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Abstract MF-LOGP, a new method for determining a single component octanol–water partition coefficients ($$LogP$$ $\mathrm{LogP}$) is presented which uses molecular formula as the only input. Octanol–water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make$$LogP$$ $\mathrm{LogP}$predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average$$RMSE$$ $\mathrm{RMSE}$= 0.77 ± 0.007,$$MAE$$ $\mathrm{MAE}$= 0.52 ± 0.003, and$${R}^{2}$$ $R^2$= 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ($$RMSE$$ $\mathrm{RMSE}$= 0.42–1.54,$$MAE$$ $\mathrm{MAE}$= 0.09–1.07, and$${R}^{2}$$ $R^2$= 0.32–0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures. Graphical Abstract 

This study investigates the interplay between digital technology and the circular economy (CE) within supply chain management through theoretical lenses. We conduct a systematic literature review to explore the theoretical underpinnings at the intersection of digital technology and CE. We determine the dominant theories and how they relate within a broader ecosystem. We further outline the promising avenues and topics for future research to foster the understanding and application of joint adoption of digital technology and CE. Contributing to the existing literature, this study develops a three-level framework (micro, meso, macro) enriched with a multi-stakeholder perspective to present in-depth insights into the interplay between digital technology and CE from theoretical viewpoints. Our proposed framework synthesizes 39 distinct theories, employed in literature to examine the dynamics between digital technology and CE, and categorizes them into five key areas: motivators, enablers, synergy, external environmental context, and multi-level stakeholders. Leveraging this framework, several research propositions, each grounded in one of the identified categories, are proposed to further explore this domain. This paper advances the theoretical discourse in the interplay between digital technology and CE and provides theoretical and practical implications for both scholars and practitioners. 

Environmental challenges and increasing resource consumption may be mitigated through organizational circular economy (CE) practices. Implementing CE practices requires organizations to rethink, develop, and implement new initiatives and processes. It has been argued that blockchain technology (BCT) can support corporate and supply chain CE practices. However, empirical evidence on whether BCT adoption can complement corporate CE practices when considering firm financial performance is virtually non-existent. Using the resource-based view and a dataset of 1766 firm-year observations of Chinese listed companies, we investigate the relationship between corporate CE practices and financial performance, as well as the moderating effect of BCT adoption. Initial findings reveal a significantly positive relationship between corporate CE practices and financial performance. However, counterintuitively, BCT adoption not only directly negatively relates to firm financial performance but also weakens the positive relationship between CE practices and financial performance. Further analysis found that these direct and indirect negative effects of BCT adoption are only observed in resource-constrained firms, supporting our argument from a resource scarcity perspective. This study provides new insights into the nuanced relationship among CE practices, BCT adoption, and financial performance from the resource-based view. These insights provide new and valuable guidance for researchers and practitioners. 

SnS2 is a two-dimensional (2D) layered semiconductor with a visible-range bandgap (~2.3eV), high charge carrier mobility, long carrier lifetimes, and good environmental stability. This study explores the impact of zero-valent metal intercalation into the van der Waals gaps of SnS2 on charge carrier dynamics. We demonstrate that metal intercalation enhances optical absorption in the yellow-to-IR range and induces metal-dependent bandgap shifts. Time-resolved THz spectroscopy reveals that different metals uniquely influence photoconductivity dynamics: We find that intercalation with Bi, Ni, and Fe shortens the photoconductivity decay times, whereas Rh intercalation results in a slower decay. These findings highlight the potential of metal intercalation to tailor SnS2 properties for diverse applications, from solar energy conversion to high-speed photodetectors. 

Lithium-ion battery (LIB) circular supply chains (CSCs) present unique safety challenges among operation processes. Blockchain technology can be a promising solution for addressing these challenges, by enabling effective tracking and verification of safety-related information throughout the supply chain. However, how blockchain can mitigate safety issues from a supply chain perspective is poorly understood. This study proposes a theory-supported framework through intervention-based research (IBR) to provide guidance for LIB CSC safety management. The conceptual framework and theoretical propositions set the foundation for research on a comprehensive blockchain ecosystem specifically designed for CSC safety management. The framework includes the design of a blockchain architecture with capabilities tailored to address safety concerns, the involvement of multiple stakeholders, and the development of a safety measurement matrix. This study suggests research and practical directions which lay the groundwork for leveraging blockchain technology to improve safety management in LIB CSC. This research contributes to sustainable supply chains by proposing a conceptual framework for mitigating safety concerns in LIB CSC, paving the way for effective blockchain implementation. In addition, this study contributes to advancing the theoretical design understanding and application of blockchain technology in CSC safety management. 

Extremophilic yeasts have favorable metabolic and tolerance traits for biomanufacturing‐ like lipid biosynthesis, flavinogenesis, and halotolerance – yet the connection between these favorable phenotypes and strain genotype is not well understood. To this end, this study compares the phenotypes and gene expression patterns of biotechnologically relevant yeasts Yarrowia lipolytica, Debaryomyces hansenii, and Debaryomyces subglobosus grown under nitrogen starvation, iron starvation, and salt stress. To analyze the large data set across species and conditions, two approaches were used: a “network‐first” approach where a generalized metabolic network serves as a scaffold for mapping genes and a “cluster‐first” approach where unsupervised machine learning co‐expression analysis clusters genes. Both approaches provide insight into strain behavior. The network‐first approach corroborates that Yarrowia upregulates lipid biosynthesis during nitrogen starvation and provides new evidence that riboflavin overproduction in Debaryomyces yeasts is overflow metabolism that is routed to flavin cofactor production under salt stress. The cluster‐first approach does not rely on annotation; therefore, the coexpression analysis can identify known and novel genes involved in stress responses, mainly transcription factors and transporters. Therefore, this work links the genotype to the phenotype of biotechnologically relevant yeasts and demonstrates the utility of complementary computational approaches to gain insight from transcriptomics data across species and conditions. 

Due to the scarcity of reliable anomaly labels, recent anomaly detection methods leveraging noisy auto-generated labels either select clean samples or refurbish noisy labels. However, both approaches struggle due to the unique properties of anomalies.Sample selectionoften fails to separate sufficiently many clean anomaly samples from noisy ones, whilelabel refurbishmenterroneously refurbishesmarginalclean samples. To overcome these limitations, we design Unity, thefirstlearning from noisy labels (LNL) approach for anomaly detection that elegantly leverages the merits of both sample selection and label refurbishment to iteratively prepare a diverse clean sample set for network training. Unity uses a pair of deep anomaly networks to collaboratively select samples with clean labels based on prediction agreement, followed by a disagreement resolution mechanism to capture marginal samples with clean labels. Thereafter, Unity utilizes unique properties of anomalies to design an anomaly-centric contrastive learning strategy that accurately refurbishes the remaining noisy labels. The resulting set, composed ofselected and refurbishedclean samples, will be used to train the anomaly networks in the next training round. Our experimental study on 10 real-world benchmark datasets demonstrates that Unity consistently outperforms state-of-the-art LNL techniques by up to 0.31 in F-1 Score (0.52 \rightarrow 0.83). 

The accurate detection of chemical agents promotes many national security and public safety goals, and robust chemical detection methods can prevent disasters and support effective response to incidents. Mass spectrometry is an important tool in detecting and identifying chemical agents. However, there are high costs and logistical challenges associated with acquiring sufficient lab-generated mass spectrometry data for training machine learning algorithms, including skilled personnel, sample preparation and analysis required for data generation. These high costs of mass spectrometry data collection hinder the development of machine learning and deep learning models to detect and identify chemical agents. Accordingly, the primary objective of our research is to create a mass spectrometry data generation model whose output (synthetic mass spectrometry data) would enhance the performance of downstream machine learning chemical classification models. Such a synthetic data generation model would reduce the need to generate costly real-world data, and provide additional training data to use in combination with lab-generated mass spectrometry data when training classifiers. Our approach is a novel combination of autoencoder-based synthetic data generation combined with a fixed, apriori defined hidden layer geometry. In particular, we train pairs of encoders and decoders with an additional loss term that enforces that the hidden layer passed from the encoder to the decoder match the embedding provided by an external deep learning model designed to predict functional properties of chemicals. We have verified that incorporating our synthetic spectra into a lab-generated dataset enhances the performance of classification algorithms compared to using only the real data. Our synthetic spectra have been successfully matched to lab-generated spectra for their respective chemicals using library matching software, further demonstrating the validity of our work. 

Autoencoders represent a significant category of deep learning models and are widely utilized for dimensionality reduction. However, standard Autoencoders are complicated architectures that normally have several layers and many hyper-parameters that require tuning. In this paper, we introduce a new type of autoencoder that we call dynamical system autoencoder (DSAE). Similar to classic autoencoders, DSAEs can effectively handle dimensionality reduction and denoising tasks, and they demonstrate strong performance in several benchmark tasks. However, DSAEs, in some sense, have a more flexible architecture than standard AEs. In particular, in this paper we study simple DSAEs that only have a single layer. In addition, DSAEs provide several theoretical and practical advantages arising from their implementation as iterative maps, which have been well studied over several decades. Beyond the inherent simplicity of DSAEs, we also demonstrate how to use sparse matrices to reduce the number of parameters for DSAEs without sacrificing the performance of our methods. Our simulation studies indicate that DSAEs achieved better performance than the classic autoencoders when the encoding dimension or training sample size was small. Additionally, we illustrate how to use DSAEs, and denoising autoencoders in general. to nerform sunervised learning tasks. 

This study examines the resilience and sustainability of supply chains amid global disruptions, with a particular focus on the essential role of reverse logistics. Through a game-theoretic approach, we explore manufacturer decisions to source from either reliable but expensive raw materials or cost-effective yet riskier recycled or recyclable materials from the reverse logistics channel. Our analysis outlines three primary sourcing strategies: sourcing exclusively from suppliers (SS), sourcing solely through retailer reverse channel (RS), and a balanced dual sourcing (DS) approach. Our findings reveal the economic viability that recycling outsourcing is influenced by market demand and disruption risks. Notably, in scenarios of constrained market potential, the cost advantage of using recycled materials from less reliable reverse logistics channels surpasses the risks associated with supply chain disruptions, suggesting a complex cost-benefit landscape amidst supply uncertainties. Moreover, the stability of suppliers emerges as a pivotal factor in strategic sourcing decisions, underscoring the need to consider both economic efficiencies and supply reliability. The study also evaluates the dynamic competition between manufacturers and retailers, shedding light on how strategic adjustments driven by sustainability and resilience goals can enhance profitability and sustainability. It was found that despite the threat of disruptions, manufacturers benefit more from engaging with risky reverse channels under specific conditions, underscoring the nuanced decision-making required in uncertain supply scenarios. This research advances sustainable supply chain management by highlighting strategic complexities and the need for understanding economic efficiencies and supply stability, offering insights for navigating disruptions and fostering resilient, sustainable supply chains.