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Numerous Late Carboniferous – Early Permian dykes are found in West Junggar and represent an important part of the Central Asian Orogenic Belt. In this contribution, we use these dykes to assess the tectonic regime and stress state in the Late Carboniferous – Early Permian. The West Junggar dykes are mainly diorite/dioritic porphyrite with minor diabase and were formed in 324–310 Ma. They have been divided into two groups based on their orientation, petrology and geochronology. Group 1 dykes mostly comprise WNW-striking dioritic porphyrite and NE-striking diorite with minor diabase and resemble the Karamay-Baogutu sanukitoid. They were probably formed from depleted mantle at a relatively high temperature and pressure with the addition of 1–2% sediment/sedimental partial melt and 0–5% trapped oceanic crust-derived melts. Group 2 dykes are ENE-striking and are similar to sanukite in the Setouchi Volcanic Belt. These dykes were also derived from depleted mantle at a shallow depth but high temperature with the addition of 2–3.5% sediment/sedimental partial melt. Magma banding and injection folds in dykes and host granitoids indicate magma flow. Paleostress analysis reveals that both groups of dykes were formed in a tensile stress field. Their emplacement is favoured by presence of pre-existing joints or fractures in the host granitoids and strata. We conclude that large-scale asthenosphere mantle upwelling induced by trapped oceanic slab-off can explain the magmatism and significant continental crustal growth of West Junggar during Late Carboniferous to Early Permian.

The objective of this study is to evaluate and improve the accuracy and stability of a strain measurement method that uses the speeded-up robust feature (SURF) method to trace the displacement of feature points in microimages and obtain the strain in objects. The microimages were acquired using a smartphone with a portable microscope, which has a broad prospect of application. An experiment was performed using an unpacked optical fiber as the experimental carrier. The matching effect of the SURF method was analyzed in the microimage, and the M-estimator sample consensus (MSAC) algorithm was used to reject outliers generated by SURF. The results indicated that the accuracy of strain measurement using the proposed method is improved by modifying the feature point tracking method and measurement method. When compared with the fiber Bragg grating (FBG) data, the maximum standard error corresponded to 2.5 με, which satisfies the requirement of structural health monitoring (SHM) in practical engineering.

Smartphones which are built into the suite of sensors, network transmission, data storage, and embedded processing capabilities provide a wide range of response measurement opportunities for structural health monitoring (SHM). The objective of this work was to evaluate and validate the use of smartphones for monitoring damage states in a three-dimensional (3D) steel frame structure subjected to shaking table earthquake excitation. The steel frame is a single-layer structure with four viscous dampers mounted at the beam-column joints to simulate different damage states at their respective locations. The structural acceleration and displacement responses of undamaged and damaged frames were obtained simultaneously by using smartphones and conventional sensors, while the collected response data were compared. Since smartphones can be used to monitor 3D acceleration in a given space and biaxial displacement in a given plane, the acceleration and displacement responses of the Y-axis of the model structure were obtained. Wavelet packet decomposition and relative wavelet entropy (RWE) were employed to analyze the acceleration data to detect damage. The results show that the acceleration responses that were monitored by the smartphones are well matched with the traditional sensors and the errors are generally within 5%. The comparison of the displacement acquired by smartphones and laser displacement sensors is basically good, and error analysis shows that smartphones with a displacement response sampling rate of 30 Hz are more suitable for monitoring structures with low natural frequencies. The damage detection using two kinds of sensors are relatively good. However, the asymmetry of the structure’s spatial stiffness will lead to greater RWE value errors being obtained from the smartphones monitoring data.

Epithelial ovarian cancer (EOC) is the deadliest gynecological cancer, and presents a major clinical challenge due to limited treatment options. Folate receptor alpha (FRα), encoded by the FOLR1 gene, is an attractive therapeutically target due to its prevalent and high expression in EOC cells. Recent basic and translational studies have explored several modalities, such as antibody-drug conjugate (ADC), monoclonal antibodies, small molecules, and folate-drug conjugate, to exploit FRα for EOC treatment. In this review, we summarize the function of FRα, and clinical efficacies of various FRα-based therapeutics. We highlight mirvetuximab soravtansine (MIRV), or Elahere (ImmunoGen), the first FRα-targeting ADC approved by the FDA to treat platinum-resistant ovarian cancer. We discuss potential mechanisms and management of ocular adverse events associated with MIRV administration.

The link between corporate digitization and green innovation is now receiving attention from all spheres of life in light of the rapidly developing digital economy and the goal of sustainable development. This study explores how corporate digitalization affects green innovation, its mediating mechanism, and moderating effects by integrating resource-based theory, attention-based view, and institutional theory. We utilize the panel data of Chinese Shanghai and Shenzhen A-share manufacturing corporation data from 2011 to 2020 as samples and use the fixed effect model in linear regression of panel data for regression analysis. Research findings: 1) corporate digitalization fosters not only green innovation directly but also promotes green innovation by enhancing human capital. 2) Executive team environmental attention encourages the beneficial correlation between human capital and green innovation. 3) Media attention promotes the favorable relationship between corporate digitalization and green innovation. 4) Heterogeneity analysis revealed that the corporate digitalization effect on green innovation is more significant when firms are more prominent in high-tech industries. The findings encourage corporations to strengthen their digital strategy, infrastructure, and applications. In addition, they can also inspire green innovation to enable companies to develop sustainably.

With the continuous promotion of digitalization and the global trend toward a low-carbon economy, the issue of whether enterprises can enhance their carbon performance with the assistance of digital technology has aroused widespread attention from both academia and industry. In order to explore whether digital technology can improve the carbon performance of manufacturing enterprises, this study, based on resource orchestration theory and signaling theory, utilizes data from China’s A-share manufacturing enterprises from 2012 to 2021 to empirically investigate the relationship between digital technology and the carbon performance of manufacturing firms. It also explores the mediating conduction path and boundary influencing factors between them. Its findings demonstrate that: digital technology is capable of improving carbon performance; green innovation (including green technology and green collaboration) has partially mediating effects; there is a catalytic role for environmental information disclosure in utilizing digital technology to enhance carbon performance. Building on this, we find that the impacts of digital technology, green innovation, and environmental information disclosure on carbon performance vary due to differences in the nature of industries and the strategic aggressiveness of enterprises. Specifically, the role of digital technology on carbon performance seems somewhat more pronounced among firms in the high-tech industry and those employing defensive and analytical strategies. Additionally, the effects generated by green innovation and environmental information are more pronounced in the high-tech industry and among enterprises that adopt analytical strategies. This study reveals the inherent mechanism of digital technology in enhancing the carbon performance of manufacturing enterprises, which provides empirical evidence for the development of digital technology and the improvement of carbon performance in manufacturing enterprises, thus helping promote low-carbon economic transformation.

To investigate the knowledge, attitudes, and willingness (KAW) of patients with thyroid disease regarding thyroid thermal ablation techniques and explore the factors associated with KAW. This cross-sectional survey was conducted at Yantai Hospital of Shandong Wendeng Orthopaedics & Traumatology and Yantai Affiliated Hospital of Binzhou Medical College between October 2022 and March 2023. This study included 632 patients; 66.14% were female. The mean knowledge, attitude, and willingness scores were 6.03 ± 2.42 (possible range: 0–10), 17.52 ± 2.91 (possible range: 5–25), and 33.02 ± 6.34 (possible range: 8–40), indicating poor knowledge, positive attitudes, and proactive willingness. Multivariable analysis showed that ≥ 51 years old, urban areas, consuming alcohol, medical treatment, and surgical treatment were independently associated with adequate knowledge. The knowledge scores, ≥ 51 years old, females, urban areas, medical treatment, and surgical treatment were independently associated with a positive attitude. Only the attitude scores were independently associated with proactive willingness. Patients with thyroid diseases have poor knowledge, positive attitudes, and proactive practice toward thermal ablation. Sustained efforts are required to increase knowledge about thyroid thermal ablation techniques.

Objective Our research aims to elucidate the significance of type 2 diabetes (T2D) and provides an insight into a novel risk model for post-cerebral infarction cognitive dysfunction (PCICD). Methods Our study recruited inpatients hospitalized with cerebral infarction in Xijing hospital, who underwent cognitive assessment of Mini-Mental State Examination (MMSE) from January 2010 to December 2021. Cognitive status was dichotomized into normal cognition and cognitive impairment. Collected data referred to Demographic Features, Clinical Diseases, scale tests, fluid biomarkers involving inflammation, coagulation function, hepatorenal function, lipid and glycemic management. Results In our pooled dataset from 924 eligible patients, we included 353 in the final analysis (age range 65–91; 30.31% female). Multivariate logistic regression analysis was performed to show that Rural Areas (OR = 1.976, 95%CI = 1.111–3.515, P  = 0.020), T2D (OR = 2.125, 95%CI = 1.267–3.563, P  = 0.004), Direct Bilirubin (OR = 0.388, 95%CI = 0.196–0.769, P  = 0.007), Severity of Dependence in terms of Barthel Index (OR = 1.708, 95%CI = 1.193–2.445, P  = 0.003) that were independently associated with PCICD, constituting a model with optimal predictive efficiency. Conclusion To the best of our knowledge, this study provides a practicable map of strategical predictors to robustly identify cognitive dysfunction at risk of post-cerebral infarction for clinicians in a broad sense. Of note, our findings support that the decline in serum direct bilirubin (DBil) concentration is linked to protecting cognitive function.

Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This paper proposes a method to solve these problems by monitoring the interstory drift with a smartphone and a laser device. In this method, a laser device is installed on the ceiling while a smartphone is fixed on a steel projection plate on the floor. Compared with a reference sensor, the method designed in this study shows that a smartphone is competent in monitoring the interstory drift. This method utilizes a smartphone application (APP) named D-Viewer to implement monitoring and data storage just in one place, which is also inexpensive. The results showed that this method has an average percent error of 3.37%, with a standard deviation of 2.67%. With the popularization of the smartphone, this method is promising in acquiring large amounts of data, which will be significant for building assessment after an earthquake.

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