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Liver cancer is a common type of cancer worldwide, with over 90% of cases being hepatocellular carcinoma (HCC). The discovery of new targets holds promise for improving the prognosis of HCC patients. Zinc finger protein 677 (ZNF677), a member of the Kruppel C2H type 2 zinc finger family, has been implicated in the progression of several tumors. However, its specific role in HCC remains unclear. In this study, we investigated the role of ZNF677 in HCC progression. Our analysis revealed low expression of ZNF677 in HCC, which correlated with patient prognosis. Functionally, ZNF677 suppressed the growth, migration, invasion, and stemness of HCC cells. Additional data confirmed its ability to block the stemness of HCC cells. Mechanistically, ZNF677 achieved these effects by targeting the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) axis. In summary, ZNF677 inhibits both growth and stemness in hepatoma cells via modulation of the PI3K/Akt axis.

In the emerging field of valleytronics, it is aimed to coherently manipulate the valley pseudospin as an information‐bearing degree of freedom. The 2D transition‐metal dichalcogenides (TMDCs) provide a unique possibility to generate an excitonic valley pseudospin, opening the way to valley information. Although significant development of valley pseudospin in layered materials has been achieved recently, looking for new TMDCs featuring robust valley phenomenon at room temperature is still desirable for practical applications. Herein, the valley pseudospin of the spiral WS2 with different layer thicknesses at room temperature is investigated by both circular and linear polarization‐resolved photoluminescence spectroscopy. In the experimental results, it is demonstrated that the spiral WS2 emerges robust valley polarization and valley coherence, the degree of circular polarization, and linear polarization gradually increase with the lift of the layer thicknesses, reaching up to 0.91 for valley polarization and 0.94 for valley coherence, respectively. The robust layer‐dependent valley pseudospin may originate from the intrinsic broken inversion symmetry, due to the spiral structure of the multilayer WS2. The robust and near‐unity valley polarization and valley coherence at room temperature in the spiral WS2 may provide a new platform for optical manipulation of the valley pseudospin for further valleytronics applications.

BackgroundSystemic inflammatory response represented by C-reactive protein to albumin ratio (CAR) was shown to be associated with long-term outcome in patients with hepatocellular carcinoma (HCC). We conducted a meta-analysis to investigate the prognostic value of preoperative CAR in patients undergoing hepatectomy for HCC.MethodsWe searched four databases (PubMed, Embase, Scopus and Cochrane Library) from inception to May 10th, 2024. Studies investigating the prognostic value of preoperative CAR in HCC patients after hepatectomy. The primary endpoints were overall survival (OS) and disease-free survival (DFS). Data from individual studies were aggregated to calculate the pooled hazard ratio (HR) using a random-effects model.ResultsA total of 11 studies included 4,066 patients were finally analyzed in the meta-analysis. Overall, the higher preoperative CAR was associated with poorer OS (HR 1.92, 95% CI 1.67 to 2.22, I2 = 0%) and DFS (HR 1.79, 95% CI 1.59 to 2.02, I2 = 0%) rate. Furthermore, subgroup analyses indicated that CAR could be a prognostic biomarker for patients with HCC regardless of regions and cut-off value.ConclusionOur meta-analysis indicates that higher preoperative CAR level is associated with poorer OS and DFS, it may be a good prognostic marker of survival outcomes after hepatectomy in patients with HCC. However, future prospective trials are necessary to validate the conclusion.Systematic review registrationThe study protocol was registered in the Open Science Framework (https://osf.io/uavt8).

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends.

IntroductionSpiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance.MethodsRecognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of inaccurate gradient backpropagation caused by Maxpooling in SNNs and address it by developing a new Maxpooling module. Furthermore, we adopt spatio-temporal block (STB) in the classification head instead of global average pooling, facilitating the aggregation of spatial and temporal features.ResultsSGLFormer demonstrates its superior performance on static datasets such as CIFAR10/CIFAR100, and ImageNet, as well as dynamic vision sensor (DVS) datasets including CIFAR10-DVS and DVS128-Gesture. Notably, on ImageNet, SGLFormer achieves a top-1 accuracy of 83.73% with 64 M parameters, outperforming the current SOTA directly trained SNNs by a margin of 6.66%.DiscussionWith its high performance, SGLFormer can support more computer vision tasks in the future. The codes for this study can be found in https://github.com/ZhangHanN1/SGLFormer.

The development of ice-melting probes is driven by the scientific need to explore the subglacial aquatic environments, such as the subglacial lakes in Antarctica and subglacial water on some extraterrestrial planets. However, during the downward melting, a deviation of the melting trajectory might occur. If left unaddressed, the accumulated deviation will eventually lead to missing the probe's target. Therefore, it is necessary to build a theoretical model to describe the deviation for predicting the future melting trajectory and building the deviation correction system. The deviation comes from any asymmetric heating condition on the melting head. Generally, the Close-Contact Melting (CCM) theory is used to model the probe's melting process without any deviation, and there is only one attempt so far that applies the CCM theory to asymmetric heating conditions. Based on this initial attempt, we successfully expanded the CCM theory with asymmetric temperature profiles to conical thermal heads. Additionally, we simplified the model solving which leads to fewer influence factors compared to existing studies. The model indicates that the melting trajectory of a conical head is linear under asymmetric heating conditions, and the inclination angle of the trajectory is related to the ratio between the head's temperatures on each side and the head's cone angle. To validate the theoretical results, laboratory experiments have also been conducted with an innovative optical positioning method.

Abstract Depth map super resolution has been paid much attention in 3D applications due to the limitation of depth sensors. Few textures in objects with clear contours along them is the most important characteristic of depth map. An efficient image representation should be directional, multiscale and anisotropic. From this we propose a novel multiscale and multidirection depth map super resolution framework with semantic inference to improve the quality of depth maps. In this framework, a multiscale and multidirection depth map contour fusion scheme captures and assembles intrinsic geometrical structures through a multiview non‐subsampled contourlet transform manner. This scheme not only isolates the discontinuities of contours but retains the smoothness along the contours. The semantic inference is also utilized to segment and label the depth map into objects/backgrounds‐level which are coplanar. Furthermore, a semantic‐aware label refinement strategy is introduced to correct the rarely inaccurate labels of the label map for upscaling the target pixel with pixels in the same object or background. Experimental results on benchmark depth map dataset demonstrate that the proposed multiscale and multidirection depth map super resolution framework with semantic inference has a significant improvement than the state‐of‐the‐art algorithms both visually and quantitatively.

A 198.8 m deep borehole was drilled through ice to subglacial bedrock in the northwestern marginal part of Princess Elizabeth Land, ~12 km south of Zhongshan Station, in January–February 2019. Three years later, in February 2022, the borehole temperature profile was measured, and the geothermal heat flow (GHF) was estimated using a 1-D time-dependent energy-balance equation. For a depth corresponding to the base of the ice sheet, the GHF was calculated as 72.6 ± 2.3 mW m−2 and temperature −4.53 ± 0.27°C. The regional averages estimated for this area based, generally, on tectonic setting vary from 55 to 66 mW m−2. A higher GHF is interpreted to originate mostly from the occurrence of metamorphic complexes intruded by heat-producing elements in the subglacial bedrock below the drill site.

Optical microcavities are compact structures that confine resonant photons in microscale dimensions for long periods of time, greatly enhancing light–matter interactions. Plentiful and profound physical mechanisms within these microcavities or functional microcavities have been extensively explored, including mode shift/splitting/broadening, lasing and gain enhancements, surface plasmon resonance, fluorescence resonance energy transferring, optical frequency comb spectroscopy, optomechanical interaction, and exceptional point. The versatility in design and the diverse range of materials, particularly composites involving metals and 2-dimensional materials, have paved a way for innovative approaches and improved performance in biochemical sensing applications. Leveraging the advantages ranging from miniaturization, high sensitivity, rapid response, and inherent stability, optical microcavity-based biochemical sensors have emerged to address the growing and increasingly complex demands of biochemical detection. This review commences with an exploration of fundamental mechanisms and structures and then delves into typical applications in recent advancements, covering the detection of biomacromolecules, cells, solid particles, liquid ions, and gas molecules. This review also culminates with a forward-looking perspective, highlighting future development trends and crucial research directions.

Very few studies have emphasized the effects of high-pressure sintering on snow density evolution, even though snow as a type of engineering material is widely used in construction engineering in cold regions for snow pavement, snow runway and polar infrastructure. This study presents new experimental results of snow densification under high pressures of up to 100 MPa for a temperature range from −3.5 to −17.3°C and uniaxial compression at the temperature of −10°C and constant strain rates from 5 × 10−4 to 10−1 s−1. Results reveal that density evolution of snow to ice under high-pressure sintering can be achieved in a wide temperature range within a duration as short as 5 min. The compressive strength of snow-sintered ice was ~1.2–2.2 times as large as that of water-frozen ice reported by previous work. The orthogonal experiment showed that pressure is a more significant factor affecting the final density in comparison with sintering temperature and time. The increased rates of ice fabrication, low limitations on temperature and reliable sintered snow strength indicate that snow-ice engineering, such as airport construction in Greenland and Antarctica, can be improved by high-pressure sintering of snow to overcome the harsh environment.

Ice cores from polar zones could determine ice dating and biodiversity. Ice cores retrieved in hot-water ice-coring drilling partly melted owing to moderate drilling speed of annular drill head with many nozzles, which was largely depended on the heat transfer efficiency of jet to ice at borehole bottom. To obtain the higher drilling speed and ice cores of good quality, this paper explored the influence of nozzle parameters on the heat transfer efficiency of annular jet to bottom ice. Main nozzle parameters affecting the heat transfer efficiency were theoretically determined firstly. Then influence law of nozzle parameters on the heat transfer were estimated by theoretical calculation and numerical simulation. The result showed the heat transfer of annular jet to bottom ice was positively correlated with the nozzle flow and negatively relevant with the nozzle diameter, nozzle angle and nozzle quantity, which was further compared with the drilling speed value in experiment to confirm its correctness. The angle was proved to be the most influential parameter, when it increased from 0° to 30°, the drilling speed decreased 75%. The result can provided reference for designers on how to promote the drilling speed in hot-water ice-coring drilling and improve ice core quality.

The melting water produced from ice during hot-point drilling can easily freeze and cause the hot-point drill to stick. Two different thermal anti-freezing methods exist to prevent water-filled boreholes from refreezing: the heating cable method and lateral heating method. However, the required power density and ice temperature distribution when using these two methods have not been fully discussed. In this study, heat transfer models of the two thermal anti-freezing methods were first developed based on a simplified heat conduction equation by considering the downward movement of the hot-point drill. Subsequently, case studies were conducted to determine the characteristics of the heat transfer during hot-point drilling. Finally, the variations in the power density, power consumption, borehole closure time, borehole closure length, and thermal layer thickness for both thermal anti-freezing methods were evaluated. We recommend that the heating cable method should be used to drill boreholes with shallow depths or small diameters in temperate ice, whereas the lateral heating method can be applied to a wide range of ice temperatures, borehole diameters, and borehole depths. Generally, the paper presents useful guidance for designing the heating cable and lateral heater and reducing the risk of borehole freezing.

A series of new synthetic armored cables were developed and tested to ensure that they were suitable for use with the RECoverable Autonomous Sonde (RECAS), which is a newly designed freezing-in thermal ice probe. The final version of the cable consists of two concentric conductors that can be used as the power and signal lines. Two polyfluoroalkoxy jackets are used for electrical insulation (one for insulation between conductors, and the other for insulation of the outer conductor). The outer insulation layer is coated by polyurethane jacket to seal the connections between the cable and electrical units. The 0.65 mm thick strength member is made from aramid fibers woven together. To hold these aramid fibers in place, a sheathing layer was produced from a polyamide fabric cover net. The outer diameter of the final version of the cable is ~6.1 mm. The permissible bending radius is as low as 17–20 mm. The maximal breaking force under straight tension is ~12.2 kN. The cable weight is only ~0.061 kg m−1. The mechanical and electrical properties and environmental suitability of the cable were determined through laboratory testing and joint testing with the probe.

Subglacial lake exploration is of great interest to the science community. RECoverable Autonomous Sonde (RECAS) provides an exploration tool to measure and sample subglacial lake environments while the subglacial lake remains isolated from the glacier surface and atmosphere. This paper presents an electronic control system design of 200 m prototype of RECAS. The proposed electronic control system consists of a surface system, a downhole control system, and a power transfer and communication system. The downhole control system is the core element of RECAS, and is responsible for sonde status monitoring, sonde motion control, subglacial water sampling and in situ analysis. A custom RS485 temperature sensor was developed to cater for the limited size and depth requirements of the system. We adopted a humidity-based measurement to monitor for a housing leak. This condition is because standard leak detection monitoring of water conductivity may be inapplicable to pure ice in Antarctica. A water sampler control board was designed to control the samplers and monitor the on/off state. A high-definition camera system with built-in storage and self-heating ability was designed to perform the video recording in the subglacial lake. The proposed electronic control system is proven effective after a series of tests.

Hot-point drills have been widely used for drilling boreholes in glaciers, ice caps and ice sheets. A hot-point drill melts ice through the thermal head at its bottom end. Penetration occurs through a close-contact melting (CCM) process, in which the ice is melted, and the meltwater is squeezed out by the exerted force applied on the thermal head. During the drilling, a thin water film is formed to separate the thermal head from the surrounding ice. For the hot-point drill, the rate of penetration (ROP) is influenced by several variables, such as thermal head shape, buoyancy corrected force (BCF), thermal head power (or temperature) and ice temperature. In this study, we developed a model to describe the CCM process, where a constant power or temperature on the working surface of a thermal head is assumed. The model was developed using COMSOL Multiphysics 5.3a software to evaluate the effects of different variables on the CCM process. It was discovered that the effect of thermal head shape and the cone angle of conical thermal head on ROP is less significant, whereas the increase in the BCF and the power (or temperature) of the thermal head can continuously enhance the ROP.

A deep ice core was drilled at Dome A, Antarctic Plateau, East Antarctica, which started with the installation of a casing in January 2012 and reached 800.8 m in January 2017. To date, a total of 337 successful ice-core drilling runs have been conducted, including 118 runs to drill the pilot hole. The total drilling time was 52 days, of which eight days were required for drilling down and reaming the pilot hole, and 44 days for deep ice coring. The average penetration depths of individual runs were 1 and 3.1 m for the pilot hole drilling and deep ice coring, respectively. The quality of the ice cores was imperfect in the brittle zone (650−800 m). Some of the troubles encountered are discussed for reference, such as armoured cable knotting, screws falling into the hole bottom, and damaged parts, among others.

Drilling and sampling are the most direct and effective methods available to study Antarctic subglacial lakes. Based on the Philberth probe, a Recoverable Autonomous Sonde (RECAS) allows for in situ lake water measurement and sampling, through the addition of an upper thermal tip and a cable recoiling mechanism. RECAS-200, a prototype of RECAS, has a drilling depth of 200 m, a surface supply voltage of 800 VAC and a downhole power of ~9.6 kW during drilling. In this study, a heating control system for RECAS-200 was designed. The system avoids the need for high-power step-down converters, by separating heating power from control power, thereby reducing the overall weight of the probe and avoiding the need to increase cable diameter. We also introduce a self-developed, small, solid-state, 800 VAC power regulator and a fuzzy PID temperature control algorithm. Their purpose was to manage the power adjustment of each heating element and to provide closed-loop temperature control of certain heating elements which can easily burn out due to overheating. Test results indicated that the proposed RECAS-200 heating control system met all our design specifications and could be easily assembled into the RECAS-200 probe.

The Antarctic subglacial drilling rig (ASDR) is designed to recover 105 mm-diameter ice cores up to 1400 m depth and 41.5 mm-diameter bedrock cores up to 2 m in length. In order to ensure safe and convenient drilling, drilling auxiliaries are designed to support fieldwork and servicing. These auxiliaries are subdivided into several systems for power supply, drill tripping in the borehole, ice core and chip processing, and drill servicing and maintenance. The required equipment also includes two generators, a drilling winch with a cable, logging winch with a cable, control desk, pipe handler with a fixed clamp, chip chamber vibrator, centrifuge, emergency devices and fitting and electrical tools. Additionally, several environmental protective measures such as a new liquid-tight casing with a thermal casing shoe and a bailing device for recovering drilling fluid from the borehole were designed. Most of the auxiliaries were tested during the summer of 2018–2019 near Zhongshan Station, East Antarctica while drilling to the bedrock to a depth of 198 m.

Drilling to the bedrock of ice sheets and glaciers offers unique opportunities for examining the processes occurring in the bed. Basal and subglacial materials contain important paleoclimatic and paleoenvironmental records and provide a unique habitat for life; they offer significant information regarding the sediment deformation beneath glaciers and its effects on the subglacial hydraulic system and geology. The newly developed and tested Antarctic subglacial drilling rig (ASDR) is designed to recover ice and bedrock core samples from depths of up to 1400 m. All of the drilling equipment is installed inside a movable, sledge-mounted, temperature-controlled and wind-protected drilling shelter and workshop. To facilitate helicopter unloading of the research vessel, the shelter and workshop can be disassembled, with individual parts weighing

A new, modified version of the cable-suspended Ice and Bedrock Electromechanical Drill (IBED) was designed for drilling in firn, ice, debris-rich ice and rock. The upper part of the drill is almost the same for all drill variants and comprises four sections: cable termination, a slip-ring section, an antitorque system and an electronic pressure chamber. The lower part of the IBED comprises an auger core barrel, reamers, a core barrel for ice/debris-ice drilling and a conventional geological single-tube core barrel or custom-made double-tube core barrel. First, the short and full-scale field versions of the IBED were tested at an outdoor testing stand and a testing facility with a 12.5 m-deep ice well. Then, in the 2018–2019 summer season, the IBED was tested in the field at a site ~12 km south of Zhongshan Station, East Antarctica, and a ~6 cm bedrock core was recovered from a 198 m-deep borehole. A total of 18 d was required to penetrate the ice sheet. The retrieved core samples of blue ice, basal ice and bedrock provided valuable information regarding the Earth's paleo-environment.

In glaciology, snow–firn temperature at 10 m is considered a representation of the mean annual air temperature at the surface (MAAT) of the studied site. Although MAAT is an important parameter in ice-sheet investigations, it has not been widely measured in Antarctica. To measure the 10 m snow–firn temperature in Antarctica, a shallow hot-point drill system is designed. In this simple and lightweight system, a hot-point drill can melt boreholes with a diameter of 34 mm in the snow–firn to a depth of 30 m and a temperature sensors string can measure the borehole temperature precisely. In the 2018/19 field season, 16 boreholes along the Zhongshan–Dome A traverse were drilled, and the borehole temperature was measured. Although certain problems existed pertaining to the hot-point drill, a total depth of ~244 m was successfully drilled at an average penetration rate of ~10 m h−1. After borehole drilling, ~12–15 h were generally required for the borehole to achieve thermal equilibrium with the surroundings. Preliminary results demonstrated that the 10 m snow–firn temperature along the traverse route was affected by the increasing altitude and latitude, and it decreased gradually with an increase in the distance from Zhongshan station.

In many cases, the efficiency and safety of a drilling project depend on the reliability of the electrical and electronic control system, as the process progresses without visual access of the operator. The electrical and electronic system provides and regulates the power supply for the drill, collects and monitors the drill data during the whole operating process, and sends and receives the control instructions and feedback signals. The entire system is composed of the surface, borehole and software subsystems. The surface subsystem serves for operating the drilling process, transmitting the drilling and environmental data, and supplying power for the drill motor and downhole control system. The borehole subsystem is generally intended for borehole data acquisition, drill motor control, power regulation and communication. The software subsystem is designed for human–computer interaction, data processing and storage, and programming of signal acquisition and transmission of data. The control system of Antarctic subglacial drilling rig was tested during the 2018–2019 summer season near Zhongshan Station, East Antarctica, in the course of drilling to the bedrock at a depth of 198 m. It exhibited a steady and efficient performance without significant system failures.

Color images have long been used as an important supplementary information to guide the super-resolution of depth maps. However, how to quantitatively measure the guiding effect of color images on depth maps has always been a neglected issue. To solve this problem, inspired by the recent excellent results achieved in color image super-resolution by generative adversarial networks, we propose a depth map super-resolution framework with generative adversarial networks using multiscale attention fusion. Fusion of the color features and depth features at the same scale under the hierarchical fusion attention module effectively measure the guiding effect of the color image on the depth map. The fusion of joint color–depth features at different scales balances the impact of different scale features on the super-resolution of the depth map. The loss function of a generator composed of content loss, adversarial loss, and edge loss helps restore clearer edges of the depth map. Experimental results on different types of benchmark depth map datasets show that the proposed multiscale attention fusion based depth map super-resolution framework has significant subjective and objective improvements over the latest algorithms, verifying the validity and generalization ability of the model.

Articular cartilage (AC), which covers the ends of bones in joints, particularly the knee joints, provides a robust interface to maintain frictionless movement during daily life due to its remarkable lubricating and load-bearing capacities. However, osteoarthritis (OA), characterized by the progressive degradation of AC, compromises the properties of AC and thus leads to frayed and rough interfaces between the bones, which subsequently accelerates the progression of OA. Hydrogels, composed of highly hydrated and interconnected polymer chains, are potential candidates for AC replacement due to their physical and chemical properties being similar to those of AC. In this review, we summarize the recent progress of hydrogel-based synthetic cartilage, or cartilage-like hydrogels, with a particular focus on their lubrication and load-bearing properties. The different formulations, current limitations, and challenges of such hydrogels are also discussed. Moreover, we discuss the future directions of hydrogel-based synthetic cartilage to repair and even regenerate the damaged AC.

Machine learning methods can establish complex nonlinear relationships between input and response variables for stadium fire risk assessment. However, the output of machine learning models is considered very difficult due to their complex “black box” structure, which hinders their application in stadium fire risk assessment. The SHapley Additive exPlanations (SHAP) method makes a local approximation to the predictions of any regression or classification model so as to be faithful and interpretable, and assigns significant values (SHAP value) to each input variable for a given prediction. In this study, we designed an indicator attribute threshold interval to classify and quantify different fire risk category data, and then used a random forest model combined with SHAP strategy in order to establish a stadium fire risk assessment model. The main objective is to analyze the impact analysis of each risk characteristic on four different risk assessment models, so as to find the complex nonlinear relationship between risk characteristics and stadium fire risk. This helps managers to be able to make appropriate fire safety management and smart decisions before an incident occurs and in a targeted manner to reduce the incidence of fires. The experimental results show that the established interpretable random forest model provides 83% accuracy, 86% precision, and 85% recall for the stadium fire risk test dataset. The study also shows that the low level of data makes it difficult to identify the range of decision boundaries for Critical mode and Hazardous mode.

Ice-core drilling to depths of 200–300 m is an important part of research studies concerned with paleoclimate reconstruction and anthropogenic climate change. However, conventional drilling methods face difficulties due to firn permeability. We have developed an electromechanical ice-core drill with air reverse circulation at the hole bottom. We believe that the new drilling system will recover ice cores faster than shallow auger drills, with high efficiency and low energy consumption. The theoretically estimated up-hole speed of the airflow should be not

Due to the greenhouse effect of SF6, the research on alternative insulation gases is currently a hot topic in high voltage engineering. The refrigerant gas such as HFC-227ea with low GWP has stable chemical properties, and high dielectric strength because of a large number of fluorine atoms. However, it is necessary to add buffer gases for relatively high liquefaction temperature(-16.7°C) of HFC-227ea. In this article, the dielectric strengths of HFC-227ea and its gas mixtures under different gas pressure, mixture composition and ratio are investigated under DC and AC electric fields. At atmospheric pressure, the results show that the breakdown voltage of HFC-227ea mixed with N2 or CO2 could only account for 60% of SF6 gas. When HFC-227ea is mixed with HFC-125, which has similar chemical composition and structure, their dielectric strength can reach at least 80% of SF6 gas and the gas mixture shows good synergistic effect. At higher gas pressure, the HFC-227ea/HFC-125 gas mixture at pressure of 0.8 MPa and low HFC-227ea content has the best insulating performance and it is suitable for a lower operating temperature, demonstrating the potential of substituting SF6 for gas insulated electrical equipment.

Stadium fires can easily cause massive casualties and property damage. The early risk prediction of stadiums will be able to reduce the incidence of fires by making corresponding fire safety management and decision making in an early and targeted manner. In the field of building fires, some studies apply data mining techniques and machine learning algorithms to the collected risk hazard data for fire risk prediction. However, most of these studies use all attributes in the dataset, which may degrade the performance of predictive models due to data redundancy. Furthermore, machine learning algorithms are numerous and applied to fewer stadium fires, and it is crucial to explore models suitable for predicting stadium fire risk. The purpose of this study was to identify salient features to build a model for predicting stadium fire risk predictions. In this study, we designed an index attribute threshold interval to classify and quantify different fire risk data. We then used Gradient Boosting-Recursive Feature Elimination (GB-RFE) and Pearson correlation analysis to perform efficient feature selection on risk feature attributes to find the most informative salient feature subsets. Two cross-validation strategies were employed to address the dataset imbalance problem. Using the smart stadium fire risk data set provided by the Wuhan Emergency Rescue Detachment, the optimal prediction model was obtained based on the identified significant features and six machine learning methods of 12 combination forms, and full features were input as an experimental comparison study. Five performance evaluation metrics were used to evaluate and compare the combined models. Results show that the best performing model had an F1 score of 81.9% and an accuracy of 93.2%. Meanwhile, by introducing a precision-recall curve to explain the actual classification performance of each model, AdaBoost achieves the highest Auprc score (0.78), followed by SVM (0.77), which reveals more stable performance under such imbalanced data.

Hot-water drilling in ice with near-bottom circulation is more advantageous than traditional hot-water drilling with all-over borehole circulation in terms of power consumption and weight. However, the drilling performance of this type of drill has been poorly studied. Initial experiments showed that drilling with single-orifice nozzles did not proceed smoothly. To achieve the best drilling performance, nozzles with different orifice numbers and structures are evaluated in the present study. The testing results show that a single-orifice nozzle with a 3 mm nozzle diameter and a nine-jet nozzle with a forward angle of 35° had the highest rate of penetration (1.7–1.8 m h−1) with 5.6–6.0 kW heating power. However, the nozzles with backward holes ensured a smoother drilling process and a larger borehole, although the rate of penetration was approximately 13% slower. A comparison of the hollow and solid thermal tips showed that under the same experimental conditions, the hollow drill tip had a lower flow rate, higher outlet temperature, and higher rate of penetration. This study provides a prominent reference for drilling performance prediction and drilling technology development of hot-water drilling in ice with near-bottom circulation.

Coast-fitting tomographic inversion that is based on function expansion using three types of normal modes (the Dirichlet, Neumann, and open boundary modes) is proposed to reconstruct current fields from the coastal acoustic tomography (CAT) data. The superiority of the method was validated while using CAT data that were obtained in 2015 in the Dalian Bay. The semidiurnal tidal and residual current fields were accurately reconstructed over the entire model domain surrounded by coasts and open boundaries. The proposed method was effective, particularly around the peripheral regions of the tomography domain and the near-coast regions outside the domain, where accurate results are not expected from the conventional inverse method based on function expansion by Fourier function series with no coast fittings. The error velocity for the semidiurnal tidal currents was 2.2 cm s−1, which was calculated from the root-mean-square-difference between the CAT-observed and inverted range-averaged currents that were obtained along the nine peripheral transmission paths. The error velocity for the residual currents estimated from the 12-h mean net residual transport at the bay mouth was 0.9 cm s−1. The errors were significantly smaller than the amplitude of the tidal and residual currents.

Ice cores from ice shelves contain abundant paleoclimatic information and provide essential information concerned with the prediction of future climatic change and global sea level variations. Efficient retrieval of ice cores is always an engineering challenge in polar ice and marine research. Here, we present design and other information of a new hot-water ice-coring drill used in combination with a hot-water drilling system that provides a rapid and environmentally friendly ice coring system. The coring system shares the surface equipment and hydraulic hose with the hot-water drilling system. Tests with the drill were carried out at an ice drill testing facility, and theoretical estimations were performed to predict the rate of penetration (ROP) and water flow rates. The results indicate the optimal water temperature for ice-coring to be 50 °C, and the most suitable water flow rate to be from 42 L/min to 55 L/min. With those drilling parameters, the maximum ROP is 27.8 m/h and the ice cores are 55−59 mm in diameter.

Coring sediments in subglacial aquatic environments offers unique opportunities for research on paleo-environments and paleo-climates because it can provide data from periods even earlier than ice cores, as well as the overlying ice histories, interactions between ice and the water system, life forms in extreme habitats, sedimentology, and stratigraphy. However, retrieving sediment cores from a subglacial environment faces more difficulties than sediment coring in oceans and lakes, resulting in low yields from the most current subglacial sediment coring methods. The coring tools should pass through a hot water-drilled access borehole, then the water column, to reach the sediment layers. The access boreholes are size-limited by the hot water drilling tools and techniques. These holes are drilled through ice up to 3000−4000 m thick, with diameters ranging from 10−60 cm, and with a refreezing closure rate of up to 6 mm/h after being drilled. Several purpose-built streamline corers have been developed to pass through access boreholes and collect the sediment core. The main coring objectives are as follows: (i) To obtain undisturbed water−sediment cores, either singly or as multi-cores and (ii) to obtain long cores with minimal stratigraphic deformation. Subglacial sediment coring methods use similar tools to those used in lake and ocean coring. These methods include the following: Gravity coring, push coring, piston coring, hammer or percussion coring, vibrocoring, and composite methods. Several core length records have been attained by different coring methods, including a 290 cm percussion core from the sub-ice-shelf seafloor, a 400 cm piston core from the sub-ice-stream, and a 170 cm gravity core from a subglacial lake. There are also several undisturbed water−sediment cores that have been obtained by gravity corers or hammer corers. Most current coring tools are deployed by winch and cable facilities on the ice surface. There are three main limitations for obtaining long sediment cores which determines coring tool development, as follows: Hot-water borehole radial size restriction, the sedimentary structure, and the coring techniques. In this paper, we provide a general view on current developments in coring tools, including the working principles, corer characteristics, operational methods, coring site locations, field conditions, coring results, and possible technical improvements. Future prospects in corer design and development are also discussed.

As a result of the severe natural environment with oxygen deficit in the Antarctic, using conventional manual work to screw and unscrew the drill pipe increases the labor intensity tremendously and causes an efficiency reduction. Therefore, it would be useful to design a clamper inside the drilling shelter, which could help to assemble and disassemble the ice core barrel with screw connection. This paper describes the design of and experiments with the clamper inside the movable drilling shelter. A related testing stand was also designed and built to experiment on relationships between the clamper motor current versus clamping torque and force. Through experimental data collation and calculation analysis, the following conclusions could be drawn: (1) according to the rotary ability of the Ice and Bedrock Electromechanical Drill (IBED) to calculate and determine the torque of clamper, the clamping torque required to provide by the clamper is 100 N·m; (2) finite element simulation and analysis of the non-standard transmission chain showed that the mechanical reliability of the subparts; (3) the experimental results showed the range of the clamping force and clamping torque of the clamper. The clamping force and clamping torque increased with the increase of clamper DC motor current, presenting proportional linear relationships. The clamper can meet the clamping requirements of IBED for screwing, unscrewing, and clamping, which will be greatly helpful when it is tested in the field.

Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity.