Search

Abstract The present investigation is dedicated to exploring the potential of utilizing gold tailings to enhance the performances of foam concrete. Tests to assess foam stability and compressive strength have been performed. The inherent yield stress exhibited by the matrix is utilized as a metric to evaluate the foam stability of the foamed concrete. Compressive strength of matrix and density ratio are employed to analyze and predict the strength of foam concrete. The experimental findings unveil that mechanical performances of foam concrete can be augmented through the incorporation of an optimal quantity of gold tailings. This enhancement can be attributed to the development of a more compact and less permeable microstructure. However, it should be noted that the inclusion of gold tailings may also have an adverse effect on the foam stability of foamed concrete. This is due to their ability to enhance the fluidity and decrease the yield stress of the base mix, thereby reducing the overall stability of the foam concrete. The main control factors of foam concrete strength are determined by using the prediction model. Compared with the foam stability, the enhancement in compressive strength of base mix, coupled with a consistent density ratio may account for the enhancement of compressive strength in foam concrete.

Abstract Background Cardiac fibrosis is significant global health problem, which is associated with numerous cardiovascular diseases, and ultimately leads to the progression to heart failure. β-adrenergic receptor (β-AR) overactivation play a role in the development of cardiac fibrosis. Miglustat (Mig) has shown anti-fibrosis effects in multiple fibrotic diseases. However, it is unclear whether and how Mig can ameliorate cardiac fibrosis induced by β-AR overactivation. Methods In vivo, mice were injected with isoproterenol (ISO) to induce cardiac fibrosis and treated with Mig. In vitro, primary cardiac fibroblasts were stimulated by ISO and treated with Mig. Levels of cardiac fibrosis, cardiac dysfunction, activation of cardiac fibroblasts were evaluated by real-time polymerase chain reaction, western blots, sirius red staining, immunohistochemistry staining and echocardiography. Through GEO data and knockdown UDP-glucose ceramide glycosyltransferase (UGCG) in primary cardiac fibroblasts, whether Mig alleviates cardiac fibrosis by targeting UGCG was explored. Results The results indicated that Mig alleviated ISO-induced cardiac dysfunction. Consistently, Mig also suppressed ISO-induced cardiac fibrosis. Moreover, Mig attenuated ISO-induced cardiac fibroblasts (CFs) activation. To identify the protective mechanism of Mig on cardiac fibrosis, several classical β-AR downstream signaling pathways, including ERK, STAT3, Akt and GSK3β, were further analyzed. As expected, ISO activated the ERK, STAT3, Akt and GSK3β in both CFs and mouse hearts, but this effect was reversed pretreated with Mig. Besides, Mig ameliorates ISO-induced cardiac fibrosis by targeting UDP-glucose ceramide glycosyltransferase (UGCG) in CFs. Conclusions Mig ameliorates β-AR overactivation-induced cardiac fibrosis by inhibiting ERK, STAT3, Akt and GSK3β signaling and UGCG may be a potential target for the treatment of cardiac fibrosis.

Abstract Virtual machine placement is a typical NP-hard problem in the field of cloud computing. Unreasonable placement schemes can result in low resource utilization and high energy consumption. To address these issues, this paper proposes a heuristic algorithm named Adaptive Boundary Grey Wolf Optimization (ABGWO). The objective is to minimize the number of physical servers, and the evaluation is conducted using Microsoft’s Azure Trace dataset from 2020. Comparative experiments are performed with genetic algorithm, particle swarm optimization algorithm, artificial bee colony algorithm, moth search algorithm, and differential evolution algorithms. The experimental results demonstrate that the ABGWO algorithm outperforms other algorithms in terms of solution quality and stability.

In the context of a globally diverse workforce with varying levels of education and precarious working conditions, this study aims to explore the impact of Social Network Analysis (SNA) on understanding and improving industrial worker skills in railway construction, amid digital transformation challenges. Employing SNA, the research dissects the intricate stakeholder interactions in railway construction projects, identifying 20 critical factors and 10 pivotal connections that significantly influence workforce development and training. The investigation reveals the critical role of digitally-oriented training programs, evolving policy frameworks, and advanced work environments in meeting the sector’s demands. Six strategic directions are introduced, focusing on policy updates, personalized training solutions, and worker well-being initiatives, tailored to navigate the digital transformation challenges effectively. This research provides a novel framework for enhancing worker competencies in the digitally evolving railway construction landscape, integrating SNA with strategic workforce development approaches. The study offers actionable strategies for stakeholders, including government bodies, educational institutions, and industry participants, emphasizing the need for collaboration to implement the proposed interventions. By addressing workforce development comprehensively, the research underscores the importance of technological and policy advancements in fostering a skilled, adaptable, and resilient workforce, contributing to societal progress through improved infrastructure projects.

Abstract Cancer is a major public health issue, with an estimated 20 million new cases and 9.7 million cancer-related deaths worldwide in 2022. Approximately 44.5% of patients experience cancer pain, significantly impacting their quality of life and causing physical and psychological burdens. Repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation technique, shows potential in managing cancer pain. This review summarizes current research on rTMS for cancer pain, focusing on pain directly caused by tumors, pain from cancer treatments, postoperative pain, and cancer-related symptoms. Additionally, rTMS shows promise in improving cancer-related fatigue, anxiety, depression, and cognitive dysfunction, which can indirectly reduce cancer pain. The analgesic mechanisms of rTMS include inhibiting nociceptive signal transmission in the spinal cord, modulating hemodynamic changes in brain regions, and promoting endogenous opioid release. High-frequency stimulation of the primary motor cortex (M1) has shown significant analgesic effects, improving patients' emotional and cognitive functions and overall quality of life. rTMS has a favorable safety profile, with most studies reporting no severe adverse events. In conclusion, rTMS holds substantial potential for cancer pain management, offering a non-invasive and multifaceted therapeutic approach. Continued research and clinical application are expected to establish rTMS as an essential component of comprehensive cancer pain treatment strategies, significantly enhancing the overall well-being of patients with cancer.

With the continuous growth of global energy demand, greenhouse gas emissions are also rising, leading to serious challenges posed by climate change. Carbon Capture, Utilization, and Storage (CCUS) technology is considered one of the key pathways to mitigate climate change. Among the CCUS technologies, CO2 storage in offshore saline aquifers has gained significant attention in recent years. This paper conducts an in-depth analysis of the Sleipner and Snøhvit projects in Norway and the Tomakomai project in Japan, exploring key issues related to the application, geological characteristics, injection strategies, monitoring systems, and simulation methods of CO2 storage in offshore saline aquifers. This study finds that CO2 storage in offshore saline aquifers has high safety and storage potential but faces several challenges in practical applications, such as geological reservoir characteristics, technological innovation, operational costs, and social acceptance. Therefore, it is necessary to further strengthen technological innovation and policy support to promote the development and application of CO2 storage in offshore saline aquifers. This study provides valuable experiences and insights for similar projects worldwide, contributing to the sustainable development of CO2 storage in offshore saline aquifers and making a greater contribution to achieving global net-zero emission targets.

Abstract The United States plays a crucial role in shaping global health through its policy decisions and engagements. Historically, bipartisan support underpinned U.S. involvement in multilateral and bilateral global health initiatives in advancing its national health, security interests, and foreign policy. However, recent decades have witnessed increased politicization and polarization of global health and fluctuating stances between Republican and Democratic administrations. This commentary speculates the potential implications of the 2024 presidential election on global health, focusing on how ideological differences between parties and previous administrative actions might affect U.S.’s strategies in addressing key global health issues, including governance, funding allocation, sexual and reproductive health policies, responses to humanitarian crises, and efforts to combat climate change. The election may be a critical juncture that could determine whether the U.S. global health strategies will continue to reflect the globalist and liberal policies typically associated with recent Democratic administrations or shift back to the isolationist tendencies observed during Trump’s presidency. The outcome will significantly determine the direction of U.S. global health policy and its broader implications for global health equity and security. The conclusions emphasize the necessity of maintaining strong international cooperation and commitment to health as a global public good.

Abstract The integration of Software-Defined Networking (SDN) in storage centers aims to enhance storage performance. However, this integration also introduces new concerns, particularly the potential eavesdropping attacks that pose a substantial risk to data privacy. By issuing flow tables (e.g., via compromised SDN switches), attackers can conveniently collect target traffic and extract confidential information with session reassembly methods. To proactively mitigate such attacks by preventing session reassembly, various moving target defense methods, such as end hopping, have been proposed. However, this study uncovers several deficiencies within existing end hopping methods. To address these deficiencies, we propose a novel linkage-field-based self-synchronizing end hopping method, which obfuscates end information (e.g., IP, Port) and linkage fields (e.g., sequence number and ID number) without third-party assistance. Furthermore, to counter the potential invalidation of end hopping methods resulting from brute-force reassembly of a small number of sessions, we propose a fake segment injection method. Extensive experiments have been conducted both in simulation and real-world environment to evaluate the effectiveness of our proposed methods. The results demonstrate that our proposed methods can effectively defend against eavesdropping attacks with acceptable performance overhead.

Abstract Metabolic disorder significantly contributes to diabetic vascular complications, including diabetic retinopathy, the leading cause of blindness in the working-age population. However, the molecular mechanisms by which disturbed metabolic homeostasis causes vascular dysfunction in diabetic retinopathy remain unclear. O-GlcNAcylation modification acts as a nutrient sensor particularly sensitive to ambient glucose. Here, we observe pronounced O-GlcNAc elevation in retina endothelial cells of diabetic retinopathy patients and mouse models. Endothelial-specific depletion or pharmacological inhibition of O-GlcNAc transferase effectively mitigates vascular dysfunction. Mechanistically, we find that Yes-associated protein (YAP) and Transcriptional co-activator with PDZ-binding motif (TAZ), key effectors of the Hippo pathway, are O-GlcNAcylated in diabetic retinopathy. We identify threonine 383 as an O-GlcNAc site on YAP, which inhibits its phosphorylation at serine 397, leading to its stabilization and activation, thereby promoting vascular dysfunction by inducing a pro-angiogenic and glucose metabolic transcriptional program. This work emphasizes the critical role of the O-GlcNAc-Hippo axis in the pathogenesis of diabetic retinopathy and suggests its potential as a therapeutic target.

In this study, the processing parameters of a laser powder bed fusion (LPBF)-fabricated CoCrNi alloy were systematically investigated for their impact on mechanical properties, microstructure, and fracture characteristics when the relative density exceeds 99%. The maximum relative density of 99.89% was attained at a laser power of 225 W, and scan speed of 1000 mm/s, accompanied by a hardness value of 276.83 HV. However, it is important to note that the highest relative density did not correspond to the maximum σUTS. Tensile testing revealed that increasing the laser power at the same scan speeds led to a decrease in elongation. The LPBF-fabricated CoCrNi alloy exhibited enhanced defect tolerance due to its cellular microstructure, which confined ductile dimple sizes to the nanoscale and impeded crack propagation through pinning effects. At scan speeds of 1000 mm/s and energy densities below 83 J/mm³, an increased presence of unmelted powder and pores was observed. Besides, it was confirmed that the crystallographic texture displayed the strongest orientation within the sample. Theoretical calculations indicated that over 50% of the yield strength is attributed to the dislocation structure. These findings contribute to the process optimization of LPBF, providing a valuable reference for adjusting process parameters to enhance material mechanical properties, particularly when targeting relative densities above 99%.

Abstract Acute lung injury (ALI) is the result of damage to the capillary endothelia and the alveolar epithelial cell caused by various direct and indirect factors, leading to significant pulmonary interstitial and alveolar edema and acute hypoxic respiratory insufficiency. A subset of ALI cases progresses to irreversible pulmonary fibrosis, a condition with fatal implications. Zafirlukast is a leukotriene receptor antagonist licensed for asthma prevention and long-term treatment. This study demonstrated a significant improvement in lung tissue pathology and a reduction in inflammatory cell infiltration in models of lipopolysaccharide (LPS)-induced ALI and bleomycin (BLM)-induced lung inflammation following zafirlukast administration, both in vivo and in vitro. Moreover, zafirlukast was found to suppress the inflammatory response of alveolar epithelial cells in vitro and lung inflammation in vivo by reducing the activation of the TLR4/NF-κB/NLRP3 inflammasome pathway. In conclusion, zafirlukast relieved lung injury and the infiltration of inflammatory cells in the lung by regulating the TLR4/NF-κB/NLRP3 pathway.

Plasma-wall interaction (PWI) is a critical concern in tokamaks because of its significant impact on the lifetimes of plasma-facing materials (PFMs), fuel retention, and plasma performance. In 2020, the Experimental Advanced Superconducting Tokamak (EAST) PFMs were upgraded to predominantly metallic walls. Following the 2021 experimental campaign, the deposition distribution and fuel retention on the surfaces of the PFMs along both the poloidal and toroidal directions were analyzed. The poloidal tests commenced at the high-field side (HFS), proceeded to the lower divertor, then to the low-field side (LFS), and finally to the upper divertor. The toroidal tests were performed at the midplane of the HFS, beginning from port A and ending at port P. The distributions of the deposits in the poloidal and toroidal directions were clearly asymmetrical. Mo and Fe particles sputtered from the first wall and inner stainless steel (SS) components were prone to deposition in the lower divertor region, as evidenced by the fact that the elemental content in the far-SOL region of the inner divertor exhibited Mo and Fe peaks, as did both the near- and far-SOL regions of the outer divertor. In addition, quick re-deposition of W and Fe was observed, as demonstrated by the fact that their contents near the erosion sources were higher than those farther away along the toroidal first wall on the HFS. This tendency was stronger for W than for Fe. Further analysis indicated that the deposits consisted of Li, C, O, W, Mo, Fe, Cu, and Ni. The Li and Fe contents were much higher than those of other metal impurities, with peak values of 8.17 μg/mm2 and 7.78 μg/mm2, respectively. The Li content decreased along the HFS first wall from 8.17 μg/mm2 at P-2 to 1.82 μg/mm2 at P-22, and then increased to 2.72 μg/mm2 at P-32 in the divertor, while the Fe content was higher around the top side and the midplane of the HFS. The deposited Li originated from routine wall conditioning and existed primarily in the form of Li2CO3. Additionally, other metal impurities deposited on the Li2CO3 surfaces exhibited various irregular shapes, often appearing as aggregated and recrystallized small particles. Furthermore, significant deuterium (D) retention on the order of 1021 atoms/m2 was measured on all the analyzed SiC-coated graphite tiles on the HFS. The D content at location P-10 at the midplane was the highest along the poloidal direction of the HFS because it was directly facing the NBI beam.

Abstract Clear cell renal cell carcinoma (ccRCC), with high mortality and poor prognosis, is the most common type of renal malignancy. It is necessary to identify new biomarkers that can serve as indicators for the detection of ccRCC at its early stages. In this study, we analyzed the role of classical zinc finger protein 692 (ZNF692) in ccRCC using datasets from The Cancer Genome Atlas (TCGA) and Single Cell Portal and immunohistochemical (IHC) staining of a tissue-microarray, and analyzed the function of ZNF692 in ccRCC cells. The analyses indicated that ZNF692 was upregulated in ccRCC samples compared with normal or paracancerous control samples (P

Abstract Postoperative delirium (POD) is a common complication in older patients with hepatocellular carcinoma (HCC) that adversely impacts clinical outcomes. We aimed to evaluate the risk factors for POD and to construct a predictive nomogram. Data for a total of 1481 older patients (training set: n=1109; validation set: n=372) who received liver resection for HCC were retrospectively retrieved from two prospective databases. The receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance. The rate of POD was 13.3% (148/1109) in the training set and 16.4% (61/372) in the validation set. Multivariate analysis of the training set revealed that factors including age, history of cerebrovascular disease, American Society of Anesthesiologists (ASA) classification, albumin level, and surgical approach had significant effects on POD. The area under the ROC curves (AUC) for the nomogram, incorporating the aforementioned predictors, was 0.798 (95% CI 0.752–0.843) and 0.808 (95% CI 0.754–0.861) for the training and validation sets, respectively. The calibration curves of both sets showed a degree of agreement between the nomogram and the actual probability. DCA demonstrated that the newly established nomogram was highly effective for clinical decision-making. We developed and validated a nomogram with high sensitivity to assist clinicians in estimating the individual risk of POD in older patients with HCC.

Abstract Background Gallbladder cancer (GBC) is the most common and lethal malignancy of the biliary tract that lacks effective therapy. In many GBC cases, infiltration into adjacent organs or distant metastasis happened long before the diagnosis, especially the direct liver invasion, which is the most common and unfavorable way of spreading. Methods Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), proteomics, and multiplexed immunohistochemistry (mIHC) were performed on GBC across multiple tumor stages to characterize the tumor microenvironment (TME), focusing specifically on the preferential enrichment of neutrophils in GBC liver invasion (GBC-LI). Results Multi-model Analysis reveals the immunosuppressive TME of GBC-LI that was characterized by the enrichment of neutrophils at the invasive front. We identified the context-dependent transcriptional states of neutrophils, with the Tumor-Modifying state being associated with oxidized low-density lipoprotein (oxLDL) metabolism. In vitro assays showed that the direct cell-cell contact between GBC cells and neutrophils led to the drastic increase in oxLDL uptake of neutrophils, which was primarily mediated by the elevated OLR1 on neutrophils. The oxLDL-absorbing neutrophils displayed a higher potential to promote tumor invasion while demonstrating lower cancer cytotoxicity. Finally, we identified a neutrophil-promoting niche at the invasive front of GBC-LI that constituted of KRT17+ GBC cells, neutrophils, and surrounding fibroblasts, which may help cultivate the oxLDL-absorbing neutrophils. Conclusions Our study reveals the existence of a subset of pro-tumoral neutrophils with a unique ability to absorb oxLDL via OLR1, a phenomenon induced through cell-cell contact with KRT17+ GBC cells in GBC-LI.

The oxidation resistance of Mg–Zn–Zr (ZK60) alloys with the addition of minor rare-earth element (RE) oxides (CeO2, La2O3) was investigated in 420 °C in air and pure O2 environments. It was revealed that the oxidation behavior of ZK60, ZK60-La2O3 and ZK60-CeO2 is different. In comparison to typical intergranular oxidation in ZK60 alloy, owing to diverse phase transformations between La and Mg, the La–Mg phase is continuously distributed in the grain boundaries, and furthermore, a dense and protective layer consisting La–Mg intermetallic phase is coated on the surface, which can effectively inhibit the oxidation of the Mg substrate, exhibiting excellent oxidation resistance. However, the presence of discontinuously distributed Ce-rich phases at the grain boundaries and the chain reaction during high-temperature exposure are the reasons for the deterioration of the oxidation resistance in ZK60-CeO2. It is anticipated that our findings will inspire the development of next-generation low-cost and excellent high-temperature performance Mg alloys.

There have been repeated security incidents in megaproject tunnels, construction security issues are particularly serious, and the management of security risks is receiving increasing attention. This study was conducted to improve the efficiency of tunnel security risk management, the security risk management process was adopted as an entry point, based on the Observe-Orient-Decision-Action (OODA) model, the influencing factors index system for the efficiency of security risk management in megaproject tunnels from four dimensions: observe, orient, decision and action. The AHP-EWM method is applied to assign index weights to model the system dynamics of security risk management efficiency. The Vensim software was used to simulate and analyse the effect of different indices on the efficiency of security risk management under different conditions, which provides a reference for improving the efficiency of security risk management. The results of the study show that building a comprehensive and uninterrupted risk information awareness network, real-time adaptation of risk control strategies based on the complexity of risks, and shortening risk management delays can effectively increase the efficiency of dynamic security risk management. This provides a theoretical and practical reference for effectively improving the efficiency of risk management for tunnel security in megaproject construction.

Abstract The burgeoning development of railway construction in plateau regions of southwest China necessitates innovative and environmentally sustainable approaches, particularly in the realm of tunnel construction, where the transfer of muck poses significant operational and environmental challenges. This research, pivoting around the application and configuration of electric muck transfer equipment in plateau railway tunnels, seeks to dissect the potentialities and impediments of transitioning from conventional diesel-powered machinery to electric alternatives, with a spotlight on mitigating environmental impacts and enhancing operational efficiency. Through an analytical lens, the study employs a case study methodology, leveraging data and insights from existing electric equipment models and their applications, provided by major manufacturers in China, to weave a comprehensive narrative around the practicalities, specifications, and challenges embedded in the adoption of electric machinery in plateau environments. The findings unveil a nuanced landscape, where the environmental and operational advantages of electric equipment are juxtaposed against a backdrop of technological, financial, and infrastructural hurdles, thereby crafting a complex tapestry of opportunities and challenges. The research further extrapolates policy recommendations and practical guidelines, advocating for a harmonized amalgamation of governmental policies, technological advancements, and strategic planning to navigate through the identified challenges and optimize the integration of electric equipment in tunnel construction practices. Envisaging future research pathways, the study underscores the criticality of perpetuating technological innovations, policy adaptations, and interdisciplinary research to further refine and enhance the application of electric muck transfer equipment in plateau railway tunnel projects, thereby contributing to the broader narrative of sustainable construction practices in challenging terrains.

This paper presents an in-depth study of the automated recognition and geometric information quantification of rebar meshes, proposing a deep learning-based method for rebar mesh detection and segmentation. By constructing a diverse rebar mesh image dataset, an improved Unet-based model was developed, incorporating residual modules to enhance the network’s feature extraction capabilities and training efficiency. The study found that the improved model maintains high segmentation accuracy and robustness even in the presence of complex backgrounds and noise. To achieve the precise measurement of rebar spacing, a rebar intersection detection algorithm based on convolution operations was designed, and the IQR (Interquartile Range) algorithm was applied to remove outliers, ensuring the accuracy and reliability of spacing calculations. The experimental results demonstrate that the proposed model and methods effectively and efficiently accomplish the automated recognition and geometric information extraction of rebar meshes, providing reliable technical support for the automated detection and geometric data analysis of rebar meshes in practical engineering applications.

Megaproject construction endeavors and technological innovation activities, led by industry–university–research (IUR) collaboration, demonstrate marked disparities in value orientations, implementing entities, and constituent components. These discrepancies lead to a mismatch between innovation demands and actual activities, as well as insufficient innovation motivation among construction entities, subsequently impacting innovation effectiveness and the commercialization of outcomes and failing to adequately support engineering construction needs. In response to this predicament, the academic community widely acknowledges IUR collaborative innovation as a solution. This research integrates fundamental theoretical analysis with a multi-case study approach and systematically dissects the distinctive features at the micro, meso, and macro levels, grounded in the collaborative innovation practices of IUR in three iconic railway engineering projects in China. Subsequently, it unravels the inherent operational mechanics of the IUR collaborative innovation system within large-scale projects. Specifically, at the micro level, the profound engagement of governments and project owners fosters a solid supportive environment and collaborative platform for IUR collaboration, while past successful cooperation experiences among key innovation entities enhance their technological and knowledge interactions. At the meso level, shared industry cognitions and values, hierarchical organizational structures, flexible institutional designs, and resource allocation strategies based on balancing risks and benefits collectively constitute the supporting system for megaproject collaborative innovation. At the macro level, the tight integration of the innovation chain and industrial chain promotes the formation of an open cooperation ecosystem, ensuring the continuity and systematic nature of innovation activities and accelerating the rapid commercialization and efficient utilization of innovation outcomes. This study not only enriches the theoretical connotations of IUR collaborative innovation in the context of major engineering projects but also provides theoretical foundations for strategy formulation and management practices for major project managers, holding significant value in guiding the innovation management of future major engineering projects.

Iterative geostatistical seismic inversion is a vital technique for estimating subsurface properties. However, a conventional single-scale strategy faces challenges in preserving large-scale geological features due to the limited restoration of the type of data template, and conventional multiscale strategies face the challenge in that it is easy to lose the large-scale structure that was previously preserved. This paper introduces a novel smoothed multiscale strategy aimed at overcoming these limitations, which comprises two components: Simulated annealing at the coarsest scale and smooth conversation between two scale grids. This approach offers a smoother way to simultaneously retain large-scale and small-scale structures, improving the overall accuracy of the subsurface property estimations. To validate the effectiveness of our approach, we apply it to both synthetic and real examples. The results show that the simulated annealing strategy at the coarsest scale grid explores the prior space and finds the best large structures to avoid the generated models trapping in the local minimal. Meanwhile, the smooth conversation strategy between two scale grids helps us avoid the damage of the coarser structure. It can be explained that a large weight is assigned to the coarse structure at the beginning of the conversation of two grid scale, reducing the likelihood of the replacing proposed local small-scale geological patterns, which prone to be accepted in the conventional multiscale strategies. The combination of the two strategies used in the proposed smoothed multiscale strategy displays a significant improvement in subsurface property estimation accuracy compared to traditional multiscale strategies. This innovation can have far-reaching implications, benefiting a wide range of geophysical applications and contributing to more accurate and informed decision-making in geological and hydrogeological assessments.

Emissivity and absorptivity are important parameters in overhead power line conductors that depend on the conductor surface condition, so their values change as the conductors age. They have an important effect on their thermal behavior, so it is important to know their value when designing dynamic line rating (DLR) strategies. DLR methods are becoming increasingly important, especially in areas with congested power lines, as they allow the full capacity of the lines to be utilized by dynamically determining their maximum allowable currents without exceeding the safe temperature limits of the conductors, thus ensuring that they operate within safe limits. However, measuring emissivity and absorptivity is non-trivial and requires complex tests that cannot be performed in the field. Using simulations based on a realistic thermoelectric model and experimental tests performed on three types of transmission line conductors in a high-current laboratory, this paper shows the importance and effect of emissivity on conductor temperature. It also presents a method for measuring the emissivity value based on adjusting the emissivity and absorptivity values in the thermoelectric model to match the experimental values. The results presented show that aged conductors allow an increase in the maximum current carrying capacity compared to new conductors in the order of 5–14 %, depending on the conductor type considered and the operating conditions.

In Software-Defined Networks (SDN), the ternary content addressable memory (TCAM) capacity in switches is limited, making them vulnerable to low-rate flow table overflow attacks. Most existing research in this field has not focused on the influence of flow entry eviction mechanisms on the effectiveness of such attacks. This paper proposes an adaptive low-rate flow table overflow attack (ALFO), which can adopt corresponding attack modes under different flow entry eviction mechanisms, significantly degrading network service quality. Due to the different features of ALFO under different attack modes, the existing attack detection methods are ineffective in this attack. Therefore, this paper proposes a detection and mitigation framework, which is called adaptive low-rate flow table overflow attack guard framework (ALFO-Guard). It extracts flow features from flow entry information in the switch and aggregates them into a current-time graph model. Then, combining graph neural networks, it performs graph anomaly detection and flow entry classification to identify attack flow entries. Finally, the attack can be eliminated by deleting the identified attack flow entries and blocking the attack flows. The effectiveness of ALFO and ALFO-Guard is validated through extensive experiments, and the experimental results demonstrate that ALFO-Guard can effectively defend against ALFO.

Objectives: This study examines the recommendation behaviors and influencing factors for the 13-valent Pneumococcal Conjugate Vaccine (PCV13) among 3579 Chinese healthcare workers (HCWs), including 1775 pediatric care providers (Peds-PCPs) and 1804 primary care providers (PCPs). Data were collected from May to July 2023 through a national cross-sectional survey using a structured questionnaire, distributed across hospitals providing pediatric services in five provincial-level administrative divisions. Methods: The sociodemographic data, vaccine knowledge, and recommendation practices were analyzed using Pearson’s chi-square test, Wilcoxson rank-sum test, and multivariate logistic regression. Results show that while PCPs are more likely to recommend PCV13, vaccine hesitancy persists among Peds-PCPs. Logistic regression revealed that higher influenza vaccination intention, salary, vaccine consultation frequency, familiarity with immunization, work ethic, and flexible schedules positively impacted HCWs’ recommendation behavior. Results: Factors influencing Peds-PCPs’ recommendations include vaccine training (OR: 1.470, CI: 1.049–2.509), safety recognition (OR: 1.986, CI: 1.163–3.391), concern over rejection (OR = 1.274, CI: 1.076–1.508) and vaccine cost (OR = 1.203, CI: 1.023–1.414). For PCPs, influencing factors were the perceived susceptibility of children to pneumonia (OR = 2.185, CI: 1.074–4.445), acceptance of herd immunity (OR: 1.717, CI: 1.101–2.677), and belief that parents with better family conditions are more likely to accept vaccine recommendations (OR = 1.229, CI: 1.024–1.477). Conclusion: This survey underscores the need for tailored interventions to address differing perceptions and enhance confidence in the safety and efficacy of vaccines among HCWs, particularly Peds-PCPs.

As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.

To study material erosion and deposition during different wall conditioning processes in the Experimental Advanced Superconducting Tokamak (EAST), two newly developed quartz crystal microbalances (QMBs) at the mid-plane of port C (C-QMB) and port J (J-QMB) with the same radial radius were used for in-situ and real-time measurements during the 2021 autumn experimental campaign. It was found that the material deposition was mainly influenced by the location of the antenna and the power of the ion cyclotron range of frequency (ICRF), rather than the location of the gas inlet, during the wall cleaning with helium plasma and siliconization assisted with ICRF plasma. The higher energy and density of plasma could result in higher deposition rates near the antenna. While, during lithium (Li) coating, the material deposition was greatly influenced by the location of the Li oven due to the deposition of unionized Li atoms. The ICRF power influenced the material deposition behavior far away from the Li ovens. Besides, a stronger effect on impurity suppression was observed with a more uniform deposition trend of Li coating. This necessitates further study on the influence of factors such as the location of the Li oven and ICRF antenna, the temperature of ovens, and ICRF power on the homogeneity of wall conditioning.

The microstructure evolution of near β-Ti alloys with initially lamellar and equiaxed microstructures subjected to dynamic loading was investigated. Results showed that during high strain rate compression, shear failure is more likely to occur in equiaxed microstructure. Owing to temperature rise and stress concentration, the lamellar α phase in adiabatic shear band (ASB) zone was recrystallized and re-melted, while the equiaxed α phase transformed in strip-shaped following the propagating direction of adiabatic shear band. During high strain rate loading, the twinning transfer was observed between β matrix and α phase, which demonstrates that twinning is the dominant deformation mode to accommodate plastic deformation.

The H8 and S1 reservoirs in the lower Shihezi Formation and Shanxi Formation of the central block in the Sulige Gas Field are typical fluvial tight sandstone reservoirs. Due to frequent river channel migrations during deposition, the reservoirs exhibit complex spatial structures with developed intra-sand mudstone interlayers. As the field has entered the middle and late stages of development, the distribution of remaining gas is intricately controlled by these interlayers, necessitating research on their distribution to understand the remaining gas patterns and types for effective extraction enhancement. However, the thinness of interlayers presents a challenge for precise prediction. Addressing this, this study delineates different interlayer types and their origins, applies reservoir architecture theory, and utilizes bounding surfaces characterization, planar and sectional distribution studies, unit scale analysis, horizontal well data, and quantitative characterization methods to investigate the internal reservoir architecture bounding surfaces. The study finely portrays the interlayer distribution, analyzes the control of reservoir architecture bounding surfaces on remaining gas, and establishes a multi-tiered reservoir architecture model in the study area. Numerical simulation of the gas reservoir clarifies the types of remaining gas enrichment. This study also identifies and quantitatively characterizes the 5–3 level architecture bounding surfaces within the sandbody, categorizing the remaining gas into bounding surfaces-controlled, well-network uncontrolled, and single-layer unperforated types, proposing targeted enhancement measures for each type. Based on the findings, four vertical wells and three horizontal wells were deployed, improving the well network density to three wells per square kilometer. The first completed horizontal well encountered an effective drilling rate of 61.7%, marking significant implications for the exploitation and recovery enhancement of similar tight sandstone gas reservoirs.

Characteristics of random ocean waves have been measured by different devices, and X-band marine radar is one of the typical devices. This study proposes an enhanced methodology for estimating the significant wave height of ocean waves through the analysis of X-band radar images, particularly leveraging the shadowing characteristics inherent within radar images. The enhancement of the shadowing-based algorithm is achieved by incorporating three different key physical properties of ocean waves. These include the spatial autocorrelation function (SACF) in the Smith function, the orthogonal property of mean surface slopes, and the relationship of high-order spectral moments. The enhanced algorithm is complementarily integrated with fast Fourier transform (FFT)-based spectral analysis, facilitating the determination of significant wave height without the necessity for supplementary reference measurements. Numerical tests have been conducted using synthetic and real radar images corresponding to various sea states to validate the accuracy and reliability of the proposed methodology. The results demonstrate that the proposed techniques consistently improve the estimation accuracy of significant wave heights for both synthetic and real radar images. Even though the measured real radar images used for validation are not exhaustive in terms of the amount of dataset and range of sea state severity, considering that the proposed technique is in its early development stage, it is inspiring that its effectiveness and physical validity have been demonstrated through the present study.

Introduction: Rice (Oryza sativa L.) is one of the most extensive crops in the world. China’s Heilongjiang Province is the northernmost rice-growing region in the world. However, rice cultivars suitable for growth in low-latitude regions may not mature normally due to their distinct climate and short frost-free period. It is necessary to precisely determine the frost-free period for each region to make the best use of the rice growth stage so as to ensure the maturity and yield of different rice cultivars in Heilongjiang Province. The time span of the heading stage is a key parameter for evaluating the adaptability of a rice cultivar to a specific rice-growing region. Given the above facts, it is of high importance to study the associated genes and sites controlling days to heading (DH) and plant height (PH) of rice in Heilongjiang Province. Bulked segregant analysis (BSA) combined with high-throughput sequencing can effectively exclude interferences from background genomic differences, making it suitable for analyzing the associated sites of complex agronomic traits in early generations.Methods: In this study, an F3 segregating population was obtained by crossing two main cultivars that are grown under different temperatures and day-light conditions in Heilongjiang. Two pools of extreme phenotypes were built for the DH and PH of the population. For SNP and InDel variants obtained from whole-genome resequencing in the pools, an association analysis was performed using the Euclidean distance (ED) algorithm and the SNP/InDel index algorithm.Results: The intersection of SNP and InDel regions associated with the phenotypes was considered to obtain the final associated sites. After excluding interferences from the cloned genes on chromosomes 2 and 7, a total length of 6.34 Mb on chromosomes 1, 3, and 10 and 3.16 Mb on chromosomes 1 and 10 were left associated with PH and DH, respectively. Then, we performed a gene annotation analysis for candidate genes in the remaining regions using multiple genome annotation databases. Our research provides basic data for subsequent gene mapping and cloning.Discussion: By mining more genetic loci associated with the days to heading and plant height of rice, we may provide abundant genetic resources for refined molecular breeding in Heilongjiang Province.

The complexity and strong heterogeneity of carbonate reservoirs with fracture-vuggy structures present significant challenges in reservoir characterization. To address these challenges, we propose a novel multi-element information fusion modeling approach. This approach is designed to integrate multiple methods and incorporate multi-probability fusion at various facies and scales, thereby bridging the gap between geological information and reservoir modeling. Our methodology involves four key steps. First, the statistics between frequency of karst and geological information are acquired, and we quantify the statistics to regression equations. Second, these regression equations are transferred to probability bodies. The probability bodies can be applied in modeling as a soft control. But just one single body can be input in modeling process. Third, multiple probability bodies are fused into a fusion probability body by a probability fusion algorithm, which can keep the potential information of probability bodies. Finally, we apply the probability body in modeling workflow. By this way, the fusion method bridges the gap between geological information and modeling. The model established through our proposed method showed a significant level of consistency with reservoir re-evaluation, achieving an impressive 90% degree of alignment. Furthermore, the history match analysis revealed a high correlation, indicating the model's reliability. The method effectively integrates various scales and types of geological information, offering an accurate approach to complex carbonate reservoir modeling.

Fibrotic skin diseases, such as keloids, are pathological results of aberrant tissue healing and are characterized by overgrowth of dermal fibroblasts. Remdesivir (RD), an antiviral drug, has been reported to have pharmacological activities in a wide range of fibrotic diseases. However, whether RD function on skin fibrosis remains unclear. Therefore, in our study, we explored the potential effect and mechanisms of RD on skin fibrosis both in vivo and in vitro. As expected, the results demonstrated that RD alleviated BLM-induced skin fibrosis and attenuates the gross weight of keloid tissues in vivo. Further studies suggested that RD suppressed fibroblast activation and autophagy both in vivo and in vitro. In addition, mechanistic research showed that RD attenuated fibroblasts activation by the TGF-β1/Smad signaling pathway and inhibited fibroblasts autophagy by the PI3K/Akt/mTOR signaling pathway. In summary, our results demonstrate therapeutic potential of RD for skin fibrosis in the future.

A novel machine learning method, named CatBoost-DF (CatBoost deep forest), is proposed to solve this existing problem of low accuracy and lack of practicality in thermal sensation prediction. In the CatBoost-DF, a cascading strategy is introduced to strengthen the association between each layer of CatBoost. To verify the accuracy and robustness of CatBoost-DF, experiments collected physiological and environmental data from hundreds of subjects with the help of sensor devices and questionnaires. Compared with existing state-of-the-art machine learning methods, CatBoost-DF shows significant superiority, with a prediction accuracy of 90%, which is 4%-39% higher than other models. Moreover, the study explored the effects of seasonal and gender factors on thermal sensation. Result shown that different seasons have different thermal sensation for males and females. Finally, CatBoost-DF is applied to predict occupants’ thermal sensation, and the “comfort range” of the important parameters HR, WS, and CTR that affect the thermal sensation is calculated experimentally.

To understand the controlling mechanism of pore-throat characteristics on gas-water relative permeability characteristics of tight sandstone gas reservoirs and better guide the increase in gas reserves and production, taking the Lower Shihezi Formation reservoir in the J72 well block of the Dongsheng Gas Field as a research object, the effects of microscopic pore-throat parameters on gas and water transport properties were studied by combining various analytical data and lithofacies division. The controlling effect of rock lithofacies on gas content and productivity was revealed.Results show that the lithofacies of the Lower Shihezi Formation in the J72 well block can be classified into conglomerate rock, gravelly sandstone, pebbly coarse-grained sandstone, medium- and fine-grained sandstone, and mudstone lithofacies, of which the pebbly coarse-grained sandstone and gravelly sandstone lithofacies are the high-quality lithofacies. Pore types are mainly secondary intergranular dissolved pores, with an average proportion of 48.5%. The proportion of intergranular dissolved pores in pebbly coarse-grained sandstone lithofacies, gravelly sandstone, and medium- and fine-grained sandstone lithofacies decreases successively.Pore space in the three lithofacies is multifractal, and the proportion of large pores gradually decreases. The high-quality lithofacies has superior physical properties and small comprehensive fractal dimensions. The high-quality lithofacies also has low clay mineral content, large average pore-throat radius, large movable gas porosity, maximum effective gas permeability during gas transport, and high gas-bearing capacity. A comprehensive study shows that high-quality lithofacies is the key factor in controlling the high and stable production of gas wells. The in-depth analysis of the microscopic characteristics of lithofacies provides useful guidance for the efficient development of tight sandstone gas reservoirs.

Travel online reviews is important experience related information for understanding an inherent personality trait, novelty seeking (NS), which influences tourism motivation and the choice of tourism destinations. Manual classification of these reviews is challenging due to their high volume and unstructured nature. This paper aims to develop a classification framework and deep learning model to overcome these limitations. A multi-dimensional classification framework was created for NS personality trait that includes four dimensions synthesized from prior literature: relaxation seeking, experience seeking, arousal seeking and boredom alleviation. Based on 30 000 reviews from TripAdvisor we propose a deep learning model using Bidirectional Encoder Representations from Transformers (BERT)- Bidirectional Gated Recurrent Unit (BiGRU) to recognize NS automatically from the reviews. The classifier based on BERT- BiGRU and NS multi-dimensional scales achieved precision and F1 scores of 93.4% and 93.3% respectively, showing that NS personality trait can be relatively accurately recognized. This study also demonstrates that the classifier based on multi-dimensional NS scales can produce satisfactory results using the deep learning model. The findings also indicate that the BERT- BiGRU model achieves the best effect compared to the same kind of deep learning models. Moreover, it proves that personality traits can be automatically identified from travel reviews based on computational techniques. For practical purposes, this study provides a comprehensive classification framework for NS, which can be used in marketing and recommendation systems operating in the tourism industry.

In Software-Defined Networks (SDN), the limited flow table capacity of switches makes them susceptible to flow table overflow attacks, which can lead to performance degradation or network corruption. Prior research has mainly focused on rate-based overflow attacks (ROA), which exhibit varying attack effects depending on the overflow rate. This study introduces a novel attack called the preemptive overflow attack (POA), which exploits flow entry eviction mechanism to preempt the flow entries of normal applications, resulting in amplified performance degradation. Notably, when using the widely deployed Least Frequently Used (LFU) eviction algorithm, POA achieves a significant impact while consuming fewer flow entries than existing ROA methods. Furthermore, the detection of POA remains challenging owing to the lack of distinctive flow features. To mitigate POA, we propose POAGuard as a defense mechanism. POAGuard incorporates a table segmentation method for table management, a score-based eviction algorithm that evicts suspicious flow entries, and a concept drift-based detection method that identifies and defends against POA. Extensive experiments are conducted to verify the effectiveness of POAGuard, and the results demonstrate that POAGuard can effectively defend against POA.

This report does some work on big data analysis of EV loads and extends automatic generation control (AGC) to automatic active power control (APC) by combining the current operating conditions of the grid and the spatial and temporal distribution characteristics of the adjustable capacity of EV loads. One year of user data from three EV charging stations provided by Chongqing is processed, cleaned, and clustered to analyze several typical EV load behaviors and the assessment of their adjustable characteristics. The data processing methods and the comparison of the K-means algorithm, the direct clustering, and the hierarchical clustering algorithm DPC clustering algorithm are also shown for this example.

Tunnel construction projects are a classic type of repetitive project, and hold a crucial position in the construction industry. The linear scheduling method (LSM) has been in the spotlight in scheduling optimization for repetitive construction projects since it was first proposed. However, the possibility of changing the construction sequence during application is very often overlooked, which is not in line with actual engineering practices. Incorporating soft logic into LSM and considering the characteristics of tunnel construction, we propose an optimization method for the tunnel construction schedule to shorten the duration by opening up an additional working surface. A mixed-integer programming (MIP) model is developed, which considers various constraints, such as construction sequence and work continuity. The exact algorithm and genetic algorithm (GA) are designed via Python, and the validity is verified through practical cases. A comparative analysis was conducted between the two algorithms and their outcomes demonstrates that the method can satisfy the restrictive constraints of tunnel construction projects. In addition, improvement strategies for tunnel construction management are recommended.

The construction of major railway projects poses significant risks, which present considerable challenges to construction management. To accurately assess the level of construction management for these projects, this study incorporated resilience theory into the field. The grounded theory method was utilized to establish a resilience evaluation indicator system for managing a major railway project construction. Additionally, a resilience evaluation model based on the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation method was proposed. This model was applied to evaluate the construction management resilience of a major railway project located in the mountainous region of southwest China. The results indicated that the project exhibits a very high overall level of construction management resilience. Specifically, it demonstrates high levels of ability to monitor and warn, an ability to resist absorption, and an ability to respond to emergencies. Additionally, it showcases high levels of ability to recover and rebuild, and an ability to learn to adapt. The evaluation results were consistent with the actual situation and verified the correctness and reliability of the method. Based on the aforementioned research findings, this paper puts forward recommendations on material redundancy and resource security from a resource perspective, and suggestions on organizational optimization and personnel capacity improvement from a subject perspective, thus indicating directions for enhancing the management level of major engineering railway constructions.

Compared with the traditional grid-following photovoltaic grid-connected converter (GFL-PGC), the grid-forming photovoltaic grid-connected converter (GFM-PGC) can provide voltage and frequency support for power systems, which can effectively enhance the stability of power electronic power systems. Consequently, GFM-PGCs have attracted great attention in recent years. When an asymmetrical short-circuit fault occurs in the power grid, GFM-PGC systems may experience transient instability, which has been less studied so far. In this paper, a GFM-PGC system is investigated under asymmetrical short-circuit fault conditions. A novel Q-V droop control structure is proposed by improving the traditional droop control. The proposed control structure enables the system to accurately control the positive- and negative-sequence reactive current without switching the control strategy during the low-voltage ride-through (LVRT) period so that it can meet the requirements of the renewable energy grid code. In addition, a dual-loop control structure model of positive- and negative-sequence voltage and current is established for the GFM-PGC system under asymmetrical short-circuit fault conditions. Based on the symmetrical component method, the composite sequence network of the system is obtained under asymmetrical short-circuit fault conditions, and positive- and negative-sequence power-angle characteristic curves are analyzed. The influence law of system parameters on the transient synchronous stability of positive- and negative-sequence systems is quantitatively analyzed through the equal area criterion. Finally, the correctness of the theoretical analysis is verified by simulation and hardware-in-the-loop experiments.

In the experiment, used Xinjiang Tianshan Special Flour and related additives as raw materials with shear force (ductility, hardness, chewability) and cooking characteristics (water absorption index, cooking loss rate), starch gelatinization degree and sensory evaluation as evaluation indices. The effects on microwave firepower, steam microwave curing time and steam quantity on dough slice quality, on the basis, the steam microwave curing process was optimized by combining response surface and principal component analysis. The results show that: Under the conditions of 60% microwave power, 3 min steam microwave curing time and 12 L steam quantity, ductility was 98.155 g·s, hardness was 2204.46 g, chewability was 5200.52 g·s, water absorption index was 142.516%, cooking loss rate was 2.79%, sensory score was 91.8 and standardized score was 0.946, which were basically consistent with the predicted value. Through the experiment, steam microwave curing process was optimized by response surface analysis (RSM) and principal component analysis (PCA), which had guiding significance for practical production.

Polyimides are polymeric materials that are widely used in spacecraft applications owing to their unique properties. However, exposure to a low-Earth-orbit environment containing atomic oxygen (AO) results in the disintegration of polymeric materials on the surface of spacecraft, thereby affecting the lifespan. Along with the development of theoretical research, the reactive force-field (ReaxFF) interatomic potential has become a robust computational method for exploring, developing and optimizing the material properties. This study employs the ReaxFF reactive-force-field molecular dynamics simulation (ReaxFF MD) program to investigate and compare the performance of two typical polyimide materials, Kapton and Upilex-S, under the impact of AO. Various aspects such as variations in the temperature, mass loss, decomposition products, and damage propagation depth were examined. Although these materials have similar elemental composition (C/H/O/N), they have different structures. Our results indicate that AO is initially adsorbed on the surfaces of both Kapton and Upilex-S. The continuous impact of AO leads to chemical reactions between AO and Kapton/Upilex-S. Erosion proceeds from the surface toward the interior of the materials. Similar to the findings of Experiment 2 conducted by the Materials International Space Station, our results also reveal that Upilex-S exhibits a lower mass loss and erosion yield than Kapton under the same AO conditions. This difference is primarily attributed to the distinct molecular structures of both Kapton and Upilex-S. Our study could provide valuable technical support for the extensive application of Upilex-S in spacecraft.

ObjectiveIn humans, arterial grayscale ultrasound texture features independently predict adverse cardiovascular disease (CVD) events and change with medical interventions. We performed this study to examine how grayscale ultrasound texture features and elastin fibers change in plaque-free segments of the arterial wall in a murine model prone to atherosclerosis.MethodsA total of 10 Apoetm1Unc/J mice (n = 5 male, n = 5 female) were imaged at 6, 16, and 24 weeks of age. Two mice were euthanized at 6 and 16 weeks and the remaining mice at 24 weeks. Texture features were extracted from the ultrasound images of the distal 1.0 mm of the common carotid artery wall, and elastin measures were extracted from histology images. Two-way analysis of variance was used to evaluate associations between week, sex, and grayscale texture features. Texture feature and elastin number comparisons between weeks were conducted using the sex-by-week two-way interaction contrasts. Sex-specific correlations between the number of elastin fibers and grayscale texture features were analyzed by conducting non-parametric Spearman's rank correlation analyses.ResultsArterial wall homogeneity changed significantly in male mice from 6 to 24 weeks, with a mean (SD) of 0.14 (0.03) units at 6 weeks and 0.18 (0.03) units at 24 weeks (p = 0.026). Spatial gray level dependence matrices-homogeneity (SGLD-HOM) also correlated with carotid artery plaque score (rs = 0.707, p = 0.033). Elastin fibers in the region of interest decreased from 6 to 24 weeks for both male and female mice, although only significantly in male mice. The mean (SD) number of elastin fibers for male mice was 5.32 (1.50) at 6 weeks and 3.59 (0.38) at 24 weeks (p = 0.023). For female mice, the mean (SD) number of elastin fibers was 3.98 (0.38) at 6 weeks and 3.46 (0.19) at 24 weeks (p = 0.051).ConclusionGrayscale ultrasound texture features that are associated with increased risk for CVD events in humans were used in a murine model, and the grayscale texture feature SGLD-HOM was shown to change in male mice from 6 weeks to 24 weeks. Structural alterations of the arterial wall (change in elastin fiber number) were observed during this time and may differ by sex.

Internal depositional architecture and sand body distribution are the main challenges faced in the development of gravity flow channel deposits in China. Despite significant progress in the exploration and development of gravity flow deposits in recent years, our understanding of the internal architecture of composite sand bodies within gravity flow channels is still limited. Gravity flow channels represent a widely developed sedimentary type in the Shahejie Formation of the Banqiao Oilfield, Huanghua Depression. The lack of understanding of the spatial stacking relationship of gravity flow channel sand bodies hinders further development and remaining oil recovery within the oilfield. Through this study, we aimed to dissect the composite channels (5th architectural units) and single channels (4th architectural units) at the study area, using a combination of well logs and seismic data. We explored the identification criteria and spatial distribution characteristics of internal architectural elements within gravity flow channel reservoirs, based on abundant drilling data, well density grids, and 3D seismic data. By identifying and delineating single channels, we were able to summarize six identification criteria for single channels, including relative elevation differences, curve shapes, and the number of interbeds. We obtained the sand body scale and aspect ratio of single channels and established three depositional architectural patterns, i.e., isolated, lateral migration, and vertical accretion, thus revealing the differences in the spatial stacking relationships of sand bodies in different structural locations (blocks). This work provides new insights into the depositional architectural patterns of gravity flow channel deposits in the Banqiao Oilfield, Huanghua Depression.

The lack of research on fine reservoir structure and sand body patterns in the Jidong Oilfield currently restricts the efficient development of the oilfield. Therefore, this article mainly focuses on the study of the main types of facies of the Shahejie Formation, sand body splicing patterns, and the degree of sand-body connectivity. The interpretation and analysis of well-logging, three-dimensional (3D) seismic, and production data were used to lay the foundation for the study and evaluate the remaining oil distribution. The results indicate that the reservoir sandstones in the study area were mainly deposited in a submerged distributary channel, mouth bar, and distributary channel flank. Using logging information to identify individual sands, a deltaic sand assemblage pattern is proposed by analyzing the sedimentary architecture. In the vertical direction, the deltaic sand body collocation style can be divided into cut-and-stack and separated types. In the lateral direction, the multi-stage sand bodies exhibit three collocation patterns: the side-cutting type, the mouth bar contact type, and the submerged distributary channel flank contact type. The degree of sand-body connectivity under different splicing patterns was analyzed and verified using production dynamic data. It was found that the sand body splicing pattern with a vertical up-cut stack and the sand body splicing pattern with a lateral up-cut had the best inter-sand-body connectivity.

The dough slice was cooked by steam microwave, the influences of microwave drying (MD), hot air drying (HD), microwave-hot air combined drying (MD-HAD), microwave-vacuum combined drying (MVD) and vacuum-freeze combined drying (VD-FD) on the drying characteristics, shear force, boiling characteristics, microstructure and organoleptic evaluation of the dough slice were researched. The results indicated that the water loss rate of the dough slice was MVD>MD>MD-HAD>HD, and the drying of the dough slice of MVD was completed in only 30 min under the conditions of minimum power, maximum load and vacuum degree. According to the shear force and boiling characteristics, MD was a better drying method, and the rehydration time was only 359.667 s. According to the microstructure, the quality of the dough slice was as following: VD-FD>MD>MVD>HD>MD-HAD. The cross section of MD dough slice had more and larger voids, which was favorable for shortening the rehydration time. The cross section of VD-FD dough slice had less voids, the size was uniform, and the distribution was uniform. According to the comprehensive sensory evaluation, the palatability, toughness, viscosity and comprehensive sensory score of MD dough slice are relatively high, indicating its good quality.

The molecular classification and tumour microenvironment in intrahepatic cholangiocarcinoma (iCCA) need further characterisation. Here, the authors perform single cell RNA-sequencing from 14 pairs of iCCA tumours and non-tumour liver tissues and propose S100P and SPP1 as markers for patient classification.

The SARS-CoV-2 coronavirus pandemic has led to increased food insecurity levels. This cross-sectional study examines the prevalence and determinants of food insecurity during the two-month (1 April to 1 June 2022) city-wide lockdown in Shanghai. The data was collected via an online questionnaire from 3230 adult Shanghai residents during the lockdown. Food insecurity was measured using an adapted version of the Household Food Insecurity Access Scale. Nearly 70% of participants reported being exposed to food insecurity. Using multivariable logistic regressions, we examined the associations between key correlates (i.e. age, income, lockdown-related income loss, migration, employment status, social capital, preparedness, and received social support) and overall food insecurity while adjusting for ethnicity, gender, education, household size, and marital status. Results showed that compared to local Shanghai residents, migrants (i.e, permanent migrants with Hukou (OR = 2.16), permanent migrants without Hukou (OR = 2.06), temporary migrants (OR = 2.74)), and participants with less than or greather than 50% lockdown-related income loss (OR = 2.60, OR = 3.09), were associated with higher odds of overall food insecurity. Participants with greater preparedness (OR = 0.66), greater bonding social capital (OR = 0.93), and greater bridging social capital (OR = 0.94), had lower odds of overall food insecurity. Targeted interventions are needed to enhance food resilience and health equity among vulnerable populations.

The study of the reservoir architecture in braided river systems has significant implications for the exploitation of remaining oil and gas reserves. However, due to the complexity of the braided river deposition process, the architecture patterns are diverse and intricate. Limited by the quality of seismic data and well network density, the characterization of underground reservoir architecture often entails considerable uncertainty. This paper investigates the architecture elements, stacking patterns, and significance of oil and gas development in the braided river deposition of the Jin 58 well area in the northern part of the Ordos Basin through typical field outcrop and core observations, and by making full use of horizontal well data. The study reveals that the Jin 58 well area is mainly characterized by four types of architecture units: braided channel, channel bar, overbank, and flood plain. Based on the data from horizontal and vertical wells, four identification criteria for single sand bodies are determined, and the vertical stacking and lateral juxtaposition styles of the architecture units, as well as the architecture patterns and internal features of the channel bar, are summarized. It is confirmed that composite sand bodies have better productivity. A three-dimensional architecture model of the braided river is established based on the results of architecture analysis. The accuracy of the architecture analysis is validated through numerical simulation, providing a basis for subsequent well deployment and other related activities.