Maarten R. Dobbelaere, Pieter P. Plehiers, R. Vijver
et al.
Abstract Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.
Fidele Benimana, Christopher Kucha, Anupam Roy
et al.
ABSTRACT The global demand for edible flowers has increased due to their diverse applications in food, nutraceuticals, and the medical field. However, issues of species identification, adulteration, contamination, and quality necessitate the use of advanced methods to authenticate product quality for edible flowers. Conventional methods are expensive, time‐consuming, and require highly skilled personnel and technical expertise. Spectroscopic methods, including Fourier transform infrared, near‐infrared, and Raman spectroscopy, are efficient, fast, and non‐destructive, providing rapid insight into the chemical structure and authenticity of edible flowers. This review systematically summarizes the recent advances in spectroscopic methods for authenticating edible flowers, including the detection of chemical changes and ensuring product integrity. The primary goal is to examine the applications of spectroscopic techniques for assessing quality changes in edible flowers during processing for food applications. Spectroscopic techniques, such as FT‐IR, NIR, and Raman spectroscopy, are rapid, accurate, and non‐destructive alternatives for authenticating the composition and quality of edible flowers. These methods enable the detection of bioactive compounds, differentiation of species, and identification of adulterants with minimal sample processing. Furthermore, chemometric models enhance data analysis, allowing for automated classification and real‐time quality monitoring of edible flowers.
Food processing and manufacture, Toxicology. Poisons
This study examines the crystallization kinetics of Ni _50−x Mn _39 Sn _11 Fe _x (x = 0, 0.5, 2, 4 at.%) amorphous thin films prepared by DC magnetron sputtering. SEM and XRD confirm their amorphous structure. Non-isothermal DSC results show that the crystallization peak temperature increases from 542.7 K to 568.0 K as Fe content rises, while the apparent activation energy increases from 96.69 to 152.93 kJ mol ^−1 , indicating enhanced resistance to crystallization. Isothermal analysis yields Avrami exponents of 1.15–1.41 (average ≈1.2), corresponding to diffusion-controlled one-dimensional growth. Local activation-energy evaluation further reveals composition-dependent differences in nucleation and growth during various stages. These quantitative kinetic parameters clarify the role of Fe in altering crystallization behavior and support the optimization of annealing conditions for Ni-Mn-Sn-based functional thin films.
Materials of engineering and construction. Mechanics of materials, Chemical technology
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.
In this work, we study an integrated fault detection and classification framework called FARM for fast, accurate, and robust online chemical process monitoring. The FARM framework integrates the latest advancements in statistical process control (SPC) for monitoring nonparametric and heterogeneous data streams with novel data analysis approaches based on Riemannian geometry together in a hierarchical framework for online process monitoring. We conduct a systematic evaluation of the FARM monitoring framework using the Tennessee Eastman Process (TEP) dataset. Results show that FARM performs competitively against state-of-the-art process monitoring algorithms by achieving a good balance among fault detection rate (FDR), fault detection speed (FDS), and false alarm rate (FAR). Specifically, FARM achieved an average FDR of 96.97% while also outperforming benchmark methods in successfully detecting hard-to-detect faults that are previously known, including Faults 3, 9 and 15, with FDRs being 97.08%, 96.30% and 95.99%, respectively. In terms of FAR, our FARM framework allows practitioners to customize their choice of FAR, thereby offering great flexibility. Moreover, we report a significant improvement in average fault classification accuracy during online monitoring from 61% to 82% when leveraging Riemannian geometric analysis, and further to 84.5% when incorporating additional features from SPC. This illustrates the synergistic effect of integrating fault detection and classification in a holistic, hierarchical monitoring framework.
Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.
Susheel Kumar Nethi, Venugopal Gunda, Nagabhishek Sirpu Natesh
et al.
Summary: Pancreatic cancer (PC) exhibits profound metabolic adaptations that support tumor progression, survival, and therapy resistance. Hypoxia-inducible factor-1α (HIF-1α) is a key regulator of these processes, promoting metabolic reprogramming and chemoresistance. Given that mitochondrial metabolites modulate HIF-1α stability, targeting mitochondrial metabolism offers a promising therapeutic strategy. Niclosamide (Nic), a clinically approved anthelmintic, disrupts mitochondrial function but is limited by poor bioavailability. To overcome this, we developed polyanhydride-based Nic nanoparticles (NicNps) to enhance bioavailability and efficacy. NicNps impaired mitochondrial function, suppressed metabolism, downregulated HIF-1α, and inhibited growth of PC cells and orthotopic gemcitabine (Gem)-resistant mouse tumor models. Notably, NicNps combined with Gem overcame therapy resistance by synergistically reducing tumor hypoxia and HIF-1α-driven metabolic reprogramming. These findings highlight NicNps as a mitochondria-targeted, nanoparticle-based therapy that enhances Nic’s bioavailability while suppressing HIF-1α-driven adaptations. NicNps in combination with Gem offer a promising strategy to overcome therapy resistance and improve treatment outcomes in patients with pancreatic cancer.
Alexander Thebelt, Johannes Wiebe, Jan Kronqvist
et al.
It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for many chemical engineering applications. This review discusses how challenging data characteristics arise in chemical engineering applications. We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult: (1) high variance, low volume data, (2) low variance, high volume data, (3) noisy/corrupt/missing data, and (4) restricted data with physics-based limitations. For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges. Finally, we identify several challenges for future research.
The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.
Developing accurate models for chemical reactors is often challenging due to the complexity of reaction kinetics and process dynamics. Traditional approaches require retraining models for each new system, limiting generalizability and efficiency. In this work, we take a step toward foundation models for chemical reactor modeling by introducing a neural network framework that generalizes across diverse reactor types and rapidly adapts to new chemical processes. Our approach leverages meta-learning to pretrain the model on a broad set of reactor dynamics, enabling efficient adaptation to unseen reactions with minimal data. To further enhance generalizability, we incorporate physics-informed fine-tuning, ensuring physically consistent adaptation to new reactor conditions. Our framework is evaluated across three integer-order fundamental reactor types - continuous stirred tank reactors, batch reactors, and plug flow reactors - demonstrating superior few-shot adaptation compared to conventional data-driven, physics-informed, and transfer learning approaches. By combining meta-learning with physics-informed adaptation, this work lays the foundation for a generalizable modeling framework, advancing the development of foundation models for chemical engineering applications. Source code is available at https://github.com/killingbear999/chemical-reactor-foundation-model.
Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito
et al.
Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.
A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.