bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling induced innovation for the low-carbon energy transition: a menu of options
Authors: Pasqualino Roberto , Peñasco Cristina , Barbrook-Johnson Peter , De Moura Fernanda Senra , Kolesnikov Sergey , Hafner Sarah , Nijsse Femke J M M , Lamperti Francesco , Hinder Ben , Melekh Yaroslav , Sharpe Simon , Jones Aled W , Anadón Laura Díaz , Lenton Timothy M , Grubb Michael 
Published: May 2024

Abstract
Induced innovation is a multi-faceted process characterized by interaction between demand-pull forces, path-dependent self-reinforcing change, and the cost reduction of technology that occurs with cumulative deployment. By endogenously including induced innovation in energy models, policy analysts and modellers could enable a mission-oriented approach to policymaking that envisions the opportunities of accelerating the low-carbon energy transition while avoiding the risks of inaction. While the integrated assessment models used in the intergovernmental panel on climate change (IPCC-IAMs) account for induced innovation, their assumptions of general equilibrium and optimality may reveal weaknesses that produce unsatisfactory results for policymakers. In this paper, we develop a menu of options for modelling induced innovation in the energy transition with non-equilibrium, non-optimal models by a three step methodology: a modelling survey questionnaire, a review of the literature, and an analysis of case studies from modelling applications within the economics of energy innovation and system transition (EEIST) programme. The survey questionnaire allows us to compare 24 models from EEIST partner institutions developed to inform energy and decarbonisation policy decisions. We find that only six models, future technological transformations, green investment barriers mode, stochastic experience curves, economy-energy-environment macro-econometric, M3E3 and Dystopian Schumpeter meeting Keynes, represent endogenous innovation—in the form of learning curves, R&D, and spillover effects. The review of the literature and analysis of case studies allow us to form a typology of different models of induced innovation alongside the IPCC-IAMs and develop a decision tree to guide policy analysts and modellers in the choice of the most appropriate models to answer specific policy questions. The paper provides evidence for integrating narrow and systemic approaches to modelling-induced innovation in the context of low-carbon energy transition, and promotes cooperation instead of competition between different but complementary approaches. These findings are consistent with the implementation of risk-opportunity analysis as a policy appraisal method to evaluate low-carbon transition pathways.
Environmental impacts from large-scale offshore renewable-energy deployment
Authors: Ouro Pablo , Fernandez Riya , Armstrong Alona , Brooks Barbara , Burton Ralph R , Folkard Andrew , Ilic Suzana , Parkes Ben , Schultz David M , Stallard Tim , Watson Francis M 
Published: May 2024

AbstractThe urgency to mitigate the effects of climate change necessitates an unprecedented global deployment of offshore renewable-energy technologies mainly including offshore wind, tidal stream, wave energy, and floating solar photovoltaic. To achieve the global energy demand for terawatt-hours, the infrastructure for such technologies will require a large spatial footprint. Accommodating this footprint will require rapid landscape evolution, ideally within two decades. For instance, the United Kingdom has committed to deploying 50 GW of offshore wind by 2030 with 90–110 GW by 2050, which is equivalent to four times and ten times more than the 2022 capacity, respectively. If all were 15 MW turbines spaced 1.5 km apart, 50 GW would require 7500 km2and 110 GW would require 16 500 km2. This review paper aims to anticipate environmental impacts stemming from the large-scale deployment of offshore renewable energy. These impacts have been categorised into three broad types based on the region (i.e. atmospheric, hydrodynamic, ecological). We synthesise our results into a table classifying whether the impacts are positive, negative, negligible, or unknown; whether the impact is instantaneous or lagged over time; and whether the impacts occur when the offshore infrastructure is being constructed, operating or during decommissioning. Our table benefits those studying the marine ecosystem before any project is installed to help assess the baseline characteristics to be considered in order to identify and then quantify possible future impacts.
p

-brane Galilean and Carrollian geometries and gravities
Authors: Bergshoeff E A , Figueroa-O’Farrill J M , van Helden K S , Rosseel J , Rotko I J , ter Veldhuis T 
Published: May 2024

Abstract
We study D-dimensional p-brane Galilean geometries via the intrinsic torsion of the adapted connections of their degenerate metric structure. These non-Lorentzian geometries are examples of G-structures whose characteristic tensors consist of two degenerate ‘metrics’ of ranks

(
p
+
1
)

and

(
D
−
p
−
1
)

. We carry out the analysis in two different ways. In one way, inspired by Cartan geometry, we analyse in detail the space of intrinsic torsions (technically, the cokernel of a Spencer differential) as a representation of G, exhibiting for generic (p, D) five classes of such geometries, which we then proceed to interpret geometrically. We show how to re-interpret this classification in terms of (

D
−
p
−
2

)-brane Carrollian geometries. The same result is recovered by methods inspired by similar results in the physics literature: namely by studying how far an adapted connection can be determined by the characteristic tensors and by studying which components of the torsion tensor do not depend on the connection. As an application, we derive a gravity theory with underlying p-brane Galilean geometry as a non-relativistic limit of Einstein–Hilbert gravity and discuss how it gives a gravitational realisation of some of the intrinsic torsion constraints found in this paper. Our results also have implications for gravity theories with an underlying (

D
−
p
−
2

)-brane Carrollian geometry.
Asymptotic analysis of fundamental solution of multi-dimensional distributed-order time-fractional diffusion equation with unit density function
Authors: Hashemzadeh Kalvari Arman , Ansari Alireza , Askari Hassan 
Published: May 2024

Abstract
In this paper, we consider the multi-dimensional distributed-order time-fractional diffusion equation with the unit density function. We introduce the new Volterra–Bessel function and give the integral representations of fundamental solutions of equations in terms of this function in the whole- and half-space. The fractional moments of fundamental solutions are also provided in the higher dimensions using the Mellin transforms. We further apply steepest descent method to find the asymptotic behaviors of solutions using the Schläfli integral of the Volterra–Bessel function. In this respect, we study the asymptotic analysis of the Volterra–Bessel function with the large parameters, and subsequently obtain the asymptotic behaviors of fundamental solutions with a discussion on the large space variable, large time variable, higher dimensions and small diffusivity constant.
Traffic modeling and accidental data analysis using GIS: A Review
Authors: Nayak Animesh , Goyal Kirti 
Published: May 2024

Abstract
Nowadays, congestion and accidents are creating major risks to cities, including delays, higher fuel usage, and compromised safety. Effective traffic modelling and accident data analysis are critical for identifying high risk identifying accident-prone locations, understanding the causes of accidents and creating focused actions to enhance traffic flow and safety. GIS is an effective tool for integrating, analysing and visualizing different geographical data relevant to transportation networks such as, traffic flow, infrastructure, and safety. It enables geographical analysis and visualization of accident hotspots by integrating accident data, road conditions, traffic numbers, and environmental factors. The use of GIS in traffic modelling and accident data analysis provides considerable benefits in urban transportation planning and management. The aim of the paper is to provide an overview of the application of GIS in traffic modelling and accidental data analysis, highlighting the methodologies, advancements, and challenges in this field. The review shall provide a comprehensive assessment of the current state of traffic modelling and accidental data analysis using GIS. It will highlight the significant contributions of GIS technology, identify key research gaps, and offer insights into future directions for enhancing transportation planning and decision-making processes.
Effect of Al7075 and activated carbon reinforced composite on optimizing WEDM responses
Authors: Ramanan G , R R Neela Rajan , Suresh S , Cho Jaehyuk , Sathishkumar V E 
Published: May 2024

Abstract
This paper presents wire cut electrical discharge machining (WEDM) response characteristics of Aluminium 7075 (Al7075) reinforced with powdered activated carbon (PAC) composite. In recent days WEDM has become a significant machining process in targeting its benefits of contributing improved material removal rate (MRR) and low surface roughness (SR). This is due the rising need for intricate, accurate, and superior structural components, the WEDM process emerges as a formidable alternative to traditional machine tools. In this work Pulse-on time (Ton), pulse-off time (Toff), discharge current (IA) and servo speed rate (SS) are the variables to be given as input and machining responses such as MRR and SR are studied. From Analysis of Variance (ANOVA) study it is found that discharge current and servo speed is the significant parameters. The optimal desirability condition is obtained with input parameters Ip: 2000 mA; Ton: 8.9 μs; Toff: 25 μs and SS: 150 rpm for the precision machining. The optimum response parameters are found as MRR 10.46 mm3/min and SR 3.32 μm. Results shows that the model designed for the prediction of MRR produces an above 98.27% and the prediction of SR is above 97.17%. The error percentage among the experimental and predicted MRR and SR were estimated. Additionally confirmatory test is performed with optimal results achieved from response surface methodology (RSM) and desirability technique. Metallurgical tests like electron backscatter diffraction analysis (EBSD) and microstructure are conducted to confirm the surface properties and atomic force morphology (AFM) analysis is applied to clarify the structural features of machined composites. The results revealed that the variation of hard deflection is caused by depression of eroded materials on the top layers of machined surface.
Controlling the interactions in a cold atom quantum impurity system
Authors: Hewitt Thomas , Bertheas Tom , Jain Manan , Nishida Yusuke , Barontini Giovanni 
Published: May 2024

Abstract
We implement an experimental architecture in which a single atom of K is trapped in an optical tweezer, and is immersed in a bath of Rb atoms at ultralow temperatures. In this regime, the motion of the single trapped atom is confined to the lowest quantum vibrational levels. This realizes an elementary and fully controllable quantum impurity system. For the trapping of the K atom, we use a species-selective dipole potential, that allows us to independently manipulate the quantum impurity and the bath. We concentrate on the characterization and control of the interactions between the two subsystems. To this end, we perform Feshbach spectroscopy, detecting several inter-dimensional confinement-induced Feshbach resonances for the KRb interspecies scattering length, that parametrizes the strength of the interactions. We compare our data to a theory for inter-dimensional scattering, finding good agreement. Notably, we also detect a series of p-wave resonances stemming from the underlying free-space s-wave interactions. We further determine how the resonances behave as the temperature of the bath and the dimensionality of the interactions change. Additionally, we are able to screen the quantum impurity from the bath by finely tuning the wavelength of the light that produces the optical tweezer, providing us with a new effective tool to control and minimize the interactions. Our results open a range of new possibilities in quantum simulations of quantum impurity models, quantum information, and quantum thermodynamics, where the interactions between a quantized system and the bath is a powerful yet largely underutilized resource.
Polyvinyl alcohol (PVA)-based films: insights from crosslinking and plasticizer incorporation
Authors: Chousidis Nikolaos 
Published: May 2024

Abstract
The properties of polyvinyl alcohol (PVA) films are intricately influenced by factors such as polymer structure, fabrication method, the addition of plasticizers and the molecular weight of monomers. This research, investigates the implication of PVA films using a solution casting method for crosslinking with boric acid (H3BO4), glycerol (C3H8O3) and citric acid (C6H8O7). This approach is compared with pure PVA films, establishing a valuable benchmark. For the experiments, tensile strength tests, physicochemical property measurements, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses were conducted to gain insights into the microstructure, surface characteristics and mineral composition of the films. This comprehensive approach aims to enhance our understanding of the intricate relationship between PVA, plasticizers and crosslinking agents, providing valuable insights for applications across diverse industries, including, construction and biomedical fields. The overarching objective of this research is to revolutionize the construction industry by developing polymer films that serve as the foundation for self-healing materials, fostering durability and innovation. The experiments revealed a significant influence of crosslinking agents on the properties of PVA films as measured.
A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles
Authors: Mary Priya , Mujeeb A 
Published: May 2024

Abstract
The biocompatibility property has made silver nanoparticles powerful candidates for various nanomedical applications. Research interest in silver nanoparticles as a viable alternative to antibiotics is gaining more attention due to their enhanced antimicrobial activity, better antibacterial activity and low cytotoxicity. Machine Learning (ML) has become a state-of-the-art analytic and modelling tool in recent times, due to its prediction capabilities and increased accuracy of the results. In this work, we present machine-learning techniques to predict the antibacterial capacity of silver nanoparticles and extended the work on antifungal studies. In the first phase, we reviewed 50 articles and collected data points for training the model, which consists of features such as core size, shape of the nanoparticle, dosage, bacteria/fungi species and zone of inhibition (ZOI). Then, we trained the data using eight different machine-learning regression algorithms and validated the models’ performance using four metrics such as RMSE, MSE, MAE and R2. Furthermore, the importance of features used in the prediction models has been evaluated. The feature importance revealed that the core size of silver nanoparticles is the prominent feature in the prediction of the antibacterial capacity. The optimum model for the prediction of antibacterial and antifungal activity has been identified. Finally, the model’s validation has also been demonstrated. This work enables researchers to utilize Machine Learning which in turn can address the challenges of time consumption, and cost in laboratory experiments while minimising the reliance on trial and error.
Transcriptome sequencing data provide a solid base to understand the phylogenetic relationships, biogeography and reticulated evolution of the genus Zamia L. (Cycadales: Zamiaceae)
Authors: Lindstrom Anders , Habib Sadaf , Dong Shanshan , Gong Yiqing , Liu Jian , Calonje Michael , Stevenson Dennis , Zhang Shouzhou 
Published: May 2024

Abstract

Background and Aims
Cycads are a key lineage to understand the early evolution of seed plants and their response to past environmental changes. However, tracing the evolutionary trajectory of cycad species is challenging when the robust relationships at inter- or infrageneric level are not well resolved.

Methods
Here, using 2901 single-copy nuclear genes, we explored the species relationships and gene flow within the second largest genus of cycads, i.e. Zamia, based on phylotranscriptomic analyses of 90 % extant Zamia species. Based on a well-resolved phylogenetic framework, we performed gene flow analyses, molecular dating and biogeographical reconstruction to examine the spatiotemporal evolution of Zamia. We also performed ancestral state reconstruction of a total of 62 traits of the genus to comprehensively investigate its morphological evolution.

Key Results
Zamia comprises seven major clades corresponding to seven distinct distribution areas in the Americas, with at least three reticulation nodes revealed in this genus. Extant lineages of Zamia initially diversified around 18.4–32.6 (29.14) million years ago in Mega-Mexico, and then expanded eastward into the Caribbean and southward into Central and South America. Ancestral state reconstruction revealed homoplasy in most of the morphological characters.

Conclusions
This study revealed congruent phylogenetic relationships from comparative methods/datasets, with some conflicts being the result of incomplete lineage sorting and ancient/recent hybridization events. The strong association between the clades and the biogeographic areas suggested that ancient dispersal events shaped the modern distribution pattern, and regional climatic factors may have resulted in the following in situ diversification. Climate cooling starting during the mid-Miocene is associated with the global expansion of Zamia to tropical South America that has dramatically driven lineage diversification in the New World flora, as well as the extinction of cycad species in the nowadays cooler regions of both hemispheres, as indicated by the fossil records.
Revision Breast Reconstruction With Biologic or Synthetic Mesh: An Analysis of Postoperative Capsular Contracture Rates
Authors: Bai Jennifer , Ferenz Sarah , Fracol Megan , Kim John Y 
Published: May 2024

Abstract

Background
Both biologic and synthetic mesh have been found to reduce the risk of capsular contracture, yet there is limited data assessing the use of these scaffold materials in revision breast reconstruction.

Objectives
This investigation sought to assess the ability of either biologic or synthetic mesh to prevent capsular contracture in the revision breast reconstruction population.

Methods
A retrospective chart review was conducted of implant-based revision reconstructions performed by the senior author between 2008 and 2023. Patient demographics and outcomes were assessed, including the incidence of Baker Grade III or IV capsular contractures. Results were compared between biologic and synthetic mesh groups using univariate and multivariate analysis.

Results
Ninety-five breasts underwent revision reconstruction with 90 (94.7%) for correction of malposition, 4 (4.2%) for size change, and 1 (1.1%) for revision after additional oncologic breast surgery. Of these breasts, 26 (27.4%) used biologic mesh and 69 (72.6%) used synthetic mesh. Capsular contracture occurred in 1 (3.8%) biologic mesh breast and 4 (5.8%) synthetic mesh breasts. There was no significant difference in the incidence of capsular contracture between the 2 groups (P = 1.000). None of the recorded demographics were risk factors for capsular contracture, including the use of biologic or synthetic mesh (P = .801).

Conclusions
Both biologic and synthetic mesh are successful at preventing capsular contracture in patients undergoing implant-based revision reconstruction. This adds to the growing evidence that both scaffold materials can be used in complex revision breast reconstruction to aid in preventing capsular contracture.

Level of Evidence: 4
Co-Delivery of Recombinant Super-Affinity VEGF and PDGF-BB Accelerates Diabetic Wound Healing by Improving Angiogenesis, Arteriogenesis and Blood Flow
Authors: D’amico R , Bicker P , Uccelli A , Hubbell J , Gürke L , Wolff T , Mujagic E , Gianni Barrera R , Banfi A 
Published: May 2024

Abstract

Background
Diabetic ulcers are characterized by local ischemia, usually without macroangiopathy. We have previously found that the combination of VEGF and PDGF-BB in a fibrin matrix is effective in stimulating arterial growth to diabetic wounds.

Aims
We developed modified forms of VEGF and PDGF-BB to bear super-affinity for the extracellular matrix (SA-VP), in order to decorate the wound tissue without the need for a biomaterial. We investigated the dose-dependent effects on local blood flow and wound healing, by applying the treatment either to the healthy tissue around the wound or to the damaged wound bed.

Methods
Full thickness wounds were created on the dorsal skin of diabetic (db/db) mice. SA-VP or saline were applied to the wound bed or divided into 4 injections around the wound edge. Wound closure and blood flow were measured on days 4 and 8 after treatment and tissues were harvested for histology on day 8. Data were analysed by Anova with Tukey’s post-hoc test for multiple comparisons.

Results
Wound healing was improved dose-dependently by SA-VP by both delivery routes. Histologically, angiogenic areas and total amount of angiogenesis were significantly increased by the highest SA-VP dose, again independent of delivery route (p = 0.034). However, the total amount of recruited arterioles was significantly increased only in the healthy tissue around the wound and only by intradermal injections of SA-VP (p = 0.008). In agreement with the arteriogenic effect, the blood flow at day 4 was significantly increased only by injections of the highest SA-VP dose around the wound (p = 0.018).

Conclusion
SA-VP treatment significantly accelerates wound healing in a diabetic murine model with a clear dose-dependent effect. Targeting the undamaged tissue around the wound by injections induces more effective arteriogenesis. However, direct treatment of the wound bed is equally effective for healing. These results support further development for a potential clinical use.