To identify associations between hemoglobin (Hb) concentration in different trimesters, Hb changes and Hb trajectories during pregnancy with risk of preterm birth (PTB) and low birth weight (LBW). A retrospective cohort study was conducted based on 18,980 participants from Haidian District Maternal and Child Health Care Hospital between January 2018 and December 2021. Maternal Hb concentrations were measured at first (0-12weeks), second (13-27weeks), and third trimesters (≥28weeks). Hb was categorized into four groups (<110, 110-119, 120-129, and ≥130 g/L), and Hb changes across different trimesters were calculated. The Latent class growth mixed model was employed to estimate Hb trajectories. Association assessment and dose-response relationship were estimated using multivariate logistic regression and restricted cubic spline. Compared with Hb 110-119 g/L, women with Hb ≥130 g/L significantly increased odds of PTB(OR:1.74,95%CI:1.29,2.31) and LBW(OR:2.31,95%CI:1.65,3.20) during the second trimester. In the third trimester, women with Hb 120-129 g/L showed 26% (OR:1.26,95%CI:1.04,1.51) increased odds of PTB and 72%(OR:1.72,95%CI:1.36, 2.17) increased odds of LBW, while the OR of Hb ≥130 g/L for PTB and LBW were 1.45(95%CI:1.12,1.86) and 2.60(95%CI:1.96,3.43), respectively. Furthermore, Hb changes across different trimesters exhibited a linear increase in odds of PTB and LBW. Women with a decline-sharp-rise trajectory during pregnancy had a 1.48-fold odds of PTB(OR:1.48,95%CI:1.26,1.75), compared to those with a decline-gradual-rise trajectory. In conclusion, elevated Hb (≥120 g/L) in mid-to-late gestation as well as insufficient Hb decline or abnormal Hb trajectory, were associated with increased odds of PTB and LBW. These findings underscore the importance of monitoring Hb during pregnancy.

The citrus industry is vital to regional economies, and scientific land suitability evaluation is essential for spatial optimisation. This study focuses on Tongcheng County in Hubei Province, which exemplifies a typical red soil hilly region that is ecologically vulnerable in southeastern China. This study integrates Geographic Information System (GIS), Analytic Hierarchy Process (AHP), and fuzzy mathematics to develop a comprehensive land suitability evaluation model that balances factor continuity with evaluation uncertainty. Multi-source spatial data (climate, topography, soil) were used to quantify fuzzy membership functions for citrus growth. Subsequently, we conducted a weighted overlay analysis based on this quantification, which resulted in the creation of a citrus cultivation suitability evaluation map for Tongcheng County. The evaluation results categorised the study area into three distinct zones: (1) The Optimal Zone (CAI ≥ 0.75), which comprises 75.8% (857 km2) of the total study area; (2) The Suitable Zone (0.55 ≤ CAI ≤ 0.75), which constitutes 19.6% (222 km2) of the total study area; and (3) The Unsuitable Zone (CAI ≤ 0.55), which represents 4.6% (52 km2) of the total study area. The research findings aligned with the actual conditions in Tongcheng County, thereby confirming the feasibility of the employed research methodology. These outcomes address research gaps and provide a replicable methodological framework for land evaluation in mountainous regions. This approach can be directly utilised in local agricultural spatial planning and the development of land-use policies. It carries substantial practical implications for advancing sustainable agricultural development and revitalising rural areas in mountainous regions.

Maternal Hb and fetal growth change dynamically throughout pregnancy. We examined the associations of time-specific Hb levels and Hb trajectories with fetal biometrics and adverse birth outcomes. This prospective study included 6844 pregnant women (mean age 26·6 (sd 3·7) years) from the Tongji-Huaxi-Shuangliu Birth Cohort. Hb levels were measured at four periods: early (6–12 gestational weeks), middle (13–27), middle-late (28–32) and late pregnancy (33–37). Fetal biometrics were assessed by ultrasound from middle to late pregnancy. Birth outcomes were obtained from medical records, including small for gestational age (SGA), low birth weight (LBW) and preterm birth. Three Hb trajectories were identified: consistent decline (Trajectory 1), consistently low (Trajectory 2) and increase from middle-late pregnancy (Trajectory 3). Compared with Trajectory 1, Trajectory 3 was associated with lower estimated fetal weight (β, −0·54; 95 % CI −0·99, −0·09) and abdominal circumference (β, −0·21; 95 % CI −0·40, −0·01) in late pregnancy and higher umbilical artery resistance index across pregnancy (β, 0·65; 95 % CI 0·31, 1·00). Trajectory 3 was also associated with higher risk of LBW (OR, 1·57; 95 % CI 1·09, 2·26). In middle-late pregnancy, higher Hb (≥ 130 g/l) was associated with higher risks of LBW (OR, 2·26; 95 % CI 1·08, 4·25) and preterm birth (OR, 2·03; 95 % CI 1·12, 3·44) compared with the reference (110–129 g/l). Elevated maternal Hb from middle-late pregnancy onwards may be associated with lower fetal weight and increased risk of LBW. Dynamic monitoring of maternal Hb may facilitate targeted nutritional management in pregnant women.

We demonstrate the effective compensation of carrier envelope phase (CEP) slip in a high-power femtosecond laser using a machine-learning (ML)-based control scheme. The compensation is achieved through fine dispersion tuning guided by a recurrent neural network (RNN) that predicts the temporal evolution of CEP slip, combined with a reinforcement-learning (RL) scheme that determines the optimal corrective actions. With this RNN+RL framework, the integrated CEP noise is reduced by more than a factor of two compared with a conventional proportional–integral–derivative controller. The proposed ML-based control methodology provides a versatile tool for stabilizing and optimizing various parameters in high-power laser systems.

Accurately predicting the melting of encapsulated phase-change materials (PCMs) is essential for optimising thermal energy storage (TES) systems, especially when natural convection dominates at high-Rayleigh-number conditions. This study conducts a pore-scale study on the constrained melting of spherical PCM capsules, using a multiple-relaxation-time lattice Boltzmann method for the thermal flow, combined with an immersed boundary method for the solid–liquid interface. A novel ray-based phase identification scheme is introduced to resolve concave phase boundaries under strong convection, thereby improving the model accuracy in high-Rayleigh-number simulations. The model is validated against analytical, numerical and experimental benchmarks, showing superior capability and accuracy. For constrained PCM melting, the melting behaviour is reproduced, and effects of boundary temperature ($T_b$), initial subcooling ($\Delta T_s$) and capsule size ($l_z$) are examined with a fixed Prandtl number ($\textit{Pr}=59.76$). Higher $T_b$ accelerates melting, whereas $\Delta T_s$ has only minor effects. Reducing $l_z$ shortens the melting time due to the smaller PCM volume, but increases the dimensionless melting time by suppressing natural convection and shifting the melting process from convection- to conduction-dominated regimes. Accordingly, a critical capsule size $l_{z,c}$ is identified, below which conduction governs the melting process. A unified Rayleigh number of $Ra_c\approx 1.9\times 10^4$ is obtained for all $l_{z,c}$ under varying $T_b$, serving as a universal threshold between the two melting regimes. For predicting liquid fraction evolutions in both conduction- and convection-dominated regimes, two empirical correlations are proposed via dimensional analysis. These findings advance the understanding of constrained PCM melting and support TES system optimisation across diverse operating conditions.

African swine fever (ASF) is a highly contagious animal disease caused by African swine fever virus (ASFV). It is listed by the World Organization for Animal Health (WOAH) as an animal disease subject to statutory reporting. ASFV, a large, enveloped double-stranded DNA virus with high genomic complexity, exhibits a case fatality rate of up to 100%, posing a significant threat to the global pig industry and food safety. To date, the absence of a safe commercial ASFV vaccine primarily stems from challenges in identifying immunogenic viral antigens, insufficient characterization of ASFV pathogenesis, and limited understanding of the virus’s immune evasion mechanisms. Here, we review the pathogenic characteristics (morphological structure, clinical symptoms, and epidemiological characteristics), molecular biological characteristics, and infection mechanism of ASFV, as well as the immune response mechanism, vaccine research, and the latest information on ASFV in other areas. This review will be in favour of understanding the current state of knowledge of ASF and developing effective vaccines to control this disease.

Design Science is the discipline that studies the creation of artifacts – products, services, and systems and their embedding in our physical, virtual, psychological, economic, and social environments. This editorial is a collective effort of the Design Science Journal’s editorial board members, past and present. The journal’s inaugural 2015 editorial, “Design Science: Why, What and How,” reflected the thoughts and vision of that first editorial board for the new journal and the discipline it represented. The present contribution offers the reflections of editors who served the journal in the past 10 years. The individual contributions were not primed and are presented here unedited for conformity or consistency. Differently from the 2015 editorial, there is no effort to synthesize the individual contributions, leaving the task to our readers, who can draw their own conclusions about the Design Science Journal and community accomplishments to date, and the challenges ahead.

The featured article introduces a much-needed theoretical framework for developing a dual-process model of life history calibration. This model accounts for the counterbalancing effects of individual energetic stresses and extrinsic mortality threats of the environment. This framework also reinstates resource availability – a key determinant of energetic conditions – into life history research, resolving its previous exclusion due to similar countervailing influences relative to extrinsic mortality threats.

High-redshift protoclusters are crucial for understanding the formation of galaxy clusters and the evolution of galaxies in dense environments. The James Webb Space Telescope (JWST), with its unprecedented near-infrared sensitivity, enables the first exploration of protoclusters beyond $ z \gt 10 $. Among JWST surveys, COSMOS-Web Data Release 0.5 offers the largest area ($\sim 0.27$ deg$^2$), making it an optimal field for protocluster searches. In this study, we searched for protoclusters at $ z \sim 9-10 $ using 366 F115W dropout galaxies. We evaluated the reliability of our photometric redshift by validation tests with the JADES DR3 spectroscopic sample, obtaining the likelihood of falsely identifying interlopers as $\sim25\%$. Overdensities ($\delta$) are computed by weighting galaxy positions with their photometric redshift probability density functions, using a 2.5 cMpc aperture and a redshift slice of $\pm 0.5$. We selected the most promising core galaxies of protocluster candidate galaxies with an overdensity greater than the 95th percentile of the distribution of 366 F115W dropout galaxies. The member galaxies are then linked within an angular separation of 7.5 cMpc to the core galaxies, finding seven protocluster candidates. These seven protocluster candidates have inferred halo masses of $ M_{\text{halo}} \sim 10^{11}\,{\rm M}_{\odot} $. The detection of such overdensities at these redshifts provides a critical test for current cosmological simulations. However, confirming these candidates and distinguishing them from low-redshift dusty star-forming galaxies or Balmer-break galaxies will require follow-up near-infrared spectroscopic observations.

Previous studies highlighted the health benefits of coffee and tea, but they only focused on the comparisons between different consumptions. Consequently, the association estimate lacked a clear interpretation, as the substitution of beverages and distribution of doses were not explicitly prescribed. We focused on the ‘relative association’ to ascertain the optimal consumption strategy (including total intake and optimal allocation strategy) for coffee, tea and plain water associated with decreased mortality. Self-reported coffee, tea and plain water intake were used from the UK Biobank. Within a compositional data analysis framework, a multivariate Cox model was used to assess the relative associations after adjusting for a range of potential confounders. The lower mortality risk was observed with at least approximately 7–8 drinks/d of total consumption. When the total intake > 4 drinks/d, substituting plain water with coffee or tea was linked to reduced mortality; nevertheless, the benefit was not seen for ≤ 4 drinks/d. Besides, a balanced consumption of coffee and tea (roughly a ratio of 2:3) associated with the lowest hazard ratios of 0·55 (95 % CI 0·47, 0·64) for all-cause mortality, 0·59 (95 % CI 0·48, 0·72) for cancer mortality, 0·69 (95 % CI 0·49, 0·99) for CVD mortality, 0·28 (95 % CI 0·15, 0·52) for respiratory disease mortality and 0·35 (95 % CI 0·15, 0·82) for digestive disease mortality than other combinations. These results highlight the importance of the rational combination of coffee, tea and plain water, with particular emphasis on ensuring adequate total intake, offering more comprehensive and explicit guidance for individuals.

Visual exploration is a task in which a camera-equipped robot seeks to efficiently visit all navigable areas of an environment within the shortest possible time. Most existing visual exploration methods rely on a static camera fixed to the robot’s body to control its own movements. However, coupling the orientation of camera with robot’s body limits the extra degrees of freedom to obtain more visual information. In this work, we adjust the camera orientation during robot motion by using a novel camera view planning (CVP) policy to improve the exploration efficiency. Specifically, we reformulate the CVP problem as a reinforcement learning problem. However, two new challenges need to be addressed: 1) determining how to learn an effective CVP policy in complex indoor environments and 2) figuring out how to synchronize it with the robot motion. To solve the above issues, we create a reward function considering factors such as exploration area, observed semantic objects, and the motion conflicts between the camera and the robot’s body. Moreover, to better coordinate the policies of the camera and the robot’s body, the CVP policy takes the body actions and the egocentric 2D spatial maps with exploration, occupancy, and trajectory information into account to make motion decisions. Experimental results show that after using the proposed CVP policy, the exploration area is expanded by 21.72% and 25.6% on average in the small-scale indoor scene with few structured obstacles and large-scale indoor scene with cluttered obstacles, respectively.