Systemic Analytics is an international peer-reviewed journal committed to fostering innovation and advancement in the field of Soft Operational Research, with a distinct emphasis on systems thinking and systemic analysis. The journal seeks to publish high-quality scientific papers that make a significant contribution to various facets of operational research, soft computing, and decision-making. Systemic Analytics champions the exploration of new theories, models, and applications across a spectrum of methodologies, including but not limited to: Soft Operational Research, Problem Structuring Methods, Large-Scale and Strategic Planning, System thinking, optimization methods, advanced algorithms, machine learning, Multi-Criteria Decision Making (MCDM), Artificial Intelligence (AI), simulation, systems dynamics, and econometric analysis. View journal details Publisher: Research Expansion Alliance
Six Sigma Performance Assessment of Coal Truck Payload Based on Non-Normal Capability and Sigma Level Analysis
Coal hauling performance is strongly influenced by truck payload consistency, which affects transportation efficiency and compliance with operational specifications. This study assesses coal truck payload performance using a Six Sigma approach supported by non-normal capability analysis and Minitab. A total of 513 payload observations from Hino 260 JD trucks were evaluated against a lower specification limit (LSL) of 25 tons and an upper specification limit (USL) of 27 tons. The analysis included process stability assessment, normality testing, distribution identification, non-normal capability analysis, PPM estimation, process yield, and Sigma Level calculation. The results showed that the payload data were non-normal, with an Anderson–Darling statistic of 8.024 and p-value <0.005. The Largest Extreme Value distribution provided the best fit. Based on the observed performance, total nonconformance was estimated at 633,528 PPM, corresponding to an observed process yield of 36.65%, a Zbench of approximately −0.34, and a Sigma Level of 1.16σ using the conventional 1.5-sigma shift. The fitted distribution also estimated an expected overall nonconformance of 686,594 PPM, equivalent to an expected yield of approximately 31.34%. The I-MR chart indicated special-cause variation and limited process stability. These findings provide a quantitative baseline for reducing payload variation, improving process centering, and strengthening operational control.
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Integrating Balanced Scorecard and Lean Six Sigma for Organizational Improvement
The Balanced Scorecard (BSC) provides a strategic performance management system for translating organizational strategy into measurable objectives, while Lean Six Sigma (LSS) provides a structured methodology for operational improvement. However, the linkage between strategic performance measurement and LSS project selection and execution remains insufficiently structured. This study develops an integrated BSC–LSS framework to establish a closed-loop mechanism between strategic objectives, organizational performance metrics, improvement project prioritization, and operational improvement. A conceptual research approach was employed to integrate established concepts of strategic performance management, performance measurement, Lean Six Sigma, and improvement project selection. The proposed framework integrates strategy development, organizational metrics, Master Improvement Story, LSS project selection and prioritization, DMAIC execution, performance review, target achievement assessment, and Problem Identification and Corrective Action (PICA). The framework extends previous integration approaches by explicitly connecting strategic performance gaps with a prioritized LSS project pipeline and subsequent performance feedback. The proposed model provides a structured mechanism for aligning improvement initiatives with organizational strategy and supporting continuous performance improvement.
The Nuclear Cap-Binding Complex as an Emerging Post-Transcriptional Regulator of Cuticular Wax Biosynthesis and Drought Tolerance in Barley
Cuticular wax is barley's first line of defense against nonstomatal water loss under drought, and the classical wax biosynthesis pathway from very-long-chain fatty acid elongation through the alkane-forming and β-diketone branches, transcriptionally coordinated by the SHINE-family regulator HvWIN1 was comprehensively reviewed alongside its wheat counterpart in 2022. Since that review, however, a distinct and largely unanticipated regulatory layer has emerged. Barley TILLING mutants in the small subunit of the nuclear cap-binding complex, CBP20, show markedly increased epicuticular wax deposition, faster stomatal closure, and improved water retention under drought apparently through post-transcriptional control of alkane-forming pathway genes rather than through the canonical HvWIN1-mediated transcriptional route. Follow-up studies in 2024 and 2025 have extended this finding to ABA sensitivity during seed germination and to distinct, stage-specific roles for the CBP20 and CBP80 subunits during reproductive development. This review synthesizes the classical barley wax biosynthesis pathway, updates it with this post-2022 cap-binding complex literature, and proposes an integrated model in which the nuclear cap-binding complex acts as a post-transcriptional checkpoint intersecting ABA signaling and the canonical wax biosynthesis transcriptional network. We identify the mechanistic relationship between these two regulatory layers as the central open question for future barley drought-tolerance breeding.
Identifying and Ranking Agile Marketing Strategies in B2B Startups in Gilan Province
Introduction: Today’s business environment is characterized by uncertainty, rapid changes, and increasing competition. Due to limited resources and complex sales cycles, B2B startups are in greater need of innovative and agile marketing approaches. Agile marketing, with its emphasis on rapid responsiveness, continuous customer interaction, and data-driven decision-making, is considered an effective solution in this regard. Methodology: This study is applied in terms of purpose and exploratory–descriptive in nature, conducted using a survey-based approach. The statistical population consisted of experts and senior managers active in the marketing domain of B2B startups in Gilan Province, from whom eight participants were selected through purposive sampling. In the first stage, a systematic review of the literature led to the identification of ten key agile marketing strategies. The content validity of these strategies was assessed using the Content Validity Ratio (CVR) and the Fuzzy Delphi method. Subsequently, the relative importance and weights of the identified strategies were determined using two multi-criteria decision-making techniques, namely Fuzzy Step-wise Weight Assessment Ratio Analysis (Fuzzy SWARA) and the Fuzzy Best–Worst Method (Fuzzy BWM). Finally, the strategies were ranked through the integrated Copeland method. Findings: The results indicated that all ten identified strategies possessed the required level of content validity. The Fuzzy SWARA method emphasized the high importance of market agility and rapid responsiveness to change, interactive marketing and customer co-creation, and marketing based on digital platforms. In contrast, the Fuzzy Best–Worst Method (Fuzzy BWM) prioritized data-driven marketing, interactive marketing, and marketing in turbulent and crisis environments. The final integrated ranking using the Copeland method revealed that interactive marketing and customer co-creation, market agility and rapid responsiveness to change, and agile innovation in the marketing process were respectively the most important strategies. Conclusion and Recommendations: The final prioritization indicates that a strong focus on active customer engagement, rapid responsiveness to market changes, and continuous innovation in marketing processes plays a pivotal role in the success of B2B startups. It is recommended that managers of these startups design their marketing structures around agile teams and continuous customer feedback mechanisms. Furthermore, the adoption of data mining and business intelligence tools is recommended to strengthen data-driven marketing capabilities, while contingency plans should be developed to effectively manage crisis situations and turbulent market conditions. Future research may employ quantitative structural approaches, such as Structural Equation Modeling, to validate the causal relationships between these agile marketing strategies and the financial performance of startups.
When Personalization Feels Like Surveillance: A Marketing Framework for Artificial Intelligence-Personalized Advertising
Artificial intelligence (AI)-personalized advertising can increase message relevance, consumer engagement, and persuasive effectiveness, yet the same targeting practices can expose the extent of consumer tracking and trigger concerns about surveillance, manipulation, and loss of autonomy. Existing research documents both the benefits and the risks of personalization, but it does not fully explain how personalization intensity, data-use transparency, and meaningful consumer control jointly shape advertising evaluations and brand-level outcomes. Through an integrative synthesis of personalized advertising, privacy-calculus theory, psychological reactance, procedural fairness, and brand-trust research, this conceptual article develops a dual-pathway framework. The value-creation pathway predicts that high-quality personalization strengthens perceived relevance and advertising value, which subsequently enhance purchase intention, brand trust, and continued engagement. The surveillance-threat pathway predicts that unexpected data use, sensitive inferences, excessive specificity, and opaque targeting practices increase perceived surveillance and intrusiveness, thereby stimulating reactance, advertising avoidance, and trust erosion. The framework distinguishes transparency from control and argues that these mechanisms become most effective when combined: transparency clarifies how and why personal data are used, whereas control allows consumers to modify, limit, or refuse personalization. Their joint presence creates procedural legitimacy and can preserve the benefits of personalization while reducing perceived vulnerability. Twelve propositions specify the principal mediating mechanisms, boundary conditions, and an inverted-U relationship between personalization intensity and net marketing response. The article also proposes a three-study empirical validation agenda and practical design principles for developing personalized advertising systems that improve relevance and effectiveness without undermining consumer autonomy, privacy expectations, or long-term brand trust.
The Problems of Translation of Denotation and Connotation Elements in Tamil Proverbs into French
The proverb is a statement, often short, that expresses a general truth of life. It gives us advice, makes an observation, or presents a teaching in a brief and memorable way, is easy to remember, and provides a quote for different situations. People also use them to express their understanding of beliefs, values, and surroundings in their daily lives. Tamil has a long history, not only in culture but also in language. The proverbs of this language play an indispensable role because people use them frequently in their verbal or oral communication or day-to-day conversation in different circumstances. This study aims to find the denotative and connotative elements in the Tamil proverb. Denotation and connotation are fundamental concepts in the study of language, playing a crucial role in communication and interpretation. Denotation is the definition of a word given in the dictionary, whereas connotation refers to emotional or cultural associations with a word beyond its literal meaning. Understanding of these two elements improves our ability to analyze the text and engage more deeply with the language. These elements pose problems of translation in the Tamil proverb into French. In terms of translating proverbs, one might note that translation recaps or reinforces an existing truth, a real experience of a specific community. Proverbs are inserted into the culture, history, society, etc. of a particular language group. This particularity requires a linguistic and extra-linguistic understanding of the community in question. The translation of proverbs faces two challenges at once: first, in terms of understanding, and then, in terms of re-expression.
Development of a Risk-Based Maintenance Framework for Improving Human Reliability in Disc Brake Maintenance Using the HEART Method
Disc brake maintenance is a critical activity in ensuring the safety and reliability of passenger vehicles. In vocational automotive workshop environments, maintenance activities are vulnerable to human error due to limited operator experience, time pressure, and inadequate work procedures. This study aims to develop a Risk-Based Maintenance (RBM) framework for improving human reliability in disc brake maintenance using the Human Error Assessment and Reduction Technique (HEART) method. The research employed a mixed-methods approach with a sequential explanatory design. Quantitative analysis was conducted through Hierarchical Task Analysis (HTA), Generic Task Type (GTT) classification, identification of Error Producing Conditions (EPCs), Assessed Proportion of Effect (APOE) evaluation, and Human Error Probability (HEP) calculation. Qualitative data were collected through observations, interviews, and Focus Group Discussions involving vocational instructors and automotive technicians. The results showed that initial inspection (HEP = 2.49), brake disc inspection (HEP = 2.48), and brake pad and caliper reassembly (HEP = 1.81) were identified as the most critical maintenance activities. The dominant EPCs included time pressure, limited operator experience, poor work procedure quality, and the absence of independent checking. Based on these findings, an RBM framework was developed to prioritize risk control actions through improved supervision, enhanced practical training, refinement of standard operating procedures, and implementation of independent inspection mechanisms. The proposed framework integrates HEART-based human reliability assessment with Risk-Based Maintenance principles to support systematic maintenance risk control and human reliability improvement in vocational automotive workshop environments.
Beyond Traditional Drivers: Artificial Intelligence and the Evolving Structure of Carbon Emissions in the United States
This study examines the determinants of carbon emissions in the United States over the period 1990–2022, with particular attention to the role of Artificial Intelligence (AI) within a broader macroeconomic setting. While traditional literature has primarily emphasized economic growth and energy consumption as the main drivers of environmental degradation, this paper extends the analysis by incorporating technological change alongside Foreign Direct Investment (FDI) and urbanization in a unified time-series framework. The empirical results indicate that economic growth and energy use continue to exert upward pressure on carbon emissions, reflecting the continued reliance on energy-intensive production and consumption patterns in the U.S. economy. FDI and urban expansion also contribute to higher emissions, although their effects vary across time and may depend on structural conditions within the economy. These findings confirm that conventional development pathways remain closely associated with environmental stress. In contrast, AI is found to have a mitigating association with carbon emissions. This relationship likely reflects improvements in production efficiency, optimization of energy use, and better allocation of economic resources driven by technological progress. However, the results also suggest that the environmental effect of AI is not independent, as it interacts with existing economic and energy structures in shaping overall emission outcomes.
Behavioral Validity of Confidence-Based Knowledge State Reporting in Multiple Choice Examinations
Multiple choice examinations remain among the most widely deployed instruments of academic and professional assessment, yet their fundamental architecture invites a systematic distortion: examinees who lack genuine knowledge may still obtain credit through random selection among alternatives. Classical scoring mechanisms offer no incentive for examinees to reveal the quality of their knowledge, and consequently the observed score conflates genuine knowledge with fortunate guessing. This article introduces and empirically investigates a structured self-reporting framework in which each examinee, for every item, declares one of three knowledge states, namely Full Knowledge (FK), Partial Knowledge (PK), or No Knowledge (NK), alongside their chosen answer. The scoring mechanism is constructed such that truthful declaration of one's epistemic state constitutes the uniquely optimal strategy in expectation, rendering guessing strictly suboptimal. The central empirical question is whether examinees, when placed within this incentive-compatible framework, do in fact report their knowledge states truthfully or whether systematic behavioral deviations, rooted in overconfidence, risk aversion, or strategic misrepresentation, emerge. Drawing on psychometric theory, Bayesian probability modeling, and the cognitive psychology of metacognition, this article formalizes the theoretical relationships between declared knowledge states and observed response accuracy, derives testable hypotheses, proposes an experimental design, and specifies a complete statistical inference framework for behavioral validation. The contribution is threefold: a formal probabilistic model linking epistemic state declarations to correctness probabilities, a rigorous hypothesis testing architecture, and an experimentally grounded methodology for assessing metacognitive honesty under incentive-compatible conditions.
Granule Size Heterogeneity in Barley Starch: Structural, Compositional, and Functional Differences between A- and B-Type Granules and Their Industrial Implications
Barley (Hordeum vulgare L.) is unique among cereal crops for the way its starch is organized not as a uniform population of granules, but as two structurally and functionally distinct classes. Large lenticular A-type granules (10–30 µm) and small, irregularly shaped B-type granules (≤6 µm) coexist in a bimodal size distribution that is rarely observed in other cereals. Numerically, B-type granules dominate (80–90% of total count), yet A-type granules account for 85–90% of total starch mass. This asymmetry is far more than a morphological curiosity: it reflects deep-seated differences in amylose content, lipid-complexed amylose concentrations, crystalline architecture, and surface porosity that collectively govern how barley starch behaves from the farm gate through to food processing, brewing, and bioethanol production. This review provides a comprehensive synthesis of current knowledge on the structural and functional differences between A- and B-type barley starch granules, considering three genotype backgrounds: normal, waxy, and high-amylose. The evidence consistently shows that B-type granules possess higher gelatinization temperatures, lower gelatinization enthalpies, greater enzyme hydrolysis rates under mild conditions, higher peak viscosities, and weaker gel network structures compared to A-type granules. Importantly, some properties including swelling factor in high-amylose genotypes and setback viscosity reverse direction depending on the genotype, underscoring the complex interplay between granule size and genetic background. The review also identifies significant research gaps in granule-size-specific chemical modification, freeze-thaw stability, and multi-scale structural characterization, and discusses how these gaps limit the full industrial exploitation of barley starch's inherent heterogeneity.
Performance and Cost Evaluation of Split–Local Material Mixtures for Coal Hauling Road Pavements: A Case Study in Coal Mining Operation
Hauling road performance plays a critical role in maintaining productivity, safety, and cost efficiency in mining operations, particularly under heavy traffic and adverse weather conditions. In coal mining areas, haul roads are frequently subjected to high axle loads, moisture-induced deterioration, and rapid loss of bearing capacity. This study investigates the effectiveness of haul road strength improvement using a mixture of split stone and locally available material known as okes through a field-based case study in a coal mining operation in Central Kalimantan, Indonesia. The hauling roads were designed to accommodate haul trucks with gross vehicle weights of up to 46 tons and an average traffic volume of 200 trips per day, supporting an annual production target of approximately 800,000 tons. Two pavement improvement methods were evaluated: a conventional Split+Basecourse mixture and an alternative Split+Okes mixture. Road performance was assessed using key indicators including California Bearing Ratio (CBR), slippery road duration after rainfall, surface deformation, and cost efficiency. The results show that both methods significantly improved road performance by reducing slippery conditions from four hours to one hour and increasing CBR values from below 10% to 72.94% and 64.50%, respectively. Although the conventional method achieved higher CBR values, the Split+Okes method delivered comparable operational performance with approximately 26% lower material cost. The findings demonstrate that the use of locally sourced materials provides a technically viable, cost-efficient, and sustainable solution for haul road improvement in mining operations.
Fuzzy Regression Model to Assess the Rainfall Variability Trend inKalahandi, Odisha: Leveraging 100 Years of Data for Trend Robustnessand Predictive Accuracy
Linear regression analysis is one of the most common statistical methods used to investigate trends in rainfall over time. However, conventional regression models usually require datasets that have been completely and accurately determined for trend analysis with rainfall, the datasets often include monthly or annual averages which can result in uncertainty in trend analysis. In order to incorporate this uncertainty, fuzzy set theory represents the ambiguity from the analyzed datasets. The fuzzy regression models consider both input and output variables to be Triangular fuzzy numbers. This study proposes a fuzzy regression approach that describes the relationships between precipitation and time in a context of excessive variability in rainfall which creates challenges for the sustainable management of water resources. Kalahandi district in Odisha has been acknowledged as one of the impoverished districts in the KBK Region. The utilization of the fuzzy regression model in addressing trend variability or trends in rainfall in Kalahandi, Odisha is a comprehensive investigation of historical rainfall records related to trends and variability of rainfall in Kalahandi, especially the region’s severe effects associated with climate change; it is of utmost importance to understand how rainfall variability trends have changed over time for local agrariens or agriculture workers and policy-makers to recognize changes in rainfall patterns and develop adapted agriculture plans and manage water resources in a variable climate. The data collection process included 100 years of fuzzy rainfall data (1923-2023) which helps to frame current rainfall variability trends in Kalahandi. From this study, it has been shown that Kalahandi is experiencing climate change or other environmental factors that are leading to a substantial increase in rainfall variability. The study methodologically establishes fuzzy regression as an alternate approach to provide a more accurate predictive model for uncertain rainfall.
Towards Sustainable Development Goals: An ARDL Analysis of Energy Efficiency, Finance, and Technology in Mitigating CO₂ Emissions in the United States
This paper examines the changing associations among economic growth, Energy Efficiency (EE), and access to finance, Information and Communication Technology (ICT), and Urbanization (URBA) and their joint impacts on Carbon Dioxide (CO₂) emissions in the United States (US) between 1990 and 2022. The analysis employs unit root tests and cointegration tests to estimate the short-run and long-run dynamics to apply the Autoregressive Distributed Lag (ARDL) model with the aid of error correction modeling and Granger causality tests. Results illustrate that Gross Domestic Product (GDP) growth and URBA significantly deteriorate environmental quality, as rising economic activities and expanding urban populations intensify fossil fuel consumption and carbon emissions. In contrast, EE, Financial Accessibility (FA), and ICT adoption exert a mitigating effect on emissions, highlighting their potential role in advancing environmental sustainability. Specifically, access to finance facilitates investment in cleaner technologies, ICT applications reduce energy intensity, and renewable energy innovations enhance efficiency. Causality analysis further indicates unidirectional effects from GDP, ICT, EE, and FA to CO₂ emissions, while URBA demonstrates a bidirectional causal link with emissions. These findings highlight how vital technological development, sustainable finance, and green energy are in ensuring that U.S. development is in line with the global climate agenda. The research is relevant to Sustainable Development Goals (SDG 7: Affordable and Clean Energy, SDG 9: Industry, Innovation, and Infrastructure, SDG 11: Sustainable Cities and Communities, and SDG 13: Climate Action) because it provides empirical data on how to strike a balance between economic growth and environmental sustainability. Policymakers are urged to prioritize green finance, ICT infrastructure, and energy transition policies to achieve long-term carbon neutrality.
Evaluation of Cargo Trends and Market Share of Import-Exportcargo Handled in Nigeria
The importance of air cargo in facilitating global trade cannot be overemphasized, thereby contributing a high value despite low volume. In Nigeria, cargo handling is dominated by NAHCO and SAHCOL, yet existing studies focus on efficiency and infrastructure with little attention to long term import-export trends or market share dynamics. The study addresses the gap by evaluating the volumes and market share of cargo (imports and exports) handled by NAHCO and SAHCOL at Murtala Muhammed International Airport, Lagos over a 10 year period (2015-2024), Secondary data were analyzed using descriptive statistics and visualized with graphs and charts. Findings revealed the market share of imports of NAHCO and SAHCOL to be 51% and 49% respectively while that of their exports are 53% and 47% respectively. With respect to the relationship between imports and export cargo for each company, NAHCO’s import is 77% in relations to its 23% export.This indicates a growth rate of 7,899,139.69kg for imports and growth of 5,653,313kg for exports. SAHCOL import is 78% in relation to its 22% exports which also indicates a growth rate of 17,636,256.4kg for imports and growth rate of 13,362,389kg for exports. These results show a highly competitive environment with growing export performance for SAHCOL.The study also reveals that NAHCO consistently handles a slightly higher proportion of overall import and export cargo (51%) compared to SAHCOL (49%), indicating a near equal distribution of market share. The study also highlights fluctuations in cargo volumes over the years. It thus recommends strengthening export support policies, diversifying Nigeria’s trade flows and enhancing data sharing mechanisms to inform future planning.
Application of Green Orienteering Problem for Tourism Case in Yogyakarta
Indonesia has a rapidly growing tourism industry, supported by numerous destinations with extraordinary natural beauty. According to the Travel Tourism Development Index (TTDI) 2024, Indonesia ranks 22nd globally and second in ASEAN. One of its prominent provinces is the Special Region of Yogyakarta, which recorded 3.2 million tourists in May 2024. The tourism industry contributed 4.1% to Indonesia’s Gross Domestic Product (GDP) in 2023, with the government targeting an increase to 4.6% by 2025. Despite its economic value, tourism accounts for 8% of global carbon emissions, with 49% originating from its transportation. The government is committed to reducing carbon emissions by 50% by 2030 and achieving net-zero emissions between 2045–2060. Many tourists rely on travel agencies to purchase tour packages. These packages are designed by travel agencies by considering routes, costs, time, and tourist satisfaction. The selection of destinations aligns with the Orienteering Problem (OP), which aims to maximize scores within time constraint, along with carbon emissions generated and trip days. This study applies a green team orienteering model to 65 destinations in Yogyakarta to maximize tourist satisfaction while minimizing costs and carbon emissions over a 3-day tour (8 hours per day). The model is solved using AMPL software and Gurobi. The optimal route includes 5 destinations on the first day, 4 destinations on the second day, and 5 destinations on the third day, achieving a total score of 3,521. Sensitivity analysis shows that increasing tour hours and the number of days leads to higher total scores. Weighting significantly influences each objective function, potentially resulting in trade-offs among objectives or dominance over others.
Compliance as a Change Agent: Strategic Leadership in Regulatory Transformation
This study addresses the crucial problem of coordinating rapidly evolving technological advancements with more conventionally static regulatory environments by examining compliance as a change agent for strategic leadership in regulatory transformation and by integrating Regulatory Technology (RegTech) into dynamic regulatory frameworks specific to the financial services sector. The main goal of this study is to create flexible regulatory frameworks that facilitate the use of cutting-edge technical solutions, ensuring compliance and enhancing the effectiveness of regulatory oversight. Through practical implementation in financial institutions, this study examines the efficacy of suggested frameworks using a combination of qualitative and quantitative research techniques. According to the results, incorporating RegTech solutions enhances real-time risk management, drastically lowers operating expenses, and streamlines compliance procedures. The study found that adaptive regulatory frameworks offer a more proactive approach that can accommodate ongoing technological developments without jeopardizing the stability or integrity of financial systems. This study's conclusion calls for a thorough review of current regulatory procedures, highlighting the need for rules as inventive and dynamic as the technology they regulate. This strategy aims to protect against new threats and create an atmosphere that encourages technology development in the financial industry.
Impacts of the Covid-19 Pandemic on Higher Education in Brazil: Longitudinal Analysis Using Data Envelopment Analysis
The aim of this study was to analyze the impacts of the Covid-19 pandemic on Higher Education in Brazil. To this end, the Academic Units of the Federal University of Ceará (UFC) were taken as a sample, which consistof Campuses, Centers, Faculties and Institutes, with a total of 17 Decision Making Units (DMUs).The Data Envelopment Analysis (DEA) methodology was applied, in BCC modeling, output oriented.Ex-ante (2018-2019) and ex-post-facto (2020-2021) longitudinal research showed a subtle increase in the relative efficiency of the units analyzed in 2020. On the other hand, in the first semester of 2021, marked by the replacement of face-to-face classes with online classes, the relative efficiency of these units fell considerably, but rose again in the following semester (2021.2). It was evident that although the relative efficiency of these units fell during the period in which the social distancing measures imposed by Covid-19 in Brazil intensified, the UFC managed to adapt to the pandemic period, with its Academic Units showing a considerable recovery in terms of their productive efficiency.
Education in The Age of AI: Challenges in Ethics, Technology, and Pedagogy
The integration of Artificial Intelligence (AI) in schools creates tremendous opportunities while introducing complicated challenges on ethical, academic, and technical levels. Ethical challenges include algorithmic prejudice, data privacy, and equal access to AI-powered educational technologies. Academically, the changing role of educators raises concerns about student engagement, personalized learning, and the balance between human and machine-driven instruction. Technically, AI implementation demands strong infrastructure, dependable data management, and safeguards against misleading. This study investigates these issues, highlighting the necessity for responsible AI implementation, teacher training, and policy frameworks to guarantee that AI improves rather than disrupts the educational landscape.
Study on Cloud-Centric IoT Transformation for AC. Manufacturing INC
Cloud computing is a business approach that is quickly changing our daily lives, and as a result, organisations are more concerned with finding a centralised, affordable solution that offers the highest uptime. This study critically analyses the advantages and disadvantages of implementing a cloud-centric Internet of Things (IoT) transformation for AC. Manufacturing Inc., a mid-sized enterprise in the automotive parts industry. The study aims to expose the integration of cloud-based IoT solutions for real-time monitoring and analytics to streamline operations, improve production efficiency, and enhance real-time decision-making.
Sustaining Smallholder Oil Palm Farmers Productivity Using Land Leasing Market Scheme In Oil Palm Producing Belt of Nigeria
The core intent of the study is to: (i) compare the profitability of the land leasing market scheme participating and non-participating smallholder oil palm farmers in oil palm producing belt of Nigeria; and (ii) assess the effect of land leasing market scheme on their productivity. Multistage sample method was employed to select 240 smallholder oil palm farmers (120 land leasing market scheme participating smallholder oil palm farmers, and 120 land leasing market scheme non-participating smallholder oil palm farmers). From the result, the net profit margin ratio for the land leasing participating farmers was 46.41%, non-participating farmers was 42.27%. The study further shows that age,2, farm size, and knowledge of land leasing market scheme had negative value and the relationship was significant with land leasing market scheme participating status. The expected oil palm productivity from land leasing participating smallholder oil palm farmers was 2,946.4 kg/hectare compared with 2,042.0 kg/hectare from non-participating farmers. The study recommends that smallholder oil palm farmers should be sensitized by all stakeholders to participate in land leasing market scheme to increase their productivity.
Sustainable development goal 2, Endogenous Switching Regression Model, Nigeria.
Powerset-Theoretic Foundations for HyperAutomata and SuperHyperAutomata
Hyperstructure theory, grounded in the powerset construction, offers a rich framework for modeling relationships among elements in a set. Its extension, superhyperstructure, employs the n-th iterated powerset to capture multi-layered hierarchical interactions [1]. A Finite Automaton is a machine with a finite set of states, recognizing regular languages via transitions over input symbols [2, 3, 4]. In this paper, we examine three computational models—classical finite automata, hyperautomata, and superhyperautomata—by providing concise mathematical definitions, illustrative examples, and key properties. Our presentation refines the powerset-based definitions originally given in [5], clarifying how hyperautomata generalize classical automata and how superhyperautomata further generalize hyperautomata. Through this unified treatment, we highlight the expressive hierarchies and closure properties that distinguish these models.
Effective Project Governance in a Full Project Lifecycle
The role of effective project governance across the full project lifecycle cannot be far-fetched from or beyond the achevement timely and effective delivery of project. For there to be an effective execution of the project and project success, several actions must be completed by the project manager, team, and all other stakeholders in the project environment. From start to finish, information technology initiatives necessitate a plethora of operations. For the project to be effectively completed, these activities need to be closely watched and coordinated to ensure a proper flow. Information technology projects are distinctive from other projects because they often combine standard project management techniques with systems development techniques. While requirements research, collecting, and analysis may not be necessary for all projects, they are crucial to information technology initiatives. The review of literature that focused on the management of projects and program that relates to information technology were to identify the project and governance activities for individual phase of the Project Management Life Cycle (PMLC) was further conducted.
Artificial Neural Networks to Investigate Mathematical Models: A Concise Review
Mathematical modelling plays an essential role in understanding numerous physical phenomena, often described by Differential Equations (DEs). However, handling these models, particularly when inundated with extensive data and inputs, poses challenges for both analytical and numerical approaches. Consequently, alternative techniques are sought. In this context, Artificial Neural Networks (ANNs) become valuable tool. Researchers have dedicated efforts to enhance ANNs capability in handling various types of DE’s such as Ordinary Differential Equations (ODE’s), Partial Differential Equations (PDE’s), and Nonlinear Partial Differential Equations (NPDE’s). This pursuit has led to advancements in learning algorithms tailored for ANNs. Currently, the Levenberg-Marquardt (LM) learning technique stands out as a popular method for training ANN models. Traditional learning techniques, marked by complexities such as the size of the Jacobian matrix, error stability, and computational intensity, have spurred the development of advanced learning techniques aimed at simplifying the training process. These advancements necessitate a thorough analysis of various ANN techniques, providing insights into selecting the most suitable approach for a given system. Consequently, the objective of this paper is systematically exploring the ANNs in solving mathematical models, shedding light on their application in diverse domains.
Fuzzy-Enhanced Lightweight CNNs for CulturallyRelevant Food Classification: Advancing Assistive Technology for Visually Impaired Nigerians
A research project fills the support technology deficit concerning assistive technologies for visually impaired Nigerians by creating lightweight Convolutional Neural Networks (CNNs) designed for indigenous food identification. We improved upon a past CNN-based Automatic Food Classification Model (AFCM) called AFCM by introducing our novel Custom Convolutional Fuzzy Neural Network (CCFNN) that features fuzzy logic and benchmark models MobileNet and LeNet-5. The database contains 4,000 images that represent 20 Nigerian swallow foods across various freshness states from fresh to 2-day-old. The images were transformed and resized to 100×100×3 to improve generalization and further enhanced through 30% image transformation methods (rotation, flipping, and zooming). The CCFNN implemented on a Lenovo T480S laptop using an Intel i7 processor and 16GB RAM demonstrated outstanding performance by attaining 95.2% accuracy when no augmentation was used and 80.9% accuracy with augmentation while having fewer than 30K parameters. This outperformed the accuracy results of AFCM that decreased to 37.4% when augmentation was activated. The combination of MobileNet and LeNet-5 operated at high accuracy (~95.7%) with light computing needs while CCFNN utilized fuzzy Gaussian inputs for understandable decisions crucial for assistive use. The study demonstrates CCFNN achieves outstanding results by maintaining an AUC-ROC of 0.9982 without augmentation and 0.9958 with augmentation which exceeds AFCM's 0.9545 with augmentation assessments. The study demonstrated that CCFNN delivered better deployment feasibility because it completed training steps in 850ms while requiring 200 times fewer parameters than MobileNet (4.2M) and running faster training steps than AFCM (998ms). Cultural adaptation of AI systems is needed according to research findings and CCFNN demonstrates strong potential to support mobile services in poor neighborhoods. The future research plan will investigate fuzzy pooling layers together with quantization methods for achieving improved real-time functionality. The proposed research brings inclusive AI by closing the discrepancy between Western-based models and localized requirements while offering sustainable solutions for assistive technology implementation in underdeveloped areas.
Sustainable Technological Expectations Using a New MCDM Approach: Sensitivity–Alternatives–Ranking Analysis (SARA)
Multi-Criteria Decision-Making (MCDM) approaches that are transparent and robust are crucial to meet the rising demand for sustainable decision-making in technology management. This paper presents a novel methodology, the Sensitivity-Alternatives-Ranking Analysis (SARA) method, that combines sensitivity testing, alternative selection, and ranking visualization to assess long-term technology expectations. The suggested approach is implemented in a case study incorporating alternative technologies, and its results are compared with the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and validated through extensive sensitivity analysis. The results demonstrate consistent and stable ranks across varying criteria weights, confirming the SARA framework's robustness. In comparison to standard MCDM methods, the SARA method enhances both interpretability and reliability through integration of technology evaluations, normalized scores, and visualization-based insights. The study suggests a practical and conceptually sound technique for long-term decision-making that may assist policymakers, researchers, and practitioners in conducting complex technical evaluations.
VlseKriterijumska optimizacija i kompromisno resenje method
Distinguishing AI-Generated Images from Real Images: A Multi-Criteria Analysis Using SF-AHP
Recent advancements in the widespread use of the internet and computers have led to the storage of vast amounts of data, increasing computational power, and enhanced computer capabilities, fundamentally transforming human life. The large data sets generated through the internet provide an unlimited source of data for Artificial Intelligence (AI). These developments, coupled with current AI techniques, challenge the traditional concepts of photography and images, particularly due to the ability to produce realistic visuals. As synthetic images produced by AI become increasingly indistinguishable from real, human-made photographs, the distinction between the two is becoming more difficult over time. Considering the development of Large Language Models (LLM), which can produce highly fluent and coherent communication almost indistinguishable from human-generated text within a few years of their introduction, it is expected that synthetic images will rapidly increase in realism as well. This situation has become a critical issue in preventing disinformation and maintaining visual credibility. This study addresses this problem and aims to present a holistic approach for distinguishing between real and synthetic images. In this context, a comprehensive evaluation framework consisting of six main criteria and eighteen sub-criteria is presented, and the relative importance of these criteria is analyzed using the Spherical Fuzzy AHP (SF-AHP) method. These importance levels should be continuously updated and discussed as technology evolves. In this study, an innovative approach is used, incorporating expert opinions (such as those from advanced language models like ChatGPT o-3), and based on SF-AHP analysis, physical consistency and sensor trace analysis emerged as the most critical determinants for distinguishing between synthetic and real images. The findings emphasize the necessity of a multi-criteria approach in AI image detection and provide insights for future validation methods.
AI Ethics Governance and Privacy in a Rapidly Advancing Digital World
As Artificial Intelligence (AI) and machine learning become increasingly integrated into everyday life, addressing their ethical implications and potential biases is crucial. This article reviews key concerns about ethics, governance, bias reduction, and privacy in AI-ML applications. While these systems show remarkable capabilities in areas like image recognition, natural language processing, and analytics, they also risk producing unfair outcomes due to biases from sources such as training data, algorithms, feature selection, and institutional practices. A comprehensive evaluation from development to deployment is essential to ensure AI systems are fair, transparent, and beneficial. This review highlights the importance of responsible development and use of AI technologies.
Project Scheduling in Uncertain Environments: An IntuitionisticFuzzy Approach to solve CPM
The Critical Path Method (CPM) in a fuzzy context is an important tool for dealing with uncertain, perhaps vague, project scheduling. This paper presents two new solution method for the Intuitionistic Fuzzy Critical Path Method (IFCPM) by defining activity durations using Triangular Intuitionistic Fuzzy Numbers (TIFNs). These methods combine forward pass and backward pass calculations, to identify the earliest and latest event times and computes, the total float for each activity. This method improves project scheduling by dealing with uncertainty and hesitancy for activity duration. To demonstrate the practical utility and usefulness of the method, two numerical examples are considered to prove it can identify the critical path in an intuitionistic fuzzy environment. The results demonstrate that the method described here improves the robustness and flexibility of the project scheduling process under uncertainty.
Intuitionistic Fuzzy Hybrid Data Mining-MAGDM Approach Using Correlated Aggregation for Solving Complex Decision Problems
This paper addresses Multiple Attribute Group Decision Making (MAGDM) problems where attribute values are represented as Intuitionistic Fuzzy Sets (IFSs). A novel decision-making framework is developed by integrating the Choquet integral operator with Intuitionistic Fuzzy Ordered Weighted Averaging (IFOWA) operators, effectively capturing both the importance and ordered influence of attributes under uncertainty. A distinguishing feature of this work is the fusion of Data Mining techniques into the MAGDM model, enabling the identification and elimination of irrelevant or redundant attributes. This integration significantly enhances the model’s efficiency and decision quality by reducing complexity and focusing on the most impactful variables. The synergy between intuitionistic fuzzy logic and data mining provides a more intelligent and scalable decision-support system. The effectiveness and practicality of the proposed approach are demonstrated through a comprehensive numerical illustration.
Issues and Challenges in Transforming Management Practices through Agility
Transforming management practices to be more agile means making organizations more flexible and responsive, but this process comes with several challenges. Key issues include cultural resistance from employees who are used to traditional methods, a lack of commitment and skills from leadership to support the change, and inadequate training for staff to understand agile practices. Additionally, existing processes may not align well with agile principles, and communication barriers between departments can hinder collaboration, making the transition difficult. Cultural resistance is a major challenge when organizations try to adopt agile management practices. This resistance often stems from a need for a significant mindset shift, as employees and leaders may feel comfortable with their current processes and hesitant to change. Additionally, there is a fear among employees that implementing agile practices could disrupt their roles and lead to job insecurity, making them reluctant to embrace new ways of working. Leadership commitment is crucial for successful agile transformation because strong support from leaders helps ensure that agile practices are genuinely implemented rather than just superficially adopted. Additionally, leaders need the right skills to navigate the complexities of agile frameworks, as a lack of knowledge can hinder progress. Without adequate training for employees and alignment with existing processes, organizations may struggle to effectively implement agile practices and achieve their goals. On another strong note, some challenging open problems are provided, to enrich the existing knowledge with new insights towards a next generation intelligent agile managerial practice.