Methodological Limitations Research Articles

Investigating the Limitations of Multi‐Model Ensembling of Climate Model Outputs in Capturing Climate Extremes

ABSTRACTIn the context of climate change, the widespread practice of directly employing Multi‐Model Ensembles (MMEs) for projecting future climate extremes, without prior evaluation of MME performance in historical periods, remains underexplored. This research addresses this gap through a comprehensive analysis of ensemble means derived from CMIP6‐based models, including both simple and weighted averages of precipitation (SEMP and WEMP) and temperature (SEMT and WEMT) time series, as well as simple (SEME) and weighted (WEME) averages of extremes based on model‐by‐model analysis. The study evaluates the efficacy of MMEs in capturing mean annual values of ETCCDI indices over India for the period 1951–2014, utilising the IMD gridded data set as a reference. The results reveal that SEME and WEME consistently align closely with IMD data across various precipitation indices. At the same time, SEMP and WEMP consistently display underestimation biases ranging from 20% to 80% across all precipitation indices, except for CWD, where there is an overestimation bias. Moreover, SEMP and WEMP consistently underestimate CDD and overestimate CWD, indicating a systematic bias in these ensemble means, while WEME and SEME demonstrate satisfactory performance. SEMT and WEMT exhibit notable underestimation in temperature indices. In summary, adopting SEME and SEMT leads to a more robust assessment of precipitation and temperature extremes, respectively. These findings highlight the limitations of traditional MME methodologies in reproducing observed extreme precipitation events across various climatic zones in India, offering essential insights for refining climate models and improving the reliability of climate projections specific to the Indian subcontinent.

Hybrid Machine Learning and Reinforcement Learning Framework for Adaptive UAV Obstacle Avoidance

This review explores the integration of machine learning (ML) and reinforcement learning (RL) techniques in enhancing the navigation and obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs). Various RL algorithms are assessed for their effectiveness in teaching UAVs autonomous navigation, with a focus on state representation from UAV sensors and real-time environmental interaction. The review identifies the strengths and limitations of current methodologies and highlights gaps in the literature, proposing future research directions to advance UAV technology. Interdisciplinary approaches combining robotics, AI, and aeronautics are suggested to improve UAV performance in complex environments.

Attitudes toward mathematics and virtual teaching of students in the context of COVID-19: validation and reliability of instruments

BackgroundThere are few instruments to assess attitudes toward mathematics and virtual teaching; Likewise, there are methodological limitations to study this phenomenon in high school students. The present study aims to evaluate the psychometric properties of the instruments of attitudes toward mathematics and the perception of virtual teaching during COVID-19 in high school students in Peru.MethodsA total of 400 students participated. The research utilized exploratory and confirmatory factor analyses to evaluate the attitudes mathematics and the perception of virtual teaching instruments’ structure, and assessed reliability through Cronbach’s Alpha (α), Ordinal Alpha (ordinal α), and Omega (Ω) indicators.ResultsExploratory factor analysis showed that Model 2, with factor loadings above 0.40, was the most suitable for both instruments. This model demonstrated acceptable fit indices (CFI and TLI > 0.90; RMSEA and SRMR <0.08) and internal consistency (α, ordinal α, and Ω > 0.60). Furthermore, confirmatory factor analysis validated a five-dimensional structure for attitudes toward mathematics and a six-dimensional structure for perceptions of virtual teaching.ConclusionBoth instruments are valid and reliable for assessing these attitudes and perceptions within the context of virtual education during the COVID-19 pandemic. Strengthening mathematical skills through targeted research and interventions is essential to addressing pandemic-induced gaps.

Direct Current to Digital Converter (DIDC): A Current Sensor

This paper introduces a systematic approach to the design of Direct Current-to-Digital Converter (DIDC) specifically engineered to overcome the limitations of traditional current measurement methodologies in System-on-Chip (SoC) designs. The proposed DIDC addresses critical challenges such as high power consumption, large area requirements, and the need for intermediate analog signals. By incorporating a current mirror in a cascode topology and managing the current across multiple binary-sized branches with the Successive Approximation Register (SAR) logic, the design achieves precise current measurement. A simple comparator, coupled with an isolation circuit, ensures accurate and reliable sensing. Fabricated using the TSMC 180 nm process, the DIDC achieves 8-bit precision without the need for nonlinearity calibration, showcasing remarkable energy efficiency with an energy per conversion of 1.52 pJ, power consumption of 117 µW, and a compact area of 0.016 mm². This innovative approach not only reduces power consumption and area, but also provides a scalable and efficient solution for next-generation semiconductor technologies. The ability to conduct online measurements during both standard operations and in-field conditions significantly enhances the performance and reliability of SoCs, making this DIDC a promising advancement in the field.

IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification

Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficult for the traditional identification methods to effectively capture the sample feature information operated by the confusion method only based on the delayed database information. The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. Through these investigations, our goal is to construct a detection system that is both more precise and efficient, capable of addressing the ever-evolving threats to cybersecurity. Pursuing research in this direction is not only vital for enhancing network security defenses and safeguarding user data, but it will also foster the advancement of related state-of-the-art technologies and further mitigate the economic and societal repercussions of malware attacks. In light of this issue, this paper proposes the Image-based Malware Classification with Multi-scale Kernels (IMCMK), a Convolutional Neural Network (CNN) architecture using multi-scale convolution kernels mixing action to improve malware variants detection capabilities. First, we propose the Multi-scale Kernels (MK) block combining deep large kernel convolution and standard small kernel convolution with shortcuts to improve the accuracy. Furthermore, we propose Multi-scale Kernel Fusion (MKF) to reduce the number of parameters that come with the large kernels. The improved Squeeze-and-Excitation (SE) block can obtain the correlation between different channels to further increase the model performance. Experimental results show that IMCMK outperforms the state-of-the-art methods in malware family classification accuracy, which has achieved 99.25 %.

Beyond aquifer boundaries: unveiling the multi-dimensional value of groundwater management in Jeju, Korea

ABSTRACT This paper presents a comprehensive analysis of groundwater valuation in Jeju Island, Korea, which relies heavily on groundwater resources. The research addresses the limitations of conventional valuation methodologies that often undervalue groundwater management and ignore spatial heterogeneity. We employ three non-market valuation aggregation methods: local benefit, national benefit, and a combined heterogeneous benefit approach. The heterogeneous benefit approach divides the population into groups based on their distinct groundwater valuations and then consolidates their willingness to pay. Using a contingent valuation method, we analyze survey data from 542 respondents in Jeju and 1,000 respondents nationwide to capture the comprehensive value of improving groundwater quality, considering both local and non-local dependencies. Results show that national benefits exceed local benefits by a factor of 21.4, and this disparity increases to 21.6 when spatial heterogeneity is included. These findings highlight a significant underestimation in previous studies focusing solely on local beneficiaries. Our approach enhances understanding of the varied values attributed to groundwater by different populations, providing crucial insights for sustainable water resource management

Contextual success and pitfalls of mHealth service for maternal and child health in Africa: An Intervention, Context, Actors, Mechanism, and Outcome (ICAMO) framework guided systematic review of qualitative evidence

IntroductionMobile health (mHealth) interventions have shown potential to improve maternal and child health outcomes in Africa, but their effectiveness depends on specific interventions, context, and implementation quality. Challenges such as limited infrastructure, low digital literacy, and sustainability need to be addressed. Further evaluation studies are essential to summarize the impact of mHealth interventions. Thus, this synthesis focuses on qualitative evidence of the impact of mHealth on maternal and child health in Africa to summarize such evidence to help policy decisions.MethodsA qualitative systematic review guided by the concepts of Intervention, Context, Mechanism, and Outcome (ICAMO) was employed in this study. The GRADE CERQual assessment and methodological constraints tools were utilized in the review to ascertain the level of confidence in the evidence and to examine the methodological limitations. The JBI checklist for qualitative research appraisal was also consulted during the review.ResultsThe current review contains 32 eligible studies from databases such as CINAHL, EMBASE, MEDLINE, Scopus, Web of Science, HINARI, and Cochrane Library. The review demonstrated substantial improvements in the HCP-woman relationship, communication system, maternal and child healthcare uptake, health-seeking behavior, and HCP skills. Economic capacities, maternal education, and the low quality of existing services challenged participants.ConclusionmHealth significantly improves maternal and child health outcomes in Africa. This review showed it can improve healthcare access, empower women, and contribute to the region's goal of universal health coverage. However, the challenges such as low partner support, high costs for services, and poor quality of current care as narrated by women need commitment from health authorities in the continent. The evidence from this review suggests that mHealth can be implemented to improve maternal and child health in Africa.Trial registrationPROSPERO: CRD42023461425.

Promoting sustainability literacy for students in Thai higher education institution: a case of Siam University

PurposeThe objectives of this study were (1) to explore and analyze sustainability literacy of undergraduate students and (2) to propose guidelines for promoting sustainability literacy of the undergraduate students at Siam University, Bangkok, Thailand.Design/methodology/approachA 5-point rating scale survey questionnaire was used to collect data from 235 students registered in General Education courses in Semester 2 of academic year 2022. The analyzed data was presented to a focus group discussion, conducted with 19 internal and external stakeholders, as a basis to propose guidelines for promoting sustainability literacy.FindingsThe students perceived that they had better knowledge about sustainable development and sustainable development goals (SDGs) and that their knowledge about and degree of participation in SDGs-related activities were at high level. Intrapersonal competence was rated as their highest sustainability competence, followed by futures-thinking, and systems-thinking. The focus-group suggested that competency-based GE curriculum should be used as a platform to enhance sustainability literacy. Guidelines concerning learning outcomes, learning content, learning culture, learning with technology, and learning assessment were then proposed.Research limitations/implicationsThe study’s methodological limitations include the use of purposive sample case of the Siam University and self-perceiving assessment survey instrument for the data collection that provide sufficient insights on the present state and problems concerning sustainability literacy of university students.Practical implicationsSustainability is one of the national pillars in Thailand 20 years strategy (2018–2037). Hence, the government encourages all educational institutions, including universities to transform themselves toward this goal. Siam University has used education program as a vehicle to meet this aspiration. Lesson learnt from the study will not only benefit the university per se but will be valuable for practices in other universities. In addition, findings and implications from the study will help accelerate the transformation of Thai higher education institutions toward sustainable universities.Social implicationsSustainability has been promoted in education and social context. Siam University is recognized as a leading university in promoting campus and community sustainability through our “living learning lab” approach. We have used social living labs for teaching and learning activities for years. Partnerships among academia, private and public sectors, as well as community members will benefit from this research.Originality/valueThe study explores the scholarly topic of significant social importance and implications in the Thai context. The present study on sustainability literacy opens a path for further research and cross-institutional and national comparative studies on the issue.

Fast Color Image Encryption Algorithm Based on DNA Coding and Multi-Chaotic Systems

At present, there is a growing emphasis on safeguarding image data, yet conventional encryption methods are full of numerous limitations. In order to tackle the limitations of conventional color image encryption methodologies, such as inefficiency and insufficient security, this paper designs an expedited encryption method for color images that uses DNA coding in conjunction with multiple chaotic systems. The encryption algorithm proposed in this paper is based on three-dimensional permutation, global scrambling, one-dimensional diffusion and DNA coding. First of all, the encryption algorithm uses three-dimensional permutation algorithms to scramble the image, which disrupts the high correlation among the image pixels. Second, the RSA algorithm and the SHA-256 hashing algorithm are utilized to derive the starting value necessary for the chaotic system to produce the key. Third, the image is encrypted by using global scrambling and one-dimensional diffusion. Finally, DNA coding rules are used to perform DNA computing. The experimental results indicate that the encryption scheme exhibits a relatively weak inter-pixel correlation, uniform histogram distribution, and an information entropy value approaching eight. This shows that the proposed algorithm is able to protect the image safely and efficiently.

Association between small intestine bacterial overgrowth and psychiatric disorders.

Small intestinal bacterial overgrowth (SIBO) is a gastrointestinal condition characterized by abnormal colonization of bacteria in the small intestine, leading to overgrowth and alteration, which is linked to gastrointestinal issues, potentially affecting neurological and mental health. Despite existing research, we still do not understand how SIBO affects tryptophan metabolism and psychiatric diseases. We investigated the literature for connections between SIBO, tryptophan metabolism disruptions, and psychiatric disorders like autism, schizophrenia, Alzheimer's, and Parkinson's diseases. We also explored the interaction between thyroid disorders and their influence on SIBO and psychiatric illnesses. PubMed and Google Scholar databases were searched using keywords and phrases, individual and in combinations, like "SIBO," "gut microbiota," "neurologic disorders," "mental disorders," "tryptophan," "dopamine," and "thyroid disease." We focused on original research and review papers that presented empirical studies conducted on animal models and human subjects published in English between February 1992 to February 2023. The initial 2 634 534 records were preliminary screened based on title and abstract and then subjected to full-text review to exclude publications with insufficient data on SIBO, lack of a psychiatric disorder component, or methodological limitations compromising the integrity of the findings. The analysis highlights the significance of the association between psychiatric disorders and SIBO, emphasizing the role of gut-microbial diversity in mental health. We advocate for more detailed studies, including longitudinal research, to clarify the causal relationships between SIBO, gut dysbiosis, and psychiatric disorders and for an integrated approach while treating complex psychiatric conditions.

Modelling nonlinear moderation effects with local structural equation modelling (LSEM): A non-technical introduction.

Understanding the differential strength of effects in the presence of a third variable, known as a moderation effect, is a common research goal in many psychological and behavioural science fields. If structural equation modelling is applied to test effects of interest, the investigation of differential strength of effects will typically ask how parameters of a latent variable model are influenced by categorical or continuous moderators, such as age, socio-economic status, personality traits, etc. Traditional approaches to continuous moderators in SEMs predominantly address linear moderation effects, risking the oversight of nonlinear effects. Moreover, some approaches have methodological limitations, for example, the need to categorise moderators or to pre-specify parametric forms of moderation. This tutorial introduces local structural equation modelling (LSEM) in a non-technical way. LSEM is a nonparametric approach that allows the analysis of nonlinear moderation effects without the above-mentioned limitations. Using an empirical dataset, we demonstrate the implementation of LSEM through the R-sirt package, emphasising its versatility in both exploratory analysis of nonlinear moderation without prior knowledge and confirmatory testing of hypothesised moderation functions. The tutorial also addresses common modelling issues and extends the discussion to different application scenarios, demonstrating its flexibility.

Systematic Review of Quantitative Risk Quantification Methods in Construction Accidents

Construction accidents pose significant risks to workers and the public, affecting industry productivity and reputation. While several reviews have discussed risk assessment methods, recent advancements in artificial intelligence (AI), big data analytics, and real-time decision support systems have created a need for an updated synthesis of the quantitative methodologies applied in construction safety. This study systematically reviews the literature from the past decade, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search identified studies utilizing statistical analysis, mathematical modeling, simulation, and artificial intelligence (AI). These methods were categorized and analyzed based on their effectiveness and limitations. Statistical approaches, such as correlation analysis, examined relationships between variables, while mathematical models, like factor analysis, quantified risk factors. Simulation methods, such as Monte Carlo simulations, explored risk dynamics and AI techniques, including machine learning, enhanced predictive modeling, and decision making in construction safety. This review highlighted the strengths of handling large datasets and improving accuracy, but also noted challenges like data quality and methodological limitations. Future research directions are suggested to address these gaps. This study contributes to construction safety management by offering an overview of best practices and opportunities for advancing quantitative risk assessment methodologies.

Curcumin as a complementary treatment in oncological therapy: a systematic review.

Curcumin, the active ingredient in turmeric, is employed by numerous cancer patients to support conventional cancer therapy. This systematic review aims to summarize the existing clinical evidence and to provide an overview of the potential benefits and risks associated with curcumin supplementation. In January 2024, we conducted a systematic search of five electronic databases (Embase, Cochrane, PsycInfo, CINAHL, and Medline) using a complex search strategy. We included randomized controlled trials on the use, effectiveness, and potential harm of additional curcumin therapy in adult patients under cancer treatment. The risk of bias was assessed using Cochrane revised Risk of Bias Tool 2.0. This systematic review included 34 randomized controlled trials involving 2580 patients out of 11143 search results. Included patients were primarily diagnosed with head and neck cancer, followed by breast, prostate, and colorectal cancer. Therapy concepts encompassed topical or systemic curcumin administration. The studies reported heterogeneous results concerning oral and skin symptoms, pain, weight alteration and changes in body composition, survival, and disease progression. Significant findings were reported for oral mucositis and weight loss. Considering risk of bias, all studies had moderate to high risk of bias. Regarding side effects, one study reported significantly more vomiting in the curcumin group. Although the results suggest promise in reducing mucositis and weight loss, a clear statement regarding the effectiveness of curcumin therapy on cancer patients cannot be made due to heterogeneous results and methodological limitations of the involved studies. Further investigations of higher quality are necessary to derive a definite recommendation for action.

Workplace bullying of psychiatric trainees: systematic review.

We aimed to systematically review primary studies exploring workplace bullying of psychiatric trainees, including rates, forms of bullying, perpetrators and help-seeking. We searched Ovid MEDLINE, PubMed, CINAHL, PsycINFO and Embase using PRISMA guidelines. The inclusion criterion was primary research papers surveying or interviewing psychiatry trainees with respect to perceived workplace bullying by staff members. Exclusion criteria were secondary research papers and papers whose only focus was bullying by patients or carers. Substantial levels of bullying were reported in all five included studies. Perpetrators were often reported to be consultants, managers or peers. Most trainees did not obtain help for bullying and harassment. All of the studies had methodological limitations. Concerning levels of workplace bullying have been reported by psychiatric trainees in the UK and abroad. Further methodologically robust studies are required to evaluate the current levels and nature of this bullying, and strategies to prevent and manage it.

Use of proton pump inhibitors and risk of severe COVID-19: A case-control study in United States Medicare beneficiaries.

Concerns have been raised regarding proton pump inhibitor (PPI) use and risk of severe coronavirus disease 2019 (COVID-19). Observational studies have yielded heterogeneous results and were subject to important methodological limitations. To examine the association between the receipt of PPIs and risk of COVID-19 hospitalizations and severe in-hospital outcomes or death. Case-control study among Medicare fee-for-service beneficiaries 66+ years old with gastroesophageal reflux disorder (GERD). Within this population, we identified cases by an incident hospital discharge diagnosis of COVID-19 from April 1 to December 11, 2020, using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) U07.1, and randomly selected up to 10 controls per case, matched on date and neighborhood. We defined PPI use as a prescription providing ≥15 days of supply in the 30 days before admission, with H2-receptor antagonist (H2RA) use as the reference to account for indication. We analyzed uncomplicated hospitalizations and hospitalizations with severe outcomes (intensive/coronary care unit admission, invasive mechanical ventilation, or death), estimating odds ratios (ORs), and 95% confidence intervals (CIs) with multinomial conditional logistic regression adjusted for demographics, comorbidities, chronic medications, and health care utilization. We matched 25,867 uncomplicated and 12,954 severe hospitalized COVID-19 cases to 146,972 and 73,104 controls, respectively. Cases tended to be older and have more comorbidities. Relative to H2RA use, we found no association of PPI use with uncomplicated COVID-19 hospitalization (OR 0.99, 95% CI 0.93-1.06) or severe COVID-19 hospitalization (OR 1.00, 95% CI 0.91-1.10). Relative to H2RA use, PPI use was not associated with uncomplicated or severe COVID-19 hospitalizations among Medicare beneficiaries with GERD.

Polyphenols and Diets as Current and Potential Nutrition Senotherapeutics in Alzheimer's Disease: Findings from Clinical Trials.

Cellular senescence, a hallmark of aging, plays an important role in age-related conditions among older adults. Targeting senescent cells and its phenotype may provide a promising strategy to delay the onset or progression of Alzheimer's disease (AD). In this review article, we investigated efficacy and safety of nutrition senotherapy in AD, with a focus on the role of polyphenols as current and potential nutrition senotherapeutic agents, as well as relevant dietary patterns. Promising results with neuroprotective effects of senotherapeutic agents such as quercetin, resveratrol, Epigallocatechin-gallate, curcumin and fisetin were reported from preclinical studies. However, in-human trials remain limited, and findings were inconclusive. In future, nutrition senotherapeutic agents should be studied both individually and within dietary patterns, through the perspective of cellular senescence and AD. Further studies are warranted to investigate bioavailability, dosing regimen, long term effects of nutrition senotherapy and provide better understanding of the underlying mechanisms. Collaboration between researchers needs to be established, and methodological limitations of current studies should be addressed.

The Time of the Stones: A Call for Palimpsest Dissection to Explore Lithic Record Formation Processes

The dissection of archaeological palimpsests has become a crucial process for achieving a diachronic understanding of the history of human groups. However, its widespread application to archaeological deposits has been hampered by both methodological and theoretical limitations, as well as by the inherent characteristics of the deposits. This paper explores whether overcoming these barriers, both methodological and theoretical, truly represents a significant shift in understanding past human behaviour, thereby motivating the pursuit of shorter timescales. To this end, we have analysed the lithic assemblages of Unit Xb from the Neanderthal site of El Salt (Alcoi, Iberian Peninsula) focusing on lithic attributes and raw material analyses, enabling the definition of raw material units and refitting sets. Considering these variables, we have applied archaeostratigraphic and spatial analyses in order to generate units of analysis whose content is compared to that of the entire unit. The defined archaeostratigraphic units display different spatial distributions and lithic composition. Some of them are attached to certain hearths and composed of refitted sets, while other units are related to areas without combustion evidence and integrated with bigger and heavier single products. Through this approach, here, we show that reducing the spatiotemporal scale of the record helps to unravel behavioural variability, reducing interpretative errors implicit in the assemblage-as-a-whole approach. This highlights the role of temporal resolution in reconstructing site formation processes and challenges research perspectives that assert the unnecessary or impossible nature of palimpsest dissection.

The Economic and Geopolitical Significance of the Brics Nations: A Comparative Analysis of Their Global Influence in the 21st Century

The research investigates how BRICS (Brazil, Russia, India, China, and South Africa) influence global economics and political leadership while noting the opportunities and obstacles the alliance encounters. As a powerful influence in finance and trade globally, BRICS confronts the Western institutions that used to dominate. The collective has challenges from its members' vast economic and political variation, even with important milestones like the setup of the New Development Bank. In addition, outside influences from political tensions, mainly with the G7 and global competition, obstruct detailed cooperation. This research examines how the future of BRICS might develop, especially with the BRICS+ initiative, which is designed to include members from the Global South. The growth shows BRICS's purpose to shape global governance more strongly. The study recognizes a requirement for better political and economic integration among BRICS and points out that greater collaboration within the group may promote sustainable growth and financial strength. Limitations in methodology and data quality are recognized along with time constraints relating to the trustworthiness of macroeconomic information from the BRICS countries. This study ends by suggesting ways to enhance BRICS collaboration and identifies new research inquiries regarding the alliance's future direction and its impact on a more diverse world order

Towards the development and enhancement of Open and Distance Learning policies in Africa: A step by step guide to the game

Purpose: This study sought to explain and understand possible steps for developing and enhancing open and distance learning (ODL) policies in Malawi. Methodology: It thus deployed descriptive and interpretive phenomenological research as advanced by Edmund Husserl and Martin Heidegger to deeply understand policies, practices and perceptions within higher education institutions (HEIs) in Malawi. It was assumed that human experiences can best be understood through ones immersion into documents, conversations and lived-experiences of the research subjects. Accordingly, we used document analyses, observations and systematic conversations followed by practical activities to enhance the available draft-ODL policies at the selected HEIs. Findings: The study established that many academics had inadequate knowledge on policy formulation using paradigmatic and marginalized philosophical theories. It was also established when policy-makers combine different theories to inform their policies, they gain more advantages than they would have otherwise done. Since education is usually confronted by complex issues, such theoretical amalgamation has paid dividends. While this study inherently took an eccentric approach to research by reconciling Husserl and Heidegger’s contradictions, it also infused some elements of empiricism and conceptual ideals ushering in a mixed methodology that culminated into a transformative paradigm. Unique Contribution to Theory, Practice and Policy: Since research is about discovering new knowledge, this study garnered its uniqueness and credibility through such methodical surprises; the approaches that transcended mere theoretical analyses to yielding a tangible ODL policy product. We acknowledge our methodological limitations although such limitations cannot lender our findings invalid. We therefore recommend researchers to try this approach, and share with us their findings.

A Study on the Role of Automation and AI in Operational Efficiency and Sustainable Mining

One of the major pillars for world’s economic growth, the mining industry today is facing challenges in maintaining efficiency and productivity while following environmental regulations, at an ever-growing demand. There is limited incorporation of technology in Indian mining practices. To address this gap, the study explores the integration of automation, artificial intelligence (AI), and the Internet of Things (IoT) to enhance operational efficiency and promote sustainable mining. The objective of the research is to evaluate and assess the contribution of advanced technology towards achieving sustainability in mining, benefits derived out of real-time monitoring, and the impact of automation on the safety of work. It is conducted with the qualitative research design approach, which has made an attempt to capture the metamorphosis of mining technologies, benefits out of integration across value chains, and areas that pose challenges through interviews and focus group discussions, carried with industry experts in various parts across India. It also compares the role of technology in mining practices across leading countries and India. These provide evidence that automation and AI are forces in the efficient management of environments and workforce security. IoT devices are quite useful in real- time monitoring and are designed for environmental footprints and resource conservation management. The paper acknowledges, however, some of the methodological limitations, which in turn places additional demands on ongoing innovation. Lastly, the research shows how work can be conducted in the future to integrate state-of-the-art technologies, blockchain, and advanced AI for further value chain enhancement in mining, focusing much on how this industry keeps adapting. Keywords: Sustainable mining, automation and AI, productivity, operational efficiency, integrating technology, real-time monitoring, worker safety, environmental footprint.