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The distinctive taste of Sichuan sauce-flavored sausage comes from an intricate microbial metabolism. The correlation between microbial composition and distinct flavor components has not been researched. The study used headspace solid-phase microextraction action with gas chromatography mass spectrometry to find flavor components and high-throughput sequencing of 16S rRNA to look at the diversity and succession of microbial communities. The correlation network model forecasted the connection between essential bacteria and the development of flavors. The study revealed that the primary flavor compounds in Sichuan sauce-flavored sausages were alcohols, aldehydes, and esters. The closely related microbes were Leuconostoc, Pseudomonas, Psychrobacter, Flavobacterium, and Algoriella. The microbes aided in the production of various flavor compounds, such as 1-octen-3-ol, benzeneacetaldehyde, hexanal, (R,R)-2,3-butanediol, and ethyl caprylate. This work has enhanced our comprehension of the diverse functions that bacteria serve in flavor development during the fermentation of Sichuan sauce-flavored sausage.

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane.

This study investigated the interplay between exit selection models and local pedestrian movement patterns within floor field frameworks. Specifically, this investigation analysed the performance of a multinomial logit exit choice model, incorporating both expected utility theory and cumulative prospect theory frameworks when coupled with three distinct local-level pedestrian movement models (FF-Von Neumann, FF-Moore, and NSFF). The expected utility theory framework considers the deterministic component as a linear relationship, while the cumulative prospect theory framework further considers the decision-maker’s risky attitudes by transforming objective terms into subjective terms using a power value function. The core objective was to comprehend how local movement dynamics, as represented by the floor field models, influence decision-making during exit selection. Comparative analyses revealed intriguing variations between the three local models, despite their shared expected utility theory-based exit choice framework. These discrepancies stemmed from the diverse pedestrian trajectory behaviours generated by each model. Consequently, these local dynamics impacted the decision-maker’s assessment of critical factors, such as the number of evacuees close to the decision-maker (NCDM) and the number of evacuees close to an exit (NCE), which the exit choice model incorporates. These assessments, in turn, significantly affected higher-level decision-making. The integration of the three models with the multinomial logit exit choice model, using either cumulative prospect theory and expected utility theory frameworks, further strengthened the observed bilateral relationship. While the specific nature of this relationship varied depending on the chosen framework and its implementation details, these consistent findings demonstrate the robustness of the results. This reinforced the influence of local-level pedestrian dynamics on higher-level exit selection, highlighting the importance of accurate crowd dynamics modelling, especially when advanced exit choice models consider local movement factors.

A bacterium was isolated and identified from the secretion of a rhesus monkey with endometritis. The morphological results showed that the strain exhibited round, convex, gray-white colonies with smooth surfaces and diameters ranging from 1 to 2 mm when cultured on Columbia blood agar at 37 °C for 24 h; on salmonella–shigella agar (S.S.) at 37 °C for 24 h, the colonies appeared round, flat, and translucent. Gram staining showed negative results with blunt ends and non-spore-forming characteristics. Molecular biology results showed that the 16S rRNA sequence of the strain revealed over 96.9% similarity with published sequences of M. morganii from different sources in the NCBI GenBank database. Morphological and molecular biology analysis confirmed that the strain (RM2023) isolated from cervical secretions of rhesus monkey was M. morganii. Drug sensitivity testing demonstrated that the isolated strain (RM2023) was sensitive to ceftriaxone, amikacin, gentamicin, cefazolin, cefuroxime, ceftazidime, levofloxacin, cotrimoxazole, norfloxacin, and tetracycline; moderately sensitive to ampicillin; and resistant to penicillin, vancomycin, ciprofloxacin, and clindamycin. The research findings provide valuable insights for disease prevention in rhesus monkeys and contribute to molecular epidemiological studies.

The collection of twelve bronze artifacts discovered in Xichuan provides invaluable historical insights into the Warring States period (476 BC to 221 BC) of ancient China. To investigate their fabrication techniques and current state of preservation, a comprehensive analysis was conducted using a metallographic microscope, a scanning electron microscope, and an electron spectrometer to examine the microstructure and elemental composition of the artifacts. The findings revealed that the copper content in these bronze artifacts varied between 41.82% and 87.95%, the tin content ranged from 6.79% to 46.88%, and the lead content was less than 28.96%. The microstructure exhibited an α-solid-solution dendritic-crystal-segregation structure, with a substantial amount of (α + δ) eutectic distributed in an island-like pattern. Lead was dispersed unevenly, appearing as small granules and large ellipsoids. The composition of these weapons aligned with their intended use, adhering to the manufacturing standards of traditional Chinese bronzes. However, their state of preservation was suboptimal, necessitating immediate protective measures. This study contributes physical evidence to the research on early Chinese bronze production and offers scientific guidance for the conservation and restoration of these bronze artifacts.

Saccharomyces cerevisiae (S. cerevisiae) and Kluyveromyces marxianus (K. marxianus) are often used as fermenters in yogurt and alcohol, and have been less studied within meat products. The yeasts were added to sauce meat, and the uninoculated group served as a control in this study to examine and compare the changing patterns of physicochemical and flavor characteristics of S. cerevisiae and K. marxianus on sauce meat during storage. The changes in moisture content, aw, pH, thiobarbituric acid reactive substances (TBARS), and other flavor characteristics were measured in sauce meat during the first, second, fourth, and sixth months after production. The following factors were examined: moisture content, aw, pH, TBARS, peroxide value (POV), acid value (AV), soluble protein (SP), free amino acid (FAA), and volatile flavoring compounds. With VIP > 1 and p < 0.05 as the screening conditions, the partial least squares model (PLS-DA) was used to assess the distinctive flavor components in the sausages. The findings demonstrated that the three groups’ changes in sauce meat were comparable during the first two months of storage but differed significantly between the 4th and 6th months. The moisture content, water activity, and pH of the sauce meat decreased gradually with the storage time; TBARS, AV, and FAA increased significantly; SP decreased significantly from 2.61 to 1.72, while POV increased to 0.03 and then decreased to 0.02. The POV and TBARS values of the yeast-infected meat were substantially lower than those of the control group, and the POV and TBARS values of the meat inoculated with S. cerevisiae were particularly decreased (p < 0.05). The POV and TBARS values of SC (S. cerevisiae group) decreased by 49.09% and 40.15%, respectively, compared to CK (the control group) at the time of storage until June. The experimental group (KM: K. marxianus group) significantly increased the SP and FAA values of the sauce meat (p < 0.05) by 32.4% and 29.84% compared to the CK group, respectively. Esters and olefins as well as alcohols and esters were much greater in meat that had been supplemented with S. cerevisiae and K. marxianus than in meat from the control group. In conclusion, inoculating sauce meat with S. cerevisiae can significantly enhance the quality and flavor of sauce meat while it is being stored.

Dietary fiber plays an important role in porcine gut health and welfare. Fiber is degraded by microbial fermentation in the intestine, and most gut microbiota related to fiber digestibility in pigs are worth pursuing. The aim of this study was to identify gut microbiota associated with the apparent total tract digestibility (ATTD) of neutral detergent fiber (NDF) and of acid detergent fiber (ADF) in pigs. Large phenotypic variations in the ATTD of NDF and of ADF were separately found among 274 Suhuai pigs. Microbial community structures were significantly different between high and low fiber digestibility groups. Fourteen genera separately dominated the communities found in the high ATTD (H-AD) of NDF and ADF samples and were in very low abundance in the low ATTD (L-AD) of NDF and ADF samples. In conclusion, norank_f__Bacteroidales_S24-7_group (p < 0.05), Ruminococcaceae_UCG-005 (p < 0.05), unclassified_f__Lachnospiraceae (p < 0.05), Treponema_2 (p < 0.01), and Ruminococcaceae_NK4A214_group (p < 0.01) were the main genera of gut microbiota affecting the ATTD of NDF in pigs. Christensenellaceae_R-7_group (p < 0.01), Treponema_2 (p < 0.05), Ruminococcaceae_NK4A214_group (p < 0.05), Ruminococcaceae_UCG-002 (p < 0.05), and [Eubacterium]_coprostanoligenes_group (p < 0.05) were the main genera of gut microbiota affecting the ATTD of ADF in pigs. The most important functions of the above different potential biomarkers were: carbohydrate transport and metabolism, general function prediction only, amino acid transport and metabolism, cell wall/membrane/envelope biogenesis, translation, transcription, replication, energy production and conversion, signal transduction mechanisms, and inorganic ion transport and metabolism. The most important metabolic pathways of the above different potential biomarkers were: membrane transport, carbohydrate metabolism, amino acid metabolism, replication and repair, translation, cell motility, energy metabolism, poorly characterized, nucleotide metabolism, metabolism of cofactors and vitamins, and cellular processes and signaling.

To evaluate the tolerance of a high-fiber diet in Erhualian pigs (Er-HL), the present investigation systematically investigated the ramifications of varying wheat bran fiber levels, specified as total dietary fiber (TDF) values of 14.07%, 16.32%, 17.99%, and 18.85%, on growth performance, fiber digestibility and gut microbiota in Er-HL, large Large White pigs (L-LW, the same physiological stage as the Er-HL) and small Large White pigs (S-LW, the same body weight as the Er-HL). Our results revealed that fiber levels exerted no discernable impact on growth performance (average daily feed intake (ADFI), and average daily gain (ADG)) of Er-HL (p > 0.05). Conversely, L-LW exhibited a decrease in ADFI and ADG with increasing fiber levels (p < 0.05). Notably, the apparent total tract digestibility (ATTD) of various fiber components, including neutral detergent fiber (NDF), acid detergent fiber (ADF), hemicellulose, TDF and insoluble dietary fiber (IDF), in Er-HL were significantly higher than those in S-LW and L-LW irrespective of diets (p < 0.05). The ATTD of cellulose and hemicellulose in Er-HL significantly decreased with increasing fiber levels (p < 0.05), yet remained statistically indifferent when comparing the 7%-wheat-bran-replaced diet (7% WRB, TDF 16.32%) to the basal diet (TDF 14.07%) (p > 0.05). The cecal microbiota of Er-HL had higher richness estimators (Chao1 and ACE) than those of S-LW and L-LW irrespective of diets (p < 0.01). Breed serves as a pivotal determinant in shaping swine gut microbiota. Thirteen genera were selected as the key bacteria related to high fiber digestibility of Er-HL. Further functional examination of these key genera elucidated an enrichment of pathways pertinent to carbohydrate metabolism in Er-HL samples compared with S-LW and L-LW samples. In summary, Er-HL exhibited high-fiber tolerance both in terms of growth performance and fiber digestibility compared with Large White pigs. Specifically, the ATTD of NDF, ADF, hemicellulose, IDF and TDF were significantly higher in Er-HL compared with L-LW and S-LW, irrespective of diets. Fiber level exerted no discernable impact on growth performance (ADFI, ADG) and the ATTD of fiber (NDF, ADF, IDF and TDF) in Er-HL. The optimum fiber level of the Er-HL was identified as 7% WRB (TDF 16.32%). Thirteen genera were ascertained to significantly contribute to high fiber digestibility of Er-HL, correlating with an enhancement of carbohydrate metabolism pathways.

Aging and metabolic disorders feedback and promote each other and are closely related to the occurrence and development of cardiovascular disease, type 2 diabetes, neurodegeneration and other degenerative diseases. Liupao tea is a geographical indication product of Chinese dark tea, with a “red, concentrated, aged and mellow” flavor quality. In this study, the aqueous extract of aged Liupao tea (ALPT) administered by continuous gavage significantly inhibited the increase of visceral fat and damage to the intestinal–liver–microbial axis in high-fat modeling of SAMP8 (P8+HFD) mice. Its potential mechanism is that ALPT significantly inhibited the inflammation and aggregation formation pathway caused by P8+HFD, increased the abundance of short-chain fatty acid producing bacteria Alistipes, Alloprevotella and Bacteroides, and had a calorie restriction effect. The results of the whole target metabolome network pharmacological analysis showed that there were 139 potential active components in the ALPT aqueous extract, and the core targets of their actions were SRC, TP53, AKT1, MAPK3, VEGFA, EP300, EGFR, HSP90AA1, CASP3, etc. These target genes were mainly enriched in cancer, neurodegenerative diseases, glucose and lipid metabolism and other pathways of degenerative changes. Molecular docking further verified the reliability of network pharmacology. The above results indicate that Liupao tea can effectively delay the body’s degenerative changes through various mechanisms and multi-target effects. This study revealed that dark tea such as Liupao tea has significant drinking value in a modern and aging society.

The increasing popularity of lithium-ion battery systems, particularly in electric vehicles and energy storage systems, has gained broad research interest regarding performance optimization, thermal stability, and fire safety. To enhance the battery thermal management system, a comprehensive investigation of the thermal behaviour and heat exchange process of battery systems is paramount. In this paper, a three-dimensional electro-thermal model coupled with fluid dynamics module was developed to comprehensively analyze the temperature distribution of battery packs and the heat carried away. The computational fluid dynamics (CFD) simulation results of the lumped battery model were validated and verified by considering natural ventilation speed and ambient temperature. In the artificial neural networks (ANN) model, the multilayer perceptron was applied to train the numerical outputs and optimal design of the battery setup, achieving a 1.9% decrease in maximum temperature and a 4.5% drop in temperature difference. The simulation results provide a practical compromise in optimizing the battery configuration and cooling efficiency, balancing the layout of the battery system, and safety performance. The present modelling framework demonstrates an innovative approach to utilizing high-fidelity electro-thermal/CFD numerical inputs for ANN optimization, potentially enhancing the state-of-art thermal management and reducing the risks of thermal runaway and fire outbreaks.

Building polymers implemented into building panels and exterior façades have been determined as the major contributor to severe fire incidents, including the 2017 Grenfell Tower fire incident. To gain a deeper understanding of the pyrolysis process of these polymer composites, this work proposes a multi-scale modelling framework comprising of applying the kinetics parameters and detailed pyrolysis gas volatiles (parent combustion fuel and key precursor species) extracted from Molecular Dynamics models to a macro-scale Computational Fluid Dynamics fire model. The modelling framework was tested for pure and flame-retardant polyethylene systems. Based on the modelling results, the chemical distribution of the fully decomposed chemical compounds was realised for the selected polymers. Subsequently, the identified gas volatiles from solid to gas phases were applied as the parent fuel in the detailed chemical kinetics combustion model for enhanced predictions of toxic gas, charring, and smoke particulate predictions. The results demonstrate the potential application of the developed model in the simulation of different polymer materials without substantial prior knowledge of the thermal degradation properties from costly experiments.

Intermediate cell states (ICSs) during the epithelial–mesenchymal transition (EMT) are emerging as a driving force of cancer invasion and metastasis. ICSs typically exhibit hybrid epithelial/mesenchymal characteristics as well as cancer stem cell (CSC) traits including proliferation and drug resistance. Here, we analyze several single-cell RNA-seq (scRNA-seq) datasets to investigate the relation between several axes of cancer progression including EMT, CSC traits, and cell–cell signaling. To accomplish this task, we integrate computational methods for clustering and trajectory inference with analysis of EMT gene signatures, CSC markers, and cell–cell signaling pathways, and highlight conserved and specific processes across the datasets. Our analysis reveals that “standard” measures of pluripotency often used in developmental contexts do not necessarily correlate with EMT progression and expression of CSC-related markers. Conversely, an EMT circuit energy that quantifies the co-expression of epithelial and mesenchymal genes consistently increases along EMT trajectories across different cancer types and anatomical locations. Moreover, despite the high context specificity of signal transduction across different cell types, cells undergoing EMT always increased their potential to send and receive signals from other cells.

In recent years, the applications of lithium-ion batteries have emerged promptly owing to its widespread use in portable electronics and electric vehicles. Nevertheless, the safety of the battery systems has always been a global concern for the end-users. The separator is an indispensable part of lithium-ion batteries since it functions as a physical barrier for the electrode as well as an electrolyte reservoir for ionic transport. The properties of separators have direct influences on the performance of lithium-ion batteries, therefore the separators play an important role in the battery safety issue. With the rapid developments of applied materials, there have been extensive efforts to utilize these new materials as battery separators with enhanced electrical, fire, and explosion prevention performances. In this review, we aim to deliver an overview of recent advancements in numerical models on battery separators. Moreover, we summarize the physical properties of separators and benchmark selective key performance indicators. A broad picture of recent simulation studies on separators is given and a brief outlook for the future directions is also proposed.

With the increase in aging in China, the health problems of older adults, especially mental health problems, have become a concern for the whole society. This article selected urban and rural older adults and analyzed the impact of social support on their mental health using a binary logistic model. It was found that under the current urban–rural dichotomy, the effects of social support on the mental health of urban and rural older adults are significantly different. In social support, first, the fairness and satisfaction with the social security system only had a significant effect on the mental health of urban older adults and had no significant effect on the rural older adults. Second, the closeness of contact with grassroots community workers had a significant impact on the mental health of older adults in urban and rural areas. From informal social support, the mental health of rural older adults was mainly influenced by the support of their children, reflecting the influence of the traditional culture of “filial piety”. Furthermore, the mental health of urban older adults was mainly influenced by neighborhood support, reflecting the importance of “close neighbors are better than distant relatives”. Based on the results of the empirical study, this article suggests that to promote the mental health of older adults, we should start by strengthening the formal social support system, establishing high-quality community service facilities, and emphasizing the role of informal social support.

The advent of immune checkpoint therapy for metastatic skin cancer has greatly improved patient survival. However, most skin cancer patients are refractory to checkpoint therapy, and furthermore, the intra-immune cell signaling driving response to checkpoint therapy remains uncharacterized. When comparing the immune transcriptome in the tumor microenvironment of melanoma and basal cell carcinoma (BCC), we found that the presence of memory B cells and macrophages negatively correlate in both cancers when stratifying patients by their response, with memory B cells more present in responders. Moreover, inhibitory immune signaling mostly decreases in melanoma responders and increases in BCC responders. We further explored the relationships between macrophages, B cells and response to checkpoint therapy by developing a stochastic differential equation model which qualitatively agrees with the data analysis. Our model predicts BCC to be more refractory to checkpoint therapy than melanoma and predicts the best qualitative ratio of memory B cells and macrophages for successful treatment.

The role that combustion plays in energy systems remains crucial in supplying the world’s ever-increasing power demands [...]

Location prediction has attracted much attention due to its important role in many location-based services. The existing location prediction methods have large trajectory information loss and low prediction accuracy. Hence, they are unsuitable for vehicle location prediction of the intelligent transportation system, which needs small trajectory information loss and high prediction accuracy. To solve the problem, a vehicle location prediction algorithm was proposed in this paper, which is based on a spatiotemporal feature transformation method and a hybrid long short-term memory (LSTM) neural network model. In the algorithm, the transformation method is used to convert a vehicle trajectory into an appropriate input of the neural network model, and then the vehicle location at the next time is predicted by the neural network model. The experimental results show that the trajectory information of an original taxi trajectory is basically reserved by its shadowed taxi trajectory, and the trajectory points of the predicted taxi trajectory are close to those of the shadowed taxi trajectory. It proves that our proposed algorithm effectively reduces the information loss of vehicle trajectory and improves the accuracy of vehicle location prediction. Furthermore, the experimental results also show that the algorithm has a higher distance percentage and a shorter average distance than the other predication models. Therefore, our proposed algorithm is better than the other prediction models in the accuracy of vehicle location predication.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

A numerical study of the fire whirl formation under symmetrical and asymmetrical entraining configuration is presented. This work aims to assess the effect of eddy-generation configuration on the evolution of the intriguing phenomenon coupled with both flow dynamics and combustion. The numerical framework implements large-eddy simulation, detailed chemistry to capture the sophisticated turbulence-chemistry interaction under reasonable computational cost. It also adopts liquid-based clean fuel with fixed injection rate and uniformed discretisation scheme to eliminate potential interference introduced by various aspects of uncertainties. The result reveals that the nascent fire whirl formulates significantly rapidly under the symmetrical two-slit configuration, with extended flame height and constrained vortex structure, compared with the asymmetrical baseline. However, its revolution orbit gradually diverges from domain centreline and eventually stabilises with a large radius of rotation, whereas the revolution pattern of that from the baseline case is relatively unchanged from the inception of nascent fire whirl. Through the analysis, the observed difference in evaluation pathway could be explained using the concept of circular motion with constant centripetal force. This methodology showcases its feasibility to reveal and visualise the fundamental insight and facilitate profound understanding of the flaming behaviour to benefit both research and industrial sectors.

Magnetorheological (MR) fluid is a smart material fabricated by mixing magnetic-responsive particles with non-magnetic-responsive carrier fluids. MR fluid dampers are able to provide rapid and reversible changes to their damping coefficient. To optimize the efficiency and effectiveness of such devices, a computational model is developed and presented where the flow field is simulated using the computational fluid dynamics approach, coupled with the magnetohydrodynamics model. Three different inlet pressure profiles were designed to replicate real loading conditions are examined, namely a constant pressure, a sinusoidal pressure profile, and a pressure profile mimicking the 1994 Northbridge earthquake. When the MR fluid damper was in its off-state, a linear pressure drop between the inlet and the outlet was observed. When a uniform perpendicular external magnetic field was applied to the annular orifice of the MR damper, a significantly larger pressure drop was observed across the annular orifice for all three inlet pressure profiles. It was shown that the fluid velocity within the magnetized annular orifice decreased proportionally with respect to the strength of the applied magnetic field until saturation was reached. Therefore, it was clearly demonstrated that the present model was capable of accurately capturing the damping characteristics of MR fluid dampers.

This paper numerically examines the characterisation of fire whirl formulated under various entrainment conditions in an enclosed configuration. The numerical framework, integrating large eddy simulation and detailed chemistry, is constructed to assess the whirling flame behaviours. The proposed model constraints the convoluted coupling effects, e.g., the interrelation between combustion, flow dynamics and radiative feedback, thus focuses on assessing the impact on flame structure and flow behaviour solely attribute to the eddy-generation mechanisms. The baseline model is validated well against the experimental data. The data of the comparison case, with the introduction of additional flow channelling slit, is subsequently generated for comparison. The result suggests that, with the intensified circulation, the generated fire whirl increased by 9.42 % in peak flame temperature, 84.38 % in visible flame height, 6.81 % in axial velocity, and 46.14 % in velocity dominant region. The fire whirl core radius of the comparison case was well constrained within all monitored heights, whereas that of the baseline tended to disperse at 0.5 m height-above-burner. This study demonstrates that amplified eddy generation via the additional flow channelling slit enhances the mixing of all reactant species and intensifies the combustion process, resulting in an elongated and converging whirling core of the reacting flow.

Many countries are making strategic plans to replace conventional vehicles (CVs) with electric vehicles (EVs), with the motivation to curb the growth of atmospheric CO2 concentration. While previous publications have mainly employed social-economic based models to predict CO2 emission trends from vehicles over the years, they do not account for the dynamics of engine and motor efficiency under different driving conditions. Therefore, this study utilized an experimentally validated vehicle dynamic model to simulate the consumption of gasoline and electricity for CVs and EVs, respectively, under eight driving cycles for different countries/regions. The CO2 emissions of CVs and EVs through 2040 were then calculated, based on the assumptions of the improvement of engine efficiency and composition of power supply chain over the years. Results reveal that, assuming that the current projections and assumptions remain valid, China would have the highest CO2 emission for EVs, followed by Japan, world average and the EU, mainly determined by the share of fossil fuels in the power grid. As for the influence of road conditions, the CO2 emission of CVs was found to be always higher than that of EVs for all countries/regions over the years. The difference is around 10−20% under highway conditions, and as high as 50−60% in crowded urban driving conditions.

Fabricating high-performance MXene-based polymer nanocomposites is a huge challenge because of the poor dispersion and interfacial interaction of MXene nanosheets in the polymer matrix. To address the issue, MXene nanosheets were successfully exfoliated and subsequently modified by long-chain cationic agents with different chain lengths, i.e., decyltrimethylammonium bromide (DTAB), octadecyltrimethylammonium bromide (OTAB), and dihexadecyldimethylammonium bromide (DDAB). With the long-chain groups on their surface, modified Ti3C2 (MXene) nanosheets were well dispersed in N,N-dimethylformamide (DMF), resulting in the formation of uniform dispersion and strong interfacial adhesion within a polystyrene (PS) matrix. The thermal stability properties of cationic modified Ti3C2/PS nanocomposites were improved considerably with the temperatures at 5% weight loss increasing by 20 °C for DTAB-Ti3C2/PS, 25 °C for OTAB-Ti3C2/PS and 23 °C for DDAB-Ti3C2/PS, respectively. The modified MXene nanosheets also enhanced the flame-retardant properties of PS. Compared to neat PS, the peak heat release rate (PHRR) was reduced by approximately 26.4%, 21.5% and 20.8% for PS/OTAB-Ti3C2, PS/DDAB-Ti3C2 and PS/DTAB-Ti3C2, respectively. Significant reductions in CO and CO2 productions were also obtained in the cone calorimeter test and generally lower pyrolysis volatile products were recorded by PS/OTAB-Ti3C2 compared to pristine PS. These property enhancements of PS nanocomposites are attributed to the superior dispersion, catalytic and barrier effects of Ti3C2 nanosheets.

In this article, the evolution of in-flame soot species in a slow speed, buoyancy-driven diffusion flame is thoroughly studied with the implementation of the population balance approach in association with computational fluid dynamics (CFD) techniques. This model incorporates interactive fire phenomena, including combustion, radiation, turbulent mixing, and all key chemical and physical formation and destruction processes, such as particle inception, surface growth, oxidation, and aggregation. The in-house length-based Direct Quadrature Method of Moments (DQMOM) soot model is fully coupled with all essential fire sub-modelling components and it is specifically constructed for low-speed flames. Additionally, to better describe the combustion process of the parental fuel, ethylene, the strained laminar flamelet model, which considers detailed chemical reaction mechanisms, is adopted. Numerical simulation is validated against a self-conducted co-flow slot burner experimental measurement. A comprehensive assessment of the effect of adopting different nucleation laws, oxidation laws, and various fractal dimension and diffusivity values is performed. The results suggest the model employing Moss law of nucleation, modified NSC law of oxidation, and adopting a fractal dimension value of 2.0 and Schmidt number of 0.9 yields the simulation result that best agreed with experimental data.

The gut microbiota plays important roles in animal health and nutrient digestibility. The characteristics of gut microbiota population in grower pigs and their correlation with apparent nutrient digestibility were assessed in previous study. Here we studied characteristics of intestinal microbiota of sows and analyzed their relationships with apparent nutrient (ether extract (EE), crude protein (CP), crude fiber (CF), neutral detergent fiber (NDF) and acid detergent fiber (ADF)) digestibility. Firmicutes and Bacteroidetes were the most dominant phyla, approximately 73% of the total sequences. Treponema, Oscillibacter and Lactobacillus were the most dominant generas, more than 49% of the total sequences. The microbiota of sows clustered separately from the microbiota of grower pigs at the age of D28 D60, D90 and D150. The abundance of Clostridium and Turicibacter was positively correlated with apparent EE digestibility. The abundance of Anaerofustis and Robinsoniella in sow fecal samples was positively correlated with apparent CF digestibility. The abundance of Collinsella and Sutterella was positively correlated with apparent NDF digestibility. The abundance of Clostridium, Collinsella, Robinsoniella and Turicibacter was positively correlated with apparent ADF digestibility. Sows have their unique gut microbial structure compared with grower pigs and some of them participate in the digestive process of different nutrients.

Recent high-profile building fires involving highly-combustible external cladding panels in Australia as well as Dubai, China, and the United Kingdom have created a heightened awareness by the public, government, and commercial entities to act on the risks associated with non-compliant building structures. In this paper, a database of fire events involving combustible aluminium composite panels was developed based on (i) review of relevant major fire events in Australia and other countries, and (ii) numerical simulation of the ignitability, fire spread, and toxic emissions associated with composite panels. Through the application of large-eddy-simulation (LES)-based computational fire field models, the associated risks for a standardized two-storey building with external cladding was considered in this study. A total of sixteen simulation cases with different initial sizes of the fire and different air cavity widths in the exterior cladding assembly were examined to investigate the tolerable situations and their influences. It was discovered that for most cases, with an initial fire size greater than 400 kW/m−2, the fire will spread from the first to second floor before the allowed egress time period.

In this study, a Large Eddy Simulation (LES) based fire field model was applied to numerically investigate the effectiveness of smoke control using smoke vents and curtains within a large-scale atrium fire. Two compartment configurations were considered: the first case with no smoke curtains installed, while the second case included a smoke curtain at the centre of the compartment to trap smoke. Based on the thermocouple results, it was found that the model predicted the gas temperature near the fire particularly well. The time development and heat transfer of the gas temperature predictions were in good agreement with the experimental measurements. Nevertheless, the gas temperature was slightly under-predicted when the thermocouple was further away from the flaming region. Overall, it was discovered that the combination of a smoke curtain and ceiling vents was a highly effective natural smoke exhaust system. However, under the same vent configuration, if the smoke curtain height is not adequate to completely block the spread of smoke, it significantly reduces the pressure differential between the compartment and the exterior, causing reduced flow rates in the outlet vents.

High-performance poly(1,4-butylene terephthalate) (PBT) nanocomposites have been developed via the consideration of phosphorus-containing agents and amino-carbon nanotube (A-CNT). One-pot functionalization method has been adopted to prepare functionalized CNTs via the reaction between A-CNT and different oxidation state phosphorus-containing agents, including chlorodiphenylphosphine (DPP-Cl), diphenylphosphinic chloride (DPP(O)-Cl), and diphenyl phosphoryl chloride (DPP(O3)-Cl). These functionalized CNTs, DPP(Ox)-A-CNTs (x = 0, 1, 3), were, respectively, mixed with PBT to obtain the CNT-based polymer nanocomposites through a melt blending method. Scanning electron microscope observations demonstrated that DPP(Ox)-A-CNT nanoadditives were homogeneously distributed within PBT matrix compared to A-CNT. The incorporation of DPP(Ox)-A-CNT improved the thermal stability of PBT. Moreover, PBT/DPP(O3)-A-CNT showed the highest crystallization temperature and tensile strength, due to the superior dispersion and interfacial interactions between DPP(O3)-A-CNT and PBT. PBT/DPP(O)-A-CNT exhibited the best flame retardancy resulting from the excellent carbonization effect. The radicals generated from decomposed polymer were effectively trapped by DPP(O)-A-CNT, leading to the reduction of heat release rate, smoke production rate, carbon dioxide and carbon monoxide release during cone calorimeter tests.

Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures.

Rail corrugation is a serious problem in a railway transportation system, aggravating the operational risk and shortening the lifetime of train–track system. In order to ensure the safety and reliability of the railway system, the detection of rail corrugation is very important. Thus, this study systematically summarizes the recent research progress of rail corrugation. First, this study introduces the definition of rail corrugation and the classification criteria. Then, the formation mechanism of rail corrugation is analyzed in detail, and its adverse consequences are investigated. Further, this study summarizes several main detection methods, which are corrugation-detection methods based on acceleration measurements, wavelet transform methods for corrugation evaluation, computer-vision-based methods for corrugation automatic detection, digital filtering algorithms for rail corrugation detection, and others. In this study, the formation mechanism and detection methods of rail corrugation are systematically described, and various corrugation-detection methods are also introduced in detail. This study not only provides a scientific basis for railway maintenance, but also lays a solid foundation for future experimental design and data analysis. This study can also guide engineering practice to improve the reliability and safety of railway systems. It also provides useful experience for future railway-engineering design and planning, as well as safer and more reliable operation. In general, this study can provide technical support for the detection of rail corrugation to ensure the safety of the rail–track system.

Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed.

In railway systems, layered structures could be induced in wheel–rail contact interfaces due to several causes, such as head hardening, work hardening, plastic deformation, and mechanical or thermal excursion-induced phase transformation. This study proposes an explicit finite element (FE) method for investigating elastic layer effects in wheel–rail rolling contact. The proposed method is first validated by comparing its solution with that of Kalker’s boundary element method (BEM) when the layer is not present, with a focus on the tractive rolling contact. To investigate general layer effects, the rail is assumed to consist of two layers, i.e., the top layer and the matrix material. The top layer is assumed to have different elastic moduli from the matrix material and then the top elastic layer effects on contact characteristics such as contact stress, contact patch, and subsurface stress are investigated. Different layer thicknesses are also considered. It is observed that a harder layer tends to introduce larger contact pressure and surface shear stress, but a smaller contact patch. A harder layer also produces larger subsurface stresses. A thicker layer may intensify these effects. The results suggest that in engineering applications, the analysis of wheel–rail rolling contact consequences such as wear and rolling contact fatigue (RCF) may need to consider the layered structures using appropriate methods.

This study aims to develop a track-side online monitoring system for malfunction detection in the suspension controllers of maglev trains during their in-service operation. The hardware module of the system includes two arrays of accelerometers deployed on an F-type rail and a data acquisition unit. The software module of the system consists of codes for three functions: (i) the identification of time intervals in relation to the passage of each suspension controller via synchrosqueezing transform; (ii) the extraction of a feature index (FI) sequence synthesized by modulating the response amplitude, frequency, and running speed; and (iii) the formulation of a Bayesian dynamic linear model for real-time malfunction detection in maglev suspension controllers. For verification of the proposed monitoring system and malfunction detection algorithm, full-scale tests have been conducted on an maglev test line using the devised system, where a maglev train was run at different speeds with malfunction occurring in the suspension controllers. The malfunction detection results of the proposed approach are exemplified via comparison with the recorded suspension gaps after the trial run of the maglev train. The fidelity of the results obtained using the extracted FI sequence and using the raw monitoring data are compared. The superiority of the proposed malfunction detection algorithm is also discussed via comparison with the results of the different train speeds.

A mathematical model was developed to determine the order of failure of layers in a two-layer ceramics composite and to determine the conditions for achieving the maximum limit load under three-point loading. The model was set in the space of three “bilayer parameters”: the ratio of the thickness of the lower layer to the whole thickness of the beam, the ratio of Young’s moduli of the lower layer to the upper layer, and the ratio of flexural strengths of the materials of the lower layer to the upper layer. The adequacy of the model obtained was confirmed by experimental results on the three-point bending of the experimental specimens. The experimental samples were two-layer composites consisting of a cermet layer TiB/Ti and a layer of α-Ti. The samples were obtained by free self-propagating high-temperature synthesis (SHS) compression and with varying their thickness. The results obtained make it possible to predict in advance which layer, based on the specific bilayer parameters, will trigger the brittle fracture mechanism as well as to set the maximum destructive load of bilayer composites.

Vibration control of composite structures coupled with distributed masses under random excitations is a significant issue. In this paper, partial and ordinary differential coupling equations are obtained from a periodic sandwich plate coupled with supported masses under random excitation. An analytical solution to the coupling equations is proposed, and the stochastic response adjustability of the system with various periodic distributions of geometrical and physical parameters is studied. Spatial periodic layer thickness and core modulus of the sandwich plate are considered based on the active‐passive periodicity strategy. The periodically distributed masses are supported on the plate by coupling springs and dampers. Partial and ordinary differential coupling equations for the system including the periodic sandwich plate and supported masses are derived and then converted into unified ordinary differential equations for multi-mode coupling vibration. Generalized system stiffness, damping and mass are functions of the periodic parameters. Expressions of frequency response function and response spectral density of the system are obtained. Numerical results show the response adjustability via the spatially periodic geometrical and physical parameters. The results have the potential for application to dynamic control or optimization of sandwich structure systems.

A response analysis method for nonlinear beams with spatial distribution parameters and non-periodic supports was developed. The proposed method is implemented in four steps: first, the nonlinear partial differential equation of the beams is transformed into linear partial differential equations with space-varying parameters by using a perturbation method; second, the space-varying parameters are separated into a periodic part and a non-periodic part describing the periodicity defect, and the linear partial differential equations are separated into equations for the periodic and non-periodic parts; third, the equations are converted into ordinary differential equations with multiple modes coupling by using the Galerkin method; fourth, the equations are solved by using a harmonic balance method to obtain vibration responses, which are used to discover dynamic characteristics including the amplitude–frequency relation and spatial mode. The proposed method considers multiple vibration modes in the response analysis of nonlinear non-periodic structures and accounts for mode-coupling effects resulting from structural nonlinearity and parametric non-periodicity. Thus, it can handle nonlinear non-periodic structures with a high parameter-varying wave in wide frequency vibration. In numerical studies, a nonlinear beam with non-periodic supports (resulting in non-periodic distribution parameters or periodicity defect) under harmonic excitations was explored using the proposed method, which revealed some new dynamic response characteristics of this kind of structure and the influences of non-periodic parameters. The characteristics include remarkable variation in frequency response and spatial mode, and in particular, vibration localization and anti-localization. The results have potential applications in vibration control and the support damage detection of nonlinear structures with non-periodic supports.

Vibration response and amplitude frequency characteristics of a controlled nonlinear meso-scale beam under periodic loading are studied. A method including a general analytical expression for harmonic balance solution to periodic vibration and an updated cycle iteration algorithm for amplitude frequency relation of periodic response is developed. A vibration equation with the general expression of nonlinear terms for periodic response is derived and a general analytical expression for harmonic balance solution is obtained. An updated cycle iteration procedure is proposed to obtain amplitude frequency relation. Periodic vibration response with various frequencies can be calculated uniformly using the method. The method can take into account the effect of higher harmonic components on vibration response, and it is applicable to various periodic vibration analyses including principal resonance, super-harmonic resonance, and multiple stationary responses. Numerical results demonstrate that the developed method has good convergence and accuracy. The response amplitude should be determined by the periodic solution with multiple harmonic terms instead of only the first harmonic term. The damping effect on response illustrates that vibration responses of the nonlinear meso beam can be reduced by feedback control with certain damping gain. The amplitude frequency characteristics including anti-resonance and resonant response variation have potential application to the vibration control design of nonlinear meso-scale structure systems.

Gelatin production is the most industry polluting process where huge amounts of raw organic materials and chemicals (HCl, NaOH, Ca2+) are utilized in the manufacturing accompanied by voluminous quantities of end-pipe effluent. The gelatinous wastewater (GWW) contains a large fraction of protein and lipids with biodegradability (BOD/COD ratio) exceeding 0.6. Thus, it represents a promising low-cost substrate for the generation of biofuels, i.e., H2 and CH4, by the anaerobic digestion process. This review comprehensively describes the anaerobic technologies employed for simultaneous treatment and energy recovery from GWW. The emphasis was afforded on factors affecting the biofuels productivity from anaerobic digestion of GWW, i.e., protein concentration, organic loading rate (OLR), hydraulic retention time (HRT), the substrate to inoculum (S0/X0) ratio, type of mixed culture anaerobes, carbohydrates concentration, volatile fatty acids (VFAs), ammonia and alkalinity/VFA ratio, and reactor configurations. Economic values and future perspectives that require more attention are also outlined to facilitate further advancement and achieve practicality in this domain.

The severe deterioration of a cement asphalt (CA) mortar layer may lead to the movement of the upper concrete slab and impair the safety of the speedy train. In this study, a test specimen simulating the structure of high-speed rail track slabs was embedded with delaminated cracks in various lateral sizes inside the CA mortar layer. Impact–echo tests (IE) were performed above the flawed and flawless locations. In present study, the IE method is chosen to assess defects in the CA mortar layer. Both traditional IE and normalized IE are used for data interpolation. The normalized IE are the simulated transfer function of the original IE response. The peak amplitudes in the normalized amplitude spectrum and the peak frequency in the traditional amplitude spectrum for the top concrete overlay were used to develop simple indicators for identifying the integrity of the CA mortar layer. The index was based on the difference of the experimental peak amplitude and frequency of the ones calculated from previously developed formulas for plates without substrates. As a result, the technique does not require an experimental baseline for the crack assessment. A field test and analysis procedure for evaluating high-speed rail slab systems are proposed.

For high-speed trains, out-of-roundness (OOR)/defects on wheel tread with small radius deviation may suffice to give rise to severe damage on both vehicle components and track structure when they run at high speeds. It is thus highly desirable to detect the defects in a timely manner and then conduct wheel re-profiling for the defective wheels. This paper presents a wayside fiber Bragg grating (FBG)-based wheel condition monitoring system which can detect wheel tread defects online during train passage. A defect identification algorithm is developed to identify potential wheel defects with the monitoring data of rail strain response collected by the devised system. In view that minor wheel defects can only generate anomalies with low amplitude compared with the wheel load effect, advanced signal processing methods are needed to extract the defect-sensitive feature from the monitoring data. This paper explores a Bayesian blind source separation (BSS) method to decompose the rail response signal and to obtain the component that contains defect-sensitive features. After that, the potential defects are identified by analyzing anomalies in the time history based on the Chauvenet’s criterion. To verify the proposed defect detection method, a blind test is conducted using a new train equipped with defective wheels. The results show that all the defects are identified and they concur well with offline wheel radius deviation measurement results. Minor defects with a radius deviation of only 0.06 mm are successfully detected.

Uncertainty in sensor data complicates the construction of baseline models for the measurement and forecasting (M&F) of high-speed rail (HSR) track slab deformation. Standard Gaussian process (GP) assumes a uniform noise throughout the input space. However, in the application to modelling of HSR structural health monitoring (SHM) data, this assumption can be unrealistic, because of its unique heteroscedastic uncertainty that is induced by dynamic train loading, electromagnetic interference, large temperature variation, and daily maintenance actions of railway track infrastructure. Therefore, this study firstly develops a novel online SHM system enabled by fiber Bragg grating (FBG) technology to eliminate electromagnetic interference on SHM data for continuous and long-term monitoring of track slab deformation, with the capacity of temperature self-compensation. To deal with different sources of uncertainty, the study explores Variational Heteroscedastic Gaussian Process (VHGP) approach while using variational Bayesian and Gaussian approximation for data modelling, estimation of the monitoring data uncertainty, and further data forecasting. The results demonstrate that the VHGP framework yields more robust regression results and the estimated confidence level can better depict the heteroscedastic variances of the noise in HSR data. Higher accuracy for both regression and forecasting is gained through VHGP and the position with maximum noise can be more accurately forecasted with a smooth varying confidence interval. Based on in-situ measurement data, the uncertainty levels for all sensors are estimated together with corresponding deformation profiles for the instrumented segment and three typical types of uncertainty are summarized during the M&F process of HSR track slab deformation.