As buildings account for nearly one-third of global energy consumption, improving their energy performance and renewable integration is essential for achieving sustainability targets. Traditional building energy management systems (BEMS), often rule-based and static, struggle to adapt to fluctuating demands, variable tariffs, and the intermittency of solar resources. This study introduces an integrated, artificial intelligence (AI)-driven BEMS framework that jointly optimizes rooftop photovoltaic (PV) sizing and adaptive demand-side management (DSM) using reinforcement learning (Q-Learning) and benchmarks its performance against two established deterministic tools: HOMER Pro for techno-economic PV sizing and particle swarm optimization (PSO) for DSM load scheduling. Using realistic hourly building loads, meteorological data, and time-of-use pricing, the Q-Learning model converged to a PV–inverter configuration closely aligned with HOMER Pro’s optimum, achieving a slightly lower net present cost (–1.85%) and a modest increase in renewable fraction (+3.1%). In DSM applications, Q-Learning consistently outperformed PSO by shifting a larger share of flexible loads and securing higher daily cost reductions. Under grid-only conditions, Q-Learning reduced energy costs by 7.58% in winter and 8.27% in summer, while PV-integrated scenarios achieved savings of 35.14% and 26.89%, respectively. These results demonstrate that reinforcement learning can effectively enhance the performance of conventional BEMS approaches by providing more adaptive scheduling aligned with tariff structures and solar availability. The proposed framework supports more efficient, flexible, and sustainable building operations, highlighting the practical potential of AI-driven energy management in modern grid-interactive environments.

The traditional Slime Mould Algorithm (SMA) often suffers from slow convergence and a tendency to fall into local optima, significantly limiting its applicability in complex optimization problems. To overcome these limitations, a Strategy-based Fractional-order SMA (SFSMA) that integrates multi-strategy integration is proposed in this paper. Population diversity was enhanced through the synergistic combination of differential evolution and fractional-order calculus, while a dynamic threshold mechanism monitored the search state in real time. These strategies collectively facilitated escape from local optima and improved convergence speed. To evaluate the performance of the proposed method, SFSMA was first compared with mainstream swarm intelligence algorithms and other fractional-order swarm intelligence variants using 12 classical benchmark functions. Experimental results confirm that SFSMA achieved significant improvements in both convergence speed and optimization accuracy. Furthermore, an SFSMA–Otsu segmentation model was developed by integrating the proposed algorithm with the two-dimensional Otsu algorithm. The model was evaluated on multiple types of images, including human, landscape, and medical images, using four quantitative metrics: peak signal-to-noise ratio, mean squared error, structural similarity index measure, and feature similarity index measure. Quantitative results demonstrate that the SFSMA–Otsu achieved substantially higher segmentation accuracy compared to existing methods. In addition, the convergence speed of SFSMA improved by approximately 82.08% compared to the traditional SMA. In conclusion, the proposed SFSMA effectively addresses the shortcomings of traditional SMA and provides an efficient and reliable solution for complex optimization and image segmentation tasks, exhibiting both theoretical value and practical potential.

This study presents a detailed parametric evaluation of solar chimney performance by combining computational fluid dynamics (CFD) simulations with advanced design-of-experiments (DOE) techniques. The analysis focuses on the effects of chimney height, collector-to-chimney AR, chimney geometry, and construction material under realistic environmental conditions. CFD modeling was used to compute airflow, temperature rise, and the resulting buoyancy-driven updraft, while DOE procedures enabled the structured assessment of main effects and interaction effects across geometric factors. The results indicate that larger collector areas relative to chimney cross-section (high AR) and the use of a divergent chimney configuration significantly enhance updraft velocity and mechanical power generation. The configuration with a 30 m chimney and AR = 6 produced an updraft of approximately 12–13 m/s and a power output close to 15 kW, representing the highest performance among all simulated cases. Important interactions between AR and chimney geometry were identified, confirming that single-factor analyses may overlook coupled behaviours relevant to system optimization. These findings offer concise design guidance for enhancing solar chimney performance under local climatic conditions and confirm the feasibility of small, well-optimized systems as a clean-energy option in high-irradiation regions. The analysis is based on steady-state simulations, which inherently simplify real conditions.

The robust model predictive control does not exploit the potentially existing statistical properties of system uncertainties, which may result in overly conservative control solutions. To address this issue, this paper proposes a novel approach of stochastic model predictive control, specifically tailored for linear time-invariant systems that are confronted with bounded additive uncertainties. The proposed method is established within the robust tube-based model predictive control framework, where chance constraints are transformed into deterministic ones. In particular, by leveraging the propagation characteristics of uncertainties, an algorithm of time-varying tube-based stochastic model predictive control is devised through computing tightened constraints along the prediction horizons. Furthermore, utilizing the infinite-horizon propagation property of uncertainties, a constant tube-based stochastic model predictive control method is derived by implementing conservatively constant tightened constraints throughout the entire prediction horizons. The feasibility and closed-loop stability results are rigorously developed, and a numerical example is provided to demonstrate the efficacy of the proposed method.

In recent years, semisubmersibles have increasingly replaced fixed and semi-compliant platforms in deep water deployments. Their larger deck area and superior operational stability under severe environmental loads have contributed to their widespread use. However, excessive heave motion remains a critical design concern. Passive and active control strategies have been explored to address this concern. The present study proposes a novel passive damping system suspended beneath the platform deck to suppress excessive heave motion. The results demonstrate the effectiveness of the proposed passive damper in controlling heave motion under low and moderate sea states. At high sea states, however, its performance declines because it does not adequately suppress second-order effects and coupling between degrees of freedom. The controlled response behavior may result from complex inter-degree-of-freedom coupling effects, including second-order interactions. It requires more detailed higher-order analysis, which is beyond the scope of the present study. Additionally, the proposed damper system is uniquely driven by the buoyancy forces generated during submergence, eliminating the need for external power. Thus, the control action is inherently self-induced through the platform’s motion, offering a simple, energy-efficient, and reliable solution for heave mitigation.

The rising demand for liquefied natural gas (LNG) has spurred research into offshore regasification platforms as a reliable solution to meet global energy needs. Buoyant leg storage and regasification platforms (BLSRPs) offer superior positional stability compared to ship-shaped alternatives; however, heave motion needs to be mitigated for safe regasification. This study proposes a semi-active response control mechanism (RCM) employing magnetorheological (MR) dampers to suppress radial buoyant leg displacements, which are directly coupled with deck heave. A baseline numerical model of the BLSRP was developed in AQWA software without an RCM, and a mathematical model incorporating the MR-RCM was formulated to evaluate the controlled system performance. Unlike previous BLSRP studies, we explicitly incorporated combined wave–wind–current environmental loading conditions. Transformation matrices were established to convert radial responses into deck heave, with influence factors calibrated for varying sea states and approach angles. Results indicate that the MR-RCM achieves 30–45% root-mean-square reductions in radial displacements under moderate and high sea conditions, resulting in significant control of deck heave. Even under very high sea states, 8–11% reductions are maintained, preventing uncontrolled escalation. Hysteresis plots validate the nonlinear energy dissipation of MR dampers and their adaptability to broadband excitations. The findings demonstrate that MR-RCM improves platform stability, reduces boil-off gas generation, and enhances safety in LNG operations. Overall, the study establishes MR-based semi-active damping as a practical and scalable solution for offshore platforms, bridging the limitations of passive devices and the complexities of fully active systems.

Ruled surfaces, defined by the motion of a straight line along a space curve, represent a fundamental class of surfaces in differential geometry with significant applications in engineering design, architectural modeling, and computer graphics. Despite their classical nature, the construction of ruled surfaces from integral curves, solutions to differential systems derived from Frenet frames, remains relatively unexplored in the literature. This paper presents a detailed geometric study of a new class of ruled surfaces constructed from integral curves associated with the Frenet frame of regular space curves with positive curvature. We focus on surfaces whose base curves are given by the integral binormal and integral normal curves of a given spatial curve. Explicit expressions for the fundamental forms, curvature properties, and striction curves are derived for six distinct types of surfaces. Necessary and sufficient conditions under which these surfaces are minimal or developable are established. A numerical example illustrates the theoretical results, highlighting potential applications in geometric modeling. This work extends the theory of ruled surfaces in differential geometry by introducing families based on integral curves and providing a complete geometric characterization via fundamental forms and curvature analysis.

The Benney–Luke (BL) equation is a fundamental nonlinear evolution equation that models long-wave propagation in fluid dynamics and other nonlinear dispersive media. To understand more complex wave interactions and higher-order dispersive effects, extended forms of the BL equation have been developed, providing improved physical realism in describing nonlinear wave phenomena. Despite these advancements, existing studies on the extended sixth-order BL equation remain limited in the systematic construction of exact analytical solutions, particularly those encompassing diverse nonlinear structures such as rogue waves, lump waves, and peak-type solutions. This paper proposes a novel roadmap of the bilinear neural network method to derive new classes of exact solutions for the extended sixth-order Benney–Luke (BL) equation. Extended from previous models due to space-lower nonlinearities, the extended sixth-order equation incorporates additional dispersive and nonlinear interaction terms, enabling more comprehensive modeling of wave dynamics in fluid systems and nonlinear phenomena. Using Hirota’s bilinear operator and a neural network-based framework, we construct a diverse spectrum of analytical solutions, including kink, rogue, lump, and peakon-type solitons. These solutions significantly expand the known solution space of the BL family and offer deeper physical insights into nonlinear wave behavior such as wave steepening, resonance, and dispersion. The extended equation not only bridges mathematical rigor and physical realism but also improves the computational efficiency and adaptability of neural network-based structures in analyzing nonlinear partial differential equations.

Green port operations require efficient and sustainable terminal management, supported by robust decision-making tools for selecting a terminal operating system (TOS). This research examines the complex process of selecting TOS in the context of green ports, aiming to identify the key criteria that influence the preference for TOS in facilitating port services. The study develops an intuitionistic fuzzy (IF) set-based hybrid decision-analytic model for TOS selection to enhance the overall performance of green ports. The IF–logarithmic decomposition of criteria importance (LODECI)–Aczel–Alsina Weighted Assessment (ALWAS) model was introduced. The IF–LODECI method was formulated for criterion weighting. It incorporated the maximum decomposition approach for robust weight stabilization. The IF–ALWAS method, based on the ALWAS method, was proposed to evaluate alternatives. The new hybrid decision-analytic model integrated Aczel–Alsina t-norm and t-conorm operations, with the IF–Aczel–Alsina weighted averaging operator as the final step. The application of the model was exemplified through a case study conducted at green ports in Türkiye, focusing on environmentally conscious criteria for TOS selection, involving six experts, 10 criteria, and five alternatives. The results revealed that “berth management and scheduling” was identified as the most significant criterion, while “Navis TOS” demonstrated the highest overall performance among the alternatives. Rigorous sensitivity analyses were conducted to validate the robustness of the proposed hybrid model and algorithm. This study presents a comprehensive decision-making framework for selecting TOS in green ports, bridging the gap between theoretical advancements and practical applications.

Fractal–fractional differential equations have emerged as a powerful mathematical framework for modeling complex systems exhibiting memory effects, non-locality, and hysteresis phenomena. This study investigates a class of fractal-fractional ordinary differential equations characterized by a power-law memory kernel and influenced by hysteresis behavior. The continuity of the function g (t, w(t)) over closed subsets of $ \mathbb{R}$, is used to establish the foundational results. A supporting lemma is introduced to facilitate the development of a uniqueness theorem. Drawing upon Borzdyko’s framework, we derive existence results pertinent to the targeted family of equations.

Widespread electrification and increasing penetration of distributed renewables increase stress on distribution networks and motivate demand-side management (DSM) strategies that coordinate flexible loads and energy storage. This study investigates a grid-connected hybrid microgrid comprising a 5 kW photovoltaic array, a 3 kW wind turbine, a 10 kW h battery energy storage system, and a mixed residential–commercial load of about 54 kW h/day under a three-level time-of-use tariff. An optimization-based energy management framework evaluates three operating strategies: (i) a baseline without DSM or storage, (ii) DSM-only load shifting of 20% of the daily demand from peak to off-peak hours, and (iii) the proposed coordinated DSM + energy storage system schedule that jointly optimizes flexible demand, battery charge–discharge, and grid exchange. Over a 24 h horizon, the coordinated strategy reduces the total daily electricity cost from 3.086 USD to 0.108 USD (96.49% savings), decreases the maximum grid import by 69.25%, eliminates photovoltaic curtailment, and increases the renewable share in load supply from 68.33% to 92.56%, while keeping the battery at an average state of charge of 53.69% with roughly one equivalent complete cycle per day. The magnitude of the cost reduction arises from the selected time-of-use schedule and the assumed day-ahead profiles used in the case study. Battery cycling is reported as an operational indicator rather than as a degradation cost term in the optimization objective. A sensitivity analysis with respect to storage capacity reveals substantial cost reductions up to approximately 10–15 kW h, with diminishing returns beyond this range. The results underscore the value of jointly designing DSM and storage scheduling for cost-effective, renewable-rich microgrids, and provide quantitative guidance for storage sizing under time-varying prices.

In this work, we establish the conditions for ensuring the existence and uniqueness of common best proximity points for non-self-mappings defined on the general metric spaces. A unified theoretical framework is formulated to cover a broad class of contraction mappings. We describe the required conditions on the real-valued functions ($\aleph, \Phi$): $[0, \infty) \rightarrow \mathbb{R}$ and verify that these secure the existence of common best proximity points for ($\aleph, \Phi$)-interpolative contractions in complete metric spaces. The study further extends this concept by examining multiple forms of interpolative proximal-type contractions, such as proximal, Ćirić-Reich-Rus, Kannan, and Hardy-Rogers variants, through the use of the auxiliary functions ($\aleph, \Phi$). Several illustrated examples are included to demonstrate the applicability of our findings. Finally, we conclude with an application involving a nonlinear fractional differential equation, showing that it fully satisfies the assumption of our main result.

Given that power systems are essential to modern life and electricity demand continues to rise, ensuring their reliable and secure operation has become a critical priority. Wireless networked control systems (WNCSs), which rely on wireless channels for communication between controllers, sensors, and actuators, are increasingly deployed in energy systems such as multi-area interconnected power systems to enhance flexibility and scalability. WNCSs are susceptible to deception attacks and time-varying communication delays that can compromise interconnection stability and deteriorate performance. This paper presents an observer-based secure control methodology that models deception via independent Bernoulli processes with unknown attack probabilities, while explicitly considering actuation and measurement delays. Using a Lyapunov stability framework, we established computationally feasible linear matrix inequality conditions enabling the co-design of the controller and observer with proven stability and disturbance rejection. A two-area interconnected power system case study validates the approach. The proposed method was tested with offline gains covering nine scenarios. Results indicate that the method sustains closed-loop performance across all nine combined attack/delay scenarios and recovers quickly even in worst-case conditions, supporting secure control of WNCSs in realistic adversarial environments.

Distributed denial-of-service (DDoS) attacks have become a major threat to the stability of critical infrastructure networks, where even short service disruptions can lead to severe operational and economic consequences. To better capture the complex dynamics of these attacks, we extend an existing epidemic-based DDoS model by employing the fractal–fractional (FF) Atangana–Baleanu (AB) operator, which effectively accounts for memory effects, network heterogeneity, and irregular traffic patterns commonly observed in cyber environments. Within this framework, we establish the existence and uniqueness of solutions and examine the Ulam– Hyers stability of the proposed system. The local stability of both infection-free and endemic equilibria is assessed to identify the conditions under which the network can maintain normal operation. Numerical simulations are performed using the Adams–Bashforth method for various combinations of fractional and fractal orders. The results show that the FFAB formulation captures slower decay, extended memory, and more realistic transient dynamics than its classical counterpart. These findings demonstrate that incorporating FF dynamics offers a more flexible and accurate representation of DDoS propagation and quarantine- based mitigation, providing valuable insights for enhancing the resilience of modern cyber-infrastructure systems.

Colorectal cancer is challenging to treat because many anticancer drugs do not achieve optimal therapeutic effects. Moreover, these drugs can cause systemic side effects, and patients often respond differently to treatment. This study provides a computational framework that merges quantitative structure-property relationship analysis with a computational methodology combining structural descriptors and ranking analysis to systematically analyze and rank 10 United States Food and Drug Administration approved medicines for colorectal cancer. A suite of degree-based and neighborhood degree-based topological indices were generated and examined for their association with essential physicochemical properties, specially molecular weight and molecular complexity. Correlation research revealed that specific degree-based indices, and neighborhood degree-based indices, exhibited strong predictive power for these properties. By employing ratio weighting alongside with the VIekriterijumsko Kompromisno Rangiranje and Technique for Order Preference by Similarity to Ideal Solution decision techniques, the medicines were ranked based on their predicted physicochemical performance. The results from both decision methods consistently showed fluorouracil as the highest ranked therapeutic agent, followed by tipiracil hydrochloride and bevacizumab, underlining their favorable structural and pharmacological properties. This comprehensive modeling technique provides a consistent and systematic strategy to aid in early-phase drug screening and inform decision-making in colorectal cancer therapy.

Sustainable tourism performance of cruise ports is a critical determinant of the long-term competitiveness of both ports and destination regions. This study aims to develop and apply a novel picture fuzzy sets (PFSs)-based decision-support system to systematically evaluate cruise ports’ sustainable tourism performance by integrating qualitative expert judgments with quantitative operational data. The proposed framework adopts a multi-attribute group decision-making structure, in which PFSs are employed to explicitly capture uncertainty, hesitation, and neutrality in expert evaluations. Criteria weights are determined using an extended PFS-based weights by envelope and slope (WENSLO) method, while cruise port rankings are obtained through a PFS-integrated multi-attributive border approximation area comparison (MABAC) approach. The applicability of the proposed PFS–WENSLO–MABAC hybrid model is demonstrated through a case study of major cruise ports in Türkiye. The results indicate that the number of cruise passengers (0.5601) and the number of cruise ships (0.4218) are the most influential determinants of sustainable tourism performance, with Kuşadası Cruise Port (0.9029) achieving the highest overall ranking. Sensitivity and robustness analyses confirm the stability of the ranking outcomes under varying weighting scenarios. The findings provide actionable insights for port authorities and policymakers by identifying priority performance dimensions and offering a reliable analytical tool to support strategic planning and sustainability-oriented decision-making in cruise tourism.

This paper introduces a new extended rectangular fuzzy b-metric-like space that generalizes the existing frameworks of the extended rectangular and fuzzy b-metric spaces. Within this setting, we establish several fixed-point theorems for Ciric-type and Banach-type contractions, accompanied by a series of corollaries, propositions, and conditions that further illustrate the proposed concept. These results unify and extend many known theorems in fuzzy metric theory. Moreover, we provided several non-trivial examples to validate of the main results. A flow diagram was provided to demonstrate the generalized structure. Additionally, we applied the fuzzy integral equation to establish the uniqueness and existence of our main result.

This paper firstly introduces the (2+1)-dimensional sixth-order breaking soliton system (SBSS) using the bilinear neural network method, providing many types of solutions. A new structure of the SBSS is investigated, and new solutions are gathered. The solutions are expressed in terms of basic activation functions and are derived by applying these activation functions. Using the Hirota bilinear operator, the original nonlinear partial differential equation is reduced to a more tractable form. The bilinear neural network approach yields various classes of solutions, including kink, rogue, peak, breather, lump-type, and spike-type solutions. These solutions are expressed in terms of elementary functions, revealing the various dynamical behaviors inherent in the system. The results obtained not only generalize known solutions but also illustrate a deeper insight into the complex wave propagation phenomena described by the sixth-order breaking soliton equation.

Retailers increasingly need decision-support tools to manage unsold inventory under operational and fiscal constraints. In this paper, we develop a reverse supply chain (RSC) model for retailers under profit–loss budgetary limitation. The retail RSC consists of multiple stores, a warehouse, and multiple vendors. Each store carries inventory that is not selling as hoped, and they want to get rid of these unwanted products to replace the space with more productive items. Our model considers two options for how a store can get rid of these products: the retailer can send the products to its warehouse if there is demand at other stores, or send them back to their vendor if there are available vendor funds. However, the retailer operates under a predetermined profit–loss budget that should be utilized as closely as possible within the fiscal cycle. The budgetary limitation is the result of profit–loss that will be incurred due to relocating products within and out of its supply chain system. This budgetary limitation, also known as the “P&L effect” in industry, is decided a year prior to an RSC activity for financial, planning, and/or taxation reasons. We model this problem as a mixed integer linear program and solve test problems using CPLEX. We then develop a heuristic solution algorithm and compare the CPLEX solution results and times with our heuristic. We summarize useful insights into our heuristic and how it can be further developed for similar optimization problems with budgetary constraints. Eventually, we outline future research topics and suggestions for RSC models for retailers.