Laboratory test on the combined cooling effect of L-shaped thermosyphons and thermal insulation on high-grade roadway construction in permafrost regions

With the completion of the Qinghai-Tibetan Railway,economic development of related areas has been greatly accelerated.This,in return,calls for building or upgrading more roadways,especially high-grade roadways.In cold regions,the thawing of permafrost can induce settlement damage of and even failure to railway (or roadway) embankments.Thermosyphons (self-powered refrigera-tion devices that are used to help keep the permafrost cool) have proved effective in mitigating thaw settlement by maintaining the thermal stability of the embankments.However,for high-grade roadway embankments of great width,stabilizing or cooling ef-fects of traditional geotechnological measures may be limited.To enhance the cooling effect of thermosyphons,an L-shaped thermosyphon was designed.A laboratory test was carried out to study the combined cooling effect of the L-shaped thermosyphon and thermal insulation applying to roadbed construction.The angle between the evaporator and condenser sections of the L-shaped thermosyphon is 134 degrees,and the L-shaped thermosyphon was inserted into the soil at an angle of 5 degrees with the road surface.The tested results show that the L-shaped thermosyphon is effective in removing heat from a roadway in winter.When the ambient air temperature is lower than the soil temperature,the thermosyphon is active and extracts the heat in the soil around it.When the ambient air temperature is higher than the soil temperature,the thermosyphon is inactive,and no heat is in-jected into the soil through the L-shaped thermosyphon.Compared to embankments with straight thermosyphons,the inner parts of the embankments with L-shaped thermosyphons were significantly cooled.It is hoped that the present study would be useful to the application of L-shaped thermosyphons in the construction of high-grade roadways in cold regions.

High-grade pancreatic intraepithelial neoplasia diagnosed based on changes in magnetic resonance cholangiopancreatography findings:A case report

BACKGROUND High-grade pancreatic intraepithelial neoplasia(PanIN)exhibits no mass and is not detected by any examination modalities.However,it can be diagnosed by pancreatic juice cytology from indirect findings.Most previous cases were diagnosed based on findings of a focal stricture of the main pancreatic duct(MPD)and caudal MPD dilatation and subsequent pancreatic juice cytology using endoscopic retrograde cholangiopancreatography(ERCP).We experienced a case of high-grade PanIN with an unclear MPD over a 20-mm range,but without caudal MPD dilatation on magnetic resonance cholangiopancreatography(MRCP).CASE SUMMARY A 60-year-old female patient underwent computed tomography for a follow-up of uterine cancer post-excision,which revealed pancreatic cysts.MRCP revealed an unclear MPD of the pancreatic body at a 20-mm length without caudal MPD dilatation.Thus,course observation was performed.After 24 mo,MRCP revealed an increased caudal MPD caliber and a larger pancreatic cyst.We performed ERCP and detected atypical cells suspected of adenocarcinoma by serial pancreatic juice aspiration cytology examination.We performed a distal pancreatectomy and obtained a histopathological diagnosis of high-grade PanIN.Pancreatic parenchyma invasion was not observed,and curative resection was achieved.CONCLUSION High-grade Pan-IN may cause MPD narrowing in a long range without caudal MPD dilatation.

Bulk geochemistry,Rb-Sr,Sm-Nd,and stable O-H isotope systematics of the Metzimevin high-grade iron ore deposit,Mbalam iron ore district,southern Cameroon

Bulk geochemistry,Sr,Nd,and O-H isotope systematics are reported for the first time on banded iron formation(BIF)-hosted high-grade iron ore at the northwestern segment of Congo Craton(CC).Located in Mbalam iron ore district,Southern Cameroon,Metzimevin iron ore deposit is a hematite-magnetite BIF system,dominated by SiO_(2)+Fe_(2)O_(3)(97.1 to 99.84 wt%),with low concentrations of clastic elements e.g.,Al_(2)O_(3),TiO_(2),and HFSE,depicting a nearly pure chemical precipitate.The REE+Y signature of the iron deposit displays strong positive Eu anomaly,strong negative Ce anomaly,and chondritic to superchondritic Y/Ho ratios,suggestive of formation by mixed seawater-high temperature hydrothermal fluids in oxidising environment.The^(87)Sr/^(86)Sr ratios of the BIF are higher than the maximum^(87)Sr/^(86)Sr evolution curves for all Archean reservoirs(bulk silicate earth,Archean crust and Archean seawater),indicating involvement of continentally-derived components during BIF formation and alteration.TheƐ_(Nd)(t)(+2.26 to+3.77)and Nd model age indicate that chemical constituents for the BIF were derived from undifferentiated crustal source,between 3.002 and 2.88 Ga.The variable and diverse O and H isotope data(−1.9‰to 17.3‰and−57‰to 136‰respectively)indicate that the Metzimevin iron ore formed initially from magmatic plumes and later enriched by magmatic-metamorphic-modified meteoric fluids.Mass balance calculations indicate mineralisation by combined leaching and precipitation,with an average iron enrichment factor of>2.67 and SiO_(2)depletion factor of>0.99.This is associated with an overall volume reduction of 28.27%,reflecting net leaching and volume collapse of the BIF protholith.

Management of retroperitoneal high-grade serous carcinoma of unknown origin:A case report

BACKGROUND Retroperitoneal high-grade serous carcinoma(HGSC)of unknown origin is a sporadic tumor that can originate from ovarian cancer.Herein,we report the case of a woman with retroperitoneal HGSC of unknown origin and describe how she was diagnosed and treated.CASE SUMMARY A 71-year-old female presented with the tumor marker CA125 elevated to 1041.9 U/mL upon a regular health examination.Computed tomography revealed retroperitoneal lymph node enlargement.Subsequently,positron emission tomography scanning revealed lesions with increased F-18 fluorodeoxyglucose uptake at the nodes.As a result,she underwent laparoscopic lymph node resection,and pathology revealed metastatic adenocarcinoma with CK7(+),PAX8(+),WT1(+),PR(-),and p53 mutational loss of expression,indicating that the origin may be from the adnexa.The patient was admitted to our ward and underwent laparoscopic staging;however,the pathological results were negative.Under the suspicion of retroperitoneal HGSC of unknown origin,chemotherapy and targeted therapy were initiated.Tumor marker levels decreased after treatment.CONCLUSION We present a case of HGSC of unknown origin managed using retroperitoneal lymphadenectomy,staging surgery,chemotherapy,and targeted therapy.

High-grade serous carcinoma of the fallopian tube in a young woman with chromosomal 4q abnormality:A case report

BACKGROUND Few studies have reported an association between an increased risk of acquiring cancers and survival in patients with 4q deletion syndrome.This study presents a rare association between chromosome 4q abnormalities and fallopian tube highgrade serous carcinoma(HGSC)in a young woman.CASE SUMMARY A 35-year-old woman presented with acute dull abdominal pain and a known chromosomal abnormality involving 4q13.3 duplication and 4q23q24 deletion.Upon arrival at the emergency room,her abdomen appeared ovoid and distended with palpable shifting dullness.Ascites were identified through abdominal ultrasound,and computed tomography revealed an omentum cake and an enlarged bilateral adnexa.Blood tests showed elevated CA-125 levels.Paracentesis was conducted,and immunohistochemistry indicated that the cancer cells favored an ovarian origin,making us suspect ovarian cancer.The patient underwent debulking surgery,which led to a diagnosis of stage IIIC HGSC of the fallopian tube.Subsequently,the patient received adjuvant chemotherapy with carboplatin and paclitaxel,resulting in stable current condition.CONCLUSION This study demonstrates a rare correlation between a chromosome 4q abnormality and HGSC.UBE2D3 may affect crucial cancer-related pathways,including P53,BRCA,cyclin D,and tyrosine kinase receptors,thereby possibly contributing to cancer development.In addition,ADH1 and DDIT4 may be potential influencers of both carcinogenic and therapeutic responses.

Transformation of marginal zone lymphoma into high-grade B-cell lymphoma expressing terminal deoxynucleotidyl transferase:A case report

BACKGROUND High-grade B-cell lymphoma(HGBL)is an unusual malignancy that includes myelocytomatosis viral oncogene(MYC),B-cell lymphoma-2(BCL-2),and/or BCL-6 rearrangements,termed double-hit or triple-hit lymphomas,and HGBL-not otherwise specific(HGBL-NOS),which are morphologically characteristic of HGBL but lack MYC,BCL-2,or BCL-6 rearrangements.HGBL is partially transformed by follicular lymphoma and other indolent lymphoma,with few cases of marginal zone lymphoma(MZL)transformation.HGBL often has a poor prognosis and intensive therapy is currently mainly advocated,but there is no good treatment for these patients who cannot tolerate chemotherapy.CASE SUMMARY We reported a case of MZL transformed into HGBL-NOS with TP53 mutation and terminal deoxynucleotidyl transferase expression.Gene analysis revealed the gene expression profile was identical in the pre-and post-transformed tissues,suggesting that the two diseases are homologous,not secondary tumors.The chemotherapy was ineffective and the side effect was severe,so we tried combination therapy including venetoclax and obinutuzumab.The patient tolerated treatment well,and reached partial response.The patient had recurrence of hepatocellular carcinoma and died of multifunctional organ failure.He survived for 12 months after diagnosis.CONCLUSION Venetoclax combined with obinutuzumab might improve the survival in some HGBL patients,who are unsuitable for chemotherapy.

Comparison of biological behavior of lacrimal gland adenoid cystic carcinoma with high-grade transformation cells

AIM:To evaluate the differences between human lacrimal gland adenoid cystic carcinoma with high-grade transformation(LACC-HGT)primar y cells cultured by high-grade transformation tissue and non-high-grade transformation(non-HGT)primary cells cultured by non-highgrade transformation tissue in proliferation,metastasis,drug susceptibility,and genes.METHODS:LACC-HGT primary cells were established by tissue block culture,and the 4^(th)to 10^(th)generation primary cells were selected as research objects.The cells were preliminarily identified by immunofluorescent staining.The differences between non-HGT and LACC-HGT primary cells in terms of proliferation,metastasis,and drug susceptibility were compared by cell counting kit-8(CCK-8)assay,wound healing,and drug sensitivity experiments.Differentially expressed genes were screened using mRNA array.Gene expression was analyzed using real-time quantitative polymerase chain reaction(RT-qPCR).RESULTS:LACC-HGT primary cells were successfully cultured by tissue block culture.Immunofluorescence staining results showed that cytokeratin(CK)and CK7 expression levels were positive in LACC-HGT primary cells.CCK-8 results showed that the proliferation ability of LACCHGT cells was significantly higher than that of non-HGT cells.Wound healing experiment showed that the migration ability of LACC-HGT cells was significantly higher than that of non-HGT cells.LACC-HGT cells were also less sensitive to cisplatin and paclitaxel than non-HGT cells.Compared with non-HGT cells,9566 differentially expressed genes were found in LACC-HGT primary cells,of which 5162 were upregulated and 4404 were down-regulated.The expression of N-acetylneuraminate pyruvate lyase(NPL),MARVEL domain containing 3(MARVELD3),syntabulin(SYBU),and allograft inflammatory factor 1(AIF1)was higher in LACCHGT cells than in non-HGT cells,whereas that of periostin(POSTN)was lower.CONCLUSION:LACC-HGT primary cells have faster proliferation,stronger migration ability,and poorer sensitivity to chemotherapy drugs than non-HGT primary cells.The expression of mRNAs in non-HGT and LACC-HGT primary cells are significantly different.These features are speculated to be the reasons why high-grade transformation tissues exhibit higher malignant degree and poorer prognosis than their counterparts.

Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis

BACKGROUND Esophageal cancer is the seventh-most common cancer type worldwide,accounting for 5%of death from malignancy.Development of novel diagnostic techniques has facilitated screening,early detection,and improved prognosis.Convolutional neural network(CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer,enabling even early detection of dysplasia.METHODS PubMed,EMBASE,Web of Science and Cochrane Library databases were searched for articles published up to November 30,2022.We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD,as well as for the invasion depth of esophageal cancer.The pooled sensitivity,pooled specificity,positive likelihood ratio(PLR),negative likelihood ratio(NLR),diagnostic odds ratio(DOR)and area under the curve(AUC)were estimated,together with the 95%confidence intervals(CI).A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model.Meta-regression and subgroup analyses were used to identify sources of hetero-geneity.RESULTS A total of 28 studies were included in this systematic review and meta-analysis.Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95(95%CI:0.92-0.97),pooled specificity of 0.92(0.89-0.94),PLR of 11.5(8.3-16.0),NLR of 0.06(0.04-0.09),DOR of 205(115-365),and AUC of 0.98(0.96-0.99).When video-based analysis was used,a pooled sensitivity of 0.85(0.77-0.91),pooled specificity of 0.73(0.59-0.83),PLR of 3.1(1.9-5.0),NLR of 0.20(0.12-0.34),DOR of 15(6-38)and AUC of 0.87(0.84-0.90)were found.Prediction of invasion depth resulted in a pooled sensitivity of 0.90(0.87-0.92),pooled specificity of 0.83(95%CI:0.76-0.88),PLR of 7.8(1.9-32.0),NLR of 0.10(0.41-0.25),DOR of 118(11-1305),and AUC of 0.95(0.92-0.96).CONCLUSION CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large,multicenter clinical trials.

基于ROAD的租车订购系统业务流程建模

基于业务架构为中心的企业架构开发思路,采用开放组架构框架(TOGAF)业务架构ACF元模型的划分,提出了一种ROAD架构迭代建模方法。针对目前ROAD元架构中业务活动模型采用IDEF0的活动模型导致的面向场景业务能力的不足,文中通过使用BPMN模型来覆盖IDEF0的活动建模,避免在描述复杂的业务过程时可能存在的局限性,并且更好地捕捉和表示所有的细节和关系,不会使得模型过于抽象。从而实现面向场景的系统建模和管理,提高业务架构设计的效率和精度。最后,文章中以旅游出行租车订购系统为例的方法,采用ROAD元架构方法建立场景化业务架构模型组,并对其进行讨论和改进,为现有架构体系提供一种面向场景的扩展思路。

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

A Case Report: High-Grade Urothelial Carcinoma in the Renal Pelvis with Complete Kidney and Ureter Duplication Featuring Heterologous Differentiation

This report describes a considerably rare case of high-grade urothelial carcinoma of the renal pelvis and ureter,presenting with heterologous differentiation,in a patient with bilateral duplicated kidneys.A 73-year-old male experienced intermittent gross hematuria for 5 months,accompanied by lower back and abdominal pain.Ultrasound and computed tomography scans revealed bilateral renal and ureteral duplication with multiple tumors in the left renal pelvis.A total nephroterectomy and bladder cuff resection were performed on the left two nephrons.Multiple space-occupying lesions were identified in the left renal pelvis and ureter.Histopathological examination showed poorly differentiated and diverse tumor cells,manifesting as sarcomatoid carcinoma,papillary adenocarcinoma,and infiltrating high-grade urothelial carcinoma.The tumor infiltrated the subcutaneous fibrous connective tissue of the renal pelvis and the full thickness of the ureter.Given the rarity of recurrent renal urothelial carcinoma with heterogeneous differentiation,comprehensive imaging and pathological assessments are vital to delineate the nature of the lesion and the direction of tissue pathological heterologous differentiation.These evaluations guide early radical surgical interventions,improving survival rates.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Local failure mechanism of sand-blocking fence in latticed dune along desert roads

The latticed dunes in the Tengger Desert are widely distributed,and the sand-blocking fence placed here are highly susceptible to local failure due to complex wind-sand activities,posing a serious threat to the safe operation of the highway.To explore the local failure mechanism of sand-blocking fence in the latticed dune area,the local failure of sand-blocking fence in the latticed dune areas along the Wuhai-Maqin Highway in China was observed.Taking the first main ridge of the latticed dune as the placement location,the structure of the wind-sand flow field of sand-blocking fence placed at top,the bottom and the middle of windward slope was analyzed by Computational Fluid Dynamics(CFD).The results show that when placed at top of the first main ridge,the wind speed near the sand-blocking fence is the highest,up to 15.23 m/s.Therefore,the wind load strength on the sand barrier is correspondingly larger,up to 232.61 N∙m-2.As the strength of material continues to decrease,the nylon net is prone to breakage.The roots of the angle steel posts are susceptible to hollowing by vortex action,which can cause sand-blocking fence to fall over in strong wind conditions.When placed at the bottom of windward slope,wind speed drop near sand-blocking fence is greatest,with the decrease of 12.48-14.32 m/s compared to the original wind speed.This is highly likely to lead to large-scale deposition of sand particles and burial of the sand-blocking fence.When placed in the middle of windward slope,sand-blocking fence is subjected to less wind load strength(168.61N∙m-2)and sand particles are mostly deposited at the bottom of windward slope,with only a small amount of sand accumulating at the root of sand-blocking fence.Based on field observations and numerical modelling results,when the sand-blocking fence is placed in latticed dune area,it should be placed in the middle of the windward slope of the first main ridge as a matter of priority.Besides the sand-blocking fence should be placed at the top of the first main ridge,and sand fixing measures should be added.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

Risk assessment of oil and gas investment environment in countries along the Belt and Road Initiative

With the implementation of the Belt and Road Initiative, China is deepening its cooperation in oil and gas resources with countries along the Initiative. In order to better mitigate risks and enhance the safety of investments, it is of significant importance to research the oil and gas investment environment in these countries for China's overseas investment macro-layout. This paper proposes an indicator system including 27 indicators from 6 dimensions. On this basis, game theory models combined with global entropy method and analytic hierarchy process are applied to determine the combined weights, and the TOPSIS-GRA model is utilized to assess the risks of oil and gas investment in 76 countries along the Initiative from 2014 to 2021. Finally, the GM(1,1) model is employed to predict risk values for 2022-2025. In conclusion, oil and gas resources and political factors have the greatest impact on investment environment risk, and 12 countries with greater investment potential are selected through cluster analysis in conjunction with the predicted results. The research findings may provide scientific decisionmaking recommendations for the Chinese government and oil enterprises to strengthen oil and gas investment cooperation with countries along the Belt and Road Initiative.

Exploring the Factors Associated with 12-Month Non-Return to Work among Motorcyclists Involved in Road Accidents

Introduction: Motorcyclists bear a disproportionate burden of morbidity and mortality from road accidents. In addition, the consequences of these accidents affect the ability of victims to return to work. This study aimed to determine the prevalence and factors associated with non-return to work among surviving motorcyclists involved in road accidents 12 months after the event. Materials and Methods: It was a cross-sectional study conducted using data from a cohort of motorcyclists involved in accidents and recruited in five hospitals in Benin from July 2019 to January 2020. The dependent variable was non-return to work 12 months after the accident (yes vs no). The independent variables were categorized into two groups: baseline and 12-month follow-up variables. Logistic regression was used to determine the factors associated with non-return to work at 12 months among the participants. Results: Among the 362 participants, 55 (15.19%, 95% CI = 11.84 - 19.29) had not returned to work 12 months after the accident. Risk factors for non-return to work identified were: smoking (aOR = 4.41, 95% CI = 1.44 - 13.56, p = 0.010), hospitalization (aOR = 2.87, 95% CI = 1.14 - 7.24, p Conclusion: The prevalence of non-return to work at 12 months was high among surviving motorcyclists involved in road accidents in Benin. Integrated support for patients based on identified risk factors should effectively improve their return to work.

Resilience assessment and optimization method of city road network in the post-earthquake emergency period

The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

A Brief Discussion on the Development of the Silk-Weaving Industry Along the “Southern Silk Road” in Yunnan

Ancient Yunnan was one of the most significant regions along China’s ancient“Southern Silk Road.”During the Nanzhao period(738–902)of the late Tang Dynasty,Yunnan’s silk-weaving industry underwent a qualitative leap as skilled silk craftsmen from the Bashu area migrated to Yunnan and introduced mulberry planting,silkworm breeding,and advanced silk-weaving techniques from Sichuan to the region.Consequently,people in Yunnan gradually acquired expertise in brocade weaving and embroidery.Many even mastered complex silk-weaving techniques.The development and progress of the silk-weaving industry in the ancient Yunnan region were intricately linked to the economic function and value of silk as both a commodity and currency along the“Southern Silk Road.”The local government in ancient Yunnan was greatly motivated by the economic interests brought by the development of silk-related industries and recognized the significance of developing the local silk industry.They even initiated a campaign to capture skilled silk craftsmen from Sichuan,aiming to foster the growth of the silk-weaving industry in Yunnan.After years of dedicated efforts from the local government in ancient Yunnan,the region emerged as a significant hub for silk production along China’s ancient“Southern Silk Road.”Despite the devastation caused by the wars in other parts of the country,Yunnan’s silk industry continued to thrive and provide ample silk products to sustain trade along this renowned route.In the contemporary era,amidst the decline of the silk-weaving industry in eastern China,Yunnan has proposed an industrial development strategy known as“relocating the silk-weaving industry from east to west.”This involves introducing advanced silk production techniques from the eastern regions into Yunnan to enhance and enrich its local silk industry,thereby establishing it as a traditional national sector and securing a competitive position within the global silk market.The historical experience of Yunnan’s silk industry demonstrated that economic development opportunities can only be seized through proactive endeavors rather than passive anticipation.The modern Yunnan silk industry,which upholds its historical traditions,continues to actively engage in international high-end technical cooperation,thus ensuring the enduring vitality of the ancient“Southern Silk Road.”

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.