Study Approach Research Articles

The Oil Resplendence: Evolution of the Oil Extraction Areas in Ecuador through the Night Luminosity

This article assesses a frequently overlooked impact, namely, the artificial illumination emanating from infrastructures associated with oil extraction installations from 2012 to 2019 located in the Amazon region, one of the places with the greatest biodiversity in the world. The primary dataset utilized in this study is derived from VIIRS nighttime satellite imagery. The significance of this research lies not only in examining light pollution but also in presenting a method for utilizing these images. The analysis focuses on Ecuador, one of the principal oil-producing nations in Latin America. The findings suggest a discernible correlation between the evolution of luminosity and the volume of crude oil production, although the luminosity shows a much higher growth rate than crude oil production. Additionally, it is observed that the elevated radiance levels in oil-rich areas yield luminous impacts comparable to those generated by major Ecuadorian cities. Consequently, this study's approach contributes to elucidating the territorial repercussions of the evolving patterns in oil consumption in a non-artificialized territory with high biodiversity. The results obtained herein aspire to instigate discourse on the environmental conditions engendered by extractive activities in their respective habitats. Furthermore, this study seeks to underscore the potential inherent in the selected data source and methodology.

DIVÓRCIO NO BRASIL NO PERÍODO PRÉ E PÓS PANDEMIA DA COVID-19: UM ESTUDO ACERCA DA JUDICIALIZAÇÃO DO DIVÓRCIO NESSE PERÍODO

Divorce in Brazil reflects changes in family, social, and legal relations over time. This study explores the judicialization of divorce, highlighting its increase during and after the Covid-19 pandemic. Pandemic challenges such as mobility restrictions drove the adoption of digital solutions to facilitate legal procedures, although logistical challenges arose. The study's qualitative approach seeks to understand the factors influencing divorce and its implications, aiming to contribute to more effective policies. Research findings indicate a significant decrease in the number of divorces in Brazil in 2020, totaling 331.2 thousand cases, followed by a significant increase in 2021, reaching a record of 386.8 thousand divorces. In 2022, there was a decrease to 68,703 cases until November, reflecting fluctuations influenced by social and economic conditions during the pandemic. The transition to the virtual environment during the pandemic facilitated divorce processes, with virtual hearings and digital document signatures, overcoming logistical challenges and delays in legal procedures. The analysis reveals significant variations in the number of divorces during and after the pandemic, reflecting social and economic changes. The transition to the virtual environment facilitated legal procedures, while changes in family structures demand supportive policies. Future studies can further explore the impact of technological adaptations on the divorce process, including the effectiveness of virtual hearings and the security of online communications.

Algorithmically Enhanced Wearable Multimodal Emotion Sensor

Sensing diverse human emotions offers significant potential for understanding deeper cognitive processes and diagnosing neurological diseases. However, capturing the full spectrum of emotions remains a significant challenge due to their intricate and subjective nature. Traditional emotion‐sensing techniques often focus on singular emotions, limiting their ability to grasp the complexity of emotional experiences. Herein, a fully printed, organic wearable sensor capable of multimodal emotion sensing by noninvasively monitoring physiological indicators such as heart rate, breathing patterns, and voice signatures is presented. The recorded signals are processed using a long short‐term memory (LSTM) neural network, achieving over 91% classification accuracy in distinguishing different emotions through a data fusion approach, showing >9% enhancement in accuracy as compared to feature fusion. As a proof of concept, Q‐learning is implemented with the data to simulate emotional responses in a robotic model. The study's approach provides a pathway to understanding complex human emotions and enhances the capabilities of effective human–machine interaction.

Results From a Randomized Trial of Intensive Glucose Management Using CGM Versus Usual Care in Hospitalized Adults With Type 2 Diabetes: The TIGHT Study.

To evaluate whether continuous glucose monitoring (CGM) could assist providers in intensifying glycemic management in hospitalized patients with type 2 diabetes. At six academic hospitals, adults with type 2 diabetes hospitalized in a non-intensive care setting were randomly assigned to either standard therapy with glucose target 140-180 mg/dL (standard group) or intensive therapy with glucose target 90-130 mg/dL guided by CGM (intensive group). The primary outcome was mean glucose measured with CGM (blinded in standard group), and the key secondary outcome was CGM glucose <54 mg/dL. For the 110 participants included in the primary analysis, mean ± SD age was 61 ± 12 years and mean HbA1c was 8.9 ± 2.3% (73.8 ± 1.6 mmol/mol). During the study, CGM-measured mean glucose was 170 mg/dL for the intensive group (n = 60) vs. 175 mg/dL for the standard group (n = 50; risk-adjusted difference -7 mg/dL, 95% CI -19 to 5; P = 0.25). Only 7% of the intensive group achieved the mean glucose target range of 90-130 mg/dL. CGM readings <54 mg/dL were infrequent (0.2% for intensive and 0.4% for standard; adjusted treatment group difference -0.1%, 95% CI -0.6 to 0.3). One severe hypoglycemia event occurred in the standard group. The study's glucose management approach using CGM did not improve glucose levels compared with standard glucose management in the non-intensive care unit hospital setting. A glucose target of 90-130 mg/dL may not be realistic in the current environment of insulin management in the hospital.

Occupational Noise Exposure and Incident Risk of Hypertension Among Construction Workers in the Greater Toronto Area (GTA)

Occupational noise poses a significant health risk, particularly for blue-collar workers, with potential consequences ranging from hearing loss to cardiovascular diseases. This research addresses the growing concern of hypertension among male construction workers in the Greater Toronto Area (GTA) due to occupational noise pollution, an issue exacerbated by the expected increase in construction industry employment. Hypertension, a major risk for cardiovascular diseases, is a common chronic condition with substantial societal implications. In Ontario, where 7.2% of the workforce is employed in construction, the need to understand and mitigate the impact of noise pollution on hypertension is urgent. This designed study, conducted over a 10-year period, will employ a cohort design, quantitatively measuring noise exposure levels using audio dosimeters by industrial hygienists and categorized into three levels: high, intermediate, and low. Annual physician evaluations will monitor incident cases of hypertension among male construction workers aged 16-65, comparing them to office workers in the same company. Existing research relies heavily on self-reported data and lacks longitudinal monitoring within the construction industry. By employing a rigorous quantitative approach, this study design aims to establish a clear association between occupational noise exposure and hypertension. The implications of the findings extend beyond individual health, impacting families, communities, and public health policies. The study's multidisciplinary approach ensures accessibility to a wide audience, emphasizing its relevance and potential to inform preventive strategies in addressing the global burden of hypertension among blue-collar workers.

NAVIGATING THE POST-PANDEMIC BUSINESS LANDSCAPE IN TRAVEL AND TOURISM INDUSTRY: A MULTIPLE CASE STUDY

&lt;p&gt;This qualitative study explores multiple cases of navigating the post-pandemic business landscape in the travel and tourism industry. The researcher involved four (4) ticketing and travel agency business owners within the Davao Region and used purposive sampling techniques in choosing the study participants. The participant's sequence number during the Case units' pseudonyms were PO1, PO2, PO3, and PO4. A comprehensive, in-depth interview was used to collect data using multiple-case designs. This study's multiple case analysis approach fits the qualitative research paradigm, which aims to explore the significance of respondents' data, experiences, and perspectives. A thematic analysis approach was utilized for PO1_CQ1.1, which pertains to Participant 1 under Central Question 1 and Sub Question 1. Thus, it is pointed out that the significant themes in handling travel and tourism service businesses in the post-pandemic business landscape were Health and Safety Protocols, Marketing and Promotion, Digital Innovations, Partnerships and Collaborations, and Flexibility and Adaptability. Significant themes in coping mechanisms with the changes brought by the post-pandemic business landscape are precautions in safety measures, virtual customer engagement and experience, resilient business operation, strategic planning, and business innovation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;SDG Indicated:&lt;/strong&gt; SDG 8 (Decent work and economic growth), SDG 9 (Industry, innovation and infrastructure) and SDG 11 (Sustainable cities and communities)&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0899/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

Engineering Phi29-DNAP Variants for Customized DNA Hydrogel Materials.

DNA hydrogels, which hold potential for use in medicine, biosensors, and tissue engineering, can be produced through enzymatic rolling circle amplification (RCA) using phi29 DNA polymerase (DNAP). This paper introduces new DNAP variants designed for RCA-based DNA hydrogel production, featuring enzymes with modified DNA binding, enhanced thermostability, reduced exonuclease activity, and protein tags for fluorescence detection or specific immobilization. We evaluated these enzymes by quantifying DNA output via quantitative PCR (qPCR) and assessing hydrogel mechanical properties through micromechanical indentation. The results showed that most variants generated similar DNA amounts and hydrogels with comparable mechanical properties. Additionally, all variants successfully incorporated non-natural nucleotides, such as base-modified dGTP derivatives and 2'fluoro-dGTP, during RCA. This study's robust analytical approach offers a strong foundation for selecting new enzymes and producing DNA hydrogels with tailored material properties.

Integrating Quantitative Data and Qualitative Insights to Understand 30-Day Readmission Rates: A Mixed-Methods Study.

The rate of patients readmitted to hospitals within 30 days of discharge is a critical indicator of healthcare quality. This study explored the factors contributing to 30-day hospital readmission rates nationally and at Arrowhead Regional Medical Center (ARMC) through a mixed-methods research design. Quantitative analysis utilized data from the Centers for Medicare & Medicaid Services (CMS) database, focusing on patient demographics, principal diagnoses, length of stay, and hospital characteristics. Multivariate regression and descriptive statistics were employed to identify predictors of 30-day readmission. The qualitative analysis sought to understand the specific medical conditions and patient profiles linked to higher readmission rates. The findings revealed that older age, specific principal diagnoses (e.g., heart failure, pneumonia, chronic obstructive pulmonary disease (COPD)), and longer initial hospital stays were associated with an increased likelihood of 30-day readmission. Gender disparities and hospital size/type also influenced readmission rates. These results provide valuable insights into the complex interplay of individual patient characteristics and hospital attributes in driving readmissions. The study's mixed-methods approach yielded a comprehensive understanding of the quantitative patterns and qualitative factors contributing to 30-day hospital readmission rates, offering important implications for healthcare quality improvement initiatives.

Machining performance analysis of wire-EDM for machining of titanium alloy using multi-objective optimization and machine learning approach

This study investigates the multi-objective optimization of process parameters in wire electrical discharge machining (WEDM) using titanium alloy grade 5 and zinc-coated brass microwire. The novelty lies in the comprehensive investigation of the effects of varying pulse on time (Ton), wire tension (WT), and wire feed rate (WF) on output responses, including surface roughness (SR), surface crack density (SCD), corner diameter (CD), and kerf width (KW). The Box-Behnken design, a sophisticated response surface methodology (RSM) technique, is employed to efficiently explore the complex relationships and interdependencies between these input and output parameters. The study's novel approach incorporates machine learning algorithms, including decision tree, light gradient boosting machine (LightGBM) and random forest, to predict the characteristics of the laser-cut components based on the input parameters. The contour plots generated provide valuable insights into the underlying patterns and associations between input and output features, aiding in the understanding of the WEDM process. The results reveal that the optimized values for SR, SCD, CD, and KW are obtained at specific machining conditions: Ton of 10 μs, WF of 3.79 m/min, and WT of 8 gf. Among the machine learning algorithms employed, random forest outperforms the others in predicting KW, SR, SCD and CD, exhibiting significantly lower mean squared error (MSE) and mean absolute error (MAE) values. This study contributes to the field of WEDM by providing a comprehensive analysis, incorporating advanced experimental design techniques and machine learning algorithms, which can aid in process optimization and quality control in manufacturing applications.

First comprehensive evaluation of genetic variability for bacterial blight resistance in wild Punica granatum L. populations from the North-Western Himalayas

Identifying and selecting disease-resistant sources from wild germplasm pools is pivotal due to their ecological benefits and cost-effectiveness. This study encompassed the survey and collection of wild pomegranate genotypes alongside commercial cultivars, followed by screening against native pathogens. Additionally, biochemical analyses were conducted to elucidate defense mechanisms in both resistant and susceptible genotypes. Cuttings from selected germplasm across four districts were obtained, grown for one year, and assessed for pathogenicity and biochemical responses. Microscopic and molecular analyses confirmed the pathogen as Xanthomonas axonopodis pv. punicae (Xap), identified by its distinctive morphology and a 1500 bp amplicon along with phylogeny analysis. Germplasm screening via detached and attached leaf assays identified genotypes with minimal disease severity, highlighting SH-14 and SH-16 as highly resistant in the attached leaf assay. Principal component analysis categorized genotypes based on disease parameters, with PC1 explaining approximately 75 % of the variability. Agglomerative hierarchical clustering further grouped genotypes into ten clusters, emphasizing clusters IX and X for their significant resistance traits. Biochemical studies revealed that the resistant genotype SH-16 exhibited higher levels of constitutive defense components like chlorophyll and vitamin C, as well as induced defense components such as phenols, flavonoids, and antioxidants, compared to the susceptible cultivar Bhagwa. These findings underscore the critical roles of both constitutive and induced defense mechanisms in conferring pomegranate resistance to Xap. The study's comprehensive approach in evaluating genetic and biochemical resistance provides valuable insights for breeding disease-resistant pomegranate varieties, marking a significant advancement in exploring pomegranate wild relatives from the Himalayan region for their resistance to bacterial blight.

Transposable Element (TE) insertion predictions from RNAseq inputs and TE impact on RNA splicing and gene expression in Drosophila brain transcriptomes

Recent studies have suggested that Transposable Elements (TEs) residing in introns frequently splice into and alter primary gene-coding transcripts. To re-examine the exonization frequency of TEs into protein-coding gene transcripts, we re-analyzed a Drosophila neuron circadian rhythm RNAseq dataset and a deep long RNA fly midbrain RNAseq dataset using our Transposon Insertion and Depletion Analyzer (TIDAL) program. Our TIDAL results were able to predict several TE insertions from RNAseq data that were consistent with previous published studies. However, we also uncovered many discrepancies in TE-exonization calls, such as reads that mainly support intron retention of the TE and little support for chimeric mRNA spliced to the TE. We then deployed rigorous genomic DNA-PCR (gDNA-PCR) and RT-PCR procedures on TE-mRNA fusion candidates to see how many of bioinformatics predictions could be validated. By testing a w1118 strain from which the deeper long RNAseq data was derived and comparing to an OreR strain, only 9 of 23 TIDAL candidates (< 40%) could be validated as a novel TE insertion by gDNA-PCR, indicating that deeper study is needed when using RNAseq data as inputs into current TE-insertion prediction programs. Of these validated calls, our RT-PCR results only supported TE-intron retention. Lastly, in the Dscam2 and Bx genes of the w1118 strain that contained intronic TEs, gene expression was 23 times higher than the OreR genes lacking the TEs. This study's validation approach indicates that chimeric TE-mRNAs are infrequent and cautions that more optimization is required in bioinformatics programs to call TE insertions using RNAseq datasets.

Detection of Two-Phase Slug Flow Film Thickness by Ultrasonic Reflection

Slug flow is a common phenomenon in closed pipes, occurring in two-phase flow where liquid and gas phases mix, impacting the custody transfer or the efficiency of chemical reactions. This study aims to detect and analyze slug flow film thickness in two-phase flow, providing detailed structural flow information. The ultrasonic or Doppler reflection method is employed to identify slug flow and detect detailed thickness. Additionally, electrical resistance tomography (ERT) is used to image and confirm the presence of slug flow. A high-speed camera records the slug flow's shape in real time, validating its existence. The ultrasonic reflection method offers high accuracy, with a measurement error rate of less than 1% based on experimental results. The study uses a homogeneous block calibration method to measure slug flow thickness. Graphical results reveal clear differences between the slug flow regime, inner pipe wall, and outer pipe wall, with the first echo of slug flow being easily observable. The accuracy of results is attributed to the combination of FPGA (Field-Programmable-Gate-Array) instruments and measurement methods, showcasing the study's novel approach. This research introduces a new perspective or novelty on slug flow in multiphase flow studies, highlighting an innovative method for detecting film thickness.

Optimization of household medical waste recycling logistics routes: Considering contamination risks.

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

INCLUSIVE PEDAGOGIES &amp; PRACTICES OF ENGLISH FOR ACADEMIC PURPOSES (EAP) IN HIGHER EDUCATION (HE): A FOCUS GROUP STUDY

This study investigates inclusive pedagogies and practices in English for Academic Purposes (EAP) within higher education (HE), addressing a critical gap in existing literature. Funded by BALEAP, the research employs a qualitative sequential exploratory design, incorporating nominal group technique and focus group discussions to capture diverse stakeholder perspectives. The study identifies key themes across micro, meso, and macro levels, providing comprehensive insights into best practices for fostering inclusive EAP teaching. At the micro level, recommendations include redefining needs analysis, implementing differentiated assignment briefs, challenging linguistic and cultural stereotypes, and promoting reflexivity among educators. Meso-level strategies emphasise decolonizing the curriculum, integrating EAP into disciplinary courses, enhancing collaboration between EAP and subject-specialist tutors, and investing in continuous professional development. At the macro level, the study highlights the necessity of providing additional resources, fostering top-down collaborations, and introducing Equity, Diversity, and Inclusion (EDI) initiatives. The findings underscore the importance of coordinated efforts among educators, institutions, and policymakers to advance inclusive teaching practices in EAP. This research contributes significantly to the discourse on inclusive education by offering actionable recommendations for enhancing the inclusivity of EAP programs in HE, ultimately aiming to create a more equitable and supportive learning environment for diverse student populations. The study's robust methodological approach and diverse participant representation ensure the reliability and relevance of its conclusions, paving the way for future research and policy development in this crucial area of higher education.

The application of curriculum analytics for improving assessments and quality assurance in higher education

Curriculum mapping is a necessary process for establishing evidence of where learning outcomes are taught and assessed in higher education programmes. Mapping ensures the credibility of the institution and programme offerings and provides students with a clear understanding of what they can expect to learn and achieve during their academic studies. Well-mapped curricula that reference relevant standards and articulate aligned assessments are foundational for effective teaching and learning. Curriculum quality assurance measures are commonly accepted as a means of assuring accurate mapping. Assurance processes are, however, resource-intensive, error-prone and reflect human biases. Few studies have adopted an analytical approach to ensure programme quality assurance steps are traceable to a fundamental level of these maps: single courses and their assessment items. In this study, we adopted a novel curriculum analytics methodology to validate the mapping of professional standards to course-based assessments in a higher education degree program. Using an Australian initial teacher education programme as a case study, we applied and analysed results from a structured methodology that may be implemented across other disciplinary programs. Practical implications for quality assurance in higher education and the methodological contributions of this novel approach are further discussed. Implications for practice or policy: Quality assurance mechanisms in higher education serve to demonstrate that specific accreditation standards have been attained or maintained. An evidence-based approach is needed to ensure curriculum mapping and quality assurance. This study's novel curriculum analytics approach provides a robust foundation for demonstrating assessments’ compliance with accreditation standards at the degree program level. A data-driven approach for quality assurance presents opportunities for wider and scalable applications across other institutions.

Determinants of intention to purchase online eco-friendly items amongst university students in Nilai, Malaysia

This study investigates the determinants of intention to purchase online eco-friendly items among university students in Nilai, Malaysia. This research builds upon previous studies regarding consumers' intentions to buy environmentally friendly items digitally. The theory of planned behavior (TPB) is used in this study since its concepts are applicable and relevant to understanding people's intentions to buy environmentally friendly goods digitally in Malaysia. The present research examined the variables influencing consumers' intentions to buy various environmentally friendly goods digitally and evaluated how well they fit into the Malaysian industry. Surveys were sent as part of the project's quantitative analysis methodology to gather data. With an expected sample population of 361 learners, this study's approach entails data collection through purposive sampling. The results of this research indicated that each independent factor employed has an impact on people's decisions to buy environmentally friendly products online in Nilai, Malaysia. It provided input on elements that might help businesses organize their advertising strategies. The findings have an impact on research as well as practice. Corporations may execute viable concepts by drawing the link between the desire to buy and acquiring knowledge about the purchasing patterns of young customers. Thus, a company's market worth and income in Malaysia may increase. The research also adds to the body of knowledge regarding people's intentions to buy eco-friendly products via the internet by offering insightful information. By addressing a gap in the existing literature, the research's focus on this specific setting advances our comprehension of how people behave while making online purchases of environmentally friendly items within the Malaysian context. So, this study helps in building a resilient eco-system aimed at sustainable growth.

Field assessments on the impact of CO2 concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests

Abstract. CO2 storage (Fs) is the cumulation or depletion in CO2 amount over a period in an ecosystem. Along with the eddy covariance flux and wind-stream advection of CO2, it is a major term in the net ecosystem CO2 exchange (NEE) equation. The CO2 storage dominates the NEE equation under a stable atmospheric stratification when the equation is used for forest ecosystems over complex terrains. However, estimating Fs remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows that lead to tremendous difficulties in capturing the true trend of CO2 changes for use in storage estimation from eddy covariance along with atmospheric profile techniques. Using measurements from Qingyuan Ker Towers equipped with NEE instrument systems separately covering mixed broad-leaved, oak, and larch forest towers in a mountain watershed, this study investigates gust periods and CO2 fluctuation magnitudes and examines their impact on Fs estimation in relation to the terrain complexity index (TCI). The gusts induce CO2 fluctuations for numerous periods of 1 to 10 min over 2 h. Diurnal, seasonal, and spatial differences (P &lt; 0.01) in the maximum amplitude of CO2 fluctuations (Am) range from 1.6 to 136.7 ppm, and these differences range from 140 to 170 s in a period (Pm) at the same significance level. Am and Pm are significantly correlated to the magnitude of and random error in Fs with diurnal and seasonal differences. These correlations decrease as CO2 averaging time windows become longer. To minimize the uncertainties in Fs, a constant [CO2] averaging time window for the Fs estimates is not ideal. Dynamic averaging time windows and a decision-level fusion model can reduce the potential underestimation of Fs by 29 %–33 % for temperate forests in complex terrain. In our study, the relative contribution of Fs to the 30 min NEE observations ranged from 17 % to 82 % depending on turbulent mixing and the TCI. The study's approach is notable as it incorporates the TCI and utilizes three flux towers for replication, making the findings relevant to similar regions with a single tower.

Industry 5.0's pillars and Lean Six Sigma: mapping the current interrelationship and future research directions

PurposeIn the evolving landscape of Industry 5.0 (I5.0), which emphasises sustainability, human-centricity and resilience, mapping the current interrelationship and future research directions, the role of Lean Six Sigma (LSS) methodology remains underexplored. Our study addresses this gap by examining the potential of LSS to support I5.0 while identifying areas for further investigation.Design/methodology/approachThis study's multifaceted approach, which includes systematic literature review (SLR), bibliographic network analysis (BNA) and expert validation (EV), provides a holistic exploration of the interaction between LSS and I5.0 as the basis for well-founded conclusions.FindingsThe analysis yields several valuable insights. Firstly, it demonstrates the absence of a direct link between LSS and I5.0. Secondly, the substantial body of literature analysed establishes connections between LSS and its pillars. Thirdly, the analysis identifies points of intersection, difference and similarity between LSS and I5.0, highlighting the potential of LSS to facilitate implementation of I5.0 through its proven methodologies, continuous improvement culture, risk management, error learning, human–machine collaboration and training and skill development.Originality/valueThis study pioneers the effort to realise the latent potential of LSS in the context of I5.0. Its systematic identification of the synergies between these paradigms fills a critical gap in the literature and gives policymakers, managers and researchers a guide for informed decision-making to maximise the benefits of I5.0 for individuals, companies, society and the planet.

Climate change vulnerability and adaptive capacity of smallholder farmers: A financing gap perspective

With climate change continuously threatening global food production, smallholder farmers are increasingly struggling to adapt to changing climatic conditions, decreasing productivity, and limited access to finance. To examine these challenges, this study used cross-sectional data collected through a multi-stage approach across three plantain-producing states in southwest Nigeria, with 100 farmers randomly selected in 5 local government areas per state to give a total of 300 farmers. A two-step Stochastic Frontier model is used to examine the relationship between climate adaptation strategies and plantain productivity while the Harold-Dorma growth equation and Multivariate Probit regression are used to assess the financing gap and its effect on adaptive capacity. Finally, the Foster Geer-Thorbecke (FGT) equation is used to measure the vulnerability of farmers to climate change. Findings show that as the financing gap of the farmers decreases, their ability to adopt climate adaptation strategies to enhance their productivity increases. However, 80% of the farmers are financially vulnerable to climate change, with 37% facing severe financial constraints. The vulnerability depth for the farmers is 56%, indicating that with $106.55 (56% of the vulnerability line of $190.26), many farmers' adaptive capacity could be enhanced to reduce their vulnerability below the vulnerability line. This study's approach provides a comprehensive approach for assessing smallholder farmers' ability to adopt adaptation strategies that increase productivity and enhance resilience. While other policy measures to enhance smallholder farmers' ability to address the challenges of climate change are important, it is crucial to prioritise actions that improve the financial status of smallholders.

On the Relationship between Iraqi EFL Teachers' Organizational Commitment and Their Leadership Style: A Case Study of Missan Province

This study looked into the connection between the leadership styles of Iraqi EFL teachers and their organisational commitment (OC). The Characteristics of Successful Teachers' Questionnaire and the Organisational Commitment Scale (OCS), which has three sub-scales: Affiliation and Pride, Identification, and Satisfactory Exchange, were given to the EFL teachers in order to get the necessary data. The "Spearman Correlation Coefficient" was utilised in this study's correlation approach to explain teacher dedication and leadership style. In the Missan province of Iraq, 147 male and female teachers were given the "Class Leadership Style" and "Organisational Commitment Scale" questionnaires for this reason. The findings showed a strong and positive correlation between teachers' organisational commitment and their leadership style.