A dual-band dual-polarized wearable antenna that applies to two different operating modes of wireless body area networks is proposed in this letter. The antenna radiates simultaneously in the ISM band at 2.45 and 5.8 GHz. It consists of a rigid button-like radiator and a flexible fabric radiator. At 2.45 GHz, an omnidirectional circularly polarized pattern is radiated by the flexible radiator, which is suitable for the on-body communication. At the same time, a linearly polarized broadside pattern for off-body communication is generated by button radiator at 5.8 GHz. The antenna has been validated in free space and human body environments. The impedance bandwidth at 2.45 and 5.8 GHz are 5% and 35%, and the gain is measured to be 0.15 and 5.95 dBi, respectively. Furthermore, the specific absorption rates are simulated. At 2.45 and 5.8 GHz, the results averaged over 1 g of body tissue are 0.128 and 0.055 W/kg. The maximum value at both bands is below the IEEE C95.3 standard of 1.6 W/kg.

The heating effect of electromagnetic waves in ion cyclotron range of frequencies (ICRFs) in magnetic confinement fusion device is different in different plasma conditions. In order to evaluate the ICRF heating effect in different plasma conditions, we conducted a series of experiments and corresponding TRANSP simulations on the EAST tokamak. Both simulation and experimental results show that the effect of ICRF heating is poor at low core electron density. The decrease in electron density changes the left-handed electric field near the resonant layer, resulting in a significant decrease in the power absorbed by the hydrogen fundamental resonance. However, quite a few experiments must be performed in plasma conditions with low electron density. It is necessary to study how to make ICRF heating best in low electron density plasma. Through a series of simulation scans of the parallel refractive index (n//) of the ICRF antenna, it is concluded that the change of the ICRF antenna n// will lead to the change of the left-handed electric field, which will change the fundamental absorption of ICRF power by the hydrogen minority ions. Fully considering the coupling of ion cyclotron wave at the tokamak boundary and the absorption in the plasma core, optimizing the ICRF antenna structure and selecting appropriate parameters such as parallel refractive index, minority ion concentration, resonance layer position, plasma current and core electron temperature can ensure better heating effect in the ICRF heating experiments in the future EAST upgrade. These results have important implications for the enhancement of the auxiliary heating effect of EAST and other tokamaks.

To address the problems of accuracy degradation, localization drift, and even failure of Simultaneous Localization and Mapping (SLAM) algorithms in unstructured environments with sparse geometric features, such as outdoor parks, highways, and urban roads, a multi-metric light detection and ranging (LiDAR) SLAM system based on the fusion of geometric and intensity features is proposed. Firstly, an adaptive method for extracting multiple types of geometric features and salient intensity features is proposed to address the issue of insufficient sparse feature extraction. In addition to extracting traditional edge and planar features, vertex features are also extracted to fully utilize the geometric information, and intensity edge features are extracted in areas with significant intensity changes to increase multi-level perception of the environment. Secondly, in the state estimation, a multi-metric error estimation method based on point-to-point, point-to-line, and point-to-plane is used, and a two-step decoupling strategy is employed to enhance pose estimation accuracy. Finally, qualitative and quantitative experiments on public datasets demonstrate that compared to state-of-the-art pure geometric and intensity-assisted LiDAR SLAM algorithms, our proposed algorithm achieves superior localization accuracy and mapping clarity, with an ATE accuracy improvement of 28.93% and real-time performance of up to 62.9 ms. Additionally, test conducted in real campus environments further validates the effectiveness of our approach in complex, unstructured scenarios.

The high comorbidity of major depressive disorder (MDD), anxiety disorders (ANX), and post-traumatic stress disorder (PTSD) complicates the study of their structural neural correlates, particularly in white matter (WM) alterations. Using fractional anisotropy (FA), this meta-analysis aimed to identify both unique and shared WM characteristics for these disorders by comparing them with healthy controls (HC). The aggregated sample size across studies includes 3,661 individuals diagnosed with MDD, ANX, or PTSD and 3,140 HC participants. The whole-brain analysis revealed significant FA reductions in the corpus callosum (CC) across MDD, ANX, and PTSD, suggesting a common neurostructural alteration underlying these disorders. Further pairwise comparisons highlighted disorder-specific differences: MDD patients showed reduced FA in the middle cerebellar peduncles and bilateral superior longitudinal fasciculus II relative to ANX patients and decreased FA in the CC extending to the left anterior thalamic projections (ATPs) when compared with PTSD. In contrast, PTSD patients exhibited reduced FA in the right ATPs compared to HC. No significant FA differences were observed between ANX and PTSD or between ANX and HC. These findings provide evidence for both shared and unique WM alterations in MDD, ANX, and PTSD, reflecting the neural underpinnings of the clinical characteristics that distinguish these disorders.

Persistent malnutrition is associated with poor clinical outcomes in cancer. However, assessing its reversibility can be challenging. The present study aimed to utilise machine learning (ML) to predict reversible malnutrition (RM) in patients with cancer. A multicentre cohort study including hospitalised oncology patients. Malnutrition was diagnosed using an international consensus. RM was defined as a positive diagnosis of malnutrition upon patient admission which turned negative one month later. Time-series data on body weight and skeletal muscle were modelled using a long short-term memory architecture to predict RM. The model was named as WAL-net, and its performance, explainability, clinical relevance and generalisability were evaluated. We investigated 4254 patients with cancer-associated malnutrition (discovery set = 2977, test set = 1277). There were 2783 men and 1471 women (median age = 61 years). RM was identified in 754 (17·7 %) patients. RM/non-RM groups showed distinct patterns of weight and muscle dynamics, and RM was negatively correlated to the progressive stages of cancer cachexia (r = –0·340, P < 0·001). WAL-net was the state-of-the-art model among all ML algorithms evaluated, demonstrating favourable performance to predict RM in the test set (AUC = 0·924, 95 % CI = 0·904, 0·944) and an external validation set (n 798, AUC = 0·909, 95 % CI = 0·876, 0·943). Model-predicted RM using baseline information was associated with lower future risks of underweight, sarcopenia, performance status decline and progression of malnutrition (all P < 0·05). This study presents an explainable deep learning model, the WAL-net, for early identification of RM in patients with cancer. These findings might help the management of cancer-associated malnutrition to optimise patient outcomes in multidisciplinary cancer care.

Overnutrition during before and pregnancy can cause maternal obesity and raise the risk of maternal metabolic diseases during pregnancy, and in offspring. Lentinus edodes may prevent or reduce obesity. This study aimed to to assess Lentinus edodes fermented products effects on insulin sensitivity, glucose and lipid metabolism in maternal and offspring, and explore its action mechanism. A model of overnutrition during pregnancy and lactation was developed using a 60 % kcal high-fat diet in C57BL6/J female mice. Fermented Lentinus edodes (FLE) was added to the diet at concentrations of 1 %, 3 %, and 5 %. The results demonstrated that FLE to the gestation diet significantly reduced serum insulin levels and homeostatic model assessment for insulin resistance (HOMA-IR) in pregnant mice. FLE can regulate maternal lipid metabolism and reduce fat deposition. Meanwhile, the hepatic phosphoinositide-3-kinase-protein kinase (PI3K/AKT) signaling pathway was significantly activated in the maternal mice. There is a significant negative correlation between maternal FLE supplementation doses and offspring body fat percentage and visceral fat content. Furthermore, FLE supplementation significantly increased offspring weaning litter weight, significantly reduced fasting glucose level, serum insulin level, HOMA-IR and serum glucose level, significantly activated liver PI3K/AKT signaling pathway in offspring, and upregulated the expression of liver lipolytic genes adipose triglyceride lipase, hormone-sensitive lipase and carnitine palmitoyltransferase 1 mRNA. Overall, FLE supplementation can regulate maternal lipid metabolism and reduce fat deposition during pregnancy and lactation, and it may improve insulin sensitivity in pregnant mothers and offspring at weaning through activation of the PI3K/AKT signaling pathway.

Kongish Daily, a Facebook page promoting Kongish – a creative, critical, and colloquial form of Hong Kong English with Cantonese inflections – has attracted a following in social media over the past decade. It has also sparked interest among sociolinguists interested in (post-)multilingual developments in East Asia. This study is built on Hansen Edwards’s (2016) premise that Hong Kong English would gain wider acceptance in Hong Kong as the cultural identity of local language users shifted amidst sociocultural transformations. We first provide an overview of the Kongish phenomenon, followed by a qualitative study involving 30 active Kongish users from diverse age groups, genders and occupations. Through semi-structured interviews, we explore users’ perceptions of language and identity. Our findings support Hansen Edwards’s prediction regarding the strengthening of Hong Kongers’ cultural identification, while revealing an evolving, counter-stereotypical Hong Kong culture as well as an opinion divide on the future trajectory of Kongish.

Emission line galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN–MLP that combines convolutional neural networks (CNNs) with multilayer perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN–MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN–MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, and Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, and Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

The robot manipulator is commonly employed in the space station experiment cabinet for the disinfection task. The challenge lies in devising a motion trajectory for the robot manipulator that satisfies both performance criteria and constraints within the confined space of an experimental cabinet. To address this issue, this paper proposes a trajectory planning method in joint space. This method constructs the optimal trajectory by transforming the original problem into a constrained multi-objective optimization problem. This is then solved and integrated with the seventh-degree B-spline curve. The optimization algorithm utilizes an indicator-based adaptive differential evolution algorithm, enhanced with improved Tent chaotic mapping and opposition-based learning for population initialization. The method employed the Fréchet distance to design a trajectory selection strategy based on the Pareto solutions to ensure that the planned trajectory complies with Cartesian space requirements. This allows the robot manipulator end-effector to approximate the desired path in Cartesian space closely. The findings indicate that the proposed method can effectively design the robot manipulator trajectory, considering both joint motion performance and end-effector motion constraints. This ensures that the robot manipulator operates efficiently and safely within the experimental cabinet.