is influenced by other factors not examined in this study.

PENGARUH KONSERVATISME AKUNTANSI, STRUKTUR MODAL DAN INVESTMENT OPPORTUNITY SET TERHADAP KUALITAS LABA

This study aims to determine the effect of accounting conservatism, capital structure, and investment opportunity set variables on earnings quality variables in basic materials companies in 2018-2022. The independent variables in this study are accounting conservatism, capital structure, and investment opportunity set, the dependent variable is earnings quality. The research method used is a quantitative method, the data sources used are secondary data. The technique used in this study is a purposive sampling technique. The population used in this study are basic materials companies listed on the IDX in 2018-2022. The sample used in this study was 12 company samples with a research observation period of 5 years in the period 2018-2022 and the number of financial reports used as samples in this study was 60. The data analysis technique used was multiple linear regression with the help of the e-views version 12 statistical processing program. The partial test results showed that the accounting conservatism variable had an effect on earnings quality, the capital structure variable had no effect on earnings quality, and the investment opportunity set variable had no effect on earnings quality, while the simultaneous test results showed that the accounting conservatism, capital structure, and investment opportunity set variables simultaneously had a simultaneous effect on earnings quality.

An adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features

Existing algorithms for trajectory simplification in AIS data processing have issues such as loss of key feature points, high subjectivity in threshold selection, and incomplete consideration of factors. In this paper, an adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features is proposed. The algorithm identifies behavioral feature intervals by extracting vessel behavioral features and constructs an adaptive segmentation threshold function to achieve adaptive segmentation of trajectories and extraction of key feature points. Then, considering the quality, similarity, and spatial variability of trajectory simplification, an adaptive simplification threshold function is constructed to automatically select the best threshold for each sub-trajectory to generate the simplified trajectory. Our experiments demonstrate that the algorithm maintains a high simplification rate while enabling adaptive simplification of the trajectory, and also retains the key feature points and trajectory shapes to the maximum extent. The effectiveness and robustness of the algorithm are verified through comparison with other algorithms, effectively avoiding the distortion of navigational behavior that may be caused by traditional methods. This provides reliable data support for maritime traffic management and decision-making.

Impact of governance quality, population and economic growth on greenhouse gas emissions: An analysis based on a panel VAR model

Unlike the previous literature on climate change, this study employed the panel vector autoregressive model to examine the impact of population growth, governance quality, and economic growth on global warming. Moreover, we used the impulse response function tool, which was developed in the same context, to better understand the reaction of the main variables of interest, per capita greenhouse gas (GHG) emissions, aftershocks on population change, governance quality, and economic growth variables. Finally, the analysis was completed by the variance decomposition of all variables. These analyses were conducted for the 33 most polluting countries in terms of GHG from 1996 to 2019. The results show that both population growth and economic growth have a positive and significant influence on climate change and can strongly explain the gradual rise in Earth's temperature. Our empirical results show that there is a negative and significant relationship between the quality of governance and GHG emissions. The results show that there is a bidirectional causality between GHG emissions, population and economic growth. Based on the findings, this study proposes several policy recommendations.

Validation and Error Minimization of Global Ecosystem Dynamics Investigation (GEDI) Relative Height Metrics in the Amazon

Global Ecosystem Dynamics Investigation (GEDI) is a relatively new technology for global forest research, acquiring LiDAR measurements of vertical vegetation structure across Earth’s tropical, sub-tropical, and temperate forests. Previous GEDI validation efforts have largely focused on top of canopy accuracy, and findings vary by geographic region and forest type. Despite this, many applications utilize measurements of vertical vegetation distribution from the lower canopy, with a wide diversity of uses for GEDI data appearing in the literature. Given the variability in data requirements across research applications and ecosystems, and the regional variability in GEDI data quality, it is imperative to understand GEDI error to draw strong inferences. Here, we quantify the accuracy of GEDI relative height metrics through canopy layers for the Brazilian Amazon. To assess the accuracy of on-orbit GEDI L2A relative height metrics, we utilize the GEDI waveform simulator to compare detailed airborne laser scanning (ALS) data from the Sustainable Landscapes Brazil project to GEDI data collected by the International Space Station. We also assess the impacts of data filtering based on biophysical and GEDI sensor conditions and geolocation correction on GEDI error metrics (RMSE, MAE, and Bias) through canopy levels. GEDI data accuracy attenuates through the lower percentiles in the relative height (RH) curve. While top of canopy (RH98) measurements have relatively high accuracy (R2 = 0.76, RMSE = 5.33 m), the accuracy of data decreases lower in the canopy (RH50: R2 = 0.54, RMSE = 5.59 m). While simulated geolocation correction yielded marginal improvements, this decrease in accuracy remained constant despite all error reduction measures. Some error rates for the Amazon are double those reported in studies from other regions. These findings have broad implications for the application of GEDI data, especially in studies where forest understory measurements are particularly challenging to acquire (e.g., dense tropical forests) and where understory accuracy is highly important.

Analisis Biaya Operasional, Harga dan Kualitas Produk Terhadap Peningkatan Pendapatan Pada UMKM Kuliner di Sidoarjo

Background – Currently, many Micro, Small and Medium Enterprises (MSMEs) in the culinary sector are seeking to capitalize on the widespread appreciation for regional products that are embedded in the cultural fabric of Indonesian society. Likewise, in the Sidoarjo area, a multitude of culinary MSME owners are trying to attract consumers by offering products that offer typical flavors native to their respective regions. Aim – The aim of this research is to determine operational cost tests, price tests and product quality tests to increase the income of MSMEs Design / methodology / approach – This research method is quantitative research with SEM-PLS as a data analysis method with the Smart PLS 3.0 program. Data was collected through a questionnaire distributed to 100 research sample respondents, namely culinary MSMEs in Sidoarjo, with each variable measured using certain statement items. Findings – The findings of this research indicate that culinary MSMEs in Sidoarjo need to focus more on pricing strategies and improving product quality to increase their income. Operational cost efficiency does not necessarily contribute significantly to increasing revenue, possibly because human resources are an obstacle. This research provides important insights for MSME players and stakeholders in formulating effective business strategies, by emphasizing the importance of price and product quality in influencing income. Conclusion - The three variables studied were Operational Costs (X1), Price (X2), and Product Quality (X3), only the Price and Product Quality variables showed a positive and significant impact on Income Increase (Y) among culinary MSME actors in Sidoarjo. Research Implication – This research was carried out to determine whether operational costs incurred could be more effective and have a positive impact on productivity and income. Limitations – Limitations This research only involved the Sidoarjo area

Soft sensor modeling based on self-organizing fuzzy neural network with clustering, merging, and splitting scheme

Abstract As a pivotal role in the control, optimization, and monitoring of contemporary industrial processes, soft sensors are frequently employed in the prediction of key quality variables. To achieve accurate prediction of key quality variables in industrial processes, a soft sensor modeling method based on the self-organizing fuzzy neural network with the clustering, merging, and splitting scheme (SOFNN-CMS) is proposed. First, the supervised fuzzy C-means clustering algorithm is proposed to identify the appropriate initial center and width of the fuzzy neural network, obtaining appropriate initial fuzzy rules. Then, a neuron merging and splitting strategy is designed to adjust the structure of the fuzzy neural network, by merging and splitting the hidden neurons according to the distance of clusters, increasing the adaptability of the fuzzy neural network. Besides, to accelerate the convergence of estimation errors, an improved Levenberg Marquardt algorithm is utilized to update neural network parameters in the training phase, realizing the soft sensor modeling of key quality variables. The effectiveness of the proposed SOFNN-CMS neural network is demonstrated on two benchmark problems and an industrial debutanizer column. Finally, the experiments showcase that the proposed SOFNN-CMS neural network can obtain better soft sensor modeling performance with a compact structure.

Pengaruh Fasilitas dan Kualitas Pelayanan terhadap Kepuasan Pengunjung di Konser Musik Blackpink “Born Pink World Tour Jakarta 2023”

The Blackpink "Born Pink World Tour Jakarta 2023" concert, organized by iMe Indonesia as the promoter, faced a flood of criticism from attendees regarding issues such as double tickets, discrepancies in the number of seating facilities, and complaints about staff service that was perceived as unfriendly and slow in resolving problems. This study aims to test the hypothesis regarding the relationship between facilities and service quality at the concert and the level of attendee satisfaction. The research method used is quantitative with a survey design. The survey technique was employed to collect primary data by providing respondents with indicator statements through an online questionnaire. The research sample consisted of one hundred respondents who were Blackpink fans attending the concert. The data obtained were processed using IBM SPSS version 26. Data analysis was performed using descriptive analysis and multiple linear regression. The results of the study indicate that the independent variables of Facilities (X1) and Service Quality (X2) simultaneously affect the dependent variable of Attendee Satisfaction (Y). The practical implication of this study is the importance for promoters to pay attention to the quality of facilities and services to enhance visitor satisfaction at music concerts.

Factors Influencing Public Interest in Using Sharia Microfinance Institution Produsts in Lamongan

Islamic Microfinance Institutions (IMI) have a strategic role with their main function being to collect funds from people with excess funds and channel the funds to people in need effectively and efficiently. The existence of IMI is expected to help people, especially those from the lower middle class, in gaining access to funding. However, the perception and attitude of the community towards bank interests and the profit sharing system show diversity. Some people still choose to accept interest and consider it the same as the profit sharing system, so an analysis of factors that influence the community's interest in using IMI products is needed. The purpose of this study was to determine the effect of knowledge, promotion, service quality, and religiosity on the community's interest in using IMI products in Lamongan. The sample used was 100 samples selected using the purposive sampling method. The data was processed using multiple linear regression analysis techniques. The test results stated that partially and simultaneously, the variables of knowledge, service quality, promotion, and religiosity had a positive effect on the community's interest in using IMI products in Lamongan.

Combined Application of Chemical Fertilizer and Organic Amendment Improved Soil Quality in a Wheat–Sweet Potato Rotation System

The long-term excessive use of chemical fertilizers may result in soil degradation, but manure and straw application is considered to be an effective approach for alleviating this problem. The aim of this study is to examine the long-term impacts of different fertilization patterns on soil quality variables in a wheat–sweet potato rotation system. Four treatments were conducted in a field trial for a duration of twelve years, including (1) no fertilizer (control, CK); (2) application of mineral fertilizers (NPK) alone; (3) NPK with crop straw return (NPKs); (4) combined use of NPK and farmyard manure (NPKm). Thirteen physical, chemical, and biological soil parameters were measured. The results showed that the NPKm and NPKs significantly improved the proportion of macroaggregates (>0.25 mm) by 24.7% and 21.9% compared to the NPK alone, respectively. The proportion of microaggregates (0.053–0.25 mm) under the NPKm was 47.4% significantly higher than the NPKs. Additionally, the NPKm resulted in a 22.2% and 19.6% increase in the SOC content than the NPK and NPKs, respectively. In terms of soil-available K, the NPKs resulted in levels that were 42.1% and 49.6% higher than the NPKm and NPK alone, respectively. Long-term fertilization significantly decreased soil pH by 0.95–1.85 units compared to the control, whereas manure application could alleviate soil acidification, as shown when the pH increased by 10.6–18.7%. The NPKm and NPKs resulted in significantly increased soil pHs by 10.6% and 18.7% compared to the NPK alone, respectively. In addition, the NPKm and NPKs increased N-acetyl-β-D-glucosaminidase activity by 52.6% and 60.3% compared to the NPK alone. Determined by the minimum data set method, the NPKm treatment exhibited the highest soil quality index, followed by the NPKs and NPK. Our findings suggested that the combined use of chemical fertilizers with organic amendments proved beneficial for enhancing soil quality.

Evaluating the Impact of the World Health Organization's Surgical Safety Checklist on Clinical Outcomes and Implementation Strategies: A Systematic Review.

This systematic review evaluates the impact and implementation strategies of the World Health Organization's Surgical Safety Checklist (WHO SSC) across diverse healthcare settings since its introduction in 2008. Our comprehensive analysis synthesizes findings from various study designs, including randomized controlled trials, qualitative studies, and meta-analyses, focusing on the checklist's effectiveness in reducing surgical complications and enhancing safety cultures within surgical teams. Despite its widespread endorsement and documented benefits, the review highlights significant variability in implementation quality and adherence, influenced by cultural, institutional, and procedural factors. The findings suggest that optimized adaptation and contextual application of the WHO SSC are crucial for maximizing its clinical benefits, particularly in low-resource settings. This review not only confirms the checklist's efficacy in improving surgical outcomes but also underscores the need for evidence-based strategies to enhance its global implementation and effectiveness.

An Integrated Dynamic and Quality Modeling Framework for Batch Processes

This manuscript considers batch process operations and addresses the challenge of identifying a model that synergistically captures the dynamic input–output behavior of continuously measured variables along with the quality variables measured only at batch termination. To this end, an optimization-based framework is developed to identify one model that captures both the dynamics between the inputs and the continuously measured output variables, measurements of which are available at every time step, and the relation between the dynamic “state” information and the terminal quality measurements. Existing approaches either do not identify the dynamic and the quality model simultaneously, or they simply connect the whole trajectory of the process variables with the qualities and do not address the dynamic relationship between the inputs and the process variables. The improved modelling performance of the model obtained from this approach is demonstrated using data from a Uni-axial Rotational Molding process, and compared with existing modelling approaches.

Analysis of the General Situation of Organic Fertilizer in China

Organic fertilizers are crucial to China's agriculture, significantly enhancing soil fertility and sustainability. Derived from sources like livestock manure, crop residues, and green manure, these fertilizers improve soil structure, water retention, and nutrient availability, thus boosting crop yields and soil health. Despite their benefits, challenges such as variability in quality, application efficiency, and balancing with inorganic fertilizers remain. Government policies and ongoing research are addressing these issues to optimize the use of organic fertilizers and support sustainable agricultural practices.