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Abstract Multiferroic materials have been intensively pursued to achieve the mutual control of electric and magnetic properties. The breakthrough progress in 2D magnets and ferroelectrics encourages the exploration of low-dimensional multiferroics, which holds the promise of understanding inscrutable magnetoelectric coupling and inventing advanced spintronic devices. However, confirming ferroelectricity with optical techniques is challenging in 2D materials, particularly in conjunction with antiferromagnetic orders in single- and few-layer multiferroics. Here, we report the discovery of 2D vdW multiferroic with out-of-plane ferroelectric polarization in trilayer NiI2 device, as revealed by scanning reflective magnetic circular dichroism microscopy and ferroelectric hysteresis loops. The evolution between ferroelectric and antiferroelectric phases has been unambiguously observed. Moreover, the magnetoelectric interaction is directly probed by magnetic control of the multiferroic domain switching. This work opens up opportunities for exploring multiferroic orders and multiferroic physics at the limit of single or few atomic layers, and for creating advanced magnetoelectronic devices.

Abstract Low-dimensional/three-dimensional perovskite heterojunctions have shown great potential for improving the performance of perovskite photovoltaics, but large organic cations in low-dimensional perovskites hinder charge transport and cause carrier mobility anisotropy at the heterojunction interface. Here, we report a low-dimensional/three-dimensional perovskite heterojunction that introduces strong aromatic conjugated low-dimensional perovskites in p-i-n devices to reduce the electron transport resistance crossing the perovskite/electron extraction interface. The strong aromatic conjugated π-conjugated network results in continuous energy orbits among [Pb2I6]2− frameworks, thereby effectively suppressing interfacial non-radiative recombination and boosting carrier extraction. Consequently, the devices achieved an improved efficiency to 25.66% (certified 25.20%), and maintained over 95% of the initial efficiency after 1200 hours and 1000 hours under ISOS-L-1I and ISOS-D-1 protocols, respectively. The chemical design of strong aromatic conjugated molecules in perovskite heterojunctions provides a promising avenue for developing efficient and stable perovskite photovoltaics.

Abstract Tuning the thermal transport properties of hybrid halide perovskites is critical for their applications in optoelectronics, thermoelectrics, and photovoltaics. Here, an effective strategy is demonstrated to modulate the thermal transport property of hybrid perovskites by halide alloying. A highly tunable thermal conductivity of mixed‐halide hybrid perovskites is achieved due to halide‐alloying and structural distortion. The experimental measurements show that the room temperature thermal conductivity of MAPb(BrxI1‐x)3 (x = 0─1) can be largely modulated from 0.27 ± 0.07 W m−1 K−1 (x = 0.5) to 0.47 ± 0.09 W m−1 K−1 (x = 1). Molecular dynamics simulations further demonstrate that the thermal conductivity reduction of hybrid halide perovskites results from the suppression of the mean free paths of the low‐frequency acoustic and optical phonons. It is found that halide alloying and the induced structural distortion can largely increase the scatterings of optical and acoustic phonons, respectively. The confined diffusion of MA+ cations in the octahedra cage is found to act as an additional thermal transport channel in hybrid perovskites and can contribute around 10–20% of the total thermal conductivity. The findings provide a strategy for tailoring the thermal transport in hybrid halide perovskites, which may largely benefit their related applications.

In current urban flood modeling, challenges arise from the inadequate consideration of heterogeneous underlying urban surface characteristics and the complexity of parameter optimization processes. This study integrates multiple machine learning methods to propose an intelligent framework for urban flood modeling that accounts for underlying surface characteristics. It began by coupling a surface runoff model with a pipe network model to form an interpretable flooding model (FM). Subsequently, utilizing the BIC-GMM machine learning method on the sample set of the urban flooding model’s parameters, this study explored the grouping trends of these parameters. The Transformer model was employed to classify different categories of urban land use, which, along with other environmental indices, aided in the construction of an Artificial Neural Network (ANN) model. This model expedites the acquisition of the sensitivity parameters. The study also proposes urban functional zoning rules incorporating “socio-driven-nature-assisted” characteristics of the underlying surface. Finally, the clustering feature thresholds of the sensitive parameters were distributed across various catchment units based on the urban functional area distribution rules. This distribution was used to select multiple observed rainfall-runoff events to determine the optimal parameters of the inundation model, culminating in the construction of the BIC-GMM-Transformer-ANN-flooding model (BGTA-FM). The experimental results indicated that the method based on this intelligent modeling framework reached a mean Nash-Sutcliffe efficiency coefficient (NSE) of 0.8. This performance represents a 0.3 and 0.15 increase in NSE compared to the Transformer-ANN-flooding model and BIC-GMM-flooding model, respectively, and significantly enhances modeling efficiency. This effectively reflects the complex underlying surface environments of the research area. Our work demonstrates the substantial potential of integrating physical knowledge with machine learning in urban flood intelligent modeling and reaffirms the critical role of applying geospatial artificial intelligence (GeoAI) in geo-environmental research and disaster management.

Abstract Background Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health. Methods This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models—XG Boost, Support Vector Machine (SVM), and Random Forest (RF)—were employed. Results Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance. Conclusions The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

The neuroinflammatory responses following ischemic stroke cause irreversible nerve cell death. Cell free-double strand DNA (dsDNA) segments from ischemic tissue debris are engulfed by microglia and sensed by their cyclic GMP-AMP synthase (cGAS), which triggers robust activation of the innate immune stimulator of interferon genes (STING) pathway and initiate the chronic inflammatory cascade. The decomposition of immunogenic dsDNA and inhibition of the innate immune STING are synergistic immunologic targets for ameliorating neuroinflammation. To combine the anti-inflammatory strategies of STING inhibition and dsDNA elimination, we constructed a DNase-mimetic artificial enzyme loaded with C-176. Nanoparticles are self-assembled by amphiphilic copolymers (P[CL35-b-(OEGMA20.7-co-NTAMA14.3)]), C-176, and Ce4+ which is coordinated with nitrilotriacetic acid (NTA) group to form corresponding catalytic structures. Our work developed a new nano-drug that balances the cGAS-STING axis to enhance the therapeutic impact of stroke by combining the DNase-memetic Ce4+ enzyme and STING inhibitor synergistically. In conclusion, it is a novel approach to modulating central nervus system (CNS) inflammatory signaling pathways and improving stroke prognosis.

Abstract Background Chronic obstructive pulmonary disease (COPD) represents a prevalent ailment, progressively surging within the ranks of coal mine laborers. The current study endeavors to elucidate the effects of dust exposure and smoking on COPD incidence amongst coal mine workers, while concurrently devising preventive strategies for this affliction. Method A nested case–control study was conducted encompassing 1,416 participants aged ≥ 18 years, spanning the duration from (2017–2018) until 2020. A meticulous matching process yielded a cohort of 708 COPD patients, each paired with a control subject, forming a harmonious 1:1 ratio. Multiple logistic regression analysis was employed to scrutinize the associations between smoking, dust exposure with COPD among coal workers. Results The COPD prevalence within the cohort of coal workers under investigation amounted to 22.66%, with an accompanying incidence density of 0.09/person-year. Following meticulous adjustment for confounding variables, it was discerned that cumulative dust exposure within the range of 47.19 ~ (OR: 1.90, 95% CI: 1.05, 3.44), 101.27 ~ (OR: 1.99, 95% CI: 1.17, 3.39), as well as smoking indices of 72 ~ (OR: 1.85, 95% CI: 1.19, 2.88), 145 ~ (OR: 1.74, 95% CI: 1.17, 2.61), 310 ~ (OR: 1.85, 95% CI: 1.23, 2.77) engender an escalated vulnerability to COPD among coal workers. Furthermore, interaction analysis discerned an absence of both multiplicative and additive interactions between dust exposure, smoking, and COPD occurrence amidst coal workers. Conclusion Dust exposure and smoking were unequivocally identified as precipitating risk factors for COPD incidence within the population of coal workers, albeit devoid of any discernible interaction between these two causal agents.

Abstract Background The global incidence of nonalcoholic fatty liver disease (NAFLD) is rapidly escalating, positioning it as a principal public health challenge with significant implications for population well-being. Given its status as a cornerstone of China's economic structure, the steel industry employs a substantial workforce, consequently bringing associated health issues under increasing scrutiny. Establishing a risk assessment model for NAFLD within steelworkers aids in disease risk stratification among this demographic, thereby facilitating early intervention measures to protect the health of this significant populace. Methods Use of cross-sectional studies. A total of 3328 steelworkers who underwent occupational health evaluations between January and September 2017 were included in this study. Hepatic steatosis was uniformly diagnosed via abdominal ultrasound. Influential factors were pinpointed using chi-square (χ2) tests and unconditional logistic regression analysis, with model inclusion variables identified by pertinent literature. Assessment models encompassing logistic regression, random forest, and XGBoost were constructed, and their effectiveness was juxtaposed in terms of accuracy, area under the curve (AUC), and F1 score. Subsequently, a scoring system for NAFLD risk was established, premised on the optimal model. Results The findings indicated that sex, overweight, obesity, hyperuricemia, dyslipidemia, occupational dust exposure, and ALT serve as risk factors for NAFLD in steelworkers, with corresponding odds ratios (OR, 95% confidence interval (CI)) of 0.672 (0.487–0.928), 4.971 (3.981–6.207), 16.887 (12.99–21.953), 2.124 (1.77–2.548), 2.315 (1.63–3.288), 1.254 (1.014–1.551), and 3.629 (2.705–4.869), respectively. The sensitivity of the three models was reported as 0.607, 0.680 and 0.564, respectively, while the precision was 0.708, 0.643, and 0.701, respectively. The AUC measurements were 0.839, 0.839, and 0.832, and the Brier scores were 0.150, 0.153, and 0.155, respectively. The F1 score results were 0.654, 0.661, and 0.625, with log loss measures at 0.460, 0.661, and 0.564, respectively. R 2 values were reported as 0.789, 0.771, and 0.778, respectively. Performance was comparable across all three models, with no significant differences observed. The NAFLD risk score system exhibited exceptional risk detection capabilities with an established cutoff value of 86. Conclusions The study identified sex, BMI, dyslipidemia, hyperuricemia, occupational dust exposure, and ALT as significant risk factors for NAFLD among steelworkers. The traditional logistic regression model proved equally effective as the random forest and XGBoost models in assessing NAFLD risk. The optimal cutoff value for risk assessment was determined to be 86. This study provides clinicians with a visually accessible risk stratification approach to gauge the propensity for NAFLD in steelworkers, thereby aiding early identification and intervention among those at risk.

Study region: The Caochangmen area in the Gulou District, Nanjing City, China, is a typical flood-prone and well-defined region. Study focus: This paper addresses the limitations of the current ''single-driver'' approach in simulating urban waterlogging, proposing a modeling methodology that integrates physical coupling models with data-driven techniques. The objective is to conduct comprehensive simulation research on urban flooding depth that leverages the respective strengths of both physical and data-driven models. A 1D pipe network runoff model and 2D surface runoff model were coupled to create an integrated model under a physical coupling framework. Based on a dataset of urban-rainfall-induced flooding depths, two data-driven modules were designed: a convolutional neural network (CNN) and improved transformer. To address the parameter uncertainty in the “model-data” coupling approach, two optimization methods were proposed for optimizing sensitive physical model parameters and the performance of the deep learning models, facilitating the spatiotemporal simulation of urban flood depths. New hydrological insights for the region: The proposed physically calibrated module exhibits a nearly linear relationship with observed water depths, confirming the suitability of the calibrated parameters for the physical driver of the integrated model within the study area. The improved transformer integrated model outperformed the CNN integrated model, achieving excellent efficiency and high accuracy. This research employed a dual-engine driving approach, wherein physical models and data are mutually integrated and mutually reinforced. This demonstration underscores the potential of the synergy between deep learning and physical knowledge in urban flooding simulation studies.

Recently there has been interest in exploring the coupling between magnons for use in information processing, however, this is hampered by the fact that such coupling is forbidden due to the different parity of the acoustic and optical magnons. Here, Comstock et al show that the interlayer Dzyaloshinskii–Moriya-Interaction in a layered hybrid antiferromagnet can allow for strong coupling between the acoustic and optical magnons, offering a pathway for magnon coherent information processing.

Control over conductivity and carrier type (electrons and holes) defines semiconductors. A primary approach to target carrier concentrations involves introducing a small population of aliovalent impurity dopant atoms. In a combined synthetic and computational study, we assess impurity doping by introducing Bi and Sn into the prototype 2D Ruddlesden-Popper hybrid perovskite phenylethylammonium lead iodide (PEA_{2}PbI_{4}). Experimentally, we demonstrate that Bi and Sn can achieve n- and p-type doping, respectively, but the doping efficiency is low. Simulations show that Bi introduces a deep defect energy level (∼0.5 eV below the conduction band minimum) that contributes to the low doping efficiency, but, to reproduce the low doping efficiency observed experimentally, an acceptor level must also be present that limits n-type doping. Experiments find that Sn achieves p-dopant behavior and simulations suggest that this occurs through the additional oxidation of Sn defects. We also study how substitutional Bi incorporation can be controlled by tuning the electrochemical environment during synthesis. First-principles impurity doping simulations can be challenging; typical dopant concentrations constitute less than 0.01% of the atoms, necessitating large supercells, while a high level of theory is needed to capture the electronic levels. We demonstrate simulations of complex defect-containing unit cells that include up to 3383 atoms, employing spin-orbit coupled hybrid density functional theory. While p- and n-type behavior can be achieved with Sn and Bi, simulations and experiments provide concrete directions where future efforts must be focused to achieve higher doping efficiency.

The Chinese Visceral Adiposity Index (CVAI) is an indicator of visceral adiposity dysfunction used to evaluate the metabolic health of the Chinese population. Steelworkers are more likely to be obese due to their exposure to special occupational factors, and have a higher prevalence of carotid atherosclerosis (CAS). This study aimed to analyze the special relationship between CVAI and CAS among steelworkers. A total of 4075 subjects from a northern steel company were involved in the cross-sectional study. Four logistic regression models were developed to analyze the correlation between CVAI and CAS. In addition, the restricted cubic spline was applied to fit the dose–response association between CVAI and CAS risk. In the study, the prevalence of CAS was approximately 25.94%. After adjustment for potential confounders, we observed a positive correlation between CVAI and CAS risk. Compared to the first CVAI quartile, the effect value odds ratio (OR) and 95% CI in the second, third, and fourth CVAI quartile were 1.523 (1.159–2.000), 2.708 (2.076–3.533), and 4.101 (3.131–5.372), respectively. Additionally, this positive correlation was stable in all subgroups except for female. Furthermore, we also found a non-linear relationship between CVAI and CAS risk (p nonlinear < 0.05). Notably, CVAI could increase the risk of CAS when higher than 106. In conclusion, our study showed that CVAI might be a reliable indicator to identify high-risk populations of CAS among steelworkers.

In this work, an ultra-thin microwave absorber with a Jerusalem cross (JC) resonator exhibiting a broad low-frequency absorption bandwidth was successfully designed and fabricated. The system is composed of a perfect electric conductor-magnetic-periodic metal three-layer structure. The top metal layer consists of a periodic JC, which can be used to obtain dual-bands and low-frequencies in L-band, S-band, and C-band applications. The simulations show that the magnetic absorber with a thickness of 2 mm exhibits two absorption peaks at 1.4 and 4.7 GHz, with a reflectivity of −10 and −8 dB, respectively. The L-band RL (reflection loss) of −4 dB bandwidth has 91% (1–1.91 GHz), whereas the S-band and the C-band of −4 dB bandwidth have 47% (2.87–5.68 GHz). The total thickness of the absorber measures only 1/100λ at 1.5 GHz. When a magnetic harmonic TM is polarized at an incidence angle of 75°, an average RL of −10 dB in the 1–8 GHz range is obtained. The equivalent L–C circuit and the power loss density clarify the mechanism behind the absorption. Such a low-frequency absorber can be used in various applications to quench microwave energy.

Infectious laryngotracheitis virus (ILTV) causes severe respiratory disease in chickens and results in huge economic losses in the poultry industry worldwide. To correlate the genomic difference with the replication and pathogenicity, phenotypes of three ILTVs isolated from chickens in China from 2016 to 2018 were sequenced by high-throughput sequencing. Based on the entire genome, the isolates GD2018 and SH2017 shared 99.9% nucleotide homology, while the isolate SH2016 shared 99.7% nucleotide homology with GD2018 and SH2017, respectively. Each virus genome contained 82 ORFs encoding 77 kinds of protein, 31 of which share the same amino acid sequence in the three viruses. GD2018 and SH2017 shared 57 proteins with the same amino acid sequence, while SH2016 shared 42 and 41 proteins with the amino acid sequences of GD2018 and SH2017, respectively. SH2016 propagated efficiently in allantoic fluid and on chorioallantoic membranes (CAMs) of SPF chicken embryo eggs, while GD2018 and SH2017 proliferated well only on CAMs. GD2018 propagated most efficiently on CAMs and LMH cells among three isolates. SH2016 caused serious clinical symptoms, while GD2018 and SH2017 caused mild and moderate clinical symptoms in chickens, although the sero of the chickens infected with those three isolates were all positive for anti-ILTV antibody at 14 and 21 days after challenge. Three ILTVs with high genetic homology showed significant differences in the replication in different culture systems and the pathogenicity of chickens, providing basic materials for studying the key determinants of pathogenicity of ILTV.

A design method of five-band polarization-insensitive metamaterial absorber (MMA) based on the slotted structures and the magnetic rubber is proposed for L-, S-, C-, X-, and Ku-band applications. The slotted structures of the top layer, which evolved from two square rings, are used to excite multi-resonance. The range of the electromagnetic (EM) parameters of a magnetic rubber substrate, which is used to adjust the equivalent impedance of the absorber to match the free space impedance in different bands, is estimated using the impedance matching principle. A series of magnetic rubber substrates based on the estimated EM parameters are prepared and measured, whose thickness is only 0.7 mm, meeting the thin design requirements. The absorption of the proposed absorber greater than 90% at 1.7 GHz, 3.87 GHz, 5.96 GHz, 9.4–10.4 GHz, and 14 GHz is achieved when the doping amount of the carbonyl iron powders is 200%. The absorbing performance of the absorber with measured EM parameter agrees well with the theoretical estimates, which validates the accuracy of the proposed design method.

Abstract In this paper, we fabricated a series of FeCoBSi multistoried patterned magnetic films with different thickness by traditional UV lithography method and DC sputtering deposition. Broad resonance band phenomenon was observed during high frequency property characterization, with full width half maximum (FWHM) of 4 GHz when the film thickness is 45 nm. The broad resonance band effect was contributed to the existence of multiple resonance peaks due to different stripe width of the combined stripe pattern, which induced distinguish shape anisotropic field in each stripe. Each resonance peak was independent due to the gap between the stripes, leading to a controllable method to tune the microwave properties of such structure. With thickness varied, the resonance band could be altered according to the mathematic prediction. This work presents an effective method for tuning the microwave resonance characterization in magnetization dynamic.

Graphene is a promising THz magneto-optical material. However, high Faraday rotation is only achievable at a low THz frequency range in single layer graphene. In this paper, we report simultaneous Faraday rotation and optical transmission enhancement in Au grating/graphene/silicon hybrid plasmonic structures across a wide frequency range from 0.43 to 24 THz using extraordinary transmission of THz spoof surface plasmons. In a broad frequency range up to 13.1 THz, the Faraday rotation and magneto-optical figure of merit in this hybrid structure can exceed the maximum value of single layer graphene at the low THz frequency range. Numerical analysis on the device dispersion relation indicates that the mechanism of simultaneous Faraday rotation and transmission enhancement is due to coupling of the TE waveguide mode with the TM hybrid waveguide-plasmon mode. Our work demonstrates the hybrid plasmonic structure as a promising candidate for THz nonreciprocal photonic device applications.

Employing first-principles calculations, structural, electronic properties of new multiferroic material Er2NiMnO6/La2NiMnO6 perovskite superlattice are investigated. This structure is computed as monoclinic phase with obvious distortion. The average in-plane anti-phase rotation angle, average out-of-plane in-phase rotation angle and other microscopic features are reported in this paper. Ni and Mn are found in this superlattice that stay high spin states. These microscopic properties play important roles in multiferroic properties. Based on these microscopic features, the relationship between the direction of spontaneous polarization and the order of substitution in neighboring A-O layers is explained. Finally, we try to enhance the electrical polarization magnitude by 32% by altering the previous superlattice as LaEr2NiMnO7 structure. Our results show that both repulsion force of A site rare earth ions and the arrangement of B site ions can exert influences on spontaneous polarization.