bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proximity induced longitudinal and transverse thermoelectric response in graphene-ferromagnetic CrBr3 vdW heterostructure
Authors: Bora Mayuri , Deb Pritam 
Published: November 2022

Abstract
The integration of longitudinal and transverse thermoelectric (TE) fosters various new opportunities in tuning the charge transport behaviour and opens a platform for efficient thermopower devices. The presence of asymmetric electronic structure supposed to accomplish large thermopower and electronic figure of merit. Herein, we investigate magnetic proximity coupled longitudinal and transverse TE behaviour in heterostructure of monolayer semimetal, graphene and a monolayer ferromagnet, CrBr3 under the framework of ab initio-based calculations and employed constant relaxation time approximation (CRTA).The integrated density of states is elevated and asymmetric near Fermi energy region due to seamless proximity integration, depicting mixed character of graphene and CrBr3. The asymmetric nature of electronic structure significantly affects the Seebeck coefficients (S) and electrical conductivity (σ/τ) of heterostructure. The consistent step-like conductance spectrum influences interfacial polarization due to agile proximity integration. The magnitude of Seebeck coefficient (S) is found to be 653 µV K−1 near Fermi level. The heterostructure observes higher electrical conductivity and power factor in n-type region of the order of 106 S m−1 and 1020 cm−3 at room temperature. The dimensionless electronic figure of merit (zT
e) advocates the heterostructure system to be an ideal TE material. Alongside longitudinal TE, we also find the heterostructure system is sensitive to anomalous Nernst effect (ANE) (transverse TE) with oscillatory nature. The Seebeck and ANE shows high degree of tunability with applied external electric field. The synergistic existence of Seebeck and ANE due to proximity integration in van der Waals atomic crystal at room temperature will provide realistic approach to experimentally fabricate and develop real-time thermopower devices.
Machine learning-driven process of alumina ceramics laser machining
Authors: Behbahani Razyeh , Yazdani Sarvestani Hamidreza , Fatehi Erfan , Kiyani Elham , Ashrafi Behnam , Karttunen Mikko , Rahmat Meysam 
Published: November 2022

Abstract
Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation of the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.
Effect of CeO2 /Er2O3 co-doping on spectroscopic properties of zinc fluoroborate glasses
Authors: Abouelmaaref Ahmed , Al-Hosiny N. M. , Alyousef Haifa A. , El-Agmy R. M. , Shaaban Kh. S. , Abdel Wahab Essam Ahmed Ali 
Published: November 2022

Abstract
The glass system 75B2O3 - 4.5ZnF2 – 0.5 Er2O3–(20-x) ZnO- x CeO2, x = (0 ≤ x ≤ 1 mol. %) in this article was produced using the melt-quenching technique. Utilizing FT-IR techniques, the structural specifics of manufactured glasses are studied. The N_4 and N_3 fractions will be used to explore the transformation in the glass system that has been obtained. The BO3 and BO4 units coexisted in the glass composition, based on FT-IR spectra. Cerium ions appear to change trigonal BO3 units into tetrahedral BO4 units, according to preliminary FT-IR studies. The Judd-Ofelt theory is used to calculate the optical properties of the current glass that is co-doped with Er/Ce. Energy levels and line strengths of the Er3+ ion are evaluated. The current work additionally provides optical gains, emission/absorption cross-sections, and optical intensity data. The compound containing 0.6 mol% provided the greatest values for optical parameters like intensity parameters and optical gain compared to all other concentrations employed in this study.
Double-Λ electromagnetically induced transparency with degenerate atomic levels
Authors: Reshetov V A 
Published: November 2022

Abstract
The general expression for the transformation matrix of the two input weak arbitrarily polarized probe fields passing through the medium under the conditions of double-Λ electromagnetically induced transparency is obtained for arbitrary values of the amplitudes, phases and polarizations of the control fields and arbitrary values of the angular momenta of resonant levels. The conditions are found for the two input weak probe fields linearly polarized in perpendicular planes to exchange their polarizations after the passage through the medium without absorption. The polarization rotation of the probe fields is studied for the atoms being initially in the equilibrium state.
Cold temperature characterization of ring triplets based on RD53A readout chip
Authors: Samy M.A.A. , Sultan D.M.S. , Lapertosa A. , Gariano G. , Dalla Betta G.-F. 
Published: November 2022

Abstract
This paper reports the Trento test setup and the cold test procedure for three triplets (ID: R4, R5, and R6) assembled with RD53A readout cheap and planar sensors. The trim and tuning of Front Ends (FE), as well as sensor I-V measurements, were made for a cold temperature (−25 °C) for an applied voltage (−30 V for R4, −10 V for R5 and R6). As a part of QC, initially, several test scans: analog, digital, threshold, ToT, noise, etc., were performed to evaluate the module response at −25 °C before applying a necessary tuning script to find a spatially uniform threshold distribution over the pixel matrix. Later, the crosstalk-based disconnected-bump and X-ray scans were made to check modules’ bump bonding quality and hits per pixel. Results reported here show a good agreement to a qualified triplet module, assuring the Trento cold test setup capacity for the ATLAS ITk QA/QC process.
Can we avert an Amazon tipping point? The economic and environmental costs
Authors: Banerjee Onil , Cicowiez Martin , Macedo Marcia N , Malek Žiga , Verburg Peter H , Goodwin Sean , Vargas Renato , Rattis Ludmila , Bagstad Kenneth J , Brando Paulo M , Coe Michael T , Neill Christopher , Marti Octavio Damiani , Murillo Josué Ávila 
Published: November 2022

Abstract
The Amazon biome is being pushed by unsustainable economic drivers towards an ecological tipping point where restoration to its previous state may no longer be possible. This degradation is the result of self-reinforcing interactions between deforestation, climate change and fire. We assess the economic, natural capital and ecosystem services impacts and trade-offs of scenarios representing movement towards an Amazon tipping point and strategies to avert one using the Integrated Economic-Environmental Modeling (IEEM) Platform linked with spatial land use-land cover change and ecosystem services modeling (IEEM + ESM). Our approach provides the first approximation of the economic, natural capital and ecosystem services impacts of a tipping point, and evidence to build the economic case for strategies to avert it. For the five Amazon focal countries, namely, Brazil, Peru, Colombia, Bolivia and Ecuador, we find that a tipping point would create economic losses of US$256.6 billion in cumulative gross domestic product by 2050. Policies that would contribute to averting a tipping point, including strongly reducing deforestation, investing in intensifying agriculture in cleared lands, climate-adapted agriculture and improving fire management, would generate approximately US$339.3 billion in additional wealth and a return on investment of US$29.5 billion. Quantifying the costs, benefits and trade-offs of policies to avert a tipping point in a transparent and replicable manner can support the design of regional development strategies for the Amazon biome, build the business case for action and catalyze global cooperation and financing to enable policy implementation.
Increasing precipitation whiplash in climate change hotspots
Authors: Chen Di , Norris Jesse , Thackeray Chad , Hall Alex 
Published: November 2022

Abstract
Throughout the world, the hydrologic cycle is projected to become more variable due to climate change, posing challenges in semi-arid regions with high water resource vulnerability. Precipitation whiplash results from hydrologic variability, and refers to interannual shifts between wet (⩾80th historical percentile) and dry (⩽20th historical percentile) years. Using five model large ensembles, we show that whiplash is projected to increase in frequency (25%–60%) and intensity (30%–100%) by 2100 across several semi-arid regions of the globe, including Western North America and the Mediterranean. These changes can be driven by increases in the frequency of wet years or dry years, or both, depending on the region. Moisture budget calculations in these regions illuminate the physical mechanisms behind increased whiplash. Thermodynamic changes generally dominate, with modulations by dynamics, evaporation, and eddies on regional or global scales. These findings highlight increasingly volatile hydrology in semi-arid regions as the 21st Century progresses.
Using reclaimed water to cope with water scarcity: an alternative for agricultural irrigation in Spain
Authors: Ballesteros-Olza Mario , Blanco-Gutiérrez Irene , Esteve Paloma , Gómez-Ramos Almudena , Bolinches Antonio 
Published: November 2022

Abstract
In water-stressed agricultural regions, reuse of reclaimed water has emerged as a promising alternative that improves supply reliability, alleviates water scarcity and contributes to circular economy. The European Union has recently launched several initiatives to facilitate the adoption of water reuse for irrigation. However, its adoption is still far below its potential in most areas. This is the case of the Western La Mancha aquifer, in central Spain, where reclaimed water reuse is considered an alternative source to groundwater that may contribute to reduce overexploitation. A stakeholder-based fuzzy cognitive map (FCM) was developed to provide insights into the current situation of reclaimed water reuse in this area, as well as to explore the outcomes of different simulated scenarios (cost recovery, agricultural transformation, social awareness and political will increase). The FCM-based dynamic simulations showed that political will increase would generate the highest increase of reclaimed water reuse in agriculture in the study area, providing the highest increase of water reuse in agriculture. Agricultural extensification and increased social awareness delivered similar positive outcomes, however, only public awareness campaigns would increase water reuse, with agricultural extensification outcomes being more oriented towards the reduction of water abstractions and pollution. The cost recovery scenario was the only one that resulted in non-desired changes, mainly caused by reduced farmers’ income due to higher costs of reclaimed water. Finally, the analysis also evidenced the key role that the newly enacted EU Water Reuse Regulation may play in promoting reclaimed water use, even reversing the negative outcomes of the cost recovery scenario.
Disentangling the mechanism of temperature and water vapor modulation on ozone under a warming climate
Authors: Zhang Junxi , Gao Yang , Leung L Ruby , Luo Kun , Wang Minghuai , Zhang Yang , Bell Michelle L , Fan Jianren 
Published: November 2022

Abstract
Temperature and water vapor have been considered as important factors affecting ozone concentrations, however, their synergistic effect on ozone, as well as its response to a warming climate remains unclear. Here, numerical experiments with different combinations of climate and emissions highlighted opposite changes of annual ozone over the contiguous U.S. under scenarios with high vs. low ozone precursor emissions in the future with warming. Comparing the occurrence of heat waves on higher vs. lower ozone days, we found significant suppression of the heat wave exacerbation of ozone by anthropogenic emissions reductions. Variations of ozone with temperature (T) and water vapor (Q) over the western U.S. and southeastern U.S. in the simulations are clearly revealed through the construction of an O3-T-Q diagram. Based on the diagram, we further identified a T-dominant regime at higher temperatures where ozone increases with temperature increase, and a Q-dominant regime at lower temperatures where ozone decreases with temperature increase due to the negative effect of water vapor. With larger anthropogenic emission reductions, ozone may increase at a lower rate with temperature in the T-dominant regime, or even decrease with temperature in the Q-dominant regime. Our results show that with large emission reductions, substantial ozone pollution due to warming and increasing heat waves may be counteracted by water vapor, partly turning the ‘climate penalty’ on ozone into ‘climate benefit’. Hence controlling anthropogenic emissions may be an increasingly important strategy in a warmer and wetter climate to improve air quality and public health.
Dynamical mean-field theory: from ecosystems to reaction networks
Authors: De Giuli Eric , Scalliet Camille 
Published: November 2022

Abstract
Both natural ecosystems and biochemical reaction networks involve populations of heterogeneous agents whose cooperative and competitive interactions lead to a rich dynamics of species’ abundances, albeit at vastly different scales. The maintenance of diversity in large ecosystems is a longstanding puzzle, towards which recent progress has been made by the derivation of dynamical mean-field theories of random models. In particular, it has recently been shown that these random models have a chaotic phase in which abundances display wild fluctuations. When modest spatial structure is included, these fluctuations are stabilized and diversity is maintained. If and how these phenomena have parallels in biochemical reaction networks is currently unknown. Making this connection is of interest since life requires cooperation among a large number of molecular species. In this work, we find a reaction network whose large-scale behavior recovers the random Lotka–Volterra model recently considered in theoretical ecology. We clarify the assumptions necessary to derive its large-scale description, and reveal the underlying assumptions made on the noise to recover previous dynamical mean-field theories. Then, we show how local detailed balance and the positivity of reaction rates, which are key physical requirements of chemical reaction networks, provide obstructions towards the construction of an associated dynamical mean-field theory of biochemical reaction networks. Finally, we outline prospects and challenges for the future.
Stationary optomagnonic entanglement and magnon-to-optics quantum state transfer via opto-magnomechanics
Authors: Fan Zhi-Yuan , Qian Hang , Li Jie 
Published: November 2022

Abstract
We show how to prepare a steady-state entangled state between magnons and optical photons in an opto-magnomechanical configuration, where a mechanical vibration mode couples to a magnon mode in a ferrimagnet by the dispersive magnetostrictive interaction, and to an optical cavity by the radiation pressure. We find that, by appropriately driving the magnon mode and the cavity to simultaneously activate the magnomechanical Stokes and the optomechanical anti-Stokes scattering, a stationary optomagnonic entangled state can be created. We further show that, by activating the magnomechanical state–swap interaction and subsequently sending a weak red-detuned optical pulse to drive the cavity, the magnonic state can be read out in the cavity output field of the pulse via the mechanical transduction. The demonstrated entanglement and state-readout protocols in such a novel opto-magnomechanical configuration allow us to optically control, prepare, and read out quantum states of collective spin excitations in solids, and provide promising opportunities for the study of quantum magnonics, macroscopic quantum states, and magnonic quantum information processing.
Effect of atomic number and pressure on plasma pinch properties and characteristic soft x-ray emission in PF1000
Authors: Malek M A , Huda M N , Islam M K , Lee S 
Published: November 2022

Abstract
In this study, the Lee code is used to compute the characteristics soft x-ray yield (Ysxr) production for nitrogen (N2), oxygen (O2), neon (Ne), and argon (Ar) and bremsstrahlung radiation for hydrogen (H2), deuterium (D2), and helium (He) with pressure variation in PF1000 of 2.5–2.6 MA for D2. In the calculation of characteristic soft x-ray, the corresponding temperature windows of the said gases are set into the code at which they are ionized to their H-like and He-like levels. The focus pinch parameters such as radius ratio (minimum radius of plasma pinch column/anode radius), ion density, specific heat ratio, pinch energy density, self-absorption correction factor, and maximum induced voltage are computed at the optimum pressure of each gas. The obtained pinch plasma temperature range (1.2–2.2) × 106 K of H2, D2, and He is sufficiently high for fully ionized plasmas and the resulting bremsstrahlung radiation (14 J) for He is significantly larger than for H2 (0.26 J) and D2 (0.62 J). The optimum Ysxr of Ne(∼9314 J) at 0.51 Torr with pinch energy density (PED) (∼26 × 108 Jm−3) is found to be the highest whilst for Ar(∼7 J) at 0.019 Torr with (∼1.2 × 108 Jm−3) is the lowest. It is found that the radius ratio (∼0.05) of Ne is 3-fold smaller than that (∼0.16) in Ar. This enhancement of compression in pinch of Ne increases the ion density significantly by a factor of 253 than in Ar gas. Thus, the results show a strong correlation of plasma pinch properties with Ysxr for various gases.
Preclinical evaluation of IAP0971, a novel immunocytokine that binds specifically to PD1 and fuses IL15/IL15Rα complex
Authors: Chen Jihong , Shen Ziyou , Jiang Xiaoling , Huang Zhenzhen , Wu Chongbing , Jiang Dongcheng , Yin Liusong 
Published: November 2022

Abstract

Background
Currently, cytokine therapy for cancer has demonstrated efficacy in certain diseases but is generally accompanied by severe toxicity. The field of antibody-cytokine fusion proteins (immunocytokines) arose to target these effector molecules to the tumor microenvironment to expand the therapeutic window of cytokine therapy. Therefore, we have developed a novel immunocytokine that binds specifically to programmed death 1 (PD1) and fuses IL15/IL15Rα complex (referred to as IAP0971) for cancer immunotherapy.

Methods
We report here the making of IAP0971, a novel immunocytokine that binds specifically to PD1 and fuses IL15/IL15Rα complex, and preclinical characterization including pharmacology, pharmacodynamics, pharmacokinetics and toxicology, and discuss its potential as a novel agent for treating patients with advanced malignant tumors.

Results
IAP0971 bound to human IL2/15Rβ proteins specifically and blocked PD1/PDL1 signaling transduction pathway. IAP0971 promoted the proliferation of CD8 + T cells and natural killer T (NKT) cells, and further activated NK cells to kill tumor cells validated by in vitro assays. In an hPD1 knock-in mouse model, IAP0971 showed potent anti-tumor activity. Preclinical studies in non-human primates following single or repeated dosing of IAP0971 showed favorable pharmacokinetics and well-tolerated toxicology profile.

Conclusion
IAP0971 has demonstrated a favorable safety profile and potent anti-tumor activities in vivo. A Phase I/IIa clinical trial to evaluate the safety, tolerability and preliminary efficacy of IAP0971 in patients with advanced malignant tumors is on-going (NCT05396391).
MNMDCDA: prediction of circRNA–disease associations by learning mixed neighborhood information from multiple distances
Authors: Li Yang , Hu Xue-Gang , Wang Lei , Li Pei-Pei , You Zhu-Hong 
Published: November 2022

Abstract
Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA–disease associations, and uncovering new circRNA–disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases. Firstly, we computed different biological attribute information of circRNA and disease separately and used them to construct multiple multi-source similarity networks. Then, we used the high-order GCN algorithm to learn feature embedding representations with high-order mixed neighborhood information of circRNA and disease from the constructed multi-source similarity networks, respectively. Finally, the deep neural network classifier was implemented to predict associations of circRNAs with diseases. The MNMDCDA model obtained AUC scores of 95.16%, 94.53%, 89.80% and 91.83% on four benchmark datasets, i.e., CircR2Disease, CircAtlas v2.0, Circ2Disease and CircRNADisease, respectively, using the 5-fold cross-validation approach. Furthermore, 25 of the top 30 circRNA–disease pairs with the best scores of MNMDCDA in the case study were validated by recent literature. Numerous experimental results indicate that MNMDCDA can be used as an effective computational tool to predict circRNA–disease associations and can provide the most promising candidates for biological experiments.