Hasil untuk "Environmental engineering"

Mina Zolfaghari, Abbas Yadegar, Atefe Rezaei et al.

In the present research, zinc oxide (ZnO) nanoparticles (NPs) were biosynthesized through reduction by Anvillea garcinii leaf extract. A. garcinii leaves contain bioactive sesquiterpenes, terpenoids, and phenolic compounds, which are likely responsible for the reduction and stabilization of ZnO NPs. Compared to conventional physicochemical approaches, this synthesis method has several advantages, including simplicity, low cost, sustainability, and replicability. In this study, the impacts of various calcination (annealing) temperatures (60 °C and 500 °C) and different pHs (8, 10, and 12) on the properties of green-synthesized ZnO NPs were evaluated. Characterization was performed by analytical instruments including UV-Vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy and X-ray diffraction (XRD) analyses, nanoparticle analyzer, and field emission scanning electron microscope (FE-SEM). The UV–Vis adsorption spectra of the ZnO NPs revealed a prominent peak at approximately 230 nm. The observed peaks in FTIR spectra align well with those reported in various studies on ZnO NPs. By microscopic observation and XRD validation, the spherical and hexagonal nature of ZnO NPs was confirmed. The pH and temperature used were effective on the particle size, so that the smallest NPs (16.4 nm) were obtained with the help of the most alkaline synthesis medium (pH 12) and oven drying (60 °C). While the largest dimension (63 nm) corresponded to the NPs synthesized under the lowest pH (8) and dried with a 500 °C furnace. Synthesized NPs exhibited high antioxidant properties. The small sizes of biosynthesized ZnO NPs and their phytochemical-coated surfaces affected their biological activity. The cytotoxic impact of NPs on the gastric cancer cells was dose-dependent, and IC50 values for ZnO prepared at 60 and 500 °C (coded as ZnO-60 and ZnO-500) were 35.11 and 42.7 μg/mL respectively. In addition, they were potent antimicrobial agents against Gram-negative bacteria Escherichia coli and 3 strains of Helicobacter pylori, and Gram-positive bacterium Staphylococcus aureus. The green synthesis of ZnO NPs represents a sustainable approach that minimizes environmental impact while producing effective nanomaterials. By using natural plant extracts, researchers can develop cost-effective and eco-friendly methods for NP production, enhancing their potential applications across diverse sectors such as medical fields, environmental science, and materials engineering.

Ömer Elri, Serkan Savaş

Manual notes and scattered messaging applications used in managing business processes compromise data integrity and abstract project tracking. In this study, an integrated system that works simultaneously on web and mobile platforms has been developed to enable individual users and teams to manage their workflows with concrete data. The system architecture integrates MongoDB, which stores data in JSON format, Node.js Express.js on the server side, React.js on the web interface, and React Native technologies on the mobile side. The system interface is designed around visual dashboards that track the status of tasks (To Do-In Progress-Done). The urgency of tasks is distinguished by color-coded labels, and dynamic graphics (Dashboard) have been created for managers to monitor team performance. The usability of the system was tested with a heterogeneous group of 10 people consisting of engineers, engineering students, public employees, branch managers, and healthcare personnel. In analyses conducted using a 5-point Likert scale, the organizational efficiency provided by the system compared to traditional methods was rated 4.90, while the visual dashboards achieved a perfect score of 5.00 with zero variance. Additionally, the ease of interface use was rated 4.65, and overall user satisfaction was calculated as 4.60. The findings show that the developed system simplifies complex work processes and provides a traceable digital working environment for Small and Medium-sized Enterprises and project teams.

Natasha M. Garner, Fabian Mahrt, Jens Top et al.

Abstract Secondary organic aerosol (SOA) comprises most of the submicron atmospheric particle mass, and often becomes internally mixed with other particles. When SOA mixes with transition metal (e.g., iron) containing particles, metal-organic complexes can form, enabling photochemical reactions that change aerosol physicochemical properties. We studied the photochemistry of α-pinene SOA formed on iron-containing ammonium sulfate seed particles at varying relative humidities (RH). Chemical composition and photochemical reduction of particles were analyzed by X-ray spectromicroscopy and infrared spectroscopy. SOA formed at low vs. high RH had different chemical functionality, including abundant carboxylic acids and alcohols. Following photolysis, carboxylic acids and unsubstituted alkanes decreased, and alcohols increased, consistent with decarboxylation reactions. Iron in SOA formed at high RH was readily photochemically reduced, but iron in SOA formed at low RH was not. Overall, RH conditions at SOA formation affect not only chemical composition but also iron-complex formation and hence photochemical processing of aerosols.

Zaher Abdel Baki, Sahar H. Abourida, Adnan Badran et al.

Bread is an indispensable staple food and a great source of complex carbohydrates, making it a potential product for fortification. The purpose of this study was to investigate the sensory, antioxidant, and nutritional properties of bread fortified with sumac (Rhus coriaria) and cactus (Opuntia ficus-indica L.) seed powder. Different levels (4, 6, and 8% w/w flour replacement) of the powdered seeds were used. Fortified bread samples were compared to control (unfortified) bread and evaluated for their moisture, nutritional composition (protein, fat, fiber, ash, carbohydrates, and energy value), sensory preference, total phenolic content (TPC), and antioxidant activity (2,2-diphenyl-1-picrylhydrazyl assay). The antioxidant capacity and TPC were significantly higher (p<0.05) for sumac- and cactus-fortified bread samples compared to the control. Nutritionally, fortification significantly increased fiber and fat content while decreasing carbohydrate content and energy value (p<0.05); protein content remained relatively stable. Sensory evaluation showed a preference for sumac-fortified bread, particularly at lower concentrations. Moisture content was significantly lower in fortified samples. This study demonstrates that fortification, particularly with 8% sumac, yielded favorable results concerning antioxidant activity, phenolic content, and sensory preference, alongside notable changes in nutritional composition.

Nayaab Azim, Sadath Ullah Khan Mohammed, Evan Phaup et al.

In recent years, the field of software engineering has experienced a considerable increase in demand for competent experts, resulting in an increased demand for platforms that connect software engineers and facilitate collaboration. In response to this necessity, in this paper we present a project to solve the lack of a proper one-stop connection platform for software engineers and promoting collaborative learning and upskilling. The idea of the project is to develop a web-based application (NEXAS) that would facilitate connecting and collaborating between software engineers. The application would perform algorithmic matching to suggest user connections based on their technical profiles and interests. The users can filter profiles, discover open projects, and form collaboration groups. Using this application will enable users to connect with peers having similar interests, thereby creating a community network tailored exclusively for software engineers.

Linda Smail, David Santandreu Calonge, Firuz Kamalov et al.

This research investigates the potential of Artificial Intelligence (AI) models to bridge the knowledge gap in environmental education among university students. By focusing on prominent large language models (LLMs) such as GPT-3.5, GPT-4, GPT-4o, Gemini, Claude Sonnet, and Llama 2, the study assesses their effectiveness in conveying environmental concepts and, consequently, facilitating environmental education. The investigation employs a standardized tool, the Environmental Knowledge Test (EKT-19), supplemented by targeted questions, to evaluate the environmental knowledge of university students in comparison to the responses generated by the AI models. The results of this study suggest that while AI models possess a vast, readily accessible, and valid knowledge base with the potential to empower both students and academic staff, a human discipline specialist in environmental sciences may still be necessary to validate the accuracy of the information provided.

David Williams, Max Hort, Maria Kechagia et al.

Software Engineering (SE) research involving the use of Large Language Models (LLMs) has introduced several new challenges related to rigour in benchmarking, contamination, replicability, and sustainability. In this paper, we invite the research community to reflect on how these challenges are addressed in SE. Our results provide a structured overview of current LLM-based SE research at ICSE, highlighting both encouraging practices and persistent shortcomings. We conclude with recommendations to strengthen benchmarking rigour, improve replicability, and address the financial and environmental costs of LLM-based SE.

Mahendra Aryal

Yi Zou, Yimei Wang, Yanhu He et al.

Previous research has primarily focused on soil erosion issues in arid and semi-arid regions, with a limited understanding of soil erosion mechanisms in tropical areas. Additionally, there is a lack of a holistic perspective to determine the spatial attribution of soil erosion. The conversion of tropical rainforests into economically driven plantations, like rubber and pulpwood, has resulted in distinct soil erosion characteristics in specific regions. To enhance our knowledge of soil erosion patterns and mechanisms in tropical regions, it is necessary to examine soil erosion in the three major watersheds of Hainan Island from 1991 to 2021, which encompass significant geographical features such as tropical island water sources and tropical rainforest national parks. The study employed the China Soil Loss Equation (CSLE) model, slope trend analysis, Pearson correlation analysis, land-use transfer matrix, and spatial attribution analysis to examine soil erosion under different scenarios. The research results indicate that scenarios driven by the combination of natural and human factors have the greatest impact on soil erosion changes in the entire study area. Co-driven increases affected 53.56% of the area, while co-driven decreases affected 21.74%. The 31-year soil erosion showed an overall increasing trend. Human factors were identified as the primary drivers of increased soil erosion in the Nandu River basin, while a combination of climate and anthropogenic factors influenced the decrease in soil erosion. In the Changhua River basin, climate and human activities contributed to the soil erosion increase, while human activities primarily caused the decrease in soil erosion. In the Wanquan River basin, climate intensified soil erosion, whereas human activities mitigated it. This study underscores the significant combined impact of human activities and natural factors on soil erosion in tropical regions. It emphasizes the importance of considering human-induced factors when implementing soil erosion control measures in tropical regions.

Yuandong Li, Zhijie Han, Rui Yang et al.

Study region: The Dajing and Xiaojing karst underground river system is the longest underground river in Guizhou Province, Southwest of China，located around the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Study focus: The water quality of underground rivers around the FAST being affected by multiple anthropogenic activities. The underground river system is an extremely non-homogeneous and anisotropic media with complex hydrodynamic and hydrochemical responses. Multi-tools (hydrochemistry, water isotope, sulfur isotope and statistical methods) were applied to accurate identification and quantification of the proportions, spatiotemporal evolution and mechanisms of karst aquifer impacted by different anthropogenic activities. New hydrological insights for the region: The synchronous variations of δ18O, δD, d-excess and δ34SO42- in the effluent and the underground rivers suggested that the underground rivers are influenced by anthropogenic activities. The relationships of ionic ratios revealed that agricultural activities, effluent from the sewage treatment plant, and water-rock interactions control the hydro-chemical characteristics of the underground river system with a distinct spatiotemporal differentiation. The response time for the water quality of Dajing and Xiaojing underground river to anthropogenic impacts were 24 and 27 days, respectively. Spatially, the PCA-ACPS-MLR and MixSIAR indicated that more impacts of anthropogenic activities on Xiaojing underground river than Dajing underground river system. A quantification of spatiotemporal response differentiation provides new insights into the precise identification and prevention of pollution in karst underground rivers.

Haoxiang Zhang, Shi Chang, Arthur Leung et al.

The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex engineering across various domains. A critical but overlooked aspect is performance engineering, which aims at ensuring FMware meets performance goals such as throughput and latency to avoid user dissatisfaction and financial loss. Often, performance considerations are an afterthought, leading to costly optimization efforts post-deployment. FMware's high computational resource demands highlight the need for efficient hardware use. Continuous performance engineering is essential to prevent degradation. This paper highlights the significance of Software Performance Engineering (SPE) in FMware, identifying four key challenges: cognitive architecture design (i.e., the structural design that defines how AI components interact, reason, and interface with classical software components), communication protocols, tuning and optimization, and deployment. These challenges are based on literature surveys and experiences from developing an in-house FMware system. We discuss problems, current practices, and innovative paths for the software engineering community.

Bingbing Liu, Haiying Wang, Ke Ji et al.

A field investigation of the indoor thermal environment and comfort in a typical office building in Qingdao was conducted. During the investigation, the indoor and outdoor environment physical parameters, such as air temperature and relative humidity (RH), were recorded in real-time. The occupants’ thermal responses, basic personal information, and adaptive behaviors were collected. The results showed that the indoor air temperature was from 18.8 °C to 21.6 °C, which was 2–3 °C lower than that during the heating season. Occupants employed heating personal comfort systems (HPCSs), such as electric heaters, air heaters, and leg radiators, to maintain thermal comfort. Based on their requirements for HPCSs, the occupants were divided into three groups-those without HPCS and did not prefer to use (Group A), those with HPCS (Group B), and those without HPCS but preferred to use (Group C). The results showed that the legs and feet thermal sensation of Group B was significantly higher than that of Group C, which made Group B more comfortable. The self-evaluated working efficiency of Group B was slightly higher than that of Group C. Notably, working efficiency was positively related to the overall thermal comfort vote (TCV).

Jenny T. Liang, Carmen Badea, Christian Bird et al.

Empirical software engineering research on production systems has brought forth a better understanding of the software engineering process for practitioners and researchers alike. However, only a small subset of production systems is studied, limiting the impact of this research. While software engineering practitioners could benefit from replicating research on their own data, this poses its own set of challenges, since performing replications requires a deep understanding of research methodologies and subtle nuances in software engineering data. Given that large language models (LLMs), such as GPT-4, show promise in tackling both software engineering- and science-related tasks, these models could help replicate and thus democratize empirical software engineering research. In this paper, we examine GPT-4's abilities to perform replications of empirical software engineering research on new data. We study their ability to surface assumptions made in empirical software engineering research methodologies, as well as their ability to plan and generate code for analysis pipelines on seven empirical software engineering papers. We perform a user study with 14 participants with software engineering research expertise, who evaluate GPT-4-generated assumptions and analysis plans (i.e., a list of module specifications) from the papers. We find that GPT-4 is able to surface correct assumptions, but struggles to generate ones that apply common knowledge about software engineering data. In a manual analysis of the generated code, we find that the GPT-4-generated code contains correct high-level logic, given a subset of the methodology. However, the code contains many small implementation-level errors, reflecting a lack of software engineering knowledge. Our findings have implications for leveraging LLMs for software engineering research as well as practitioner data scientists in software teams.

Marvin Muñoz Barón, Marvin Wyrich, Daniel Graziotin et al.

Searching for clues, gathering evidence, and reviewing case files are all techniques used by criminal investigators to draw sound conclusions and avoid wrongful convictions. Similarly, in software engineering (SE) research, we can develop sound methodologies and mitigate threats to validity by basing study design decisions on evidence. Echoing a recent call for the empirical evaluation of design decisions in program comprehension experiments, we conducted a 2-phases study consisting of systematic literature searches, snowballing, and thematic synthesis. We found out (1) which validity threat categories are most often discussed in primary studies of code comprehension, and we collected evidence to build (2) the evidence profiles for the three most commonly reported threats to validity. We discovered that few mentions of validity threats in primary studies (31 of 409) included a reference to supporting evidence. For the three most commonly mentioned threats, namely the influence of programming experience, program length, and the selected comprehension measures, almost all cited studies (17 of 18) did not meet our criteria for evidence. We show that for many threats to validity that are currently assumed to be influential across all studies, their actual impact may depend on the design and context of each specific study. Researchers should discuss threats to validity within the context of their particular study and support their discussions with evidence. The present paper can be one resource for evidence, and we call for more meta-studies of this type to be conducted, which will then inform design decisions in primary studies. Further, although we have applied our methodology in the context of program comprehension, our approach can also be used in other SE research areas to enable evidence-based experiment design decisions and meaningful discussions of threats to validity.

Eriks Klotins, Michael Unterkalmsteiner, Panagiota Chatzipetrou et al.

Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect data related to engineering goals, challenges, and practices in start-up companies to ascertain trends and patterns characterizing engineering work in start-ups. Such data allows researchers to understand better how goals and challenges are related to practices. This understanding can then inform future studies aimed at designing solutions addressing those goals and challenges. Besides, these trends and patterns can be useful for practitioners to make more informed decisions in their engineering practice. Method: We use a case survey method to gather first-hand, in-depth experiences from a large sample of software start-ups. We use open coding and cross-case analysis to describe and identify patterns, and corroborate the findings with statistical analysis. Results: We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among start-ups. We have mapped these goals, challenges, and practices to start-up life-cycle stages (inception, stabilization, growth, and maturity). Thus, creating the progression model guiding software engineering efforts in start-ups. Conclusions: We conclude that start-ups to a large extent face the same challenges and use the same practices as established companies. However, the primary software engineering challenge in start-ups is to evolve multiple process areas at once, with a little margin for serious errors.

Nowshin Nawar Arony, Kezia Devathasan, Ze Shi Li et al.

Retaining diverse, underrepresented students in computer science and software engineering programs is a significant concern for universities. In this chapter, we describe the INSPIRE: STEM for Social Impact program at the University of Victoria, Canada, which leverages the three principles of self-determination theory competence, relatedness, and autonomy in the design of strategies to empower women and other underrepresented groups in using software and other engineering solutions to approach sustainability, community-driven problems. We also describe lessons learned from a first successful year that involved over 30 students, 6 community partners (sustainability problem owners), and over 20 industry and academic mentors and reached out to more than 200 solution end users in our communities. Finally, we provide recommendations for universities and organizations who may want to adopt our approach. In the program 24 diverse students (in terms of gender, sexual orientation, ethnicity, academic standing, and background) divided into six teams paired with six community partners worked on solving society impactful problems and developed solutions for a number of respective community partners. Each team was supported by an experienced upper year student and mentors from industry and community throughout the program. The experiential learning approach of the program allowed the students to learn a variety of soft and technical skills while developing a solution that has a social and/or environmental impact. Having a diverse team and creating a solution for real end users motivated the students to actively collaborate with their peers, community partners, and mentors resulting in the development of an inclusive network. A network of like minded people is crucial in empowering underrepresented individuals and inspiring them to remain in the computer science and software engineering fields.

Abbas H. Abdulabbas, Murtada A. Ismael

The loads in the deep beams are transmitted diagonally from the load area to the support area by means of the strut and the tie. It is characterized by having a small span to depth ratio. which causes the distribution of stresses to be non-linear within the beam, which motivates researchers to study the effect of the placing of longitudinal hollows and the extent to which these holes affect the behavior and distribution of stresses for these types of beams. In addition to the advantages added by longitudinal hollow to the beam such as reducing weight and passing various electrical and mechanical services...etc. This study investigated the effect of making longitudinal circular holes (with a diameter of 50mm) with a slope on the structural behavior of three deep beams with a solid sample as a reference where the slope used was 0%, 4.3%, and 7.8%. The results showed that making holes reduces the load capacity of the deep beam, a decrease in the failure load was observed by 7.56%, 8.96%, and 11.2% for hollow beams with a slope of 0%, 4.3%, and 7.8%, respectively. Also, the appearance of flexural cracking increased by 2.66%, 2.66%, and 6.66%.and 2.14%, 3.52%, and 7.14%, respectively, for shear cracks. While the effect was small for the neutral axis location as well as for the vertical deflection.

Atieh Alipour, Farnaz Yarveysi, Hamed Moftakhari et al.

Abstract The current Tropical Cyclones (TCs) scaling system, Saffir‐Simpson Hurricane Wind Scale (SSHWS), characterizes the hazardousness of these events solely based on wind speed. This is despite the fact that TCs are classic examples of compound hazards during which multiple hazard drivers that are wind, storm surge, and intense rainfall interact and yield in impacts greater than the sum of individuals. Studies have shown that people's decision to evacuate is highly related to the estimated SSHWS category. Thus, the current SSHWS ‐based classification of TCs yields an underestimation of the hazardousness of TCs and so may misguide the threatened communities. Here, we propose a new scaling system that uses Copulas for categorizing TCs based on the likelihood of a given set of severity for rainfall, surge, and wind speed. We use a variety of data sources to obtain the timing and intensity of wind speed, rainfall along the track, and the associated maximum surge for 102 TCs that have made landfall in the United States' Atlantic and Gulf coasts between 1979 and 2020. Comparing the outputs of our scaling system with official damage reporting for the costliest TCs in the history of the United States, we show that the proposed approach significantly improves TC hazard communication and can be useful for informing decision makers and emergency responders.

Runqiu Chen, Huaijun Tu, Tingtao Chen

Heavy metal (HM) exposure remains a global occupational and environmental problem that creates a hazard to general health. Even low-level exposure to toxic metals contributes to the pathogenesis of various metabolic and immunological diseases, whereas, in this process, the gut microbiota serves as a major target and mediator of HM bioavailability and toxicity. Specifically, a picture is emerging from recent investigations identifying specific probiotic species to counteract the noxious effect of HM within the intestinal tract via a series of HM-resistant mechanisms. More encouragingly, aided by genetic engineering techniques, novel HM-bioremediation strategies using recombinant microorganisms have been fruitful and may provide access to promising biological medicines for HM poisoning. In this review, we summarized the pivotal mutualistic relationship between HM exposure and the gut microbiota, the probiotic-based protective strategies against HM-induced gut dysbiosis, with reference to recent advancements in developing engineered microorganisms for medically alleviating HM toxicity.

Kegong Diao, Luigi Berardi, Daniele B. Laucelli et al.

Pipe re-sizing of water distribution networks (WDNs) aims at improving the service performance to the required level, while minimizing the cost of replacing pipes in the network. The main challenge comes from the identification of the most effective pipes to re-size from a large number of interacting components. Performing a global search over all pipes in large WDNs does not guarantee a feasible and efficient solution due to the enormous search space, even by employing advanced algorithms, e.g., evolutionary algorithms. This paper proposes a novel method to reduce the search space for optimal re-sizing based on topological metrics from Complex Network Theory and hydraulic metrics, while providing suboptimal solutions comparable to the full search solutions, i.e., considering all pipes as candidates. The topological metrics are based on the edge-betweenness tailored for WDN analysis. Hydraulic metrics are unit head loss and flow rates of pipes computed based on simulation of the WDN in the current configuration. The optimal re-sizing plans obtained, particularly those using edge betweenness, were tested on a real WDN. The results are comparable with the full search solutions but they are much more efficient to obtain and require replacing mostly contiguous pipes, i.e., easier for practical fieldwork. HIGHLIGHTS Developed a novel method for search space reduction for optimal re-sizing of water distribution networks.; The method selects a subset of pipes for re-sizing based on topological and hydraulic metrics.; Using indirect topological metrics produces better optimization solutions than using direct metrics.; The solution based on indirect edge betweenness selection is comparable with the solution from full search.;