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Correction: Severe, but not moderate asthmatics share blood transcriptomic changes with post-traumatic stress disorder and depression

Exploring the impact of urban spatial morphology on land surface temperature: A case study in Linyi City, China

The increasing population density and impervious surface area have exacerbated the urban heat island effect, posing significant challenges to urban environments and sustainable development. Urban spatial morphology is crucial in mitigating the urban heat island effect. This study investigated the impact of urban spatial morphology on land surface temperature (LST) at the township scale. We proposed a six-dimensional factor system to describe urban spatial morphology, comprising Atmospheric Quality, Remote Sensing Indicators, Terrain, Land Use/Land Cover, Building Scale, and Socioeconomic Factors. Spatial autocorrelation and spatial regression methods were used to analyze the impact. To this end, the township-scale data of Linyi City from 2013 to 2022 were collected. The results showed that LST are significantly influenced by urban spatial morphology, with the strongest correlations found in the factors of land use types, landscape metrics, and remote sensing indices. The global Moran’s I value of LST exceeds 0.7, indicating a strong positive spatial correlation. The High-High LISA values are distributed in the central and western areas, and the Low-Low LISA values are found in the northern regions and some scattered counties. The Geographically Weighted Regression (GWR) model outperforms the Spatial Error Model (SEM) and Ordinary Least Squares (OLS) model, making it more suitable for exploring these relationships. The findings aim to provide valuable references for town planning, resource allocation, and sustainable development.

Navigating the shots: Parental willingness to immunize their children with COVID-19 vaccines in Saudi Arabia explored through a systematic review and meta-analysis

Introduction Although COVID-19 vaccines have been recommended for children and adolescents since 2021, suboptimal vaccination uptake has been documented. No previous systematic review/meta-analysis (SRMA) investigated parents’ willingness to administer COVID-19 vaccines for their children in Saudi Arabia. Accordingly, this SRMA aimed to estimate parents’ willingness to immunize their children with COVID-19 vaccines in Saudi Arabia and to identify reasons and determinants influencing parents’ decisions. Methods This SRMA adhered to the PRISMA guidelines and the protocol was registered on PROSPERO (ID: CRD42023492760). An extensive systematic search was performed across electronic databases including Pub Med, Pub Med Central, ISI Web of science, Web of Science Core Collection, Medline, KCI-Korean Journal Database, ProQuest, and SciELO, to identify relevant studies published from January 1, 2020 to October 30, 2023. A random-effects model was utilized to estimate the pooled effects considering the expected variability across studies. Heterogeneity, risk of bias, publication bias and quality of studies were considered and evaluated by relevant appropriate tests to ensure robust results. Results Twenty-five studies with 30,844 parents were included. The overall pooled rate of parents who intended to immunize their children with COVID-19 vaccines was 48.0% (95% CI: 41.0–54.0%) with high heterogeneity (I2 = 99.42%). The main reason for parents to vaccinate children was to protect child, family and community from COVID-19. Perceived efficacy/safety of vaccines were the most significant determinants associated with parents’ willingness to vaccinate children. Conclusion This was the first SRMA from Saudi Arabia which emphasized the priority to focus on vaccine-related factors as main/key strategy of COVID-19 vaccines’ drivers to convince parents in a logical way based on accurate cumulative and emerging scientific data about efficacy and safety of COVID-19 vaccines to optimize their uptake by children/adolescents. This SRMA can provide valuable insights for development of evidence-based policies to improve parental willingness to vaccinate children, which is crucial for controlling SARS-CoV-2 spread and promoting herd immunity in the community particularly if the virus continues to pose a major threat.

Enhancing MaaS user satisfaction through strategic marketing: The synergy of sustainability and service experience

As urbanization intensifies and the need for sustainable transportation grows, Mobility as a Service (MaaS) emerges as a promising solution to urban mobility challenges. This study seeks to explore the underlying mechanisms of MaaS from a sustainability perspective and to assess its impact on service experience and user satisfaction. Additionally, it examines how user satisfaction influences the broader adoption of MaaS. To address these objectives, relevant hypotheses were posited, and hypothetical models were constructed based on a comprehensive review of the literature. The interconnections among sustainability, service experience, and user satisfaction within MaaS were rigorously analyzed employing both a survey methodology and structural equation modeling for data analysis. The findings support five hypotheses, affirming that sustainability significantly influences the MaaS service experience, which in turn impacts user satisfaction. Furthermore, sustainability directly contributes to user satisfaction and is crucial for its enhancement. User satisfaction also positively affects the dissemination of MaaS services. Notably, the study identifies a critical mediating role of service experience in the utilization of MaaS, linking sustainability and user satisfaction. This research offers both theoretical insights and practical guidance for understanding the operational dynamics of MaaS and improving the user experience.

Development of a service blueprint for blockchain services

As blockchain has been actively applied in various services, a tool for visualizing the complex service processes reflecting the characteristics of blockchain has been required. A service blueprint is a tool to visualize all key systems and encounters in service delivery. Although several blueprints already exist, they have limitations to systematically visualize and analyze blockchain service processes. This study develops a Blockchain Service Blueprint (BSB) specialized in visualizing and analyzing blockchain service processes. A comprehensive literature review and an analysis of blockchain services were conducted to identify characteristics of blockchain services and limitations of existing blueprints. The BSB was developed based on the derived key components of blockchain service processes, so that it has the optimal structure with key elements to visualize complex processes of blockchain services. The usefulness of the BSB was verified by both comparisons with traditional blueprints and expert interviews. The proposed BSB can intuitively and clearly visualize a service process between customers and service providers in blockchain services. Using the Blockchain Service Blueprint (BSB), providers can identify and improve service processes to enhance sustainability, and this study offers researchers cases and a development process that demonstrate the BSB’s effectiveness across various blockchain services.

Dual-hybrid intrusion detection system to detect False Data Injection in smart grids

Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures. This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. The main objective is to enhance the accuracy and robustness of IDS in smart grids. The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data’s spatial and temporal features. The dataset used for evaluation, the Industrial Control System (ICS) Cyber Attack Dataset (Power System Dataset) consists of various FDIA scenarios simulated in a smart grid environment. Experimental results demonstrate that the proposed IDS framework significantly outperforms traditional methods. The hybrid feature selection effectively reduces the dimensionality of the dataset, improving computational efficiency and detection performance. The hybrid deep learning classifier performs better in key metrics, including accuracy, recall, precision, and F-measure. Precisely, the proposed approach attains higher accuracy by accurately identifying true positives and minimizing false negatives, ensuring the reliable operation of smart grids. Recall is enhanced by capturing critical features relevant to all attack types, while precision is improved by reducing false positives, leading to fewer unnecessary interventions. The F-measure balances recall and precision, indicating a robust and reliable detection system. This study presents a practical dual-hybrid IDS framework for detecting FDIAs in smart grids, addressing the limitations of existing IDS techniques. Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.

Identification of key regulators in pancreatic ductal adenocarcinoma using network theoretical approach

Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease with poor clinical outcomes, which is mainly because of delayed disease detection, resistance to chemotherapy, and lack of specific targeted therapies. The disease’s development involves complex interactions among immunological, genetic, and environmental factors, yet its molecular mechanism remains elusive. A major challenge in understanding PDAC etiology lies in unraveling the genetic profiling that governs the PDAC network. To address this, we examined the gene expression profile of PDAC and compared it with that of healthy controls, identifying differentially expressed genes (DEGs). These DEGs formed the basis for constructing the PDAC protein interaction network, and their network topological properties were calculated. It was found that the PDAC network self-organizes into a scale-free fractal state with weakly hierarchical organization. Newman and Girvan’s algorithm (leading eigenvector (LEV) method) of community detection enumerated four communities leading to at least one motif defined by G (3,3). Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. Transcription Factor and mi-RNA of the key regulators were obtained. Recognizing the therapeutic potential and biomarker significance of PDAC Key regulators, we also identified approved drugs for specific genes. However, it is imperative to subject Key regulators to experimental validation to establish their efficacy in the context of PDAC.

From impact metrics and open science to communicating research: Journalists’ awareness of academic controversies

This study sheds light on how journalists respond to evolving debates within academia around topics including research integrity, improper use of metrics to measure research quality and impact, and the risks and benefits of the open science movement. It does so through a codebook thematic analysis of semi-structured interviews with 19 health and science journalists from the Global North. We find that journalists’ perceptions of these academic controversies vary widely, with some displaying a highly critical and nuanced understanding and others presenting a more limited awareness. Those with a more in-depth understanding report closely scrutinizing the research they report, carefully vetting the study design, methodology, and analyses. Those with a more limited awareness are more trusting of the peer review system as a quality control system and more willing to rely on researchers when determining what research to report on and how to vet and frame it. While some of these perceptions and practices may support high-quality media coverage of science, others have the potential to compromise journalists’ ability to serve the public interest. Results provide some of the first insights into the nature and potential implications of journalists’ internalization of the logics of science.

Genomic characteristics and molecular epidemiology of MRSA from medical centers in Mexico: Results from the Invifar network

Introduction The methicillin-resistant Staphylococcus aureus (MRSA) genome varies by geographical location. This study aims to determine the genomic characteristics of MRSA using whole-genome sequencing (WGS) data from medical centers in Mexico and to explore the associations between antimicrobial resistance genes and virulence factors. Methods This study included 27 clinical isolates collected from sterile sites at eight centers in Mexico in 2022 and 2023. Antibiotic susceptibility testing was performed using VITEK 2. In addition, WGS was performed using a NovaSeq platform, and a bioinformatic analysis was conducted using several tools. Results In this study, 21 strains were CC5, five were CC8, and one was CC93. Moreover, six strains were identified as ST5(CC5)-MRSA-IIa- t895, four strains were found to be ST1011(CC5)-MRSA-IIa-t895, five strains were found to be ST1011(CC5)-MRSA-IIa-t9364, one strain was found to be ST1011(CC5)-MRSA-IIa-t8116, another was found to be ST1011(CC5)-MRSA-IIa-t62, three were found to be ST8(CC8)-MRSA-IVa-t8, one strain was ST5(CC5)-MRSA-IVa-t2, one strain was as ST93(CC93)-MRSA-IVa-t3949, two strains were ST9003(CC8)-MRSA-IVa-t18492, and three strains were ST9034(CC5)-MRSA-V-t2. All SCCmec IIa strains showed resistance to levofloxacin and ciprofloxacin, and all but two strains were resistant to clindamycin. Among the strains that harbored the type IIa cassette, most had the aadD, blaZ, and ermA_SDS genes and the erm A gene. Multiple genes for adhesion, enzymes, immune evasion, and secretion system were detected, regardless of SCCmec type. Of the SCCmec IVa strains, most harbored the Panton-Valentine leukocidin encoding genes. Conclusion In this study, the most frequently detected CC was CC5, followed by CC8, and CC93, and the most frequently detected MRSA ST was ST1011, followed by ST5. Most SCCmec elements were found to be type IIa, followed by type IVa. High MIC values were observed for ciprofloxacin, erythromycin, and clindamycin, particularly within SCCmec IIa. Of the SCCmec IVa strains, most harbored the lukS-PV and lukF-PV genes.

Using machine learning to forecast peak health care service demand in real-time during the 2022–23 winter season: A pilot in England, UK

During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022–23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. Forecast uncertainty was modelled usings a novel generalised additive model for location, scale and shape (gamlss) approach which enabled prediction intervals to vary according to the level of the forecast activity. The winter of 2022–23 was atypical because the demand for healthcare services in children was exceptionally high, due to RSV circulating in the community and increased concerns around invasive group A streptococcal (iGAS) infections. However, our short-term forecasts proved to be adaptive forecasting a new higher peak once the increasing demand due to iGAS started. Thus, we have demonstrated the utility of our approach, adding forecasts to existing surveillance systems.

Normal-incidence mid-infrared photodetection via intraband transitions in InGaAs/InP multiple quantum well nanowire arrays

Recently, InGaAs/InP multiple quantum well nanowires grown by selective area epitaxy have been demonstrated with uniform morphology and high optical quality. The InGaAs quantum wells wrapping around the nanowire core are formed with both axial and radial components. As such the radial quantum well configuration presents a unique advantage for the realization of intraband absorption of normal-incidence light in the nanowires, which cannot be achieved in conventional planar quantum well structures due to polarization selection rules. We report here mid-infrared intraband transitions within the atmospheric window (3–5 μm) in InP nanowire arrays embedded with five InGaAs quantum wells under normal-incidence light. The light absorption coefficient of the quantum wells is modeled, and the absorption peak indicates a bound-to-continuum transition. The intraband photocurrent shows a linear dependence on the incident power, while the interband photoresponse is sublinear due to the surface states of the nanowires. These nanowires with radial quantum wells open up great opportunities for developing next-generation mid- to long-wavelength infrared photodetectors and focal plane arrays.

Plasma-induced optically active defects in hexagonal boron nitride

Hexagonal boron nitride (hBN) has been the subject of numerous research efforts in the last decade. Of particular interest is the creation of optically active defects in hBN because of their easy integration, e.g., in van der Waals heterostructures, and their room temperature photon emission. Many methods to create such defects in hBN are still under investigation. In this work, we present our approach to creating single defect emitters in hBN using remote plasma with different plasma species and report on the outcome statistically. We have used argon, nitrogen, and oxygen plasmas and report statistics on the emitters, produced by the different gas species and their optical properties. In particular, we examine the emission of the exfoliated flakes before and after the plasma processes without an annealing step to avoid creating emitters that are not caused by the plasma exposure. Our findings suggest that the purely physical argon plasma treatment is the most promising route for creating optically active defect emitters in hBN by plasma exposure.

Measurements of in-plane thermophysical properties on nanoscale-thick films by lock-in thermography

We demonstrate a versatile technique for measuring the in-plane thermal conductivity, in-plane thermal diffusivity, and volumetric heat capacity of nanoscale-thick films by means of lock-in thermography. The technique relies on the thermal analyses of imaged lock-in temperature distribution over the surface of films generated by an on-chip line heater. This enables simultaneous estimation of the properties for a free-standing membrane or multilayered thin films deposited on the membrane. We validate the usability of this technique by determining the thermophysical properties of Ni films with different nanoscale thicknesses. This technique also enables measurements under an external magnetic field, facilitating investigation of magneto-thermal transport properties. Thus, the proposed approach will be useful for exploring nanoscale thermal transport properties in thin films and thermal management systems.

Temporal uniaxial crystal in a dispersion-modulated lattice model

Time reflection and refraction, as temporal analogs to spatial phenomena, provide a degree of freedom for manipulating wave dynamics within the temporal domain. In this study, we investigate the dynamics of Gaussian wave packets at time interfaces within one-dimensional lattice models, providing insights into generalized temporal refraction and dispersion control. By redefining time reflection as temporal negative refraction, we propose a generalized temporal Snell's law based on the equivalent refractive index, which effectively predicts both temporal positive and negative refraction. Furthermore, we introduce a “temporal uniaxial crystal” by extending our investigation to a three-band model without symmetry constraints, characterized by double temporal positive and negative refraction. Our approach not only deepens the understanding of time refraction but also offers a versatile tool for studying complex wave behaviors in time-variant systems.

Electrochemical reduction for modulating spin state of nickel: A pathway to improved water and seawater oxidation

Understanding the electronic structure of catalysts is crucial for analyzing electrocatalyst behavior. Here, we present a straightforward method to modify the electronic configuration of active sites in nickel-iron-niobium layered double hydroxides (NiFeNb-LDHs) via electrochemical reduction (ER), uncovering key factors that enhance oxygen evolution reaction (OER) activity. The results indicate that ER-NiFeNb-LDHs display excellent OER performance and long-term stability over 60 h in various electrolytes (271.99 mV@50 mA cm−2 in 1M KOH and 280.56 mV@50 mA cm−2 in 1M KOH +0.5M NaCl). Furthermore, the cell voltage of the two-electrode electrolyzer ER-NiFeNb-LDHs ǁ Pt/C achieves a current density of 50 mA cm−2 at an ultra-low voltage of 1.58 V, significantly outperforming the commercial RuO2ǁPt/C. X-ray absorption spectroscopy, magnetic characterization, and density functional theory calculations reveal that the unsaturated coordination environment created by ER modifies the electronic state distribution between eg and t2g orbitals, effectively lowering the spin state of nickel and enhancing its OER activity.

Microstructure modulation and mechanical properties of multicomponent Fe50Cr14Mo14C9B8Tm5 bulk metallic glass through controlled crystallization processing

Fe50Cr14Mo14C9B8Tm5 bulk metallic glass was prepared at a cooling rate of 6.36 × 104 K s−1. Its Young's modulus and compressive strength were modulated through controlled crystallization processing. A low cooling rate induced the formation of the Tm2Mo2C3 phase while preserving free volume. Thus, the elastic deformation limit was increased to 2.39%. After low-temperature annealing, the (Fe, Cr)23(C, B)6 phase was formed at first. As the annealing temperature increased, four types of crystalline phases appeared as dispersions in this alloy, where the hardness of the Fe3Mo3C phase reached 32.5 GPa. Even when the annealing temperature reached 1260 K, the average grain size was only 180 nm. The large volume fraction of the Fe3Mo3C phase and uniform microstructure of crystallized alloy resulted in a high strength of 4.01 GPa and a low Young's modulus of 237.11 GPa.

Room-temperature topological spin textures and magnetic-field-induced skyrmion-bimeron switching in FeSnN3 monolayer

Two-dimensional (2D) polar magnets have received considerable attention due to their intrinsic ability to host Dzyaloshinskii–Moriya interaction (DMI), which is crucial for generating topological spin textures such as skyrmions and bimerons. The ability to switch between skyrmions and bimerons is considered to be important for developing future computing architectures based on multiple different topological bits. Here, using first-principles calculations and Monte Carlo simulations, we predict that the FeSnN3 monolayer with a polar structure is a 2D ferromagnetic half-metal, exhibiting an out-of-plane magnetic anisotropy energy of 0.181 meV, a high Curie temperature TC of 510 K, and a substantial DMI of 2.96 meV. Micromagnetic simulations demonstrate that the DMI-induced skyrmions in the FeSnN3 monolayer can persist above room temperature under feasible magnetic fields. Notably, skyrmion-bimeron switching can be achieved by altering the direction of the external magnetic field. Our findings not only suggest that the FeSnN3 monolayer is a promising candidate for developing spintronic devices based on topological spin textures but also provide alternative insights into skyrmion-bimeron switching through magnetic field.

Weak coupling of buckled germanene with high Fermi velocity on semiconducting Cu2Te

Despite its promise, growing a quasi-freestanding monolayer of germanene with Dirac cone signature remains a significant attention. Synthesizing germanene on semiconductor surfaces is highly desirable to preserve its linear energy dispersion near the K points, which has been experimentally challenging. Here, we report the molecular beam epitaxy of monolayer germanene on semiconducting Cu2Te supported by Cu(111). Scanning tunneling microscopy/spectroscopy (STM) revealed a low-buckled honeycomb lattice of germanene, exhibiting an intrinsic Dirac cone at the K point. By combining STM measurements with theoretical simulations, we confirm that germanene atoms occupy threefold hollow sites on Cu2Te via van der Waals interaction. Remarkably, by dI/dV spectra fitting, we find the prepared germanene owns the Fermi velocity of (6.9 ± 0.1) × 105 m/s, which is slightly higher than the density functional theory calculated 4.6 × 105 m/s with considering the dielectric constant of the underlying Cu2Te, implying the weak coupling of germanene with the substrate. This work provides a platform for further exploring the ballistic charge transport properties of germanene with a Dirac cone.

Stress release-induced piezoresponse in lead-free piezoceramics with high-symmetry morphotropic phase boundary

The (1 − x)(Bi0.5Na0.5)TiO3-xBaTiO3 (BNT-BT) system has attracted a great deal of interest because it presents a morphotropic phase boundary (MPB) between the rhombohedral and tetragonal phases for 0.05 < x < 0.08. Identifying the MPB in the BNT-BT system often results in materials exhibiting a high-symmetry (pseudo)cubic x-ray diffraction pattern. However, this singular composition exhibits ferroelectricity, which has been explained as a consequence of a field-induce phase transformation. Here, we demonstrate that the stress release after poling, from the virgin state of the sample, is a crucial phenomenon to obtain piezoelectric response in MPB BNT-BT. The mechanism behind the unusual poling–depoling process in piezoceramics exhibiting high-symmetry MPB is elucidated by combining x-ray diffraction measurements and advanced Raman spectroscopy. This underscores the importance of post-poling stress release from the virgin state as a critical factor in attaining piezoelectric response in lead-free piezoceramics with high-symmetry MPB configurations.

Trace Yb doping-induced cationic vacancy clusters enhance thermoelectrics in P-type PbTe

Alloying has been widely used to enhance thermoelectric (TE) performance, but achieving high TE performance remains challenging due to strong coupling between electrical and thermal transport in lead telluride-based materials. In this Letter, trace doping of the rare earth element Yb in a Pb0.95Na0.04Te matrix effectively regulates charge carriers by competing with cation vacancies. This mechanism optimizes carrier concentration and phonon scattering, resulting in a high power factor of ∼27 μW cm−1 K−2 and a low lattice thermal conductivity of ∼0.42 W m−1 K−1 at 823 K in Pb0.94Na0.04Yb0.01Te. First-principles calculations reveal that Yb doping induces local lattice distortions in PbTe, potentially forming pseudo-nanostructures in localized regions. This strategy leads to a peak zT of ∼2.4 at 823 K and an average zT of ∼1.4 from 303 to 823 K in Pb0.94Na0.04Yb0.01Te. Our findings suggest that the competition between dopant cations and cation vacancies reduces thermal conductivity via local lattice distortions while simultaneously improving electrical conductivity at high temperatures. This synergistic control of electrical and thermal transport offers an approach for boosting zT in TE materials.