To establish a cuproptosis-related lncRNA signature and evaluate its prognostic value and potential immunological relevance in gastric cancer (GC). Transcriptomic data from the cancer genome atlas and gene expression omnibus were analyzed using Pearson correlation and least absolute shrinkage and selection operator Cox regression to construct the prognostic model. Model performance was assessed by Kaplan-Meier analysis, time-dependent receiver operating characteristic curves, and decision curve analysis. An mRNA-based RS score was further validated in Gene Expression Omnibus datasets and the IMvigor210 cohort. Expression of 2 key long noncoding RNAs (AC245041.1 and LINC01614) was confirmed by qRT-PCR in an independent GC cohort. A 25-lncRNA cuproptosis-related signature effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly poorer survival. The model outperformed conventional clinical variables. Functional enrichment suggested involvement of immune regulation and mitochondrial pathways related to cuproptosis, and the low-risk group displayed a more immunologically active profile. The RS score showed consistent predictive performance across external cohorts, and qRT-PCR validated the prognostic relevance of AC245041.1 and LINC01614. This cuproptosis-related lncRNA signature demonstrates potential as a prognostic indicator and may provide insights into immunotherapy relevance, offering a promising direction for personalized GC management.

Travel time estimation (TTE) is a critical function in intelligent driving systems. Current research and applications related to TTE primarily focus on urban environments. The objective of this study is to develop TTE methods that are applicable to wilderness areas characterized by plateau and mountainous topography. We selected Transformer, which has greater robustness in capturing long-distance dependencies than LSTM, to develop a Transformer-based model. The model simultaneously integrates positional encoding and multi-head self-attention mechanisms with the objective of enhancing the accuracy of travel time predictions based on a substantial number of trajectory points in wilderness settings. A meta-learning strategy was employed to improve the model’s generalization ability, thereby ensuring its applicability for accurate travel time estimation across a range of challenging environments. Two datasets were constructed based on measurements from two selected areas in eligible plateau and mountainous regions of western China. For each dataset, two categories of features were defined: terrain-weather features and spatio-temporal features. These categories were established in accordance with the influence of seven specific features on traffic conditions in both urban and wilderness areas. Experiments were conducted on both datasets utilizing terrain-weather features. When evaluated alongside the five models that are most commonly utilized in urban settings, the mean absolute percentage error (MAPE) of our model exhibited a 14.89% improvement in plateau environments and a 12.20% improvement in mountainous environments in comparison with the most effective model, namely MetaTTE-GRU. These findings substantiate the assertion that the proposed model is an effective means of estimating travel times in complex environments, and that it exhibits superior accuracy compared to existing LSTM-based estimation models.

Skin cutaneous melanoma (SKCM) arises from melanocytes and is an aggressive form of skin cancer. If left untreated, most melanomas will metastasize, posing a major health risk. GADD45B, a member of the GADD45 family, is known to be involved in DNA damage repair; however, its specific role in SKCM remains largely unclear. In this study, we comprehensively investigated the function of GADD45B in SKCM. By integrating 26 SKCM-related datasets from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), cBioPortal for Cancer Genomics (cBioPortal), Gene Expression Omnibus (GEO), and other databases, we conducted functional enrichment, immune infiltration, and single-cell analyses using R. Additionally, transcriptome sequencing of 30 human SKCM cell lines, phenotype characterization of 29 SKCM lines in vitro, and macrophage polarization analysis were performed. We found that GADD45B expression was significantly downregulated in SKCM patients compared to normal controls (p < 0.001), and higher GADD45B levels correlated with better prognosis (p < 0.05). GADD45B also showed high diagnostic accuracy, with an area under the curve (AUC) of 0.986. GO and KEGG analyses revealed a strong association between GADD45B and immune-related pathways. Gene Set Variation Analysis (GSVA) and single-cell sequencing suggested that GADD45B may serve as a novel immune checkpoint, predominantly expressed in macrophages and promoting M1 polarization. In vitro, overexpression of GADD45B significantly inhibited SKCM cell proliferation, potentially via suppression of the PI3K/Akt signaling pathway, and also reduced chemotherapy resistance. Furthermore, in vivo experiments using a xenograft mouse model demonstrated that GADD45B overexpression significantly suppressed tumor growth. Mice injected with GADD45B-overexpressing tumor cells exhibited smaller tumor volumes from day 15 onwards compared to controls, with markedly reduced tumor volume and weight at the endpoint. These results underscore the potential of GADD45B as an effective tumor suppressor in SKCM. In conclusion, our findings highlight GADD45B as a key regulator in SKCM progression, capable of restraining tumor cell proliferation and enhancing apoptosis. GADD45B holds promise as a novel diagnostic and prognostic biomarker and a potential target for SKCM immunotherapy.

Current road extraction models from remote sensing images based on deep learning are computationally demanding and memory-intensive because of their high model complexity, making them impractical for mobile devices. This study aimed to develop a lightweight and accurate road extraction model, called Road-MobileSeg, to address the problem of automatically extracting roads from remote sensing images on mobile devices. The Road-MobileFormer was designed as the backbone structure of Road-MobileSeg. In the Road-MobileFormer, the Coordinate Attention Module was incorporated to encode both channel relationships and long-range dependencies with precise position information for the purpose of enhancing the accuracy of road extraction. Additionally, the Micro Token Pyramid Module was introduced to decrease the number of parameters and computations required by the model, rendering it more lightweight. Moreover, three model structures, namely Road-MobileSeg-Tiny, Road-MobileSeg-Small, and Road-MobileSeg-Base, which share a common foundational structure but differ in the quantity of parameters and computations, were developed. These models varied in complexity and were available for use on mobile devices with different memory capacities and computing power. The experimental results demonstrate that the proposed models outperform the compared typical models in terms of accuracy, lightweight structure, and latency and achieve high accuracy and low latency on mobile devices. This indicates that the models that integrate with the Coordinate Attention Module and the Micro Token Pyramid Module surpass the limitations of current research and are suitable for road extraction from remote sensing images on mobile devices.

The causes of frequent corrosion and leakage of 7 ℃ water condenser in second-line distillation were thoroughly investigated and analyzed from the aspects of device design,water quality control and process medium,and the solutions were put forward.

Aiming at the problem of poor desorption effect of hydrochloric acid,a systematic investigation and analysis was carried out by means of industry investigation,consultation of equipment manufacturers,inquiry of design data,on-site equipment running status investigation,and symposium. Finally,the reasons were found out and the problems were solved.

Construction of real-world driving cycle is of great significance for designing and assessing the energy management strategy of electric vehicles. In this study, five methods of driving cycle construction are investigated, namely, the random selection method, principal component analysis-based method, clustering analysis method, Markov chain-based method, and the optimization-based method. Urban driving conditions in Shenyang, China, are used as a case study to construct the driving cycles using the five methods, respectively. Based on the above efforts, an evaluation method of driving cycle effectiveness is proposed from the three perspectives, namely, accuracy, operability, and reproducibility. Characteristic parameters, speed–acceleration probability distribution, impact on energy control effect, dependence on data volume, and result repeatability are considered specifically. The assessing results of the common methods are proposed by making systematic comparisons. The results disclose the advantages and disadvantages of each method and obtain the ranking of five methods in three performance indexes. The presented results are expected to provide a theoretical basis and useful guidance for establishing real-world driving cycles in practical engineering applications.

针对高速列车一位转向架受到来流冲击产生较强气动噪声的特点,基于声类比方法,采用安装普通排障器和底部后端设置平行凹坑排障器的高速列车转向架区域简化模型和车头比例模型,计算分析平行凹坑对转向架区域流场和气动噪声特性的影响,并进行声学风洞测试,验证数值模拟气动噪声的降噪效果.结果表明:在高速列车排障器底部后端设置凹坑,可抑制转向架区域流场剪切层的生长和发展,削弱转向架舱前缘流动分离并缓和尾流与舱内各部件的流动冲击作用,干扰转向架周围大尺度湍涡的形成和脱落,抑制排障器与转向架区域几何体表面压力脉动的形成,减少气动噪声的产生;声学风洞中列车模型排障器底部后端采用扰流措施后,转向架部位噪声源面积缩小,噪声幅值降低约1dB(A),气动噪声得到有效控制.

Ferroelectric field‐effect transistors (FeFETs) with 2D semiconductors as channel materials have been fabricated to achieve miniaturized size, high storage, and low power consumption. The FeFETs are studied based on few‐layer MoS2 sheets on the non‐lead Bi0.85La0.15Fe0.92Mn0.08O3 (BLFMO) ferroelectric films with a large remnant polarization (Pr ≈36 μC cm−2). In FeFETs, the conductivity states of the 2D semiconductor can be tuned by the ferroelectric polarization. It is found that the MoS2‐based FeFETs display a large memory windows exceeding 25 V, a high on/off ratio (>105), remarkable program/erase ratio (≈104), competitive retention, endurance, and high‐speed performance. Moreover, the 2D based FeFETs exhibit switchable multi‐bit data storage by applying different amplitudes of negative gate voltage pulses to enhance the data storage density. On the basis of these characteristics, the 2D‐FeFETs are potentially able to meet the need for scalability, capacity, retention, and endurance of nonvolatile memory.

以苝为母体,通过引入全氟联苯基,设计合成一种新型多氟代苝基荧光分子3-(全氟-[1,1'-联苯]-4-基)苝(PFD).经核磁共振氢谱(1H NMR)及高分辨质谱(HRMS)等技术对目标产物进行结构验证,并结合量子化学计算,系统研究其紫外-可见吸收、荧光发射、量子产率及电化学性质.结果表明:在溶液中,PFD于300～475 nm波长范围内展现出强而宽的特征吸收,于425～600 nm波长范围内展现出具有精细结构的荧光发射,乙腈溶液中的最大发射波长(λem max)为460 nm,相对荧光量子产率(Φem)为96％;在固态下,PFD于560 nm处表现出黄光发射,固态绝对荧光量子产率为30％.基于此,将PFD作为发光材料涂敷于蓝光驱动的发光二极管(LED)芯片上,并成功制备出黄光发射的LED器件,此器件的国际照明委员会(CIE)色度坐标为(0.43,0.54),色纯度为93.7％.这种全氟联苯基取代的苝衍生物PFD在新型荧光材料的设计合成及低成本LED发光器件制备等方面都具有潜在的应用前景.

上海是浙江建筑企业在外施工规模最大的建筑市场之一.据不完全统计,浙江进沪建筑企业有800多家、从业人员近20万人.随着国家"碳达峰碳中和"战略的不断推进,如何实现绿色转型发展成为一个重要课题.根据浙江进沪建筑企业在沪实际情况,对与浙江业务主管部门联系相对紧密的200多家企业中的20％(42家)进行了问卷调查,并分别选取了国企、民企、总包、分包等不同类型4家具有代表性的企业进行走访调研.

The simulation of human brain neurons by synaptic devices could be an effective strategy to break through the notorious "von Neumann Bottleneck" and "Memory Wall". Herein, opto-electronic synapses based on layered hafnium disulfide (HfS2) transistors have been investigated. The basic functions of biological synapses are realized and optimized by modifying pulsed light conditions. Furthermore, 2 × 2 pixel imaging chips have also been developed. Two-pixel visual information is illuminated on diagonal pixels of the imaging array by applying light pulses (λ = 405 nm) with different pulse frequencies, mimicking short-term memory and long-term memory characteristics of the human vision system. In addition, an optically/electrically driven neuromorphic computation is demonstrated by machine learning to classify hand-written numbers with an accuracy of about 88.5%. This work will be an important step toward an artificial neural network comprising neuromorphic vision sensing and training functions.

The requirement for detecting faulty equipment is widespread in the industry. In today's big data era, using big data features to detect equipment is facing new opportunities and challenges. In order to improve the efficiency of faulty equipment detection, we found the characteristics of the binary tree that are suitable for manufacturing processes of the current status. In practice, this paper establishes a binary tree model for the manufacturing process. Then, in order to analyze and study the breadth traversal process and depth traversal process of bad nodes in binary tree, this paper proposes a location method of breadth and depth ratio. Finally, the location range of the faulty equipment (bad node) is quickly found due to the influence of breadth and depth ratio. The experimental results show the difference between the existing mechanisms of detection and position. In terms of accuracy and efficiency, both methods of deep traversal and breadth traversal have their own advantages in different devices and processes. To compare with the two methods, the detection method of breadth and depth ratio in this paper has the characteristics of higher efficiency, feasibility and accuracy.

The development of information technology and the advancement of the process of educational informatization have provided technical support for education and education reform. The virtual simulation teaching mode is no longer a new thing for people, and it is more and more accepted by people familiar with it. Induction and analysis of short message texts generated by students’ online learning can fully understand students, make effective adjustments to the classroom, guide teachers’ teaching practice, and optimize the classroom, thereby improving students’ learning ability, practical ability, innovation ability, and collaboration. To study the effect of virtual simulation education on improving the skills of medical students, this paper is based on video training and education of virtual reality side-cut high-simulation simulator using case analysis and literature analysis methods. It collects data from databases and builds on online education. The model was developed, and a large number of relevant pieces of literature were read and analyzed through the literature survey method. The research results show that virtual simulation teaching can effectively improve students’ technical thinking and ability. Students’ technical thinking and ability are about 30% higher than traditional teaching methods, and the cooperation consciousness between students reaches 0.8 consciousness, which can solve the contradiction between popularization and improvement. And to solve the problems of polarization and transformation of underachievers, students’ personalities can be fully displayed and developed, and education and teaching will be on the track of quality education. This shows that training videos based on virtual reality technology can play a very good role in improving students’ technical mastery.

This paper studied the impact of COVID-19 on garlic price and found a model with high accuracy to predict garlic price to provide reference for relevant personnel in the garlic industry. Through the analysis of the average weekly price of garlic over the years, and analysis of garlic prices at specific time points since the outbreak in 2020. It was found that the outbreak had a relatively large impact on garlic prices, which kept garlic prices low relative to previous years. In order to better respond to emergencies. Therefore, it is particularly important to find a better forecasting model for garlic price prediction. It can provide a reference for people engaged in garlic industry. The CEEDMAN-LSTM combined model is used to forecast the average weekly garlic price in 2020, and the prediction results show that the model is suitable for the prediction of garlic price.

The electromagnetic guided wave transducer has been widely used in pipeline detection in recent years due to its non-contact energy conversion characteristics. Based on the Weidemann effect, an electromagnetic guided wave transducer that can realize the locating defect of the steel pipe was provided. Firstly, the principle of the transducer was analyzed based on the Weidemann effect. The basic structure of the transducer and the basic functions of each part were given. Secondly, the key structural parameters of the transducer were studied. Based on the size of the magnets and the coils, a protype electromagnetic guided wave transducer based on Wiedemann effect was developed. Finally, the experiments were carried out on the steel pipe with a defect using the developed transducer. The results show that the transducer can actuate and receive the T(0,1) and T(1,1) modes in the steel pipe. The axial positioning of the defect is located by moving the transducer axially. The circumferential positioning of the defect is located by rotating the transducer. Additionally, missed detection can be effectively avoided by rotating the transducer.

The wall-thinning measurement of ferromagnetic plates covered with insulations and claddings is a main challenge in petrochemical and power generation industries. Pulsed eddy current testing (PECT) is considered as a promising method. However, the accuracy is limited due to the interference factors such as lift-off and cladding. In this study, by decoupling analytic solution, a feature only sensitive to plate thickness is proposed. Based on the electromagnetic waves reflection and transmission theory, cladding-induced interference is firstly decoupled from the analytical model. Moreover, by using the first integral mean value theorem, interferences of insulation and the lift-off are decoupled, too. Hence, the method is proposed by calculating Euclidean distances between the normalized detection signal and normalized reference signal as the feature to assess wall thinning. Its effectiveness under various conditions is examined and results show that the proposed feature is only sensitive to the ferromagnetic plate thickness. Finally, the experiment is carried on to verify this method practicable.

Background Reports have proven that shorter door-to-needle time (DTN time) indicates better outcomes in AIS patients received intravenous thrombolysis. Efforts have been made by hospitals and centers to minimize DTN time in many ways including introducing a stroke nurse. However, there are few studies to discuss the specific effect of stroke nurse on patients’ prognosis. This study aimed to compare consecutive AIS patients before and after the intervention to analyze the effect of stroke nurse on clinical outcome of AIS patients. Methods In this retrospective study, we observed 1003 patients from November 2016 to December 2020 dividing in two groups, collected and analyzed AIS patients’ medical history, clinical assessment information, important timelines, 90 mRS score, etc. Comparative analysis and mediation analysis were also used in this study. Results A total of 418 patients was included in this study, and 199 patients were enrolled in the stroke nurse group and 219 was in the preintervention group. Baseline characteristics of patients showed no significant difference except there seems more patients with previous ischemic stroke history in the group of stroke nurse. ( p = 0.008). The median DTN time significantly decreased in the stroke nurse group (25 min versus 36 min, p < 0.001) and multivariate logistic regression analysis showed the 90-day mRS clinical outcome significantly improved in the stroke nurse group ( p = 0.001). Mediation analysis indicated the reduction of DTN time plays a partial role on the 90 days mRS score and the stroke nurse has some direct effect on the improvement of clinical outcome ( p = 0.006). Conclusions The introduction of stroke nurse is beneficial to clinical outcome of AIS patients and can be use of reference in other hospitals or centers.

Kisspeptin-10 (Kp-10) is a peptide hormone that regulates normal physiological processes. The mechanism of Kp-10 in milk synthesis is still unclear. Therefore, bovine mammary epithelial cells (BMECs) were used to study the mechanism by which Kp-10 affects milk synthesis in BMECs. The GPR54 inhibitor and SIRT6 overexpression plasmid and siRNA were used to study the mechanism of regulating milk protein and milk fat synthesis by Kp-10. The results showed that 100 nM Kp-10 increased milk synthesis in BMECs. SIRT6 overexpression could significantly reduce the milk protein and milk fat synthesis in BMECs. Moreover, overexpression of SIRT6 reversed the activation of the Kp-10-induced mTOR signaling pathway. Further analysis suggested that SIRT6 might regulate the signal transduction of mTOR at the transcriptional level. These results strongly suggested that Kp-10/GPR54 activated the mTOR signaling pathway by inhibiting SIRT6 expression and then increased the milk synthesis in BMECs.

Overpressure measurement is an important approach to evaluate the power of shock wave (SW) monitoring. Traditional wired monitoring systems exhibit the limitations of high-cost, heavyweight, troublesome maintenance, and big-data transmission in SW monitoring. In this article, a new lightweight FPGA-based wireless overpressure node (LFWON) with the resistance to high-temperature and high-pressure environment for SW monitoring. The proposed LFWON is based on the Spartan-6 XC6SLX92TQG144C FPGA circuit, via a serial peripheral interface to the RF transceiver and data bus to the NAND flash chip for data management. To validate the LFWON, experimental tests in terms of dynamic parameters and network quality are performed in a real blast testing with 8-kg trinitrotoluene. This article is conducted to provide new insights into how the antishocking structure and sensing algorithm of wireless sensor node is designed in SW monitoring for acquiring overpressure accurately. The results show that the errors of ΔP(7 m-12 m), t d (>6 m), and I + (3 m-24 m) from proposed LFWON are below 20% in comparison with wired system. In addition, the RSSI value of LFWON should be set above -70 dBm for stable communication quality.