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Direct-fed microbials (DFMs) are feed additives containing live naturally existing microbes that can benefit animals’ health and production performance. Due to the banned or strictly limited prophylactic and growth promoting usage of antibiotics, DFMs have been considered as one of antimicrobial alternatives in livestock industry. Microorganisms used as DFMs for ruminants usually consist of bacteria including lactic acid producing bacteria, lactic acid utilizing bacteria and other bacterial groups, and fungi containing Saccharomyces and Aspergillus . To date, the available DFMs for ruminants have been largely based on their effects on improving the feed efficiency and ruminant productivity through enhancing the rumen function such as stabilizing ruminal pH, promoting ruminal fermentation and feed digestion. Recent research has shown emerging evidence that the DFMs may improve performance and health in young ruminants, however, these positive outcomes were not consistent among studies and the modes of action have not been clearly defined. This review summarizes the DFM studies conducted in ruminants in the last decade, aiming to provide the new knowledge on DFM supplementation strategies for various ruminant production stages, and to identify what are the potential barriers and challenges for current ruminant industry to adopt the DFMs. Overall literature research indicates that DFMs have the potential to mitigate ruminal acidosis, improve immune response and gut health, increase productivity (growth and milk production), and reduce methane emissions or fecal shedding of pathogens. More research is needed to explore the mode of action of specific DFMs in the gut of ruminants, and the optimal supplementation strategies to promote the development and efficiency of DFM products for ruminants.

I construct a daily business cycle index based on quarterly GDP growth and textual information contained in a daily business newspaper. The newspaper data are decomposed into time series representing news topics, while the business cycle index is estimated using the topics and a time-varying dynamic factor model where dynamic sparsity is enforced upon the factor loadings using a latent threshold mechanism. The resulting index classifies the phases of the business cycle with almost perfect accuracy and provides broad-based high-frequency information about the type of news that drive or reflect economic fluctuations. In out-of-sample nowcasting experiments, the model is competitive with forecast combination systems and expert judgment, and produces forecasts with predictive power for future revisions in GDP. Thus, news reduces noise. Supplementary materials for this article are available online.

The accurate detection of glucose in wound exudate is critically important for monitoring chronic wound healing. While photoelectrochemical (PEC) sensors are widely used, they often suffer from material degradation caused by redox reactions during operation. Herein, we introduce a dark‐field microscopy (DFM) setup integrated with a PEC system, creating a PEC‐DFM hybrid‐sensing platform. This system allows for conjoint measurement of photocurrent and monitoring of nanoparticle behavior via scattering spectroscopy. We further introduce Ag NP@Au NCs nanocomposites to drastically improve sensor performance, achieving a 193‐fold increase in signal‐to‐noise ratio. The PEC‐DFM hybrid‐sensing system guides the optimization of the nanocomposite's structure, enabling exclusion of damaged nanoparticles during measurement. Moreover, through continuous, long‐term (up to 1800 s) observation of scattering spectrum parameters, we decipher the photocurrent generation mechanism, confirming electron transfer from Au NCs to Ag NP inside single Ag NP@Au NCs nanocomposite. Finally, a non‐invasive sensing mechanism based on molecular‐level competition for glucose is developed, enabling reversible (20 rounds) and ultrasensitive detection of glucose with a detection limit of 0.49 p M . This work not only provides a powerful tool for wound management but also offers profound insights into the design of advanced hybrid sensing systems.

The design for manufacturing and assembly (DFMA) is a family of methods belonging to the design for X (DfX) category which goal is to optimize the manufacturing and assembly phase of products. DFMA methods have been developed at the beginning of the 1980s and widely used in both academia and industries since then. However, to the best of the authors’ knowledge, no systematic literature reviews or mapping has been proposed yet in the field of mechanical design. The goal of this paper is to provide a systematic review of DFMA methods applied to mechanical and electro-mechanical products with the aim to collect, analyse, and summarize the knowledge acquired until today and identify future research areas. The paper provides an overview of the DFMA topic in the last four decades (i.e., from 1980 to 2021) emphasizing operational perspectives such as the design phase in which methods are used, the type of products analysed, the adoption of quantitative or qualitative metrics, the tool adopted for the assessment, and the technologies involved. As a result, the paper addresses several aspects associated with the DFMA and different outcomes retrieved by the literature review have been highlighted. The first one concerns the fact that most of the DFMA methods have been used to analyse simple products made of few components (i.e., easy to manage with a short lead-time). Another important result is the lack of valuable DFMA methods applicable at early design phases (i.e., conceptual design) when information is not detailed and presents more qualitative than quantitative data. Both results lead to the evidence that the definition of a general DFMA method and metric adaptable for every type of product and/or design phase is a challenging goal that presents several issues. Finally, a bibliographic map was developed as a suitable tool to visualize results and identify future research trends on this topic. From the bibliometric analysis, it has been shown that the overall interest in DFMA methodologies decreased in the last decade.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal.

CO 2 based Enhanced Oil Recovery (EOR) in unconventional reservoirs is an emerging technology. Scientific research efforts are directed towards understanding the propagation of CO 2 front due to the complex interplay between CO 2 injection and saturation, and reservoir’s constitutive relationships. Conventional methods for characterising CO 2 -EOR rely on high-fidelity numerical solutions that often result in over or under prediction of CO 2 geosequestration. In this study, we develop a novel hybrid Computational Fluid Dynamics (CFD) and Machine Learning (ML) framework that allows for rapid CO 2 geosequestration prediction and its optimal injection. Very low or high injection rates have been shown to result in low sweep efficiency or excessive entry pressure, while an intermediate injection rate offers the best balance between the two. CFD data-driven Gaussian Process Regression (GPR) and Extreme Gradient Boosting (XGBoost) models have been developed, trained and tested for predicting CO 2 saturation in the reservoir. Comparative analysis indicates that GPR outperforms XGBoost in terms of its predictive performance and robustness. Through the analysis of layer-resolved CO 2 front displacement and development of data-driven surrogate models, this study contributes a novel framework for CO 2 -EOR predictive modelling and optimising injection strategies in naturally fractured reservoirs.

This paper aims to propose an Industry 4.0 implementation model relevant to the composite manufacturing industry and offer it to academia and manufacturing practice in order to aid successful change and adoption. The research scope is defined at an intersection of challenges within the composites industry, as well as Industry 4.0. A critical review of relevant papers was used to establish key trends and gaps in professional practice. Exposed challenges and opportunities were then synthesized to propose a conceptual framework for implementing Industry 4.0. Findings suggest that the predicted growth of the composites sector depends on the paradigm shift in manufacturing. Industry 4.0, including automation, and horizontally and vertically integrated business models are seen as enablers. However, the value proposition or organizational resistance in establishing such integration is not sufficiently addressed or understood by the industry. Achieving a successful design for manufacturing (DFM), or, more generally, design for excellence (DFX), is identified as the target performance objectives and key business process enablers used to introduce Industry 4.0 technology. The identified key gap in professional practice indicate the lack of a model used for structuring and implementing Industry 4.0 technology into composite businesses. The existence of an identified gap, evidenced by the lack of literature and available knowledge, reinforces the need for further research. To enable further research, and to facilitate the introduction of Industry 4.0 in composite manufacturing firms, a conceptual implementation framework based on the systems engineering V model is proposed. The paper concludes with topics for further investigation.

This paper presents a time-explicit, fully coupled, hydro-mechanical model to simulate the two-phase flow process in fractured porous media, considering the geomechanical effect. Two solvers are developed, and mutual hydro/mechanical interactions are considered: (i) a novel finite volume discrete fracture–matrix model (FV-DFM) for two-phase flow, through both pores and fractures; and (ii) a combined finite-discrete element method (FDEM) for mechanical responses (e.g., deformation and fracturing). Particularly, a novel two-phase exchange flow model is applied at the matrix–fracture interface, which overcomes the difficulty in realistically capturing the discontinuity (e.g., pressure, saturation, and normal flux) across fractures. Meanwhile, non-uniform time steps of fracture and matrix flow are adopted to improve computational efficiency while maintaining accuracy. The performance of the proposed model is validated against existing results and applied to a practical waterflooding process considering fracture propagation. Results show that the model can well predict the two-phase flow process (e.g., pressure/saturation field, reservoir recovery) in fractured reservoirs, and reveal the important HM coupled effect on the flow process (e.g., the stress-dependent permeability and fracture propagation), with important implication for hydrocarbon reservoirs and CO2 geological storage.

This study investigates how the well-documented link between economic activity and carbon dioxide emissions, identified in previous research in energy and environmental economics, can be used to improve nowcasting carbon dioxide emissions in a data-rich environment. We compare classic and recent improvements in dynamic factor models with linear and nonlinear machine learning algorithms that have been shown to be effective in previous research. These machine learning algorithms are implemented in the mixed data sampling framework. The recent improvements in dynamic factor models include the use of nondifferenced data, which has increased prediction accuracy, especially during economically volatile periods. Additionally, there are structurally augmented dynamic factor models, which combine machine learning methods with dynamic factor models. For the structurally augmented models, we use machine learning algorithms to select the most important variables, which then augment the dynamic factor models. Our findings indicate that dynamic factor models outperform alternative approaches for carbon dioxide emission nowcasting. Specifically, models based on nondifferenced data demonstrate superior predictive ability with principal component extraction, whereas models using differenced data yield better results with Kalman filter extraction. These findings are essential for developing effective nowcasting models that enable timely emission assessments, which are critical for advancing ambitious climate policies. Our research, therefore, contributes to the ongoing discourse on the challenges of sustainable development by employing econometric models for the dynamic links between economic activities and environmental outcomes.

Previous studies of direct-fed microbial (DFM) supplements showed variable effects on the microbiota and physiology of dairy cows. The main aims of this study were to investigate the milk microbiota of cows supplemented with a lactobacilli-based DFM compared to untreated cows; describe the changes; and quantify the association between the taxa and cow productivity. The study followed seventy-five Holstein–Friesian dairy cows supplemented with a DFM over 16 months compared to seventy-five non-supplemented cows. Twenty-five cows from each group were sampled for microbiota analysis. The top taxa significantly associated with the variables were as follows: Age (Mammaliicoccus_319276, Turicibacter), milk production (Turicibacter, Bifidobacterium_388775), DIM (Stenotrophomonas_A_615274, Pedobacter_887417), milk fat percentage (Pseudomonas_E_647464, Lactobacillus), calendar month (Jeotgalicoccus_A_310962, Planococcus), milk protein percentage (Tistrella, Pseudomonas_E_650325), experimental group (Enterococcus_B, Aeromonas), SCC (Paenochrobactrum, Pseudochrobactrum), and trimester of pregnancy (Dyadobacter_906144, VFJN01 (Acidimicrobiales)). These were identified using multivariable analysis. Twenty-six genera were associated with the differences between experimental groups, including Pseudomonas, Lactococcus and Staphylococcus. Microbial taxa that changed in relative abundance over time included Atopostipes, Brevibacterium and Succinivibrio. Many of these genera were also part of the core microbiota. Supplementation with the DFM significantly altered the milk microbiota composition in the dairy cows, highlighting the impact of long-term DFM supplementation on microbial communities.