Underwater robots conducting inspections require autonomous obstacle avoidance capabilities to ensure safe operations. Training methods based on reinforcement learning (RL) can effectively develop autonomous obstacle avoidance strategies for underwater robots; however, training in real environments carries significant risks and can easily result in robot damage. This paper proposes a Sim-to-Real pipeline for RL-based training of autonomous obstacle avoidance in underwater robots, addressing the challenges associated with training and deploying RL methods for obstacle avoidance in this context. We establish a simulation model and environment for underwater robot training based on the mathematical model of the robot, comprehensively reducing the gap between simulation and reality in terms of system inputs, modeling, and outputs. Experimental results demonstrate that our high-fidelity simulation system effectively facilitates the training of autonomous obstacle avoidance algorithms, achieving a 94% success rate in obstacle avoidance and collision-free operation exceeding 5000 steps in virtual environments. Directly transferring the trained strategy to a real robot successfully performed obstacle avoidance experiments in a pool, validating the effectiveness of our method for autonomous strategy training and sim-to-real transfer in underwater robots.

Using three waves (2011–15) of CHARLS data, we analyze the short-term effects of widowhood on cognitive function among older Chinese. Fixed-effect models show that widowhood has significant adverse effects on cognition for rural elders but not for urban ones. Furthermore, compared to rural men, rural women exhibit greater declines in cognition, especially in fluid cognition. We explore the possible mechanism from the neighborhood perspective. The results show that community sports and entertainment facilities and public services can effectively mitigate the negative impact of widowhood on cognitive function for rural widows. Sports and entertainment facilities can mainly enhance word recall ability, especially delayed word recall. Public services such as elderly health centers focusing on the healthcare function for the elderly can also improve the word recall ability of rural widows. On the other hand, family-based elderly care centers mainly increase the cognition ability of mental intactness.

Cryphodera guangdongensis n. sp. was collected from the soil and roots of Schima superba in Guangdong province, China. The new species is characterised by having a nearly spherical female, with dimensions of length × width = 532.3 (423.8–675.3) × 295.6 (160.0–381.2) μm, stylet length of 35.7 (31.1–42.1) μm, protruding vulval lips, a vulval slit measuring 54.2 (47.4–58.9) μm, an area between the vulva and anus that is flat to concave, and a vulva–anus distance 49.3 (41.1–57.6) μm. The male features two lip annules, a stylet length of 31.7 (27.4–34.8) μm and basal knobs that are slightly projecting anteriorly, while lateral field is areolated with three incisures and spicules length of 27.1 (23.7–31.0) μm. The second stage juvenile is characterised by a body length of 506.1 (441.8–564.4) μm long, two to three lip annules, a stylet length 31.2 (29.7–33.2) μm which is well developed, basal knobs projecting anteriorly, a lateral field that is areolate with three incisures, and a narrow rounded tail measuring 63.2 (54.2–71.3) μm long, with a hyaline region of 35.6 (27.4–56.6) μm long that is longer than the stylet. Based on morphology and morphometrics, the new species is closely related to C. sinensis and C. japonicum within the genus Cryphodera. The phylogenetic trees constructed based on the ITS-rRNA, 28S-rRNA D2–D3 region, and the partial COI gene sequences indicate that the new species clusters with other Cryphodera species but maintains in a separated subgroup. A key to the species of the genus Cryphodera is also provided in this study.

The incorporation of trace metals into land snail shells may record the ambient environmental conditions, yet this potential remains largely unexplored. In this study, we analyzed modern snail shells (Cathaica sp.) collected from 16 sites across the Chinese Loess Plateau to investigate their trace metal compositions. Our results show that both the Sr/Ca and Ba/Ca ratios exhibit minimal intra-shell variability and small inter-shell variability at individual sites. A significant positive correlation is observed between the shell Sr/Ca and Ba/Ca ratios across the plateau, with higher values being recorded in the northwestern sites where less monsoonal rainfall is received. We propose that shell Sr/Ca and Ba/Ca ratios, which record the composition of soil solution, may be controlled by the Rayleigh distillation in response to prior calcite precipitation. Higher rainfall amounts may lead to a lower degree of Rayleigh distillation and thus lower shell Sr/Ca and Ba/Ca ratios. This is supported by the distinct negative correlation between summer precipitation and shell Sr/Ca and Ba/Ca ratios, enabling us to reconstruct summer precipitation amounts using the Sr/Ca and Ba/Ca ratios of Cathaica sp. shells. The potential application of these novel proxies may also be promising for other terrestrial mollusks living in the loess deposits globally.

The interaction of helminth infections with type 2 diabetes (T2D) has been a major area of research in the past few years. This paper, therefore, focuses on the systematic review of the effects of helminthic infections on metabolism and immune regulation related to T2D, with mechanisms through which both direct and indirect effects are mediated. Specifically, the possible therapeutic role of helminths in T2D management, probably mediated through the modulation of host metabolic pathways and immune responses, is of special interest. This paper discusses the current possibilities for translating helminth therapy from basic laboratory research to clinical application, as well as existing and future challenges. Although preliminary studies suggest the potential for helminth therapy for T2D patients, their safety and efficacy still need to be confirmed by larger-scale clinical studies.

This meta-analysis assesses the relationship between vitamin D supplementation and incidence of major adverse cardiovascular events (MACEs). Pubmed, Web of science, Ovid, Cochrane Library and Clinical Trials were used to systematically search from their inception until July 2024. Hazard ratios (HR) and 95% confidence intervals (95%CI) were employed to assess the association between vitamin D supplementation and MACEs. This analysis included 5 randomized controlled trials (RCTs). Pooled results showed no significant difference in the incidence of MACEs (HR: 0.96; p=0.77), expanded MACEs (HR: 0.96; p=0.77) between the vitamin D intervention group and the control group. Further, the vitamin D intervention group had a lower incidence of myocardial infarction (MI), but the difference was not statistically significant (HR: 0.88, 95%CI: 0.77-1.01; p=0.061); nevertheless, vitamin D supplementation had no effect on the reduced incidence of stroke (p=0.675) or cardiovascular death (p=0.422). Among males (p=0.109) and females (p=0.468), vitamin D supplementation had no effect on the reduced incidence of MACEs. For participants with a body mass index (BMI)<25 kg/m2, the difference was not statistically significant (p=0.782); notably, the vitamin D intervention group had a lower incidence of MACEs for those with BMI≥25 kg/m2 (HR: 0.91, 95%CI: 0.83-1.00; p=0.055). Vitamin D supplementation did not significantly contribute to the risk reduction of MACEs, stroke and cardiovascular death in the general population, but may be helpful for MI. Notably, effect of vitamin D supplementation for MACEs was influenced by BMI. Overweight/obese people should be advised to take vitamin D to reduce the incidence of MACEs.

Inspired by the need to theoretically understand the naturally occurring interactions between internal waves and mesoscale phenomena in the ocean, we derive a novel model equation from the primitive rotational Euler equations using the multi-scale asymptotic expansion method. By applying the classic balance $\epsilon =\mu ^2$ between nonlinearity (measured by $\epsilon$) and dispersion (measured by $\mu$), along with the assumption that variations in the transverse direction are of order $\mu$, which is smaller than those in the propagation direction, we arrive at terms from the classic Kadomtsev–Petviashvili equation. However, when incorporating background shear currents in two horizontal dimensions and accounting for Earth’s rotation, we introduce three additional terms that, to the best of the authors’ knowledge, have not been addressed in the previous literature. Theoretical analyses and numerical results indicate that these three terms contribute to a tendency for propagation in the transverse direction and an overall variation in wave amplitudes. The specific effects of these terms can be estimated qualitatively based on the signs of the coefficients for each term and the characteristics of the initial waves. Finally, the potential shortcomings of this proposed equation are illuminated.

The primary focus of this article is to capture heterogeneous treatment effects measured by the conditional average treatment effect. A model averaging estimation scheme is proposed with multiple candidate linear regression models under heteroskedastic errors, and the properties of this scheme are explored analytically. First, it is shown that our proposal is asymptotically optimal in the sense of achieving the lowest possible squared error. Second, the convergence of the weights determined by our proposal is provided when at least one of the candidate models is correctly specified. Simulation results in comparison with several related existing methods favor our proposed method. The method is applied to a dataset from a labor skills training program.

The fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is a highly destructive polyvorous pest with a wide host range and the ability to feed continuously with seasonal changes. This destructive pest significantly damages crops and can also utilize non-agricultural plants, such as weeds, as alternative hosts. However, the adaptation mechanisms of S. frugiperda when switching between crop and non-crop hosts remain poorly understood, posing challenges for effective monitoring and integrated pest management strategies. Therefore, this study aims to elucidate the adaptability of S. frugiperda to different host plants. Results showed that corn (Zea mays L.) was more suitable for the growth and development of S. frugiperda than wheat (Triticum aestivum L.) and goosegrass (Eleusine indica). Transcriptome analysis identified 699 genes differentially expressed when fed on corn, wheat, and goosegrass. The analysis indicated that the detoxification metabolic pathway may be related to host adaptability. We identified only one SfGSTs2 gene within the GST family and investigated its functional role across different developmental stages and tissues by analysing its spatial and temporal expression patterns. The SfGSTs2 gene expression in the midgut of larvae significantly decreased following RNA interference. Further, the dsRNA-fed larvae exhibited a decreased detoxification ability, higher mortality, and reduced larval weight. The findings highlight the crucial role of SfGSTs2 in host plant adaptation. Evaluating the feeding preferences of S. frugiperda is significant for controlling important agricultural pests.

This study explored mental workload recognition methods for carrier-based aircraft pilots utilising multiple sensor physiological signal fusion and portable devices. A simulation carrier-based aircraft flight experiment was designed, and subjective mental workload scores and electroencephalogram (EEG) and photoplethysmogram (PPG) signals from six pilot cadets were collected using NASA Task Load Index (NASA-TLX) and portable devices. The subjective scores of the pilots in three flight phases were used to label the data into three mental workload levels. Features from the physiological signals were extracted, and the interrelations between mental workload and physiological indicators were evaluated. Machine learning and deep learning algorithms were used to classify the pilots’ mental workload. The performances of the single-modal method and multimodal fusion methods were investigated. The results showed that the multimodal fusion methods outperformed the single-modal methods, achieving higher accuracy, precision, recall and F1 score. Among all the classifiers, the random forest classifier with feature-level fusion obtained the best results, with an accuracy of 97.69%, precision of 98.08%, recall of 96.98% and F1 score of 97.44%. The findings of this study demonstrate the effectiveness and feasibility of the proposed method, offering insights into mental workload management and the enhancement of flight safety for carrier-based aircraft pilots.

Temporal variability and methodological differences in data normalization, among other factors, complicate effective trend analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) wastewater surveillance data and its alignment with coronavirus disease 2019 (COVID-19) clinical outcomes. As there is no consensus approach for these analyses yet, this study explored the use of piecewise linear trend analysis (joinpoint regression) to identify significant trends and trend turning points in SARS-CoV-2 RNA wastewater concentrations (normalized and non-normalized) and corresponding COVID-19 case rates in the greater Las Vegas metropolitan area (Nevada, USA) from mid-2020 to April 2023. The analysis period was stratified into three distinct phases based on temporal changes in testing protocols, vaccination availability, SARS-CoV-2 variant prevalence, and public health interventions. While other statistical methodologies may require fewer parameter specifications, joinpoint regression provided an interpretable framework for characterization and comparison of trends and trend turning points, revealing sewershed-specific variations in trend magnitude and timing that also aligned with known variant-driven waves. Week-level trend agreement corroborated previous findings demonstrating a close relationship between SARS-CoV-2 wastewater surveillance data and COVID-19 outcomes. These findings guide future applications of advanced statistical methodologies and support the continued integration of wastewater-based epidemiology as a complementary approach to traditional COVID-19 surveillance systems.

This paper presents a low-profile miniaturized dual-band antenna utilizing the quarter-mode substrate integrated waveguide (QMSIW) structure. The two modes of TE110 and TE220 of a single QMSIW structure are employed, enabling a dual-band operation. The frequency ratio between the two bands can be tuned by loading a capacitive structure, which is comprised of a capacitive-loaded patch and a short circuit post, inside the QMSIW structure. By introducing parasitic QMSIW structures through magnetic coupling, a dual-band antenna with enhanced bandwidths is achieved. The antenna has dimensions of smaller than 400 mm2 (0.048λL2) with a uniform height of 1.4 mm (0.016λL). Measurement results indicate that the −6 dB impedance bandwidths of the antennas can cover the 5G N78 (3.3–3.6 GHz) and N79 (4.8–5 GHz) bands, and the average efficiencies is better than −2.5 dB. To the authors’ knowledge, the proposed designs offer dual-wideband operation while having the smallest planar dimension compared to the previously reported antennas. Furthermore, an extended electric coupling dual-band antenna configuration is also described and measured, which achieves similar bandwidth extension as the proposed antenna.

Posttraumatic stress disorder (PTSD) is a heterogenous disorder with frequent diagnostic comorbidity. Research has deciphered this heterogeneity by identifying PTSD subtypes and their neural biomarkers. This review summarizes current approaches, symptom-based group-level and data-driven approaches, for generating PTSD subtypes, providing an overview of current PTSD subtypes and their neural correlates. Additionally, we systematically assessed studies to evaluate the influence of comorbidity on PTSD subtypes and the predictive utility of biotypes for treatment outcomes. Following the PRISMA guidelines, a systematic search was conducted to identify studies employing brain imaging techniques, including functional magnetic resonance imaging (fMRI), structural MRI, diffusion-weighted imaging (DWI), and electroencephalogram (EEG), to identify biomarkers of PTSD subtypes. Study quality was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We included 53 studies, with 44 studies using a symptom-based group-level approach, and nine studies using a data-driven approach. Findings suggest biomarkers across the default-mode network (DMN) and the salience network (SN) throughout multiple subtypes. However, only six studies considered comorbidity, and four studies tested the utility of biotypes in predicting treatment outcomes. These findings highlight the complexity of PTSD’s heterogeneity. Although symptom-based and data-driven methods have advanced our understanding of PTSD subtypes, challenges remain in addressing the impact of comorbidities and the limited validation of biotypes. Future studies with larger sample sizes, brain-based data-driven approaches, careful account for comorbidity, and rigorous validation strategies are needed to advance biologically grounded biotypes across mental disorders.

In this work, a compact active integrated antenna based on a highly compatible antenna-in-package (AiP) solution is proposed. It consists of two sections, namely, a cover plate integrated with an antenna and a package backplane that carries a GaN power amplifier (PA) die. The proposed AiP solution not only provides efficient interconnection between the antenna and the GaN PA die while providing physical shielding, but also provides impedance compensation for the die to improve the matching performance. Besides, a plated through hole array is designed inside the package backplane to significantly improve heat dissipation performance. The proposed AiP solution is compatible with radio frequency integrated circuit (RFIC) dies with different pin arrangements. Two prototypes are fabricated and measured for validation. The first prototype is the active integrated antenna based on the GaN PA, which shows an impedance bandwidth of 25.7–28.7 GHz, a peak gain of 31 dBi, and a dimension of 8 mm × 8 mm × 1.7 mm. Another prototype is based on a GaN front-end module (FEM) die integrating the PA and low noise amplifier, which demonstrates better EVM and ACPR than the conventional design with separate antenna and FEM.