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.

This study introduces a low-profile, broadband antenna with filtering features and tunable radiation nulls. The antenna consists of an arc-shaped slot, a sawtooth square slot, a Y-shaped filtering branch, two rectangular metal cavities, and curved current loops. High-frequency current balancing technology is used in this research, two rectangular metal cavities are added above the slot to balance the current strength and reduce cross-polarization. By introducing a Y-shaped filtering branch based on the reverse diversion technique, the filtering capability of the antenna can be significantly enhanced. The electric and magnetic field intensity in the specific area is enhanced through arc-shaped slot tuning technology, and the bandwidth is effectively broadened. The radius adjustment of the sector-shaped feeding network controls the position of the high-frequency radiation null, and the curved current loops control the low-frequency radiation null, the two modulate to regulate the roll-off rate of the radiation characteristic. Experimental tests demonstrate an impedance matching bandwidth greater than 55%, a peak gain of 4.5 dBi, and out-of-band suppression of 25 and 21 dB in the low and high-frequency bands, respectively. Moreover, the cross-polarization level obtained in the xoz plane is lower than –35 dB. The designed antenna demonstrates considerable potential for broadband filtering applications.

Ultra-thin liquid sheets generated by impinging two liquid jets are crucial high-repetition-rate targets for laser ion acceleration and ultra-fast physics, and serve widely as barrier-free samples for structural biochemistry. The impact of liquid viscosity on sheet thickness should be comprehended fully to exploit its potential. Here, we demonstrate experimentally that viscosity significantly influences thickness distribution, while surface tension primarily governs shape. We propose a thickness model based on momentum exchange and mass transport within the radial flow, which agrees well with the experiments. These results provide deeper insights into the behaviour of liquid sheets and enable accurate thickness control for various applications, including atomization nozzles and laser-driven particle sources.

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.

The World Cancer Research Fund and the American Institute for Cancer Research recommend a plant-based diet to cancer survivors, which may reduce chronic inflammation and excess adiposity associated with worse survival. We investigated associations of plant-based dietary patterns with inflammation biomarkers and body composition in the Pathways Study, in which 3659 women with breast cancer provided validated food frequency questionnaires approximately 2 months after diagnosis. We derived three plant-based diet indices: overall plant-based diet index (PDI), healthful plant-based diet index (hPDI) and unhealthful plant-based diet index (uPDI). We assayed circulating inflammation biomarkers related to systemic inflammation (high-sensitivity C-reactive protein [hsCRP]), pro-inflammatory cytokines (IL-1β, IL-6, IL-8, TNF-α) and anti-inflammatory cytokines (IL-4, IL-10, IL-13). We estimated areas (cm2) of muscle and visceral and subcutaneous adipose tissue (VAT and SAT) from computed tomography scans. Using multivariable linear regression, we calculated the differences in inflammation biomarkers and body composition for each index. Per 10-point increase for each index: hsCRP was significantly lower by 6·9 % (95 % CI 1·6%, 11·8%) for PDI and 9·0 % (95 % CI 4·9%, 12·8%) for hPDI but significantly higher by 5·4 % (95 % CI 0·5%, 10·5%) for uPDI, and VAT was significantly lower by 7·8 cm2 (95 % CI 2·0 cm2, 13·6 cm2) for PDI and 8·6 cm2 (95 % CI 4·1 cm2, 13·2 cm2) for hPDI but significantly higher by 6·2 cm2 (95 % CI 1·3 cm2, 11·1 cm2) for uPDI. No significant associations were observed for other inflammation biomarkers, muscle, or SAT. A plant-based diet, especially a healthful plant-based diet, may be associated with reduced inflammation and visceral adiposity among breast cancer survivors.

Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.

Cartographer is an algorithm that was open sourced by Google in 2016 and adapted to multiple sensors. To address issues of the original algorithm, such as the negative impact of outlier point cloud on the scan matching, and low accuracy of position fusion. This paper preprocesses the sensor data and presents HT-Carto, an improved hybrid point-cloud filtering system, and a tightly coupled LiDAR/IMU framework based on Cartographer’s front-end. The inertial measurement unit (IMU) provides initial values for the point cloud, and the IMU pre-integration combines the scan-matched pose to construct the factors, which are added as constraints to the factor graph. The result is used to update the current pose and work as odometer residuals at the back-end. The optimization of the selected strategy during point cloud preprocessing, PassThrough, and RadiusOutlierRemoval are combined to ensure quality. An actual vehicle is used in complex indoor environment to verify the stability and robustness of HT-Carto. Compared to the Cartographer, Karto, Hector, and GMapping, HT-Carto demonstrates better localization and mapping, it can obtain a more precise trajectory.

The betatron radiation source features a micrometer-scale source size, a femtosecond-scale pulse duration, milliradian-level divergence angles and a broad spectrum exceeding tens of keV. It is conducive to the high-contrast imaging of minute structures and for investigating interdisciplinary ultrafast processes. In this study, we present a betatron X-ray source derived from a high-charge, high-energy electron beam through a laser wakefield accelerator driven by the 1 PW/0.1 Hz laser system at the Shanghai Superintense Ultrafast Laser Facility (SULF). The critical energy of the betatron X-ray source is 22 ± 5 keV. The maximum X-ray flux reaches up to 4 × 109 photons for each shot in the spectral range of 5–30 keV. Correspondingly, the experiment demonstrates a peak brightness of 1.0 × 1023 photons·s−1·mm−2·mrad−2·0.1%BW−1, comparable to those demonstrated by third-generation synchrotron light sources. In addition, the imaging capability of the betatron X-ray source is validated. This study lays the foundation for future imaging applications.

For binary plug nozzle, the plug cone is exposed to high-temperature mainstream flow, making it one of the nozzle’s high-temperature components. This paper uses the Realizable k-ε turbulence model and the reverse Monte Carlo method to numerically investigate the aerodynamic and infrared radiation characteristics of the plug nozzle. Various slot cooling configurations were adopted to study the nozzle’s infrared radiation in detail. Results indicate that compared to the baseline nozzle, the plug nozzle’s performance is slightly reduced due to the decrease in effective area of flow over the plug cone. Introducing slot cooling at the rear edge provides significant infrared suppression benefits at low detection angles and notably reduces infrared radiation discrepancy with baseline nozzle at high detection angles. The cooling air from slots causes the nozzle jet to exhibit a ‘thermal layered’ feature. With the same total coolant mass flow, the ‘leading edge + trailing edge’ cooling configuration can lower the area-averaged wall temperature of the plug cone by 5.5% – 12.3%. However, its infrared radiation intensity at each detection angle on the pitch detection plane is higher than that of the ‘trailing edge’ configuration. The significance of leading-edge cooling is focused more on thermal protection for the plug. Thus, it is essential to balance coolant mass flow distribution between infrared radiation suppression and thermal protection.

Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

The Central Asian Orogenic Belt is the world’s largest accretionary orogenic belt, associated with the closure of the Paleo-Asian Ocean (PAO). However, the final closure timing of the eastern PAO remains contentious. The Permian-Triassic sedimentary sequences in the Wangqing area along the Changchun-Yanji suture zone offer important clues into this final closure. New data on petrology, whole-rock geochemistry, zircon U-Pb geochronology and zircon Hf isotopes of sedimentary rocks from the Miaoling Formation and Kedao Group in Wangqing area provide new insights into the final closure of the eastern end of the PAO. The maximum deposition ages of the Miaoling Formation and Kedao Group have been constrained to the Late Permian (ca. 253 Ma) and early Middle Triassic (ca. 243 Ma), respectively. These sedimentary rocks exhibit similar geochemical characteristics, showing low textural and compositional maturities, implying short sediment transport, with all detrital zircons suggesting their origins from felsic igneous rocks. The εHf(t) values of the Miaoling Formation range from −6.09 to 12.43 and from −2.20 to 7.59 for the Kedao Group, implying these rocks originated from NE China. Considering our new data along with previously published data, we propose that a reduced remnant ocean remained along the Changchun-Yanji suture zone in the early Middle Triassic (ca. 243 Ma), suggesting the final closure of the eastern PAO likely occurred between the latest Middle Triassic and early Late Triassic.

This research aimed to develop biomarkers for estimating ammonia (NH3) emissions from dairy cattle manure over a 15-day in vitro incubation system. To generate different levels of NH3 emissions, the experiment utilized four manure experimental groups: 1 urinary nitrogen (U) to 1 faecal nitrogen (F) ratio (CT), 2 U to 1 F ratio (2U1F), and CT and 2U1F with lignite application (CT + L and 2U1F + L, respectively). The addition of lignite to ruminant manure aimed to enhance environmental sustainability through its beneficial properties. Three biomarkers, nitrogen (N) isotopic fractionation (δ15N), N: potassium (K) ratio, and N: phosphorus (P) ratio, were investigated. Manure δ15N increased linearly when NH3 emission increased in CT and 2U1F groups (R2 = 0.79 and 0.90, respectively; P ≤ 0.001), while manure N: P decreased when NH3 emission increased in CT + L and 2U1F + L groups (R2 = 0.73 and 0.85, respectively; P ≤ 0.001). No useful relationship was found between N: K and NH3 emission, apart from in 2U1F group (R2 = 0.84; P ≤ 0.001). The experiment found manure δ15N and N: P are complementary biomarkers to predict NH3 emissions, from non-lignite and lignite groups, respectively.

Precise pose estimation is crucial to various robots. In this paper, we present a localization method using correlative scan matching (CSM) technique for indoor mobile robots equipped with 2D-LiDAR to provide precise and fast pose estimation based on the common occupancy map. A pose tracking module and a global localization module are included in our method. On the one hand, the pose tracking module corrects accumulated odometry errors by CSM in the classical Bayesian filtering framework. A low-pass filter associating the predictive pose from odometer with the corrected pose by CSM is applied to improve precision and smoothness of the pose tracking module. On the other hand, our localization method can autonomously detect localization failures with several designed trigger criteria. Once a localization failure occurs, the global localization module can recover correct robot pose quickly by leveraging branch-and-bound method that can minimize the volume of CSM-evaluated candidates. Our localization method has been validated extensively in simulated, public dataset-based, and real environments. The experimental results reveal that the proposed method achieves high-precision, real-time pose estimation, and quick pose retrieve and outperforms other compared methods.

The influence of the SNP rs174575 (C/G) within the fatty acid desaturase 2 gene on the levels of long-chain PUFA was determined through statistical meta-analysis. Six databases were searched to retrieve the relevant literature. Original data were analysed using Stata 17·0, encompassing summary statistics, tests for heterogeneity, assessment of publication bias, subgroup analysis and sensitivity analysis. A total of ten studies were identified and grouped into twelve trials. Our results showed that individuals who carried the minor G allele of rs174575 had significantly higher dihomo-γ-linolenic acid levels (P = 0·005) and lower arachidonic acid levels (P = 0·033) than individuals who were homozygous for the major allele. The subgroup analysis revealed that the G-allele carriers of rs174575 were significantly positively correlated with linoleic acid (P = 0·002) and dihomo-γ-linolenic acid (P < 0·001) and negatively correlated with arachidonic acid (P = 0·004) in the European populations group. This particular SNP showed a potential association with higher concentrations of dihomo-γ-linolenic acid (P = 0·050) and lower concentrations of arachidonic acid (P = 0·030) within the breast milk group. This meta-analysis has been registered in the PROSPERO database (ID: CRD42023470562).

The high-altitude balloon proposed in this paper is a long-life balloon carrying a payload through a cable that flies at 20km altitude in near space. A dynamic model of the system, including the thermodynamics of the buoyancy body coupled with a hanging model of the pod, is developed using the Newton–Euler method. The buoyancy body consists of a helium balloon and a ballonet. A differential pressure difference-based altitude adjustment is achieved by tracking the pressure difference at the target altitude. A dynamic simulation of the buoyancy body with a slung pod in autonomous vertical takeoff and altitude regulation processes is presented. The internal thermodynamic variations and pressure differential of the buoyancy body are given. The air mass exchange and blower flow control of the ballonet are validated. The altitude holding error is analysed. The maximum pull force that the cable can withstand is calculated, and the maximum attitude angles of the pod during the ascending and descending processes are depicted. Simulation results provide basic knowledge for the structural design and payload installation of pods.

High-elevation environments present harsh challenges for the pursuit of agropastoral subsistence strategies and relatively little is known about the mechanisms early communities employed to adapt to such locations successfully. This article presents the sequential carbon and oxygen analysis of archaeological caprine teeth from Bangga (c. 3000–2200 BP), which is approximately 3750masl on the Tibetan Plateau. Made visible through this method, intra-tooth variation in isotopic composition allows insights into herding strategies that possibly included the provisioning of livestock with groundwater and agricultural fodder and summer grazing in saline or marsh environments. Such intensive provisioning differs markedly from lower-elevation agropastoralism.

Achieving optimal nutritional status in patients with penetrating Crohn’s disease is crucial in preparing for surgical resection. However, there is a dearth of literature comparing the efficacy of total parenteral nutrition (TPN) v. exclusive enteral nutrition (EEN) in optimising postoperative outcomes. Hence, we conducted a case-matched study to assess the impact of preoperative EEN v. TPN on the incidence of postoperative adverse outcomes, encompassing overall postoperative morbidity and stoma formation, among penetrating Crohn’s disease patients undergoing bowel surgery. From 1 December 2012 to 1 December 2021, a retrospective study was conducted at a tertiary centre to enrol consecutive patients with penetrating Crohn’s disease who underwent surgical resection. Propensity score matching was utilised to compare the incidence of postoperative adverse outcomes. Furthermore, univariate and multivariate logistic regression analyses were conducted to identify the risk factors associated with adverse outcomes. The study included 510 patients meeting the criteria. Among them, 101 patients in the TPN group showed significant improvements in laboratory indicators at the time of surgery compared with pre-optimisation levels. After matching, TPN increased the occurrence of postoperative adverse outcomes (92·2 % v. 64·1 %, P = 0·001) when compared with the EEN group. In the multivariate analysis, TPN showed a significantly higher OR for adverse outcomes than EEN (OR = 4·241; 95 % CI 1·567–11·478; P = 0·004). The study revealed that penetrating Crohn’s disease patients who were able to fulfil their nutritional requirements through EEN exhibited superior nutritional and surgical outcomes in comparison with those who received TPN.

This study investigates the impact of molecular thermal fluctuations on compressible decaying isotropic turbulence using the unified stochastic particle (USP) method, encompassing both two-dimensional (2-D) and three-dimensional (3-D) scenarios. The findings reveal that the turbulent spectra of velocity and thermodynamic variables follow the wavenumber (k) scaling law of ${k}^{(d-1)}$ for different spatial dimensions $d$ within the high wavenumber range, indicating the impact of thermal fluctuations on small-scale turbulent statistics. With the application of Helmholtz decomposition, it is found that the thermal fluctuation spectra of solenoidal and compressible velocity components (${\boldsymbol {u}}_{s}$ and ${\boldsymbol {u}}_{c}$) follow an energy ratio of 1 : 1 for 2-D cases, while the ratio changes to 2 : 1 for 3-D cases. Comparisons between 3-D turbulent spectra obtained through USP simulations and direct numerical simulations of the Navier–Stokes equations demonstrate that thermal fluctuations dominate the spectra at length scales comparable to the Kolmogorov length scale. Additionally, the effect of thermal fluctuations on the spectrum of ${\boldsymbol {u}}_{c}$ is significantly influenced by variations in the turbulent Mach number. We further study the impact of thermal fluctuations on the predictability of turbulence. With initial differences caused by thermal fluctuations, different flow realizations display significant disparities in velocity and thermodynamic fields at larger scales after a certain period of time, which can be characterized by ‘inverse error cascades’. Moreover, the results suggest a strong correlation between the predictabilities of thermodynamic fields and the predictability of ${\boldsymbol {u}}_{c}$.

Two 10-day in vitro experiments were conducted to investigate the relationship between nitrogen (N) isotope discrimination (δ15N) and ammonia (NH3) emissions from sheep manure. In Exp. 1, three different manure mixtures were set up: control (C); C mixed with lignite (C + L); and grape marc (GM), with 5, 4 and 5 replications, respectively. For C, urine and faeces were collected from sheep fed a diet of 550 g lucerne hay/kg, 400 g barley grain/kg and 50 g faba bean/kg; for C + L, urine and faeces were collected from sheep fed the C diet and 100 g ground lignite added to each incubation system at the start of the experiment; for GM, urine and faeces were collected from sheep fed a diet consisting of C diet with 200 g/kg of the diet replaced with GM. In Exp. 2, three different urine-faeces mixtures were set up: 2U:1F, 1.4U:1F and 1U:1F with urine to faeces ratios of 2:1, 1.4:1 and 1:1, respectively, each with 5 replications. Lignite in C + L led to significantly lower cumulative manure-N loss by 81 and 68% in comparison with C and GM groups, respectively (P = 0.001). Cumulative emitted manure NH3-N was lower in C + L than C and GM groups by 35 and 36%, respectively (P = 0.020). Emitted manure NH3-N was higher in 2U:1F compared to 1.4U:1F and 1U:1F by 18 and 26%, respectively (P < 0.001). This confirms the relationship between manure δ15N and cumulative NH3-N loss reported by earlier studies, which may be useful for estimating NH3 losses.