Application Of Quality Management Research Articles

Understanding oxidation potential and degradation mechanism of acid-treated TiO2 coupled g-C3N4 S-scheme heterojunction photocatalyst for the removal of gaseous formaldehyde

In this work, a step (S)-scheme heterojunction catalyst is synthesized by coupling acid-treated TiO2 (ATT) with graphitic carbon nitride nanosheet (g-C3N4: GCN) and used for adsorption-photocatalytic removal of gaseous formaldehyde (FA: 100 - 500 ppm). The acid treatment of TiO2 proves to be an effective approach for improving the surface porosity of ATT with more abundant active sites for efficient catalytic reactions. Similarly, the S-scheme heterojunction displays superior redox potential (i.e., availability of free e- at conduction band of GCN (reduction potential of −0.485 eV/NHE) and free h+ at valance band of ATT (oxidation potential 2.79 eV/NHE)). As such, in a dynamic packed-bed flow reactor, the improved textural and optical properties of ATT-GCN (bed mass = 10 mg) offer outstanding adsorption and photocatalytic oxidation potential, effectively removing 86% of 200 ppm FA vapor at a reaction rate of 70.2 nmole mg−1 min−1 under slightly humidified conditions (e.g., 20% relative humidity (RH) and 21% O2) when exposed to 32-W UV-A light source. Under these operational conditions, the ATT-GCN achieves the superior performance metrics (e.g., quantum yield of 5.1E-02 molec. photon-1, space–time yield of 5.1E-03 molec. photon-1 mg−1, and specific clean air delivery rate of 515.4 L g-1 h−1). In-situ Kelvin probe microscope analysis of ATT-GCN postulates the formation of a strong built-in electric field to improve the separation of charge carriers. Considerations on the degradation pathway and intermediate dynamics with the aid of in-situ DRIFTS analysis suggest that the presence of water molecules (e.g., 20% RH) is beneficial for accelerating the oxidation and mineralization of FA molecules relative to a dry gas stream. The overall outcomes of this work are expected to help deliver new paths for the construction of advanced photocatalytic systems for upscaled applications toward air quality management.

Optimizing indoor air models through k-means clustering of nanoparticle size distribution data

Sectional physics-based aerosol models imply a computational effort that hinders their use in building digital twins, real-time predictive control, and computer-based iterative optimization versus black-box approaches. The innovation of this paper lies in the proposal of a novel systematic methodology to optimize the number of size bins in sectional reduced-order models for particle concentration simulations. This allows its application in indoor air quality management and overcomes generalizability and data-dependency issues of black-box models. This method, based on k-means clustering, aims to ensure precision when tackling relatively fast fine and ultrafine particle simulations targeting the reduction of time and resource consumption from experimentally-determined aerosol size distribution time series. Consequently, three tests were carried out using combustion aerosols inside a custom-designed emission chamber to simulate emission hotspots in non-commercial and occupational settings. 2-, 3-, 4-, and 5-cluster classifications were evaluated for data coming from 13 particle size bins through silhouette analysis and the study of their temporal profiles. Results show that the 4-cluster classification summarizes the behavior of data in the 10–420 nm range, ensuing up to a 77 % improvement in the model's computational demand. Moreover, this method allows an accurate definition of the necessary size ranges to calculate nanoparticle concentrations inside the chamber and facilitates the interpretation of aerosol behavior and processes through the resulting clusters' temporal profiles.

Short-term air pollution prediction using graph convolutional neural networks

Pollution is a major concern in the present day, causing multiple illnesses and deaths, specifically in developing countries in Asia and Africa. While it has drawn worldwide attention as governments try to issue laws to meet certain criteria for air pollution levels, pollution concentration forecasting has become a major challenge. Particularly, short term forecasting will help to gain information regarding concentrations of harmful pollutants for the upcoming hours and enable better decision-making with regards to controlling air pollution. In this paper, we investigate spatio-temporal graph-based models to determine the best methods for spatial and temporal analysis of data. The models have the additional capacity to perform multi-variate predictions of correlated data, i.e., predicting multiple pollutant concentrations simultaneously, thus requiring lower computational efforts. A real-world pollution dataset measured over Delhi, India, is used to comparing the proposed models with baselines, which shows the Spatio-Temporal Graph Convolution Neural Network (STGCN) models to be performing better than others. For a better understanding of model architectures with the most effective strategies for spatial and temporal data analysis, three models, namely STGCN-A, STGCN-B, STGCN-C have been developed. The models have been compared with 6 other baselines over multiple forecasting horizons of 1 h, 24 h, and 48 h timesteps using various metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE). On the PM2.5 dataset of Delhi, STGCN-B achieves a performance of 10.53 MAE, 6.92 RMSE and 25.25 MAPE for a 1 h forecast, while STGCN-C achieves 20.18 MAE, 14.73 RMSE and 55.45 MAPE for a 24 h forecast. In general, both structures achieve similar results, with STGCN-C being better in many cases. They are further analysed through observation-prediction graphs and Taylor diagrams, which give an insight into our findings. The models are additionally validated on a benchmark real-world dataset from California, USA for better understanding of the spatio-temporal relations and model performances on a more stable dataset, where STGCN-C performs best for PM2.5 with 4.30 RMSE, 1.98 MAE, 25.96 MAPE for 1 h predictions for univariate data and 3.63 RMSE, 1.88 MAE and 25.91 MAPE in multivariate forecasting. The developed spatio-temporal graph-based models hold promising applications in urban air quality management, aiding policymakers in implementing targeted interventions to mitigate pollution-related health risks. Furthermore, these models can support public health agencies by providing timely and accurate forecasts of pollutant concentrations, enabling proactive measures to safeguard community well-being. Our study showcases the efficacy of spatio-temporal graph-based models in accurately forecasting air pollutant concentrations, with particular emphasis on short-term predictions. By leveraging multi-variate capabilities, our proposed models demonstrate superior performance compared to baseline approaches across various forecasting horizons.

Analysis of The Effect of Total Quality Management on Organizational Performance in Small and Medium Enterprises with Innovation Process Speed as a Mediating Variable in SMES in the Culinary Sector in Bogor City

The increasing competitiveness of the business environment, especially in the culinary industry, has encouraged small and medium-sized enterprises to focus on improving their organizational performance. This study aims to determine and analyze the effect of total quality management on organizational performance in small and medium enterprises with the speed of the innovation process as a mediating variable in culinary SMEs in Bogor City. The method used in this research is quantitative. The population in this study were 100 respondents of culinary SMEs in Bogor City. This study uses purposive sampling technique to determine the sample. Based on the results showed that TQM significantly has a positive influence on organizational performance, TQM significantly has a positive influence on the speed of the innovation process, the speed of the innovation process significantly has a positive influence on organizational performance, and there is a positive relationship and influence between Total Quality Management and organizational performance mediated by the speed of the innovation process. The results of this study provide implications for SMEs regarding the application of Total Quality Management. SMEs are expected to focus on services and products that meet consumer demand, reduce production costs, and offer affordable prices without sacrificing quality, thereby increasing consumer loyalty and business competitiveness. This research has implications for the need for SMEs to invest in training and development of effective TQM systems and pay attention to market dynamics to continue to innovate and meet consumer expectations.

Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)

Abstract. Severe airborne particulate matter (PM, including PM2.5 and PM10) pollution in India has caused widespread concern. Accurate PM concentrations are fundamental for scientific policymaking and health impact assessment, while surface observations in India are limited due to scarce sites and uneven distribution. In this work, a simple structured, efficient, and robust model based on the Light Gradient-Boosting Machine (LightGBM) was developed to fuse multisource data and estimate long-term (1980–2022) historical daily ground PM concentrations in India (LongPMInd). The LightGBM model shows good accuracy with out-of-sample, out-of-site, and out-of-year cross-validation (CV) test R2 values of 0.77, 0.70, and 0.66, respectively. Small performance gaps between PM2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 µg m−3) indicate low overfitting risks. With great generalization ability, the openly accessible, long-term, and high-quality daily PM2.5 and PM10 products were then reconstructed (10 km, 1980–2022). This showed that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations have significantly increased (p<0.05) in most regions since 2000 (0.34 µgm-3yr-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Programme, and PM2.5 concentrations declined in most regions (−0.78 µgm-3yr-1) during 2018–2022. Severe PM2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % confidence interval (CI) [0.65, 0.80]) million in 2000 to 1.22 (95 % CI [1.03, 1.41]) million in 2019, particularly in the IGP, where attributable mortality increased from 0.36 million to 0.60 million. LongPMInd has the potential to support multiple applications of air quality management, public health initiatives, and efforts to address climate change. The daily and monthly PM2.5 and PM10 concentrations are publicly accessible at https://doi.org/10.5281/zenodo.10073944 (Wang et al., 2023a).

Evaluation of industrial and consumer 3-D resin printer fabrication of microdevices for quality management of genetic resources in aquatic species

Aquatic germplasm repositories can play a pivotal role in securing the genetic diversity of natural populations and agriculturally important aquatic species. However, existing technologies for repository development and operation face challenges in terms of accuracy, precision, efficiency, and cost-effectiveness, especially for microdevices used in gamete quality evaluation. Quality management is critical throughout genetic resource protection processes from sample collection to final usage. In this study, we examined the potential of using three-dimensional (3-D) stereolithography resin printing to address these challenges and evaluated the overall capabilities and limitations of a representative industrial 3-D resin printer with a price of US$18,000, a consumer-level printer with a price <US$700, and soft lithography, a conventional microfabrication method. A standardized test object, the Integrated Geometry Sampler (IGS), and a device with application in repository quality management, the Single-piece Sperm Counting Chamber (SSCC), were printed to determine capabilities and evaluate differences in targeted versus printed depths and heights. The IGS design had an array of negative and positive features with dimensions ranging from 1 mm to 0.02 mm in width and depth. The SSCC consisted of grid and wall features to facilitate cell counting. The SSCC was evaluated with polydimethylsiloxane (PDMS) devices cast from a typical photoresist and silicon mold. Fabrication quality was evaluated by optical profilometry for parameters such as dimensional accuracy, precision, and visual morphology. Fabrication time and cost were also evaluated. The precision, reliability, and surface quality of industrial-grade 3-D resin printing were satisfactory for operations requiring depths or heights larger than 0.1 mm due to a low discrepancy between targeted and measured dimensions across a range of 1 mm to 0.1 mm. Meanwhile, consumer-grade printers were suitable for microdevices with depths or heights larger than 0.2 mm. While the performance of either of these printers could be further optimized, their current capabilities, broad availability, low cost of operation, high throughput, and simplicity offer great promise for rapid development and widespread use of standardized microdevices for numerous applications, including gamete quality evaluation and “laboratory-on-a-chip” applications in support of aquatic germplasm repositories.

Exploratory Data Analytics of Air Pollutant Data for Air Quality Management Application

Purpose: With escalating concerns over the detrimental impacts of air pollution on public health and the environment, this study embarks on a comprehensive exploration of air quality dynamics, focusing on Ogun state metropolis of Nigeria. The primary objective is to contribute to the burgeoning field of air quality management by harnessing the power of data analytics, exploratory data analysis, and advanced python programming libraries. The study seeks to address critical questions regarding the current state of air quality, major sources of pollution, government interventions, and individual contributions to better air quality. Methodology: The research employs Exploratory Data Analysis to unveil intrinsic patterns and trends within historical air quality datasets extracted from IoT devices located across Ogun state. This initial phase aims to discern key statistical parameters, including mean concentrations, variations, and correlations among various pollutants. Subsequently, the study employs Topic Modeling to extract latent themes and sentiments from qualitative data, specifically focusing on public opinions gathered through digital surveys. Resulting topics provide insights into public perceptions regarding air quality, pollution sources, and the efficacy of governmental interventions. Findings: Experimental result is revealing of the diversity of the dataset. The EDA returned a mean Particulate Matter concentration of approximately 12.65 µg/m³, while the mean Nitric Oxide concentration is approximately 13.56 µg/m³. Notable correlations include a strong positive correlation between PM2.5 and PM10 (0.69), indicating a substantial association between fine and coarse particulate matter. Additionally, there is a noteworthy positive correlation between NO and Nox (0.97), suggesting a high degree of correlation between nitric oxide and nitrogen oxide levels. However, negative correlations are observed, such as the substantial negative correlation between RH (relative humidity) and PM2.5 (-0.46), implying an inverse relationship between humidity and fine particulate matter levels. The study develops an innovative air quality management application. Unique Contribution to Theory, Practice and Policy: The application aims to empower both the public and policymakers with actionable insights, fostering informed decision-making and collaborative efforts towards improving air quality. It is highly recommended that industry experts should continue to imbibe ethical standards in data acquisition and deployment practices.

Improving the non-structural preparedness of the selected hospital based on the FOCUS-PDCA1 model: action research

IntroductionWith the intensification of the country’s development process, the expansion of cities and population, and the inclusion of Iran in the accident-prone category, reducing the vulnerability of non-structures has received more attention from the organizations involved. In addition to damage to communities and infrastructure, accidents can affect hospitals and their non-organizational components. Hospitals, as the front line of providing medical services after accidents, must maintain their stability, ensure the safety of their patients and employees, and continue to operate without interruption as in normal conditions. Therefore, it is necessary to evaluate the non-structural safety and their preparedness to ensure they can perform acceptable in critical conditions.MethodsThis applied research was conducted in 2023 (September to December) using the participatory action research method in all selected hospital departments. The level of non-structural preparedness of the hospital was checked using the valid “Hospital Safety Index” questionnaire and the non-structural weaknesses of the hospital were identified. Then, in action research using the FOCUS-PDCA model, a program was implemented to improve the non-structural preparedness of different departments of hospitals in the face of accidents and disasters. The non-structural readiness level of the hospital was compared before and after the implementation of the change.ResultsBased on the evaluation conducted in the present study, the lowest level of safety was observed in the water supply system, office furniture and appliances, and fuel storage. The waste management systems, the fire protection system, and the long-distance communication systems were at a desirable performance level. Although in the evaluation before the change, the overall score of the hospital was 71.01%, and it had a desirable performance level in non-structural factors, in all the involved parts of the hospital, the sensitive, critical, and practical parts in the operation of the hospital had an average and sometimes low safety level. According to the obtained safety score, the safety level of the selected hospital before the change was 7 out of 10 (level seven of safety evaluation = medium). After the change and corrective measures, the non-structural safety assessment score was 76.93, and the hospital’s safety level was raised by one step to 8 out of 10 (8th level of safety assessment = relatively favorable).ConclusionThe present study showed that the application of Total Quality Management (TQM), primarily its application tool FOCUS-PDCA, is efficient and helpful in improving the non-structural preparedness of hospitals. Using action research in the health field in accidents and disasters can open blind knots in different dimensions of preparedness (structural, non-structural, and functional).

An efficient approach for discovering Graph Entity Dependencies (GEDs)

Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social media networks and entity resolution. In this paper, we study the discovery problem of GEDs—finding a minimal cover of valid GEDs in a given graph data. We formalise the problem, and propose an effective and efficient approach to overcome major bottlenecks in GED discovery. In particular, we leverage existing graph partitioning algorithms to enable fast GED-scope discovery, and employ effective pruning strategies over the prohibitively large space of candidate dependencies. Furthermore, we define an interestingness measure for GEDs based on the minimum description length principle, to score and rank the mined cover set of GEDs. Finally, we demonstrate the scalability and effectiveness of our GED discovery approach through extensive experiments on real-world benchmark graph data sets; and present the usefulness of the discovered rules in different downstream data quality management applications.

Penerapan Total Quality Management Pada Dunia Pendidikan Dalam Meningkatkan Kinerja Tenaga Kependidikan

This research aims to analyze the application of Total Quality Management (TQM) in improving the performance of education staff. In an era of globalization that demands improving the quality of education, TQM is considered a strategic approach that is able to increase the efficiency and effectiveness of education personnel. The research method used is a case study with a qualitative approach. Data collection was carried out through direct observation, in-depth interviews with school principals, teachers and administrative staff, as well as analysis of documents related to the implementation of TQM in selected vocational schools. The research results show that the implementation of TQM contributes significantly to improving the performance of education personnel in several main aspects: first, through strengthening a quality culture that emphasizes the importance of student satisfaction and achieving high education standards; second, continuous work process improvement through the Plan-Do-Check-Act (PDCA) cycle; and third, increasing job satisfaction and professionalism of educational staff through continuous training and career development programs. In addition, key factors that support the successful implementation of TQM in the educational environment include strong commitment from school management, active involvement of all stakeholders, and effective and transparent communication. This research also identifies challenges in implementing TQM, such as resistance to change and limited resources, but emphasizes that the long-term benefits of implementing TQM are much greater. The conclusion of this research is that TQM is an effective and efficient strategy in improving the performance of educational staff in vocational schools, and therefore, can be adopted more widely by other educational institutions. Successful implementation of TQM will have a positive impact on the overall quality of education, so that it can produce graduates who are competent and ready to compete in the world of work.

The degree of applying quality management database in Jordanian food companies from managers perspectives

The degree of applying quality management database in Jordanian food companies from managers perspectives

Application of Total Quality Management on Operational Performance

Purpose – The purpose of this study is to find out how customer satisfaction, employee empowerment, continuous quality improvement, and fact-based management are elements of TQM that can affect operational performance. Methodology/approach – This study is conducted in Quantitative Method by gathering primary data from employee of Bank Mandiri in the Special Region of Yogyakarta (DIY). The sampling technique is purposive sampling. The data that has been obtained is processed using the Smart PLS statistical tool. Findings – It was found that customer satisfaction, employee empowerment, and continuous quality improvement were proven to have a negative influence on operational performance. While fact-based management in this study is proven to have a positive influence on operational performance. Novelty/value – This study contributes to deliver knowledge about how the components of total quality management represented by customer satisfaction, employee empowerment, continuous quality improvement, and fact-based management affect operational performance of Banking Company, specifically Bank Mandiri in the Special Region of Yogyakarta (DIY) which has never been studied before.

The Impact of Total Quality Management (TQM), Supply Chain Management (SCM) and Knowledge Management (KM) on Corporate Performance through the Corporate Competitiveness (Case Study of PT Brantas Abipraya (Persero))

In many countries, competitiveness management and business performance are considered as a key factor in a company's success. In general, companies that achieve rapid progress are those that can implement various management strategies to improve company performance appropriately in order to produce great company performance. PT Brantas Abipraya (Persero) is pursuing various strategies to increase the company's competitiveness by implementing Total Quality Management (TQM), Supply Chain Management (SCM), and Knowledge Management (KM) to improve the company's performance. This research aims to analyze the impact of Total Quality Management (TQM), Supply Chain Management (SCM), and Knowledge Management (KM) on company performance through increasing company competitiveness. The type of research used by researchers is causative research, which is a type of characteristic research with problems in the form of a cause-and-effect relationship between two or more variables. The researcher used a survey research design on 180 respondents with a probability sampling technique, namely a sampling technique that provides an equal opportunity for each element (member) of the population to be selected as a member of the sample. The data were analyzed using the structural equation modeling (SEM) method using the LISREL 10.0 program. The research results show that the application of Total Quality Management (TQM), Supply Chain Management (SCM), and Knowledge Management (KM) has a positive and significant effect on the company's competitiveness. Competitiveness itself and the application of Total Quality Management (TQM), Supply Chain Management (SCM), and Knowledge Management (KM) also have a positive and significant effect on company performance, and the application of Total Quality Management (TQM), Supply Chain Management (SCM), and Knowledge Management (KM) through competitiveness has a positive and significant effect on the company performance of PT Brantas Abipraya (Persero).

The Application of Quality Management in Chip Production

This article explores the importance and specific applications of quality management in the chip production process. As a core link in high-tech manufacturing, chip production has extremely strict requirements for product quality. Quality management ensures the stability of the chip production process and the reliability of product quality through a series of methods and means. This article first introduces the basic process of chip production and the key links of quality control, and then analyzes in detail the core elements of quality management, including quality control, quality assurance, and quality improvement. In addition, the article also analyzes the main problems faced in chip production quality management through practical cases. Finally, the specific application of quality management in chip production was elaborated, including process control, design and process defects, supply chain management, process technology innovation, and prevention and resolution of personnel management issues. And the future development trend of chip production quality management was discussed. Through this study, the aim is to provide effective quality management strategies and practical methods for chip production enterprises, and promote the sustainable and healthy development of the chip industry.

Social Quality Management in the Lens of Philosophy and Methodology of Science

Relevance. Quality problems have been relevant for many centuries. At the same time, quality is viewed from the perspective of various sciences and industries indirectly: as a category identified with any sphere. Quality management is also of interest to many scientists and specialists, but most often in the technical or economic sphere. At the same time, its social aspect is obvious. Moreover, in the context of social management (social) quality management is an effective tool aimed at ensuring and improving the quality of life. However, despite the widespread application and adaptation of (social) quality management in various fields of science and practice, philosophical understanding is presented quite selectively. In this regard, in this study, it is advisable to present social quality management from the perspective of philosophy and methodology of science.Purpose. In the work, the author sets himself the purpose of conducting a comprehensive analysis of social quality management from a philosophical and methodological perspective.Objectives. To achieve this goal, it is relevant to solve the following tasks: analysis of social quality management as a post-non-classical concept of the philosophy of complexity; assessment and interpretation of social quality management in a broad plane of humanitarianism; attempt to define the philosophical understanding of social quality management.Methodology. To achieve this goal, the author uses in his work such methods and methodologies as the theory of complexity (complexity), the semiotic-digital dimension, and the methodology for constructing sociotechnical landscapes of digital reality. Results. The paper presents the results of a comprehensive analysis of social quality management from a philosophical and methodological perspective.Conclusions. Based on the results of the study, the author's understanding of social quality management in a philosophical and methodological context is substantiated; social quality management is presented as a concept of the complexity paradigm; the interrelationships of social quality management and the sociotechnical landscape of digital reality in a single field of modern humanities are established.

The Influence of Total Quality Management, Lean Manufacturing, and Entrepreneurial Orientation on Operating Performance in Semarangan Batik Entrepreneurs in Semarang City

Operations management is very important for the production process, the performance it produces is very important for the sustainability of the company. Further research is needed because of the many production problems faced by batik craftsmen in Semarang City. This study aims to study how the application of Total Quality Management, entrepreneurial orientation, and Lean Manufacturing impacts on operating performance. This study used questionnaires as a data collection method. Simple random sampling was used to select the sample, and 59 people were selected. Multiple linear regression analysis was used to evaluate the effect of the independent variable on the dependent variable of the study. The results showed that total quality management improved operating performance (H1 received). The second hypothesis says Lean Manufacturing improves operations performance is accepted, and the third hypothesis says entrepreneurial orientation improves operations performance is accepted. The results of the coefficient of determination test show that the three independent variables affect operating performance by 53%. It is recommended to Semarang batik craftsmen in Semarang City to provide training to their employees, improve their production methods, and reduce their dependence on intermediaries in sales.

Agroindustry Development Strategy for Palm Sugar in Bone Regency

Bone Regency is one of the potential palm sugar producing areas because it has many sources of raw materials. However, palm sugar is still produced traditionally, resulting in low product quality. This study aims to understand the overview of palm sugar industry and analyze the appropriate development strategy to be applied to the palm sugar industry in Bone Regency. The research method applied is descriptive quantitative. The data analysis method uses descriptive analysis, SWOT and AHP. The results of alternative strategies using SWOT are SO strategy: increased promotion for marketing expansion online and offline. WO strategy: improvement of palm sugar production infrastructure to meet export product standards. ST strategy: improving product quality through training/technical guidance on the application of quality management. WT strategy: increased commitment and cooperation between palm stakeholders in strengthening palm sugar agroindustry, namely the quality of export-ready palm sugar products. The priority strategy based on the AHP results, especially the main determinant factor is human resources, with the responsible actor being the government. The final goal of the palm sugar agroindustry development strategy is the improvement of palm sugar production facilities and infrastructure to meet export product standards.

Flow control by circular cavities in lateral flow porous membranes.

This research explores the flow penetration in porous media by virtue of capillary action and geometric control of the liquid imbibition rate in microfluidic paper-based analytical devices (μPADs) having applications in food quality management, medical diagnostics, and environmental monitoring. We examine changes in flow resistance and membrane geometry, aiming to understand factors influencing capillary penetration rates for various practical applications. We conducted experiments and simulations using lateral porous membranes and altered the flow resistance by changing the liquids or the paper channel geometry by adding cavities. From experiments, it was revealed that by creating a circular cavity in the paper channel, the penetration rate was sufficiently altered. Moreover, increasing the cavity size and type of liquid (w.r.t. viscosity) also caused a decrease in the flow rate. Imbibition rates were also influenced by the position of the cavities in the paper channel. The maximum delay for water was almost 2 times with a 16 mm circular cavity located at 3 cm from strip bottom edge. Overall, we attained a maximum delay in the case of castor oil which was almost 85 times slower than water and 3.7 times slower than olive oil. A good agreement was observed with CFD analysis. We believe that this research would help in developing advance techniques to enhance the flow control strategies in μPADs and indicators.

Quality Management Procedures and Applications for Implementing Manufacturing Integration Modeling in an Engineering Company

Quality Management Procedures and Applications for Implementing Manufacturing Integration Modeling in an Engineering Company

Exploring Quality in Higher Education: An Introduction and Theoretical Framework

Purpose: This paper aims to provide an in-depth understanding of the diverse dimensions that contribute to educational excellence, encompassing factors like teaching methodologies, curriculum design, assessment practices, and institutional culture. This exploration establishes a foundational framework for evaluating, enhancing, and ensuring the delivery of high-quality education, guiding institutions toward continuous improvement, and meeting the evolving needs of students and society. Methodology: Exploratory research involves the collection of information as per the keywords identified using various websites and search engines like Google, Google Scholar, and AI-based GPTs using original, review, and AI-generated focus group information this relevant information are analysed as per the research framework used and suggestions are derived. Results/Outcome: A concise summary of the paper's objectives, methodologies, key findings, and implications regarding quality in higher education are presented. This includes an exploratory analysis of the introduction and theoretical frameworks in quality in higher education. This includes the importance of quality in higher education within the contemporary educational landscape, the significance of exploring quality theories and frameworks in enhancing educational outcomes and institutional effectiveness, and definitions &amp; interpretations of quality in higher education from scholarly perspectives. Originality/Values: Several issues like various Dimensions of Quality, Principles, and applications of Total Quality Management (TQM) in HEIs, Quality Assurance and Accreditation, Benchmarking and Continuous Improvement, Challenges in Implementing Quality Measures, and Some of the potential strategies and recommendations for addressing the challenges and advancing quality in higher education. Type of Paper: Exploratory Research.