Scheduling optimization of electric energy meter distribution vehicles for intelligent batch rotation.

As industrial technology continues to advance through integration, society's demand for electricity is rapidly increasing. To meet the requirements of refined grid management and address the elevated challenges arising from the increased electrical load, this paper delves into the investigation of distribution vehicle scheduling for the practical scenario of batch rotation of smart meters. Initially, based on the practical distribution task requirements of a provincial metrology verification center, a multi-level optimization model is constructed for the batch rotation and distribution vehicle scheduling of smart meters. The primary objective is to maximize the enhancement of smart meter distribution efficiency while minimizing the overall distribution cost. Moreover, this paper introduces a refined Grey Wolf Optimization algorithm (OLC-GWO) based on Opposition-based Learning, Levy flight strategy, and Cauchy mutation to solve the model. By generating an opposite population to improve the quality of initial feasible solutions and further harnessing the global search capabilities of Levy flight and Cauchy mutation operators, the algorithm's effectiveness is enhanced. The algorithm is subjected to testing using multiple benchmark functions and its performance is compared with variants of GWO, as well as several cutting-edge intelligent optimization algorithms including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Honey Bee Algorithm (HBA). The results indicate that OLC-GWO exhibits excellent performance in terms of convergence speed and optimization capability. Finally, the improved algorithm is subjected to simulation experiments by incorporating order data from the practical distribution operations of a provincial metrology verification center. The outcomes verify the efficiency of the proposed algorithm, reinforcing the practical significance of the established model in addressing the real-world challenge of batch rotation and distribution vehicle scheduling for smart meters.

Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation.

Crops are constantly challenged by different environmental conditions. Seed treatment using nanomaterials is a cost-effective and environmentally friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly increase the stress resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined heat-drought stress, respectively. Metabolomics data reveal that ZnO nanopriming treatment, with the highest SRI value, mainly regulates the pathways of amino acid metabolism, secondary metabolite synthesis, carbohydrate metabolism, and translation. Understanding the mechanism of seed nanopriming is still difficult due to the variety of nanomaterials and the complexity of interactions between nanomaterials and plants. Using the nanopriming data, we present an interpretable structure-activity relationship (ISAR) approach based on interpretable machine learning for predicting and understanding its stress mitigation effects. The post hoc and model-based interpretation approaches of machine learning are integrated to provide complementary advantages and may yield more illuminating or trustworthy results for researchers or policymakers. The concentration, size, and zeta potential of nanoparticles are identified as dominant factors for correlating root dry weight under salinity stress, and their effects and interactions are explained. Additionally, a web-based interactive tool is developed for offering prediction-level interpretation and gathering more details about a specific nanopriming treatment. This work offers a promising framework for accelerating the agricultural applications of nanomaterials and may contribute to nanosafety assessment.

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment.

Understanding plant uptake and translocation of nanomaterials is crucial for ensuring the successful and sustainable applications of seed nanotreatment. Here, we collect a dataset with 280 instances from experiments for predicting the relative metal/metalloid concentration (RMC) in maize seedlings after seed priming by various metal and metalloid oxide nanoparticles. To obtain unbiased predictions and explanations on small datasets, we present an averaging strategy and add a dimension for interpretable machine learning. The findings in post-hoc interpretations of sophisticated LightGBM models demonstrate that solubility is highly correlated with model performance. Surface area, concentration, zeta potential, and hydrodynamic diameter of nanoparticles and seedling part and relative weight of plants are dominant factors affecting RMC, and their effects and interactions are explained. Furthermore, self-interpretable models using the RuleFit algorithm are established to successfully predict RMC only based on six important features identified by post-hoc explanations. We then develop a visualization tool called RuleGrid to depict feature effects and interactions in numerous generated rules. Consistent parameter-RMC relationships are obtained by different methods. This study offers a promising interpretable data-driven approach to expand the knowledge of nanoparticle fate in plants and may profoundly contribute to the safety-by-design of nanomaterials in agricultural and environmental applications.

Integrating machine learning interpretation methods for investigating nanoparticle uptake during seed priming and its biological effects.

Seed priming by nanoparticles is an environmentally-friendly solution for alleviating malnutrition, promoting crop growth, and mitigating environmental stress. However, there is a knowledge gap regarding the nanoparticle uptake and the underlying physiological mechanism. Machine learning has great potential for understanding the biological effects of nanoparticles. However, its interpretability is a challenge for building trust and providing insights into the learned relationships. Herein, we systematically investigated how the factors influence nanoparticle uptake during seed priming by ZnO nanoparticles and its effects on seed germination. The properties of the nanoparticles, priming solution, and seeds were considered. Post hoc interpretation and model-based interpretation of machine learning were integrated into two ways to understand the mechanism of nanoparticle uptake during seed priming and its biological effects on seed germination. The results indicated that nanoparticle concentration and ionic strength influenced the shoot fresh weight mainly by controlling the nanoparticle uptake. The nanoparticle uptake had a significant slowdown when the nanoparticle concentration exceeded 50 mg L-1. Although other factors, such as zeta potential and hydrodynamic diameter, had no obvious effects on nanoparticle uptake, their biological effects cannot be ignored. This approach can promote the safer-by-design strategy of nanomaterials for sustainable agriculture.

In silico nanosafety assessment tools and their ecosystem-level integration prospect.

Engineered nanomaterials (ENMs) have tremendous potential in many fields, but their applications and commercialization are difficult to widely implement due to their safety concerns. Recently, in silico nanosafety assessment has become an important and necessary tool to realize the safer-by-design strategy of ENMs and at the same time to reduce animal tests and exposure experiments. Here, in silico nanosafety assessment tools are classified into three categories according to their methodologies and objectives, including (i) data-driven prediction for acute toxicity, (ii) fate modeling for environmental pollution, and (iii) nano-biological interaction modeling for long-term biological effects. Released ENMs may cross environmental boundaries and undergo a variety of transformations in biological and environmental media. Therefore, the potential impacts of ENMs must be assessed from a multimedia perspective and with integrated approaches considering environmental and biological effects. Ecosystems with biodiversity and an abiotic environment may be used as an excellent integration platform to assess the community- and ecosystem-level nanosafety. In this review, the advances and challenges of in silico nanosafety assessment tools are carefully discussed. Furthermore, their integration at the ecosystem level may provide more comprehensive and reliable nanosafety assessment by establishing a site-specific interactive system among ENMs, abiotic environment, and biological communities.

Preparation and characterization of cross-linked starch nanocrystals and self-reinforced starch-based nanocomposite films.

In this study, starch-based nanocomposite films reinforced by cross-linked starch nanocrystals (CSNCs) were successfully prepared. CSNCs were obtained by cross-linking reaction between starch nanocrystals (SNCs) and sodium hexametaphosphate (SHMP). Through the characterization and comparison of SNCs and CSNCs in microscopic morphology, degree of substitution, swelling degree, XRD spectrum, and FTIR spectrum, the successful progress of the cross-linking reaction was confirmed. Besides, the effects of adding CSNCs on physiochemical properties of the nanocomposite films including mechanical properties, water vapor permeability, and contact angle were studied. The results confirmed that CSNCs had good enhancement effects on the physicochemical properties of starch-based films due to the self-reinforcing effect, and when the CSNCs content reached 10%, the nanocomposite film had the best overall performance. We further evaluated the cytotoxicity of the nanocomposite. Taken together, it is believed that the reported self-reinforced starch-based films are very promising for food packaging and preservation.

Predicting and investigating cytotoxicity of nanoparticles by translucent machine learning.

Safety concerns of engineered nanoparticles (ENPs) hamper their applications and commercialization in many potential fields. Machine learning has been proved as a great tool to understand the complex ENP-organism-environment relationship. However, good-performance machine learning models usually exist as black boxes, which may be difficult to build trust and whose ways of expressing knowledge rarely directly map to forms familiar to scientists. Here, we present an approach for uncovering causal structure in nanotoxicity datasets by mutual-validated and model-agnostic interpretation methods. Model predictions can be explained from feature importance, feature effects, and feature interactions. The utility of this approach is demonstrated through two case studies, the cytotoxicity of cadmium-containing quantum dots and metal oxide nanoparticles. Further, these case studies indicate the efficacy and impacts at two scales: (i) model interpretation, where the most relevant features for correlating cytotoxicity are identified and their influence on model predictions and interactions with other features are then explained, and (ii) model validation, where the difference among interpretation results of different methods (or the difference between interpretation results and well-known toxicity mechanisms) may reflect some inherent problems in the used dataset (or the developed models). Our approach of integrating machine learning models and interpretation methods provides a roadmap for predicting the toxicity of ENPs in a translucent way.