Abstract: In the context of the transformation of urban construction from incremental expansion to inventory renewal, the reuse of industrial remnants has gradually shifted from the issue of spatial transformation to that of scene construction. Taking Tianjin as the research object, based on the scene theory framework, by comprehensively applying kernel density spatial analysis and network comment text mining methods, the research is conducted from two levels: spatial structure and public perception. The study found that the three main models of industrial park, museum, and commercial area have significant differences in spatial distribution and accessibility conditions; the network comment analysis further indicates that public perception shows structural differences in the dimensions of neighborhood environment, appropriateness of objects, activities, and values, which essentially stem from the transmission effect of spatial structure on behavior and experience, and then through influencing activity frequency and population structure, shaping differentiated scenes such as "creative life", "consumption leisure", and "historical culture". At the same time, different models generally have problems such as superficial cultural expression, entertainment-oriented experience, and insufficient value recognition. Based on this, a scenario-based renewal strategy oriented towards mechanism optimization is proposed, providing theoretical basis and practical paths for the spatial translation and cultural regeneration of industrial remnants.

Abstract: The downtime and maintenance associated with the failure of a wind turbine gearbox can be significant, leading to high repair costs. Currently, when warning signals are received through the condition-monitoring system, wind farms typically perform maintenance on the gearbox to ensure continued operation. However, reducing power not only leads to an imbalance between the life of the transmission system and the amount of electricity generated, but also reduces revenue; Moreover, it faces the dilemma of being unable to accurately grasp the health status of the gear transmission system, which increases the difficulty of life extension. To address the above issues, this study proposes a gearbox life extension strategy based on wind turbine control methods. This approach breaks through the limitation of traditional methods where damage assessment is decoupled from operating conditions, and transforms the previous research status where life and power generation optimization were treated as separate entities. And the effectiveness of the life extension strategy was validated using actual operating data from China. The results demonstrated that the proposed strategy could extend the gearbox's life and enhance total power generation.

Abstract: Since tires from end-of-life vehicles are not entirely biodegradable and pose a serious environmental problem, their disposal has grown to be a significant global environmental concern. One technique to decrease these environmental issues is incorporating waste rubber to make sustainable green concrete. This study examined the usage of waste supplementary cementitious materials (SCMs) such as fly ash (FA), metakaolin (MK), marble powder (MP), slag (SL), and silica fume (SF) for surface precoating of crumb rubber (CR) to improve the mechanical properties of the produced crumb rubber concrete (CRC) by strengthening the bond between CR and cement paste in the Interfacial Transition Zone (ITZ). The CR replaced (0, 15%, and 25%) of sand by weight in the preparation of CRC mixtures. A total of eleven CRC mixes were cast to investigate the fresh properties, compressive strength, and splitting tensile strength. In addition, the compressive stress-strain curve was investigated, and peak stress, peak strain, energy absorption, toughness, and modulus of elasticity have been evaluated. The outcomes showed that pre-coating CR using FA, followed by MK, has the maximum effect in increasing the CRC compressive performance. The 25% substitution of sand with FA-treated CR increased compressive strength after 28 days, splitting tensile strength, peak stress, toughness, and modulus of elasticity by 34.7%, 23.7%, 34.8%, 26.1%, and 25.2%, respectively, in comparison to the same percentage of untreated CR. The proposed approach demonstrates a viable pathway for integrating waste materials and SCM-based technologies to develop high-performance, sustainable cementitious composites.

Abstract: Construction management, from the contractor's perspective, is led by the Construc-tion Manager (CM). The work motivation and leadership style of the CM are critical variables for the successful execution of construction projects. Scientific literature identifies participative leadership as the most effective style for mitigating conflicts among various stakeholders. However, analyzing the specific variables that influence a CM's conflict resolution capacity remains an underexplored area. Furthermore, while the CM must act as a leader for their team (subcontractors, suppliers, etc.), they remain accountable to the contractor’s senior management. Therefore, this study aims to ana-lyze the mediating role of CM motivation in the relationship between leadership and conflict resolution capacity using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research contributes to identifying the factors that influence the CM’s conflict resolution capacity during the execution phase, thereby enhancing best prac-tices in knowledge management within the construction industry.

Abstract: Sensors are a fundamental component of Structural Health Monitoring (SHM) systems. Among the different types of sensors, piezoelectric (PZT) sensors are widely used due to their desirable properties, such as dual actuation–sensing capability, high sensitivity, low cost, and suitability for real-time monitoring. In addition to proper sensors, SHM also requires effective signal processing techniques for interpreting the data acquired by the sensors. Recently, the rapid advancement of Artificial Intelligence (AI) has significantly improved the automated SHM of structures and demonstrated how effective SHM can become when combined with artificial intelligence. Thus, the use of appropriate sensors, effective signal processing techniques, and AI can significantly enhance SHM performance. Guided by these developments, this paper presents a critical review of signal processing and machine learning approaches in PZT-based SHM systems, with emphasis on engineering structures. The fundamental principles of PZT sensing and wave propagation are first outlined. Next, signal processing techniques and their importance in SHM are discussed with a focus on recent advancements in the use of AI in PZT-based SHM. This work also discusses the Hybrid frameworks that integrate signal processing with data-driven AI models which are promising directions for improving robustness and accuracy of SHM. Finally, existing key challenges such as environmental variability, sensor degradation, data scarcity, and model generalization are discussed, along with future directions including physics-informed learning, transfer learning, explainable AI, and baseline-free SHM systems.

Abstract: This study explores the development of lattice-based panels for satellite applications using Direct Metal Laser Sintering and aimed to optimize lightweight, high-strength structures suitable for CubeSat deployment. Three lattice configurations namely Body-Centered Cubic, Octet, and Gyroid were evaluated. While Gyroid lattices exhibited the highest compressive strength at 13,825.8 N, the BCC lattice was selected for the final design due to superior manufacturability and weight reduction potential. The final optimized panel weighed 185.7 g, achieving an 11.4% reduction from the initial rib-type design and a 65.2% reduction from a solid panel. Finite Element Analysis and mechanical testing confirmed that the fabricated structures met the necessary mechanical requirements for aerospace launch conditions.

Abstract: The increasing penetration of distributed renewable energy resources and electric vehicles has transformed microgrids into complex multi-prosumer systems that require coordinated control. Traditional centralized and local independent control strategies fail to exploit distributed flexibility and often lead to sub-optimal renewable utilization and inefficient energy management. This paper proposes a new method for coordinating these multi-prosumer microgrids using a hybrid coordination framework that utilizes Federated Learning for forecasting, game theory for energy trading, and blockchain for transaction recording through a decentralized network of peer to peer transactions between prosumers. Additionally, using the principles of model predictive control the battery algorithm was trained to make optimal decisions about present and probable future conditions of each microgrid node. By conducting simulations on heterogeneous networks of multi-prosumer microgrids, the system demonstrated a significant increase in renewable energy utilization by up to 91.2% and provided for greater coordination across three (3) microgrids through energy trading, fairness, and energy efficiency while also maintaining adequate levels of voltage regulation and power quality. In comparison, the baseline controller only achieved a lower operational cost. The results revealed essential trade-offs between local optimality and system coordination leading to the design of next generation decentralized microgrids.

Abstract: The fast growth of wireless communication systems and the growing need for very high data rates have been the driving force behind the creation of sixth-generation (6G) technologies that operate in the terahertz (THz) frequency region. This research represents the design and analysis of a small compact microstrip patch antenna that works in the terahertz (THz) frequency range for 6G cellular connectivity. The Rogers RT5880 substrate and annealed copper are used in the design of the suggested antenna, which aims for a 593 GHz resonance frequency. A progressive design technique that incorporates slotting and geometric optimization has been used to develop a castle shaped antenna which improve impedance matching and bandwidth to overcome the inherent constraints of traditional microstrip antennas. Excellent impedance matching is shown by the final design's near-ideal voltage standing wave ratio (VSWR) and return loss (S11) of –48.76 dB. It achieves a broad impedance bandwidth of 154.88 GHz, which far outperforms many current systems. The antenna exhibits consistent radiation characteristics in the broadside direction, a gain of 8.005 dBi, a directivity of 8.727 dBi, and an efficiency of around 91.73%. The proposed design performs very well in terms of bandwidth and efficiency, while also preserving compact dimensions and structural simplicity, as shown by a comparative comparison with most current literature. These results validate the suitability of the proposed antenna for high-speed, short-range THz communication systems in future 6G networks.

Abstract: In recent years, the rapid expansion of low-temperature facilities—such as cold storage and indoor ice and snow venues—has underscored their pronounced vulnerability to fire, as evidenced by multiple severe incidents. Due to their distinct environmental conditions, existing theoretical frameworks, technical approaches, and standards exhibit limited applicability. Consequently, the fire risk characteristics of such facilities remain insufficiently defined, and systematic methods for hazard identification and assessment are lacking. This study conducts a detailed analysis of fire incident data from representative low-temperature facilities to identify the fire risks characteristics across all lifecycle stages, including construction, renovation and expansion, operation, maintenance, and demolition. An integrated framework combining the WBS/RBS matrix and CN methods is then proposed to establish a structured methodology for full lifecycle fire hazard identification and classification. The results address critical gaps, including the absence of clearly defined lifecycle fire risk profiles and a robust scientific basis for hazard identification, and provide a technical foundation for lifecycle fire risk management in low-temperature facilities.

Abstract: This work introduces a compact multi-resonant metamaterial absorber designed to achieve efficient electromagnetic absorption over several microwave frequency bands. The proposed configuration is based on a hybrid resonator arrangement that promotes strong electromagnetic interaction and enables multiple resonant modes within a single unit cell. Consequently, six distinct absorption peaks are obtained at 2.4, 5.21, 6.88, 9.77, 12.61, and 14.99~GHz, covering S-, C-, X-, and Ku-band applications. The absorber exhibits high absorption performance, exceeding 97\% across most operating frequencies, which indicates effective impedance matching with free space and efficient energy dissipation mechanisms. The absorption characteristics are further examined through surface current distributions, electric field confinement, and effective medium analysis, demonstrating that the multi-band response originates from the interaction of multiple resonant elements and intrinsic material losses. Moreover, the proposed structure maintains stable performance for different polarization angles and oblique wave incidence, confirming its polarization-insensitive and angularly stable behavior. To validate the design, a prototype is fabricated and experimentally characterized using a free-space measurement setup, showing close agreement with the simulated results. The compact geometry, low fabrication cost, and scalability of the proposed absorber make it a promising candidate for applications such as electromagnetic interference mitigation, radar cross-section reduction, and modern wireless communication systems.

Abstract: The proliferation of high-speed railway (HSR) networks necessitates frequent construction activities adjacent to operational lines, posing significant risks to the structural integrity and safety of existing infrastructure. This study addresses the critical need for a comprehensive framework to assess and monitor the deformation of HSR piers throughout the entire construction process of a new, nearby bridge, which includes the cumulative effects of both substructure and superstructure construction. A hybrid methodology integrating quantitative risk assessment and real-time, non-contact monitoring was developed and implemented. A risk evaluation model was established using the Analytic Hierarchy Process (AHP) to structure the problem, combined with Triangular Fuzzy Numbers to handle the inherent uncertainties in expert judgments. The Fuzzy Comprehensive Evaluation method was then employed to quantify the risk levels of various construction stages. Concurrently, a vision-based monitoring system utilizing Digital Image Correlation (DIC) technology was deployed to capture the three-dimensional deformation of adjacent HSR piers with high precision and frequency. The case study, focusing on the construction of a new bridge crossing the operational Beijing-Shanghai HSR, demonstrated the application of this framework. The risk assessment model identified the pile cap and pier construction phase as the highest-risk stage, with a risk weight of 0.311. The DIC monitoring system, validated against total station measurements with a relative error of less than 5%, recorded the cumulative pier deformations throughout 31 distinct construction stages. The maximum recorded deformations in the transverse, longitudinal, and vertical directions were all maintained within the early warning threshold of ±1.2 mm stipulated by railway regulations. The study confirms that the integrated AHP-Fuzzy and DIC framework provides a robust paradigm for proactive risk management in adjacent-line construction projects. The risk model accurately predicted the most critical construction phase, and the DIC system offered a reliable and efficient solution for real-time safety assurance. The findings validate that with appropriate risk-informed monitoring, the impact of new bridge construction on existing HSR infrastructure can be effectively controlled within safe limits, offering a valuable reference for similar engineering projects globally.

Abstract: We present TADI (Tool-Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence-based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real-time objects, 15,634 production records, formation tops, and perforations into a dual-store architecture: DuckDB for structured queries over 12 tables with 65,447 rows, and ChromaDB for semantic search over 36,709 embedded documents. Twelve domain-specialized tools, orchestrated by a large language model via iterative function calling, support multi-step evidence gathering that cross-references structured drilling measurements with daily report narratives. The system parses all 1,759 DDR XML files with zero errors, handles three incompatible well naming conventions, and is backed by 95 automated tests plus a 130-question stress-question taxonomy spanning six operational categories. We formalize the agent's behavior as a sequential tool-selection problem and propose the Evidence Grounding Score (EGS) as a simple grounding-compliance proxy based on measurements, attributed DDR quotations, and required answer sections. The complete 6,084-line, framework-free implementation is reproducible given the public Volve download and an API key, and the case studies and qualitative ablation analysis suggest that domain-specialized tool design, rather than model scale alone, is the primary driver of analytical quality in technical operations.

Abstract: Rapid urbanization and intensifying climate risks are placing unprecedented pressure on cities to transition toward sustainable and resilient models. Achieving Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action) requires intelligent systems capable of interpreting complex urban dynamics and enabling proactive, adaptive decision-making. This paper presents a PRISMA-guided rapid review examining the role of Agentic Artificial Intelligence (AAI)–autonomous, goal-directed systems with multi-step reasoning, tool use, and multi-agent coordination–in advancing urban sustainability and climate resilience. Studies were required to exhibit at least two attributes: autonomous decision-making, multi-step planning, tool use or environmental interaction, and multi-agent coordination. From 920 records, 70 peer-reviewed studies were synthesized, covering smart mobility, infrastructure planning, waste management, emergency response, climate monitoring, emissions tracking, renewable energy forecasting, and multi-hazard early warning systems. Results show that despite rapid progress, AAI applications remain fragmented and domain-specific. To address this, a unified Agentic AI–Digital Twin framework is proposed, integrating real-time sensing, urban–climate co-simulation, multi-agent coordination, and adaptive decision intelligence. A Pareto-based optimization approach balances competing sustainability goals. Key challenges in interoperability, data governance, ethics, and scalability are identified, alongside a research roadmap for integrated intelligent urban ecosystems.

Abstract: The purpose of this paper is to investigate, collect, and analyze the different technologies that are being integrated into vehicle automation systems. These technologies can range from LIDAR/RADAR sensors, voice recognition, and AI models. With the continued push for the development of AI and au- tonomous vehicles in both the economy and among the populace, designers and engineers are more incentivized than ever to break new ground. As technology in the industry changes, so must the priorities of its developers. First, data and analysis on the safety of autonomous vehicles will be provided, providing context for the importance of the topic. Second, an overview of the research and development of the technology used to address the previous concerns is provided. Third, an examination of the successes and failures of the technology in regard to those concerns will be made. Lastly, this paper will explore the emerging breakthroughs and future advancements that will drive the mass adoption of autonomous vehicles, specifically those that can be scaled up to civilian automobiles.

Abstract: This paper describes the design and implementation of PolyUAnalog, a modular and open-source polyphonic analog synthesizer. The architecture utilizes the AS3397 analog voice chip, with each voice managed by a dedicated RP2040 microcontroller. System coordination, including MIDI processing and voice allocation, is handled by a central conductor board communicating over an I²C bus. Technical implementation details and associated measurements are provided regarding real-time DCO pitch stabilization via a PID feedback loop and the generation of high-resolution control voltages using filtered Pulse Width Modulation (PWM). The complete hardware schematics and C++ software stack are documented to facilitate replication, modification, and further development within the electronic musical instrument community.

Abstract: Kindergarten toilet design critically influences children's autonomy, hygiene behaviours, and psychological well-being. Yet comparative architectural evaluations in conflict-affected and developing regions remain scarce, particularly in Iraq, where facilities typically adhere only to minimum regulatory standards. This study develops and applies a structured evaluation framework to assess child-centred innovation in kindergarten toilet facilities, identifying design weaknesses and opportunities to inform architects, policymakers, and implementing institutions. The study evaluated ten kindergartens in Erbil using a literature-derived framework comprising four domains: Autonomy & Functionality, Health & Hygiene, Safety & Comfort, and Aesthetics & Sustainability, operationalised through 14 quantitative indicators and assessed via a five-point scoring rubric. Data sources included architectural drawings and systematic on-site observations. Overall innovation scores ranged from 3.1 to 4.3 (scale 1-5). While basic safety requirements were universally met, significant deficiencies emerged in inclusive design (accessible fixtures present in only 3/10 facilities, 30%), advanced hygiene technologies (sensor-activated fixtures in only 2/10, 20%), and aesthetic-environmental quality (mean score 2.4/5). Higher-performing facilities demonstrated closer classroom-toilet proximity (≤ 6m vs. >15m), distributed rather than centralised layouts, and integrated child-scale fixtures. Current kindergarten toilet design in Erbil achieves functional adequacy but consistently fails to deliver inclusivity, technological innovation, and spatial quality. Policy revision beyond minimum compliance toward child-centred performance standards is urgently required.

Abstract: Large language models (LLMs) and foundation models (FMs) are reshaping petroleum engineering at a pace no previous wave of artificial intelligence has matched. Between 2022 and 2026 the field went from zero petroleum specific LLMs to eighteen domain-specialized models, more than a dozen subsurface foundation models, and more than twenty commercial industry platforms, while annual publication counts grew more than five-fold from 2020 to 2024. This survey integrates those developments into a single framework. We analyze 296 verified references spanning 2003–2026 across 14 thematic areas and six petroleum sub-disciplines plus one cross-cutting category (geophysics, drilling, reservoir, production, petrophysics, completions, and cross-cutting), from classical natural-language-processing baselines through today’s vision–language models, retrieval-augmented generation stacks, and autonomous agents. Our organizing contributions include (i) a positioning matrix against 25 prior surveys, (ii) a bubble-plot taxonomy of sub-disciplines against AI paradigms, (iii) seven application-category tables, six additional thematic tables, and a dedicated maturity-model table (fourteen tables in total), (iv) a catalog of public petroleum AI systems and enabling substrate, and (v) the PetroLLM Maturity Model — a five-level scaffold (L1 Conversational Q&A, L2 Document Intelligence and Retrieval, L3 Domain-Specialized LLMs, L4 Autonomous Agents and Copilots, L5 Self-Improving Foundation-Model Ecosystems) that situates every surveyed system on a common ladder. The paper closes with a bibliometric snapshot (trends, sub-discipline distribution, method distribution, institutional footprint) and an open-research agenda spanning data, benchmarks, physics integration, safety, multilinguality, and standards. Our headline findings: geophysics leads, reservoir and production lag, petroleum benchmarks are scarce, industry deployments outpace academic publication, and L5 self-improving ecosystems remain aspirational but within a realistic 2030 horizon.

Abstract: Accurate prediction of nitrogen oxide (NOx) emissions from marine medium-speed four-stroke diesel engines is crucial for meeting increasingly stringent environmental standards. This paper focuses on optimizing the first and most significant reaction of the extended Zeldovich mechanism for the formation of nitric oxide (NO). A numerical engine model was developed and validated against experimental measurements of combustion pressure, power, and emissions at 81.95% of the Maximum Continuous Rating (MCR). The research analyzes the influence of various chemical reaction rate constants (k_1,f) on the accuracy of NO concentration predictions. The results demonstrate that by carefully selecting the kinetic parameters, the deviation of the numerical model can be reduced to only -0.93%. Utilizing the optimized constant for the primary Zeldovich reaction k_1,f = 1.8*10¹⁴ *e^(-38300/T), significantly improves the reliability of combustion and emission formation simulations.

Abstract: This work demonstrated improvements in the photovoltaic performance metrics of a dye-sensitized solar cell (DSSC) through the application of Eu-doped strontium silicate – Sr2SiO4:Eu3+, luminescent downshifting (LDS) material. The material converted underuti-lized high energy ultraviolet (UV) photon into lower energy visible photon for better spec-tral responsivity in the DSSC. The LDS material was prepared by the conventional solid state technique. Surface morphology was examined by scanning electron microscope (SEM). Photoluminescence (PL) measurement was applied for the fluorescence emission. The photovoltaic performances of the bare and LDS enhanced devices were analyzed from the photovoltaic current – voltage measurement. Compared to the bare DSSC, the cell with Sr2SiO4:Eu3+ LDS phosphor material had an enhancement of 14.8 % in the short circuit current density (Jsc), from 0.243 – 0.279 mA/cm2. The open circuit voltage (Voc) yielded an improvement of 10 % from 580 – 638 mV. Maximum power output (Pmax) produced a boost of 26.5 % from 0.0136 – 0.0172 mW and the efficiency improvement at 26.6 % from 1.09 – 1.38 %. The coefficient of variation was introduced to evaluate device reproducibility. The device with the incorporation of Sr2SiO4:Eu3+ LDS phosphor, depicted a coefficient of variation of 8.5 %, suggesting good DSSC reproducibility consistency.

Abstract: The quality and functionality of urban public spaces are often limited by inadequate nighttime lighting, low sensory comfort, and inefficient construction waste management, reducing usability and perceived safety. This study aims to develop and evaluate sensory materials for the sustainable revitalization of public spaces through the design of photo-luminescent and aromatic bricks using recycled materials. A mixed-method experimental approach was adopted, including prototype fabrication and performance assessment through illuminance, luminance, volatile compound release, and physical property tests. The results indicate that the developed bricks achieve illuminance levels of approximately 2 lux under real conditions, within the recommended range for low-activity pedestrian areas, and exhibit moderate and sustained aromatic release with functional performance up to 0.90 m. In addition, the materials show adequate physical stability, mechanical re-sistance, and controlled water absorption. These findings suggest that the proposed mate-rials can contribute to improving nighttime visibility and sensory perception in public spaces. This study proposes an integrated approach combining material innovation and urban design, supporting the use of passive multisensory strategies for sustainable public space revitalization.