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The development of digital finance represents a new paradigm for the delivery of financial services that has exerted an external shock on the off-balance sheet (OBS) activities of traditional commercial banks. In response, commercial banks have embarked on a digital transformation to mitigate the challenges posed by digital finance. However, the impact of external shocks and internal responses on banks' OBS activities, especially the effect of internal responses, needs to be further clarified in order to inform commercial banks' decision-making. Using a dataset consisting of 42 Chinese commercial banks' operating data from 2013 to 2022, this paper employs a two-way fixed effects model and a moderation analysis to conduct an empirical analysis. The results show that digital finance has a significant inhibitory effect on OBS activities; furthermore, digital transformation of commercial banks strengthens this inhibitory effect, indicating that its benefits are outweighed by costs of investment and competitive losses. Additionally, the net interest margin significantly amplifies the inhibitory effect, suggesting a trade-off between income from core business activities and OBS activities under external competitive pressure. Based on these research findings, it is recommended that commercial banks seek differentiated competitive strategies and optimize the product structure of their OBS activities. Furthermore, digital transformation strategies should take into account the overall interests of the bank and strike a balance between long-term and short-term benefits. [ABSTRACT FROM AUTHOR]

Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset's data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model's stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data's outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results. [ABSTRACT FROM AUTHOR]

Glycosidases are essential for the industrial production of functional oligosaccharides and many biotech applications. A novel β-galactosidase/α-L-arabinopyranosidase (PpBGal42A) of the glycoside hydrolase family 42 (GH42) from Paenibacillus polymyxa KF-1 was identified and functionally characterized. Using pNPG as a substrate, the recombinant PpBGal42A (77.16 kD) was shown to have an optimal temperature and pH of 30 °C and 6.0. Using pNPαArap as a substrate, the optimal temperature and pH were 40 °C and 7.0. PpBGal42A has good temperature and pH stability. Furthermore, Na+, K+, Li+, and Ca2+ (5 mmol/L) enhanced the enzymatic activity, whereas Mn2+, Cu2+, Zn2+, and Hg2+ significantly reduced the enzymatic activity. PpBGal42A hydrolyzed pNP-β-D-galactoside and pNP-α-L-arabinopyranoside. PpBGal42A liberated galactose from β-1,3/4/6-galactobiose and galactan. PpBGal42A hydrolyzed arabinopyranose at C20 of ginsenoside Rb2, but could not cleave arabinofuranose at C20 of ginsenoside Rc. Meanwhile, the molecular docking results revealed that PpBGal42A efficiently recognized and catalyzed lactose. PpBGal42A hydrolyzes lactose to galactose and glucose. PpBGal42A exhibits significant degradative activity towards citrus pectin when combined with pectinase. Our findings suggest that PpBGal42A is a novel bifunctional enzyme that is active as a β-galactosidase and α-L-arabinopyranosidase. This study expands on the diversity of bifunctional enzymes and provides a potentially effective tool for the food industry. [ABSTRACT FROM AUTHOR]

Polyamide 4 (PA4) is a biobased and biodegradable polyamide. The high hydrogen bond density of PA4 bestows it with a high melting point that is close to its thermal decomposition temperature, thereby limiting the melt processing of PA4. In this study, PA4 was blended with polyamide 6 (PA6) and further modified with copolyamide 4/6 (R46). The effects of composition on the crystallization behavior of the blends were studied. The results demonstrated that the binary PA4/PA6 (B46) and ternary PA4/PA6/R46 (B46/R46) blends formed two crystalline phases (PA4- and PA6-rich phases) through crystallization-induced phase separation. With increasing PA6 content, the thermal stability and crystallinity of the B46 blend increased and decreased, respectively, and the contribution of PA6 toward the crystallization of the PA4-rich phase diminished. Molecular dynamics simulations showed the molecular chain orientation of the B46 blends well. The melting points, crystallinities, and grain sizes of the B46/R46 blends were lower than those of the B46 blends. The crystallization of the PA4-rich phase was restrained by the dilution effect of molten-state PA6, and the nucleation and crystallization of the PA6-rich phase were promoted by the presence of crystallized PA4. The B46 blends with 30–40 wt% PA6 had the best mechanical properties. [ABSTRACT FROM AUTHOR]

Rainfall has a significant impact on surface runoff and erosion in a watershed, and there is a lack of information about the features of rainfall regimes and how they affect runoff and soil erosion. In the paper, based on 59 rainfall events from 2021 to 2022 in the lower Yellow River Culai Mountain sub-watershed, various statistical analysis methods were used to preliminarily explore the rainfall regime features and their influence on surface runoff and soil erosion. The results showed that the rainfall in the watershed was divided into three regimes: Rainfall Regime I had the highest frequency of occurrence, reaching 74.58%, and Rainfall Regime III was the main power source for surface runoff and soil erosion. The paper filtered out three indicators (P, I, and I30) to analyze the degree of influence of rainfall features on surface runoff and erosion, and the results show that precipitation is the main influencing factor affecting the variation in surface runoff, and the maximum 30 min rainfall intensity is the main factor impacting the variation in sediment yield. The results can provide a theoretical basis for soil conservation, hydrological forecasting, and non-point source pollution management. [ABSTRACT FROM AUTHOR]

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security. [ABSTRACT FROM AUTHOR]

Featured Application: The research results of this paper have been applied to the evaluation of ship curved plate forming in the field of shipbuilding. The evaluation method proposed in this article can save manufacturing hours and construction costs. Designers can predict the forming difficulty before actual processing. Green manufacturing and energy saving can be achieved through optimized design schemes. It can also provide ideas for research about plate forming difficulty evaluation in the fields of aerospace manufacturing, chemical pressure vessel and precision manufacturing. To solve the problem that the lack of reference for hull plate division leads to the difficulty and low efficiency of curved plate forming, the forming difficulty evaluation of complex curved hull plates is researched. Considering the characteristics in curved plate forming process, the forming difficulty evaluation model of complex curved hull plates based on grey relational analysis is established. The evaluation process of forming difficulty for curved plates is designed. The influence law of parameters and forming parameters on the forming difficulty of curved plates are revealed. The weight coefficients of different influencing factors on the forming difficulty of curved plates are quantified. Taking the real hull plate of a container ship as an example, the evaluation results for the forming difficulty of curved plates are calculated based on the evaluation model. The rationality of the evaluation model is verified by comparing the evaluation results of curved plate forming and the process time data. The research can provide a reference for the reasonable division of hull plates and reduce the forming difficulty of curved plates. [ABSTRACT FROM AUTHOR]

Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this paper, aiming at addressing the issue of poor recognition performance at low signal-to-noise ratios (SNRs) and the inability of deep features to effectively distinguish among all modulation types, we propose an optimization scheme for modulation recognition based on fine-tuning and feature re-extraction. In the proposed scheme, the network is firstly trained with the signals at high SNRs; then, the trained network is fine-tuned to the untrained network at low SNRs. Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. The simulation results demonstrate that the proposed optimization scheme can maximize the performance of the neural network in the recognition of signals that are easily confused and at low SNRs. Notably, the average recognition accuracy of the proposed scheme was 91.28% within an SNR range of −8 dB to 18 dB, which is an improvement of 8% to 17% in comparison with four existing schemes. [ABSTRACT FROM AUTHOR]

This study proposes an optimal control method for connected autonomous vehicles (CAVs) through signalized intersections to reduce the energy consumption of mixed human-driven vehicles (HDVs) and CAV traffic. A real-time optimal control model was developed to optimize the trajectory of each CAV by minimizing energy consumption during the control period while ensuring traffic efficiency and safety. The control conditions of the CAVs were analyzed under different driving scenarios considering the impact of signal phase timing and preceding vehicles. Additionally, a method is proposed for CAVs to guide other vehicles directly and reduce the energy consumption of the entire signalized intersection. Simulation experiments using MATLAB and SUMO were conducted to evaluate the performance of the proposed method under various traffic conditions, such as different levels of saturation, market penetration rates (MPRs), and the green ratio. The performance was measured using average energy consumption and an average time delay. The results show that the proposed method can effectively reduce vehicle energy consumption without compromising traffic efficiency under various conditions. Moreover, under traffic saturation, the proposed method performs better at a high MPR and green ratio, especially at 40–60% MPR. [ABSTRACT FROM AUTHOR]

As a widespread pollutant, bisphenol A (BPA) has created a serious threat to ecosystem and human health. Therefore, expanding the available microbial resources used to screen highly efficient BPA-degrading bacteria with BPA as the sole carbon source is very important for the removal of this pollutant from the environment. In this study, the BPA degradation rate of Pseudomonas sp. P1 to 30 mg/L was 96.89% within 120 h. Whole genome sequencing showed that the genome of strain P1 was composed of a single circular chromosome with a full length of 6.17 Mb, which contained 5636 predicted coding genes. Comparative genomic analysis showed that strain P1 contained 210 functional genes related to BPA degradation. It was confirmed that BPA degradation genes ferredoxin (bisdA), P450 (bisdB), CotA and Lac in strain P1 were highly expressed under the induction of BPA. Combined with the identification of metabolites, the route of BPA degradation by Pseudomonas was proposed. A new metabolite, 4-vinylphenol, was detected for the first time in pathway Ⅰ. In pathway Ⅱ, BPA is directly oxidized to phenol and 4-isopropenyl phenol in the presence of laccase, which is rarely reported in the process of bacterial degradation of BPA. This study confirmed that strain P1 had good tolerance to various environmental factors at the gene level and enriched the degradation mechanism of BPA. [ABSTRACT FROM AUTHOR]

Capacity drop (CD) at overloaded bottlenecks is a puzzling traffic flow phenomenon with some internal and complicated mechanisms at the microscopic level. Capacity drop is not only important for traffic flow theory and modelling, but also significant for traffic control. A traffic model evaluating traffic control measures needs to be able to reproduce capacity drop in order to deliver reliable evaluation results. This paper delivers a comprehensive overview on the subject from the behavioral mechanism perspective, as well as from microscopic and macroscopic simulation points of view. The paper also conducts comparable studies to replicate capacity drop at freeway ramp merges from both macroscopic and microscopic perspectives. Firstly, the subject is studied using the macroscopic traffic flow model METANET with respect to ramp merging scenarios with and without ramp metering. Secondly, one major weakness of commercial microscopic traffic simulation tools in creating capacity drop at ramp merges is identified and a forced lane changing model for ramp-merging vehicles is studied and incorporated into the commercial traffic simulation tool AIMSUN. The extended AIMSUN carefully calibrated against real data is then examined for its capability of reproducing capacity drop in a complicated traffic scenario with merging bottlenecks. The obtained results demonstrate that reproducible capacity drop can be delivered for the targeted bottlenecks using both macroscopic and microscopic simulation tools. [ABSTRACT FROM AUTHOR]

The fabrication of a coffee ring was studied in this work to improve its sensitivity in detecting trace analytes based on surface-enhanced Raman scattering (SERS). Gold nanoparticles were synthesized with diameters of ~40 nm through the sodium citrate reduction method, and rhodamine 6G (R6G) was employed as a probe to evaluate the performance of the fabricated coffee rings. The results showed that the coffee ring formed from the water-washed gold nanoparticles presented more orderly and regular morphology as well as better SERS properties than the unwashed ones. Furthermore, both the concentration and the amount of gold nanoparticles were found to affect its SERS performance. Using the optimized coffee ring as a SERS substrate, trace R6G with a concentration of 5 × 10−8 M was detected. This sensing platform could realize aflatoxin B1 (AFB1) detection down to 5 × 10−7 M and was demonstrated to function well in real-sample testing. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA) pollution poses an increasingly serious problem. BPA has been detected in a variety of environmental media and human tissues. Microbial degradation is an effective method of environmental BPA remediation. However, BPA is also biotoxic to microorganisms. In this study, Rhodococcus equi DSSKP-R-001 (R-001) was used to degrade BPA, and the effects of BPA on the growth metabolism, gene expression patterns, and toxicity-resistance mechanisms of Rhodococcus equi were analyzed. The results showed that R-001 degraded 51.2% of 5 mg/L BPA and that 40 mg/L BPA was the maximum BPA concentration tolerated by strain R-001. Cytochrome P450 monooxygenase and multicopper oxidases played key roles in BPA degradation. However, BPA was toxic to strain R-001, exhibiting nonlinear inhibitory effects on the growth and metabolism of this bacterium. R-001 bacterial biomass, total protein content, and ATP content exhibited V-shaped trends as BPA concentration increased. The toxic effects of BPA included the downregulation of R-001 genes related to glycolysis/gluconeogenesis, pentose phosphate metabolism, and glyoxylate and dicarboxylate metabolism. Genes involved in aspects of the BPA-resistance response, such as base excision repair, osmoprotectant transport, iron-complex transport, and some energy metabolisms, were upregulated to mitigate the loss of energy associated with BPA exposure. This study helped to clarify the bacterial mechanisms involved in BPA biodegradation and toxicity resistance, and our results provide a theoretical basis for the application of strain R-001 in BPA pollution treatments. [ABSTRACT FROM AUTHOR]

Continuous cropping and soilborne diseases affect soil properties and soil microbial diversity and structure, which are the main factors posing obstacles to the continuous cropping of ginseng. This study explored the response of the physicochemical properties, enzyme activity, and microbial community of ginseng rhizosphere soil to continuous cropping and soilborne disease (root rot of ginseng). We used woodland soil without ginseng planting as a control to study these changes. The results showed that continuous cropping and soilborne disease significantly affected soil physicochemical properties, enzyme activities, and microbial communities. The levels of total nitrogen, hydrolyzable nitrogen, organic matter, and soil pH decreased significantly, while available phosphorus, available potassium, total phosphorus, and total potassium showed significant accumulation after continuous cropping. The activities of urease, catalase, sucrase, acid phosphatase, alkaline phosphatase, and polyphenol oxidase decreased significantly after continuous cropping. Using MiSeq high-throughput sequencing, we found that the alpha diversity and the number of bacterial and fungal communities significantly changed after continuous cropping and soilborne disease. A redundancy analysis suggests that soil physicochemical properties and enzyme activities also affect soil microbial communities. In summary, this study revealed the effects of continuous cropping and soilborne disease on soil and provides a theoretical basis for alleviating soilborne disease in ginseng. [ABSTRACT FROM AUTHOR]

The global epidemic of obesity is tightly associated with numerous comorbidities, such as type II diabetes, cardiovascular diseases and the metabolic syndrome. Among the key features of obesity, some studies have suggested the abnormal expansion of adipose-tissue-induced local endogenous hypoxic, while other studies indicated endogenous hyperoxia as the opposite trend. Endogenous hypoxic aggravates dysfunction in adipose tissue and stimulates secretion of inflammatory molecules, which contribute to obesity. In contrast, hypoxic exposure combined with training effectively generate exogenous hypoxic to reduce body weight and downregulate metabolic risks. The (patho)physiological effects in adipose tissue are distinct from those of endogenous hypoxic. We critically assess the latest advances on the molecular mediators of endogenous hypoxic that regulate the dysfunction in adipose tissue. Subsequently we propose potential therapeutic targets in adipose tissues and the small molecules that may reverse the detrimental effect of local endogenous hypoxic. More importantly, we discuss alterations of metabolic pathways in adipose tissue and the metabolic benefits brought by hypoxic exercise. In terms of therapeutic intervention, numerous approaches have been developed to treat obesity, nevertheless durability and safety remain the major concern. Thus, a combination of the therapies that suppress endogenous hypoxic with exercise plans that augment exogenous hypoxic may accelerate the development of more effective and durable medications to treat obesity and comorbidities. [ABSTRACT FROM AUTHOR]

With the development of urbanization, the hourly PM2.5 concentration in the air is constantly changing. In order to improve the accuracy of PM2.5 prediction, a prediction model based on the Classification and Regression Tree (CART) and Broad Learning System (BLS) was constructed. Firstly, the CART algorithm was used to segment the dataset in a hierarchical way to obtain a subset with similar characteristics. Secondly, the BLS model was trained by using the data of each subset, and the validation error of each model was minimized by adjusting the window number of the mapping layer in the BLS network. Finally, for each leaf in the tree, the global BLS model and the local BLS model on the path from the root node to the leaf node are compared, and the model with the smallest error is selected. The data collected in this paper come from the Chine Meteorological Historical Data website. We selected historical data from the Huaita monitoring station in Xuzhou city for experimental analysis, which included air pollutant content and meteorological data. Experimental results show that the prediction effect of the CART–BLS model is better than that of RF, V-SVR, and seasonal BLS models. [ABSTRACT FROM AUTHOR]

The highway on-ramp merging area is one of the major sections that form traffic bottlenecks. In a connected vehicle environment, V2V and V2I technologies enable real-time exchange of information, including position, speed, and acceleration. To improve the efficiency of vehicle merging at the on-ramp, this study proposes a cooperative merging control strategy for network-connected autonomous vehicles. First, the central controller designs the merging sequence and safety space for vehicles passing through the confluence point. Then, a trajectory optimization model was constructed based on vehicle longitudinal dynamics, and the PMP algorithm was used to determine the optimal control input. Finally, all vehicles follow the optimal trajectory so that the ramp vehicles merge smoothly into the mainline. Simulations verify that the proposed algorithm performs better than FIFO, with 13.2% energy savings, 41.4% increase in average speed, and 50.4% reduction in travel time over the uncontrolled merging scenario. The method is further applied to different traffic flow conditions and the results show that it can significantly improve traffic safety and mobility, while effectively reducing vehicle energy consumption. However, the traffic operation improvement is not satisfactory under low traffic demand. [ABSTRACT FROM AUTHOR]

As a potential air control measure, RF-based surveillance is one of the most commonly used unmanned aerial vehicles (UAV) surveillance methods that exploits specific emitter identification (SEI) technology to identify captured RF signal from ground controllers to UAVs. Recently many SEI algorithms based on deep convolution neural network (DCNN) have emerged. However, there is a lack of the implementation of specific hardware. This paper proposes a high-accuracy and power-efficient hardware accelerator using an algorithm–hardware co-design for UAV surveillance. For the algorithm, we propose a scalable SEI neural network with SNR-aware adaptive precision computation. With SNR awareness and precision reconfiguration, it can adaptively switch between DCNN and binary DCNN to cope with low SNR and high SNR tasks, respectively. In addition, a short-time Fourier transform (STFT) reusing DCNN method is proposed to pre-extract feature of UAV signal. For hardware, we designed a SNR sensing engine, denoising engine, and specialized DCNN engine with hybrid-precision convolution and memory access, aiming at SEI acceleration. Finally, we validate the effectiveness of our design on a FPGA, using a public UAV dataset. Compared with a state-of-the-art algorithm, our method can achieve the highest accuracy of 99.3% and an F1 score of 99.3%. Compared with other hardware designs, our accelerator can achieve the highest power efficiency of 40.12 Gops/W and 96.52 Gops/W with INT16 precision and binary precision. [ABSTRACT FROM AUTHOR]

The natural runoff mechanism of the Dawen River, the main tributary of the lower Yellow River, has been stressed in recent years as a result of human activity, and the hydrological situation has changed dramatically. In this paper, various hydrological statistical methods such as the Mann–Kendall nonparametric test, cumulative anomaly, ordered clustering, sliding T test, and rainfall–runoff double-cumulative curve were used to study the evolution characteristics of hydrological factors in Dawen River. The result revealed that the rainfall and runoff of the Dawen River decreased overall from 1956 to 2016, but the downward trend was not clear, and the runoff variance was high, with 1978 as the variation point. The IHA/RVA and PCA were used to comprehensively evaluate the hydrological variability of the Dawen River, and nine representative indicators were screened out. The overall change was 58%, which is mild, and the difference in hydrological change between the IHA index system and the PCA index system was just 7%, which was predictable. The hydrological situation of the Dawen River has undergone huge changes, and there has been a serious dry-off phenomenon since 1978. The biology, habitat, and structure of the Dawen River have all been irreversibly impacted by changes in its hydrological regime. Furthermore, the key influencing aspect of hydrological variation is the vast building of water conservation schemes. The findings could serve as a theoretical foundation for integrated water resource management and ecological conservation. [ABSTRACT FROM AUTHOR]

From the perspective of damage mechanics, the damage parameters were introduced as the characterizing quantity of the decrease in the mechanical properties of powder superalloy material FGH96 under fatigue loading. By deriving a damage evolution equation, a fatigue life prediction model of powder superalloy containing inclusions was constructed based on damage mechanics. The specimens containing elliptical subsurface inclusions and semielliptical surface inclusions were considered. The CONTA172 and TARGE169 elements of finite element software (ANSYS) were used to simulate the interfacial debonding between the inclusions and matrix, and the interface crack initiation life was calculated. Through finite element modeling, the stress field evolution during the interface debonding was traced by simulation. Finally, the effect of the position and shape size of inclusions on interface debonding was explored. [ABSTRACT FROM AUTHOR]

Simple Summary: Various plant extracts are used as functional nutritional factors to keep the health and improve the performance of animals. This research investigated the effects of bilberry extract (effective ingredient: anthocyanin) on growth performance, meat quality, antioxidant status, and immune function of yellow-feathered chickens. Results showed that dietary supplementation with bilberry extract enhanced relative weight of the bursa of Fabricius and broadly increased activities of antioxidant enzymes of chickens; indices of meat quality were improved without impact on growth performance. The finding indicated that bilberry extract might be considered as a new additive to improve the health and meat quality of yellow-feathered chickens. The experiment was conducted to investigate the effects of bilberry extract on growth performance, meat quality, antioxidant status, and immune function of yellow-feathered chickens. A total of 360 female hatchling Lingnan chickens were randomly allocated to three treatments with 6 replicates of 20 chickens per replicate. Birds were fed a basal diet supplemented with 0 (the control group), 100 (B100), and 400 (B400) mg/kg of bilberry extract for 63 d. Compared with the controls, (1) dietary supplementation with bilberry extract did not affect the growth performance of chickens from 1 to 63 d. (2) At 21 d, the relative weight of the bursa of Fabricius was increased (p < 0.05) by dietary supplementation with 400 mg/kg bilberry extract. Bilberry extract decreased the concentrations of IgY and IgM in blood plasma of 63-d chickens (p < 0.05). (3) For 21-d chickens, dietary supplementation with 400 mg/kg bilberry extract increased (p < 0.05) the activity of GSH-Px in blood plasma and jejunal mucosa (p < 0.05). Supplementation with 100 mg/kg bilberry extract increased (p < 0.05) the activities of T-SOD in jejunal mucosa and GSH-Px in the liver and decreased (p < 0.05) the MDA concentration in the liver. For chickens at the age of 63 d, both levels of bilberry extract increased activity of T-SOD in blood plasma (p < 0.05) and reduced MDA concentration in the jejunum (p < 0.05). (4) Supplementation with bilberry extract in the diet decreased the MDA concentration (B100) in muscle of 63-d chickens at 45 min postmortem and increased (p < 0.05) the activity of T-SOD (B400) at 4 d postmortem. (5) In breast muscle at 63 d, birds supplemented with bilberry extract (B400) had increased pH and drip loss while drip loss was reduced in the B100 treatment (p < 0.05); treatments did not affect inosinic acid or intramuscular fat contents. In conclusion, dietary supplementation of yellow-feathered chickens with bilberry extract enhanced the relative weight of the bursa of Fabricius, and broadly increased activities of antioxidant enzymes; indices of meat quality were improved without impact on growth performance. Considering the results in the current research, 100 mg/kg bilberry extract was recommended when supplemented in chickens. [ABSTRACT FROM AUTHOR]

Simple Summary: Manganese is an indispensable trace element, necessary for the normal development and activity of tissues such as bones. The low Mn content in corn–soybean meal diets used in production and the inefficient intestinal absorption of Mn in broilers calls for the need for optimizing the supplemental provision of Mn to broilers. The current study examined the effect of an optimized dietary supplemental Mn for growth performance, tibial characteristics, immune function and meat quality of yellow-feathered broilers and recommended that optimal supplementation with Mn in diets for birds to achieve the best performance was 52 (d 1 to d 21), 60 (d 22 to d 42), and 68 mg/kg (d 43 to d 63). This study provides a rational recommendation for the appropriate dietary nutrient levels and provides a scientific basis for establishing nutritional standards of yellow-feathered broilers. This experiment investigated the effect of an optimized supplemental dietary manganese (Mn) on growth performance, tibial characteristics, immune function and meat quality, of yellow-feathered broilers. In three rearing periods, birds were fed for 21-d periods, from d 1 (starter), d 22 (grower) and d 43 (finisher), respectively, with basal diets (containing 16, 17, and 14 mg/kg analyzed Mn, respectively) supplemented with 0, 20, 40, 60, 80, 100, 120 and 140 mg/kg Mn. For starter phase broilers, supplemental manganese affected feed to gain ratio (F/G), and the minimum value was observed with 120 mg/kg manganese. During the grower phase, ADG increased quadratically (p < 0.05) with supplemental Mn and was maximal with 54 mg/kg additional manganese estimated using the regression equation. There was no influence of supplemental manganese on growth performance of broilers during the finisher phase (p > 0.05). The thymic relative weight of broilers were linearly (p < 0.05) and quadratically (p < 0.05) increased with supplemental Mn and maxima were obtained with 95 and 110 mg/kg additional Mn at 42 d and 63 d. The bone density of the tibia in broilers at d 21, 42 and 63 were increased quadratically (p < 0.05) by supplemental Mn, and optimal supplementation for the three phases was 52, 60 and 68 mg/kg, respectively. The weight, diameter, breaking strength and bone density of the tibia of 63-d broilers were influenced (p < 0.05) by supplemental manganese. The lightness (L*) value (linear, p < 0.05) and yellowness (b*) value (p < 0.05) of the breast muscle were decreased by dietary manganese supplementation, and the optimal supplementation, based on L*, was 86 mg/kg. In conclusion, supplemental Mn affected the growth performance, thymic relative weight, tibial characteristics, and the meat color of yellow-feathered broilers. From the quadratic regressions, the optimal supplementation of yellow-feathered broilers at the starter, grower and finisher phases to achieve the best performance was 52, 60, and 68 mg/kg, respectively. [ABSTRACT FROM AUTHOR]

Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (~6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from −2 to 2 mm/day, and the differences for SM of the top thin layer range from −2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections. [ABSTRACT FROM AUTHOR]

In order to reveal the effect of Mo addition on the microstructure and mechanical properties, (CoCuFeNi)100-xMox (x = 0, 10, 15, 19, and 25, x values in atomic ratio) high entropy alloys were prepared by vacuum arc-melting. The results showed that with Mo addition, the μ phase formed and serious separation occurred in the high entropy alloys. The content of μ phase increased with the increase in Mo content. The microstructure of the alloys changed from an initial single-phase face-center-cubic (FCC) solid solution structure (x = 0) to a hypoeutectic microstructure (x = 15), then to a full eutectic microstructure (x = 19), and finally to a hypereutectic microstructure (x = 25). Coherent interface between μ phase and FCC phase was observed. The (CoCuFeNi)81Mo19 alloy with fully eutectic microstructures exhibited the highest yield strength of 557 MPa and fracture strength of 767 MPa in tensile tests at room temperature. The fracture surface revealed that the formation of great amounts of the μ phase resulted in the loss of ductility of (CoCuFeNi)100-xMox alloys. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occurrence consistency, and elevation dependency, from grid-cell to regional scales. The results show that the nine satellite-based products exhibited different agreement with gauge-based reference data with median correlation coefficients ranging from 0.15 to 0.95. Three products (climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA)) generally presented the best performance with the reference data, even in complex terrain regions, given their root mean square errors (RMSE) of less than 25 mm/mon. The climate prediction center merged analysis of precipitation (CMAP) product has relatively coarse spatial resolution, but it also exhibited good performance with a bias of less than 20% in watershed scale. Two other products (precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PER-CCS) and climate prediction center morphing technique-raw (CMORPH-RAW)) overestimated precipitation with median RMSEs of 87 mm/mon and 45 mm/mon, respectively. All the precipitation products generally exhibited better agreement with the reference data for rainy season and lower-elevation regions. All of the products captured precipitation occurrence well, with hit event over 60%, and similar percentages of missed and false event. According to the evaluation, the four products (CHIRPS, MSWEP, TMPA, and CMAP) revealed that the annual precipitation over the TP fluctuated between 333 mm/yr and 488 mm/yr during the period 2003 to 2015. The study indicates the importance of integration of multiple data sources and post-processing (e.g., gauge data fusion and elevation correction) for satellite-based products and have implications for selection of suitable precipitation products for hydrological modeling and water resources assessment for the TP. [ABSTRACT FROM AUTHOR]

Simple Summary: Iron (Fe) is an essential trace mineral for all living organisms, playing important roles in oxygen and electron transport as well as in DNA synthesis. At present, requirements of chickens for trace elements are still based on past National Research Council (NRC) (1994) standards. It is important, therefore, to accurately re-evaluate and satisfy the Fe requirements of modern broiler chicken, and especially important, non-Western breeds such as Chinese yellow broilers, for their optimal growth and health. Therefore, the aim of the present study was to investigate the effects of Fe on growth performance, immune organs and meat quality in Chinese yellow broilers. Based on the findings, feeding yellow-feathered broilers on a conventional corn-soy-based diet satisfies their requirements without additional Fe at ages 1 to 21, and 22 to 42 d, while 90 mg/kg in the finisher phase improved meat quality, and from the QP (quadratic polynomial) models of the key meat quality variables pH of breast muscle and drip loss of breast muscle, the optimal dietary Fe level was 89 to 108 mg/kg, and daily Fe fed allowance was 11 to 13 mg in the finisher phase (43 to 63 d). The objective of three trials was to investigate the effects of dietary Fe on growth performance, immune organ indices and meat quality of Chinese yellow broilers during the whole growth period. A total of 1440 1-day-old, 1440 22-day-old, and 1080 43-day-old Lingnan yellow male broilers were randomly assigned to one of six dietary treatments with six replicates per treatment (40 birds per replicate for both 1 to 21 d and 22 to 42 d, 30 birds for 43 to 63 d). Additional Fe (0, 20, 40, 60, 80, and 100 mg/kg) was added as FeSO4 • H2O to the three basal diets (calculated Fe 50 mg/kg, analyzed 48.3, 49.1, 48.7 mg/kg, respectively). The calculated final dietary Fe concentrations in Starter, Grower and Finisher phases were 50, 70, 90, 110, 130, and 150 mg/kg. The results showed that average daily gain (ADG), average daily feed intake (ADFI) and feed conversion rate (FCR) of the broilers were not influenced by the different levels of Fe (p> 0.05). Weight indices of the spleen, thymus and bursa of Fabricius were not influenced (p > 0.05) by the different levels of Fe during three 21-day experimental periods. Hematocrit, and Fe contents of the liver and kidney were not affected by different levels of Fe (p> 0.05). The diet with 150 mg/kg of Fe increased the a* (relative redness) value of breast muscle compared to the 50 and 70 mg/kg diets at 24 h post mortem (p< 0.05). The diet with 90 mg/kg Fe increased the pH of breast muscle compared to broilers fed 50 or 150 mg/kg Fe (p < 0.05) 45 min after slaughter. The diet with 90 mg/kg Fe decreased drip loss of breast muscle compared to 150 mg/kg Fe (p< 0.05). These data suggest that feeding yellow-feathered broilers on a conventional corn-soy based diet satisfies their requirements without additional Fe at ages 1 to 21, and 22 to 42 d, while 90 mg/kg in the finisher phase improved meat quality, and from the QP (quadratic polynomial) models of the key meat quality variables, pH of breast muscle and drip loss of breast muscle, the optimal dietary Fe level was 89 to 108 mg/kg, and daily Fe fed allowance was 11 to 13 mg in the finisher phase (43 to 63 d). [ABSTRACT FROM AUTHOR]

Simple Summary: Recently, there has been an increasing worldwide interest in limiting or banning the use of synthesized antibiotic growth promoters used in animal production, so as to avoid the development of the drug resistance of human pathogens. This is accelerating the efforts of scientists to present new, natural and safe antibiotic alternatives that are adequate for animal and poultry production. This study contributes by evaluating some potent antibiotic alternatives in growing yellow feathered chicks, including mushroom polysaccharides, nano-copper, copper loaded chitosan, and lysozyme, versus a common antibiotic (Aureomycin). The dietary addition of the tested substances has demonstrated several beneficial effects, particularly in the case of polysaccharides (100 mg/kg) and lysozyme (500 mg/kg), on jejunal barrier, and immunity of yellow-feathered chicks aged 4–24 days, with comparable effects to the antibiotic treatment. This study investigated the influence of dietary supplementation with some antibiotic alternatives on growth performance, intestinal barrier, and immunity of lipopolysaccharide (LPS) challenged chicks. Wenshi females, aged 4 days, were allocated randomly into eight groups, each with six replicates of 20 birds (n = 120/treatment), which received a basal diet supplemented with 0 (control), 0 (LPS), 200 mg/kg aureomycin, 50 mg/kg mushroom polysaccharide, 100 mg/kg mushroom polysaccharide, 500 mg/kg nano-copper, 300 mg/kg copper loaded chitosan, and 500 mg/kg lysozyme for 21 days. On day 18 and 20, the control birds were injected with 0.5 mL saline solution, the other treatments were injected with 0.5 mL saline containing 500 µg LPS/kg body weight (BW). The results indicated that LPS treatment reduced the BW, average daily gain (ADG), and daily feed intake (ADFI) than the controls (p < 0.05), and the antibiotic and the tested alternatives could not retrieve the normal BW, ADG, and ADFI. The tested additives reduced several negative effects of LPS; they reduced diamine oxidase activity and inflammatory mediators in plasma, jejunal mucosa, spleen and thymus, increased content of immunoglobulin in plasma and jejunal mucosa, and decreased gene expression of inducible nitric oxide synthase and Cyclooxygenase 2 in jejunal mucosa. [ABSTRACT FROM AUTHOR]

Although a lot of work has been conducted on car-following modeling, model calibration and validation are still a great challenge, especially in the era of autonomous driving. Most challengingly, besides the immediate benefit incurred with a car-following action, a smart vehicle needs to learn to evaluate the long-term benefits and become foresighted in conducting car-following behaviors. Driving memory, which plays a significant role in car-following, has seldom been considered in current models. This paper focuses on the impact of driving memory on car-following behavior, particularly, historical driving memory represents certain types of driving regimes and drivers' maneuver in coordination with the variety of driving regimes. An autoencoder was used to extract the main features underlying the time-series data in historical driving memory. Long short-term memory (LSTM) neural network model has been employed to investigate the relationship between driving memory and car-following behavior. The results show that velocity, relative velocity, instant perception time (IPT), and time gap are the most relevant parameters, while distance gap is insignificant. Furthermore, we compared the accuracy and robustness of three patterns including various driving memory information and span levels. This study contributes to bridging the gap between historical driving memory and car-following behavior modeling. The developed LSTM methodology has the potential to provide personalized warnings of dangerous car-following distance over the next second. [ABSTRACT FROM AUTHOR]

Freeway congestion may spill back for several kilometers, blocking a number of on/off-ramps upstream. As a consequence, flows at the off-ramps may be substantially reduced, and vehicles bound for the off-ramps are trapped in the mainstream congestion, causing intensified spillback of congestion that blocks even more off-ramps further upstream. Such off-ramp blockage is readily understood and its impact is empirically recognized, but there is a lack of analysis to provide more insights. In this paper, some flow conditions for the activation of bottlenecks and congestion propagation are first established, and the mechanism of the off-ramp blockage is theoretically explored. Macroscopic and microscopic simulations are conducted to demonstrate the analytical results, and some general relations between the total demand, total inflow, total off-ramp outflow, and the number of vehicles within a freeway system are examined. [ABSTRACT FROM AUTHOR]

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method. [ABSTRACT FROM AUTHOR]

Innovative technologies and traffic data sources provide great potential to extend advanced strategies and methods in travel behaviour research. Considering the increasing availability of real-time vehicle trajectory data and stimulated by the advances in the modelling and analysis of big data, this paper developed a hybrid unsupervised deep learning model to study driving bahaviour and risk patterns. The approach combines Autoencoder and Self-organized Maps (AESOM), to extract latent features and classify driving behaviour. The specialized neural networks are applied to data from 4032 observations collected from Global Positioning System (GPS) sensors in Shenzhen, China. In two case studies, improper vehicle lateral position maintenance, speeding and inconsistent or excessive acceleration and deceleration have been identified. The experiments have shown that back propagation through multi-layer autoencoders is effective for non-linear and multi-modal dimensionality reduction, giving low reconstruction errors from big GPS datasets. [ABSTRACT FROM AUTHOR]