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The temperature instability of piezoelectric properties in KNN-based ceramics has been a major bottleneck impeding them toward practical applications. Herein, we prepared multilayer composite ceramics using two kinds of textured thick films with different polymorphic phase transition temperatures. The composite ceramics simultaneously exhibited outstanding piezoelectric properties (d 33 = 400 ± 20 pC/N; S = 0.18 % at 40 kV/cm) and piezoelectric temperature stability (d 33 and S variation within 5 % and 2 %, respectively over the temperature range of room temperature to 180 °C). The synergistic contribution of slightly fluctuating ε r ·P r values, the robust phase and domain structures, and the complementary effect of piezoelectric fluctuation between the two composite layers led to the outstanding temperature stability of piezoelectric properties. Furthermore, the piezoelectric energy harvester produced using KNN-T 1 /T 2 composite ceramics exhibits a large power density of 5.4 mW/cm3. These results prove great potential of KNN-T 1 /T 2 ceramics in high temperature piezoelectric devices. [ABSTRACT FROM AUTHOR]

The rapid and efficient elimination of CO by-products in mining and tunneling blasting operations poses a significant challenge. In this study, an in-situ synchronous elimination method (ISSE) for CO was proposed based on catalytic oxidation principles by deploying nano-scale catalysts in boreholes. Initially, Co 3 O 4 catalyst was prepared and characterized. Subsequently, the catalyst was processed into a packing bag for in-situ CO elimination within boreholes. Field experiments were conducted at the Lujiatuo coal mine to verify the ISSE technology, and the effects of packaging materials, charging configuration, catalyst concentration, and length-to-diameter ratio of the packing bag on CO elimination efficiency were investigated. The experimental results revealed that when utilizing a nylon water cannon mud bag and charging 35 g of catalyst at the orifice, the peak CO concentration decreased from 735 ppm to 243 ppm, achieving an elimination efficiency of 66.9 % and reducing CO exceedance time by 28.5 %. These findings indicate that the ISSE technology surpasses existing techniques in terms of CO eliminating during mining and tunneling operations rapidly and effectively, that are expected to find widespread application in blasting production. [Display omitted] • An in-situ synchronous elimination (ISSE) method is proposed. • The influencing factors of ISSE method has been investigated. • The CO elimination rate achieved 66.9 % in field tests. [ABSTRACT FROM AUTHOR]

Ultrahigh molecular weight polyethylene (UHMWPE) is widely used in industrial, military, and civilian applications owing to its excellent properties. However, the high entanglement degree of UHMWPE melt leads to great difficulties in diffusion and crystallization, which causes poor interfacial strength and mechanical properties of pre-melted UHMWPE, restricting its high-quality processing and high-value recycling. To this end, we propose a rapid interface stitching technology with a pulsed vibration force driving free volume homogenization and chain segment orientation. For the first time, pulse vibration molding (PVM) is applied for the hot-pressing of pre-melted UHMWPE, successfully realizing the interface stitching and product strengthening. Compared with those of the convention molding (CM) samples at 180 °C, the interfacial adhesive fracture toughness of the adhesive samples processed using PVM is increased by 105.9 % and the work of tension of the recycled samples processed using PVM is also increased by 49.09 %, respectively. The fracture strength of the PVM recycled samples at 180 °C reaches 91.77 % of that of the pure samples and the yield strength is also higher, which significantly enhances the recycling of UHMWPE. The characterization results of the interfacial molecular chain entanglement and crystallization show that PVM strengthens the molecular chain diffusion and induces crystallization by promoting the orientation of chain segments, especially at the interface. Consequently, we believe that the proposed technology can play a significant role in the UHMWPE processing methods that involve surface healing. [Display omitted] • A rapid interface stitching technology with a pulsed vibration force driving free volume homogenization and chain segment orientation was proposed. • The processing-structure-properties relationships of pre-melted UHMWPE at different processing conditions were established. • The response law of the interfacial entanglement and crystal structure is proposed. • The interfacial strengthening and the high-value recycling of UHMWPE are realized at a low time cost. [ABSTRACT FROM AUTHOR]

Land allocation and pricing substantially impact carbon emissions, yet their nonlinear effects remain understudied. This research employs ensemble machine learning models to examine the complex relationships between land conveyance and per capita carbon emissions across 104 major Chinese cities from 2009 to 2017. The results reveal that keeping industrial land allocations below 35% helps reduce emissions, whereas higher ratios increase emissions. Allocating over 8% and 33% to business and public land respectively also lowers emissions. Land prices demonstrate heterogeneity – a higher residential land price promotes efficiency only when its relative price level to the comprehensive land price is below 1.1. The findings highlight customised policies balancing development and emissions reduction, based on local conditions and development stages, can forge sustainable pathways. Overall, the nonlinear modelling quantifies nuanced emissions responses to land allocation thresholds and strategic pricing incentives. By considering these complex mechanisms, urban planners can devise tailored strategies that simultaneously nurture growth and curb emissions. The novel method and evidence-based insights contribute to planning support systems and sustainable policy-making. • Estimating land conveyance's nonlinear effects on emissions by ensemble learning. • Allocation and price of land display nonlinear relationships on carbon emissions. • Land use within suitable ratios and pricing incentives promotes carbon reductions. [ABSTRACT FROM AUTHOR]

• An inspection network with a dynamic training method is proposed. • Dynamic training method with multi-branch block to enhance representation ability. • Activate features in various channels for specific tasks to assist downstream multitasking. • Pixel-wise soft weights with Gaussian shape priors are designed to supervise training. • The inference speed and prediction accuracy are increased. High-precision identification and real-time localization for irregular-shaped steel surface defects are crucial for shipbuilding quality control. Although traditional lightweight networks enable real-time defect inspection, the incurred model cannot achieve precise inspection for the defects with large variations in aspect ratios of ship plates. This paper proposes a lightweight inspection network with a dynamic feature extractor and task alignment detection head (INDT) for multi-class steel plate surface defects to address this obstacle. A dynamic structure expansion training strategy based on a re-parameterization multi-branch block is constructed to achieve real-time inspection containing multi-scale information. Furthermore, fed with multi-scale information, the task alignment head with a preprocess for multi-task to concentrate task-oriented features into specific channels. Besides, a soft-weighted sample assignment algorithm with dynamic priors to irregular defects is developed to supervise high-precision model training. The experiments show that the INDT achieves higher precision among all the benchmark methods with lossless accelerating inference. [ABSTRACT FROM AUTHOR]

Although different types of drugs are available for postmenopausal osteoporosis, the limitations of the current therapies including drug resistances and adverse effects require identification of novel anti-osteoporosis agents. Here, we defined that norlichexanthone (NOR), a natural product, is a ligand of estrogen receptor-alpha (ER α) and revealed its therapeutic potential for postmenopausal osteoporosis. We used mammalian-one hybrid assay to screen for ER α modulators from crude extracts of several plant endophytes. As a result, NOR purified from the extract of endophyte ARL-13 was identified as a selective ER α modulator. NOR directly bound to ER α with an affinity in nanomolar range, revealing that it is a natural ligand of ER α. NOR induced osteoblast formation in MC3T3-E1 precursor cells. Conversely, NOR inhibited receptor activator of nuclear factor-kappa B ligand (RANKL)-induced osteoclast formation in both RAW264.7 macrophages and mouse primary monocytes. Mechanistically, NOR inhibited RANKL-induced association of ER α and TRAF6 to prevent ER α -mediated TRAF6 activation via Lys63-linked ubiquitination. Importantly, NOR exhibited potent anti-osteoporosis efficacy in an ovariectomized mouse model. Comparing to estrogen, NOR was of much less capability in stimulating endometrial hyperplasia and promoting mammalian cancer cell proliferation. Taken together, our study identified NOR as a natural and high affinity ligand of ER α with substantial anti-osteoporosis but less estrogenic activity. Image 1 [ABSTRACT FROM AUTHOR]

• The clinical and magnetic resonance imaging features of PSP were analyzed. • FDG positron emission tomography brain imaging was performed. • Characteristics of cerebral glucose metabolism were explored to increase accuracy. • Studying the clinical and MRI features of PSP can be helpful in the diagnosis. Progressive supranuclear palsy (PSP) is a kind of neurodegenerative disease. In the present study, the clinical and magnetic resonance imaging (MRI) features of PSP were analyzed, and fluorodeoxyglucose positron emission tomography (FDG PET) brain imaging was performed to preliminarily explore the characteristics of cerebral glucose metabolism, so as to make a more accurate and differential diagnosis. In accordance with the diagnostic criteria, the clinical symptoms, the unified Parkinson's disease rating scale (UPDRS), the mini-mental state examination (MMSE), the activities of daily living (ADL), and the magnetic resonance image (MRI) of 19 patients with PSP were examined and FDG-PET brain imaging was performed. Visual analysis and statistical parameter diagram were used to analyze the characteristics. Concerning the clinical characteristics, 84.21% of the patients with PSP had early recurrent falls, 63.15% had vertical supranuclear ophthalmoplegia, and 47.37% had symptoms of dementia. The MRI scans showed that 57.89% of the patients had a "hummingbird" sign in the middle sagittal position. The FDG-PET imaging indicated that glucose metabolism in the midbrain and frontal lobe was decreased in patients with PSP. Studying the clinical and MRI features of patients with PSP can be helpful in the diagnosis, especially as some signs could be found in the early stage by FDG PET, enhancing early diagnosis and differentiation. [ABSTRACT FROM AUTHOR]

Recent advances in manufacturing technologies witness the digital transformation of traditional manufacturing systems. Digital Twin is one technology being studied and applied in various aspects of production. Reconfigurable Manufacturing System (RMS), as an advanced manufacturing paradigm, proposes new challenges for Digital Twin application. More flexibility is required for Digital Twin to adapt to the dynamic rearrangement of RMS, supporting the full lifecycle of the systems. But how this can be achieved is yet to be explored. Against the background, this paper aims to realise Digital Twin-driven RMS reconfiguration. By integrating and clarifying the prerequisites of Cyber–Physical System (CPS) and Manufacturing Execution System (MES), Digital Twin Monitoring and Simulation Integrated Platform (DTMSIP) is proposed for RMS. With transparent communication and decoupled structure, DTMSIP can perform real-time online supervision and high-fidelity offline simulation of the production of RMS. Accordingly, the method of implementing DTMSIP is introduced by building Digital Twin mapping based on Twinning Entity (TE). Moreover, how the CPS-MES-DTMSIP architecture serves the RMS evolution is discussed by proposing the procedure of DTMSIP-driven RMS reconfiguration. To validate the proposed procedure, DTMSIP software is realised for an existing RMS using Unreal Engine 4 (UE4). Through DTMSIP monitoring and simulation of planned RMS configurations, the performance of planned configurations is calculated, and the optimised configuration is obtained. [ABSTRACT FROM AUTHOR]

• A three-dimensional thermal-fluid-mechanical coupling model was established. • A novel structure tube was proposed considering comprehensive factors. • The novel structure is applicable for high non-uniform heat flux. • The proposed tube can reduce maximum temperature and thermal stress by 25 K and 29%. Supercritical CO 2 has been a promising alternative working medium in coal-fired power plants, high-temperature solar power systems, and fuel cells. In these cases, supercritical carbon dioxide is in a round tube under high temperature and pressure with high non-uniform heat flux. In this study, a novel structure tube is proposed by optimizing the coupling between circumferential heat flux and tube thickness on the heat-absorbing side to improve its comprehensive performance. A thermal-fluid-mechanical coupling model was developed. The novel structure can reduce maximum temperature and temperature difference effectively for the half-cycle uniform and non-uniform heat flux. The maximum thermal stress decreased by 23 and 29% and the maximum temperature decreased by 29.6 and 24.8 K for half-cycle uniform and non-uniform heat flux when the Eccentricity increased from 0 to 0.4. A comparison was made between the proposed structure, thin-walled tube, and traditional tube. Results showed that the performance of the proposed tube is the best. The maximum temperature decreases by approximately 55 K compared with the traditional and thin-walled tubes. The maximum equivalent stress of proposed tube is the smallest. Furthermore, the structure will improve the safety and economics of the tube. [ABSTRACT FROM AUTHOR]

Fueled by the recent proliferation of energy-efficient and energy-autonomous or self-powered nanotechnology-based wearable smart systems, human motion intention prediction (MIP) plays a critical role in a wide range of applications, such as rehabilitation and assistive robotics, to enable more natural, biologically inspired, and seamless integrated motion assistance task execution, including for elders and physically impaired patients. With the increasing complexity of human-machine interactions and the need for personalized assistance, there is a growing demand for real-time and accurate MIP systems. This review aims to provide a comprehensive understanding of the interdisciplinary field of MIP, under the logic of its physiological foundations, by discussing state-of-the-art sensing technologies, including brain-computer interfaces (BCI), electromyography (EMG), and motion sensors, alongside the relevant data processing techniques and decoding algorithms. We emphasize the importance of fostering collaboration among scholars from different domains to capture the intricate dependencies between the set of stimuli and responses of the central nervous system and the activation of the complex set of muscles and joints that produce human motion. By offering insights into the recent advancements and future prospects of the field, this review seeks to stimulate further research and innovation in the rapidly evolving area of human motion intention prediction, for a future where technologies understand and respond to complex human intentions patterns, anticipating their needs. [Display omitted] • Wearable sensors and AI data processing for Motion Intention Prediction (MIP). • Multimodal sensing from Brain to human motion task actuation via the CNS and PNS. • Materials and devices for brain-computer interface, electromyography, and IMUs. • Latest breakthroughs in transfer learning and explainable AI for MIP data decoding. • Exploring challenges and applications of biocompatible, efficient, and ethical MIP. [ABSTRACT FROM AUTHOR]

Accurate snow depth (SD) information at global scales is highly important for validation and calibration of hydrological model, snowmelt runoff prediction, and global water supplies estimation. Passive microwave remote sensing has been proven to be an efficient method to detect SD at regional or global scales. However, numerous heterogeneous snow property parameters and forest parameters affect the microwave radiation characteristics, which is a great challenge for SD retrieval. In this study, a novel SD retrieval algorithm that comprehensively considered the multiple sensitive factors was developed at global scales by coupling the Microwave Emission Model of Layered Snowpacks (MEMLS) model and machine learning (ML) technique. The exponential correlation length (Pec), snow density (ρ), and forest cover fraction (f f o r e s t) are three sensitive parameters for retrieving SD from passive microwave observations based on the extended Fourier amplitude sensitivity test (EFAST) algorithm. The key parameter Pec , which characterizes the snow microstructure information inside snowpacks, was optimized by minimizing the brightness temperature between satellite observations and microwave radiative transfer model simulations by integrating the MEMLS, forest impact, and atmospheric radiation attenuation. The random forest (RF) ML technique was used, and the optimal combination of independent variables was selected by correlation heatmap and importance ranking of variables. Six selected essential parameters, including Pec , vertical polarized brightness temperature differences between 18.7 and 36.5 GHz, ρ from ERA5, f f o r e s t , elevation, and longitude together with target SD output were used to train the RF model. Seventy percent of the entire data at meteorological stations were randomly chosen to train the RF model, and the remaining 30% were adopted as the validation data from 2013 to 2020 to ensure the mutual independence of training data and validation data. Compared with independent in situ measurements, the overall RMSE of SD is 13.6 cm, which was noticeably lower than those of the international SD remote sensing products and reanalysis datasets, including AMSR2 (36.2 cm), GlobSnow 3.0 (25.3 cm), ERA5 (38.2 cm), and ERA5-land (29.9 cm). The results demonstrated that the new proposed algorithm, by coupling the MEMLS model with the ML technique, could significantly enhance the overall performance in SD retrieval at global scales. • A novel method to retrieve snow depth (SD) at global scales is proposed by considering snow metamorphism and forest influence. • Exponential correlation length (Pec), snow density, and forest cover fraction are three sensitive parameters for retrieving SD from passive microwave observations. [ABSTRACT FROM AUTHOR]

As urbanization and societal needs grow, the demand for inclusive, efficient, and sustainable underground public transportation has increased, necessitating a greater focus on enhancing passengers' thermal comfort. However, limited research has addressed the subway station environment, and restricted datasets are challenging to build an accurate nonlinear model. Therefore, this study proposes a thermal comfort data generation algorithm based on 5161 measured data via the Variational Autoencoder (VAE) framework to break data collection constraints and increase thermal comfort model accuracy. By employing a gradient boosting algorithm on the extensively generated dataset, this study investigated factors influencing thermal comfort in subway stations, emphasizing individual gender differences and dynamic environmental variables. The VAE model enhanced the thermal comfort dataset, resulting in a 29% and 35% increase in male and female test set accuracy, respectively. The study suggests that males with a BMI over 27 were more sensitive to temperature. As for the gender-friendly environmental features, people feel most comfortable at temperatures between 24 and 29 °C and relative humidity levels between 20% and 70%. Additionally, the study reveals that a stable environment promotes enhanced thermal comfort for passengers. These insights contribute to developing sustainable, equitable, and user-friendly underground public transportation systems. • Generated model improved thermal comfort model accuracy by 29% and 35%. • 24–29 °C is a comfortable temperature domain for both females and males. • Relative humidity between 20% and 70% is comfortable for both genders. • Males are heat-sensitive, while females are cold-sensitive. • Stable environments enhance thermal comfort in subways. [ABSTRACT FROM AUTHOR]

The emergence of urban big data is transforming the existing research paradigms in urban studies. New theories and analytical methods are required to meet the methodological challenges. This paper empirically compares a data-driven approach and an urban-system-model approach through a case study of modelling the commuting patterns in Beijing. For the data-driven approach, the novel location-based-services (LBS) data are explored to identify the employment-residence location of the service users. For the modelling approach, a spatial equilibrium model is calibrated for base year 2010 and is used to simulate the commuting patterns for Beijing 2015 based on exogenous development projections. The results of the two approaches are then compared against the benchmark statistics for Beijing 2015. The comparison shows that the LBS data perform better in detecting residence locations than employment locations. The model prediction fits better with the benchmark, while the errors of the LBS data tend to vary significantly across space. For amplifying the LBS sample data to represent the full population, uniform scale factor thus should be avoided. In addition, the ineffectiveness of representing short-distance commuting for the LBS data is revealed by the comparison with the model predicted flows. In light of the strength and weakness of the respective approach, the prospect of a collaborative use of big data and urban system models is explored in the conclusion. [ABSTRACT FROM AUTHOR]

Al x CrFeNi multi-component alloys were fabricated by vacuum arc-melting to study the effect of homogenization on microstructures of as-cast and homogenized alloys using X-ray diffraction, scanning electron microscopy and transmission electron microscopy. Besides, electrical resistivity and thermal conductivity of as-cast and homogenized alloys were also investigated. The results showed that no obvious differences of crystal structures and phase compositions between these as-cast and homogenizing alloys were presented. However, transmission electron microscope indicated that the ordered B2 NiAl intermetallic nanoparticles, which were distributed on the disordered BCC [Fe, Cr] solid solution of as-cast Al 1.2 CrFeNi alloy, disappeared after homogenization. For Al x CrFeNi alloys, the electrical resistivity increased with Al addition and dramatic increase of thermal conductivity was presented with increasing the homogenization temperatures. However, homogenization could decrease both electrical resistivity and thermal conductivity of as-cast alloys. The main factors influencing the changes of electrothermal properties of Al x CrFeNi alloys were the electron scattering affected by crystal defects, phase boundaries and temperatures. [ABSTRACT FROM AUTHOR]

• The finite-time and fixed-time bipartite consensus of MASs over signed graph while considering both leaderless and leader-following MASs are investigated. • In contrast to the existing methods to address FNTBC and FXTBC, the impulsive control was employed, which has a better performance in convergence speed and less state information transmission. • Some sufficient criteria and the settling time which are based on the proposed impulsive control protocols are derived. This paper studies the finite-/fixed-time bipartite consensus (FNTBC and FXTBC) of multi-agent systems (MASs) over signed graph via discontinuous impulsive control while considering both leaderless and leader-following MASs. In contrast to the existing methods of FNTBC and FXTBC, the impulsive control has a better performance in convergence speed and less state information transmission, which is more practical and flexible in real applications. To realize FNTBC and FXTBC for leaderless and leader-following MASs, a class of distributed impulsive control protocols is proposed. Then by utilizing impulsive control theory and finite-/fixed-time stability theory, some sufficient criteria and the settling time which are based on the proposed impulsive control protocols for FNTBC and FXTBC for leaderless and leader-following MASs are derived. It has been shown that the settling time for FNTBC depends on initial conditions of systems, while this limitation is removed for FXTBC. Finally, the proposed impulsive protocols are validated by some simulations, separately. [ABSTRACT FROM AUTHOR]

Zebrafish irf2bp2a , an ortholog of human IRF2BP2 , is specifically expressed in the developing liver at growth stage. As a multi-functional protein, the role of irf2bp2a during hepatogenesis remains unclear. Here we take advantage of an irf2bp2a knockout line to show that the deficiency of irf2bp2a can induce apoptosis of hepatic cells through aberrant p53 activation at the early stage of embryonic development. Mechanistic studies reveal that within the IRF2BP2 -null hepatic cells, more MDM2 molecules, the E3 ubiquitin ligase of P53, can be sequestrated into the IRF2-MDM2 complex, which in turns stabilizes P53 protein. Moreover, irf2bp2a is demonstrated as a direct downstream target of c/ebpα. Thus, a C/ebpα-Irf2bp2a-P53 axis controls liver development in zebrafish. Overall, our findings indicate a stage-specific role of irf2bp2a on liver organogenesis by regulating p53 pathway. • The size of the developing liver is reduced in irf2bp2a -deficient embryos. • Deficiency of irf2bp2a leads to decreased proliferation and enhanced apoptosis of liver cells • P53 activation leads to the defective liver development • A C/ebpα-Irf2bp2a-P53 axis controls liver development in zebrafish [ABSTRACT FROM AUTHOR]

Sm 3+ and Eu 3+ co-doped CaWO 4 phosphors were synthesized by the solid-state reaction. The phase composition and photoluminescence properties of as-synthesized samples were characterized using X-ray power diffraction (XRD) and photoluminescence spectra (PL). The results show that their excitation spectra present strong absorption at 393 nm near-UV light and 465 nm blue light, which match well with commercial LED chips. The maximum emission peak excited under 393 nm was observed at about 615 nm due to the 5 D 0 → 7 F 2 transition of Eu 3+ ions. In the Eu 3+ and Sm 3+ co-doped system, the intensity of the main emission peak ( 5 D 0 → 7 F 2 transition of Eu 3+ ions) was effectively strengthened because of the energy transition from Sm 3+ to Eu 3+ . In addition, the doping concentration of Eu 3+ and Sm 3+ were optimized. [ABSTRACT FROM AUTHOR]

• Plant growth is constrained by soil space. • Plant responses to soil space are independent of functional groups. • Plant responses to soil space are species-specific and size-dependent. • Plant species performed better in deeper vertical space. • Plant species responded similarly to horizontal space. Plant growth is constrained not only by the availability of soil nutrients, but also by the physical underground space where it grows. However, few studies have considered the effect of dimensional variations of soil space on the growth of a large set of plants and whether the effect differs among plant functional groups. We grew each of 19 common grassland plant species from three functional groups (grasses, legumes, and forbs) in three types of soil space [a cylindrical space of 15 cm in diameter and 15 in height (D15H15), a cylindrical space of 30 cm in diameter and 15 cm in height (D30H15), and a cylindrical space of 15 cm in diameter and 60 cm in height (D15H60)] with the same amount of essential nutrients. Irrespective of plant functional groups, plant species performed better in the soil with deeper vertical space (D15H60) than in the soil with shallower vertical space (D15H15 and D30H15) in terms of root biomass. Shoot mass and/or total mass in the different types of soil space varied with species. Within each functional group, species with larger body sizes tended to accumulate more shoot mass in soils with deeper vertical space (D15H60), while those with smaller body sizes did not respond differently. These results suggest that the dimensional variations of soil space, especially variations in the vertical dimensions, can be a key factor influencing the performance of plant species. [ABSTRACT FROM AUTHOR]

Recently, the contamination and synergistic impact of aflatoxin B1 (AFB1) and bacterial endotoxin (lipopolysaccharide, LPS) have attracted people's attention. Non-toxic or non-lethal dose of AFB1 with the synergy of LPS could cause serious consequences on animal health. This study aimed to evaluate the effects of glucose oxidase (GOD) and Bacillus subtilis (BS) on growth performance, feed conversion rate (FCR), liver function indices, intestinal mucosal barrier, enterotoxin and visceral lesions in broilers exposed to AFB1 and LPS. One-day-old yellow feather broilers (n = 240) were randomly allocated into 4 groups, with 10 chicks per cage in 6 replicates for each group, including the control group (CON), the infected group (INF) (AFB1 100 μg/kg + LPS 1 mg/bird), GOD group (INF + GOD 400 U/kg), and BS group (INF + BS 2 × 10^10 colony forming units/kg). The animal experiment lasted for 21 days, during which the broilers were raised and vaccinated depending on the Yellow Feather Broiler Breeding Management Manual. Our results showed that the exposure to AFB1 and LPS reduced the final weight (FW), average daily gain (ADG), and average daily feed intake (ADFI) of the poultry, and it increased the FCR (P < 0.05). Moreover, liver functions (aspartate aminotransferase AST, alanine transaminase ALT, total protein TP, albumin ALB, and liver lesion score) and intestinal functions (diamine oxidase, endotoxin, and intestinal lesion scores) were equally impaired (P < 0.05). The addition of GOD or BS in broiler diets counteracted the toxins-exhibited impacts on broiler growth performance and FCR (P < 0.05). In addition, the main contribution of GOD was observed in improving broiler liver functions (P < 0.05), while the principal contribution of BS was reflected in improving intestinal functions of the broilers (P < 0.05), the level of which was recovered back to that in the control group (P > 0.05). Overall, GOD and BS introduced different benefits on improving liver anti-oxidative stress and protecting the intestinal mucosal barrier, respectively. These differences might have been resulted from their own different mechanisms of action, but the combination of GOD and BS is promising to be applied in diets for protecting poultry from toxins-induced illnesses. • The exposure to aflatoxin B1 (AFB1) and lipopolysaccharide (LPS) reduced the growth performance, liver functions and intestinal functions of yellow feather broilers. • The addition of glucose oxidase (GOD) or bacillus subtilis (BS) in broiler diets counteracted the toxins-induced impacts on broiler growth performance. • The main contribution of GOD was observed in improving broiler liver functions. • The principal contribution of BS was reflected in improving intestinal functions of the broilers. • The combination of GOD and BS is promising to be applied in diets for protecting poultry from toxins-induced illnesses. [ABSTRACT FROM AUTHOR]

The proportion of buildings occupying underground space has increased with three-dimensional urban development. Thermal comfort is crucial to the design of underground spaces and plays an important role in the optimization of building environment controls. Owing to limitations in recording various practical environmental parameters, it is difficult to access large data and further to establish an accurate forecasting model for the thermal comfort of an underground space. This paper addresses the problem from the perspective of data enhancement. A model for generating underground space data based on a variational autoencoder is proposed. The model maps data of the thermal comfort of an underground space to a highly compressed latent layer space and generates data in an unsupervised manner. The forecasting models were trained using the generated data, resulting in accuracy improvements of 41.34%–45.31%. Hence, the proposed generative model can learn effective real data features. The results also demonstrate that the adjustment of ventilation is more effective than the adjustment of the temperature and relative humidity in improving the thermal comfort of an underground space. The findings of this research will provide better thermal comfort evaluation for the operational management of building environment in underground spaces. • A thermal comfort data generative model expands limited real data sets. • R-squared of a forecasting model based on generated data is substantially higher. • The generated forecasting models have a lower risk of overfitting. • Air temperature and its difference between adjacent spaces are equally important. • The adjustment of ventilation is a more effective means to improve thermal comfort. [ABSTRACT FROM AUTHOR]