Weeds significantly reduce sugarcane (Saccharum officinarum L.) production by 30% to 50% and cause complete crop loss in severe cases. Guangxi, a central sugarcane-growing region in southern China, faces significant challenges due to the proliferation of weeds severely impacting crop tillering, yield, and quality. In this study, we surveyed and identified 35 weed species belonging to 16 families in Longzhou, Nongqin, and Qufeng, with significant threats posed by purple nutsedge (Cyperus rotundus L.), bermudagrass [Cynodon dactylon (L.) Pers.], hairy crabgrass [Digitaria sanguinalis (L.) Scop.], black nightshade (Solanum nigrum L.), white-edge morningglory [Ipomoea nil (L.) Roth], and ivy woodrose [Merremia hederacea (Burm. f.) Hallier f.]. The application of 81% MCPA-ametryn-diuron achieved greater than 90% control within 15 d. Although herbicides are effective, they can unintentionally harm sugarcane, indicating a need for tolerant genotypes. Therefore, we comprehensively evaluated herbicide-induced phytotoxic responses and identified tolerant sugarcane genotypes over 3 yr of trials conducted on 222 genotypes across Guangxi. We quantified phytotoxicity by counting the number and severity of affected leaves. The ANOVA revealed statistically significant main and interaction effects among genotype, crop cycle, and location. Cluster and discriminant analyses classified the genotypes into five groups: 21 highly tolerant (HT), 68 tolerant, 75 moderately tolerant, 18 susceptible, and 40 highly susceptible. The 21 HT genotypes demonstrated strong potential to be used as parental lines for breeding herbicide-tolerant varieties, to inform precision breeding strategies, and to increase tolerance to herbicide stress in sugarcane.

Dot array deposition through electrohydrodynamic (EHD) printing is widely used for high resolution and material utilization advantages. However, the conventional printing method is subject to a printing frequency limit known as the capillary frequency of the meniscus oscillation, where the jet directly contacts the substrate. This makes the printing frequency of EHD printing maintain at a low level and that is difficult to improve. In this work, a method for high-frequency EHD printing through continuous pinch-off is proposed. The characteristic frequency is broken through. A model is established to reveal the printing mechanism by combining the Poisson–Nernst–Planck equation and the phase field method. The unreal charge leakage is prevented by constructing a transition function for the fluid’s properties. The stability of the Taylor cone’s deformation and the droplets’ generation is studied. The measurement criterion for printing frequency is determined. The suitable printing height that can prevent the jet from directly contacting the substrate is obtained by investigating its influence on the printing states and frequency. The phase diagram considering the liquid’s conductivity and viscosity is presented to distinguish whether the printing is based on the end-pinching or Rayleigh–Plateau instability. The influence of the conductivity, viscosity, flow rate and printing voltage on the printing frequencies is studied quantitatively. Finally, scaling laws for printing frequency are proposed by theoretical analyses and summarizing the numerical data. This work could be beneficial for further enhancing the printing frequency of EHD printing.

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.

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.

Against the proliferation of large language model (LLM) based Artificial Intelligence (AI) products such as ChatGPT and Gemini, and their increasing use in professional communication training, researchers, including applied linguists, have cautioned that these products (re)produce cultural stereotypes due to their training data. However, there is a limited understanding of how humans navigate the assumptions and biases present in the responses of these LLM-powered systems and the role humans play in perpetuating stereotypes during interactions with LLMs. In this article, we use Sequential-Categorial Analysis, which combines Conversation Analysis and Membership Categorization Analysis, to analyze simulated interactions between a human physiotherapist and three LLM-powered chatbot patients of Chinese, Australian, and Indian cultural backgrounds. Coupled with analysis of information elicited from LLM chatbots and the human physiotherapist after each interaction, we demonstrate that users of LLM-powered systems are highly susceptible to becoming interactionally entrenched in culturally essentialized narratives. We use the concepts of interactional instinct and interactional entrenchment to argue that whilst human–AI interaction may be instinctively prosocial, LLM users need to develop Critical Interactional Competence for human–AI interaction through appropriate and targeted training and intervention, especially when LLM-powered tools are used in professional communication training programs.

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 composite dietary antioxidant index (CDAI) has been identified as a critical factor in the pathogenesis of certain inflammatory diseases. The study aimed to investigate the relationship between CDAI and Helicobacter pylori infection using cross-sectional design. In this study, participants from the 1999–2000 National Health and Nutrition Examination Survey were analysed using logistic and Cox regression analyses to assess the associations between H. pylori infection and CDAI, encompassing vitamin A, vitamin C, vitamin E, carotene, Zn, Se and Cu. The results demonstrated a negative correlation between CDAI scores and H. pylori infection, revealing a non-linear relationship between the odds of H. pylori infection and CDAI as a continuous variable. Subsequently, a two-sample Mendelian randomisation study was conducted utilising genome-wide association study summary statistics to explore the causal relationship between antioxidant levels and H. pylori infection. We found that the intake of Cu was a protective factor in the occurrence of H. pylori infection but did not support a causal association between circulating Cu levels and H. pylori infection. The prevalence of H. pylori infection was found to be elevated among individuals of older age, lower education levels, limited socio-economic status, smokers, diabetes and those with hypertension. The study suggests that higher CDAI is linked to decreased odds of H. pylori infection, and further prospective studies are needed to confirm the association. Our findings may have significant implications for the prevention and management of H. pylori-related diseases.

This study marks the first update on Malaysia’s marine tardigrades after more than 50 years, presenting both the discovery of a new species, Batillipes malaysianus sp. nov., and a new record, Batillipes rotundiculus. The specimens were collected from the intertidal zone at Pantai Pancur Hitam, Labuan, Malaysia, during two separate sampling efforts. Despite extensive sampling, the density of marine tardigrades in the area was found to be exceptionally low, with only a single specimen of B. malaysianus sp. nov. and a limited number of other Batillipes individuals collected. The new species, B. malaysianus sp. nov., is distinguished by unique morphological features, including setae scattered across the ventral cuticle – a trait not observed in any other species of the genus – and constricted primary clavae, a characteristic absent in other species of the B1 toe arrangement group. Additionally, B. rotundiculus represents the first confirmed record of this species in Malaysia, expanding its known distribution. This study also updates the global species count of Batillipes to 42, incorporating recent taxonomic changes and this new addition. An updated dichotomous key for the genus is provided, incorporating all species described to date. These findings underscore the importance of exploring understudied marine habitats and highlight the potential for discovering more tardigrade species in Malaysia.

This paper introduces a distributed online learning coverage control algorithm based on sparse Gaussian process regression for addressing the problem of multi-robot area coverage and source localization in unknown environments. Considering the limitations of traditional Gaussian process regression in handling large datasets, this study employs multiple robots to explore the task area to gather environmental information and approximate the posterior distribution of the model using variational free energy methods, which serves as the input for the centroid Voronoi tessellation algorithm. Additionally, taking into consideration the localization errors, and the impact of obstacles, buffer factors and centroid Voronoi tessellation algorithms with separating hyperplanes are introduced for dynamic robot task area planning, ultimately achieving autonomous online decision-making and optimal coverage. Simulation results demonstrate that the proposed algorithm ensures the safety of multi-robot formations, exhibits higher iteration speed, and improves source localization accuracy, highlighting the effectiveness of model enhancements.