Hasil untuk "Environmental engineering"

Y. Adewuyi

J. P. Giraldo, Honghong Wu, G. Newkirk et al.

I. Chang, Minhyo Lee, A. Tran et al.

Abstract Various applications of biopolymer-based soil treatment (BPST) in geotechnical engineering have been implemented in recent years, including dust control, soil strengthening and erosion control. Despite BPST methods can ensure the effectiveness of engineering while meeting environmental protection requirements, BPST technology requires further validation in terms of site applicability, durability, and economic feasibility. This study aims to provide a state-of-the-art review and future prospective of BPST. Current biopolymer types, engineered and assessed in laboratory scales, are described along with site implementation attempts. The effect of biopolymers on soil behavior is reviewed with regard to geotechnical engineering application and practice, including soil consistency limits, strength parameters, hydraulic conductivity, soil-water characteristics, and erosion control. The economic feasibility and sustainability of BPST application in ground improvement and earth stabilization practices is discussed. This review postulates biopolymers to be a promising new, environmentally friendly ground improvement material for geotechnical and construction engineering practice.

Jing Ke, B. Wang, Y. Yoshikuni

To support an ever-increasing population, modern agriculture faces numerous challenges that pose major threats to global food and energy security. Plant-associated microbes, with their many plant growth-promoting (PGP) traits, have enormous potential in helping to solve these challenges. However, the results of their use in agriculture have been variable, probably because of poor colonization. Phytomicrobiome engineering is an emerging field of synthetic biology that may offer ways to alleviate this limitation. This review highlights recent advances in both bottom-up and top-down approaches to engineering non-model bacteria and microbiomes to promote beneficial plant-microbe interactions, as well as advances in strategies to evaluate these interactions. Biosafety, biosecurity, and biocontainment strategies to address the environmental concerns associated with field use of synthetic microbes are also discussed.

Baotong Zhu, Dong Wang, Na Wei

The drastically increasing amount of plastic waste is causing an environmental crisis that requires innovative technologies for recycling post-consumer plastics to achieve waste valorization while meeting environmental quality goals. Biocatalytic depolymerization mediated by enzymes has emerged as an efficient and sustainable alternative for plastic treatment and recycling. A variety of plastic-degrading enzymes have been discovered from microbial sources. Meanwhile, protein engineering has been exploited to modify and optimize plastic-degrading enzymes. This review highlights the recent trends and up-to-date advances in mining novel plastic-degrading enzymes through state-of-the-art omics-based techniques and improving the enzyme catalytic efficiency and stability via various protein engineering strategies. Future research prospects and challenges are also discussed.

A. Paço, T. Lavrador

Tiancheng Pu, Wenhao Zhang, Minghui Zhu

Strong metal-support interactions (SMSI) represent a classic yet fast-growing area in catalysis research. The SMSI phenomenon results in encapsulation and stabilization of active metal nanoparticles (NPs) with the support material and has been found for multiple types of catalysts that significantly impact the catalytic performance through stabilization of supported particles and regulation of the interfacial interactions. Engineering the strong-metal support interactions provide a promising approach to steer catalytic performance in various chemical processes, which serves as an effective tool to tackle energy and environmental challenges. Our minireview covers characterization, theory, catalytic activity, and structural and environmental sensitivity of the SMSI phenomena. By providing the overview and outlook of the cutting-edge techniques and methodologies in this multidisciplinary research field, we not only look forward to bringing insights into further exploitation of SMSI in catalysis but also inspiring rational designs and characterization into the broad field of material science and physical chemistry.

Liang Cheng, M. Shahin, D. Mujah

M. Khosravani, T. Reinicke

Abstract The introduction of new materials is tied to the rapid development of manufacturing processes. Additive manufacturing (AM) has been employed to fabricate a solid three-dimensional (3D) part directly from a computer-aided design (CAD) data. AM is an industrial process that has been widely employed in different industries in the past few years. In fact, this 3D printing is a rapid prototyping technology that involves a series of techniques. Although several challenges and limitations exist in the AM process currently, AM is expected to revolutionize the manufacturing process of engineering components. However, despite the numerous applications of AM techniques in various industries, their environmental impacts are not well documented. AM affords significant changes in production cost, energy consumption, and manufacturing lead times. All these issues have been considered to develop this technology to have higher efficiencies and lower environmental impacts. In this paper, we briefly review AM methods and discuss their environmental impacts. Furthermore, we present the main advantages and disadvantages of AM processes involving polymers. It can be concluded that AM is advantageous in some cases, exhibiting lower energy consumption and comprising shorter manufacturing processes. The analysis and discussion indicate the advantages, limitations, and future research directions for the industrial applications of AM.

E. Kanjo, Eman M. G. Younis, C. Ang

The detection and monitoring of emotions are important in various applications, e.g. to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g.EEG and GSR), environmental data (e.g. audio and weather), videos (e.g. for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)

Xudong Zhang, Peng Ye, Yajun Wu

The increasing production of sludge poses significant environmental risks. Sludge disposal and transport are costly because of the high water content (WC). Reducing the WC of sludge is the most efficient strategy to decrease treatment costs. However, the sludge contains a large amount of hydrophilic organic matter, causing poor dewaterability. Therefore, research on preconditioning and mechanical dewatering has great significance for advanced sludge dewatering. In this study, the features of sludge, the advantages and disadvantages of preconditioning methods, and the action mechanisms (including physical, chemical, and biological preconditioning) are thoroughly described. In addition, the dewatering principle and engineering applications of mechanical dewatering techniques are introduced in this manuscript, especially the application of vacuum preloading as an in-situ dewatering technology in sludge. Finally, cost analysis of different conditioning and mechanical dewatering methods is conducted to explore their application feasibility. This manuscript provides new insights for engineering applications of preconditioning methods and mechanical dewatering technology.

R. Saratale, G. Saratale, Han-Seung Shin et al.

Golnoush Abaei, Mojtaba Shahin, Maria Spichkova

The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.

Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik et al.

As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.

Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr et al.

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

Hao Zhang, Hao Zhang, Hao Zhang et al.

BackgroundHigh-altitude hypoxia disrupts intestinal homeostasis by impairing the epithelial barrier, triggering inflammation, and promoting microbial translocation. Berberine (BER), a natural isoquinoline alkaloid with antimicrobial and anti-inflammatory properties, has shown potential in protecting intestinal integrity; however, its efficacy under hypoxic conditions and its interaction with the gut microbiota remain unclear.MethodsA chronic hypoxia mouse model was used to investigate the protective effects of BER against intestinal injury. Microbiota dependency was assessed through antibiotic-mediated depletion and fecal microbiota transplantation (FMT), combined with 16S rRNA gene sequencing, metabolomics, and immune profiling. The functional role of a BER-responsive bacterium was validated by oral administration in antibiotic-treated mice.ResultsBER supplementation restored epithelial barrier integrity, including tight junctions, antimicrobial peptide expression, and goblet cell function, while reducing inflammation and epithelial apoptosis under hypoxic conditions. BER also reshaped gut microbial composition and network structure, accompanied by coordinated alterations in cecal metabolites, particularly purine metabolites and bile acids. Microbiota depletion abolished the protective effects of BER, whereas FMT from BER-treated donors recapitulated these effects, confirming a microbiota-dependent mechanism. Among BER-responsive taxa, Bacteroides thetaiotaomicron (B. thetaiotaomicron) emerged as a key effector, correlating with metabolite profiles and barrier integrity. Oral administration of B. thetaiotaomicron alone protected against hypoxia-induced intestinal injury, restoring mucin production and antimicrobial peptide expression, and attenuating inflammation and apoptosis. Mechanistically, both BER and B. thetaiotaomicron reactivated bile acid–FXR signaling and normalized intestinal immune homeostasis, including T-cell subset distribution.ConclusionThese findings demonstrate that BER protects against hypoxia-induced intestinal injury through microbiota-dependent metabolic and immune regulation. B. thetaiotaomicron acts as a central mediator of this protective effect, highlighting microbiota-targeted strategies as potential interventions for maintaining intestinal homeostasis under hypoxic stress.

Xiang-Jing Kong, Jianrong Li

Abstract Given the current global energy and environmental issues resulting from the fast pace of industrialization, the discovery of new functional materials has become increasingly imperative in order to advance science and technology and address the associated challenges. The boom in metal–organic frameworks (MOFs) and MOF-derived materials in recent years has stimulated profound interest in exploring their structures and applications. The preparation, characterization, and processing of MOF materials are the basis of their full engagement in industrial implementation. With intensive research in these topics, it is time to promote the practical utilization of MOFs on an industrial scale, such as for green chemical engineering, by taking advantage of their superior functions. Many famous MOFs have already demonstrated superiority over traditional materials in solving real-world problems. This review starts with the basic concept of MOF chemistry and ends with a discussion of the industrial production and exploitation of MOFs in several fields. Its goal is to provide a general scope of application to inspire MOF researchers to convert their focus on academic research to one on practical applications. After the obstacles of cost, scale-up preparation, processability, and stability have been overcome, MOFs and MOF-based devices will gradually enter the factory, become a part of our daily lives, and help to create a future based on green production and green living.

Renee Obringer, Benjamin A. Rachunok, Debora Maia-Silva et al.

a The National Socio-Environmental Synthesis Center, University of Maryland, 1 Park Place, Annapolis, MD 21401 United States b Environmental and Ecological Engineering, Purdue University, 500 Central Drive, West Lafayette, IN 47907 United States c School of Industrial Engineering, Purdue University, 315N. Grant Street, West Lafayette, IN 47907 United States d Massachusetts Institute of Technology Energy Initiative, 307 Ames Street E19, Cambridge, MA 02142 United States e The Whitney and Betty MacMillan Center for International and Area Studies, Yale University, 34 Hillhouse Avenue, New Haven, CT 06520, United States f Centre for Environmental Policy, Imperial College London, 16-18 Princes Gardens, London SW7 1NE, United Kingdom

Erik de Lima Andrade, D. C. da Cunha e Silva, Eligelcy Augusta de Lima et al.

Environmental noise has been growing in recent years, causing numerous health problems. Highly sensitive environments such as hospitals deserve special attention, since noise can aggravate patients’ health issues and impair the performance of healthcare professionals. This work consists of a systematic review of scientific articles describing environmental noise measurements taken in hospitals between the years 2015 and 2020. The researchers started with a consultation of three databases, namely, Scopus, Web of Science, and ScienceDirect. The results indicate that for the most part, these studies are published in journals in the fields of medicine, engineering, environmental sciences, acoustics, and nursing and that most of their authors work in the fields of architecture, engineering, medicine, and nursing. These studies, which are concentrated in Europe, the Americas, and Asia, use as reference values sound levels recommended by the World Health Organization. Leq measured in hospital environments showed daytime values ranging from 37 to 88.6 dB (A) and nighttime values of 38.7 to 68.8 dB (A). Leq values for outdoor noise were 74.3 and 56.6 dB (A) for daytime and nighttime, respectively. The measurements were taken mainly inside hospitals, prioritizing more sensitive departments such as intensive care units. There is a potential for growth in work carried out in this area, but research should also include discussions about guidelines for improvement measures aimed at reducing noise in hospitals.

Zirui Li, Stephan Husung, Haoze Wang

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.