The objective of this article is to explore the significance of labour-related laws and regulations, taking account of the role of the Chinese Communist Party (CPP) and the Chinese government, particularly concerning labour, unemployment, and reform challenges. The analysis demonstrates that stringent labour laws are associated with lower unemployment rates due to increased job security and protection for workers. The study indicates that the CPP significantly shapes and influences labour-related policies and regulations, and so economic growth.

Data-driven neural word embeddings (NWEs), grounded in distributional semantics, can capture various ranges of linguistic regularities, which can be further enriched by incorporating structured knowledge resources. This work proposes a novel post-processing approach for injecting semantic relationships into the vector space of both static and contextualized NWEs. Current solutions to retrofitting (RF) word embeddings often oversimplify the integration of semantic knowledge, neglecting the nuanced differences between relationships, which may result in suboptimal performance. Instead of applying multi-thresholding to distance boundaries in metric learning, we compute taxonomic similarity to dynamically adjust these boundaries during the semantic specialization of word embeddings. Benchmark evaluations on both static and contextualized word embeddings demonstrate that our dynamic-fitting (DF) approach produces SOTA correlation results of 0.78 and 0.76 on SimLex-999 and SimVerb-3500, respectively, highlighting the effectiveness of incorporating multiple semantic relationships in refining vector semantics. Our approach also outperforms existing RF methods in both supervised and unsupervised semantic relationships recognition tasks. It achieves top accuracy scores for hypernymy detection on the BLESS, WBLESS, and BIBLESS datasets (0.97, 0.89, and 0.83, respectively) and an F1 score of over 0.60 on four types of semantic relationship classification in the shared Subtask-2 of CogALex-V, surpassing all participant systems. In the analogy reasoning task of the Bigger Analogy Test Set, our approach outperforms existing RF methods on inferring relational similarity. These consistent improvements across various lexical semantics tasks suggest that our DF approach can effectively integrate distributional semantics with symbolic knowledge resources, thereby enhancing the representation capacity of word embeddings in downstream applications.

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.

Turbulence closures are essential for predictive fluid flow simulations in both natural and engineering systems. While machine learning offers promising avenues, existing data-driven turbulence models often fail to generalise beyond their training datasets. This study identifies the root cause of this limitation as the conflation of generalisable flow physics and dataset-specific behaviours. We address this challenge using symbolic regression, which yields interpretable, white-box expressions. By decomposing the learned corrections into inner-layer, outer-layer and pressure-gradient components, we isolate universal physics from flow-specific features. The model is trained progressively using high-fidelity datasets for plane channel flows, zero-pressure-gradient turbulent boundary layers (ZPGTBLs), and adverse pressure-gradient turbulent boundary layers (PGTBLs). For example, direct application of a model trained on channel flow data to ZPGTBLs results in incorrect skin friction predictions. However, when only the generalisable inner-layer component is retained and combined with an outer-layer correction specific to ZPGTBLs, predictions improve significantly. Similarly, a pressure-gradient correction derived from PGTBL data enables accurate modelling of aerofoil flows with both favourable and adverse pressure gradients. The resulting symbolic corrections are compact, interpretable, and generalise across configurations – including unseen geometries such as aerofoils and Reynolds numbers outside the training set. The models outperform baseline Reynolds-averaged Navier–Stokes closures (e.g. the Spalart–Allmaras and shear stress transport models) in both a priori and a posteriori tests. These results demonstrate that explicit identification and retention of generalisable components is key to overcoming the generalisation challenge in machine-learned turbulence closures.

A novel entomopathogenic nematode (EPN) species, Steinernema tarimense n. sp., was isolated from soil samples collected in a Populus euphratica forest located in Yuli County within the Tarim Basin of Xinjiang, China. Integrated morphological and molecular analyses consistently place S. tarimense n. sp. within the ‘kushidai-clade’. The infective juvenile (IJ) of new species is characterized by a body length of 674–1010 μm, excretory pore located 53–80 μm from anterior end, nerve ring positioned 85–131 μm from anterior end, pharynx base situated 111–162 μm from anterior end, a tail length of 41–56 μm, and the ratios D% = 42.0–66.6, E% = 116.2–184.4, and H% = 25.5–45.1. The first-generation male of the new species is characterized by a curved spicule length of 61–89 μm, gubernaculum length of 41–58 μm, and ratios D% = 36.8–66.2, SW% = 117.0–206.1, and GS% = 54.8–82.0. Additionally, the tail of first-generation female is conoid with a minute mucron. Phylogenetic analyses of ITS, 28S, and mt12S sequences demonstrated that the three isolates of S. tarimense n. sp. are conspecific and form a sister clade to members of the ‘kushidai-clade’ including S. akhursti, S. anantnagense, S. kushidai, and S. populi. Notably, the IJs of the new species exhibited faster development at 25°C compared to other Steinernema species. This represents the first described of an indigenous EPN species from Xinjiang, suggesting its potential as a novel biocontrol agent against local pests.

The automation of assembly operations with industrial robots is pivotal in modern manufacturing, particularly for multispecies, low-volume, and customized production. Traditional programing methods are time-consuming and lack adaptability to complex, variable environments. Reinforcement learning-based assembly tasks have shown success in simulation environments, but face challenges like the simulation-to-reality gap and safety concerns when transferred to real-world applications. This article addresses these challenges by proposing a low-cost, image-segmentation-driven deep reinforcement learning strategy tailored for insertion tasks, such as the assembly of peg-in-hole components in satellite manufacturing, which involve extensive contact interactions. Our approach integrates visual and forces feedback into a prior dueling deep Q-network for insertion skill learning, enabling precise alignment of components. To bridge the simulation-to-reality gap, we transform the raw image input space into a canonical space based on image segmentation. Specifically, we employ a segmentation model based on U-net, pretrained in simulation and fine-tuned with real-world data, significantly reducing the need for labor-intensive real image segment labels. To handle the frequent contact inherent in peg-in-hole tasks, we integrated safety protections and impedance control into the training process, providing active compliance and reducing the risk of assembly failures. Our approach was evaluated in both simulated and real robotic environments, demonstrating robust performance in handling camera position errors and varying ambient light intensities and different lighting colors. Finally, the algorithm was validated in a real satellite assembly scenario, achieving a success rate of 15 out of 20 tests.

Tuberculosis (TB) remains a significant public health concern in China. Using data from the Global Burden of Disease (GBD) study 2021, we analyzed trends in age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and disability-adjusted life years (DALYs) for TB from 1990 to 2021. Over this period, HIV-negative TB showed a marked decline in ASIR (AAPC = −2.34%, 95% CI: −2.39, −2.28) and ASMR (AAPC = −0.56%, 95% CI: −0.62, −0.59). Specifically, drug-susceptible TB (DS-TB) showed reductions in both ASIR and ASMR, while multidrug-resistant TB (MDR-TB) showed slight decreases. Conversely, extensively drug-resistant TB (XDR-TB) exhibited upward trends in both ASIR and ASMR. TB co-infected with HIV (HIV-DS-TB, HIV-MDR-TB, HIV-XDR-TB) showed increasing trends in recent years. The analysis also found an inverse correlation between ASIRs and ASMRs for HIV-negative TB and the Socio-Demographic Index (SDI). Projections from 2022 to 2035 suggest continued increases in ASIR and ASMR for XDR-TB, HIV-DS-TB, HIV-MDR-TB, and HIV-XDR-TB. The rising burden of XDR-TB and HIV-TB co-infections presents ongoing challenges for TB control in China. Targeted prevention and control strategies are urgently needed to mitigate this burden and further reduce TB-related morbidity and mortality.

Nutrition intervention is an effective way to improve flesh qualities of fish. The effect of feed supplementation with glutamate (Glu) on flesh quality of gibel carp (Carassius gibelio) was investigated. In trial 1, the fish (initial weight: 37.49 ± 0.08 g) were fed two practical diets with 0 and 2% Glu supplementation. In trial 2, the fish (37.26 ± 0.04 g) were fed two purified diets with 0 and 3% Glu supplementation. The results after feeding trials showed that dietary Glu supplementation increased the hardness and springiness of muscle, whether using practical or purified diets. Glu-supplemented diets increased the thickness and density of myofibres and collagen content between myofibres. Furthermore, Glu promoted muscle protein deposition by regulating the IGF-1-AKT-mTOR signalling pathway, and enhanced the myofibre hypertrophy by upregulating genes related to myofibre growth and development (mef2a, mef2d, myod, myf5, mlc, tpi and pax7α). The protein deposition and myofibre hypertrophy in turn improved the flesh texture. In addition, IMP content in flesh increased when supplementing Glu whether to practical or to purified diet. Metabolomics confirmed that Glu promoted the deposition of muscle-flavoured substances and purine metabolic pathway most functioned, echoed by the upregulation of key genes (ampd, ppat and adsl) in purine metabolism. The sensory test also clarified that dietary Glu improved the flesh quality by enhancing the muscle texture and flavour. Conclusively, dietary Glu supplementation can improve the flesh quality in this fish, which can further support evidence from other studies more generally that improve flesh quality of cultured fish.

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.

We strengthen two results of Moretó. We prove that the index of the Fitting subgroup is bounded in terms of the degrees of the irreducible monomial Brauer characters of the finite solvable group G and it is also bounded in terms of the average degree of the irreducible Brauer characters of G that lie over a linear character of the Fitting subgroup.

The extracellular matrices, such as the haemolymph, in insects are at the centre of most physiological processes and are protected from oxidative stress by the extracellular antioxidant enzymes. In this study, we identified two secreted superoxide dismutase genes (PxSOD3 and PxSOD5) and investigated the oxidative stress induced by chlorpyrifos (CPF) in the aquatic insect Protohermes xanthodes (Megaloptera: Corydalidae). PxSOD3 and PxSOD5 contain the signal peptides at the N-terminus. Structure analysis revealed that PxSOD3 and PxSOD5 contain the conserved CuZn-SOD domain, which is mainly composed of β-sheets and has conserved copper and zinc binding sites. Both PxSOD3 and PxSOD5 are predicted to be soluble proteins located in the extracellular space. After exposure to different concentrations of sublethal CPF, MDA content in P. xanthodes larvae were increased in a dose-dependent manner; SOD and CAT activities were also higher in CPF-treated groups than that in the no CPF control, indicating that sublethal CPF induces oxidative stress in P. xanthodes larvae. Furthermore, PxSOD3 and PxSOD5 expression levels and haemolymph SOD activity in the larvae were downregulated by sublethal CPF at different concentrations. Our results suggest that the PxSOD3 and PxSOD5 are putative extracellular antioxidant enzymes that may play a role in maintaining the oxidative balance in the extracellular space. Sublethal CPF may induce oxidative stress in the extracellular space of P. xanthodes by reducing the gene expression and catalytic activity of extracellular SODs.

The whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) is economically one of the most threatening pests in tomato cultivation, which not only causes direct damage but also transmits many viruses. Breeding whitefly-resistant tomato varieties is a promising and environmentally friendly method to control whitefly populations in the field. Accumulating evidence from tomato and other model systems demonstrates that flavonoids contribute to plant resistance to herbivorous insects. Previously, we found that high flavonoid-producing tomato line deterred whitefly oviposition and settling behaviours, and was more resistant to whiteflies compared to the near-isogenic low flavonoid-producing tomato line. The objective of the current work is to describe in detail different aspects of the interaction between the whitefly and two tomato lines, including biochemical processes involved. Electrical penetration graph recordings showed that high flavonoid-producing tomato reduced whitefly probing and phloem-feeding efficiency. We also studied constitutive and induced plant defence responses and found that whitefly induced stronger reactive oxygen species accumulation through NADPH oxidase in high flavonoid-producing tomato than in low flavonoid-producing tomato. Moreover, whitefly feeding induced the expression of callose synthase genes and resulted in callose deposition in the sieve elements in high flavonoid-producing tomato but not in low flavonoid-producing tomato. As a consequence, whitefly feeding on high flavonoid-producing tomato significantly decreased uptake of phloem and reduced its performance when compared to low flavonoid-producing tomato. These results indicate that high flavonoid-producing tomato provides phloem-based resistance against whitefly infestation and that the breeding of such resistance in new varieties could enhance whitefly management.

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 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.

Unmanned surface vehicles (USVs) frequently encounter inadequate energy levels while navigating to their destinations, which complicates their successful berthing in intricate harbor environments. A bacterial foraging optimization algorithm (BFO) is proposed that takes energy consumption into account and incorporates multiple constraints (MC-BFO). The energy consumption model is redefined for wind environments, enhancing the sensitivity of USVs to wind conditions. Additionally, a reward function is integrated into the algorithm, and the fitness function is reconstructed to improve the goal orientation of the USV. This approach enables the USV to maintain a reasonable path length while pursuing low energy consumption, resulting in more practical navigation. Constraining the USV’s sailing posture for smoother paths and restricting the USV’s heading and speed near the berthage facilitate safe berthing. Finally, three distinct experimental environments are established to compare the paths generated by MC-BFO, BFO, and genetic algorithm under both downwind and upwind conditions, ensuring consistency in relevant parameters. Data on sailing posture, energy consumption, and path length are collected, generalized, and analyzed. The results indicate that MC-BFO effectively reduces energy consumption while maintaining an acceptable path length, resulting in smoother and more coherent paths compared to traditional segmented planning. In conclusion, this method significantly enhances the quality of the berthing path.

This paper presents a general approach to synthesizing closed-loop robots for machining and manufacturing of complex quadric surfaces, such as toruses, helicoids, and helical tubes. The proposed approach begins by employing finite screw theory to describe the motion sets generated by prismatic, rotational, and helical joints. Subsequently, generatrices and generating lines are put forward and combined for type synthesis of serial kinematic limbs capable of generating single-DoF translations along spatial curves and two-DoF translations on complex quadric surfaces. Following this manner, the two-DoF translational motion patterns on these complex quadric surfaces are algebraically defined and expressed as finite screw sets. Type synthesis of close-loop robots having the newly defined motion patterns can thus be carried out based upon analytical computations of finite screws. As application of the presented approach, closed-loop robots for machining toruses are synthesized, resulting in four-DoF and five-DoF standard and derived limbs together with their corresponding assembly conditions. Additionally, brief descriptions of robots for machining helicoids and helical tubes are provided, along with a comprehensive list of all the feasible limbs for these kinds of robots. The robots synthesized in this paper have promised applications in machining and manufacturing of spatial curves and surfaces, enabling precise control of machining trajectories ensured by mechanism structures and achieving high precision with low cost.

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.

An actively controllable cascaded proton acceleration driven by a separate 0.8 picosecond (ps) laser is demonstrated in proof-of-principle experiments. MeV protons, initially driven by a femtosecond laser, are further accelerated and focused into a dot structure by an electromagnetic pulse (EMP) on the solenoid, which can be tuned into a ring structure by increasing the ps laser energy. An electrodynamics model is carried out to explain the experimental results and show that the dot-structured proton beam is formed when the outer part of the incident proton beam is optimally focused by the EMP force on the solenoid; otherwise, it is overfocused into a ring structure by a larger EMP. Such a separately controlled mechanism allows precise tuning of the proton beam structures for various applications, such as edge-enhanced proton radiography, proton therapy and pre-injection in traditional accelerators.