IntroductionEnvironmentally friendly pork production is crucial to the pig industry, where the enhancement of growth performance and feed efficiency with reduced environmental impacts is favored. This study aimed to evaluate the effect that protease supplementation in a low crude protein diet has on the growth performance, nutrient digestibility, nitrogen retention, and gut microbiome in growing pigs.MethodsEighty pigs (Landrace × Yorkshire × Duroc; 24.72 kg) were selected, and based on initial body weight and sex, randomly allocated to one of the following dietary treatments: H, 16% crude protein (CP) diet; L, 14% CP diet; L+E1, low CP diet + 0.1% protease; and L+E2, low CP diet + 0.2% protease. Each treatment comprised four replicates with five pigs per pen. ResultsPigs fed a low CP diet with protease supplementation showed a significantly higher body weight, average daily gain, and feed conversion ratio than those fed a high CP diet. In addition, ammonia emissions were lower in the L+E2 group than in the L group. Based on microbiome analysis, the L+E1 and L+E2 groups showed an increased Firmicutes-to-Bacteroidota ratio and elevated expression of pathways related to carbohydrate metabolism, coinciding with higher concentrations of short-chain fatty acids, such as butyrate and propionate, which support intestinal health. Additionally, the predicted function of the microbiota of pigs fed protease exhibited reduced nitrogen and sulfur metabolism, suggesting a potential reduction in excreted odorous compounds. DiscussionThese findings highlight the role of protease in enhancing growth performance and feed efficiency by modulating gut microbial composition and metabolic functions and reducing noxious gas emissions. Also, potential feed-cost savings are inferred from lower CP formulation.
Abstract Volcanic eruptions produce plumes of ash, gas and aerosols that present a risk to aviation at all standard flight levels. Here, we investigate atmospheric dispersal of volcanic emissions, whether and how they infiltrate aircraft, and whether ground-level public health exposure thresholds can be related to the pressurised cabin environment. We then review the limited evidence for physical and mental health, and behavioural impacts, resulting from volcanic emissions entering aircraft. Serious health risks are considered low for healthy individuals, but respiratory irritation is likely for a high exposure scenario to sulfur dioxide (SO2). Asthmatics are particularly sensitive to SO2, with even relatively low, short exposures, potentially resulting in severe respiratory impacts. Negative group behaviours are not expected but individual distress is possible. Communicating this evidence to the aviation industry may result in more informed decision-making on flightpath alterations and triggering of emergency protocols, both before and during volcanic emission encounters.
Environmental protection, Disasters and engineering
Abstract The 10 000‐m ultradeep dolomite reservoir holds significant potential as a successor field for future oil and gas exploration in China's marine craton basin. However, major challenges such as the genesis of dolomite, the formation time of high‐quality reservoirs, and the preservation mechanism of reservoirs have always limited exploration decision‐making. This research systematically elaborates on the genesis and reservoir‐forming mechanisms of Sinian–Cambrian dolomite, discussing the ancient marine environment where microorganisms and dolomite develop, which controls the formation of large‐scale Precambrian–Cambrian dolomite. The periodic changes in Mg isotopes and sedimentary cycles show that the thick‐layered dolomite is the result of different dolomitization processes superimposed on a spatiotemporal scale. Lattice defects and dolomite embryos can promote dolomitization. By simulating the dissolution of typical calcite and dolomite crystal faces in different solution systems and calculating their molecular weights, the essence of heterogeneous dissolution and pore formation on typical calcite and dolomite crystal faces was revealed, and the mechanism of dolomitization was also demonstrated. The properties of calcite and dolomite (104)/(110) grain boundaries and their dissolution mechanism in carbonate solution were revealed, showing the limiting factors of the dolomitization process and the preservation mechanism of deep buried dolomite reservoirs. The in situ laser U‐Pb isotope dating technique has demonstrated the timing of dolomitization and pore formation in ancient carbonate rocks. This research also proposed that dolomitization occurred during the quasi‐contemporaneous or shallow‐burial periods within 50 Ma after deposition and pores formed during the quasi‐contemporaneous to the early diagenetic periods. And it was clear that the quasi‐contemporaneous dolomitization was the key period for reservoir formation. The systematic characterization of the spatial distribution of the deepest dolomite reservoirs in multiple sets of the Sinian and the Cambrian in the Chinese craton basins provides an important basis for the distribution prediction of large‐scale dolomite reservoirs. It clarifies the targets for oil and gas exploration at depths over 10 000 m. The research on dolomite in this study will greatly promote China's ultradeep oil and gas exploration and lead the Chinese petroleum industry into a new era of 10 000‐m deep oil exploration.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
In response to the global challenge of climate change, financial institutions are increasingly called upon to assess and disclose their carbon emissions. Various global carbon quantification and reporting standards were developed, such as the Greenhouse Gas (GHG) Protocol, Task Force on Climate-related Financial Disclosures (TCFD), Partnership for Carbon Accounting Financials (PCAF) and others. Unfortunately, the now diverse landscape of standards increases the complexity for institutions seeking to develop voluntary carbon quantification and reporting. This study addresses the complexity issue by developing a criteria-based tool that summarizes the various components and requirements of the carbon standards. We propose eight criteria that summarize the standards’ key elements, requirements and relevance to the financial industry. We analyze seven major carbon quantification and reporting standards, systematically evaluating them against our tool. By doing so, we provide financial institutions with valuable insights in selecting appropriate standards to inform their emissions quantification and reporting decisions.
Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.
Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi
et al.
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.
Background. The mineral resource base is a strategic component of Russia’s economic security and a critical tool for strengthening the country’s position in the international arena. At the same time, economic turmoil and geopolitical tensions negatively affect the quality of resource auditing in the oil and gas and other extractive industries of the Russian Federation. This have an effect on the investment attractiveness of exploration projects, which are highly important for the continuous reproduction of the country’s mineral resource base.Aim and objectives. In order to consider the potential of mineral resource auditing for augmentation of Russia’s wealth, the following objectives were undertaken: 1) to analyze the structure and state of audit methods in Russian extractive industries; 2) to study the prospects and possibilities of applying international experience in auditing Russian extractive industries; 3) to apply new tools and approaches to optimize auditing activities in the domestic practice.Materials and methods. Both domestic and foreign research publications were reviewed, along with the reports of the Ministry of Natural Resources and the Environment of the Russian Federation and the Federal Subsoil Resources Management Agency, analytical materials and audit guides by CAAF, INTOSAI, and AFROSAI-E McKinsey. A set of general scientific and special methods Y were used: forecasting methods to assess the state and prospects of financing geological exploration in the Russian Federation; methods of comparative analysis and synthesis, induction and deduction to study the international experience of auditing in the extractive industry.Results. It is proposed to attract financial resources at various stages of geological exploration through the creation of special-purpose direct investment funds. Attention is also paid to such methods as streaming and acquisition of fixed profit margins from production activities in exchange for an advance payment, which can be applied at later stages of geological exploration.Conclusion. In order to attract investments for geological exploration of subsoil resources, Russian companies should apply new tools and approaches, as well as international best practices. In combination, this will facilitate the process of attracting funds alternative to budgetary resources, thereby diversifying the methods of financial support, and will increase the investment attractiveness of the Russian geological industry.
The research and application of risk analysis and evaluation for underground gas storage facilities are critical due to their diverse equipment, complex process flows, and numerous risk factors. In particular, corrosion failure accidents in ground process pipelines at these facilities have become increasingly common in recent years. Effective and accurate analysis of the causes of these corrosion failures is essential for ensuring the safe operation of underground gas storage facilities. This article presents a risk assessment methodology that leverages data and knowledge fusion. The process begins with a statistical analysis of the corrosion failure data from ground process pipelines in underground gas storage facilities, from which a Bayesian corrosion prediction model is developed. This model serves as the foundation for analyzing the basic events that lead to corrosion failure in these pipelines. Subsequently, a knowledge model of corrosion failure is established, and a detailed analysis of corrosion causes is conducted using the fault tree specific to corrosion failure in ground process pipelines. The importance of each basic event within the fault tree is quantified through the structural importance coefficient assigned to each event. The analysis categorizes the influencing factors of corrosion failure into four main groups. A judgment matrix is then created to determine the relative weight values of these different influencing factors. This matrix is crucial for setting the weight factors in the fuzzy comprehensive evaluation, which ultimately determines the risk level of corrosion failure in ground process pipelines at underground gas storage facilities. By applying examples of corrosion risk assessments for ground process pipelines, this study provides a scientific basis for enhancing safety management and operational practices at underground gas storage facilities.
Petroleum refining. Petroleum products, Gas industry
Sensors play a crucial role in various fields by enabling the detection and analysis of a wide range of substances, including hazardous substance detection, environmental and food safety monitoring, pharmaceutical industry, gas analysis, and others. Research continues to identify and develop sensor matrix materials that can increase the sensitivity, selectivity and responsiveness of sensors. Silica, an oxide mineral is a potential matrix material for sensor applications because of its unique characteristics. It has a large pore structure and modifiable pore size distribution. Silica’s stable chemical properties, high-temperature resistance and corrosion resistance make it an ideal matrix material for a wide range of sensor applications. In recent years, silica cellulose also become a potential material for sensor applications. Silica cellulose is produced by combining silica with cellulose components from natural materials, such as rice husk ash, bamboo leaf ash, rice straw ash, and other plant fibers. This article provides a comprehensive exploration of various methods of synthesis and characterization of silica and silica cellulose materials. The methods include sol-gel, acid leaching, alkaline extraction, and other techniques for extracting cellulose from natural sources. In addition, sensor applications that have been tested using this material are also discussed, including its use in detecting molecular compounds, food and environmental applications. The development of silica and silica cellulose materials based on natural materials is considered because of their sustainability. By continuing to explore the potential of these materials, it is hoped that it can make a significant contribution in the development of sensor technology that is more innovative, environmentally friendly and sustainable.
With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
J. Aaron Mendoza-Rodarte, Katarzyna Gas, Manuel Herrera-Zaldívar
et al.
Diluted magnetic semiconductors (DMS) have attracted significant attention for their potential in spintronic applications. Particularly, magnetically-doped GaN is highly attractive due to its high relevance for the CMOS industry and the possibility of developing advanced spintronic devices which are fully compatible with the current industrial procedures. Despite this interest, there remains a need to investigate the spintronic parameters that characterize interfaces within these systems. Here, we perform spin Hall magnetoresistance (SMR) measurements to evaluate the spin transfer at a Pt/(Ga,Mn)N interface. We determine the transparency of the interface through the estimation of the real part of the spin mixing conductance finding $G_r = 2.6\times 10^{14} \, Ω^{-1} m^{-2}$, comparable to state-of-the-art yttrium iron garnet (YIG)/Pt interfaces. Moreover, the magnetic ordering probed by SMR above the (Ga,Mn)N Curie temperature TC provides a broader temperature range for the efficient generation and detection of spin currents, relaxing the conditions for this material to be applied in new spintronic devices.
As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry.Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. Keywords: Robotics, Metaverse, Digital Twin, VR/AR, AI/ML, Foundation Model;
Davide Frizzo, Francesco Borsatti, Alessio Arcudi
et al.
Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.
Lawal Abubakar, Nor Azah Yusof, Abdul Halim Abdullah
et al.
Due to the release of hazardous heavy metals from various industries, water pollution has become one of the biggest challenges for environmental scientists today. Mercury Hg(II) is regarded as one of the most toxic heavy metals due to its ability to cause cancer and other health issues. In this study, a tailor-made modern eco-friendly molecularly imprinted polymer (MIP)/nanoporous carbon (NC) nanocomposite was synthesized and examined for the uptake of Hg(II) using an aqueous solution. The fabrication of the MIP/NC nanocomposite occurred via bulk polymerization involving the complexation of the template, followed by polymerization and, finally, template removal. Thus, the formed nanocomposite underwent characterizations that included morphological, thermal degradation, functional, and surface area analyses. The MIP/NC nanocomposite, with a high specific surface area of 884.9 m<sup>2</sup>/g, was evaluated for its efficacy towards the adsorptive elimination of Hg(II) against the pH solution changes, the dosage of adsorbent, initial concentration, and interaction time. The analysis showed that a maximum Hg(II) adsorption effectiveness of 116 mg/g was attained at pH 4, while the Freundlich model fitted the equilibrium sorption result and was aligned with pseudo-second-order kinetics. Likewise, thermodynamic parameters like enthalpy, entropy, and Gibbs free energy indicated that the adsorption was consistent with spontaneous, favorable, and endothermic reactions. Furthermore, the adsorption efficiency of MIP/NC was also evaluated against a real sample of condensate from the oil and gas industry, showing an 87.4% recovery of Hg(II). Finally, the synthesized MIP/NC showed promise as a selective adsorbent of Hg(II) in polluted environments, suggesting that a variety of combined absorbents of different precursors is recommended to evaluate heavy metal and pharmaceutical removals.