Search Results (44,654)

This study presents an AI-driven computational framework for optimizing the orientation and spatial deployment of photovoltaic (PV) systems installed on uneven terrain, with the objective of enhancing energy efficiency and supporting sustainable energy development. The proposed methodology integrates PVsyst-based numerical simulations with statistical modeling and bio-inspired heuristic optimization algorithms, forming a hybrid machine learning–assisted decision-making approach. A heuristic–parametric optimization strategy was employed to evaluate multiple tilt and azimuth configurations, aiming to maximize specific energy yield and overall system performance, expressed through the performance ratio (PR). The model was validated using site-specific climatic data from Veracruz, Mexico, and identified an optimal azimuth orientation of approximately 267.3°, corresponding to an estimated PR of 0.8318. The results highlight the critical influence of azimuth orientation on photovoltaic efficiency and demonstrate strong consistency between simulation outputs, statistical analysis, and intelligent optimization results. From an industrial perspective, the proposed framework reduces planning uncertainty and energy losses associated with suboptimal configurations, enabling more reliable and cost-effective photovoltaic system design, particularly for installations on uneven terrain. Moreover, the methodology significantly reduces planning time and potential installation costs by eliminating the need for preliminary physical testing, offering a scalable and reproducible AI-assisted tool that can contribute to lower levelized energy costs, enhanced system reliability, and more efficient deployment of photovoltaic technologies in the renewable energy industry. Future work will extend the model toward a multivariable machine learning framework incorporating tilt angle, climatic variability, and photovoltaic technology type, further strengthening its applicability in real-world environments and its contribution to Sustainable Development Goal 7: affordable and clean energy. Full article

Effective drug delivery in oncology is challenged by a hierarchy of biological barriers—from abnormal vasculature and dense stroma to cellular immunosuppression and specialized interfaces like the blood–brain barrier. This review provides a contemporary analysis of smart nanoformulations through the lens of a rational, stage-gated design pipeline. We first deconstruct the solid tumor microenvironment as a multi-tiered obstacle (systemic, stromal, cellular), establishing a barrier-specific foundation for nanocarrier design. The core of the review articulates an architectural toolkit, detailing how intrinsic nanoparticle properties precondition in vivo identity via the protein corona, which in turn informs the selection of advanced ligands for cellular targeting and programmed intracellular trafficking. This integrated framework sets the stage for exploring sophisticated applications, including endogenous and externally triggered responsive systems, bio-orthogonal activation, immuno-nanoformulations, and combination strategies aimed at overcoming multidrug resistance. By synthesizing these components into a cohesive design philosophy, this review moves beyond a catalog of advances to offer a blueprint for engineering next-generation nanotherapeutics. We critically assess the translational landscape and contend that this hierarchical design approach is essential for developing more effective, personalized, and clinically viable cancer treatments. Full article

This article proposes a strategic framework for Kyiv’s post-conflict sustainability and resilience under brittle, anxious, non-linear, and incomprehensible (BANI) conditions. We integrate adaptive governance, circular-economy reconstruction, and city-scale digital capabilities, including AI-enabled analytics, IoT sensing, and urban digital twins. Building on recent assessments of Ukraine’s reconstruction needs, we outline a socio-technical model that links sustainability and resilience objectives under shock risk and budget constraints and show how an illustrative five-year optimisation can rebalance investments toward distributed renewables and early-warning infrastructure. The example portfolio achieves an end-horizon composite performance of F_optimized(5) = 0.65 (on a 0–1 normalised index where 1 indicates achieving the policy-defined targets; 0.65 indicates ~65% progress toward those targets at year 5, improving on the baseline allocation under shocks), indicating improved robustness relative to a baseline allocation. We emphasise that effective implementation depends on secure-by-design digital architecture, participatory prioritisation of indicators and weights, and iterative monitoring that supports rapid adaptation as conditions evolve. The framework provides a pragmatic roadmap for Kyiv and similarly vulnerable cities seeking a low-carbon recovery while reducing systemic brittleness and mitigating anxiety-driven decision-making delays. Full article

In recent years, the design of public spaces surrounding school buildings has gained growing attention in urban planning and child-friendly city agendas. This paper examines the role of tactical urbanism in creating more Liveable School Surroundings (LSS) and introduces the LSS framework as a new lens for interpreting school-adjacent spaces as threshold environments where safety, autonomy, sustainable mobility, social interaction, and play converge. Methodologically, it develops a 12-indicator evaluation grid structured around four dimensions and applies it to a systematic comparative analysis of 30 interventions implemented in Milano, Bologna, and Torino. The analysis provides new empirical evidence on the effectiveness of tactical urbanism in this domain. Findings show that tactical interventions can rapidly enhance perceived safety and social interaction, often outperforming permanent solutions in terms of spatial reconfiguration and activation, while revealing limitations in the domains of play, climatic comfort, and cycling integration. The comparative analysis also reveals the modest scale of Italian initiatives compared to international programs, underscoring the need for stronger governance and long-term planning tools. By positioning tactical urbanism as an experimental device and a strategic lever for school-centered public space regeneration, the study offers an original contribution to international debates on child-friendly planning and proximity-based urban policies. Full article

The growing presence of autonomous AI systems, such as self-driving cars and humanoid robots, raises critical ethical questions about how these technologies should make moral decisions. Most existing moral machine (MM) models rely on secular, utilitarian principles, which prioritize the greatest good for the greatest number but often overlook the religious and cultural values that shape moral reasoning across different traditions. This paper explores how theological perspectives, particularly those from Christian, Islamic, and East Asian ethical frameworks, can inform and enrich algorithmic ethics in autonomous systems. By integrating these religious values, the study proposes a more inclusive approach to AI decision making that respects diverse beliefs. A key innovation of this research is the use of large language models (LLMs), such as ChatGPT (GPT-5.2), to design with human guidance MM architectures that incorporate these ethical systems. Through Python 3 scripts, the paper demonstrates how autonomous machines, e.g., vehicles and humanoid robots, can make ethically informed decisions based on different religious principles. The aim is to contribute to the development of AI systems that are not only technologically advanced but also culturally sensitive and ethically responsible, ensuring that they align with a wide range of theological values in morally complex situations. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience. Full article

The aviation industry aims to reduce environmental impact by adopting alternative propulsion systems, including hydrogen-based, hybrid-electric, and all-electric architectures, requiring a new Thermal Management System (TMS). In addition, new design methods are needed for the TMS, at the system and component levels, to handle various fluids and varying fluid properties. Within the TMS, heat exchangers are critical components that may require significant space and must be considered early in the design process. This paper presents a parametric sizing methodology for heat exchangers suitable for early design phases within a Multidisciplinary Design Analysis and Optimization (MDAO) framework, specifically for cryogenic heat transfer. The method combines physical equations with validated empirical relationships, using iterative solver algorithms for sizing. To address multi-variable design challenges, the methodology integrates discretization schemes for fluid properties, temperature, and energy calculations, and constraint-based optimization with a weighted-sum approach for solution selection. The methodology is validated with a commercial heat exchanger, and cross-validated with a cryogenic Heat Exchanger (HX). A case study for an all-electric hydrogen fuel cell aircraft architecture with a 7.6 MW propulsion system is presented to demonstrate the effectiveness of the methodology. The presented heat exchanger performance can be predicted across multiple conditions quickly enough to enable large design space exploration. Overall, the presented model is a crucial element for the design of a TMS for future aircraft with hydrogen-based propulsion systems. Full article

Moray (Cusco, Peru) represents one of the most sophisticated examples of Inca agricultural engineering, where architecture, environmental management, and constructive systems converge to generate controlled microclimates for agricultural experimentation. Recognized as an important archaeological heritage site, Moray provides valuable insight into ancestral Andean strategies for adapting agriculture to complex high-altitude environments. However, the site is increasingly exposed to environmental pressures associated with climatic variability, soil erosion, structural collapses, and tourism intensity. This study aims to analyze the relationship between microclimates, geometric design, and constructive sustainability of the Moray archaeological complex through integrated spatial, functional, and constructive analyses, supported by digital tools such as Google Earth Pro, AutoCAD 2023, SketchUp 2023, and environmental simulations developed by Andrew Marsh. The research examines the geometric configuration of the circular terraces, which present radii between 45 and 65 m, heights ranging from 3 to 5 m, and slope variations between 14% and 48%, generating temperature gradients of 12–15 °C between upper and lower levels. These conditions enabled the Incas to experiment with and adapt diverse ecological species across different thermal zones. The study also evaluates the irrigation and infiltration systems composed of gravel, sand, and stone layers that ensured soil stability and moisture regulation. Climate data from SENAMHI (2019–2024) indicate that Moray is located in a semi-arid meso-Andean environment, reinforcing its interpretation as an ancestral environmental laboratory. The results demonstrate Inca mastery in integrating environmental design, hydrological engineering, and agricultural experimentation while also identifying current conservation challenges related to erosion processes, structural deterioration, and tourism pressure. This research contributes to understanding Moray as a climate-sensitive heritage system, offering insights relevant to contemporary strategies for sustainable agriculture, climate adaptation, and heritage conservation in Andean regions. Full article

This paper presents an open-architecture mechatronic drive platform for operating a three-phase BLDC servomotor in an industrial robotic axis. A sequential and iterative mechatronic design methodology is adopted, integrating electronic design, digital control, mechanical development, and experimental prototyping, with emphasis on open-loop operation. The electronic circuit was designed using schematics and a PCB and validated in Proteus Design Suite 8.15 (Labcenter Electronics Ltd., London, UK) to verify switching sequences and inverter behavior. The power stage is based on a six-switch insulated-gate bipolar transistor (IGBT) inverter module, complemented by an independent snubber protection board and a dedicated digital gate-drive control board. The mechanical enclosure was designed using computer-aided design (CAD), CAD software tools (Shapr3D, version 5.911.0 (9224), Shapr3D Zrt., Budapest, Hungary), and fabricated via 3D printing. Switching behavior was simulated in Octave using parameters from a real industrial BLDC servomotor (Yaskawa SGMAH series) extracted from a Motoman robotic axis. The contribution is design-oriented in a mechatronic engineering sense, emphasizing accessibility, openness, and experimental enablement of industrial drive hardware rather than control-performance optimization. An industrial Yaskawa BLDC servomotor from the Motoman robot is used to determine switching sequences and safe operating parameters. Experimental open-loop tests were conducted by directly commanding the six inverter switching sectors, resulting in the stable synchronous rotation of the motor on the developed electromechanical platform. Full article

The rapid digital transformation of higher education institutions (HEIs) has created new opportunities to promote sustainability-focused teaching, learning, and assessment. At the same time, traditional assessment methods often fail to accurately measure complex skills needed for sustainability, such as systems thinking, critical reflection, and real-world problem-solving. This study examines the integration of adaptive learning platforms with performance-based assessment (PBA) as an innovative way to support inclusive, sustainability-oriented learning in higher education. Based on principles of Education for Sustainable Development (ESD), Universal Design for Learning (UDL), and constructivist learning theory, the study investigates how adaptive learning technologies tailor instruction for diverse learners while PBAs offer genuine measures of sustainability skills. Using a mixed-methods approach, data were gathered from forty-eight undergraduate students enrolled in an inclusive education course that used an adaptive learning module and PBA tasks. Learning analytics, rubric-based performance scores, and student perception surveys were analyzed to explore effects on engagement, accessibility, and skill development. The results show that this combined method enhances student inclusion, supports differentiated learning pathways, boosts engagement in sustainability tasks, and yields more complete evidence of sustainability competencies than traditional assessments. The study provides a framework for HEIs aiming to align digital transformation initiatives with sustainability objectives. It emphasizes the potential of integrating adaptive learning and PBA to promote innovative, inclusive, and sustainability-focused assessment practices. Implications for policy, curriculum design, and future digital sustainability efforts are also discussed. Full article

Understanding the behavior of light non-aqueous phase liquids (LNAPLs) in urban subsurface environments is essential to developing effective pollution control strategies, designing remediation systems, and managing waste and resources sustainably. Oil leakage from urban industrial facilities, underground pipelines, and fueling systems often leads to contamination that is challenging to characterize due to complex soil structures, limited access beneath densely built infrastructure, and dynamic groundwater conditions. In this study, we integrate a large-scale soil tank experiment with multiphase flow simulations to elucidate LNAPL distribution mechanisms under fluctuating groundwater conditions. A 2.4-m-by-2.4-m-by-0.6-m soil tank was used to visualize oil movement with high-resolution multispectral imaging, enabling a quantitative evaluation of saturation distribution over time. The results showed that a rapid rise in groundwater can trap 60–70% of the high-saturation LNAPL below the water table. In contrast, a subsequent slow rise leaves 10–20% residual saturation within pore spaces. These results suggest that vertical redistribution caused by groundwater oscillation significantly increases residual contamination, which cannot be evaluated using static groundwater assumptions. Comparisons with a commonly used NAPL simulator revealed that conventional models overestimate lateral spreading and underestimate trapped residual oil, thus highlighting the need for improved constitutive models and numerical schemes that can capture sharp saturation fronts. These results emphasize that an accurate assessment of LNAPL contamination in urban settings requires an explicit consideration of groundwater fluctuation and dynamic multiphase interactions. Insights from this study support rational monitoring network design, reduce uncertainty in remediation planning, and contribute to sustainable urban environmental management by improving risk evaluation and preventing the long-term spread of pollution. Full article

Central nervous system (CNS) disorders such as depression severely impair human health. Targeted inhibition of the GluN1-GluN2A receptor is a promising therapeutic strategy, but current drugs often have adverse effects. To develop novel candidate drugs, this study utilized the (S)-ketamine and GluN1-GluN2A receptor complex as a structural template and conducted de novo drug design with the DrugFlow platform. An integrated strategy of molecular docking-based virtual screening combined with high-throughput binding free energy (∆G_binding) calculations from large-scale molecular dynamics (MD) simulations identified three promising antagonists. The ∆G_binding values of these compounds are all below −18.98 kcal/mol, indicating stronger binding affinity than (S)-ketamine, and they demonstrate promising drug-like properties and development potential. 200-ns MD simulations confirmed their stable receptor binding and mechanism consistent with (S)-ketamine. Electrophysiological recordings revealed that, at a concentration of 10 μM, Compounds A1, A2, and A3 produced concentration-dependent inhibition of GluN1-GluN2A receptor-mediated currents, with fractional inhibition values of 24.26%, 35.36%, and 41.76%, respectively. These findings demonstrate the compounds’ potential as CNS disorder therapeutics, requiring further experiments to validate efficacy and advance development for conditions like depression. Full article

An extended operational space kinematics and dynamics formulation is presented for the control of redundant non-serial compound robotic manipulators. A broad spectrum of high-load-capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely on Jacobian pseudoinverses and local null-space projections, a globally valid, differential-geometry-based, multi-valued inverse kinematic mapping is defined at the configuration level, with the explicit self-motion parameterization of manipulator redundancy. The formulation yields coupled second-order ordinary differential equations of manipulator dynamics on the product space of task variables and self-motion coordinates. This enables the direct integration of system dynamics with control strategies, such as model predictive control or feedback design, while maintaining task constraint compliance. The methods presented are validated through the simulation and control of a non-serial compound material loader manipulator with multiple degrees of redundancy, demonstrating advantages in generality, numerical accuracy, and trajectory smoothness. Full article

The exponential growth of digital data and the escalating sophistication of cyber threats have intensified the demand for secure yet computationally efficient encryption methods. Conventional algorithms (e.g., AES-based schemes) are cryptographically strong and widely deployed; however, some implementations can face performance bottlenecks in large-scale or real-time workloads. While many modern systems seed from hardware entropy sources and employ standardized cryptographic PRNGs/DRBGs, security can still be degraded in practice by weak entropy initialization, misconfiguration, or the use of non-cryptographic deterministic generators in certain environments. To address these gaps, this study introduces FileCipher. This novel file-encryption framework integrates a chaos-enhanced Cryptographically Secure Pseudorandom Number Generator (CPRNG) based on the State-Based Tent Map (SBTM). The proposed design achieves a balanced trade-off between security and efficiency through dynamic key generation, adaptive block reshaping, and structured confusion–diffusion processes. The SBTM-driven CPRNG introduces adaptive seeding and multi-key feedback, ensuring high entropy and sensitivity to initial conditions. A multi-threaded Java implementation demonstrates approximately 60% reduction in encryption time compared with AES-CBC, validating FileCipher’s scalability in parallel execution environments. Statistical evaluations using NIST SP 800-22, SP 800-90B, Dieharder, and TestU01 confirm superior randomness with over 99% pass rates, while Avalanche Effect analysis indicates bit-change ratios near 50%, proving strong diffusion characteristics. The results highlight FileCipher’s novelty in combining nonlinear chaotic dynamics with lightweight parallel architecture, offering a robust, platform-independent solution for secure data storage and transmission. Ultimately, this paper contributes a reproducible, entropy-stable, and high-performance cryptographic mechanism that redefines the efficiency–security balance in modern encryption systems. Full article

This study presents an integrated experimental simulation and multi-objective optimization methodology that maximizes energy production and optimizes economic performance in the design of wind power plants (WPPs). The relationship between five fundamental design parameters (wind speed (XWS), hub height (XHH), rotor diameter (XRD), turbine spacing (XTS), and row spacing (XRS)) and five techno-economic outputs (annual AC energy (YAEP), net present value (YNPV), levelized cost of energy (YLCOE), net cost of capital (YNCCpw), and total BOS cost (YTBC)) is systematically investigated using a Multi-Level Full Factorial Experimental Design (DoE) for four different US regions (Southern Wyoming, Southern California, Northeastern West Virginia, and South Florida). The optimization was performed by applying a multi-objective desirability function to regression models derived from 1200 NREL SAM simulation data points, thereby simultaneously evaluating five design parameters across five techno-economic responses. ANOVA results revealed that 77.5% of the variability in annual energy production was due to wind speed and 21.4% to rotor diameter, clearly demonstrating the decisive role of resource quality in project feasibility. Optimization identified the optimal configuration (XRS = 5, XTS = 3, XWS = 10.157 m/s, XHH = 120 m, XRD = 70 m) that provided a balanced trade-off between conflicting objectives, achieving 575.16 GWh of YAEP, $42.02 million of YNPV, $43.66 million of YTBC, 2.368 cents/kWh of YLCOE, and $1.508/W of YNCCpw. The study emphasizes that resource evaluation precedes technological optimization in the planning phase of wind energy projects, demonstrating that integrating DoE, simulation, and multi-objective optimization provides a strong framework for achieving realistic, feasible, and economically sustainable WPPs. The novelty of this approach lies in its ability to simultaneously account for environmental stochasticity and economic feasibility, providing a robust computational roadmap for stakeholders to maximize energy efficiency while minimizing levelized costs. Full article