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This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save energy by reducing energy consumption, improve the digging accuracy by minimizing the deviation between the desired and dug surfaces of the ground, and prevent colliding with subsurface objects. For this purpose, computer vision was employed to improve monitoring and verification. Five types of experiments were carried out in this investigation. The first two and the other three examined the impact of energy conservation in PSO- and PFM-based path generation, respectively. Finally, the results from these experiments were compared to identify and show the effect of optimal path generation.

Objective(s): 5-Fluorouracil (5-FU) is currently the main drug used in chemotherapy for gastric cancer (GC). The main clinical problems of 5-FU therapy are insensitivity and acquired resistance to 5-FU. The mechanism of GC cell resistance to 5-FU is currently unknown.Materials and Methods: This study employed next-generation sequencing (NGS) to analyze the differentially expressed genes (DEGs) in chemotherapy-sensitive and non-sensitive GC tissues. In addition, a bioinformatics analysis was conducted using the GC dataset of GEO, and further validated and explored through in vitro experiments.Results: Thyroid adenoma-associated gene (THADA) was highly expressed in GC tissues from chemotherapy-sensitive patients and was an independent prognostic factor in GC patients receiving postoperative 5-FU adjuvant chemotherapy. Notably, heightened THADA expression in GC cells was associated with the down-regulation of autophagy-related proteins (LC-3, ATG13, ULK1, and TFEB). Furthermore, the PI3K/AKT/mTOR signaling pathway and mTORC1 signaling pathway were remarkably increased in patients with elevated THADA expression. THADA expression was associated with mTOR, the core protein of the mTOR signaling pathway, and related proteins involved in regulating the mTORC1 signaling pathway (mLST8, RHEB, and TSC2). THADA exhibited inhibitory effects on autophagy and augmented the sensitivity of GC cells to 5-FU through the PI3K/AKT/mTOR signaling pathway.Conclusion: The findings suggest that THADA may be involved in the regulatory mechanism of GC cell sensitivity to 5-FU. Consequently, the detection of THADA in tumor tissues may bring clinical benefits, specifically for 5-FU-related chemotherapy administered to GC patients with elevated THADA expression.

A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods, despite its few demerits. Sufficient progress in accuracy and depth computation speed has manifested during the period. Over the years, stereo depth estimation has been provided with various training modes, such as supervised, self-supervised, and unsupervised, before deploying it for real-time performance. These modes are to be used depending on the application and/or the availability of datasets for training. Deep learning, on the other hand, has provided the stereo depth estimation methods with a new life to breathe in the form of enhanced accuracy and quality of images, attempting to successfully reduce the residual errors in stages in some of the methods. Furthermore, depth estimation from a single RGB image has been intricate since it is an ill-posed problem with a lack of geometric constraints and ambiguities. However, this monocular depth estimation has gained popularity in recent years due to the development in the field, with appreciable improvements in the accuracy of depth maps and optimization of computational time. The help is mostly due to the usage of CNNs (Convolutional Neural Networks) and other deep learning methods, which help augment the feature-extraction phenomenon for the process and enhance the quality of depth maps/accuracy of MDE (monocular depth estimation). Monocular depth estimation has seen improvements in many algorithms that can be deployed to give depth maps with better clarity and details around the edges and fine boundaries, which thus helps in delineating between thin structures. This paper reviews various recent deep learning-based stereo and monocular depth prediction techniques emphasizing the successes achieved so far, the challenges acquainted with them, and those that can be expected shortly.

Path planning is the most fundamental necessity for autonomous mobile robots. Traditionally, the path planning problem was solved using analytical methods, but these methods need perfect localization in the environment, a fully developed map to plan the path, and cannot deal with complex environments and emergencies. Recently, deep neural networks have been applied to solve this complex problem. This review paper discusses path-planning methods that use neural networks, including deep reinforcement learning, and its different types, such as model-free and model-based, Q-value function-based, policy-based, and actor-critic-based methods. Additionally, a dedicated section delves into the nuances and methods of robot interactions with pedestrians, exploring these dynamics in diverse environments such as sidewalks, road crossings, and indoor spaces, underscoring the importance of social compliance in robot navigation. In the end, the common challenges faced by these methods and applied solutions such as reward shaping, transfer learning, parallel simulations, etc. to optimize the solutions are discussed.

Objectives: Plasmacytoid dendritic cells (pDCs) play a crucial role in the microenvironment of tumor. Evidences has been shown that chemokine receptor 9 (CCR9) is an important molecule that attracts pDCs homing to the digestive tract and the latter are involved in the formation of digestive tract immune tolerance. The aim of this study was to explore the role of CCR9-CCL25 interaction in pDC-mediated immunosuppression microenvironment of gastric cancer (GC). Materials and methods: Regulatory T cells (Tregs) and pDCs were detected by immunohistochemistry. CCR9, which expressed on pDC was visualized by immunofluorescence. Western Blot was applied to evaluate the expression of CCL-25. Total pDCs, CCR9+pDCs, CCR9−pDCs, total Tregs, inducible costimulator + (ICOS) Tregs and ICOS−Tregs in peripheral blood and draining lymph nodes were analyzed by flow cytometry. Plasma concentration of the cytokines were measured by enzyme-linked immunosorbent assay Results: Total Tregs, pDCs and CCR9+pDCs were higher in GC tissue. CCL-25 was over-expressed in carcinoma tissue. Peripheral total pDCs, CCR9−pDCs, total Tregs, ICOS+ Tregs, ICOS− Tregs were significantly increased in GC patients. More total pDCs, CCR9+ pDCs, total Tregs, ICOS+ Tregs were found in metastatic lymph nodes. Plasma concentrations of IL-6 and IL-10 were significantly higher in GC patients. More CCR9+ pDCs were found infiltrating carcinoma tissue in patients with later T staging and lymph node metastasis and conferred a poor prognosis. Conclusion: CCR9-CCL25 interaction might play an important role in mediating PDC homing to metastatic lymph nodes and carcinoma tissue, which contributed to the formation of tumor immunosuppressive microenvironment and poor prognosis.

Abstract Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region. This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression. General health indicators are extracted from the partial discharge process. The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction. The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic. Besides, only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction. Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack, even with only 50 cycles for model fine-tuning, which can save about 90% time for the aging experiment. Thus, it largely reduces the time and labor for battery pack investigation. The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell, which can reveal the weakest cell for maintenance in advance.

Recycling nutrients in municipal sewage sludge (MSS) to soil would support sustainable development. In this study, a comprehensive recycling using specific plants able to grow in the fresh MSS and an indirect application technique was developed. Fresh MSS was placed in permeable containers next to Handroanthus chrysanthus plants to provide indirect fertilization. Sludge treatment plants (Alocasia macrorrhiza and Pennisetum hybridum) were grown directly on the Fresh MSS to produce plant biomass and treat MSS. The basal diameters of the H. chrysanthus plants were markedly increased by the treatment. Nutrients were extracted from MSS more readily and more biomass was produced by the P. hybridum than the A. macrorrhiza plants. The heavy metal contents of the soil did not increase significantly and not generate potential ecological risk, but the organic matter, nitrogen, and phosphorus contents increased markedly. The fresh MSS leachate met the relevant fecal coliform and heavy metal irrigation water standards. At the end of the treatment, the MSS mass had markedly decreased and the treated MSS was used as a seedling substrate for two garden plant seedlings. The net carbon emissions from the comprehensive recycling are estimated as -15.79 kg CO2e (CO2 equivalent) per ton fresh sludge, in contrast, the emissions from composting treatment are estimated as 8.15 kg CO2e. The method allows nutrients in MSS to be recycled without causing heavy metal pollution and without net carbon emission, while gives gardening products with commercial value.

Summary: Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.

Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain a real-time pseudo point cloud from image sensors (cameras) instead of laser-based sensors (LiDARs). Previous studies (such as PSMNet-based point cloud generation) built the algorithm based on accuracy but failed to operate in real time as LiDAR. We propose an approach to use different depth estimators to obtain pseudo point clouds similar to LiDAR to achieve better performance. Moreover, the depth estimator has used stereo imagery data to achieve more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms other existing approaches on KITTI depth prediction while yielding point clouds significantly faster than other approaches as well. Additionally, the proposed approach is evaluated on the KITTI stereo benchmark, where it shows effectiveness in runtime.

This research focuses on the preparation of cellulose nanocrystals (CNCs) from Pennisetum hydridum fertilized by municipal sewage sludge (MSS) through sulfuric acid hydrolysis in different acid concentrations (40–65%), temperature (room temperature ∼55°C), and reaction time (50–120 min). The results showed that the obtained CNC possessed stable dispersion in water. The length of CNCs reached 272.5 nm under the condition of room temperature (RT), 65% acid concentration, and 120 min reaction time, and the diameter was within 10 nm. Furthermore, Fourier transform infrared (FTIR) showed that the CNC still kept the cellulose type I structure. The crystallinity of CNCs increased to the maximum by 18.34% compared with that of delignified Pennisetum hydridum fibers. Thermogravimetry (TG) illustrated the thermal stability of CNCs was lower than that of delignified Pennisetum hydridum fibers due to the introduction of sulfate groups in the cellulose. This study demonstrated that Pennisetum hydridum fertilized by MSS might be a suitable raw material for CNCs. This implies meaningful resource utilization of MSS and Pennisetum hydridum.

Abstract Background Isolated palmar dislocation of distal radioulnar joint is a rare injury. It can easily lead to misdiagnosis. Previous literature reports were all rotation violence. We reported a patient with direct impact violence. Case presentation We report a 31-year-old male laborer presented to our hospital with an acute trauma. Severe tenderness and limited mobility were seen in his right wrist. He received an x-ray film examination and diagnosed as the isolated palmar dislocation of distal radioulnar joint. The treatment was closed reduction and splint fixation. After half a year, the patient gained a functional recovery of his previously afflicted wrist. Conclusions To the best of our knowledge, this is the first case of isolated palmar dislocation of distal radioulnar joint caused by a direct impact violence. Patients and physicians should be aware of the properties of this mechanism of injury so that early diagnosis and treatment can be achieved.

The anode state of charge (SOC) and degradation information pertaining to lithium-ion batteries (LIBs) is crucial for understanding battery degradation over time. This information about each cell in a battery pack can help prolong the battery pack’s life cycle. Because of the limited observability, estimating the anode state and capacity fade is difficult. This task is even more challenging for the cells in a battery pack, as the current through the individual cell is not constant when cells are connected in parallel. Considering these challenges, this paper presents a novel method to set up three-electrode cells by using the battery’s casing as a reference electrode for building a three-electrode battery pack. This work is a continuation of the authors’ previous research. An unknown input observer (UIO) is employed to estimate the anode SOC of an individual battery in the battery pack. To ensure the stability of a defined Lyapunov function, the UIO parameter matrices are expressed as a linear matrix inequality (LMI). The anode SOC of a lithium nickel manganese cobalt oxide (NMC) battery is estimated by using the standard graphite potential (SGP) and state of lithiation (SOL) characteristic curve. The anode capacity is then calculated by using the total charge transferred in a charging cycle and the estimated SOC of the anode. The degradation of the battery is then evaluated by comparing the capacity fading of the anode to the total charge carried to the cell. The proposed method can estimate the anode SOC and capacity fade of an individual battery in a battery pack, which can monitor the degradation of the individual batteries and the battery pack in real time. By using the proposed method, we can identify the over-degraded batteries in the pack for remaining useful life analysis on the battery.

This paper aims to design thermal dummy cells (TDCs) that can be used in the development of lithium-ion battery thermal management systems. Based on physical property and geometry of real 18,650 cylindrical cells, a three-dimensional model of TDCs was designed, and it is used to numerically simulate the thermal performance of TDCs. Simulations show that the TDC can mimic the temperature change on the surface of a real cell both at static and dynamic current load. Experimental results show that the rate of heating resistance of TDC is less than 0.43% for temperatures between 27.5 °C and 90.5 °C. Powered by a two-step voltage source of 12 V, the temperature difference of TDCs is 1 °C and 1.6 °C along the circumference and the axial directions, respectively. Powered by a constant voltage source of 6 V, the temperature rising rates on the surface and in the core are higher than 1.9 °C/min. Afterwards, the proposed TDC was used to simulate a real cell for investigating its thermal performance under the New European Driving Cycle (NEDC), and the same tests were conducted using real cells. The test indicates that the TDC surface temperature matches well with that of the real battery during the NEDC test, while the temperature rise of TDC exceeds that of the real battery during the suburban cycle. This paper demonstrates the feasibility of using TDCs to replace real cells, which can greatly improve safety and efficiency for the development of lithium-ion battery thermal management systems.

Interleukin-17 (IL-17) is prevalent in tumor tissue and suppresses effective anti-tumor immune responses. However, the source of the increased tumor-infiltrating IL-17 and its contribution to tumor progression in human gastric cancer remain poorly understood. In this study, we enrolled 112 gastric cancer patients, immunofluorescence was used to evaluate the colocalization of CD3, CD4, CD56, CD20, CD68, and mast cell tryptase (MCT) with IL-17. Immunohistochemistry was used to evaluate the distribution of microvessel density (CD34), CD66b(+), CD68(+), and FoxP3(+) cells in different microanatomical areas. Prognostic value was determined by Kaplan-Meier analysis and a Cox regression model. The results showed that mast cells, but not T cells or macrophages, were the predominant cell type producing IL-17 in gastric cancer. Significant positive correlations were detected between densities of mast cell-derived IL-17 and microvessels, neutrophils, and regulatory T cells (Tregs). Furthermore, we found that the majority of vascular endothelial cells expressing Interleukin-17 receptor (IL-17R). Kaplan-Meier analysis revealed that increasing intratumor infiltrated mast cells and IL-17(+) cells, as well as MCT(+) IL-17(+) cells, were significantly associated with worse overall survival. These findings indicated that mast cells were the major source of IL-17 in gastric cancer, and intratumor IL-17 infiltration may have promoted tumor progression by enhancing angiogenesis in the tumor microenvironment through the axis of IL-17/IL-17R. IL-17-positive mast cells showed a prognostic factor in gastric cancer, indicating that immunotherapy targeting mast cells might be an effective strategy to control intratumor IL-17 infiltration, and consequently reverse immunosuppression in the tumor microenvironment, facilitating cancer immunotherapy.