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Abstract Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice.

Digital technologies (DTs) have resulted in innovations that have fueled global knowledge-based economic development. This systematic review of the literature focuses on the relationship between DTs and innovation. Drawing on the extant literature, we investigate 685 articles published in 41 journals from 1997 to 2023. We present the current state of different types of articles and the theoretical perspectives applied in the previous research. Based on topic modeling analyses and interpretations of existing work, we develop a meta-framework that distinguishes between the direct and indirect effects of DTs on innovation and considers four levels of potential heterogeneity affecting the relationship. Finally, we propose an agenda that emphasizes the avenues for future research on DTs and innovation.

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

Abstract Recently, advanced adenoma (AA) has been recognized as a target for colorectal cancer (CRC) screening. However, the fecal occult blood test (FOBT), the primary non-invasive screening method, shows limited sensitivity in detecting AA. This study investigates the relationship between adenoma characteristics and FOBT false-negative results. In a retrospective cohort study conducted from 2015 to 2022, we examined 342 inpatients with AA who underwent colonoscopy and received qualitative FOBT. FOBT sensitivity was analyzed about various adenoma characteristics, and logistic regression models were employed to investigate the relationship between adenoma features and FOBT false-negative outcomes. FOBT sensitivity in AA inpatients was 52.63%. Significant differences in sensitivity were observed based on adenoma location (left vs. right), morphology (with or without pedunculation), and size (≤ 10 mm vs. > 10 mm). After adjusting for several potential confounders, FOBT showed a reduced false-negative rate in AA with large-sized (OR, 0.49; 95% CI 0.31–0.77), left-sided location (OR, 0.53; 95% CI 0.31–0.89), and pedunculated morphology (OR, 0.73; 95% CI 0.43–1.24). AA with large size, left-sided location, and pedunculated morphology independently contribute to a decreased rate of FOBT false-negative results. However, these adenoma characteristics are not actively modifiable. Therefore, novel non-invasive methods are needed to improve AA detection accuracy.

Postpubertal testicular teratoma exhibits malignant biological behavior and has metastatic potential. We report a case of a 17-year-old patient diagnosed with postpubertal testicular teratoma with massive retroperitoneal metastasis. The pathological examination revealed a mature teratoma without any other components. However, the patient had a significantly increased level of AFP, and 18F-FDG PET/CT showed the retroperitoneal metastasis had increased FDG uptake, with a SUVmax of 15.6, suggesting the coexistence of other germ cell tumor components, and the patient might have a poor prognosis. After resection of the retroperitoneal tumor, PET/CT further revealed multiple abdominal and pelvic metastases, with a SUVmax of 22.5. Therefore, the patient received a cycle of chemotherapy and follow-up PET/CT imaging showed the achievement of complete metabolic response after the treatment. In this case, PET/CT played a crucial role in detecting metastasis, compensating for the limitations of pathological sampling, thus establishing a definitive diagnosis and predicting prognosis. And it was evident that PET/CT also has the advantage of evaluating therapeutic efficacy.

Fibrosis is a progressive pathological process participating in the progression of many diseases and can ultimately result in organ malfunction and failure. Around 45% of deaths in the United States are believed to be attributable to fibrotic disorders, and there are no favorable treatment regiments available to meet the need of blocking fibrogenesis, reversing established fibrosis, and curing diseases, especially in the terminal stage. Therefore, early detection and continuous monitoring provide valuable benefits for patients. Among all the advanced techniques developed in recent years for fibrosis evaluation, molecular imaging stands out with its distinct advantage of visualizing biochemical processes and patterns of target localization at the molecular and cellular level. In this review, we summarize the current state of the art in molecular imaging of benign fibrosis diseases. We will first introduce molecular pathways underlying fibrosis processes and potential targets. We will then elaborate on molecular probes that have been developed thus far, expounding on their mechanisms and current states of translational advancement. Finally, we will delineate the extant challenges impeding further progress in this area and the prospective benefits after overcoming these problems.

Ultrasonic assisting is considered to be a promising technology for industrial applications due to its ability to improve the quality of the WAAMed component. A specially designed ultrasonically vibrating torch clamp (UV torch clamp) was used to introduce the UV in the WAAM process of Al alloy for the first time. UV effect on the forming accuracy, microstructure and mechanical properties of WAAMed Al thin-walled component were investigated. The results show that the UV applied via the UV torch clamp changed the metal transfer mode, and increased the transfer frequency by at least 7.42% compared with that without UV at various Wire Feed Speed ( WFS s). The applied UV caused slight increases in the surface roughness of the deposited thin-wall components at low WFS s and obvious decreases at high WFS s, the most significant reduction occurred with WFS = 8 m min ^−1 , the surface roughness decreased from 1.04 to 0.44. The grain size and the pore size and density in the deposit were significantly reduced by the UV, and the higher the WFS, the greater the grain refinement effectiveness and the pore suppression effectiveness. The UTS and fracture elongation of the specimen with UV application increased by at least 4.6% (from 277 Mpa to 290 Mpa for longitudinal specimen with WFS = 5 m min ^−1 , ) and 3.3% (from 21.42% to 22.12% for vertical specimen with WFS = 8 m min ^−1 ) respectively at various WFS s compared to the specimen without UV application.

The scales of the power distribution networks in real-world power grids expand quickly while the network structures are becoming more and more complex. The power grid companies analyze the power distribution networks in different business scenarios with different topology models. In this work, we propose a hierarchical graph model to describe the medium-voltage distribution network (which is a typical power distribution network in power grids) based on homeomorphic transformation. The hierarchical graph model preserves the basic network topology described by the traditional Common Information Model (CIM). Firstly, the nodes in the distribution network topology are classified according to graph theory. Secondly, three typical business scenarios of distribution network topology analysis are summarized, and the original model is simplified by progressive dimensionality reduction method to meet the analysis requirements of different scenarios, the simplified method consists of three abstract levels: critical path, core path and minimal path, and can effectively reduce the space complexity of the model while maintaining the topological properties. Thirdly, a multi-level distribution network topology construction and mapping method based on the graph database is proposed. It is used to realize the rapid conversion and traceability between different levels of topology. Finally, a practical distribution network in a county is used as an example to verify the effectiveness of the proposed method in the aspects such as topology rendering and path searching. The evaluation indicates that the proposed model can visualize the distribution network intuitively. The model can also speed up the visualization and path searching significantly.

BackgroundRhabdomyosarcoma (RMS) is the most frequent soft sarcoma in children and adolescents. Alveolar rhabdomyosarcoma (ARMS) is a relatively rare subtype that is characterized by aggressive behavior and an unsatisfactory prognosis. An ARMS can arise anywhere but most commonly occurs at extremity sites with a very small fraction in the retroperitoneum. The utility of 2-Deoxy-2-[fluorine-18]-fluoro-D-glucose (18F-FDG) positron emission tomography combined with computed tomography (PET/CT) remains to be established in ARMS.Case ReportA 3-year-old female child was accidentally found with a large left upper abdominal mass for a day. CT examination indicated a huge soft tissue mass in the left retroperitoneum extending superiorly to the level of the left hilus renalis and inferiorly to the left acetabulum in the pelvic cavity, with intrusion into the lumbar foramens. 18F-FDG PET/CT found a mass in the left retroperitoneum from the level of T12 to the left acetabulum, with the maximum standardized uptake value (SUVmax) of about 7.0, and a CT value of about 39 HU, invading the left L3-5 intervertebral foramina and protruding into the spinal canal, with unclear boundary with the spinal cord. Retroperitoneal tumor resection and the repair operation of vascular exploration were performed. An ARMS was confirmed by postoperative biopsy, immunohistochemical staining, and genetic detection with the rupture of the fork head in rhabdomyosarcoma (FKHR). The patient received chemotherapy and was in a good condition with no recurrence and obvious complications.ConclusionRetroperitoneal ARMS is rare and indicates a poor outcome with the potential to involve vital organs and intrude into the spinal canal. Accurate diagnosis and staging using PET/CT would contribute to better risk stratifications and appropriate treatment individually.

The calculation and analysis of a power distribution network (PDN) require accurate device parameters. However, a PDN has many points, and the distribution area is very wide. The PDN parameters are influenced by manual entry, and most are relatively random. Additionally, these parameters are affected by the operating status. Thus, this paper proposes an algorithm that accurately identifies PDN parameters based on the Markov chain and Monte Carlo (MCMC) method. The algorithm assumes that the PDN parameters conform to a nonlinear probability space. The parameters are the line resistance $R_{L} $ , line reactance $X_{L} $ , short-circuit loss $P_{k} $ , short-circuit voltage percentage $U_{k}$ %, no-load loss $P_{0} $ , no-load current percentage $I_{0} $ %, etc. The algorithm in this paper uses the Monte Carlo method to provide parameter values that conform to the initial probability distribution and then combines the data collected from the actual feeder to perform power flow calculations to obtain the loss function. The data include the head and end voltages and active and reactive power on the low voltage side. The Markov chain and loss function update the initial parameter probability distribution. The low voltage side voltage of the power flow calculation is iteratively calculated under the new given parameters to obtain the new loss function, and finally, the PDN line and transformer parameter values are identified. Actual feeder data verification results show that this MCMC PDN parameter identification method can obtain high-precision parameter values without phase angle information; additionally, this method is insensitive to the initial values and exhibits fast convergence.

Accurate device parameters play a critical role in the calculation and analysis of power distribution networks (PDNs). However, device parameters are always affected by the operating status and influenced by manual entry. Besides, the distribution area of PDN is very wide, which brings more challenges to parameter identification work. Therefore, developing appropriate algorithms for accurately identifying PDN parameters has attracted much more attention from researchers recently. Most of the existing parameter identification algorithms are gradient-free and based on heuristic schemes. Herein, an adaptive gradient-based method is proposed for parameter identification in PDN. The analytical expressions of the gradients of the loss function with respect to the parameters are derived, and an adaptive updating scheme is utilized. By comparing the proposed method and several heuristic algorithms, it is found that the errors in both three criteria via our solution are much lower with a much smoother and more stable convergence of loss function. By further taking a linear transformation of the loss function, the method of this work significantly promotes the parameter identification performance with much lower variance in repeat experiments, indicating that the proposed method in this work achieves a more robust performance to identify PDN parameters. This work gives a practical demonstration by utilizing the gradient-based method for parameter identification of PDN.

Accurate network parameters are of great importance for the accurate control of the power distribution network (PDN). In fact, the line parameters of the PDN are always affected by external operating conditions. However, most of the line parameters in the PDN account are static parameters. In order to obtain the dynamic parameters that reflect the line operating condition, this study presents a method that uses only the RMS voltage of the first section of the line and the RMS voltage and power at the low-voltage side of the transformer. This study introduces the processing method of abnormal measurement data, constructs a derivative-free identification equation represented by a matrix, and uses the designed loss function combined with a heuristic method to solve the equation. An actual feeder is used in the experimental part. The experimental data show that the method has some antipower noise ability, and the identification accuracy of this method is better than that of the genetic algorithm and random search algorithm.

Thanks to their strong maneuverability and high load capacity, car-like robots with non-holonomic constraints are often used in logistics to improve efficiency. However, it is difficult to plan a safe and smooth optimal path in real time on the restricted narrow roads of the logistics park. To solve this problem, an optimization-based motion planning method inspired by the Timed-Elastic-Band algorithm is proposed, called Narrow-Roads-Timed-Elastic-Band (NRTEB). Three optimization modules are added to the inner and outer workflow of the Timed-Elastic-Band framework. The simulation results show that the proposed method achieves safe reversing planning on narrow roads while the jerk of the trajectory is reduced by 72.11% compared to the original method. Real-world experiments reveal that the proposed method safely and smoothly avoids dynamic obstacles in real time when navigating forward and backward. The motion planner provides a safer and smoother trajectory for car-like robots on narrow roads in real time, which greatly enhances the safety, robustness and reliability of the Timed-Elastic-Band planner in logistics parks.

The depth completion task aims to generate a dense depth map from a sparse depth map and the corresponding RGB image. As a data preprocessing task, obtaining denser depth maps without affecting the real-time performance of downstream tasks is the challenge. In this paper, we propose a lightweight depth completion network based on secondary guidance and spatial fusion named SGSNet. We design the image feature extraction module to better extract features from different scales between and within layers in parallel and to generate guidance features. Then, SGSNet uses the secondary guidance to complete the depth completion. The first guidance uses the lightweight guidance module to quickly guide LiDAR feature extraction with the texture features of RGB images. The second guidance uses the depth information completion module for sparse depth map feature completion and inputs it into the DA-CSPN++ module to complete the dense depth map re-guidance. By using a lightweight bootstrap module, the overall network runs ten times faster than the baseline. The overall network is relatively lightweight, up to thirty frames, which is sufficient to meet the speed needs of large SLAM and three-dimensional reconstruction for sensor data extraction. At the time of submission, the accuracy of the algorithm in SGSNet ranked first in the KITTI ranking of lightweight depth completion methods. It was 37.5% faster than the top published algorithms in the rank and was second in the full ranking.

To improve the conversion efficiency of propionic acid in the post-anaerobic fermentation of biogas slurry, the anaerobic fermentation process using biogas slurry with a high acid content was simulated in an anaerobic reactor at 35 ± 0.5 °C using sodium propionate as the sole substrate. The effects of different electron acceptors (NO3−, SO42− and Fe3+) on propionic acid conversion and the succession of microbial community structures were investigated. The results showed that the experimental group with the electron acceptor NO3− exhibited the best anaerobic fermentation effect, with a maximum propionate removal rate of 94%, which was 36% higher than the control group without an electron acceptor. The maximum methane production rate was 307.6 mL/g COD, an increase of 30% compared with the control group. Thauera, Aquabacterium, Desulfomicrobium, Clostridium_sensu_stricto_1, and other functional microorganisms were all enriched. The dominant functional genes related to redox reactions, such as K03711, K00384, and K03406, were highly enriched in the reactor when Fe3+ and NO3− were added. The study shows that adding an electron acceptor can enhance interactions between microorganisms, achieve efficient propionate conversion, and improve methane production in the system.

Considering the rapid convergence of the longitudinal and lateral tracking errors of the platoon, a finite-time tracking control method for the longitudinal and lateral directions of the intelligent bus platoon is proposed. Based on the bus platoon model and desired motion trajectory, a distributed longitudinal and lateral finite-time sliding mode tracking control framework of the platoon is designed. Considering the finite-time convergence of the sliding mode of the system, a nonsingular integral terminal sliding mode (NITSM) is designed. An adaptive power integral reaching law (APIRL) is proposed for the finite-time accessibility of the system approaching mode. Based on NITSM-APIRL, a distributed longitudinal and lateral finite-time sliding mode tracking controller for the bus platoon is designed, and a Lyapunov function is created to analyze the finite-time stability and string stability of the system. Based on the Trucksim/Simulink joint simulation experiment platform, the control performance of the method is contrasted with the existing methods, and the actual vehicle test verification is completed by relying on the National Intelligent Connected Vehicle testing zone, which proves the practicability of the method.

As the scale of China's interconnected power grid continues to expand, traditional serial computing methods are no longer sufficient for the rapid analysis and computation of electrical networks with tens of thousands of nodes due to their small scale and low efficiency. To enhance the capability of online grid analysis, this paper introduces an accelerating batched power flow calculation method based on a heterogeneous CPU-GPU platform. This method, based on the fast decoupled method, combined with the tremendous parallel computing capability of GPUs with the multi-threaded parallel processing of CPUs, efficiently resolves the exceeding bus type conversion issues in GPU-batched power flow calculation and improves the accuracy of the power flow calculations. Then, a binary-based power flow data exchange format was introduced, which utilizes a single binary file for data exchange. This format significantly minimizes I/O time overhead and reduces file size, further enhancing the method's efficiency. Case studies on real-world power grids demonstrate its high accuracy and reliability. Compared to the traditional single-threaded power flow calculation method, this method dramatically reduces time consumption in batch power flow calculations. It proves the significant advantages of dealing with large-scale power flow calculations. [ABSTRACT FROM AUTHOR]