Search Results (5,000)

Objective: This study investigates how artificial intelligence (AI) research relates to firm value, focusing on dominant thematic trends, theoretical foundations, and global collaboration patterns. Methods: A PRISMA-guided systematic review was conducted on 219 peer-reviewed articles published between 2013 and May 2025 in the Web of Science Social Sciences Citation Index. Bibliometric techniques, including co-word, co-citation, and collaboration network analyses, were performed using the bibliometrix (version 4.2.3) in R (version 4.4.2) package to map key concepts, intellectual structures, and international research partnerships. Results: The literature is primarily grounded in strategic management theories such as the resource-based view (RBV) and dynamic capabilities. Emerging research streams emphasize digital transformation, big data analytics, and decision support systems. Frequently co-occurring terms include “firm performance,” “artificial intelligence,” “dynamic capabilities,” “information technology,” and “decision-making.” Collaboration mapping highlights the United States, United Kingdom, and China as leading hubs, with increasing contributions from emerging economies such as India, Malaysia, and Saudi Arabia. The alignment between co-word and co-citation structures reflects a shift from foundational theory to applied AI capabilities in firm-value creation. Implications: By integrating a systematic review with advanced bibliometric and science-mapping methods, this work establishes a structured foundation for theory development, empirical testing, and policy formulation in AI-driven business landscapes. Full article

In response to the actual needs of pure copper bonding wires, it is crucial to develop a chromium-free passivator that is environmentally friendly and has excellent corrosion resistance. In this study, three different composite organic formulations of chromium-free passivation solutions are selected: 2-Amino-5-mercapto-1,3,4 thiadiazole (AMT) + 1-phenyl-5-mercapto tetrazolium (PMTA), 2-mercaptobenzimidazole (MBI) + PMTA, and Hexadecanethiol (CHS) + sodium dodecyl sulfate (SDS). The performance analysis and corrosion mechanism were compared with traditional hexavalent chromium passivation through characterization techniques such as XRD, SEM, and XPS. The results show that the best corrosion resistance formula is the combination of the PMTA and MBI passivation agent, and all its performances are superior to those of hexavalent chromium. The samples treated with this passivation agent corrode within 18 s in the nitric acid drop test, which is better than the 16 s for Cr⁶⁺ passivation. The samples do not change color after being immersed in salt water for 48 h. Electrochemical tests and high-temperature oxidation test also indicate better corrosion resistance than Cr⁶⁺ passivation. Through the analysis of functional groups and bonding, the excellent passivation effect is demonstrated to be achieved by the synergistic action of the chemical adsorption film formation of PMTA and the anchoring effect of MBI. Eventually, a dense Cu-PMTA-BMI film is formed on the surface, which effectively blocks the erosion of the corrosive medium and significantly improves the corrosion resistance. Full article

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across various charging contexts and control scenarios. This paper categorizes RL methodologies from value-based to actor–critic and hybrid frameworks, and explores their integration with optimization techniques, forecasting models, and multi-agent coordination strategies. By examining key design aspects—including agent structures, training schemes, coordination mechanisms, reward formulation, data usage, and evaluation protocols—this review identifies broader trends across central control dimensions such as scalability, uncertainty management, interpretability, and adaptability. In addition, the review assesses common baselines, performance metrics, and validation settings used in the literature, linking algorithmic developments with real-world deployment needs. By bridging theoretical principles with practical insights, this work provides comprehensive directions for future RL applications in EVCS control, while identifying methodological gaps and opportunities for safer, more efficient, and sustainable operation. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

Cement-based materials are essential construction components, yet their complex microstructures critically govern mechanical performance and durability. This study investigates the micro-textural characteristics and mechanical properties of cement paste modified with grinding aids (triethanolamine, TEA; maleic acid triethanolamine ester, MGA) and polycarboxylate-based superplasticizer (PCA). Moving beyond qualitative SEM limitations, we employ advanced image-based quantitative techniques: grayscale-based texture analysis for statistical evaluation and fractal dimension analysis for geometric quantification of microstructural irregularity. Results demonstrate that grinding aids enhance particle dispersion and reduce agglomeration, resulting in a more uniform micro-texture characterized by lower grayscale variability and reduced fractal dimensions. PCA superplasticizers further significantly enhance fluidity and compressive strength. The optimal formulation (MGA + PCA) achieved a 20% increase in 28-day compressive strength compared to control samples. The fractal dimension D_B exhibits a positive correlation with compressive strength, while energy and correlation values show a negative correlation; in contrast, entropy and contrast values demonstrate a positive correlation. This research advances quantitative microstructure characterization in cementitious materials, offering insights for tailored additive formulations to enhance sustainability and efficiency in concrete production. Full article

We design a phase rendering technique that exploits the link between the angular deviations of a beam and the optical implementation of cross-correlations. We employ two suitably coded gratings, which are incorporated as part of a new device here called a dark-field, Lau interferometer. To this end, we use a first grating whose unit cell is coded with the white and black versions of a Barker sequence. We employ a second grating that is coded as the Babinet’s complementary of the first grating. We describe the cross-correlation operation by using a compact matrix formulation, which is amenable to numerical evaluation. Full article

With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

Power outages in non-faulted zones caused by system failures significantly reduce the reliability of distribution networks. To address this issue, this paper proposes a fault self-healing technique based on the integration of soft open points (SOPs) and network reconfiguration. A mathematical model for power restoration is developed. The model incorporates SOP operational constraints and the stochastic output of photovoltaic (PV) distributed generation. And this formulation enables the determination of the optimal network reconfiguration strategy and enhances the restoration capability. The study first analyzes the operational principles of SOPs and formulates corresponding constraints based on their voltage support and power flow regulation capabilities. The stochastic nature of PV power output is then modeled and integrated into the restoration model to enhance its practical applicability. This restoration model is further reformulated as a second-order cone programming (SOCP) problem to enable efficient computation of the optimal network configuration. The proposed method is simulated and validated in MATLAB R2019a. Results demonstrate that combining the SOP with the reconfiguration strategy achieves a 100% load restoration rate. This represents a significant improvement compared to traditional network reconfiguration methods. Furthermore, the second-order cone programming (SOCP) transformation ensures computational efficiency. The proposed approach effectively enhances both the fault recovery capability and operational reliability of distribution networks with high penetration of renewable energy. Full article

The misuse of drone technology poses significant risks to public and personal safety, emphasizing the need for accurate and efficient aerial target detection to prevent detection failures due to randomly distributed airborne targets and mitigate interference from undesired directions. Unlike conventional beam-synthesis techniques that often require either a large number of array elements or iterative numerical optimization, the proposed method analytically derives the excitation distribution by solving a newly formulated weighted-constraint problem, thereby fully accounting for mutual coupling between elements and ensuring both computational efficiency and design accuracy. In this communication, a 10 × 4 planar microstrip antenna array with a wide flat-top beam and low sidelobe is designed based on the extended method of maximum power transmission efficiency. The optimized distribution of excitations for the antenna array, which achieves a shaped beam with uniform gain over the desired angular range while suppressing sidelobe levels (SLLs) outside the shaped region, is derived by analytically solving a newly formulated weighted constraint problem. To reduce the number of antenna elements and enhance radiation characteristics, the inter-element spacings in the E-plane and H-plane are set to 0.55 λ₀ and 0.75 λ₀, where λ₀ is the free-space wavelength at 3.5 GHz. Measurement results indicate that the flat-top beam in the E-plane has a wide half-power beamwidth (HPBW) of 51.2° and a low SLL of −30.1 dB, while the beam in the H-plane has a narrow HPBW of 20.1° and a low SLL of −30.5 dB, thereby demonstrating its capability in aerial target detection and airborne tracking applications. Full article

The Minimum Spanning Tree (MST) problem addresses the challenge of identifying optimal network pathways for critical infrastructure systems, including transportation grids, communication backbones, power distribution networks, and reliability optimization frameworks. However, inherent uncertainties stemming from disruptive events demand robust analytical models for effective decision-making. This research introduces an uncertainty-theoretic framework to assess MST stability in uncertain network environments through novel constructs: lower set tolerance (LST) and dual lower set tolerance (DLST). Both LST and DLST provide quantifiable measures characterizing the resilience of element sets relative to edge-weighted MST configurations. LST captures the maximum simultaneous risk variation preserving current MST optimality, while DLST identifies the minimal variation required to invalidate it. We evaluate MST robustness by integrating uncertain reliability measures and risk factors, with emphasis on computational methods for set tolerance determination. To overcome computational hurdles in set tolerance derivation, we establish bounds and exact formulations within an uncertainty programming paradigm, offering enhanced efficiency compared with conventional re-optimization techniques. Full article

Anastrepha fraterculus (Diptera: Tephritidae) is a major fruit pest in several countries of South America and is mass-reared for use in integrated pest control strategies, including the sterile insect technique (SIT), and as a host for rearing biocontrol agents. Optimizing these rearing protocols requires a deeper understanding of how larval diet impacts adult traits. This study investigated the effects of three larval diet formulations differing in nutrient composition on larval development and adult fitness traits. All diets contained inactive non-hydrolyzed brewer’s yeast and sucrose. Two of them included wheat germ, either alone (wheat germ diet) or combined with mashed carrot (carrot diet), whereas the corn flour diet did not contain wheat germ. The carrot diet produced the heaviest pupae, adults with longer wings, and the lowest rate of deformed adults. The corn flour diet prolonged larval and pupal development and increased adult lipid and carbohydrate content. Both the corn flour and carrot diets led to greater glycogen accumulation and more skewed weight distributions compared to the wheat germ diet. Present results highlight how larval diet composition determines developmental traits with direct consequences on adult physiology in A. fraterculus. These characteristics could enhance the effectiveness of control programs such as SIT and other biological control strategies. Full article

Cocoa pod husk (CPH) has the potential to be utilized for polyphenol extraction to be used in functional food formulations and pharmaceutical formulations due to its health benefits. However, polyphenols are sensitive to environmental factors that reduce their stability and functionality. Therefore, encapsulation is necessary to protect their antioxidant capacity, mask undesirable flavours and smells, and, at the same time, allow the release of polyphenols in the gastrointestinal phases. This study encapsulated polyphenols using complex coacervation (CC) and spray drying (SD) with gum arabic (GA), sodium alginate (SA), chitosan (C), and gelatine (G), and evaluated yield (EY), encapsulation efficiency (EE), loading efficiency (LE), and bioaccessibility through in vitro digestion. The results showed that in the encapsulation using CC, the highest LE of 36.95 ± 7.63% was obtained using SA-G. In SD, significant differences in LE were observed among the tested encapsulant ratios, with the highest LE of 34.77 ± 1.2% achieved using GA (1:3). Bioaccessibility varied significantly depending on the encapsulation technique and encapsulating agent (EA) used. Using GA and spray drying (SD), the highest polyphenol release was achieved at 76.55 ± 5.10%, in contrast to only 6.41 ± 1.61% for the non-encapsulated extract. In conclusion, both techniques for encapsulating polyphenols extracted from CPH are efficient. However, SD allows for greater polyphenol bioaccessibility. Full article

Hybrid polysaccharide-based aerogels offer significant potential as advanced drug delivery platforms due to their tunable structure, high porosity, and biocompatibility. In this study, aerogel microparticles were synthesized using alginate, pectin, carrageenan, and their hybrid formulations via an emulsion–gelation technique followed by supercritical fluid CO₂ extraction. The resulting aerogels exhibit mesoporous structures with specific surface areas ranging from 324 to 521 m²/g and pore volumes between 1.99 and 3.75 cm³/g. Comprehensive characterization (SEM, gas sorption, XRD, TGA, DSC, and FTIR) confirmed that hybridization improved morphological uniformity and thermal stability compared to single polymer aerogels. Ibuprofen was used as a model drug to evaluate loading efficiency and release kinetics. Among all formulations, the alginate/carrageenan (2:1) hybrid showed the highest drug loading efficiency (93.5%) and a rapid release profile (>90% within 15 min), closely matching the performance of commercial ibuprofen tablets. Drug release followed Fickian diffusion, as confirmed by the Korsmeyer–Peppas model (R² > 0.99). These results highlight the potential of hybrid polysaccharide aerogels as vehicles for drug delivery and other fast-acting therapeutic applications. Full article