Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision

Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems. However, existing V2X perception methods focus on static scenes from mainly vehicle-based vision, which is constrained by sensor capabilities and communication loads. To adapt V2X perception models to dynamic scenes, we propose to build V2X perception from road-to-vehicle vision and present Adaptive Road-to-Vehicle Perception (AR2VP) method. In AR2VP,we leverage roadside units to offer stable, wide-range sensing capabilities and serve as communication hubs. AR2VP is devised to tackle both intra-scene and inter-scene changes. For the former, we construct a dynamic perception representing module, which efficiently integrates vehicle perceptions, enabling vehicles to capture a more comprehensive range of dynamic factors within the scene.Moreover, we introduce a road-to-vehicle perception compensating module, aimed at preserving the maximized roadside unit perception information in the presence of intra-scene changes.For inter-scene changes, we implement an experience replay mechanism leveraging the roadside unit's storage capacity to retain a subset of historical scene data, maintaining model robustness in response to inter-scene shifts. We conduct perception experiment on 3D object detection and segmentation, and the results show that AR2VP excels in both performance-bandwidth trade-offs and adaptability within dynamic environments

Experience of management of pediatric upper gastrointestinal perforations: a series of 30 cases

BackgroundThis study aimed to explore the characteristics of pediatric upper gastrointestinal (UGI) perforations, focusing on their diagnosis and management.MethodsBetween January 2013 and December 2021, 30 children with confirmed UGI perforations were enrolled, and their clinical data were analyzed. Two groups were compared according to management options, including open surgical repair (OSR) and laparoscopic/gastroscopic repair (LR).ResultsA total of 30 patients with a median age of 36.0 months (1 day–17 years) were included in the study. There were 19 and 11 patients in the LR and OSR groups, respectively. In the LR group, two patients were treated via exploratory laparoscopy and OSR, and the other patients were managed via gastroscopic repair. Ten and three patients presented the duration from symptom onset to diagnosis within 24 h (p = 0.177) and the number of patients with hemodynamically unstable perforations was 4 and 3 in the LR and OSR groups, respectively. Simple suture or clip closure was performed in 27 patients, and laparoscopically pedicled omental patch repair was performed in two patients. There was no significant difference in operative time and length of hospital stay between the LR and OSR groups. Treatment failed in two patients because of severe sepsis and multiple organ dysfunction syndrome, including one with fungal peritonitis.ConclusionSurgery for pediatric UGI perforations should be selected according to the general status of the patient, age of the patient, duration from symptom onset, inflammation, and perforation site and size. Antibiotic administration and surgical closure remain the main strategies for pediatric UGI perforations

Identification and treatment of intestinal malrotation with midgut volvulus in childhood: a multicenter retrospective study

BackgroundIntestinal malrotation is a rare condition, and its delayed diagnosis can lead to fatal consequences. This study aimed to investigate the identification and treatment of malrotation in children.MethodsClinical data, imaging, operative findings, and early postoperative outcomes of 75 children with malrotation were retrospectively analyzed.ResultsThe mean age was 6.18 ± 4.93 days and 51.26 ± 70.13 months in the neonatal group (56 patients) and non-neonatal group (19 patients), respectively. Sixty-seven patients were under the age of 1 year at the time of diagnosis. The occurrence of bilious vomiting and jaundice was significantly higher in the neonatal group (89.29%) than that in the non-neonatal group (37.5%), p < 0.05 and p < 0.01, respectively. The incidence of abnormal ultrasound (US) findings was 97.30% and 100%, respectively, and the sensitivities of the upper gastrointestinal series were 84.21% and 87.5%, respectively. Sixty-six (88%) patients had midgut volvulus, including in utero volvulus (two patients) and irreversible intestinal ischemia (four patients). Most neonates (89.29%) underwent open Ladd's procedure with a shorter operative time (p < 0.01). Reoperation was performed for postoperative complications (four patients) or missed comorbidities (two patients).ConclusionsNon-bilious vomiting was the initial symptom in >10% of neonates and nearly 40% of non-neonates. This highlights the importance for emergency physicians and surgeons to be cautious about ruling out malrotation in patients with non-bilious vomiting. Utilizing US can obviate the need for contrast examinations owing to its higher diagnostic accuracy and rapid diagnosis and can be recommended as a first-line imaging technique. Additionally, open surgery is still an option for neonatal patients

Effects of Seven-Year Fertilization Reclamation on Bacterial Community in a Coal Mining Subsidence Area in Shanxi, China

The restoration of soil fertility and microbial communities is the key to the soil reclamation and ecological reconstruction in coal mine subsidence areas. However, the response of soil bacterial communities to reclamation is still not well understood. Here, we studied the bacterial communities in fertilizer-reclaimed soil (CK, without fertilizer; CF, chemical fertilizer; M, manure) in the Lu’an reclamation mining region and compared them with those in adjacent subsidence soil (SU) and farmland soil (FA). We found that the compositions of dominant phyla in the reclaimed soil differed greatly from those in the subsidence soil and farmland soil (p < 0.05). The related sequences of Acidobacteria, Chloroflexi, and Nitrospirae were mainly from the subsided soil, whereas those of Alphaproteobacteria, Planctomycetes, and Deltaproteobacteria were mainly derived from the farmland soil. Fertilization affected the bacterial community composition in the reclaimed soil, and bacteria richness and diversity increased significantly with the accumulation of soil nutrients after 7 years of reclamation (p < 0.05). Moreover, soil properties, especially SOM and pH, were found to play a key role in the restoration of the bacterial community in the reclaimed soil. The results are helpful to the study of soil fertility improvement and ecological restoration in mining areas

The Research of Air Combat Intention Identification Method Based on BiLSTM + Attention

In the process of air combat intention identification, expert experience and traditional algorithm are relied on to analyze enemy aircraft combat intention in a single moment, but the identification time and accuracy are not excellent. In this paper, from the dynamic attributes of an airspace fighter air combat target and the dynamic and time series changing characteristics of the battlefield environment, we introduce the bidirectional long short-term memory neural network (BiLSTM + Attention) intention identification method based on the attention mechanism for air combat intention identification. In this method, five kinds of state parameters, including target maneuver type, distance, flight velocity, altitude and heading angle, were taken as datasets. The BiLSTM + Attention was used to extract enemy aircraft intention features. By introducing attention mechanism, the weight coefficients of characteristic states corresponding to air combat victories were corrected. Finally, it was input into the SoftMax function to obtain the category of the enemy’s intention. Experimental results showed that the proposed method can effectively identify enemy aircraft in the case of high complexity, multidimensional and large amount of data. Compared with bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), long short-term memory based on attention mechanisms (LSTM + Attention) and support vector machine (SVM) classification, the proposed method had higher accuracy and lower loss value

Experimental Study of the Heat-Transfer Performance of an Extra-Long Gravity-Assisted Heat Pipe Aiming at Geothermal Heat Exploitation

The installation and operation of enhanced geothermal systems (EGS) involves many challenges. These challenges include the high cost and high risk associated with the investment capital, potential large working-fluid leakage, corrosion of equipment, and subsiding land. A super-long heat pipe can be used for geothermal exploitation to avoid these problems. In this paper, a high aspect-ratio heat pipe (30 m long, 17 mm in inner diameter) is installed vertically. Experiments are then carried out to study its heat-transfer performance and characteristics using several filling ratios of deionized water, different heating powers, and various cooling-water flowrates. The results show that the optimal filling-ratio is about 40% of the volume of the vaporizing section of the heat pipe. Compared with a conventional short heat pipe, the extra-long heat pipe experiences significant thermal vibration. The oscillation frequency depends on the heating power and working-fluid filling ratio. With increasing cooling-water flow rate, the heat-transfer rate of the heat pipe increases before it reaches a plateau. In addition, we investigate the heat-transfer performance of the heat pipe for an extreme working-fluid filling ratio; the results indicate that the lower part of the heat pipe is filled with vapor, which reduces the heat-transfer to the top part. Based on the experimental data, guidelines for designing a heat pipe that can be really used for the exploitation of earth-deep geothermal energy are analyzed

Logging-based identification and evaluation of karst fractures in the eastern Right Bank of the Amu Darya River, Turkmenistan

Carbonate gas reservoir in the eastern area on the Right Bank of the Amu Darya River, Turkmenistan, are of low-porosity and with developed fractures. In this area, fractures control reservoir properties and natural gas production, and karst fractures are the most important kind of fractures, so their identification and evaluation are quite necessary. In this paper, fracture types were identified and their occurrence was extracted by using conventional logging and image logging data after core calibration. Then, the distribution characteristics of karst fractures and their controlling effect on reservoirs were studied according to the identification results. And the following research results were obtained. First, karst fractures are mainly of high angle with the characteristic of mono system and the interactive relation of genesis. Second, they are mainly distributed in the upper XVhp layer of Callovian–Oxford Stage and the lower XVa2–XVI layer. Third, they are the main effective fractures in this area. The dissolved pores are connected effectively through the expanded karst fractures by dissolution, and consequently reservoirs of high porosity and permeability are formed and they are the important reservoir type and high-yield gas reservoir in this area. Fourth, karst fractures are related to high-yield wells and high-yield layers in this area, and they also control the distribution of high-yield reservoirs in the lower part of Callovian–Oxford Stage. It is concluded that by virtue of imaging logging and conventional logging data, karst fractures, unfilled fractures, semi-filled fractures and fully filled fractures can be identified and evaluated better. Furthermore, the identification and evaluation of karst fractures deepen the understanding on fractured reservoirs in this area, improve the reservoir evaluation effect, and provide the basis for the target horizon and azimuth optimization of horizontal wells and highly deviated wells. And it is also indicated that the reservoirs with developed karst fractures are the subsequent important drilling targets. Keywords: Turkmenistan, Eastern area on the Right Bank of Amu Darya, Carbonate gas reservoir, Karst fractures, Filling characteristic, Imaging logging, Log response, Natural gas production rat

Development of Mg–6Al–4Sn–1Zn alloy sheets with ultra-high strength by combining extrusion and high-speed rolling

Achievement of ultra-high yield strength of above 400 MPa for rare-earth-free magnesium (Mg) alloys is quite difficult via traditional thermal-mechanical processing. In this study, Mg–6Al–4Sn–1Zn alloy (ATZ641) sheet with the yield strength of 410 MPa, ultimate tensile strength of 442 MPa, and elongation of 5.1 %, was fabricated by combining extrusion and high-speed rolling (HSR). The results showed significant refinement in the grains of high-speed rolled ATZ641 alloy due to dynamic recrystallization (DRX) effectively promoted by particle-stimulated nucleation (PSN) of pre-existing relatively coarse Mg17Al12 precipitates uniformly distributed along grain boundaries, formed during extrusion prior to HSR. Meanwhile, the Mg17Al12 nano-precipitates with high number density, mainly due to dynamic precipitation during HSR and submicron Mg2Sn precipitates formed during both extrusion and HSR, suppress DRX and thus promote the formation of a certain amount of subgrains via particle pinning. The excellent mechanical properties are mainly attributed to fine-grain strengthening, high density residual dislocations and presence of numerous hybrid particles such as bimodal size (micron and nanometer) Mg17Al12 precipitates and submicron Mg2Sn precipitates as well as a large amount of subgrains. This study offers an important notion for developing rare-earth-free Mg alloys with ultra-high strength

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data