Influence of local strain heterogeneity on high piezoelectricity in 0.5Ba(Zr0.2Ti0.8)O3−0.5(Ba0.7Ca0.3)TiO3 ceramics

Dielectric and mechanical spectroscopies have been used to investigate ferroelectric transitions and twin wall dynamics in the lead-free ceramic 0.5Ba(Zr0.2Ti0.8)O3−0.5(Ba0.7Ca0.3)TiO3 (abbreviated as BZT-50BCT), which is known to have a high piezoelectric coefficient (d33>545pC/N). Results from dynamical mechanical analysis in the frequency range 0.2–20 Hz and resonant ultrasound spectroscopy in the frequency range ∼0.1–1.2MHz confirm the existence of three phase transitions with falling temperature, at ∼360K (cubic-tetragonal), ∼304K (tetragonal-orthorhombic), and ∼273K (orthorhombic-rhombohedral). In comparison with BaTiO3, however, the transitions are marked by rounded rather than sharp minima in the shear modulus. The pattern of acoustic loss is also quite different from that shown by BaTiO3 in having a broad interval of high loss at low temperatures, consistent with a spectrum of relaxation times for interactions of ferroelastic twin walls. Differences in the dielectric properties also suggest more relaxor like characteristics for BZT-50BCT. It is proposed that the overall pattern of behavior is significantly influenced by strain heterogeneity at a local length scale in the perovskite structure due to the substitution of cations with different ionic radii. The existence of this strain heterogeneity and its influence on the elastic behavior near the transition points could be contributory factors to the development of adaptive nanoscale microstructures and enhanced piezoelectric properties

Digital Inclusive Finance, Entrepreneurial Activity of Farmers, and Urban–Rural Income Disparity: Evidence from China

In recent years, although the ratio of urban to rural per capita income in China has exhibited a continuous downward trend, the income gap between urban and rural areas remains significantly higher than that of developed countries. Addressing this inequality and achieving common prosperity has become one of the key objectives of national development. Utilizing a sample from mainland China, this paper empirically analyzes the role of digital inclusive finance in narrowing the urban–rural income gap by employing the Theil index to assess this disparity. The findings indicate that digital inclusive finance significantly alleviates the urban–rural income gap; however, its impact varies across different dimensions and regions. Furthermore, threshold effect analysis reveals a non-linear relationship between digital financial inclusion and the urban–rural income gap, with farmers’ entrepreneurial activities serving as an important moderating factor in this process. The results of this paper provide new insights into the understanding of urban–rural income distribution issues and offer valuable policy recommendations for addressing imbalances in urban–rural development

LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application

Coupling between phase transitions and glassy magnetic behaviour in Heusler alloy Ni 50 Mn 34 In 8 Ga 8

Abstract: The transition sequence in the Heusler alloy Ni50Mn34In8Ga8 has been determined from measurements of elasticity, heat flow and magnetism to be paramagnetic austenite → paramagnetic martensite → ferromagnetic martensite at ∼335 and ∼260 K, respectively, during cooling. The overall pattern of elastic stiffening/softening and acoustic loss is typical of a system with bilinear coupling between symmetry breaking strain and the driving structural/electronic order parameter, and a temperature interval below the transition point in which ferroelastic twin walls remain mobile under the influence of external stress. Divergence between zero-field-cooling and field-cooling determinations of DC magnetisation below ∼220 K indicates that a frustrated magnetic glass develops in the ferromagnetic martensite. An AC magnetic anomaly which shows Vogel–Fulcher dynamics in the vicinity of ∼160 K is evidence of a further glassy freezing process. This coincides with an acoustic loss peak and slight elastic stiffening that is typical of the outcome of freezing of ferroelastic twin walls. The results suggest that local strain variations associated with the ferroelastic twin walls couple with local moments to induce glassy magnetic behaviour

Coupling between phase transitions and glassy magnetic behaviour in Heusler Alloy Ni50Mn34In8Ga8

The transition sequence in the Heusler alloy Ni50Mn34In8Ga8 has been determined from measurements of elasticity, heat flow and magnetism to be paramagnetic austenite → paramagnetic martensite → ferromagnetic martensite at ~335 and ~260 K, respectively, during cooling. The overall pattern of elastic stiffening/softening and acoustic loss is typical of a system with bilinear coupling between symmetry breaking strain and the driving order parameter in a temperature interval below the transition point in which ferroelastic twin walls remain mobile under the influence of external stress. Divergence between zero-field-cooling (ZFC) and field-cooling (FC) determinations of DC magnetisation below ~220 K indicates that a frustrated magnetic glass develops in the ferromagnetic martensite. An AC magnetic anomaly which shows Vogel-Fulcher dynamics in the vicinity of ~160 K is evidence of a further glassy freezing process. This coincides with an acoustic loss peak and slight elastic stiffening that is typical of the outcome of freezing of ferroelastic twin walls. The results indicate that local strain variations associated with the ferroelastic twin walls couple with local moments to induce glassy magnetic behaviour

Hardware implementation of memristor-based artificial neural networks

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Unforeseen plant phenotypic diversity in a dry and grazed world

23 páginas..- 4 figuras y 7 figuras.- 50 referencias y 90 referenciasEarth harbours an extraordinary plant phenotypic diversity1 that is at risk from ongoing global changes2,3. However, it remains unknown how increasing aridity and livestock grazing pressure—two major drivers of global change4,5,6—shape the trait covariation that underlies plant phenotypic diversity1,7. Here we assessed how covariation among 20 chemical and morphological traits responds to aridity and grazing pressure within global drylands. Our analysis involved 133,769 trait measurements spanning 1,347 observations of 301 perennial plant species surveyed across 326 plots from 6 continents. Crossing an aridity threshold of approximately 0.7 (close to the transition between semi-arid and arid zones) led to an unexpected 88% increase in trait diversity. This threshold appeared in the presence of grazers, and moved toward lower aridity levels with increasing grazing pressure. Moreover, 57% of observed trait diversity occurred only in the most arid and grazed drylands, highlighting the phenotypic uniqueness of these extreme environments. Our work indicates that drylands act as a global reservoir of plant phenotypic diversity and challenge the pervasive view that harsh environmental conditions reduce plant trait diversity8,9,10. They also highlight that many alternative strategies may enable plants to cope with increases in environmental stress induced by climate change and land-use intensification.This research was funded by the European Research Council (ERC Grant agreement 647038 1004 [BIODESERT]) and Generalitat Valenciana (CIDEGENT/2018/041). N.G. was supported by CAP 20–25 (16-IDEX-0001) and the AgreenSkills+ fellowship programme which has received funding from the European Union’s Seventh Framework Programme under grant agreement FP7-609398 (AgreenSkills+ contract). F.T.M. acknowledges support from the King Abdullah University of Science and Technology (KAUST), the KAUST Climate and Livability Initiative, the University of Alicante (UADIF22-74 and VIGROB22-350), the Spanish Ministry of Science and Innovation (PID2020-116578RB-I00), and the Synthesis Center (sDiv) of the German Centre for Integrative Biodiversity Research Halle–Jena–Leipzig (iDiv). Y.L.B.-P. was supported by a Marie Sklodowska-Curie Actions Individual Fellowship (MSCA-1018 IF) within the European Program Horizon 2020 (DRYFUN Project 656035). H.S. is supported by a María Zambrano fellowship funded by the Ministry of Universities and European Union-Next Generation plan. L.W. acknowledges support from the US National Science Foundation (EAR 1554894). G.M.W. acknowledges support from the Australian Research Council (DP210102593) and TERN. M.B is supported by a Ramón y Cajal grant from Spanish Ministry of Science (RYC2021-031797-I). L.v.d.B. and K.T. were supported by the German Research Foundation (DFG) Priority Program SPP-1803 (TI388/14-1). A.F. acknowledges the financial support from ANID PIA/BASAL FB210006 and Millenium Science Initiative Program NCN2021-050. A.J. was supported by the Bavarian Research Alliance for travel and field work (BayIntAn UBT 2017 61). A.L. and L.K. acknowledge support from the German Research Foundation, DFG (grant CRC TRR228) and German Federal Government for Science and Education, BMBF (grants 01LL1802C and 01LC1821A). B.B. and S.U. were supported by the Taylor Family-Asia Foundation Endowed Chair in Ecology and Conservation Biology. P.J.R. and A.J.M. acknowledge support from Fondo Europeo de Desarrollo Regional through the FEDER Andalucía operative programme, FEDER-UJA 1261180 project. E.M.-J. and C.P. acknowledge support from the Spanish Ministry of Science and Innovation (PID2020-116578RB-I00). D.J.E. was supported by the Hermon Slade Foundation. J.D. and A.Rodríguez acknowledge support from the FCT (2020.03670.CEECIND and SFRH/BDP/108913/2015, respectively), as well as from the MCTES, FSE, UE and the CFE (UIDB/04004/2021) research unit financed by FCT/MCTES through national funds (PIDDAC). S.C.R. acknowledges support from the US Department of Energy (DE-SC-0008168), US Department of Defense (RC18-1322), and the US Geological Survey Ecosystems Mission Area. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government. E.H.-S. acknowledges support from Mexican National Science and Technology Council (CONACYT PN 5036 and 319059). A.N. and C. Branquinho. acknowledge the support from FCT—Fundação para a Ciência e a Tecnologia (CEECIND/02453/2018/CP1534/CT0001, PTDC/ASP-SIL/7743/ 2020, UIDB/00329/2020), from AdaptForGrazing project (PRR-C05-i03-I-000035) and from LTsER Montado platform (LTER_EU_PT_001). Field work of G.P. and J.M.Z. was supported by UNRN (PI 40-C-873).Peer reviewe

Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands

14 páginas.- 4 figuras.- 67 referencias.- The online version contains supplementary material available at https://doi.org/10.1038/s41477-024-01670-7Perennial plants create productive and biodiverse hotspots, known as fertile islands, beneath their canopies. These hotspots largely determine the structure and functioning of drylands worldwide. Despite their ubiquity, the factors controlling fertile islands under conditions of contrasting grazing by livestock, the most prevalent land use in drylands, remain virtually unknown. Here we evaluated the relative importance of grazing pressure and herbivore type, climate and plant functional traits on 24 soil physical and chemical attributes that represent proxies of key ecosystem services related to decomposition, soil fertility, and soil and water conservation. To do this, we conducted a standardized global survey of 288 plots at 88 sites in 25 countries worldwide. We show that aridity and plant traits are the major factors associated with the magnitude of plant effects on fertile islands in grazed drylands worldwide. Grazing pressure had little influence on the capacity of plants to support fertile islands. Taller and wider shrubs and grasses supported stronger island effects. Stable and functional soils tended to be linked to species-rich sites with taller plants. Together, our findings dispel the notion that grazing pressure or herbivore type are linked to the formation or intensification of fertile islands in drylands. Rather, our study suggests that changes in aridity, and processes that alter island identity and therefore plant traits, will have marked effects on how perennial plants support and maintain the functioning of drylands in a more arid and grazed world.This research was supported by the European Research Council (ERC grant 647038 (BIODESERT) awarded to F.T.M.) and Generalitat Valenciana (CIDEGENT/2018/041). D.J.E. was supported by the Hermon Slade Foundation (HSF21040). J. Ding was supported by the National Natural Science Foundation of China Project (41991232) and the Fundamental Research Funds for the Central Universities of China. M.D.-B. acknowledges support from TED2021-130908B-C41/AEI/10.13039/501100011033/Unión Europea Next Generation EU/PRTR and the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. O.S. was supported by US National Science Foundation (Grants DEB 1754106, 20-25166), and Y.L.B.-P. by a Marie Sklodowska-Curie Actions Individual Fellowship (MSCA-1018 IF) within the European Program Horizon 2020 (DRYFUN Project 656035). K.G. and N.B. acknowledge support from the German Federal Ministry of Education and Research (BMBF) SPACES projects OPTIMASS (FKZ: 01LL1302A) and ORYCS (FKZ: FKZ01LL1804A). B.B. was supported by the Taylor Family-Asia Foundation Endowed Chair in Ecology and Conservation Biology, and M. Bowker by funding from the School of Forestry, Northern Arizona University. C.B. acknowledges funding from the National Natural Science Foundation of China (41971131). D.B. acknowledges support from the Hungarian Research, Development and Innovation Office (NKFI KKP 144096), and A. Fajardo support from ANID PIA/BASAL FB 210006 and the Millennium Science Initiative Program NCN2021-050. M.F. and H.E. received funding from Ferdowsi University of Mashhad (grant 39843). A.N. and M.K. acknowledge support from FCT (CEECIND/02453/2018/CP1534/CT0001, SFRH/BD/130274/2017, PTDC/ASP-SIL/7743/2020, UIDB/00329/2020), EEA (10/CALL#5), AdaptForGrazing (PRR-C05-i03-I-000035) and LTsER Montado platform (LTER_EU_PT_001) grants. O.V. acknowledges support from the Hungarian Research, Development and Innovation Office (NKFI KKP 144096). L.W. was supported by the US National Science Foundation (EAR 1554894). Y.Z. and X.Z. were supported by the National Natural Science Foundation of China (U2003214). H.S. is supported by a María Zambrano fellowship funded by the Ministry of Universities and European Union-Next Generation plan. The use of any trade, firm or product names does not imply endorsement by any agency, institution or government. Finally, we thank the many people who assisted with field work and the landowners, corporations and national bodies that allowed us access to their land.Peer reviewe