Statistical Techniques Research Articles

The Impact of Lexical Bundle Length on L2 Oral Proficiency

Lexical bundles (LBs) are crucial in L2 oral proficiency, yet their complexity in terms of length is under-researched. This study therefore examines the relationship between longer and shorter LBs and oral proficiency among 150 L2 learners of varying proficiency levels at a UK university. Through the analysis of oral presentation data (scores ranging from intermediate to advanced) and employing a combined text-internal and text-external approach (two- to five-word bundles), this study advances an innovative text-internal LB refinement procedure, thus isolating the unique contribution of LB length. Robust regression, dominance analysis, and random forest statistical techniques reveal the predictive power of bigram mutual information (MI) and longer three-to-five-word sequences on higher proficiency scores. Our results show that learners using higher MI score bigrams tend to perform better in their presentations, with a strong positive impact on scores (b = 14.38, 95% CI [8.01, 20.76], t = 4.42; dominance weight = 58.63%). Additionally, the use of longer three-to-five-word phrases also contributes to better performance, though to a lesser extent (dominance weight = 18.80%). These findings highlight the pedagogical potential of a nuanced approach to the strategic deployment of LBs, particularly bigram MI, to foster oral proficiency. Suggestions for future LB proficiency research are discussed in relation to L2 speech production models.

Using the methodology of transformation of geophysical fields in the study of dike-type ore systems in the zone of influence of the Chai-Yurinsky deep fault

Identification of prospecting geophysical signs of promising areas at the level of ore nodes and fields, deposits and ore structures (ore-localizing structures) has always been one of the main contents of geophysical research. However, especially at the deposit – ore structures level, very often the solution of these tasks becomes very difficult and difficult to implement. The problem is that, in general, rocks in the research area are quite contrasting in their physical properties; however, ore-bearing dike bodies represented by beresitized diorite-porphyrites do not always confidently differ in their physical characteristics from the sandy, carbonaceous, sulfidized and shale sedimentary rocks that contain them. The use of approaches based on statistical processing techniques in the interpretation of geophysical data made it possible to establish geological and geophysical signs of gold-quartz mineralization of the dike type within the study area and thus predict gold mineralization.The paper presents the results of processing geophysical materials in order to create a list of features characteristic of gold-quartz deposits of the dyke type in the Central-Kolyma region (Magadan Oblast).The geophysical research complex consisted of electrical profiling and magnetic exploration. MMPOS-2 magnetometers were used to measure the geomagnetic field. The coordinates of the measurement points were determined using a GPS receiver structurally connected to a magnetometer. The methodology of the work provided for the introduction of variations of the geomagnetic field. The variation of the Earth’s magnetic field was taken into account using the SURV software. The work by the electrical profiling method was carried out using an instrument complex: AIE-2 using the median gradient method in modification of induced polarization (SG-VP) at a frequency of 0.3 Hz. Supply line AB=800-900 m, receiving line MN=10 m.The presented set of works is capable of solving the problems of forecasting gold mineralization in the zone of influence of the Chai-Yurinsky fault of the Yana-Kolyma metallogenic system. Based on the interpretation of the geophysical data, prospecting geological and geophysical signs of gold-quartz mineralization within the ore field are outlined.One of the promising methodological methods of interpretation in determining the search signs of potentially ore zones is electrical exploration by the method of induced polarization.

Pengembangan Media Pembelajaran Big Book “Garuda Pancasila” untuk Meningkatkan Pemahaman Nilai Pancasila Siswa kelas 3

Development (R&D). This model is implemented through 4D with 4 stages: 1) Define, 2) Design, 3) Develop, 4) Disseminate. The types of data applied are quantitative and qualitative data. Data was collected through interviews, questionnaires and tests. With research instruments in the form of interview guides, validation instruments which include validation instruments for content/material experts and validation instruments for learning media experts, this questionnaire has teacher and student response questionnaires to test the practicality of the product, as well as instruments for testing the effectiveness and effectiveness of the product from students. . Data were analyzed using descriptive statistical data analysis techniques. Quantitative data was obtained from a Likert scale score questionnaire and qualitative data was in the form of scores from validator suggestions. The results explain that the development of Big Book media in the validation stage got a score of 96% in the very good group, then in the validation stage the learning media design expert got a score of 62% in the very good category, after validation was applied the learning media design expert got a score of 76% in the category Good. In order to see the effectiveness of the Big Book learning process for increasing knowledge of Pancasila values, pre and post tests were applied to the sample and then applied through the Wilcoxon test and obtained sig. < 0.05 then there is a difference between the values. before and after applying the product. Testing via the Mann-Whitney test on sig. ∝= 0.05. The value is obtained from the hypothesis < 0.05, namely. 0.002 means that this big book learning media received a positive response from the teachers because the learning media can provide increased understanding of Pancasila values ​​for class III A students at SD Catholic 14 Manado.

KNOWLEDGE REGARDING DIGITAL TRANSACTIONS AMONG WORKING WOMEN

A digital transaction refers to the exchange of value or payment between two parties using electronic means, such as through the internet or other digital channels. Both the payer and the payee use digital methods to send and receive money, while making digital payments. The study was conducted in Hisar district during the year 2022-23 with a sample size of 160 working women (80 each from teaching and non-teaching) selected randomly from four colleges of the university. A set of 6 independent variables and 1 dependent variable was selected for the study.The collected data was analysed with suitable statistical tools and techniques such as frequency, percentage, chi-square test and correlation to reveal major findings. The findings revealed that the majority of respondents from the teaching staff were in the age group of 35-45 years were married and lived in urban areas, whereas the majority of respondents from the non-teaching staff were in the age group of 25-35 years married and majority of them residing in urban areas. Almost cent percent majority from the teaching staff had exposure to digital transactions and the majority of them were using digital transactions for more than 3 years with 40 percent having internet as source of information. From the non-teaching staff, the majority of respondents had exposure to digital transactions with 28.75 percent using it for 1-2 years having localite source of information. The knowledge level was assessed using 20 statements and found that majority of the teaching staff reported having a medium degree of understanding of digital transactions, compared to a low level of knowledge among the non-teaching staff. A negative correlation was found between the age and knowledge of the respondents in both teaching and non-teaching staff.

KORELASI KEPEMIMPINAN KEPALA SEKOLAH TERHADAP EFEKTIVITAS PEMBELAJARAN DI ERA KURIKULUM MERDEKA DI SEKOLAH MADRASAH IBTIDAIYAH SWASTA DI PEKANBARU

The aim of this research is to find out whether there is a correlation between school principal leadership and learning effectiveness in their curriculum era at private Madrasah Ibtidaiyah schools in Pekanbaru. This research is quantitative research. This research involved 354 people, including teachers, school principals and students from four private Madrasah Ibtidaiyah located in the city of Pekanbaru. . As a research sample, respondents who are met by chance and are willing to participate will be taken by chance (accidental sampling method). After the data was collected, the Spearman Rank statistical technique was used to analyze the correlation between two variables using Spearman rho. The results of this research are that there is a positive correlation between school principal leadership and learning effectiveness in their curriculum era at private Madrasah Ibtidaiyah schools in Pekanbaru.

TRAINING NEED ASSESSMENT OF AGRI-INPUT DEALERS

In India, research and extension are two crucial components of agricultural growth. To achieve continuous expansion in agriculture, an effective extension system can quickly disseminate farm technology throughout farming communities. Agricultural input dealers play a significant role in the production and sale of agricultural products in addition to the extension services provided to farmers by the state Department of Agriculture and other organizations. Due to their easy accessibility in rural regions, they build a solid connection and help to enhance the agricultural extension system by offering the farming community useful services in a way that disseminates technology. Therefore, it is crucial that they take refresher training courses to provide themselves with the most recent agricultural expertise and information. Hence keeping this in mind, the present investigation was conducted in Jorhat, Golaghat, Morigaon and Udalguri districts of Assam during 2021-22 to ascertain training needs of agro-input dealers. A total 120 numbers of Agri-input dealers were selected as respondents by proportionate allocation method from each of the four districts. Data were collected by using pretested structured interview schedule. The needs assessment were done in the training areas viz., manures and fertilizers , pesticides, seeds, farm machineries, computer application and record keeping with three point continuum of 'much needed', 'needed' and 'not needed' having the value of 3,2 and 1 respectively. The data were analyzed with the help of proper statistical techniques and it was revealed that 96.66 percent of the respondents require training on integrated nutrient management practices, 70 percent have much need for pest protection of stored seeds, training on certification techniques of seeds (76.66 %), latest farm implements and machinery (78.06 %) and 75.97 percent have much need for training on record keeping software. A few characteristics of the dealers like age, experience in dealership, input supply and annual income have significantly negative relationship with their training needs.

KORELASI KEPEMIMPINAN KEPALA SEKOLAH TERHADAP EFEKTIVITAS PEMBELAJARAN DI ERA KURIKULUM MERDEKA DI SEKOLAH MADRASAH IBTIDAIYAH SWASTA DI PEKANBARU

The aim of this research is to find out whether there is a correlation between school principal leadership and learning effectiveness in their curriculum era at private Madrasah Ibtidaiyah schools in Pekanbaru. This research is quantitative research. This research involved 354 people, including teachers, school principals and students from four private Madrasah Ibtidaiyah located in the city of Pekanbaru. . As a research sample, respondents who are met by chance and are willing to participate will be taken by chance (accidental sampling method). After the data was collected, the Spearman Rank statistical technique was used to analyze the correlation between two variables using Spearman rho. The results of this research are that there is a positive correlation between school principal leadership and learning effectiveness in their curriculum era at private Madrasah Ibtidaiyah schools in Pekanbaru.

The Influences of Misinformation on Incidences of Politically Motivated Violence in Europe

Misinformation has become increasingly prevalent in online media. Transnational misinformation, in particular, poses an increasing threat to the security and stability of modern nation-states. To this end, at least some anecdotal evidence suggests a direct relationship between misinformation and domestic acts of politically motivated violence. Yet, such claims lack systematic empirical evidence, especially as it relates to transnational misinformation. To address this, this paper analyzes the effects of transnational misinformation on several distinct forms of domestic political violence across Europe, sharpening our empirical understanding of the purported association between misinformation and political violence. This is achieved through the fusion of a fine-grained spatial-temporal dataset of confirmed instances of news-based misinformation with daily event data on incidents of political conflict. These combined data are analyzed using a variety of statistical techniques for the period covering January 2016–May 2022. Findings imply a positive association between transnational misinformation and several forms of political violence in Europe. Furthermore, this association is more reliable for civilian-to-government violence events than for violence between civilian actors. Altogether this paper accordingly provides novel and nuanced empirical evidence for the pernicious effects of transnational misinformation on political violence.

Distinct plasma metabolomic signatures differentiate autoimmune encephalitis from drug-resistant epilepsy.

Differentiating forms of autoimmune encephalitis (AE) from other causes of seizures helps expedite immunotherapies in AE patients and informs studies regarding their contrasting pathophysiology. We aimed to investigate whether and how Nuclear Magnetic Resonance (NMR)-based metabolomics could differentiate AE from drug-resistant epilepsy (DRE), and stratify AE subtypes. This study recruited 238 patients: 162 with DRE and 76 AE, including 27 with contactin-associated protein-like 2 (CASPR2), 29 with leucine-rich glioma inactivated 1 (LGI1) and 20 with N-methyl-d-aspartate receptor (NMDAR) antibodies. Plasma samples across the groups were analyzed using NMR spectroscopy and compared with multivariate statistical techniques, such as orthogonal partial least squares discriminant analysis (OPLS-DA). The OPLS-DA model successfully distinguished AE from DRE patients with a high predictive accuracy of 87.0 ± 3.1% (87.9 ± 3.4% sensitivity and 86.3 ± 3.6% specificity). Further, pairwise OPLS-DA models were able to stratify the three AE subtypes. Plasma metabolomic signatures of AE included decreased high-density lipoprotein (HDL, -(CH2)n-, -CH3), phosphatidylcholine and albumin (lysyl moiety). AE subtype-specific metabolomic signatures were also observed, with increased lactate in CASPR2, increased lactate, glucose, and decreased unsaturated fatty acids (UFA, -CH2CH=) in LGI1, and increased glycoprotein A (GlycA) in NMDAR-antibody patients. This study presents the first non-antibody-based biomarker for differentiating DRE, AE and AE subtypes. These metabolomics signatures underscore the potential relevance of lipid metabolism and glucose regulation in these neurological disorders, offering a promising adjunct to facilitate the diagnosis and therapeutics.

Integrating RADEC Model and AI to Enhance Science Literacy: Student Perspectives

This study aims to evaluate the use of the RADEC Model supported by Artificial Intelligence (AI) in enhancing students' science literacy. The research methodology involves data collection through student questionnaires, which include questions related to their understanding of scientific concepts, contexts, and attitudes after using the RADEC Model. Data analysis employs descriptive statistical techniques to assess students' agreement levels regarding the provided statements. The results indicate that most students exhibit high levels of agreement regarding the effectiveness of the RADEC Model in enhancing their understanding of science concepts, boosting learning motivation, facilitating problem-solving, and developing critical thinking skills. Positive impacts are also observed in improving student engagement in collaborative discussions and increasing their interest in further exploration of science. This research makes a significant contribution by highlighting the importance of integrating AI technology into educational approaches to enhance students' science literacy, with broad implications for improving the quality of science education in the future. In conclusion, the integration of the AI-based RADEC Model is an innovative step to increase the effectiveness of science learning in elementary schools, so prospective elementary school teachers must be able to master this technology to provide personalized learning experiences and improve students' science literacy.

The Investigation of the Entrepreneurial Intention of the Younger Generation in the Tourism and Hospitality Industry

The hospitality and tourism industry is considered as one of Thailand’s most important industries. This current study thus pursues to investigate the significant influences of subjective norms, attitudes toward hospitality, perceived behavioural control, and hospitality behaviour on the hospitality entrepreneurial intention. The data used for this current study was collected from 414 respondents fromcollege students in tourism and hospitality programmes in Thailand. The data was systematically analysed adopting the statistical technique of Structural EquationModelling after reliability and validity testing. The findings showed that perceivedbehavioural control, hospitality behaviour, attitude towards hospitality, andsubjective norms, had positive influences on the hospitality entrepreneurial intention. The originality and benefits for the practitioners of this study included the expansionof the use of the theory of planned behaviour from the field of psychology intotourism, hospitality and entrepreneurship. The implications and practical recommendations included the government and agencies should enhance the positive image of the hospitality industry to create the positive attitudes towards the hospitality and tourism industry. In addition, policy makers, related hospitalitymanagers and education institutions can provide supports, such as promoting clubs or associations for young entrepreneurs to raise the awareness and interests for becoming entrepreneurs in the future.

Cox proportional hazards regression in small studies of predictive biomarkers

Predictive biomarkers are essential for personalized medicine since they select the best treatment for a specific patient. However, of all biomarkers that are evaluated, only few are eventually used in clinical practice. Many promising biomarkers may be erroneously abandoned because they are investigated in small studies using standard statistical techniques which can cause small sample bias or lack of power. The standard technique for failure time endpoints is Cox proportional hazards regression with a multiplicative interaction term between binary variables of biomarker and treatment. Properties of this model in small studies have not been evaluated so far, therefore we performed a simulation study to understand its small sample behavior. As a remedy, we applied a Firth correction to the score function of the Cox model and obtained confidence intervals (CI) using a profile likelihood (PL) approach. These methods are generally recommended for small studies of different design. Our results show that a Cox model estimates the biomarker-treatment interaction term and the treatment effect in one of the biomarker subgroups with bias, and overestimates their standard errors. Bias is however reduced and power is increased with Firth correction and PL CIs. Hence, the modified Cox model and PL CI should be used instead of a standard Cox model with Wald based CI in small studies of predictive biomarkers.

Performance of Different ANNs in Microtremor H/V Peak Classification

Abstract The microtremor horizontal-to-vertical (H/V) technique is extensively used to both assess the seismic amplification potential of soils and, in combination with other surface wave-based techniques, to reconstruct the near-surface seismic stratigraphy. The H/V peak frequencies are traditionally interpreted in terms of soil resonances, and, in this case, they are assigned a “stratigraphic origin.” However, not all H/V peaks mark subsoil resonances, and some of them have “anthropic” or “artefactual” origin. Recognizing the nature of H/V peaks should be mandatory before any stratigraphic interpretation. Nonetheless, this problem is not given the attention it deserves. Because this classification is not easy to achieve using standard statistical techniques, we decided to train two supervised neural networks: a traditional artificial neural network using a set of input values extracted from the individual (horizontal and vertical) microtremor spectra and a convolutional neural network working on images of the microtremor spectra. The nets were trained on an Italian dataset and tested on a U.S. dataset, collected by different operators and with different instruments. Both the nets achieved a classification accuracy of ∼90%; however, the convolutional one showed a greater generalization capability compared to the traditional one. Such machine learning algorithms can be useful tools to discriminate the origin of H/V peaks, complementing the traditional SESAME guidelines, which do not go into much detail on this topic.

Microstructural and mechanical characterization of direct energy deposited 316L stainless steel developed using an evolutionary computational approach

Wire arc additive manufacturing (WAAM) is a technology that has revolutionized the fabrication of large metallic components in various industries. However, previous studies have predominantly relied on trial and error or statistical techniques to determine process parameters, neglecting the potential of evolutionary algorithms for optimizing. In the present work, detailed investigations were performed on the WAAM-based gas tungsten arc welding to predict and optimize the process parameters of the 316L austenitic stainless steel. The input process parameters viz., current, wire feed speed, travel speed, and wire feed direction play a significant role in determining the single bead geometry. The study investigated the influence of these parameters on bead characteristics (such as height to width ratio and percentage dilution) and surface roughness. Multiobjective optimization was carried out using both the response surface methodology-desirability approach (RSM-DA) and the non-dominated sorting genetic algorithm-II (NSGA-II). In the case of NSGA-II, the optimal parameters were determined through a fuzzy decision method and it was validated experimentally. The NSGA-II approach showed superior performance over the traditional RSM-DA model in predicting the responses. In addition, the thin-walled structure printed for the parameters suggested using RSM-DA and NSGA-II approach. In a comprehensive property-based comparative analysis, the NSGA-II optimized parameters demonstrated improved mechanical properties attributed to increased low-angle grain boundaries and kernel average misorientation values. This study highlights the effectiveness of evolutionary computation methods in achieving superior performance and mechanical characteristics in the fabrication of SS316L components.

The Effect of Family Social Support, Physical Health, and Religiosity on the Quality of Life and Happiness of the Elderly in Tabanan Regency

This research aimed to explore the impact of family social support, physical health, and religiosity on the quality of life and happiness among elderly individuals in Tabanan Regency. Employing both quantitative and qualitative approaches, primary and secondary data were collected using a questionnaire as the main instrument. Structural Equation Modeling (SEM) was utilised in order to test the research hypotheses. The study utilised a non-probability sampling method employing accidental sampling to gather data from a cohort of 203 elderly individuals aged 60 years and older residing in Tabanan Regency. The independent variables were family social support, physical health, and religiosity, with quality of life serving as the mediating variable, and happiness as the dependent variable. Descriptive statistical techniques and differential analysis were applied to examine the relationships among these variables. The results revealed that family social support significantly influenced the quality of life and happiness of the elderly. Physical health also positively contributed to both quality of life and happiness. While religiosity has a significant relationship with the quality of life and happiness of the elderly, its impact is less pronounced compared to the other factors. The scientific novelty of this research stems from its integrated approach in analysing multidimensional factors - family social support, physical health, and religiosity - and their collective impact, thereby offering fresh perspectives on the mediating role of quality of life. Practically, the study offers valuable information for policymakers and practitioners to develop targeted interventions that enhance elderly well-being. The use of SEM further provides a robust methodological framework for future research.

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.

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale.

Evaluating how pollutant loads react to changes in land use/land cover (LULC) is a challenging task due to the intricate relationships among the many elements within a watershed. However, the difficulty in connecting LULC change and nonpoint source (NPS) pollution loads to streams may be lessened by combining hydrological modeling with geospatial tools and multivariate statistics. The objective of this study was to investigate the long-term effects of LULC change on NPS pollution loads in a highly human-dominated catchment, in central Ethiopia. In the study, hydrologic modeling was used to estimate the NPS parameters from multispectral Landsat images, and multivariate statistical techniques were then used to extract major LULC types that explain the variances of NPS loads between 1981 and 2020. The results demonstrated that there were human-induced LULC changes in the area, as the built-up and agricultural landscapes are rising (186.4% and 5.8%, respectively), and shrub and forest lands are decreasing (67.1% and 41%, respectively). As a result of these changes, the concentrations of nitrate (NO3), total P, total N, organic N, and organic P loads were increased by 69.41, 19.83, 18.45, 18.88, and 24.05%, respectively. Reductions in natural vegetation, as well as agriculture intensification, are the major contributors to the NPS pollutant losses to surface water sources. The result also revealed that pollution nutrients are strongly related to deforestation and agricultural land expansion. Proper adaptation strategies should be implemented to minimize the negative impact of LULC changes in the area.

Impacts of Computer-Managed Instruction in Basic Technology Education in Junior Secondary Schools in Anambra State

This study utilised a cross-sectional survey design in order to evaluate the impact of computer-managed instruction (CMI) on teaching basic technology in junior secondary schools in Anambra State. Covering the Onitsha Education Zone, the study employed a multi-stage sampling to select 20 schools and 840 students and teachers. Data was collected through a structured questionnaire assessing CMI availability and impact, validated by experts and tested for reliability. The questionnaires were administered over a month, with data analysed using the SPSS software and statistical techniques. The results indicated a positive correlation between CMI integration and enhanced learning experiences, with students reporting increased motivation, interest, and engagement in Basic Technology lessons. Additionally, CMI is found to enhance problem-solving abilities, confidence in learning, and performance outcomes. Both teachers and students recognize the transformative possibilities of CMI, despite differences in their perceptions. These findings underscored the importance of embracing technological advancements in education to cultivate dynamic learning environments. The study offers practical implications for educational stakeholders, advocating for the continued integration of CMI to meet the evolving needs of students and prepare them for success in a digital age.

Integrated Assessment of Heavy Metal Contamination in Urban Road Dust: Implications for Human Health and Ecosystem Sustainability in Abeokuta, Nigeria

This research aims to comprehensively investigate the heavy metal contamination in road dust within the rapidly expanding urban area of Abeokuta, Nigeria. Focusing on potentially hazardous metals such as Cr, Co, As, Ni, Cu, Zn, Cd, Fe, and Pb, the study employs multivariate statistical techniques to identify sources, assess ecological and health hazards, and determine metal concentrations. Through the analysis of twenty road dust samples, variations in metal concentrations are observed, leading to the application of the geo-accumulation index, contamination factor, contamination degree, and pollution load index for contamination assessment. The results reveal varying degrees of contamination, with notable concentrations of Pb and Ni, indicating a deteriorating urban environment. Ecological risk assessments underscore potential threats to local flora and wildlife, while health risk evaluations emphasize urgent corrective actions needed, particularly for children exposed to heightened carcinogenic and non-carcinogenic hazards. Factor analysis and correlation studies unveil common anthropogenic origins for several metals, shedding light on the need for pollution mitigation techniques in the face of rapid urbanization and industrialization. The research contributes valuable insights to environmental and public health evaluations in the Abeokuta region, emphasizing the importance of informed decision-making for policymakers and the public.

Leveraging Lean Six Sigma principles in an Indian tertiary care hospital: a case study

PurposeWorkflow optimisation is crucial for establishing a viable acute stroke (AS) intervention programme in a large tertiary care centre. This study aims to utilise Lean Six Sigma (LSS) principles to enhance the hospital's workflow.Design/methodology/approachThe Action Research methodology was used to implement the project and develop the case study. The study took place in a large tertiary care academic hospital in India. The Define-Measure-Analyse-Improve-Control approach optimised the workflow within 6 months. Lean tools such as value stream mapping (VSM), waste audits and Gemba were utilised to identify issues involving various stakeholders in the workflow. Sigma-level calculations were used to compare baseline, improvement and sustainment status. Additionally, statistical techniques were effectively employed to draw meaningful inferences.FindingsLSS tools and techniques can be effectively utilised in large tertiary care hospitals to optimise workflow through a structured approach. Sigma ratings of the processes showed substantial improvement, resulting in a five-fold increase in clinical outcomes. Specifically, there was a 43% improvement in outcome for patients who underwent acute stroke revascularisation. However, certain sigma ratings deteriorated during the control and extended control (sustainment) phases. This indicates that ensuring the sustainability of quality control interventions in healthcare is challenging and requires continuous auditing.Research limitations/implicationsThe article presents a single case study deployed in a hospital in India. Thus, the generalisation of outcomes has a significant limitation. Also, the study encounters the challenge of not having a parallel control group, which is a common limitation in quality improvement studies in healthcare. Many studies in healthcare quality improvement, including this one, are limited by minimal data on long-term follow-up and the sustainability of achieved results.Originality/valueThis study pioneers the integration of LSS methodologies in a large Indian tertiary care hospital, specifically targeting AS intervention. It represents the first LSS case study applied in the stroke department of any hospital in India. Whilst most case studies discuss only the positive aspects, this article fills a critical gap by unearthing the challenges of applying LSS in a complex healthcare setting, offering insights into sustainable quality improvement and operational efficiency. This case study contributes to the theoretical understanding of LSS in healthcare. It showcases its real-world impact on patient outcomes and process optimisation.