bluuebunny/crossref_metadata_2025_split huggingface dataset:
String (Gravi)photons, “Dark Brane Photons”, Holography and the Hypercharge Portal
Authors: Anastasopoulos P. , Bianchi M. , Consoli D. , Kiritsis E. 
Published: May 2021

AbstractThe mixing of graviphotons and dark brane photons to the Standard Model hypercharge is analyzed in full generality, in weakly‐coupled string theory. Both the direct mixing as well as effective terms that provide mixing after inclusion of SM corrections are estimated to lowest order. The results are compared with Effective Field Theory (EFT) couplings, originating in a hidden large‐N theory coupled to the SM where the dark photons are composite. The string theory mixing terms are typically subleading compared with the generic EFT couplings. The case where the hidden theory is a holographic theory is also analyzed, providing also suppressed mixing terms to the SM hypercharge.
Orientations of hexagonal chains with extremal values of the Randić index
Authors: Bermudo Sergio , Monsalve Juan , Rada Juan 
Published: May 2021

AbstractLet D be a digraph with set of vertices V(D) and set of arcs A(D). Recently, the Randić index of D was extended to digraphs as
urn:x-wiley:00207608:media:qua26744:qua26744-math-0001
where and are the out‐degree and in‐degree of u and v, respectively. In this paper we study the extremal values of R over the set of all orientations of hexagonal chains with k hexagons.
Consensus‐based unscented Kalman filtering over sensor networks with communication protocols
Authors: Sheng Li , Huai Wuxiang , Niu Yichun , Gao Ming 
Published: May 2021

AbstractThis article is concerned with the consensus‐based distributed filtering problem for a class of general nonlinear systems over sensor networks with communication protocols. In order to avoid data collisions, the stochastic protocol and the Round‐Robin protocol are respectively introduced to schedule the data transmission between each node and its neighboring ones. A consensus‐based unscented Kalman filtering (UKF) algorithm is developed for the purpose of estimating the system states over sensor networks subject to communication protocols. Moreover, the exponential boundedness of estimation error in mean square is proved for the proposed algorithm. Finally, compared with the extended Kalman filtering, an experimental simulation example is provided to validate the effectiveness of the consensus‐based UKF algorithm.
Inspired by Vitamin a for Anti-Ageing: Searching for Plant-Derived Functional Retinoid Analogues
Authors: Sadgrove N. J. , Oblong J. E. , Simmonds M. S. J. 
Published: May 2021

Abstract

Background
Cosmetic treatments that inspire one's appearance to resemble their younger portrait often utilize ingredients that confer acute effects, particularly hydration by creating hydrophobic barriers or transient elevation of barrier water content. But superior therapies successfully promote morphogenesis of the dermal-epidermal junction, inspiring extracellular matrix (ECM) formation. This can be achieved by agonism of the very well-known retinoid nuclear receptors using the endogenous ligand all-trans retinoic acid (tRA), tRA precursors or plant-based functional analogues, with reduced side effects.

Aims, Materials and Methods
While there are already many promising cosmetic ingredients available from the world's flora, higher potency is favoured, so increasing known candidates is a worth undertaking. Functional analogues of retinoic acid can be identified by culturing fibroblasts with lipophilic candidates from the plant kingdom and assessing gene-arrays. Modern approaches to validating these findings include the coculturing of fibroblasts with keratinocytes as a measure to predict the potential effects of crosstalk.

Results and Discussion
In this regard, the most promising plant-derived candidates are of terpene or meroterpene origin, including derivatives of squalene and phytol. Surprisingly pimaric or abietic acids and labdane diterpenes are also noteworthy agonists of the retinoic acid receptor, stimulating collagen expression in dermal fibroblasts.

Conclusion
There are numerous derivatives of these terpenes available from the world's flora and research conducted thus far encourages further screening of these chemical candidates.
Preparation of All‐Cellulose Composites Based on Controlled Dissolution Procedure
Authors: Zhang Qian , Wang Zhaoqi , Hao Sufen , Huang Jintian 
Published: May 2021

AbstractIn this study, all‐cellulose composite materials are prepared through the dissolution and regeneration process using waste kraft papers as raw material in the sodium hydroxide/thiourea/urea solvent system. The degree of conversion is controlled by adjusting the microenvironment of the cellulose solvents, and the composite membrane is prepared by mixing the regenerated cellulose with the insoluble cellulose, which aims at adjusting the morphology and mechanical properties of the composite material. The shape of the regenerated cellulose after the dissolution and regeneration change from the original fiber into a sphere as shown by SEM images. The composite films show different phenomena, the insoluble part, and the matrix phase form a dense and strong network structure. The presence of this insoluble part increases the strength of the matrix phase and fundamentally changes the brittleness of the regenerated cellulose. This work provides a green and promising method to prepare high‐performance, environmentally friendly all‐cellulose composite materials.
Progress in the sustainable recycling of spent lithium‐ion batteries
Authors: Fan Min , Chang Xin , Meng Qinghai , Wan Li‐Jun , Guo Yu‐Guo 
Published: May 2021

AbstractLithium‐ion batteries (LIBs) are booming in multiple fields due to a rapid development in the last decade. However, limited by operational lifespans, a growing number of spent LIBs reaching the end of their lives are consequently faced with serious accumulation and descended to hazardous waste. Without proper disposal, the spent LIBs will inevitably cause negative influence on the ecology and undermine the sustainable manufacture of LIBs. The initial research of recycling strategies mainly focused on the optimization of metallurgical processes. Recently, the sustainability of the recycling process has attracted much more attention and become an important factor. Here, we summarize the recent progress of the spent LIBs recycling from a sustainable perspective, especially discussing the green innovations in recycling strategies for spent LIBs. Through this article, we expect to reveal the challenges and developing tendency of the recycling strategies and provide a guideline for future researches on processing spent LIBs and beyond, like the recycling of the solid‐state lithium metal batteries.
Displaced new physics at colliders and the early universe before its first second
Authors: Calibbi Lorenzo , D’Eramo Francesco , Junius Sam , Lopez-Honorez Laura , Mariotti Alberto 
Published: May 2021

Abstract
Displaced vertices at colliders, arising from the production and decay of long-lived particles, probe dark matter candidates produced via freeze-in. If one assumes a standard cosmological history, these decays happen inside the detector only if the dark matter is very light because of the relic density constraint. Here, we argue how displaced events could very well point to freeze-in within a non-standard early universe history. Focusing on the cosmology of inflationary reheating, we explore the interplay between the reheating temperature and collider signatures for minimal freeze-in scenarios. Observing displaced events at the LHC would allow to set an upper bound on the reheating temperature and, in general, to gather indirect information on the early history of the universe.
Remarks on holographic models of the Kerr-AdS5 geometry
Authors: Amado Julián Barragán , da Cunha Bruno Carneiro , Pallante Elisabetta 
Published: May 2021

AbstractWe study the low-temperature limit of scalar perturbations of the Kerr-AdS5black-hole for generic rotational parameters. We motivate the study by considering real-time holography of small black hole backgrounds. Using the isomonodromic technique, we show that corrections to the extremal limit can be encoded in the monodromy parameters of the Painlevé V transcendent, whose expansion is given in terms of irregular chiral conformal blocks. After discussing the contribution of the intermediate states to the quasinormal modes, we perform a numerical analysis of the low-lying frequencies. We find that the fundamental mode is perturbatively stable at low temperatures for small black holes and that excited perturbations are superradiant, as expected from thermodynamical considerations. We close by considering the holographic interpretation of the unstable modes and the decaying process.
A model-driven approach for engineering customizable MES with the application to the food and beverage industry
Authors: Chen Xinyu , Nophut Christoph , Voigt Tobias 
Published: May 2021

AbstractThe Manufacturing Execution System (MES) is a process-oriented IT solution collecting and managing information from the shop floor manufacturing processes. Because of the programming and customization effort required for specific production processes, the MES implementation is not widespread in the food and beverage industry, as most food and beverage manufacturers are small- and medium-sized enterprises with limited resources to invest in MES. For engineering the MES that should be customized according to the target production processes, a model-driven approach has been presented in this paper with six phases covering the entire lifecycle of the MES engineering process. By using this approach, MES can be automatically generated and sustainably improved, which has the potential to reduce the complexity of implementation as well as the resources required for the engineering of customizable MES. Based on two use cases in the processing and packaging areas in the food and beverage industry, the feasibility and practicality of the presented approach have been proven.
Bayesian graphical models for modern biological applications
Authors: Ni Yang , Baladandayuthapani Veerabhadran , Vannucci Marina , Stingo Francesco C. 
Published: May 2021

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.
Implicit incentives for fund managers with partial information
Authors: Angelini Flavio , Colaneri Katia , Herzel Stefano , Nicolosi Marco 
Published: May 2021

AbstractWe study the optimal asset allocation problem for a fund manager whose compensation depends on the performance of her portfolio with respect to a benchmark. The objective of the manager is to maximise the expected utility of her final wealth. The manager observes the prices but not the values of the market price of risk that drives the expected returns. Estimates of the market price of risk get more precise as more observations are available. We formulate the problem as an optimization under partial information. The particular structure of the incentives makes the objective function not concave. Therefore, we solve the problem by combining the martingale method and a concavification procedure and we obtain the optimal wealth and the investment strategy. A numerical example shows the effect of learning on the optimal strategy.
Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability
Authors: Peres David J. , Cancelliere Antonino 
Published: May 2021

AbstractRainfall intensity-duration landslide-triggering thresholds have become widespread for the development of landslide early warning systems. Thresholds can be in principle determined using rainfall event datasets of three types: (a) rainfall events associated with landslides (triggering rainfall) only, (b) rainfall events not associated with landslides (non-triggering rainfall) only, (c) both triggering and non-triggering rainfall. In this paper, through Monte Carlo simulation, we compare these three possible approaches based on the following statistical properties: robustness, sampling variation, and performance. It is found that methods based only on triggering rainfall can be the worst with respect to those three investigated properties. Methods based on both triggering and non-triggering rainfall perform the best, as they could be built to provide the best trade-off between correct and wrong predictions; they are also robust, but still require a quite large sample to sufficiently limit the sampling variation of the threshold parameters. On the other side, methods based on non-triggering rainfall only, which are mostly overlooked in the literature, imply good robustness and low sampling variation, and performances that can often be acceptable and better than thresholds derived from only triggering events. To use solely triggering rainfall—which is the most common practice in the literature—yields to thresholds with the worse statistical properties, except when there is a clear separation between triggering and non-triggering events. Based on these results, it can be stated that methods based only on non-triggering rainfall deserve wider attention. Methods for threshold identification based on only non-triggering rainfall may have the practical advantage that can be in principle used where limited information on landslide occurrence is available (newly instrumented areas). The fact that relatively large samples (about 200 landslides events) are needed for a sufficiently precise estimation of threshold parameters when using triggering rainfall suggests that threshold determination in future applications may start from identifying thresholds from non-triggering events only, and then move to methods considering also the triggering events as landslide information starts to become more available.
“It Happens, But I’m Not There”: On the Phenomenology of Childbirth
Authors: Trigg Dylan 
Published: May 2021

AbstractPhenomenologically grounded research on pregnancy is a thriving area of activity in feminist studies and related disciplines. But what has been largely omitted in this area of research is the experience of childbirth itself. This paper proposes a phenomenological analysis of childbirth inspired by the work of Merleau-Ponty. The paper proceeds from the conviction that the concept of anonymity can play a critical role in explicating the affective structure of childbirth. This is evident in at least two respects. First, the concept of anonymity gives structural specificity to the different levels of bodily existence at work in childbirth. Second, the concept of anonymity can play a powerful explanatory role in accounting for the sense of strangeness accompanying childbirth. To flesh these ideas out, I focus on two attributes of birth, sourced from first-person narratives of childbirth. The first aspect concerns the sense of leaving one’s body behind during childbirth while the second aspect concerns the sense of strangeness accompanying the first encounter with the baby upon successful delivery. I take both of these aspects of childbirth seriously, treating them as being instructive not only of the uniqueness of childbirth but also revealing something important about bodily life more generally. Accordingly, the paper unfolds in three stages. First, I will critically explore the concept of anonymity in Merleau-Ponty; second, I will apply this concept to childbirth; finally, I will provide an outline of how childbirth sheds light on the broader nature of bodily life.
Metabolomics: towards acceleration of antibacterial plant-based leads discovery
Authors: Sieniawska Elwira , Georgiev Milen I. 
Published: May 2021

AbstractThe wide and not yet fully uncovered potential of plant secondary metabolites make plants a rich source of drug leads. Metabolomics enables the study of the metabolic perturbations taking place in bacterial cells under the influence of plant-based bioactive molecules. It reveals the changes in metabolic pathways within bacteria, reflecting the reprogramming of the biochemical networks. From this point of view, metabolomics is valuable in understanding the alteration of cell functions when bacteria are subjected to metabolic stress caused by treatment with secondary metabolites, that inhibit their growth. In this review the application of metabolomics in revealing bacteria response to plant-derived secondary metabolites is presented. Metabolomics may be a way to select antibacterial plant-based bioactive secondary metabolites and to understand their mode of action. Therefore, herein the usefulness of metabolomic approach in screening for antimicrobials from plants, as well as in exploring the target points in bacteria metabolism and in uncovering the mechanisms of bacteria adaptation and resistance to natural antimicrobials are discussed. Basic chemometrics and molecular networking are successfully applied for the identification of antimicrobial molecules in complex plant mixtures. Determination of antibacterial modes of action is done through classification strategy, pathway analysis and integration of transcriptomics, genomics and metabolomics, whereas, comparative metabolomics and integrative approach is useful in revealing the bacterial mechanisms of resistance.
Economics of autonomous equipment for arable farms
Authors: Lowenberg-DeBoer James , Franklin Kit , Behrendt Karl , Godwin Richard 
Published: May 2021

AbstractBy collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.
Causal effect of renal function on venous thromboembolism: a two-sample Mendelian randomization investigation
Authors: Yuan Shuai , Bruzelius Maria , Larsson Susanna C. 
Published: May 2021

AbstractWhether renal function is causally associated with venous thromboembolism (VTE) is not yet fully elucidated. We conducted a two-sample Mendelian randomization (MR) study to determine the causal effect of renal function, measured as estimated glomerular filtration rate (eGFR), on VTE. Single-nucleotide polymorphisms associated with eGFR were selected as instrumental variables at the genome-wide significance level (p < 5 × 10−8) from a meta-analysis of 122 genome-wide association studies including up to 1,046,070 individuals. Summary-level data for VTE were obtained from the FinnGen consortium (6913 VTE cases and 169,986 non-cases) and UK Biobank study (4620 VTE cases and 356,574 non-cases). MR estimates were calculated using the random-effects inverse-variance weighted method and combined using fixed-effects meta-analysis. Genetically predicted decreased eGFR was significantly associated with an increased risk of VTE in both FinnGen and UK Biobank. For one-unit decrease in log-transformed eGFR, the odds ratios of VTE were 2.93 (95% confidence interval (CI) 1.25, 6.84) and 4.46 (95% CI 1.59, 12.5) when using data from FinnGen and UK Biobank, respectively. The combined odds ratio was 3.47 (95% CI 1.80, 6.68). Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. This MR-study supported a casual role of impaired renal function in VTE.
Predicting COVID-19 cases using bidirectional LSTM on multivariate time series
Authors: Said Ahmed Ben , Erradi Abdelkarim , Aly Hussein Ahmed , Mohamed Abdelmonem 
Published: May 2021

AbstractTo assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.