Hasil untuk "Environmental engineering"

G. Ellison

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R. Albuquerque, Y. Koskinen, Chendi Zhang

This paper presents an industry equilibrium model where firms can choose to engage in corporate social responsibility (CSR) activities. We model CSR activities as an investment in customer loyalty and show that CSR decreases systematic risk and increases firm value. These effects are stronger for firms producing differentiated goods and when consumers' expenditure share on CSR goods is small. We find supporting evidence for our predictions. In our empirical tests, we address a potential endogeneity problem by instrumenting CSR using data on the political affiliation of the firm's home state, and data on environmental and engineering disasters and product recalls.

Santiago Correa, Abigail K. Grosskopf, Hector Lopez Hernandez et al.

Advances in hydrogel technology have unlocked unique and valuable capabilities that are being applied to a diverse set of translational applications. Hydrogels perform functions relevant to a range of biomedical purposes—they can deliver drugs or cells, regenerate hard and soft tissues, adhere to wet tissues, prevent bleeding, provide contrast during imaging, protect tissues or organs during radiotherapy, and improve the biocompatibility of medical implants. These capabilities make hydrogels useful for many distinct and pressing diseases and medical conditions and even for less conventional areas such as environmental engineering. In this review, we cover the major capabilities of hydrogels, with a focus on the novel benefits of injectable hydrogels, and how they relate to translational applications in medicine and the environment. We pay close attention to how the development of contemporary hydrogels requires extensive interdisciplinary collaboration to accomplish highly specific and complex biological tasks that range from cancer immunotherapy to tissue engineering to vaccination. We complement our discussion of preclinical and clinical development of hydrogels with mechanical design considerations needed for scaling injectable hydrogel technologies for clinical application. We anticipate that readers will gain a more complete picture of the expansive possibilities for hydrogels to make practical and impactful differences across numerous fields and biomedical applications.

L. Cabeza, L. Rincón, Virginia Vilariño et al.

S. Balachandar, J. Eaton

I. Angelidaki, M. Alves, D. Bolzonella et al.

S. Atsumi, T. Hanai, J. Liao

C. Bowler, M. Montagu, D. Inzé

Robin Smith

J. Odling-Smee, K. Laland, M. Feldman

A. Akcil, Soner Koldas

J. DeJong, Michael B. Fritzges, K. Nüsslein

T. Chen, N. Murata

Yu Ma, Jiahang Zhang, Qixuan Zhou et al.

Rotenone, a widely used agricultural pesticide has been linked to an increased risk of Parkinson’s disease (PD) following chronic exposure. Our previous study found that rotenone not only caused motor deficits in mice, but was also able to cause cognitive deficits, a common non-motor symptom in PD. This study aimed to explore whether activation of the SIRT1-Nrf2 axis confers neuroprotection against rotenone-induced cognitive impairments. We found that rotenone significantly decreased the expression and activation of SIRT1 and Nrf2 in the hippocampus of mice. Pharmacological activation of SIRT1 and Nrf2 using resveratrol and tert-butylhydroquinone (TBHQ), respectively, markedly ameliorated rotenone-induced learning and memory impairments and neuronal damage. Mechanistically, resveratrol and TBHQ suppressed microglial activation and the expression of proinflammatory genes (iNOS, TNFα, IL-1β), attenuated C3-CR3 signaling, and restored the levels of synaptic proteins (PSD95, mBDNF, TrkB), indicating suppression of abnormal glia-mediated synaptic pruning. Furthermore, SIRT1-Nrf2 axis activation reduced iron accumulation and lipid peroxidation in the hippocampus, accompanied by increased GPX4 and decreased COX2 and ACSL4 expression, suggesting suppression of neuronal ferroptosis. Collectively, our findings demonstrate that activation of the SIRT1-Nrf2 axis alleviates rotenone-induced cognitive deficits by inhibiting microglia-mediated synaptic pruning and neuronal ferroptosis. These results provide mechanistic insight and a potential therapeutic target for pesticide-induced neurotoxicity and PD-related cognitive dysfunction.

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.

Ahmed M. Zobaa, Hazem H. Abdelnabi, Rodan M. Reda et al.

Abstract Corona discharge has been recognized for centuries, with sailors reporting the bluish glow of St. Elmo’s fire on ship masts during storms. In the early development of high-voltage engineering, researchers such as Townsend and Peek described the physical basis of this phenomenon as the ionization of air around a conductor when the electric field exceeds the strength of the surrounding medium. The result is a partial discharge that produces visible light, hissing sounds, ozone, and other reactive gases, while also creating radio interference and ultraviolet radiation. In modern transmission systems, these effects appear as wasted power, accelerated wear of insulators, shortened equipment lifetime, and environmental concerns. Although corona has been studied for decades, it continues to challenge the reliable and economical operation of high-voltage networks, particularly under changing weather conditions. This study investigates the phenomenon by analyzing its causes, effects, and mitigation strategies through a combination of theoretical modelling, simulation, and statistical analysis. Using MATLAB Simulink and Python, simulations were conducted under varying environmental conditions—including temperature, humidity, and pressure—as well as electrical parameters such as voltage and conductor design, using observed data to ensure practical relevance. Comparable data sources may be used in other national or regional contexts. Key statistical techniques, including linear and multiple regression, analysis of variance (ANOVA), t-tests, and Monte Carlo simulations, were applied to determine the most influential factors affecting corona discharge losses. Results confirmed that higher voltage levels and unfavorable environmental conditions significantly increase corona loss, while increased conductor spacing and the use of corona rings emerged as the most effective mitigation strategies. An economic analysis based on probabilistic modelling estimated potential annual savings of up to 455 million Egyptian pounds (EGP) for the Egyptian grid, serving as a representative case study. The analytical framework is general and can be applied to other national transmission systems with appropriate data. The findings offer data-driven insights for improving transmission efficiency, minimizing power losses, and enhancing the overall reliability and cost-effectiveness of high-voltage power systems.

Shahab Hosseini, Abiodun Ismail Lawal, Francois Mulenga

Blast-induced ground vibration (BIGV) is one of the detrimental environmental consequences of blasting operations in mining and civil engineering. Hence, accurate prediction of BIGV is highly imperative. Therefore, different novel artificial intelligence (AI) methods such as Bayesian regularized neural network (BRNN), Bayesian regularized causality-weighted neural network (BRCWNN) and Z-number-based Bayesian regularized causality-weighted neural network (Z-BRCWNN) are proposed in this study for the reliable prediction of BIGV in a dolomitic marble quarry using the obtained field data. The outcome of the proposed models is subjected to rigorous statistical analyses. The outcome of analyses revealed that the Z-BRCWNN model outperformed the other models with 70%, 82% and 82% threshold statistic values evaluated at the 5%, 10% and 15% confidence levels for the testing phase and 63%, 91% and 91% threshold values for the validation phase evaluated at the same levels as above. The sensitivity analysis conducted revealed that the distance from the measuring point to the blasting point (DI) has the highest influence on BIGV.

Denisa BILÍKOVÁ, Petr AIGEL, Michał JUSZCZYK et al.

This study addresses the impending challenge of construction and demolition waste (CDW) generation from the Czech Republic’s extensive panel housing estates, constructed between the 1950s and 1990s. These structures, representing a significant portion of the national housing stock, will eventually reach their operational lifespan, necessitating systematic waste management strategies. A novel estimation methodology is proposed to quantify demolition waste volumes through material-specific decomposition of panel building structures. The T06B panel system, widely deployed in Czech housing estates, serves as a selected case study. The structure, according to the Waste Catalogue, is used for the classification of specific waste types. From a cost perspective, individual fees for waste disposal or recycling are taken from the budgeting program database. The proposed methodology facilitates the predictive modelling of both demolition waste quantities and associated financial expenditures for disposal/recycling of individual waste categories, such as concrete, bricks, iron, plastic, etc.

F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi et al.

This study provides an in-depth evaluation of sea level rise (SLR) and its varied effects across the coastal regions of southern Africa. Utilizing data collected between 1993 and 2022, we analyze SLR patterns alongside land subsidence phenomena, based on observations from 10 strategically located tide gauges and X-TRACK satellite altimetry datasets. To ensure greater accuracy, the Coastal Altimetry Approach was adopted to refine nearshore measurements. Findings indicate that in areas such as Cape Town, sea-level rise rates reach around 6.3 mm/year, which is nearly twice the current global average of 3.3 mm/year. The interaction between rapid sea-level rise and subsidence rates surpassing 2.2 mm/year presents significant threats to coastal communities, critical infrastructure, and natural ecosystems. Moreover, the study highlights how seismic activity contributes to coastal dynamics, illustrating the role of earthquake-induced subsidence in magnifying the impacts of SLR. By incorporating seismic factors into the analysis, a more comprehensive understanding of the interplay between natural and human-induced drivers of sea-level variability is achieved. Additionally, the study examines the broader effects of SLR on Africa’s culturally and historically important coastal heritage sites, emphasizing the urgent need for proactive coastal management and climate adaptation efforts.

Sharon Guardado, Risha Parveen, Zheying Zhang et al.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.