Hasil untuk "Environmental technology. Sanitary engineering"

Klara Borowa, Andrzej Zalewski, Lech Madeyski

The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

A.Y.A. Mohamed, Laurence Gill, Alejandro Monleon et al.

Microbial aggregates of different sizes in aerobic granular sludge (AGS) systems have been shown to exhibit distinct microbial community compositions. However, studies comparing the microbial activities of different-sized aggregates in AGS systems remain limited. In this study, genome-resolved metatranscriptomics was used to investigate microbial activity patterns within differently sized aggregates in a full-scale AGS plant. Our analysis revealed a weak correlation between the relative abundance of metagenome-assembled genomes (MAGs) and their transcriptomic activity, indicating that microbial abundance does not directly correspond to metabolic activity within the system. Flocculent sludge (FL; <0.2 mm) predominantly featured active nitrifiers and fermentative polyphosphate-accumulating organisms (PAOs) from Candidatus Phosphoribacter, while small granules (SG; 0.2–1.0 mm) and large granules (LG; >1.0 mm) hosted more metabolically active PAOs affiliated with Ca. Accumulibacter. Differential gene expression analysis further supported these findings, demonstrating significantly higher expression levels of key phosphorus uptake genes associated with Ca. Accumulibacter in granular sludge (SG and LG) compared to flocculent sludge. Conversely, Ca. Phosphoribacter showed higher expression of these genes in the FL fraction. This study highlights distinct functional roles and metabolic activities of crucial microbial communities depending on aggregate size within AGS systems, offering new insights into optimizing wastewater treatment processes.

Hammami Saber, Romdhane Hela, Soualmia Amel et al.

Sediment transport plays a vital role in river management and flood protection, particularly in regions prone to erosion and deposition. The study aims to assess the impact of roughness modification on the sediment transport process in the Medjerda, Tunisia’s longest perennial river, following a decade of dredging activities implemented for flood protection measures in the Boussalem city. We used the Telemac Sisyphe model to stimulate sediment 17.8 km section, which regularly undergoes dredging crossing the city of Boussalem. This section contains two distinct parts: first a smooth riverbed followed by the variable roughness on both sides of the banks, which is influenced by the existing vegetation cover. The study developed four simulation scenarios, with a smooth riverbed maintained in call cases while the roughness of the second part increasing from smooth to rough. The model-generated outputs facilitated a comprehensive longitudinal and transverse comparative analysis, focusing on flow velocity, shear stress, and bed evolution profile in response to varying roughness levels. The results show a reduction in erosion and deposition phenomena as the roughness as the bank’s roughness increases. this the crucial role of vegetation in stabilizing river banks by, strengthening the cohesion of the riverbed, thus minimizing erosion risks and excessive sediment transport, ultimately maintaining the riverbed’s integrity. These findings contribute to understanding of sedimentation patterns in the Medjerda River and facilitated the prediction of potential impacts on its fluvial morphology.

Gopal Krishan, Vivek Diwakar, S. D. Khobragade et al.

Abstract Groundwater quality assessment is critical due to its susceptibility to a range of natural and anthropogenic influences, which, if unmanaged, can pose serious environmental and public health risks. This study investigates the hydrogeochemical characteristics and groundwater quality evaluation in the southwestern districts of Punjab, India, with a focus on sustainable resource management. A total of 242 groundwater samples were systematically collected during the summer of 2019 across the districts of Mansa, Fazilka, Muktsar, Bathinda, Firozpur, and Faridkot. The samples were analyzed for almost all major cations, anions and other physicochemical parameters. Relative abundance of cations was Na+ > Mg2+ > Ca2+ > K & anions were SO4 2− > HCO3 − > Cl− > NO3 − > F. Elevated concentrations of sulphate & nitrate were detected, highlighting the impact of agrochemical inputs. The plots of Wilcox and USSL plots revealed a declining trend in groundwater suitability for irrigation, affecting both shallow and deep aquifer sources, due to increasing salinity and sodium hazards. Hydrochemical data was interpreted using Gibbs diagram, Piper’s trilinear plot and Durov diagram to understand the various geochemical processes affecting the groundwater quality. Hydrochemical analysis indicates that rock–water interactions, evaporation & anthropogenic processes predominantly control groundwater composition, as evidenced by high levels of sodium and chloride. This study is significant as the surface water resources are limited and the quality and quantity of groundwater are deteriorating with time due to anthropogenic inputs. These findings underscore the necessity of continuous monitoring and informed groundwater management strategies to mitigate contamination and ensure long-term sustainability.

Mugeng Liu, Siqi Zhong, Weichen Bi et al.

Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy et al.

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Jan Pišl, Marc Rußwurm, Lloyd Haydn Hughes et al.

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over $588^{^{\prime}}000$ sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data.

Alisa Luangrath, Joorim Na, Pandi Kalimuthu et al.

Biodegradable plastics (BPs) are widely used as alternatives to non-BPs due to their inherent ability to undergo facile degradation. However, the ecotoxicological impact of biodegradable microplastics (MPs) rarely remains scientific documented especially to aquatic ecosystem and organisms compared to conventional microplastics. Therefore, this study aimed to investigate the ecotoxicity of biodegradable polylactic acid (PLA) MPs to Daphnia magna with that of conventional polyethylene (PE) MPs with and without ultraviolet (UV) treatment (4 weeks). The acute toxicity (48 h) of PLA MPs was significantly higher than that of PE MPs, potentially attributable to their elevated bioconcentration resulting from their higher density. UV treatment notably reduced the particle size of PLA MPs and induced new hydrophilic functional groups containing oxygen. Thus, the acute lethal toxicity of PLA MPs exhibited noteworthy increase, compared to before UV treatment after UV treatment, which was greater than that of UV-PE MPs. In addition, UV-PLA MPs showed markedly elevated reactive oxygen species concentration in D. magna compared to positive control. However, there was no significant increase in the level of lipid peroxidation, possibly due to successful defense by antioxidant enzymes (superoxide dismutase and catalase). These findings highlight the ecotoxicological risks of biodegradable MPs to aquatic organisms, which require comprehensive long-term studies.

Luis D. Loor-Urgilés, Tabata N. Feijoó, Carlos A. Martínez-Huitle et al.

Abstract Chemical oxygen demand-COD is essential for water pollution control and monitoring and is also used to validate wastewater treatment technologies. Conventional COD determination use of costly toxic inputs that do not align with Sustainable Development Goals 6. To address these environmental challenges, photocatalytic (PC)- and photoelectrocatalytic (PEC)-COD sensors have emerged as a solution. This comprehensive review examines PC-COD and PEC-COD sensors in terms of nanomaterials used and their properties, focusing on how multiple variables influence PC activity and sensor performance. Analytical principles and operational variables affecting performance in COD determination are discussed. Finally, a series of materials and conditions are proposed to improve the viability of PEC-COD sensors currently and in the future.

Johan Cederbladh, Antonio Cicchetti

In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.

Egberto Selerio

This research focuses on decentralized wastewater treatment (DEWAT) technologies for the Philippines that is motivated by the limited suitable wastewater treatment infrastructure in the country. A multi-criteria analysis (MCA), using the Analytic Hierarchy Process (AHP) and Delphi method, was employed to evaluate DEWAT technologies based on life cycle costs and wastewater treatment efficiency parameters such as CODt, BOD5, TSS, NH4-N, TP, and hydraulic retention time. A two-factor Analysis of Variance (ANOVA) without replication was used to assess statistical differences between technologies. The analysis revealed that the Downflow Hanging Sponge (DHS) filter, Multi-Soil Layering (MSL) systems, and Moving Bed Biofilm Reactors (MBBRs) are the top-performing technologies, with no statistically significant differences in their overall performance. The DHS filter ranked highest, excelling in energy efficiency and nutrient removal, making it ideal for resource-scarce environments. MSL systems were noted for their broad-spectrum contaminant removal, while MBBRs demonstrated flexibility and scalability for semi-urban areas. A thorough analysis is carried out for these DEWAT technologies and insights for applicability in the Philippine context are provided.

E. I. Burt, E. I. Burt, G. R. Goldsmith et al.

<p>Determining the sources of water provisioning streams, soils, and vegetation can provide important insights into the water that sustains critical ecosystem functions now and how those functions may be expected to respond given projected changes in the global hydrologic cycle. We developed multi-year time series of water isotope ratios (<span class="inline-formula"><i>δ</i>¹⁸</span>O and <span class="inline-formula"><i>δ</i>²</span>H) based on twice-monthly collections of precipitation, lysimeter, and tree branch xylem waters from a seasonally dry tropical montane cloud forest in the southeastern Andes mountains of Peru. We then used this information to determine indices of the seasonal origins, the young water fractions (<span class="inline-formula"><i>F</i>_yw</span>), and the new water fractions (<span class="inline-formula"><i>F</i>_new</span>) of soil, stream, and tree water. There was no evidence for intra-annual variation in the seasonal origins of stream water and lysimeter water from 1 m depth, both of which were predominantly comprised of wet-season precipitation even during the dry seasons. However, branch xylem waters demonstrated an intra-annual shift in seasonal origin: xylem waters were comprised of wet-season precipitation during the wet season and dry-season precipitation during the dry season. The young water fractions of lysimeter (<span class="inline-formula">&lt;</span> 15 %) and stream (5 %) waters were lower than the young water fraction (37 %) in branch xylem waters. The new water fraction (an indicator of water <span class="inline-formula">≤</span> 2 weeks old in this study) was estimated to be 12 % for branch xylem waters, while there was no significant evidence for new water in stream or lysimeter waters from 1 m depth. Our results indicate that the source of water for trees in this system varied seasonally, such that recent precipitation may be more immediately taken up by shallow tree roots. In comparison, the source of water for soils and streams did not vary seasonally, such that precipitation may mix and reside in soils and take longer to transit into the stream. Our insights into the seasonal origins and ages of water in soils, streams, and vegetation in this humid tropical montane cloud forest add to understanding of the mechanisms that govern the partitioning of water moving through different ecosystems.</p>

Charles Onyutha, Saeed Golian, Saeed Golian et al.

Mirella Kanerva, Nguyen Minh Tue, Tatsuya Kunisue et al.

The Atlantic salmon (Salmo salar) population in the Baltic Sea consists of wild and hatchery-reared fish that have been released into the sea to support salmon stocks. During feeding migration, salmon migrate to different parts of the Baltic Sea and are exposed to various biotic and abiotic stressors, such as organohalogen compounds (OHCs). The effects of salmon origin (wild or hatchery-reared), feeding area (Baltic Main Basin, Bothnian Sea, and Gulf of Finland), and OHC concentration on the differences in hepatic proteome of salmon were investigated. Multi-level analysis of the OHC concentration, transcriptome, proteome, and oxidative stress biomarkers measured from the same salmon individuals were performed to find the key variables (origin, feeding area, OHC concentrations, and oxidative stress) that best account for the differences in the transcriptome and proteome between the salmon groups. When comparing wild and hatchery-reared salmon, differences were found in xenobiotic and amino acid metabolism-related pathways. When comparing salmon from different feeding areas, the amino acid and carbohydrate metabolic pathways were notably different. Several proteins found in these pathways are correlated with the concentrations of polychlorinated biphenyls (PCBs). The multi-level analysis also revealed amino acid metabolic pathways in connection with PCBs and oxidative stress variables related to glutathione metabolism. Other pathways found in the multi-level analysis included genetic information processes related to ribosomes, signaling and cellular processes related to the cytoskeleton, and the immune system, which were connected mainly to the concentrations of Polychlorinated biphenyls and Dichlorodiphenyltrichloroethane and their metabolites. These results suggest that the hepatic proteome of salmon in the Baltic Sea, together with the transcriptome, is more affected by the OHC concentrations and oxidative stress of the feeding area than the origin of the salmon.

Jan Heine, Herbert Palm

The increasing pressure within VUCA (volatility, uncertainty, complexity and ambiguity) driven environments causes traditional, plan-driven Systems Engineering approaches to no longer suffice. Agility is then changing from a "nice-to-have" to a "must-have" capability for successful system developing organisations. The current state of the art, however, does not provide clear answers on how to map this need in terms of processes, methods, tools and competencies (PMTC) and how to successfully manage the transition within established industries. In this paper, we propose an agile Systems Engineering (SE) Framework for the automotive industry to meet the new agility demand. In addition to the methodological background, we present results of a pilot project in the chassis development department of a German automotive manufacturer and demonstrate the effectiveness of the newly proposed framework. By adopting the described agile SE Framework, companies can foster innovation and collaboration based on a learning, continuous improvement and self-reinforcing base.

Gianina Giacosa, Codey Barnett, Daniel G. Rainham et al.

The pulp and paper industry is a major contributor to water and air pollution globally. Pulp and paper processing is an intensive energy consuming process that produces multiple contaminants that pollute water, air, and affect ecological and human health. In Canada, the National Pollutant Release Inventory (NPRI) is used to assess the release of air pollutants into the atmosphere from industrial facilities (including pulp and paper mills) and provides a repository of annual emissions reported by individual facilities. This study compared annual air emissions of carbon monoxide, nitrogen oxides, total particulate matter (TPM), PM_2.5, PM₁₀, sulphur dioxide, and volatile organic compounds from nine different pulp and/or paper mills in Atlantic Canada from three provinces (Nova Scotia, New Brunswick, and Newfoundland and Labrador) between 2002 and 2019. Results revealed that annual releases were several orders of magnitude higher than federal reporting thresholds suggested by Environment and Climate Change Canada. Pulp mills emit higher pollutant loads than those producing paper. The highest exceedance of a reporting threshold was for particulate matter (PM_2.5) at Northern Pulp in Nova Scotia. The emissions of PM_2.5 were on average (over a 17-year period) about 100,000% above the reporting threshold of 0.3 tonnes per year.