Search Results (2,372)

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of ($R^2 =~76\%$), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R² reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article

This study investigates the acceptance of an AI-powered decision-support chatbot among professionals in a marketing and sales context, addressing a gap in technology acceptance research by examining data-intensive decision environments that remain underexplored. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes an extended model incorporating Behavioral Intention, Performance Expectancy, Effort Expectancy, Social Influence, Output Quality, Time Saving, Source Trustworthiness, Cognitive Load, and Chatbot Self-Efficacy. An experimental study was conducted with 106 professionals using a chatbot-enhanced business analytics platform to complete marketing KPI analysis tasks. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that Behavioral Intention to use decision-support chatbots is significantly influenced by Performance Expectancy, Effort Expectancy, and Social Influence. Performance Expectancy is strongly driven by Output Quality, Time Saving, and Source Trustworthiness, while Effort Expectancy is significantly shaped by reduced Cognitive Load and higher Chatbot Self-Efficacy. The findings suggest that chatbot acceptance in professional decision-making depends not only on usability and performance beliefs but also on cognitive relief, trust in information sources, and efficiency gains, highlighting important implications for both theory and the design of AI-based decision-support systems. Full article

To address the inaccurate scheduling of electric–hydrogen integrated stations (EHISs) caused by the limited accuracy of conventional mechanistic models for proton exchange membrane (PEM) electrolyzers, this study proposes a deep reinforcement learning (DRL)-based scheduling strategy incorporating a data-driven electrolyzer model. First, a deep XGBoost model is developed to characterize the hydrogen production behavior of the PEM electrolyzer, thereby replacing the traditional mechanistic model and reducing prediction errors. Second, the EHIS scheduling problem is formulated as a constrained Markov decision process (CMDP) that explicitly considers user demand and carbon emission constraints. Third, an improved deep Q-network (DQN) algorithm integrating Lagrangian relaxation and the template policy-based reinforcement learning (TPRL) method is designed to solve the scheduling problem, which enhances convergence speed and generalization performance under similar operating scenarios. The simulation results demonstrate that the proposed method can effectively alleviate the decision-making risks introduced by model inaccuracies and significantly improve the operational profitability of the station while satisfying user demand and carbon emission constraints. Full article

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

Sewage sludge management remains a critical challenge in Greece, where increasing regulatory pressure, environmental constraints, and limited stakeholder participation complicate regional decision-making. In particular, the revision of regional Waste Management Plans requires decision-support approaches that are both technically robust and socially legitimate. This study develops and applies a participatory, data-driven multi-criteria decision analysis framework to evaluate sustainable sewage sludge management strategies in the Region of Eastern Macedonia and Thrace. The framework combines structured stakeholder participation with quantitative performance assessment, enabling transparent, reproducible, and systematic comparison of alternative sewage sludge management options. Four realistic sludge management alternatives—composting fr agriculture, forestry use, land restoration, and thermal drying with energy recovery were assessed against fifteen economic, environmental, and social sub-criteria. Data were collected through structured questionnaires administered to forty-four representatives from five stakeholder groups: utilities (water and sewerage service providers), local authorities, scientists/experts, end-users, and citizens. Group preferences were aggregated using equal group weighting to ensure balanced representation. The results show that environmental and economic criteria outweigh social aspects. The highest mean weights were assigned to compliance with environmental requirements for products derived from the disposal method (0.105) and compliance with stricter national environmental legislation (0.104), followed by energy intensity (0.097), installation cost (0.065), and operation and maintenance (O&M) cost (0.061). Overall rankings identified composting and thermal drying as the most preferred options, followed by land restoration and forestry use; sensitivity analysis (±10% variation in sub-criterion weights) confirmed ranking stability. The proposed framework enhances decision transparency by embedding measurable criteria and stakeholder inputs within a structured analytical process. From a policy perspective, it addresses participation gaps in Greek waste planning and offers a transferable decision-support tool for future regional planning. Further extensions may include integration with life cycle assessment and cost–benefit analysis to support adaptive updates under circular economy objectives. Full article

This study aims to determine the current seismic resistance of two masonry minarets that were severely damaged during the 6 February 2023 Kahramanmaraş earthquakes, while also evaluating whether a model-updating approach based on experimental dynamic characteristics can reliably capture the actual seismic behavior and collapse mechanism of such structures under real earthquake conditions. The dynamic characteristics of the minarets were identified using Operational Modal Analysis (OMA) based on previous in-situ vibration measurements. These characteristics were used to calibrate finite element models through a model-updating process employing Multi-Criteria Decision-Making (MCDM) methods. The initial modal analyses revealed discrepancies of up to 13.7% in natural frequencies and 9.7% in mode shapes. After applying MCDM methods to a wide set of model variants, these differences were reduced to 2.0% and 9.2%, respectively, improving the agreement between numerical and experimental results. Once the most representative models were obtained, nonlinear seismic analyses were performed using actual ground motion records from the earthquake. The results included evaluations of peak displacements, base shear forces, and principal stresses. The concentration of principal stresses near the transition zone showed good qualitative agreement with the observed collapse locations, indicating a reasonable consistency between numerical results and observed damage patterns. These findings demonstrate the value of integrating OMA-based model updating with MCDM methods and support a data-driven framework for assessing the seismic performance of historical masonry structures. Full article

Lean production philosophy and sustainability approach have become a critical framework for efficiency improvement, waste reduction, and promoting sustainable manufacturing practices. In the age of artificial intelligence (AI), there is a synergy, which has now found new dimensions, data-driven decision-making, predictive analytics, and operational agility. AI technologies promise to transform industrial processes by converging lean production and sustainability principles, a synergy explored in this paper. AI APIs enable the use of AI to improve resource utilization, reduce environmental pressure, and maintain economic growth inherent to all business sectors while also fostering social accountability. In this study, a robust regression model is employed to study the role of AI in moderating the lean practices and sustainability outcomes relationship, using a sample of 528 manufacturing firms. The results show that the contribution of AI technologies to economic, ecological, and social sustainability is effectively multiplied by that of lean production. This research offers a framework to help practitioners and policymakers optimize production systems in line with Sustainable Development Goals. Finally, the study delivers actionable recommendations for navigating skill gaps and cybersecurity risks that were identified. In sum, this paper contributes to the rapidly emerging conversation by providing empirical evidence on AI’s moderating role in the lean–sustainability relationship and offering a strategic framework for practitioners. Full article

The construction sector contributes to global CO₂ emissions and resource consumption, highlighting the need for sustainable design strategies. In this context, building envelope performance plays a key role, supported by digital technologies. This study proposes an automated BIM-MCDM workflow to select the optimal wall stratigraphy with Aerogel, EPS, and Rock Wool thermal insulation layers. The evaluation indicators are organized into three thematic clusters: Thermal Performance (TPI), Environmental Sustainability (ESI), and Economic Indicators (EI). Insulation alternatives and indicators are modeled in Autodesk Revit, enabling parametric variation in insulation layers and generating multiple stratigraphic configurations. Performance indicators are automatically calculated through a BIM-VPL integration using Dynamo, Microsoft Excel, and Tally. Fully interoperable parametric scripts enable data extraction from the BIM model, regulatory compliance verification, and the transfer of results back to the BIM model. Finally, indicator values are weighted and evaluated using an MCDM analysis based on the AHP method, fully implemented in Dynamo. The results indicate that EPS ranks first due to its strong performance in TPI and ESI, followed by Aerogel, influenced by EI, and Rock Wool, which shows a lower contribution to ESI. This research contributes to data-driven decision-making and the digitalization of sustainability-oriented performance assessment for building envelopes. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

The integration of emerging technologies and real-world data is transforming the landscape of hematologic clinical trials. Artificial intelligence (AI) offers remarkable capabilities for predictive modeling, patient stratification, and adaptive trial design, while circulating tumor DNA (ctDNA) provides a minimally invasive biomarker for disease monitoring, the early detection of relapse, and treatment response assessment. Concurrently, real-world evidence (RWE) complements traditional clinical trial data by capturing treatment effectiveness, safety, and patient outcomes in broader, heterogeneous populations. This review examines the synergistic potential of AI, ctDNA, and RWE to optimize trial design and decision-making in hematologic malignancies. We discuss methodological innovations, including AI-driven patient selection, ctDNA-guided adaptive interventions, and the incorporation of RWE for external control arms and post-marketing surveillance. Key challenges, such as data standardization, regulatory considerations, and ethical implications, are also addressed. By integrating these advanced tools, clinical trials in hematology can achieve greater efficiency, precision, and translatability, ultimately accelerating the development of personalized therapies and improving patient outcomes. Full article

Enterprise decision making in marketing, finance, and audit remains fragmented, leading to inefficient budget allocation and incomplete risk assessment. This study proposes an integrated, Big Data-driven decision-support framework that unifies Large Language Models (LLMs), attention-based marketing mix modeling, and multi-agent, game-theoretic optimization to coordinate cross-functional decisions. The architecture combines five modules: LLM-enhanced customer segmentation and customer lifetime value prediction, attention-weighted marketing mix modeling, multi-agent LLM systems for hierarchical budget optimization, attention-informed Markov multi-touch attribution, and LLM-augmented audit quality assessment. Empirical validation on a large-scale e-commerce dataset with 2.8 million customers and USD 156 million in marketing expenditure shows that marketing return on investment increases from 4.2 to 6.78 (61.4% relative improvement), financial forecasting error (MAPE) decreases from 12.8% to 4.7% (63.3% reduction), fraud detection accuracy improves by 29.8%, the Audit Quality Index reaches 0.951, and customer lifetime value prediction accuracy improves from 76.4% to 91.3%. By operationalizing the convergence of LLMs, attention mechanisms, and game-theoretic reasoning within a unified and empirically validated framework, the study delivers both theoretical advances and practically deployable tools for integrated business intelligence in digital economies. Full article

Within the Industry 5.0 paradigm, the management of critical assets requires advanced digital architectures capable of ensuring resilience and operational sustainability. The present systematic review analyzes the state of the art in predictive maintenance (PdM) technologies for turbines and turbomachinery, providing a technical examination of anomaly and fault detection frameworks, extended to remaining useful life (RUL) estimation and root cause analysis (RCA). The work addresses inherent sectoral challenges, ranging from the processing of high-dimensional multivariate time series (MTS) from Supervisory Control and Data Acquisition (SCADA) systems to labeled data scarcity and signal non-stationarity in real-world environments. Both purely data-driven frameworks and hybrid physics-informed models, such as Physics-Informed Neural Networks (PINNs), are critically evaluated against performance indicators. A significant contribution of this study lies in the classification of methodologies based on their readiness for real-time inference, emphasizing the role of Explainable AI (XAI) in providing transparent insights to domain experts, who remain central to decision-making processes. The primary objective of this review is to offer an analytical overview of progress to date against current technological gaps, tracing a clear trajectory for future developments. In this regard, the adoption of Generative AI and Large Language Models (LLMs) is identified as a fundamental step toward evolving into interactive, human-centric decision support systems. Full article