Chase Humiston, Mehmet Cetin, Anderson Rodrigo de Queiroz
Battery Energy Storage Systems (BESS) can benefit from price volatility in electricity markets, but frequent cycling increases degradation and reduces long-term value. This study develops a rolling-horizon dispatch framework in which battery operation is fully price-driven, while degradation is evaluated separately to isolate the effect of degradation model choice. A 48 h look-ahead window is solved repeatedly and advanced by 24 h, with only the first 24 h of decisions implemented and remaining capacity carried forward. Degradation is assessed using three widely used model classes: Linear-Calendar (LC), Energy-Throughput (ET), and Cycle-Based rainflow (CB) models. The framework is applied to Electric Reliability Council of Texas (ERCOT) 15 min real-time prices for 2024 (Houston Zone). LC and ET result in limited annual capacity loss (≈2%) and modest economic impact, while the CB model predicts substantially higher degradation and large negative valuation. Sensitivity analysis shows that CB-based results are highly dependent on parameter calibration. Overall, the results highlight the strong influence of degradation modeling choices on BESS valuation under rolling-horizon operation.
We introduce a framework for Foundational Analysis of Safety Engineering Requirements (SAFER), a model-driven methodology supported by Generative AI to improve the generation and analysis of safety requirements for complex safety-critical systems. Safety requirements are often specified by multiple stakeholders with uncoordinated objectives, leading to gaps, duplications, and contradictions that jeopardize system safety and compliance. Existing approaches are largely informal and insufficient for addressing these challenges. SAFER enhances Model-Based Systems Engineering (MBSE) by consuming requirement specification models and generating the following results: (1) mapping requirements to system functions, (2) identifying functions with insufficient requirement specifications, (3) detecting duplicate requirements, and (4) identifying contradictions within requirement sets. SAFER provides structured analysis, reporting, and decision support for safety engineers. We demonstrate SAFER on an autonomous drone system, significantly improving the detection of requirement inconsistencies, enhancing both efficiency and reliability of the safety engineering process. We show that Generative AI must be augmented by formal models and queried systematically, to provide meaningful early-stage safety requirement specifications and robust safety architectures.
Abstract Understanding the relationship between tropical heavy rainfall and large-scale circulation provides valuable insights for improving the climate models. Here we use Gaussian Mixture Model to identify two distinct types of heavy rainfall over the tropical Pacific, “strong deep convection” and “moderately strong deep convection,” using satellite-borne precipitation radar measurements. They differ in two typical climatological deep convection-related rainfall modes between the western and eastern Pacific regions. The occurrence frequency of moderately strong deep convection is significantly different between the western and eastern Pacific, potentially linked to the Walker circulation. The enhanced Walker circulation appears to weaken the local Hadley circulation, thereby reducing strong deep convective activity in the eastern Pacific. This increases moderately heavy rainfall and decreases diabatic heating, which can affect global climate. We propose incorporating the close link between large-scale Walker circulation and mesoscale heavy convective rainfall into the current climate models.
Luigia Bosa, Simona Mattioli, Anna Maria Stabile
et al.
The aim of this study was to analyze how recombinant rabbit NGF (Nerve Growth Factor) encapsulated in chitosan (rrβNGFch) affects sperm viability, motility, capacitation, acrosome reaction (AR), kinetic traits, and apoptosis after 30 min and 2 h of storage. Specific intracellular signaling pathways associated with either cell survival, such as protein kinase B (AKT) and extracellular signal-regulated kinases 1/2 (ERK1/2), or programmed cell death, such as c-Jun N-terminal kinase (JNK), were also analyzed. The results confirmed the effect of rrβNGFch on capacitation and AR, whereas a longer storage time (2 h) decreased all qualitative sperm traits. AKT and JNK did not show treatment-dependent activation and lacked a correlation with functional traits, as shown by ERK1/2. These findings suggest that rrβNGFch may promote the functional activation of sperm cells, particularly during early incubation. The increase in capacitation and AR was not linked to significant changes in pathways related to cell survival or death, indicating a specific action of the treatment. In contrast, prolonged storage negatively affected all sperm parameters. ERK1/2 activation correlated with capacitation, AR, and apoptosis, supporting its role as an NGF downstream mediator. Further studies should analyze other molecular mechanisms of sperm and the potential applications of NGF in assisted reproduction.
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data-driven methods face challenges in capturing inherently complex and interconnected processes and are further constrained by limited observational data in many environmental applications. Foundation models, which leverages large-scale pre-training and universal representations of complex and heterogeneous data, offer transformative opportunities for capturing spatiotemporal dynamics and dependencies in environmental processes, and facilitate adaptation to a broad range of applications. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in common environmental use cases including forward prediction, data generation, data assimilation, downscaling, inverse modeling, model ensembling, and decision-making across domains. We also detail the process of developing these models, covering data collection, architecture design, training, tuning, and evaluation. Through discussions on these emerging methods as well as their future opportunities, we aim to promote interdisciplinary collaboration that accelerates advancements in machine learning for driving scientific discovery in addressing critical environmental challenges.
Owing to their remarkable thermal–hydraulic performance, vapor chambers have diverse applications, especially for cooling electronic devices. However, the performances of circular and noncircular pillars throughout the entire vapor chamber under identical wick volume conditions have been compared in very few studies. In this study, a series of noncircular pillar arrays featuring biomimetic tree-like fractal networks is introduced. The incorporation of fractal pillars significantly enhances the thermal–hydraulic performance of the vapor chamber because the gas–liquid interface area is extended. Compared to a vapor chamber with circular pillars, the maximum condensation surface temperature difference, total thermal resistance, and liquid pressure drop of this vapor chamber with fractal pillars are reduced by up to 57.8 %, 13.8 %, and 10.9 %, respectively. In particular, with large branch levels and pillar diameters, the fractal structure is pivotal for boosting the overall performance. When the number of pillars is increased, the fractal structure's impact on the thermal performance reaches a limit, whereas its boosting effect on the flow performance weakens. This study is a pioneering exploration of the application of stretched geometries in pillar-type vapor chambers, with fractal pillars utilized in showcase demonstrations.
Artificial Intelligence, machine learning (AI/ML) has allowed exploring solutions for a variety of environmental and climate questions ranging from natural disasters, greenhouse gas emission, monitoring biodiversity, agriculture, to weather and climate modeling, enabling progress towards climate change mitigation. However, the intersection of AI/ML and environment is not always positive. The recent surge of interest in ML, made possible by processing very large volumes of data, fueled by access to massive compute power, has sparked a trend towards large-scale adoption of AI/ML. This interest places tremendous pressure on natural resources, that are often overlooked and under-reported. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle for informing policymakers, stakeholders to adequately implement standards and policies and track the policy outcome over time. For these policies to be effective, AI's environmental impact needs to be monitored in a spatially-disaggregated, timely manner across the globe at the key activity sites. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations. We present a case study around Northern Virginia, United States that hosts a growing number of datacenters and observe changes in multiple satellite-based environmental metrics. We then discuss the steps to expand this methodology for comprehensive assessment of AI's environmental impact across the planet. We also identify data gaps and formulate recommendations for improving the understanding and monitoring AI-induced changes to the environment and climate.
Larissa Barbosa, Sávio Freire, Rita S. P. Maciel
et al.
[Context and Motivation] Several studies have investigated attributes of great software practitioners. However, the investigation of such attributes is still missing in Requirements Engineering (RE). The current knowledge on attributes of great software practitioners might not be easily translated to the context of RE because its activities are, usually, less technical and more human-centered than other software engineering activities. [Question/Problem] This work aims to investigate which are the attributes of great requirements engineers, the relationship between them, and strategies that can be employed to obtain these attributes. We follow a method composed of a survey with 18 practitioners and follow up interviews with 11 of them. [Principal Ideas/Results] Investigative ability in talking to stakeholders, judicious, and understand the business are the most commonly mentioned attributes amongst the set of 22 attributes identified, which were grouped into four categories. We also found 38 strategies to improve RE skills. Examples are training, talking to all stakeholders, and acquiring domain knowledge. [Contribution] The attributes, their categories, and relationships are organized into a map. The relations between attributes and strategies are represented in a Sankey diagram. Software practitioners can use our findings to improve their understanding about the role and responsibilities of requirements engineers.
Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. Finally, we provide a survey of tools for prompt engineers.
Ehsan Inam Ullah, Shakil Ahmad, Muhammad Fahim Khokhar
et al.
Run off river schemes are getting widespread importance as they are considered environmentally safe. However, number of studies and the consequent information regarding impacts of run off river schemes is very limited worldwide. Present study attempted to analyze impacts of Ghazi Barotha Hydropower Plant, which is a run off river scheme situated in Khyber Pakhtunkhwa province of Pakistan. This study attempted to analyze impacts of this run off river scheme on hydrological and ecological conditions of downstream areas. Data on river discharge, groundwater levels, agriculture area, vegetation and bare soil was utilized for this study. All data sets between the year 1990 till 2020 were analyzed. Hydrological impacts were analyzed through secondary data analysis, whereas ecological impacts were studied through remote sensing technique. Statistical methods were applied to further draw conclusions between hydrological and ecological interrelationships. Results showed that after functioning of Ghazi Barotha, there was 47% and 91% reduction of river discharge, in summer and winter seasons respectively. Groundwater level dropped by 50%. Agriculture area reduced by 1.69% and 9.11% during summer and winter respectively, whereas land under bare soil increased. River water diversion was considered to be responsible for groundwater reduction, as strong correlation was found between both. Agriculture land recovery, in post Ghazi Barotha period, was premised at intense groundwater mining, as groundwater level and agriculture area were significantly related (p < 0.05). Governments’ groundwater development schemes, and a shift into motorized groundwater mining were major factors behind further groundwater exploitation in study area. This study came to the conclusion that Ghazi Barotha Hydropower Plant had impacted flow regime of Indus River, as well as groundwater levels and land use of downstream area along the river. These effects were triggered by inappropriate compensatory measures and uncontrolled water resource exploitation.
Anh Nguyen-Duc, Beatriz Cabrero-Daniel, Adam Przybylek
et al.
Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios. We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering. We identified 78 open Research Questions (RQs) in 11 areas of Software Engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology. GenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research.
Fueled by the soaring popularity of large language and foundation models, the accelerated growth of artificial intelligence (AI) models' enormous environmental footprint has come under increased scrutiny. While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity -- the fact that AI's environmental footprint can be disproportionately higher in certain regions than in others -- has emerged, raising social-ecological justice concerns. This paper takes a first step toward addressing AI's environmental inequity by balancing its regional negative environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (GLB) to explicitly address AI's environmental impacts on the most disadvantaged regions. We run trace-based simulations by considering a set of 10 geographically-distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.
We develop a numerical method for the computation of a minimal convex and compact set, $\mathcal{B}\subset\mathbb{R}^N$, in the sense of mean width. This minimisation is constrained by the requirement that $\max_{b\in\mathcal{B}}\langle b , u\rangle\geq C(u)$ for all unit vectors $u\in S^{N-1}$ given some Lipschitz function $C$. This problem arises in the construction of environmental contours under the assumption of convex failure sets. Environmental contours offer descriptions of extreme environmental conditions commonly applied for reliability analysis in the early design phase of marine structures. Usually, they are applied in order to reduce the number of computationally expensive response analyses needed for reliability estimation. We solve this problem by reformulating it as a linear programming problem. Rigorous convergence analysis is performed, both in terms of convergence of mean widths and in the sense of the Hausdorff metric. Additionally, numerical examples are provided to illustrate the presented methods.
Ports offer an effective way to facilitate the global economy. However, massive carbon emission during port operating aggravates the atmospheric pollution in port cities. Capturing characteristics of port carbon emission is vital to reduce GHG (greenhouse gas) in the maritime realm as well as to achieve China’s carbon neutral objective. In this work, an integrated framework is proposed for exploring the driving factors of China ports’ emissions combined with stochastic effects on population, affluence and technology regression (STIRPAT), Global Malmquist-Luenberger (<i>GML</i>) and multiple linear regression (MLR). The port efficiency is estimated for each port and the potential driving factors of carbon emission are explored. The results indicate that port carbon emissions have a strong connection with port throughput, productivity, containerization and intermodal transshipment. It is worth noting that the containerization ratio and port physical facility with fossil-free energy improvement have positively correlated with carbon emissions. However, the specific value of waterborne transshipment shows a complex impact on carbon dioxide emission as the ratio increases. The findings reveal that China port authorities need to improve containerization ratio and develop intermodal transportation; meanwhile, it is responsible for port authorities to update energy use and improve energy efficiency in ways to minimize the proportion of non-green energy consumption in accordance with optimizing port operation management including peak shaving and intelligent management systems under a new horizon of clean energy and automatic equipment.
Abstract The Liyuan courtyard buildings are considered as contemporary architectural symbols of the spirit in Qingdao, China. The sustainability potentials embodied in the building is evaluated by building performance simulations analysis based on field investigation in this case study. Two models with optimization refurbishment were made through building simulation software. One model with façade supplemented in the insulation layers of the envelope walls and the other model with further upgrade with consideration of recycling materials mixed were discussed and estimated with building performance simulation method. The energy performance in the building and both scenarios designed can improve the energy efficiency, while the advanced model could achieve better result in the building energy behavior dramatically. Technologies innovation are proved to be good tools to improve energy performance the existing buildings by renovation actions such as insulation improvement and so on. It is concluded the sustainability regain its authentic appearance while achieve energy efficiency embodied within contemporary buildings through adaptational renovation strategies. Multicriteria considerations might influence the balanced between different factors when making decisions in the building restoration project, it is also expected to empower the fresh glory in the development of building protection and restoration.
Building information modeling (BIM) is a data-based tool for engineering, design, and building management, which is used to parameterize and model various types of information. This information may then be shared and introduced throughout the building life-cycle phases (planning, design, construction and operation, and maintenance (O&M)). Therefore, BIM allows engineers to correctly understand and efficiently respond to varying types of building information as well as serves as a foundation for cooperation between the design and construction teams. Hence, BIM plays an important role in increasing productivity, minimizing costs, and shortening construction times. In this article, we present a case example where BIM was used for intelligent construction of a large wastewater-treatment plant (WTP). The process involved intelligent design and simulation techniques, management and simulation of construction works, digital delivery solutions, BIM-based Internet of things O&M, and environmental monitoring. BIM was used to digitalize the construction, delivery, and O&M processes of the WTP. According to this case example, we also offer suggestions on how deep learning and intelligent control techniques can be used to enhance intelligent O&M in WTPs.