Abstract Reliable streamflow projections are essential for effective water‐resource management and climate adaptation. However, streamflow projections are associated with large uncertainties due to divergent precipitation projections from climate models, which directly propagate into hydrological estimates. Observation‐constrained approaches that condition future projections on past observations have been shown to reduce such uncertainties; however, they have not been applied to streamflow projections across the Indian rivers. Using long‐term streamflow and global mean surface temperature observations, climate model projections, hydrological modeling, and a Bayesian detection–attribution framework, we developed observational constrained streamflow projections for nine major Indian rivers. The method reduces the 5–95% confidence interval of future streamflow projections by nearly one‐third compared to raw multimodel ensembles, with constraint strength controlled by internal streamflow variability and inter‐model spread in the unconstrained ensemble. Projection uncertainty is further reduced to ∼20% when considering projections based only on skillful climate models. Constrained projections indicate significant increases in streamflow in the near‐, mid‐, and far‐future periods, except for the Cauvery basin, which shows a near‐term decline. Applying the method to raw precipitation projections reveals comparable constraint strength and increases confidence in the results, given the strong dependence of Indian river flows on precipitation. Our findings underscore the importance of combining skillful climate models with post‐processing constraint methods to substantially reduce model‐based uncertainty. Overall, our results provide critical insights into future streamflow changes across Indian rivers, supporting long‐term water‐resource planning and climate‐resilient management.
La regulación de los recursos naturales en proyectos de ingeniería civil constituye uno de sus principales pilares para el desarrollo sostenible, especialmente en aquellos espacios donde la extracción y uso de recursos como la arena, los sedimentos y los residuos de la construcción son capaces de propiciar altos impactos ambientales. Esta revisión sistemática indaga en investigaciones recientes en gobernanza sobre la extracción de arena, la economía circular de la construcción, las prácticas de dragado, las compras públicas ecológicas y las evaluaciones de impacto ambiental, con el objetivo de determinar alternativas que concilien el avance económico con la protección/conciencia ecológica. Se incorporan veintinueve artículos seleccionados en función de los criterios de inclusión criterios de idoneidad temáticas y robustez metodológica para regiones como Ghana, Malasia, Vietnam y otras partes del mundo. Los resultados destacan que la extracción ilegal de la arena degrada la fertilidad del suelo y las comunidades agrícolas; la economía circular genera menos desperdicio debido al reciclado de materiales; las políticas de compras públicas ecológicas extreman los criterios ambientales aplicados a las licitaciones; y la evaluación de impacto ambiental demanda simplificaciones de los modelos de forma coherente para no provocar un cierto retraso, pero, a la vez, sin limitar una cierta protección.
Viorica Patraucean, Iro Armeni, Mohammad Nahangi
et al.
Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.
Thermal runaway characteristics and hazards of lithium-ion batteries under low ambient pressure in-flight conditions are studied in a dynamic pressure chamber. The influence of ambient pressures (95 kPa and 20 kPa) and packaging forms (cylindrical and pouch commercial batteries) were especially investigated. The results show that the values of heat release, temperature, and CO2 concentration decrease with the reduction in pressure from 95 kPa to 20 kPa, while the total hydrocarbon and CO increase. Without violent fire, explosion, and huge jet flames, the thermal hazards of TR fire under 20 kPa are lower, but the amount of toxic/flammable gas emissions increases greatly. The amount of CO and hydrocarbons varies inversely with the thermal hazards of fire. Under low-pressure environments of cruise altitude, the thermal hazards of TR fire for pouch cells and the toxic/potentially explosive hazards of gas emissions of cylindrical cells need more attention. The performance of TR hazards for two packaging types of battery is also different. Pouch cells have higher thermal hazards of fire and lower combustible/toxic emitted gases than cylindrical cells. The thermal runaway intensity of individual cells decreases under lower ambient pressure, but the burning intensity increases dramatically when thermal runaway occurs in a battery pack. The open time of a safety valve (rupture of the bag) is shortened, but the trigger time for a thermal runaway varies for different formats of batteries under 20 kPa. Those results may be helpful for the safety warning and hazard protection design of Li batteries under low-pressure conditions.
Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce <b>TSET</b> (<b>T</b>riplet <b>S</b>tructure <b>E</b>nhanced <b>T</b>5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model’s understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named “semantic transformation” to fortify the model’s grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% <i>F</i>1 and 93.1% QM on LC-QuAD 2.0, 75.85% <i>F</i>1 and 61.76% QM on QALD-9 plus, 51.37% <i>F</i>1 and 40.05% QM on QALD-10).
Information retrieval aims to find the most important data for specific queries. The challenge is retrieving relevant data efficiently due to the large search area. Existing solutions lead to unnecessary processing costs. Additionally, identifying the main focus of the query is crucial for targeted retrieval. Current methods struggle to address these issues effectively. To overcome these challenges, we have proposed a goal-question-indicator (GQI) approach for personalized learning inquiry (PLA). This approach allows for efficient retrieval of variable-sized data with reduced processing requirements. We have also presented the open learning analytics platform's (Open-LAP) pointer motor segment, which helps end users specify goals, generates discussion topics, and provides self-characterizing pointers.
P. Morato, C. Andriotis, K. Papakonstantinou
et al.
In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies.
In the past decades, nanomaterials have become one of the focal points in civil engineering research. When added to cement-based construction materials (e.g., concrete), it results in significant improvements in their strength and other important properties. However, the final mix characteristics depend on many variables that must be taken into account. As such, there is no general consensus regarding the influence upon the original material of certain nano-sized additives, the optimum dosage or the synergistic effect of two or more nano-materials. This is also the case for titanium dioxide (TiO2) and nanoclay (NC). The paper focuses on reporting the existing research data on the use of the above-mentioned materials when added to mortar and concrete. The collected data is summarized and presented in terms of strength and durability properties of cement mortar and concrete containing either TiO2 or NC. Both nano-materials have been proven, by various studies, to increase the strength of the composite, at both room and elevated temperature, when added by themselves in 0.5%~12% for TiO2 and 0.25%~6% for NC. It can be inferred that a combination of the two with the cementitious matrix can be beneficial and may lead to obtaining a new material with improved strength, elastic and durability properties that can be applied in the construction industry, with implications at the economic, social and environmental levels.
Ramzi A. Abd Alsaheb, Mohammed A. Atyia, Jaafar Kamil Abdullah
et al.
Succinic acid is an essential base ingredient for manufacturing various industrial chemicals. Succinic acid has been acknowledged as one of the most significant bio based building block chemicals. Rapid demand for succinic acid has been noticed in the last 10 years. The production methods and mechanisms developed. Hence, these techniques and operations need to be revised. Recently, an omnibus rule for developing succinic acid is to find renewable carbohydrate Feedstocks. The sustainability of the resource is crucial to disintegrate the massive use of petroleum based-production. Accordingly, systematically reviewing the latest findings of bacterial production and related fermentation methods is critical. Therefore, this paper aims to study the latest research and assess the findings statistically comprehensively. The current review attempt is a step toward comprehending all the conditions surrounding succinic acid production from raw materials, microorganisms, and fermentation methods.
Chemical engineering, Engineering (General). Civil engineering (General)
Shaojuan Zhang, Xuwei Xue, Eduward Tangdiongga
et al.
In order to meet the massively increasing requirements of big-data applications, data centers (DCs) are key infrastructures to cope with the associated demands, such as high performance, easy scalability, low cabling complexity and low power consumption. Many research efforts have been dedicated to traditional wired data center networks (DCNs). However, DCNs’ static and rigid topology based on optical cables significantly limits their flexibility, scalability, and even reconfigurability. The limitations of this wired connection can be addressed with optical wireless technology, which avoids cable complexity problems while allowing dynamic adaption and fast reconfiguration. Here, we propose and investigate a novel optical wireless data-center network (OW-DCN) architecture based on nanoseconds semiconductor optical amplifier (SOA)-based wavelength selectors and arrayed waveguide grating router (AWGR) controlled by fast field-programmable gate array (FPGA)-based switch schedulers. The full architecture, including the design, packet-switching strategy, contention solving methodology, and reconfiguration capability, is presented and demonstrated. Dynamic switch scheduling with a FPGA-based switch scheduler processing optical label and software-defined network (SDN)-based reconfiguration were experimentally confirmed. The proposed OW-DCN was also achieved with a power penalty of less than 2 dB power penalty at BER < 1 × 10<sup>−9</sup> for a 50 Gb/s OOK transmission and packet-switching transmission.