Johann Fredrik Jadebeck, Wolfgang Wiechert, Katharina Nöh
Bayesian analysis is particularly useful for inferring models and their parameters given data. This is a common task in metabolic modeling, where models of varying complexity are used to interpret data. Nested sampling is a class of probabilistic inference algorithms that are particularly effective for estimating evidence and sampling the parameter posterior probability distributions. However, the practicality of nested sampling for metabolic network inference has yet to be studied. In this technical report, we explore the amalgamation of nested sampling, specifically diffusive nested sampling, with reversible jump Markov chain Monte Carlo. We apply the algorithm to two synthetic problems from the field of metabolic flux analysis. We present run times and share insights into hyperparameter choices, providing a useful point of reference for future applications of nested sampling to metabolic flux problems.
Mechanical drawing. Engineering graphics, Physical and theoretical chemistry
The festival 'I fumi della fornace' originates from a rumor that attributed the eccentricity and artistic talents of the boys and girls of Valle Cascia, a small hamlet in the province of Macerata, to the fumes produced by the former Smorlesi furnace. This tale of distortion evolved into a foundational myth, a second baptism directed against any certain nature. The entire place is enveloped by this baptismal movement that has never reconciled with itself: a celebration like a perpetual new beginning. Thus, from a seemingly catastrophic place, Valle Cascia transforms into a laboratory of imaginative rebuilding: an attempt to reconstitute the world, becoming a formula that is both lasting and soluble.
The festival is an opportunity for a poetic rediscovery of the area, aimed at reactivating and raising awareness of those forgotten places - which here are transformed into open-air laboratories. Every intervention on the territory cultivates and reverses the original etymology: the park of secular oaks, the brick kiln, and the old disused sources are all brought to a second life. The fumes - resulting from the firing of bricks which once produced surplus value - have today become fabulous fumes from which we want to start to overturn the geography of the community.
خوردگی شیرین نوعی از خوردگی در صنعت نفت است که در آن گاز CO2 با آب واکنش داده و کربنیک اسید ایجاد شده و باعث خوردگی در فولاد میشود. یکی از روشهای مقابله با خوردگی استفاده از بازدارنده خوردگی است. در این پژوهش، اثربازدارنده طبیعی عصاره غلاف نخود فرنگی به عنوان بازدارنده خوردگی جدید برای فولاد کم کربن مورد استفاده در صنایع گاز (API 5L X42) در محلول 3.5 درصد وزنی NaCl اشباع شده با CO2 در غلظتهای مختلف مورد بررسی قرار گرفت. روشهای الکتروشیمیایی از قبیل آزمون قطبش و طیفسنجی امپدانس الکتروشیمیایی مورد استفاده قرار گرفت. نتایج نشان داد که حداقل زبری سطح در عصاره با غلظت ppm 100 ، 20.607 نانومتر بوده وهمچنین بهترین اثربخشی عصاره با درصد 69.76= IE در غلظت ppm 100 در مقایسه با سایر غلظتها است و نتایج حاصل از SEM و XRD نشاندهنده تشکیل یک لایه محافظ روی سطح نمونهها است و عملکرد عصاره به صورت یک بازدارنده مختلط است.
Jeffrey De Rycke, Alfredo Pironti, Marco Ariola
et al.
Magnetic confinement nuclear fusion offers a promising solution to the world’s growing energy demands. The DEMO reactor presented here aims to bridge the gap between laboratory fusion experiments and practical electricity generation, posing unique challenges for magnetic plasma diagnostics due to limited space for diagnostic equipment. This study employs Bayesian inference and Gaussian process modeling to integrate data from pick-up coils, flux loops, and saddle coils, enabling a qualitative estimation of the plasma current density distribution relying on only external magnetic measurements. The methodology successfully infers total plasma current, plasma centroid position, and six plasma–wall gap positions, while adhering to DEMO’s stringent accuracy standards. Additionally, the interchangeability between normal pick-up coils and saddle coils was assessed, revealing a clear preference for saddle coils. Initial steps were taken to utilize Bayesian experimental design for optimizing the orientation (normal or tangential) of pick-up coils within DEMO’s design constraints to improve the diagnostic setup’s inference precision. Our approach indicates the feasibility of Bayesian integrated data analysis in achieving precise and accurate probability distributions of plasma parameter crucial for the successful operation of DEMO.
Mechanical drawing. Engineering graphics, Physical and theoretical chemistry
Yining Hong, Christopher S. Timperley, Christian Kästner
Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.
Peter Yichen Chen, Minghao Guo, Hanspeter Pfister
et al.
Computer graphics, often associated with films, games, and visual effects, has long been a powerful tool for addressing scientific challenges--from its origins in 3D visualization for medical imaging to its role in modern computational modeling and simulation. This course explores the deep and evolving relationship between computer graphics and science, highlighting past achievements, ongoing contributions, and open questions that remain. We show how core methods, such as geometric reasoning and physical modeling, provide inductive biases that help address challenges in both fields, especially in data-scarce settings. To that end, we aim to reframe graphics as a modeling language for science by bridging vocabulary gaps between the two communities. Designed for both newcomers and experts, Graphics4Science invites the graphics community to engage with science, tackle high-impact problems where graphics expertise can make a difference, and contribute to the future of scientific discovery. Additional details are available on the course website: https://graphics4science.github.io
Hashini Gunatilake, John Grundy, Rashina Hoda
et al.
Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.
Mirko Daneluzzo, Michele Zannoni, Giorgio Dall’Osso
et al.
The field of research involving design and well-being, finds in the development of responsive technologies and Artificial Intelligence the latest tools for mutual influence and transformation.
Research on this topic, shared at the Disrupting Geographies in the Design World forum, testified to the multiple and contemporary fields of investigation related both to technological development and the human sphere.
The paper proposes a clustering of research on this topic into two sets: the first group gathers research and design approaches that increase human knowledge toward digital data, the second brings together insights on behavioral changes generated by design using emerging technologies.
The results of the debate and the categorizations open new focus points aimed at improving, through the discipline of design, individual and collective well-being.
امروزه با رشد آگاهی در زمینه محیطزیست و تأکید بیشتر بر فرآیندهای پاک، استفاده از رنگزاهای طبیعی مورد توجه بسیاری قرارگرفته است. در این پژوهش رنگزای میکروبی فیکوسیانین به عنوان یک رنگزای طبیعی برای رنگرزی نخ های پشمی استفاده شد. به منظور بهبود خواص رنگی کالای پشمی، از اصلاحات سطحی پلاسمای تحت خلا، پرتو فرابنفش و همچنین دندانه زیستی کیتوسان استفاده شد. برای بررسی اصلاحات انجام شده، ثباتهای شستشویی، نوری، مالشی، قدرت رنگی و فعالیت ضدمیکروبی نمونهها مورد بررسی قرار گرفت. همچنین، به منظور بررسی گروههای عاملی و ریختشناسی الیاف از طیفسنجی فروسرخ (FTIR) و میکروسکوپ الکترونی روبشی (SEM) استفاده شد. نتایج نشان داد، بیشترین قدرت رنگی (8.69) مربوط به نمونه اصلاح سطحی شده با فرابنفش و دندانه داده شده با دی کرومات پتاسیم است. همچنین، همه نمونههای رنگرزی شده فعالیت ضدمیکروبی عالی از خود نشان دادند.
Mariia Vorobchuk, Kalina Pashkevych, Olga Yezhova
et al.
Classification of design codes by the level of complexity of their design was cre-ated for the first time. The obtained results are key to understanding the role of the QR code as a full-fledged work of art in the context of modern design. In the future, this will allow us to single out the most noticeable trends in the development of coded information in the conditions of modern visual culture.
The necessary dialogue between users, students, and professionals in the field of sustainable design requires a precise understanding of the conceptual meaning of the term “sustainability”. This concept is multi-componential, and different categories of actors in the design scene attribute varying components and varying weights to each component. This research, conducted on a sample of 304 participants, employed qualitative analysis to map the differences between the groups, both for the static definition of the concept and the definition anchored in perceptual data. Our research finds significant differences in conceptualization between the different groups: the idea of sustainability is substantially different between designers and users. The differences identified by this study are essential for designers who aim to create in a way that aligns with the categorization of users, in order to trigger an effective circular process.
Learning complex disciplines traditionally requires the adoption of equally articulated methods and strategies. Design does not escape this principle, on the contrary it enhances some of its peculiar characteristics: very different skills, often not aligned development and application times, tools complexity, declare an environment where methods and practice had always defined a non-linear educational scenario (Cennamo et al., 2011). The paper discusses the matter while bringing a case study specifically defined with the purpose to test a new educational scenario where a student is put into conditions to experiment the entire Design process by himself, being connected with different competences inside a complex multidisciplinary environment, in order to develop a physical Design solution for a user with disabilities.
Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context description, study classification, generalizability, and the handling of validity threats. This paper presents a reflective analysis aiming to share insights that can enhance the quality and impact of case study reporting. We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies. Additionally, particular focus is placed on articulating generalizable findings and thoroughly discussing generalizability threats. We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound, resonate with, and apply to software engineering practitioners and the broader academic community. The reflections and recommendations offered in this paper aim to ensure that insights from case studies are transparent, understandable, and tailored to meet the needs of both academic researchers and industry practitioners. In doing so, we seek to enhance the real-world applicability of academic research, bridging the gap between theoretical research and practical implementation in industry.
Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi
et al.
Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.
Taking ideas to market can be a long, iterative, and complex process. When dealing with new bio-based materials, understanding factors that help bridge the lab-to-market gap and how materials are selected for new product development have the potential to speed up the transition to a circular bioeconomy. This article defines abstract and conceptual material demonstrators and explores how they support the innovation process in different ways. Nine roles are identified, including how material demonstrators contribute to generating and expressing new ideas, enable a shared understanding of technology, support the discovery of market value and the visualization of potential applications as well as helping to articulate internal and external strategies and communications. Abstract and conceptual material demonstrators are exemplified with both technology-driven and market-driven bio-based materials used in packaging.
This paper presents a comparatively simple approach for showthrough and strikethrough print defect detection using computer vision method. Showthrough and strikethrough are common printing problem and are typically functions of a paper’s opacity. Under normal lighting condition the visibility of printing on the reverse side of printed paper is termed as showthrough whereas the penetration of ink to the other side is termed as strikethrough. Moreover the intensity of showthrough pixel is extremely low thus it is difficult to identify the showthrough pixel from the printed area. On the other hand strikethrough is the result of penetration of ink through paper and depends on the absorbent nature of paper. Comparatively the intensity of the strikethrough pixel is higher than that of the showthrough but due to similar intensity of the ink of the printed pixel and strikethrough pixel, both overlapped with each other in the foreground of the image. These print defects can degrade the image quality as well as print production. In this study, the detection of these two print defects achieved using histogram equalization technique, to enhance the contrast between foreground and back ground pixels. A global thresholding algorithm was applied on a histogram equalized image to segment the printed area from the background of the image. Pixels in the background which are considered as showthrough and strike through pixels are identified by image subtraction. The pictorial representations of the results show the remarkable potential of the proposed technique which can be possible alternative of present subjective measures of showthrough and strikethrough.
AbstractTemporal networks can naturally model real‐world complex phenomena such as contact networks, information dissemination and physical proximity. However, nodes and edges bear real‐time coordinates, making it difficult to organize them into discrete timeslices, without a loss of temporal information due to projection. Event‐based dynamic graph drawing rejects the notion of a timeslice and allows each node and edge to retain its own real‐valued time coordinate. While existing work has demonstrated clear advantages for this approach, they come at a running time cost. We investigate the problem of accelerating event‐based layout to make it more competitive with existing layout techniques. In this paper, we describe the design, implementation and experimental evaluation of MultiDynNoS, the first multi‐level event‐based graph layout algorithm. We consider three operators for coarsening and placement, inspired by Walshaw, GRIP and FM3, which we couple with an event‐based graph drawing algorithm. We also propose two extensions to the core algorithm: AutoTau and Bend Transfer. We perform two experiments: first, we compare MultiDynNoS variants to existing state‐of‐the‐art dynamic graph layout approaches; second, we investigate the impact of each of the proposed algorithm extensions. MultiDynNoS proves to be competitive with existing approaches, and the proposed extensions achieve their design goals and contribute in opening new research directions.
Lydia Anggraini, Annisa Nur Wahyuni, Rendi Hernawan
et al.
This paper examines the use of waste energy in a 3x1 MW Gas Engine Power Plant (GEPP) on Bawean Island, Indonesia. The feasibility method uses water-lithium bromide (H2O-LiBr) technology as absorption refrigeration technology. In addition, bananas are also used for cold storage to overcome waste energy utilization. The cold storage is placed in the 300 m3 area with a 100 kg load capacity for a banana with a temperature of 5oC, 85% humidity, 24 hours of operation, 1292 W cooling load, and 371 TR. This system is used because it utilizes a cheap energy source that dissipates heat from gas and has no ecological hazards, such as ozone layer depletion and global warming. The exhaust gas temperature is 500oC. Moreover, cooling loads for cold storage, which are used with thermodynamic models, and consistent fluid properties, performance, and size of cold storage were also investigated. The results obtained show that higher cold storage output comes from internal factors as compared to external factors. In addition, the absorption refrigerant with Tevaporation is 5oC, capacity 403 TR and Qabsorption is 984 kW, Qgenerator is 1066 kW, Qevaporation is 1411 kW, Qcondenser is 1493 kW, with an absorption coefficient of performance (COP) of 1.32 and power consumption of 158,25 kW. Furthermore, after calculations, analysis, and field experiments, it shows that the internal factor of the cooling load is higher than the external factor sourced from bananas in the cold storage. This phenomenon occurs probably due to the product being refrigerated, following the soar cooling capacity. Thus, the waste energy in PLTGU 3x1 MW has tried to be utilized by the refrigerant absorption system.
The Princeton Research Software Engineering Group has grown rapidly since its inception in late 2016. The group, housed in the central Research Computing Department, comprised of professional Research Software Engineers (RSEs), works directly with researchers to create high quality research software to enable new scientific advances. As the group has matured so has the need for formalizing operational details and procedures. The RSE group uses an RSE partnership model, where Research Software Engineers work long-term with a designated academic department, institute, center, consortium, or individual principal investigator (PI). This article describes the operation of the central Princeton RSE group including funding, partner & project selection, and best practices for defining expectations for a successful partnership with researchers.