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Abstract The intrauterine device (IUD) is an important and highly effective means of contraception. Migration of the IUD, post implantation, out of the uterus is an infrequent complication, and its subsequent migration into the urinary bladder with formation of secondary bladder calculi, is even more infrequently reported. The authors report a 51 year old woman who had had her last child delivered via cesarean section 16 years ago. She underwent an IUD insertion in the next few months but was subsequently lost to follow up. Before detection of the ectopic state of the IUD she complained of dysuria and dyspareunia for two weeks. Clinically detected suprapubic discomfort on palpation. After a series of imaging studies and cystoscopy, the presence of an IUD stump with stones attached to it in the patient’s bladder were confirmed. Cystoscopy followed by laser lithotripsy of the calculus was performed, as the first step in the treatment. After failure to remove the IUD cystoscopically, further cystotomy was made, however one side end of the cap structure of the IUD could not be located, necessitating abdominal exploration and irrigation till retrieval in totality. The patient made an uneventful recovery. The case report emphasizes the importance of excluding uncommon etiolgies like ectopic IUDs as a cause of immobile adherent bladder calculi in women. Complete removal necessitates good preoperative knowledge of the type of IUD, as structural degradation might lead to separation of its components. An adequate consent for a possible exploration of the abdomen in case of a missing component should also be taken.

Abstract Background Hepatitis B remains a significant global health concern with widespread communicability. Nevertheless, data on its burden and trends in children and adolescents were limited. We aim to evaluate the global, regional, and national trends of total burden related to hepatitis B in children and adolescents aged 0–19 years from 1990 to 2021. Methods The age-standardized incidence, prevalence, mortality, and disability-adjusted life years (DALYs) were calculated by the Global Burden of Disease (GBD) study from 1990 to 2021. These indicators were stratified by sex, age, socio-demographic index (SDI), and disease stage. We calculated the correlation between them and SDI. The temporal trends were examined using the annual average percentage change (AAPC) and joinpoint regression. Results The global age-standardized incidence of hepatitis B in children and adolescents decreased from 1385.20 per 100,000 population in 1990 to 418.68 per 100,000 population in 2021, with an AAPC of -3.76%. Similarly, age-standardized DALYs decreased from 70.78 per 100,000 population to 36.31 per 100,000 population, with an AAPC of -2.13%. The age-standardized prevalence (AAPC − 3.53%) and mortality (AAPC − 2.09%) of hepatitis B also decreased significantly. From 1990 to 2021, the age-standardized incidence and prevalence among males exhibited a higher trend compared to females, although both declined over time. These two indicators also decreased across all age subgroups, with consistently higher rates observed in the 15–19 age group compared to other age groups. The burden of hepatitis B demonstrated a notable reduction in countries with high-middle SDI, while it was highest in countries with low SDI. In 2021, Central sub-Saharan Africa and West sub-Saharan Africa reported the highest age-standardized incidence. For age-standardized DALYs, South Asia was the only region to experience an increase (AAPC 1.09%), while East Asia showed the largest decline (AAPC − 7.58%). Alcohol and drug use remained important risk factors for DALYs among people aged 15–19 years. Furthermore, the impact of drug use on disease burden was increasing, particularly in high-SDI countries. Conclusions The global burden and trends of hepatitis B decreased significantly in children and adolescents, exhibiting regional and national variations. Management of alcohol and drug use remains a major challenge for people aged 15–19 years.

Abstract Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.

Abstract Background Recognition of seasonal trends in bacterial infection and drug resistance rates may enhance diagnosis, direct therapeutic strategies, and inform preventive measures. Limited data exist on the seasonal variability of Acinetobacter baumannii. We investigated the seasonality of A. baumannii, the correlation between temperature and meropenem resistance, and the impact of temperature on this bacterium. Results Meropenem resistance rates increased with lower temperatures, peaking in winter/colder months. Nonresistant strain detection exhibited temperature-dependent seasonality, rising in summer/warmer months and declining in winter/colder months. In contrast, resistant strains showed no seasonality. Variations in meropenem-resistant and nonresistant bacterial resilience to temperature changes were observed. Nonresistant strains displayed growth advantages at temperatures ≥ 25 °C, whereas meropenem-resistant A. baumannii with β-lactamase OXA-23 exhibited greater resistance to low-temperature (4 °C) stress. Furthermore, at 4 °C, A. baumannii upregulated carbapenem resistance-related genes (adeJ, oxa-51, and oxa-23) and increased meropenem stress tolerance. Conclusions Meropenem resistance rates in A. baumannii display seasonality and are negatively correlated with local temperature, with rates peaking in winter, possibly linked to the differential adaptation of resistant and nonresistant isolates to temperature fluctuations. Furthermore, due to significant resistance rate variations between quarters, compiling monthly or quarterly reports might enhance comprehension of antibiotic resistance trends. Consequently, this could assist in formulating strategies to control and prevent resistance within healthcare facilities.

Abstract The functionally graded composite structures with viscoelastic polymers inherits the excellent performance of functionally graded composites and also possesses large damping performance, which has broad application prospects in the aerospace and mechanical engineering fields. However, due to the complexity of the structure itself, there is limited literature available on its theoretical modeling for efficient solutions. To predict its dynamic performance, a simplified dynamic model of the functionally graded composite structures with viscoelastic polymers is established. This model takes into account the displacement transfer relationship between the functional graded composite layer and the viscoelastic polymer layer. The governing differential equations are obtained by applying the Navier method and complex modulus theory. These equations are then solved using the Rayleigh–Ritz method. The validity of the theoretical model is confirmed by comparing it with existing literature and the results obtained from ANSYS software. Additionally, the model that has been developed is used to analyze how the graded index and elastic modulus of the structure, as well as its geometric parameters, affect its vibration and damping characteristics.

Traditional low-order flux sliding mode observer (FSMO) and quadrature phase-locked loop (QPLL) structures generally encounter issues such as estimated signal chattering and inadequate dynamic performance. To overcome these challenges, this paper proposes an iterative fast super-twisting flux sliding mode observer (IFST-FSMO) and a tangent quadrature phase-locked loop (TQPLL) for sensorless control of surface-mounted permanent magnet synchronous motors (SPMSMs). Building on the traditional super-twisting algorithm (STA), the IFST-FSMO is proposed to accelerate convergence and enhance chattering suppression, which incorporates a linear term and utilizes the hyperbolic tangent function to replace the intrinsic sign function. Notably, the feedback matrix is redesigned to ensure the algorithm’s stability during speed reversal. Furthermore, an iterative calculation strategy is implemented under low-speed and light-load conditions, improving steady-state accuracy of estimated flux while avoiding increased computational burden at medium and high speeds. Regarding position estimation, a novel TQPLL with correction factor is proposed, utilizing the tangent function of the electrical angle error to achieve normalization and bandwidth adaptation. Ultimately, the proposed method is implemented on a motor test platform. Comparative experimental results demonstrate that the IFST-FSMO combined with TQPLL exhibits superior dynamic response and steady-state accuracy, while achieving efficient speed reversal.

Abstract Objective Tumor-specific growth factor (TSGF) is an immune-related factor that offers good performance in the clinical management of human cancers. However, the role of serum TSGF in esophageal squamous cell carcinoma (ESCC) has not been fully clarified. Methods A total of 562 ESCC cases were collected in our study, with available information on preoperative serum levels of TSGF at diagnosis. Preoperative serum TSGF was detected using the rate method. We retrospectively analyzed its correlation with clinicopathological features of ESCC and survival. Results The cut-off value of serum TSGF was determined to be 60.5 U/mL by receiver operating characteristic (ROC) analysis. Serum TSGF was associated with gender (P

Summary: Craniofacial microsomia (CFM) is a congenital defect that usually results from aberrant development of embryonic pharyngeal arches. However, the molecular basis of CFM pathogenesis is largely unknown. Here, we employ the zebrafish model to investigate mechanisms of CFM pathogenesis. In early embryos, tet2 and tet3 are essential for pharyngeal cartilage development. Single-cell RNA sequencing reveals that loss of Tet2/3 impairs chondrocyte differentiation due to insufficient BMP signaling. Moreover, biochemical and genetic evidence reveals that the sequence-specific 5mC/5hmC-binding protein, Sall4, binds the promoter of bmp4 to activate bmp4 expression and control pharyngeal cartilage development. Mechanistically, Sall4 directs co-phase separation of Tet2/3 with Sall4 to form condensates that mediate 5mC oxidation on the bmp4 promoter, thereby promoting bmp4 expression and enabling sufficient BMP signaling. These findings suggest the TET-BMP-Sall4 regulatory axis is critical for pharyngeal cartilage development. Collectively, our study provides insights into understanding craniofacial development and CFM pathogenesis.

This paper presents the design and implementation of an ISO-compliant ocean profiling observation system for wave-powered vertical profiler. This system aims to provide a comprehensive, scalable, and interoperable solution for high-resolution, real-time oceanic observation. As a part of this system, we introduce a wave-powered vertical profiler, known as “Wave Master,” designed to offer enhanced stability and reliability for long-term oceanic data collection. The core of the paper focuses on the Ocean Profiling Observation Complex Virtual Instrument (OPO-CVI), a comprehensive system developed in alignment with ISO 21851 standard. OPO-CVI seamlessly integrates data collection, transmission, storage, and visualization. Specifically, OPO-CVI addresses the challenges of information isolation, system rigidity, and lack of modularity in traditional ocean profiling methods by standardizing data formats and transmission protocols, allowing for seamless integration of new observation elements, and employing a modular architecture for enhanced scalability and reusability. By offering detailed technical insights into the OPO-CVI architecture and its compliance with ISO 21851 standard, this paper aims to contribute significantly to the advancement of standardized, efficient, and reliable oceanic observation systems.

L10 FePd is increasingly recognized as a potential candidate for magnetic tunnel junctions (MTJs), yet there remains room for enhancing device performance. In this work, we fabricated fully-integrated L10 FePd-based perpendicular MTJ devices and achieved a significant increase in tunnel magnetoresistance, reaching ∼65%, compared to the previous record of 25%. Notably, we observed bi-directional switching with a low switching current density of about 1.4 × 105 A/cm2, which outperforms the typical spin-transfer torque (STT) MTJ by about one order of magnitude. We propose two possible mechanisms to elucidate the switching process and associated device performance: (1) The voltage-controlled exchange coupling-driven switching of the bottom CoFeB layer; (2) The STT-driven switching of the exchange-coupled L10 FePd–CoFeB composite. While additional research is necessary, these findings may further advance the integration of L10 FePd into spintronic devices, potentially enabling low-energy memory and logic technologies.

Salmonella enterica serovar London is known to colonize the human digestive tract and can cause gastroenteritis and diarrhea. The excretion of S. London in the stool can spread into the environment. However, S. London colonization of the gut is not known to cause infection of the soft tissues. Here, we report a case of S. London infection of the skin and soft tissue of the leg and heel.

Insufficient sample is a common problem in hyperspectral image (HSI) classification and an important factor causing to low accuracy. Adding weak supervision mechanism to model training is an effective way to solve this problem. In this article, we propose a weakly supervised HSI classification method with few samples based on intradomain sample expansion, which is anchored on the spatial correlation between samples. First, to reduce the negative effects of spectral mixing on pseudolabel generation and classifier training, we introduce the continuous side window filter to smooth the original HSIs band-by-band. Second, in order to better balance the correctness and representativeness of the generated pseudolabels, a novel method for sample selection and pseudolabel generation is proposed in this article. The method contains two branches, the segmentation graph based and the neighborhood relationship based. In the branch based on segmentation graphs, the polygon segmentation graph generated by the false color image is used to select the expansion samples. As a complementary branch, the branch based on neighborhood relationship exploits the neighborhood relationship and spectral similarity between samples to further select samples. Finally, in the classification stage, this article uses the broad learning system as a classifier to obtain the classification graph. Experiments on three publicly available datasets show that the method in this article can effectively achieve sample expansion and improve the classification accuracy and stability of HSIs in the case of insufficient labeled samples.

Abstract Little is known about the effects of transcutaneous electrical acupoint stimulation (TEAS) for children with attention-deficit/hyperactivity disorder (ADHD). Here, we carried out a 4 week randomized clinical trial in which patients aged 6–12 years old with an ADHD diagnosis received TEAS or sham TEAS. The primary outcome measure was the investigator-rated Clinical Global Impression-Improvement (CGI-I) score at week 4. Secondary outcomes included changes from baseline to week 4 in the investigator-rated Clinical Global Impression-Severity of Illness (CGI-S) score, the Conners’ Parent/Teacher Rating Scales-Revised: Short Form (CPRS-R: S/CTRS-R: S) score, go/no-go task performance, and functional near-infrared spectroscopy (fNIRS)-based oxygenated hemoglobin level within the prefrontal cortex. At week 4, the CGI-I score indicated improvement in 33.3% of the TEAS group compared with 7.7% of the sham group (P = 0.005). The TEAS group had a greater decrease in the mean CGI-S score (−0.87) than the sham TEAS group (−0.28) (P = 0.003). A greater enhancement in the mean cerebral oxygenated hemoglobin within the prefrontal cortex was found in the TEAS group (0.099 mM mm) compared with the sham TEAS group (0.005 mM mm) (P

Abstract New particle formation (NPF) is a global phenomenon that significantly influences climate. NPF also contributes to haze, with pronounced negative impacts on human health. Theory and observations both show that nucleation is favored during clean days and inhibited during haze episodes due to a high pre‐existing condensation sink (CS). Here we show that the surprising occurrence of NPF during haze days in Beijing is associated with a high concentration of sulfuric acid dimers. With both field observations and model simulations, we demonstrate that downward mixing of sulfur dioxide (SO2) from the residual layer aloft enhances ground level SO2, which in turn elevates sulfuric acid dimer after rapid SO2 oxidation in the polluted air. Our results address a key gap between the source of SO2 and its atmospheric oxidation products during haze conditions in a megacity, Beijing, providing a missing link in a complete chain describing NPF in the polluted atmosphere.

ABSTRACT Clostridioides difficile infection (CDI), recurrently reported as an urgent threat owing to its increased prevalence and mortality, has attracted significant attention. As the use of antibiotics to treat CDI has many limitations, such as high recurrence rate, the need to actively seek and develop other drugs that can effectively treat CDI with fewer side effects has become a key issue in CDI prevention and treatment. This study aimed to evaluate the inhibitory effect of Galla chinensis (GC) and its main component, tannic acid (TA), against C. difficile in vitro and its therapeutic effect on CDI in vivo. When GC and TA concentrations were 250 and 64 mg/L, respectively, the cumulative antibacterial rate against C. difficile reached 100%. The sub-MIC of TA significantly inhibited C. difficile sporulation, toxin production, and biofilm formation in vitro. Compared with the CDI control group, TA-treated mice lost less weight and presented a significantly improved survival rate. TA significantly reduced the number of spores in feces, decreased serum TcdA level, and increased serum interleukin 10 (IL-10). Based on the inhibitory effect of TA on C. difficile in vitro and its therapeutic effect on the CDI mouse model, we consider TA as a potentially effective drug for treating CDI. IMPORTANCE Clostridioides difficile is one of the major pathogens to cause antibiotic-associated diarrhea. Although antibiotic treatment is still the most commonly used and effective treatment for CDI, the destruction of indigenous intestinal microbiota by antibiotics is the main reason for the high CDI recurrence rate of about 20%, which is increasing every year. Moreover, the growing problem of drug resistance has also become a major hidden danger in antibiotic treatment. GC has been used to treat diarrhea in traditional Chinese medicine. In the present study, we evaluated the inhibitory effect of TA, the main component of GC, on dissemination and pathogenic physiological functions of C. difficile in vitro, as well as its therapeutic efficacy in a CDI model. Overall, TA is considered to be a potentially effective drug for CDI treatment.

Laboratory biosafety has become a core focus in biological analysis, owing to the frequent occurrence of laboratory-acquired infections caused by the leakage of pathogenic microorganisms. For this purpose, the authors developed a safe pretreatment device combining a sealing technique with a direct injection technique. In this study, several bacteria and viruses were used to validate the filtration effect of the invention. Data show that the new device can completely filter bacteria and that the filtration rates for hepatitis B virus and hepatitis C virus reached 94% and 96%, respectively. The results show that the new preparation device can effectively block these pathogens and can improve biological safety and provide powerful protection for technicians.

Background: Alpha-fetoprotein-positive gastric cancer (AFPGC) is a subtype of gastric cancer that is rare in clinical practice and extremely malignant. Malignant tumors are often associated with hemorrhage, thrombosis, and even disseminated intravascular coagulation (DIC). The D-dimer test is used as a sensitive index in the diagnosis of DIC and fresh thrombosis in malignant tumors. Therefore, this study aims to investigate the relationship between D-dimer values and the clinical characteristics and prognosis of patients with serum AFPGC (AFP ⩾ 15 μg/L) patients. Methods: Overall, 120 healthy subjects and 120 AFP-negative gastric cancer (AFP < 15μg/L) patients from May 2017 to July 2018 at the Shanxi Cancer Hospital served as the control group in this retrospective cohort study. Additionally, 120 patients with pretreatment advanced serum AFP were chosen to analyze clinicopathologic features and factors that affect prognosis. The predictor was the D-dimer, and the outcome variable was overall survival (OS). Other variables included age, sex, tumor site, T-stage, distant metastasis, and preoperative serum tumor biomarkers. Differences in OS rate were analyzed by GraphPad Prism 9.2.0.332. The Cox regression model was used for univariate and multivariate analysis. Results: In comparison to AFP-negative gastric cancer, we discovered that D-dimer had a meaningfully higher presentation in patients with AFPGC ( P < .001). Based on D-dimer median levels, the AFPGC patients were divided into two groups, including 39 patients with low D-dimer (

Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many methods have been continuously proposed in recent years. However, it is still challenging to achieve high accuracy classification in applications. One of the main reasons is the lack of labeled data. Due to the limitation of spatial resolution, manual labeling of HSI data is time-consuming and costly, so it is difficult to obtain a large amount of labeled data. In such a situation, many researchers turn their attention to the study of HSI classification with small samples. Focusing on this topic, this paper provides a systematic review of the research progress in recent years. Specifically, this paper contains three aspects. First, considering that the taxonomy used in previous review articles is not well-developed and confuses the reader, we propose a novel taxonomy based on the form of data utilization. This taxonomy provides a more accurate and comprehensive framework for categorizing the various approaches. Then, using the proposed taxonomy as a guideline, we analyze and summarize the existing methods, especially the latest research results (both deep and non-deep models) that were not included in the previous reviews, so that readers can understand the latest progress more clearly. Finally, we conduct several sets of experiments and present our opinions on current problems and future directions.

Sulfate aerosols are an important component of wintertime haze events in China, but their production mechanisms are not well known. Here, the authors show that transition metal-catalyzed oxidation of SO2 on aerosol surfaces could be the dominant sulfate formation pathway in Northern China.

The development of breast cancer is closely linked to the estrogen receptor ERα, which is also considered to be an important target for the treatment of breast cancer. Therefore, compounds that can antagonize ERα activity may be drug candidates for the treatment of breast cancer. In drug development, to save manpower and resources, potential active compounds are often screened by establishing compound activity prediction model. For the 1974 compounds collected, the top 20 molecular descriptors that significantly affected the biological activity were screened using LASSO regression models combined with 10-fold cross-validation method. Further, a regression prediction model based on the MLP fully connected neural network was constructed to predict the bioactivity values of 50 new compounds. To measure the validity of the model, the model loss term was specified as the mean squared error (MSE). The results showed that the MLP-based regression prediction model had a loss value of 0.0146 on the validation set. This model is therefore well trained and the prediction strategy used is valid. The methods developed by this paper may provide a reference for the development of anti-breast cancer drugs.

Coal-derived fly ash is a major aerosol composition in the atmosphere and presents a major challenge in understanding the atmospheric environment. In this study, the heterogeneous uptake of hydrogen peroxide and sulfur dioxide on coal fly ash was investigated using a Knudsen cell reactor coupled with a quadrupole mass spectrometer. The uptake coefficients were measured as a function of the initial mixing ratio of H2O2 and SO2 from 10 to 60 ppbv, and the temperature dependence of the uptake coefficients was also carried out over a range from 253 to 313 K. The mixing ratio of H2O2 and SO2 showed little effect on the uptake coefficients for these heterogeneous processes. As a function of temperature, the initial uptake coefficients of H2O2 and SO2 on coal fly ash were (0.48–1.65) × 10−4 and (1.50–8.54) × 10−5, respectively, which decreased with an increase in temperature. The steady-state uptake coefficients of H2O2 on coal fly ash were in the range of 2.46 × 10−5 to 4.84 × 10−5, which increased with an increase in temperature. Furthermore, we examined the synergistic effects between SO2 and H2O2 in their reaction on coal fly ash and found the existence of gaseous H2O2 appeared to enhance the ability of SO2 uptake on coal fly ash. Therefore, this finding suggests that the oxidation of SO2 on the surface of coal fly ash by gaseous H2O2 would promote SO2 consumption and transformation.

Abstract The role of new particle formation (NPF) events and their contribution to haze formation through subsequent growth in polluted megacities is still controversial. To improve the understanding of the sources, meteorological conditions, and chemistry behind air pollution, we performed simultaneous measurements of aerosol composition and particle number size distributions at ground level and at 260 m in central Beijing, China, during a total of 4 months in 2015–2017. Our measurements show a pronounced decoupling of gas-to-particle conversion between the two heights, leading to different haze processes in terms of particle size distributions and chemical compositions. The development of haze was initiated by the growth of freshly formed particles at both heights, whereas the more severe haze at ground level was connected directly to local primary particles and gaseous precursors leading to higher particle growth rates. The particle growth creates a feedback loop, in which a further development of haze increases the atmospheric stability, which in turn strengthens the persisting apparent decoupling between the two heights and increases the severity of haze at ground level. Moreover, we complemented our field observations with model analyses, which suggest that the growth of NPF-originated particles accounted up to ∼60% of the accumulation mode particles in the Beijing–Tianjin–Hebei area during haze conditions. The results suggest that a reduction in anthropogenic gaseous precursors, suppressing particle growth, is a critical step for alleviating haze although the number concentration of freshly formed particles (3–40 nm) via NPF does not reduce after emission controls.

Due to profound impact on climate and human health, air quality has attracted attention from all levels of the civil society. The key step in the provision of required tools for the society to tackle the complex air quality problem is to characterize it in a comprehensive manner with a long-term perspective. Here, we describe a continuous and comprehensive observation station and its accompanying state-of-the-art instrumentation that was established to investigate the complex urban atmospheric environment in a rapidly developing Chinese Megacity. The station, located in downtown Beijing, aims to study air quality by identifying the major atmospheric pollutants and key processes determining their formation and loss mechanisms. A few hundreds of parameters are continuously measured with the state-of-the-art instruments, including trace gas concentrations, aerosol particle size distributions, and mass concentrations, covering aerosol particle chemical composition from molecules to micrometer-sized aerosol particles. This produced long-term, comprehensive big data with around $$1 \times {10^{11}}$$ bytes per year. In this paper, we provide an overview on the facilities of the station, the instrumentation used, the workflow of continuous observations and examples of results from 2018 to 2019 and a basis for establishing a modern long-term, comprehensive atmospheric urban observation station in other megacities.

Since the emergence of deep neural network (DNN), it has achieved excellent performance in various research areas. As the combination of DNN and reinforcement learning, deep reinforcement learning (DRL) becomes a new paradigm for solving differential game problems. In this study, we build up a reinforcement learning environment and apply relevant DRL methods to a specific bio-inspired differential game problem: the dog sheep game. The dog sheep game environment is set on a circle where the dog chases down the sheep attempting to escape. According to some presuppositions, we are able to acquire the kinematic pursuit and evasion strategy. Next, this study implements the value-based deep Q network (DQN) model and the deep deterministic policy gradient (DDPG) model to the dog sheep game, attempting to endow the sheep the ability to escape successfully. To enhance the performance of the DQN model, this study brought up the reward mechanism with a time-out strategy and the game environment with an attenuation mechanism of the steering angle of sheep. These modifications effectively increase the probability of escape for the sheep. Furthermore, the DDPG model is adopted due to its continuous action space. Results show the modifications of the DQN model effectively increase the escape probabilities to the same level as the DDPG model. When it comes to the learning ability under various environment difficulties, the refined DQN and the DDPG models have bigger performance enhancement over the naive evasion model in harsh environments than in loose environments.

The linear stability properties of the microtearing mode are investigated in the edge and core regimes of the National Spherical Torus Experiment (NSTX) using the particle-in-cell method based gyrokinetic code GEM. The dependence of the mode on various equilibrium quantities in both regions is compared. While the microtearing mode in the core depends upon the electron-ion collisions, in the edge region, it is found to be weakly dependent on the collisions and exists even when the collision frequency is zero. The electrostatic potential is non-negligible in each of the cases. It plays opposite roles in the core and edge of NSTX. While the microtearing mode is partially stabilized by the electrostatic potential in the core, it has substantial destabilizing effect in the edge. In addition to the spherical tokamak, we also study the microtearing mode for parameters relevant to the core of a standard tokamak. The fundamental characteristics of the mode remain the same; however, the electrostatic potential in this case is destabilizing as opposed to the core of NSTX. The velocity dependence of the collision frequency, which is crucial for the mode to grow in slab calculations, is not required to destabilize the mode in toroidal devices. [ABSTRACT FROM AUTHOR]

The demand for accurate indoor localization has become more and more urgent in location-based services (LBS). Since the traditional indoor localization is based on a single fingerprint, which is susceptible to environmental changes, it is hard to obtain high accuracy. In order to fill this gap, we propose a new multiple fingerprints method used in the indoor environment without extra measurement, and it can reduce the uncertainty caused by a single fingerprint. The multiple fingerprints are based on the signal Subspace and the received signal strength index (RSSI) and others, where the signal Subspace represents the characteristic representation of the received array signal and the RSSI represents the signal strength. First, we propose an algorithm named Subspace gradient boost decision tree (Subspace-GBDT) to obtain a strong classifier, which can make a successful prediction. Second, we propose another algorithm named double threshold assisted mode (DTAM) to combine multiple fingerprints. It can achieve accurate prediction by setting two different threshold parameters, the threshold of samples number and classifiers number, and reduce errors of matching. The experiment results show that the proposed method can obtain higher cumulative distribution function (CDF) value and localization accuracy than the traditional methods.

Prostate cancer is one of the most prevalent cancers among men. Early detection of this cancer could effectively increase the survival rate of the patient. In this paper, we propose a Bi-attention adversarial network for the prostate cancer segmentation automatically. The proposed architecture consists of the generator network and discriminator network. The generator network aims to generate the predicted mask of the input image, while the discriminator network aims to further improve the generator performance with adversarial learning by discriminating the generator predicted mask and the true label mask. For better improving the segmentation performance, we combine two attention mechanisms with the generator network to learn more global and local features. Extensive experiments on the T2-weighted (T2W) images have demonstrated our model could achieve state-of-the-art segmentation performance compared with other methods.

Through a sol-gel method, a honeycomb-like Na _3 V _2 (PO _4 ) _3 /C cathode material for sodium-ion batteries was synthesized from a spent electroless nickel plating bath. The obtained Na _3 V _2 (PO _4 ) _3 /C exhibited excellent electrochemical properties, with the initial discharge capacity of 108.3 mAh·g ^−1 at 0.2 C and the capacity retention of 99.3% after 300 cycles, which was comparable to that of powders prepared from chemical reagents. This study will be helpful for the value-adding utilization of the spent electroless nickel plating bath.

Global electromagnetic gyrokinetic simulations show the existence of near threshold conditions for both a high-n kinetic ballooning mode (KBM) and an intermediate-n kinetic version of peeling-ballooning mode (KPBM) in the edge pedestal of two DIII-D Η-mode discharges. When the magnetic shear is reduced in a narrow region of steep pressure gradient, the KPBM is significantly stabilized, while the KBM is weakly destabilized and hence becomes the most-unstable mode. Collisions decrease the KBM's critical β and increase the growth rate. [ABSTRACT FROM AUTHOR]

A natural fueling mechanism that helps to maintain the main core deuterium and tritium (DT) density profiles in a tokamak fusion reactor is presented. In H-mode plasmas dominated by ion-temperature gradient (ITG) driven turbulence, cold DT ions near the edge will naturally pinch radially inward toward the core. This mechanism is due to the quasineutral heat flux dominated nature of ITG turbulence and still applies when trapped and passing kinetic electron effects are included. Fueling using shallow pellet injection is augmented by an inward pinch of cold DT fuel. The natural fueling mechanism is investigated using the gyrokinetic turbulence code GEM [Y. Chen and S. E. Parker, J. Comput. Phys. 220, 839 (2007)] and is analyzed using quasilinear theory. Profiles similar to those used for conservative International Thermonuclear Experimental Reactor [R. Aymar et al., Nucl. Fusion 41, 1301 (2001)] transport modeling that have a completely flat density profile are examined and it is found that natural fueling actually reduces the linear growth rate and energy transport. Natural fueling requires a two-component plasma and ion-ion and charge-exchange collisions set limits on this favorable effect. [ABSTRACT FROM AUTHOR]

Essential tremor (ET) is a neurological disorder characterized by rhythmic, involuntary shaking of a part or parts of the body. The most common tremor is seen in the hands/arms and fingers. This paper presents an evaluation of ETs monitoring based on finger-to-nose test measurement as captured by small wireless devices working in shortwave or S-band frequency range. The acquired signals in terms of amplitude and phase information are used to detect a tremor in the hands. Linearly transforming raw phase data acquired in the S-band were carried out for calibrating the phase information and to improve accuracy. The data samples are used for classification using support vector machine algorithm. This model is used to differentiate the tremor and nontremor data efficiently based on secondary features that characterize ET. The accuracy of our measurements maintains linearity, and more than 90% accuracy rate is achieved between the feature set and data samples.