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A broadband online X-ray spectrometer has been designed and commissioned at the SUD beamline of the Shanghai Soft X-ray Free-Electron Laser Facility, which can deliver both SASE and seeded FEL pulses to user experiments, spanning the photon energy range of 50–620 eV. The resolving powers of the spectrometer calibrated via online measurement at 92 eV and 249 eV are ∼20000 and ∼15000, respectively, and the absolute photon energy is characterized by an electron time-of-flight spectrometer. The high energy resolution provided by the spectrometer can differentiate the fine structure in the FEL spectrum, to determine its pulse length.

Abstract Transformerless grid‐connected inverters (GCIs) are universally utilized in the PV system. However, they have the shoot‐through issue and common mode leakage current (CMLC). To address the above‐mentioned issues, a transformerless common‐ground GCI without shoot‐through issue is proposed. The proposed topology has one common ground between the PV array and the grid that eliminates the CMLC. The topology is the combination of two buck‐boost converters, so it has the boost capability. Each buck‐boost converter operates in half‐line period, so the proposed inverter is a single‐stage system. Only two switches operate in high frequency in positive half‐line period and only one switch operates in high frequency in negative half‐line period. Therefore, efficiency of the system can be improved. There is no current flowing through the body diode of the MOSFET. Additionally, the presented inverter has no shoot‐through problem. The operating principle is elaborated. At last, simulation and experimental results of a 250 W proposed inverter validate the theoretical analysis. The maximum efficiency of the system is 96.2%.

The Shanghai Soft X-ray Free-Electron Laser (SXFEL) is the first X-ray free-electron laser facility in China. The SASE beamline, which consists of a pink-beam branch and a mono-beam branch, is one of the two beamlines in the Phase-I construction. The pink-beam branch opened for users in 2023 after successful first-round beamline commissioning. In this paper, the design of the beamline is presented and the performance of the pink-beam branch is reported. The measured energy-resolving power of the online spectrometer is over 6000 @ 400 eV. The focusing spot size of the pink beam is less than 3 µm in both the horizontal and vertical at the endstation.

Graphite, a key anode material in lithium-ion batteries, primarily relies on the Acheson graphitization furnace (AGF) for production. This research focuses on the power supply module of the AGF, particularly the electrodes and their power transmission clamps. A three-dimensional transient electric-thermal-fluid coupling model was developed to numerically analyze the temperature and electric field distributions during operation. The study revealed that heat conduction through furnace electrodes dominates temperature rise. Notably, clamping plates within transmission clamps exhibit high temperatures and gradients, posing a thermal failure risk. Efficient cooling plate design with liquid-cooled channels is crucial for temperature control. Additionally, maintaining high electrode temperatures reduces resistivity, lowering power consumption in the power supply module. This study provides insights into optimizing AGF power supply module design, emphasizing the importance of effective cooling strategies for clamping plates and the benefits of maintaining elevated electrode temperatures for energy efficiency.

The rapid progress in cultivated meat research has engendered considerable attention towards the edible scaffolding biomaterials employed in the production. Cellulose has the advantages in availability, edibility, animal-free origin, etc., which show its potential in wide fields. This review begins by presenting the fundamental physical and chemical properties of cellulose from different sources, including plant and bacterial cellulose. Subsequently, we summarize the application of cellulose especially in cultivated meat and tissue engineering. Furthermore, we explore various methods for preparing cellulose-based scaffolds for cultivated meat, encompassing five specific structural variations. In the end, associated with utilizing cellulose in cultivated meat production, we address several primary challenges surrounding to cell adhesion, scaling up, processibility and mechanical properties, and provide potential innovations. This review underscores the potential of cellulose as a versatile biomaterial in the cultivated meat industry and provides insight into addressing critical challenges for its integration.

Due to geographic isolation and habitat disturbance, the Qinling giant pandas (QGPs) were divided into six habitat patches and accordingly assigned to six local populations in the Fourth National Survey on Giant Pandas. Whether the artificial division of QGP local population is of rationality and whether it is reflected by genetic structure remain unclear. To answer the questions, we conducted microsatellite marker analysis with 12 loci from 94 individuals identified via faeces samples collected from two local populations in the Qinling Mountains. Based on analysis of molecular variance (AMOVA), the genetic variation of the Xinglongling local population and Niuweihe local population mainly occurred within populations (81.22 %), with a relatively high genetic differentiation (Fst = 0.18782) and low gene flow (Nm = 0.340282) between Xinglongling and Niuweihe local population. The Mantel test revealed a significant positive correlation between pairwise genetic distance and geographic distance (R = 0.136, p = 0.001). The study reflected that the partition of two local populations is consistent with the division of habitat patches, and also confirmed the geographic distance has influences on genetic distance between QGP individuals in Qinling Mountains. These findings offer important references for effective conservation of QGPs, especially small populations, avoiding the loss of genetic diversity of the QGP population.

Abstract Background There are no recommendations for specific suture materials in transtibial pullout repair of medial meniscus posterior root tears. This study aimed to evaluate the clinical outcomes of transtibial pullout repair of medial meniscus posterior root tears using ultrahigh-molecular-weight polyethylene sutures and suture tape. Methods We retrospectively reviewed the data of 36 patients (27 women and 9 men, mean age 64.1 years) who had undergone transtibial pullout repair of medial meniscus posterior root tears between November 2018 and December 2019. Two groups of 18 patients each received either two different cord-like sutures or suture tape. Clinical parameters were assessed preoperatively and on second-look arthroscopy (mean postoperative period 12 months). The meniscal healing status was assessed using a previously published scoring system (ranging from 0 to 10), and the incidence rate of suture cut-out was assessed on second-look arthroscopy. Results All clinical scores significantly improved in both groups, with no significant between-group differences on second-look arthroscopy. The arthroscopic meniscal healing scores significantly differed between sutures (mean 6.7 points) and suture tape (mean 7.4 points; p = 0.044). No significant between-group difference in the suture cut-out rate was observed. Conclusions This study found no significant differences in the clinical outcomes between ultrahigh-molecular-weight polyethylene sutures and suture tape. Favorable clinical outcomes were obtained using both types of suture; however, the usefulness of suture tape appears to be limited.

Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks.

The current popular one-shot multi-object tracking (MOT) algorithms are dominated by the joint detection and embedding paradigm, which have high inference speeds and accuracy, but their tracking performance is unstable in crowded scenes. Not only does the detection branch have difficulty in obtaining the accurate object position, but the ambiguous appearance of features extracted by the re-identification (re-ID) branch also leads to identity switches. Focusing on the above problems, this paper proposes a more robust MOT algorithm, named CSMOT, based on FairMOT. First, on the basis of the encoder–decoder network, a coordinate attention module is designed to enhance the information interaction between channels (horizontal and vertical coordinates), which improves its object-detection abilities. Then, an angle-center loss that effectively maximizes intra-class similarity is proposed to optimize the re-ID branch, and the extracted re-ID features are made more discriminative. We further redesign the re-ID feature dimension to balance the detection and re-ID tasks. Finally, a simple and effective data association mechanism is introduced, which associates each detection instead of just the high-score detections during the tracking process. The experimental results show that our one-shot MOT algorithm achieves excellent tracking performance on multiple public datasets and can be effectively applied to crowded scenes. In particular, CSMOT decreases the number of ID switches by 11.8% and 33.8% on the MOT16 and MOT17 test datasets, respectively, compared to the baseline.

The massive emission of CO2 has caused a series of environmental problems, including global warming, which exacerbates natural disasters and human health. Cu-based catalysts have shown great activity in the reduction of CO2, but the mechanism of CO2 activation remains ambiguous. In this work, we performed density functional theory (DFT) calculations to investigate the hydrogenation of CO2 on Cu(211)-Rh, Cu(211)-Ni, Cu(211)-Co, and Cu(211)-Ru surfaces. The doping of Rh, Ni, Co, and Ru was found to enhance CO2 hydrogenation to produce COOH. For CO2 hydrogenation to produce HCOO, Ru plays a positive role in promoting CO dissociation, while Rh, Ni, and Co increase the barriers. These results indicate that Ru is the most effective additive for CO2 reduction in Cu-based catalysts. In addition, the doping of Rh, Ni, Co, and Ru alters the electronic properties of Cu, and the activity of Cu-based catalysts was subsequently affected according to differential charge analysis. The analysis of Bader charge shows good predictions for CO2 reduction over Cu-based catalysts. This study provides some fundamental aids for the rational design of efficient and stable CO2-reducing agents to mitigate CO2 emission.

Abstract Background Low-temperature swelling of cotton linter cellulose and subsequent gelatinization in trifluoroacetic acid (TFA) greatly enhance rates of enzymatic digestion or maleic acid–AlCl3 catalyzed conversion to hydroxymethylfurfural (HMF) and levulinic acid (LA). However, lignin inhibits low-temperature swelling of TFA-treated intact wood particles from hybrid poplar (Populus tremula × P. alba) and results in greatly reduced yields of glucose or catalytic conversion compared to lignin-free cellulose. Previous studies have established that wood particles from transgenic lines of hybrid poplar with high syringyl (S) lignin content give greater glucose yields following enzymatic digestion. Results Low-temperature (− 20 °C) treatment of S-lignin-rich poplar wood particles in TFA slightly increased yields of glucose from enzymatic digestions and HMF and LA from maleic acid–AlCl3 catalysis. Subsequent gelatinization at 55 °C resulted in over 80% digestion of cellulose in only 3 to 6 h with high-S-lignin wood, compared to 20–60% digestion in the wild-type poplar hybrid and transgenic lines high in guaiacyl lignin or 5-hydroxy-G lignin. Disassembly of lignin in woody particles by Ni/C catalytic systems improved yields of glucose by enzymatic digestion or catalytic conversion to HMF and LA. Although lignin was completely removed by Ni/C-catalyzed delignification (CDL) treatment, recalcitrance to enzymatic digestion of cellulose from the high-S lines was reduced compared to other lignin variants. However, cellulose still exhibited considerable recalcitrance to complete enzymatic digestion or catalytic conversion after complete delignification. Low-temperature swelling of the CDL-treated wood particles in TFA resulted in nearly complete enzymatic hydrolysis, regardless of original lignin composition. Conclusions Genetic modification of lignin composition can enhance the portfolio of aromatic products obtained from lignocellulosic biomass while promoting disassembly into biofuel and bioproduct substrates. CDL enhances rates of enzymatic digestion and chemical conversion, but cellulose remains intrinsically recalcitrant. Cold TFA is sufficient to overcome this recalcitrance after CDL treatment. Our results inform a ‘no carbon left behind’ strategy to convert total woody biomass into lignin, cellulose, and hemicellulose value streams for the future biorefinery.

Forage availability and quality directly impact animal performance, ultimately affecting productivity and health. This study aimed to understand the interaction between qualitative and quantitative vegetation availability and feed intake of goats on alpine pastures in the Chinese Altai Mountains. The daily grazing routes of three goats from a local herding family were monitored with GPS devices set at a logging rate of 64 s during spring and the early and late summer season in 2013 and 2014. The quantity and quality of vegetation along their grazing routes was determined, and the amount of feces excreted was measured in a total of five goats per season for the indirect determination of the animals’ feed intake. The grazing routes were longer in spring than in summer, leading to larger grazing areas visited in spring. Vegetation on offer ranged from 980 to 2400 kg dry mass per hectare and was similar in the spring and summer seasons but higher in 2013 than in 2014. Feed consumption of forage and nutrients did not significantly differ between seasons and years, respectively, suggesting that the goats’ nutrient intake was not restricted by interannual variability of forage on offer. Regular monitoring of animal numbers and of vegetation quantity and quality on the mountain rangelands can help responsible government agencies to estimate forage offtake of small ruminants in order to timely adjust grazing pressure in the study region.

Siamese networks are one of the most popular directions in the visual object tracking based on deep learning. In Siamese networks, the feature pyramid network (FPN) and the cross-correlation complete feature fusion and the matching of features extracted from the template and search branch, respectively. However, object tracking should focus on the global and contextual dependencies. Hence, we introduce a delicate residual transformer structure which contains a self-attention mechanism called encoder-decoder into our tracker as the part of neck. Under the encoder-decoder structure, the encoder promotes the interaction between the low-level features extracted from the target and search branch by the CNN to obtain global attention information, while the decoder replaces cross-correlation to send global attention information into the head module. We add a spatial and channel attention component in the target branch, which can further improve the accuracy and robustness of our proposed model for a low price. Finally, we detailly evaluate our tracker CTT on GOT-10k, VOT2019, OTB-100, LaSOT, NfS, UAV123 and TrackingNet benchmarks, and our proposed method obtains competitive results with the state-of-the-art algorithms.

Metabolic reprogramming is a cancer hallmark. Although the reprogramming of central carbon has been well documented, the role of sulfur metabolism has been largely overlooked. Additionally, the effects of sulfur are sometimes contradictory in tumorigenesis. In this study, we aimed to investigate the gene expression profile in hepatocellular carcinoma (HCC) and the effects of reactive sulfur species (RSS) on HCC tumor cells. Furthermore, the cell imaging technology was applied to discover some potential anti-cancer compounds. Gene Set Enrichment Analysis (GSEA) of Gene Expression Omnibus (GEO) dataset (GSE102083) revealed that sulfur amino acid-related metabolism and vitamin B6 binding activity in HCC tissues were downregulated. Calculation of the interaction network identified nine hub genes, among which eight were validated by differential expression and survival analysis in the TCGA_LIHC cohort, and two (CSE and CBS) had the highest enrichment degree. The metabolomics analysis suggested that the hub genes were associated with RSS metabolism including H2S, H2S2, cystine, cysteine, homocysteine, cystathionine, and methionine. The cell viability assay demonstrated that H2S2 had significant anti-cancer effects in HCC SNU398 tumor cells. The cell imaging assay showed that treatment with H2S2 remarkably increased intracellular sulfane sulfur content. On this basis, the anti-cancer activity of some other sulfane sulfur compounds, such as DATS and DADS, was further verified. Lastly, according to the fact that HCC tumor cells preferentially take in cystine due to high expression of SLC7A11 (a cystine/glutamate transporter), persulfided cysteine precursor (PSCP) was tested for its sulfane sulfur release capability and found to selectively inhibit HCC tumor cell viability. Collectively, this study uncovered sulfur metabolism in HCC was reprogrammed, and provided a potential therapeutic strategy for HCC by donating sulfane sulfur.

Abstract Background The invention of efficient systems for lignocellulose conversion is essential for economically feasible production of bio-based chemicals and biofuels. One limiting step is highly selective processes to quickly decrystallize the compact cellulose structure for efficient hydrolysis. We evaluated the impact of trifluoroacetic acid (TFA) and phosphorous acid (PA)-induced swelling of crystalline cellulose on enhancement of enzymatic digestion. Results In this study, two swelling agents, TFA and PA, are compared and found to be highly efficient for cellulose decrystallization at low temperatures within 1 h. After treatment, the microfibril structure of swollen celluloses was observed to develop distinct microscopic morphology and subsequent enzymatic hydrolysis resulted over 90% cellulose conversion within 24 h. The crystalline cellulose change was determined by reduction of loss of X-ray diffractability, and loss of resistance to enzymatic hydrolysis. NMR results suggest that both TFA and PA efficiently converted most of the crystalline cellulose regions to amorphous regions through cellulose chain relocation that inhibits recrystallization. It was found that the swelling mechanism is different between TFA and PA. To the best of our knowledge, it is the first time to compare and quantify the cellulose regions transformation by swelling agents. Conclusion This study shows the low-temperature swelling of different celluloses in TFA and PA reduces recalcitrance of crystalline cellulose to enzymatic hydrolysis. TFA and PA are both ideal candidate swelling agents for a closed system for ease of solvent recovery by either simple distillation or filtration. This study provides potentially useful agents in large-scale deconstruction of biomass.

Understanding how climate change alters the spatial aggregation of sympatric species is important for biodiversity conservation. Previous studies usually focused on spatial shifting of species but paid little attention to changes in interspecific competitions under climate change. In this study, we evaluated the potential effects of climate change on the spatial aggregation of giant pandas (Ailuropoda melanoleuca) and three sympatric competitive species (i.e., black bears (Ursus thibetanus), golden takins (Budorcas taxicolor), and wild boars (Sus scrofa)) in the Qinling Mountains, China. We employed an ensemble species distribution modeling (SDM) approach to map the current spatial distributions of giant pandas and sympatric animals and projected them to future climate scenarios in 2050s and 2070s. We then examined the range overlapping and niche similarities of these species under different climate change scenarios. The results showed that the distribution areas of giant pandas and sympatric species would decrease remarkably under future climate changes. The shifting directions of the overlapping between giant pandas and sympatric species vary under different climate change scenarios. In conclusion, future climate change greatly shapes the spatial overlapping pattern of giant pandas and sympatric species in the Qinling Mountains, while interspecific competition would be intensified under both mild and worst-case climate change scenarios.

Abstract Background The crystallinity of cellulose is a principal factor limiting the efficient hydrolysis of biomass to fermentable sugars or direct catalytic conversion to biofuel components. We evaluated the impact of TFA-induced gelatinization of crystalline cellulose on enhancement of enzymatic digestion and catalytic conversion to biofuel substrates. Results Low-temperature swelling of cotton linter cellulose in TFA at subzero temperatures followed by gentle heating to 55 °C dissolves the microfibril structure and forms composites of crystalline and amorphous gels upon addition of ethanol. The extent of gelatinization of crystalline cellulose was determined by reduction of birefringence in darkfield microscopy, loss of X-ray diffractability, and loss of resistance to acid hydrolysis. Upon freeze-drying, an additional degree of crystallinity returned as mostly cellulose II. Both enzymatic digestion with a commercial cellulase cocktail and maleic acid/AlCl3-catalyzed conversion to 5-hydroxymethylfurfural and levulinic acid were markedly enhanced with the low-temperature swollen cellulose. Only small improvements in rates and extent of hydrolysis and catalytic conversion were achieved upon heating to fully dissolve cellulose. Conclusions Low-temperature swelling of cellulose in TFA substantially reduces recalcitrance of crystalline cellulose to both enzymatic digestion and catalytic conversion. In a closed system to prevent loss of fluorohydrocarbons, the relative ease of recovery and regeneration of TFA by distillation makes it a potentially useful agent in large-scale deconstruction of biomass, not only for enzymatic depolymerization but also for enhancing rates of catalytic conversion to biofuel components and useful bio-products.

Region proposal network (RPN) based trackers employ the classification and regression block to generate the proposals, the proposal that contains the highest similarity score is formulated to be the groundtruth candidate of next frame. However, region proposal network based trackers cannot make the best of the features from different convolutional layers, and the original loss function cannot alleviate the data imbalance issue of the training procedure. We propose the Spatial Cascaded Transformed RPN to combine the RPN and STN (spatial transformer network) together, in order to successfully obtain the proposals of high quality, which can simultaneously improves the robustness. The STN can transfer the spatial transformed features though different stages, which extends the spatial representation capability of such networks handling complex scenarios such as scale variation and affine transformation. We break the restriction though an easy samples penalization loss (shrinkage loss) instead of smooth L1 function. Moreover, we perform the multi-cue proposals re-ranking to guarantee the accuracy of the proposed tracker. We extensively prove the effectiveness of our proposed method on the ablation studies of the tracking datasets, which include OTB-2015 (Object Tracking Benchmark 2015), VOT-2018 (Visual Object Tracking 2018), LaSOT (Large Scale Single Object Tracking), TrackingNet (A Large-Scale Dataset and Benchmark for Object Tracking in the Wild) and UAV123 (UAV Tracking Dataset).

Because the target users of the assistive-type lower extremity exoskeletons (ASLEEs) are those who suffer from lower limb disabilities, customized gait is adopted for the control of ASLEEs. However, the customized gait is unable to provide stable motion for variable terrain, for example, flat, uphill, downhill, and soft ground. The purpose of this paper is to realize gait detection and environment feature recognition for AIDER by developing a novel wearable sensing system. The wearable sensing system employs 7 force sensors as a sensing matrix to achieve high accuracy of ground reaction force detection. There is one more IMU sensor that is integrated into the structure to detect the angular velocity. By fusing force and angular velocity data, four typical terrain features can be recognized successfully, and the recognition rate can reach up to 93%.

Microwave heating was applied in the liquefaction of corn stover with polyhydric alcohol to establish a rapid process for converting corn stover into polyols. With ethylene glycol (EG) as liquefacient and 3.5% sulfuric acid as a catalyst, the residue content was reduced to 4.7% after 20 min of microwave liquefaction at 160 °C. Effects of liquefaction parameters on the residue content were investigated. It was found that the reaction temperature had a greater influence on the residue content than the sulfuric acid concentration and reaction time. The liquefied mixture was characterized as complexes of ester and ether type polyols by means of Fourier transform infrared spectroscopy. 3-(2-Methyl-1,3-dioxolan-2-yl) propanoic acid was characterized as the main degradation product of corn stover, besides the large amount of condensation product of EG.

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. The existing spatially regularized discriminative correlation filter (SRDCF) method learns partial-target information or background information when experiencing rotation, out of view, and heavy occlusion. In order to reduce the computational complexity by creating a novel method to enhance tracking ability, we first introduce an adaptive dimensionality reduction technique to extract the features from the image, based on pre-trained VGG-Net. We then propose an adaptive model update to assign weights during an update procedure depending on the peak-to-sidelobe ratio. Finally, we combine the online SRDCF-based tracker with the offline Siamese tracker to accomplish long term tracking. Experimental results demonstrate that the proposed tracker has satisfactory performance in a wide range of challenging tracking scenarios.

Algicidal bacteria offer a promising option for killing cyanobacteria. Therefore, a new Alcaligenes aquatilis strain F8 was isolated to control Microcystis aeruginosa in this study. The algicidal activity of strain F8 was dependent on the cell density of M. aeruginosa, and the maximal algicidal rate of the free bacterium reached 88.45% within 72 h. With a view to its application to the control of M. aeruginosa in the natural environment, strain F8 was immobilized in sodium alginate beads, but immobilization of the strain decreased its algicidal rate compared to that of the free bacterium. However, addition of wheat bran to the sodium alginate matrix used to immobilize strain F8 not only eliminated the adverse effects of immobilization on the bacteria but also resulted in an 8.83% higher algicidal rate of the immobilized than free bacteria. Exclusion and recovery methods were used to identify key ingredients of wheat bran and gain insight into the mechanism underlying the observed enhancement of algicidal activity. This analysis indicated that certain factors in wheat bran, including vitamins B1, B2, B9, and E were responsible for promoting bacterial growth and thereby improving the algicidal rate of immobilized strain F8. Our findings indicate that wheat bran is able to improve the algicidal efficiency of A. aquatilis strain F8 for killing M. aeruginosa and is a good source of not only carbon and nitrogen but also vitamins for bacteria.

In coal seam gas (CSG) wells, water is periodically removed from the wellbore in order to keep the bottom-hole flowing pressure at low levels, facilitating the desorption of methane gas from the coal bed. In order to calculate gas flow rate and further optimize well performance, it is necessary to accurately monitor the liquid level in real-time. This paper presents a novel method based on autocorrelation function (ACF) analysis for determining the liquid level in CSG wells under intense noise conditions. The method involves the calculation of the acoustic travel time in the annulus and processing the autocorrelation signal in order to extract the weak echo under high background noise. In contrast to previous works, the non-linear dependence of the acoustic velocity on temperature and pressure is taken into account. To locate the liquid level of a coal seam gas well the travel time is computed iteratively with the non-linear velocity model. Afterwards, the proposed method is validated using experimental laboratory investigations that have been developed for liquid level detection under two scenarios, representing the combination of low pressure, weak signal, and intense noise generated by gas flowing and leakage. By adopting an evaluation indicator called Crest Factor, the results have shown the superiority of the ACF-based method compared to Fourier filtering (FFT). In the two scenarios, the maximal measurement error from the proposed method was 0.34% and 0.50%, respectively. The latent periodic characteristic of the reflected signal can be extracted by the ACF-based method even when the noise is larger than 1.42 Pa, which is impossible for FFT-based de-noising. A case study focused on a specific CSG well is presented to illustrate the feasibility of the proposed approach, and also to demonstrate that signal processing with autocorrelation analysis can improve the sensitivity of the detection system.

Background: Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. Methods: This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n=136; external validation, n=81; prospective, n=26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. Results: Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNIpositive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P=0.021). Conclusions: We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application. [ABSTRACT FROM AUTHOR]