Edge computing has emerged as a significant computing trend alongside the rapid expansion of the Internet of Things (IoT). Computing operations at the network’s edge offer a solution to the high latency and service overload challenges often associated with cloud computing. Additionally, the Secure Hash Algorithm-3 (SHA-3) plays a crucial role in ensuring data integrity and is implemented for numerous applications in the IoT field. Therefore, integrating the SHA-3 accelerator into edge computing is essential. Moreover, recent studies about SHA-3 have primarily focused on achieving high performance and optimizing resource utilization for SHA-3. However, these studies have overlooked the crucial aspect of data transfer between the external memory and the hash function block, as transfer time plays a significant role. This paper proposes an efficient SHA-3 architecture designed for System-on-Chip (SoC) Field Programmable Gate Array (FPGA) to address the aforementioned challenges and be suitable for real edge computing. The architecture contains three key techniques. First, the harmony of padding and Direct Memory Access (DMA) for managing the data transfer process and enhancing performance efficiency based on Serial Input to Parallel Output (SIPO) and a barrel shifter. Secondly, implementing internal pipelining within the Round Function (RF) to reduce critical path delays and optimize resource utilization. Finally, designing four modes (SHA3-224, SHA3-256, SHA3-384, and SHA3-512) to cater to various applications. Our architecture is implemented and tested on the DE10-Standard Development Kit (Cyclone V SX SoC-5CSXFC6D6F31C6N), which is integrated into the FPGA and is controlled by a Dual-Core ARM Cortex-A9 processor. The result is up to 38.34 Gbps in throughput and 5.61 Mbps/ALM in efficiency for the RF computation, 28.02 Gbps in throughput, and 3.63 Mbps/ALM for the full proposed architecture.

As an important support for power system with high penetration of renewable energy, energy storage system (ESS) has changed the traditional model of simultaneous implementation of electricity production and consumption, and its installed capacity under the source-grid-load scenario is rising year by year, but it faces the problems of insufficient utilization and benefits. This study analyzes the functional combination of ESS under the source-grid-load scenarios, and proposes a comprehensive benefit evaluation method of energy storage projects based on fuzzy decision-making trial and evaluation laboratory (DEMATEL) and super efficiency data envelopment analysis (DEA) model, which considers the perspective of economic and social environment. Firstly, the functional requirements of energy storage on the source-grid-load scenarios are explored respectively, and the characteristics of various functions are analyzed to form functional combination schemes. Secondly, index modeling is carried out from three aspects: the whole life cycle cost, functional combination benefit and social environmental benefit, and the comprehensive benefit evaluation index system of energy storage project is proposed, which includes both economic and social environment. Then, the intuitionistic trapezoidal fuzzy number is combined with DEMATEL to form an effective analysis method for the input-output relationship of the indexes, and the comprehensive evaluation is realized based on the super efficiency DEA model. Finally, the effectiveness of the proposed evaluation method and the rationality of the functional combination are verified under source-grid-load scenarios respectively.

The 2019-nCoV has been identified as the cause of an outbreak of respiratory illness in Wuhan, Hubei Province, China beginning in December 2019. This epidemic had spread to 19 countries with 11,791 confirmed cases, including 213 deaths, as of January 31, 2020. The World Health Organization declared it as a Public Health Emergency of International Concern. This study analyzed and discussed 70 research articles published until January 31, 2020 for a better understanding of the epidemiology, causes, clinical diagnosis, prevention and control of this virus. Studies thus far have shown origination in connection to a seafood market in Wuhan, but specific animal association has not been confirmed. The reported symptoms include fever, cough, fatigue, pneumonia, headache, diarrhea, hemoptysis, and dyspnea. Preventive measures such as masks, hand hygiene practices, avoidance of public contact, case detection, contact tracing, and quarantines are effective for reducing the transmission. To date, no specific antiviral treatment is proven effective, hence, infected people primarily rely on symptomatic treatment and supportive care. Although these studies had relevance to control a public emergency, more research need to be conducted to provide valid and reliable ways to manage this kind of public health emergency in both short- and long- term.

(1) Background: The rapid development of information technology has changed the traditional concept of space, gradually shifting the "container space" to the "relational space" . The constraints of geographical spatial elements are diminishing, and the theory of virtual agglomeration is gaining attention. (2) Methods: In this study, we constructed an evaluation system for virtual agglomeration, comprising four primary indicators: virtual infrastructure level, virtual output level, virtual input level, and virtual service level. The TOPSIS method was used to calculate the virtual agglomeration level for each province, and the DEA-Malmquist method was employed to measure provincial total factor productivity (TFP) in China. Using panel data from 30 provincial regions in China from 2013 to 2021, we examined the mechanism and impact path of virtual agglomeration on TFP. (3) Result: There is an inverted U-shaped relationship between virtual agglomeration and TFP in China. The overall development level of virtual agglomeration is relatively low, within the range where the marginal effect of virtual agglomeration on TFP is positive but diminishing, indicating significant potential for further development. The impact of virtual agglomeration on TFP is stronger in the Yangtze River Economic Belt compared to non-Yangtze River areas, and the highest stable point of TFP is higher in central provinces than in eastern and western provinces. (4) Conclusions: This study provides a theoretical basis and practical reference for improving the level of virtual agglomeration.

Some management practices, such as maintaining birds under high group density, used in the poultry industry may cause birds stress, leading to injurious behaviors, such as injurious pecking, aggression, and cannibalism. In addition, some management practices used to prevent severe injuries in birds may cause pain. Beak trimming (BT), removal of 1/3 to 1/2 of a beak, is a routine husbandry procedure practiced in laying hens to prevent or reduce injurious behaviors. However, BT causes tissue damage, which may increase somatosensory sensitization of the damaged nerve tissues, resulting in pain (acute, chronic or both) in the treated birds because the beak is a complex, functional organ with an extensive nerve supply. BT has already been heavily regulated or prohibited in several European countries and, in time, this trend will impact the practice used in the United States poultry industry. With the growing public concern for poultry welfare there is a pressing need to identify and develop alternatives to BT. Probiotics defined “as a source of live (viable) naturally occurring microorganisms (direct-fed microbials)” have been used as dietary supplements or functional foods to target gut microbiota (microbiome) for prevention or therapeutic treatment of mental diseases including social stress-induced psychiatric disorders in humans and various experimental animals. In our studies, chickens were used as an animal model to assess if dietary supplementation of probiotic, Bacillus subtilis, reduces injurious behaviors following social challenge. Hens of Dekalb XL strain, an aggressive line, were used in the studies. Our results indicate that dietary supplementation of the Bacillus subtilis based probiotic reduces aggressive behaviors in chickens. These results suggest dietary probiotics could be a suitable strategy for increasing hosts’ health status and welfare conditions.

A flexible asymmetric supercapacitor (ASCs) is successfully developed by using the composite of MoO3 and graphene oxide (GO) electrochemically deposited on carbon cloth (CC) (MoO3/rGO/CC) as the negative electrode, the MnO2 deposited on CC (MnO2/CC) as the positive electrode and Na2SO4/polyvinyl alcohol (PVA) as gel electrolyte. The results show that the introduction of GO layer can remarkably increase the specific capacitance of MoO3 from 282.7 F g-1 to 341.0 F g-1. Furthermore, the combination of such good electrode materials and a neutral gel electrolyte renders the fabrication of high-performance ASC with a large operating potential difference of 1.6 V in 0.5 mol L-1 Na2SO4 solution of water. Furthermore, the ASCs exhibit excellent cycle ability and the capacitance can maintain 87 % of its initial value after 6000 cycles. The fact that a light-emitting diode can be lighted up by the ASCs indicates the device’s potential applications as energy storage devices. The encouraging results demonstrate a promising application of the composite of MoO3 and GO in energy storage devices.

Solar Energy has enormous potential to fulfill the energy requirements of the world and can be extracted using solar cells. However, the solar cells are affected by poor efficiency and further affected by wind speed, orientation of the panel, temperature and dust deposition. There are different cleaning technologies devised by many industry experts to clean the solar panels. However, they are facing drawbacks when deployed in the solar farm. An efficient cleaning system, along with an added cooling system must be devised, so that the solar panels must be cleaned and cooled often to maximize the energy production. This paper presents a low-cost, energy-efficient, smart and innovative dust cleaning and cooling system for Photovoltaic (PV) Panels. The system is designed, fabricated, fully-automated using Programmable Logic Controller (PLC) and tested successfully. A battery-charging kiosk, capable of charging two, 24V Lead Acid Batteries embedded within this prototype, shall provide clean-energy in a sustainable manner to the rural communities of the developing nations. The user can check the status of the battery such as battery voltage, battery temperature, and state of charge on the Human Machine Interface (HMI) panel while charging the batteries

This study explored what motivates university counselors. By analyzing various factors like personal values and work environment, they found counselors experience greater career satisfaction when their work aligns with their own values and societal impact, and when they receive recognition and opportunities for growth. Universities can utilize these findings to create a more supportive environment for counselors by highlighting their impact, offering development opportunities, and fostering a positive work culture. This can ultimately lead to a more motivated and effective counseling team, benefiting both the counselors and the students they serve.

According to the model of proactive motivation, we constructed a moderated impact model to examine the relationship between employees perceived overqualification (POQ), transformational leadership (TL), constructive deviant behaviour (CDB), and the generational differences in their relationships. We examined research hypotheses using data of 525 individuals. Results revealed that POQ positively affects CDB, and TL strengthened the effect of POQ on positive toward CDB. Furthermore, the effect of POQ on CDB is strongest among individuals born in the 1980s, followed by those born in the 1990s, and the moderating effect of TL is strongest among individuals born in the 1980s, and not significant among those born in the 1990s. Implications, limitations, and future directions of the present study are discussed.

The biodiversity loss in urban areas has attracted public concern, which is one of the urgent global environmental issues. This study used bibliometric methods to analyze 3351 publications from 1995-2021 that retrieved from the Web of Science and visually represented the state-of-the-art of researches in city biodiversity field. The prolific authors, journals(sources), institutions, and countries are clearly identified. The most cited and influential paper proposed a conceptual framework of associations between urban green space, and ecosystem and human health, and then concluded that green infrastructure could physically and psychologically benefits people by ecosystem services it provides, and make a better socio-economic benefit. The theme hotspots are urbanization, urban ecology, ecosystem services, urban planning, green infrastructure, urban forest and urban park et al. Ingo Kowarik is the most productive author in terms of number of publications. Michael L. McKinney is the most cited authors by the publications in analyzed corpus, who identified how urbanization harms native ecosystems, however, a well ecologically educated population could greatly enhance species richness in all ecosystems. Urban park, garden, fragmented green spaces and green corridor networks could help enhance city biodiversity. In general, the city biodiversity research presents a trend that involves intensive global cooperation, and become more comprehensive.

Lemonomycin (1) was first isolated from the fermentation broth of Streptomyces candidus in 1964. The complete chemical structure was not elucidated until 2000 with extensive spectroscopic analysis. Lemonomycin is currently known as the only glycosylated tetrahydroisoquinoline antibiotic. Its potent antibacterial activity against Staphylococcus aureus and Bacillus subtilis and complex architecture make it an ideal target for total synthesis. In this short review, we summarize the research status of lemonomycin for biological activity, biosynthesis and chemical synthesis. The unique deoxy aminosugar-lemonose was proposed to play a crucial role in biological activity, as shown in other antibiotics, such as arimetamycin A, nocathiacin I, glycothiohexide α, and thiazamycins. Given the self-resistance of the original bacterial host, the integration of biosynthesis and chemical synthesis to pursue efficient synthesis and further derivatization is in high demand for the development of novel antibiotics to combat antibiotic-resistant infections.

Protein phosphorylation and ADP-ribosylation (ADPr), as two types of post-translational modifications (PTM), are the process of adding phosphate group and ADP-ribose moieties to proteins, respectively. Although both PTM types can occur on many amino acid types, serine is the most common. Serine phosphorylation (pS), serine ADPr (SADPr), and their in situ crosstalks (pSADPr) play essential roles in biological processes. Although in silico classifiers have been developed for predicting pS and SADPr sites, the classifier for predicting pSADPr sites is unavailable. In this study, we developed classifiers to predict pSADPr sites. Specifically, we collected 3250 human pSADPr, 7520 SADPr, 151,227 pS and 80,096 unmodified serine sites. Based on them, we investigated the characteristics of pSADPr sites and constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. Three of them (e.g. Convolutional Neural Network with the One-Hot encoding, dubbed CNNOH) performed better than the rest two. For instance, CNNOH had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites.Therefore, it is challenging to distinguish pSADPr sites from SADPr sites compared to the other two. It is consistent with our observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Furthermore, we used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. However, none of the ensemble classifiers showed better performances, suggesting that the base classifiers have good enough performances. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

As the embedded device and internet of things (IoTs) concept prevalent in today's world, there is an increasing demand for the security and performance requirements on deploying these devices to private and public sectors. The crucial part of it is to protect privacy, confidentiality and integrity, meanwhile, maintain an adequate level of performance during transmission, storage and access of critical information. While the conventional cryptography methods, such as the Advanced Encryption Standard (AES), SHA$-$2 hashing method and RSA and Diffie Hellman for message authentication and identification, work well on systems which have reasonable processing power and memory capabilities. These do not scale well into a world with embedded systems and sensor networks, in conjunction with their nature of smaller size and lower cost. Thus, in the context of resource constrained device, lightweight cryptography methods are proposed to overcome many of the problems of conventional cryptography possessed. This includes constraints related to physical size, processing requirement, memory limitation, energy drain and production cost. This paper provides a survey of the architectures that are defined as replacements for conventional ciphers within an IoTs space and discuss some trends in the design of future lightweight algorithms. The performance metrics are carefully chosen to reflect and assess the suitability for embedded devices. The aim of this research is to identify the various performance metrics applied on different types of symmetric block ciphers especially lightweight ciphers and compare the results on versatile platforms with stream ciphers and public key methods. The comparative analysis on efficient LW cipher will be tested against other similar block ciphers on both MacBook Pro with Intel core and resource constrained device Raspberry Pi with ARM processor.

In nature and engineering applications, flexible fiber beds covering biological surfaces can play a role in reducing resistance. These fibers deform under the action of fluids, and this deformation affects the fluid flow state, forming a complex fluid-solid interaction phenomenon. To quantitatively analyze such issues, the physical model is simplified to the deformation problem of an soft hair bed caused by Stokes flow, and the deformation problem of a single fiber caused by Stokes flow is further studied. The deformation problem of an elastic single fiber in a channel caused by Stokes flow can be described by a nonlinear integral equation. We have obtained a new series solution, which has been compared with the previous perturbation method to verify the accuracy and effectiveness of the series solution. Meanwhile, we have further provided an extended form of flexible fiber deformation through experimental fitting. This fluid-solid interaction problem involves multiple fields and is very important in many natural and engineering systems. The research in this paper can not only help us better understand complex phenomena in nature but also delve into the interaction mechanism between fluids and solids, providing a theoretical basis for future scientific research and engineering applications.

Excessive CO₂ emissions and alternative energy fuels are two major difficult issues. The utilization of CO₂ into fine chemicals is an optimal route. Diethyl carbonate (DEC) is an extremely versatile chemical intermediate. DEC is used in gasoline, pharmaceutical, chemical and other fields. DEC synthesis from CO₂ and ethanol is a typical green synthetic route. Ni-Cu@Na₃PW₁₂O₄₀ catalysts were synthesized by two novel methods of supported and mixed. The catalyst prepared by mixed method showed nice catalytic performance. It was confirmed that water removal was the key to improving conversion efficiency. In the presence of dehydrating agent of ethylene, ethanol conversion increased from ca. 3% to ca. 40%. Propylene oxide (PO) was participated in the reaction and ethanol conversion continued to reach to ca.90% while DEC selectivity dropped by half. Under optimal conditions, our Ni-Cu@Na₃PW₁₂O₄₀ catalyst effectively solved the two major issues above.

This study measured secondary students’ digital literacy using a digital game-based assessment system that was designed and developed based on the Evidence-Centered Game Design (ECGD) approach. A total of 188 secondary students constituted the valid samples in this study. Fine-grained behavioral data generated from students’ gameplay processes were collected and recorded with the assessment system. The Delphi method was used to extract feature variables related to digital literacy from the process data, and the Analytic Hierarchy Process (AHP) method was used to construct the measurement model. The assessment results of the ECGD-based assessment had a high correlation with standardized test scores, which have been shown to be reliable and valid in prior large-scale assessment studies.

With the burgeoning growth of online retail, the issue of counterfeit products has become a significant threat to both consumer trust and brand integrity, posing challenges to sustainable market practices. Online retail platforms are promoted to redesign their channel structures and enhance brand traceability to tackle counterfeits using blockchain technology. To investigate the interactions between blockchain technology and the channel structures of online retail platforms, we design a game theory model incorporating a manufacturer with(out) blockchain participation selling products via an online retail platform that operates an agency channel and/or a reselling channel. We show that the critical trade-off of blockchain participation comprises the brand assurance effect of the blockchain, the channel competition intensification effect, and the blockchain adoption cost. The platform is capable of strategically designing the channel structure to encourage (while discouraging) blockchain participation by the manufacturer. Our analysis highlights the complementarity between blockchain participation and the channel structure. Furthermore, the effect of the consumer’s belief in the reselling channel, the commission fee in the agency channel, and the substitutability between channels are investigated in depth. The research provides valuable insights for online retail platforms and policymakers seeking to foster a sustainable and trustworthy e-commerce ecosystem.

Ethylene is an ideal CO2 product in an electrocatalytic CO2 reduction reaction (CO2RR), which has high economic value. In this paper, Al–doped octahedral Cu2O (Al–Cu2O) catalyst was synthesized by a simple wet chemical method. The selectivity of CO2RR products was improved by doping Al onto the surface of octahedral Cu2O by regulating the Al content. The Al–Cu2O was used as an efficient electrocatalyst for CO2RR with selective ethylene production. The Al–Cu2O exhibited a high Faradic efficiency (FEC2H4) of 44.9% at -1.23 V (vs. RHE) in CO2 saturated 0.1 M KHCO3 electrolyte. Charge transferring from Al atom to Cu atom take place after Al doping in Cu2O, thereby optimizing the electronic structure, which facilitates CO2RR to ethylene production. The DFT calculation showed that the Al–Cu2O catalyst can effectively reduce the adsorption energy of the *CHCOH intermediate and promote the mass transfer of charges, thus improving the FEC2H4. After Al doping into Cu2O, the center of d orbitals shift positively, which makes the d–band closer to the Fermi level. Furthermore, the density of electronic states increases, which was conducted to the interaction between Cu atoms and intermediates, thus accelerating the electrochemical CO2 reduction process. This work proved that the metal doping strategy can effectively improve the catalytic properties of Cu2O, thus providing a useful way for CO2 cycling and green production of C2H4.

The Loess Plateau is an important region for vegetation restoration in China, however, changes in soil organic carbon (SOC), soil nutrients, and stoichiometry after restoration in this vulnerable ecoregion are not well understood. Typical restoration types, including orchardland (OL), grassland (GL), shrubland (SL), and forestland (FL) were chosen to examine changes in the stocks and stoichiometry of SOC, soil total nitrogen (TN), and soil total phosphorus (TP) at different soil depths and recovery times. Results showed that SOC stocks first increased and then stabilized in OL, GL, and SL at 0–30 cm depth, while in FL, stocks gradually increased. Soil TN stocks first increased and then decreased in OL, SL, and FL with vegetation age at 0–30 cm depth, while soil TP stocks showed little variation between restoration types. In the later stages of restoration, the stocks of SOC and soil TN at 0–30 cm soil depth were still lower than those in natural grassland (NG) and natural forest (NF). The overall C:N, C:P, and N:P ratios increased with vegetation age. Additionally, the SOC, soil TN and soil TP stocks, and C:N, C:P, and N:P ratios decreased with soil depth. The FL had the highest rate of change in SOC and soil TN stocks, at 0-10 cm soil depth. These results indicate a complex response of SOC, soil TN, and soil TP stocks and stoichiometry to vegetation restoration, which could have important implications for understanding C, N, and P changes and nutrient limitations after vegetation restoration.

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost‐effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naïve integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with Fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

Existing Knowledge Graph (KG) models for commonsense question answering present two challenges: (i) existing methods retrieved entities related to questions from the knowledge graph, which may extract noise and irrelevant nodes, and (ii) lack of interaction representation between questions and graph entities. However, current methods mainly focus on retrieving relevant entities with some noisy and irrelevant nodes. In this paper, we propose a novel Retrieval-augmented Knowledge Graph (RAKG) model, which solves the above issues through two key innovations. First, we leverage the density matrix to make the model reason along the corrected knowledge path and extract an enhanced knowledge graph subgraph. Second, we fuse representations of questions and graph entities through a bidirectional attention strategy, in which two representations fuse and update by Graph Convolutional Network (GCN). To evaluate the performance of our method, we conduct experiments on two widely-used benchmark datasets CommonsenseQA and OpenBookQA. The case study gives insight into findings that the augmented subgraph provides reasoning along the corrected knowledge path for question answering.

Background: Potential calcium-related adverse events of vitamin D supplement use have not been addressed in large-scale, real-world data so far. Methods: Leveraging data from the UK Biobank, encompassing 445,493 individuals aged 40-69, we examined associations of high 25-hydroxyvitamin (25[OH]D) levels ≥100 nmol/L and vitamin D supplementation with hypercalcemia (plasma calcium >2.6 mmol/L), kidney stones, and atherosclerosis assessments (pulse wave arterial stiffness index and carotid intima-medial thickness). Regression models were comprehensively adjusted for 49 covariates. Results: Approximately 1.5% of the participants had high 25(OH)D levels, 4.3% regularly used vitamin D supplements, and 20.4% reported regular multivitamin use. At baseline, the hypercalcemia prevalence was 1.6%, and 1.1% were diagnosed with kidney stones during follow-up. High 25(OH)D levels were neither associated with calcium-related adverse events nor atherosclerosis assessments. Vitamin D and multivitamin supplementation were associated with an increased prevalence of hypercalcemia (odds ratios and 95% confidence intervals: 1.46[1.32-1.62] and 1.11[1.04-1.18], respectively) but were neither associated with atherosclerosis nor future kidney stones. Conclusion: High 25(OH)D levels observable in routine care were not associated with any adverse outcome. Vitamin D users have a slightly higher prevalence of hypercalcemia, possibly due to co-supplementation with calcium, but without higher atherosclerosis prevalence or risk of kidney stones.

Abstract: There are a large number of cross-linked chemical bonds such as tight disulfide bonds, hydrogen bonds,and salt bonds in keratin, which makes it difficult to break the structural stability of the protein using conventional methods. The degradation of feather waste using keratinases produced by microorganisms has the advantage of being efficient and environmentally friendly. In this study, 23 strains capable of producing transparent circles were obtained via preliminary screening with skim milk as the sole carbon and nitrogen source, and 9 strains capable of degrading feathers were obtained via rescreening with feathers as the sole carbon and nitrogen source. Finally, through the determination of enzyme activity, the strain CY-A with a high ability to degrade feathers was obtained. The strain was identified as Bacillus tequilensis via morphological observation, physiological and biochemical identification, and 16 S rRNA molecular biology. The keratinase gene (bta) of Bacillus tequila was cloned, and its full-length gene sequence was 1110 bp, containing an open reading frame of 1089, encoding 369 amino acids (GeneBank accession number OR733336). The physicochemical properties, secondary structure, and tertiary structure of the recombinant protein were analyzed using bioinformatics tools such as ProtParm, SOPMA, MEGA, and SWISS-MODELS. It was found that the relative molecular mass was 37.953 kD and that the isoelectric point was 8.48. The protein was a hydrophilic protein. The keratinase bta gene was ligated to the expression vector pET28a(+) using T4 ligase, and the recombination expression plasmid pET28-bta was constructed and transformed into E. coli BL21(DE3) to obtain recombinant engineered bacteria. The expressed keratinase was purified using a Ni2+ affinity chromatography column to obtain recombinant bta keratinase bta with a molecular weight of 37.953 kD. The identification and characterization of this keratinase gene provides some theoretical basis for the further enhancement of keratinase activity by means of genetic engineering.

p53 is among the most frequently mutated tumor suppressor genes given its prevalence in >50% of all human cancers, including high grade serous endometrial cancers and ovarian cancers. In addition to loss of tumor suppression function, many mutated p53 (Mutp53) proteins acquire gain-of-function (GOF) activities as oncogenes to promote cancer progression, which manifest through aberrant expression of p53. As we have come to see, statins induce CHIP-mediated degradation of mutp53 by blocking the interaction between mutp53 and DNAJA1. Therefore, targeting critical downstream pathways of mutp53 provides an alternative strategy for treating cancers expressing mutp53. In this review, we summarize recent advances with Wee1 inhibitors and mevalonate pathway inhibitors, particularly statins, regarding their use in gynecological cancers with p53 mutations.

This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

Urban morphology exhibits fractal characteristics, which can be described by multifractal scaling. Multifractal parameters under positive moment orders primarily capture information about cen-tral areas with relatively stable growth, while those under negative moment orders mainly reflect information about marginal areas with more active growth. However, effectively utilizing mul-tifractal spectrums to uncover the spatio-temporal variations of urban growth remains a challenge. To addresses this issue, this paper proposes a multifractal measurement by combining theoretical principles and empirical analysis. To capture the difference between growth stability in central areas and growth activity in marginal areas, an index based on generalized correlation dimension Dq is defined. This index takes the growth rate of Dq at extreme negative moment order as the numerator, and that at extreme positive moment order as the denominator. During the stable stage of urban growth, the index demonstrates a consistent pattern over time. While during the active stage, the index may exhibit abnormal fluctuations or even jumps. This indicates that the index can reveal spatio-temporal information about urban evolution that cannot be directly observed through multifractal spectrums alone. By integrating this index with multifractal spectrums, we can more comprehensively characterize the evolutionary characteristics of urban spatial structure.

The purpose of this study is to examine the relationship between the institutional investors which can affect financial performance for corporate sustainability on the income smoothing. Therefore, this study focus on the connection between the nature of stock rights and income smoothing in China. For this study, hypotheses were established on the relationship each state-controlled companies, income smoothing, and information equilibrium of individual investors, and empirical analysis was conducted through related variables. The analysis results are summarized in three categories as follows. First, this research finds that state-controlled firms (CONTs) prefer income smoothing activities compared to non-state-controlled firms for the long-term sustainable development of firms using data from 2011 to 2019. Second, this study found out that Institutional investors support the behavior of CONTs to smooth their earnings because this behavior is seen as an attempt by CONTs to convey valuable private information to other investors. Third, we was able to discover that institutional investors' monitoring effect is predominantly driven by pressure-resistant institutional investors. This research complements the lack of empirical research on income smoothing and enable to give a guideline that the type of stock rights is a critical key determinant of participation in income smoothing activities for stable growth and sustainability in the future.

This general review assesses the impact of physical exercise on oxidative and nitrosative stress and the counteractive role of antioxidants, drawing from a systematic literature search up to March 2024 in databases like PubMed and Web of Science. The review highlights studies on the effect of exercise on the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) and antioxidant responses in both human and animal models based on rigorous inclusion and exclusion criteria. Due to the variety of exercise types and participants' characteristics, a narrative synthesis approach was adopted. The findings indicate that moderate exercise boosts antioxidant defenses, known as hormesis, whereas excessive physical activity may increase oxidative and nitrosative damage. The review also explores the complex effects of antioxidant supplementation, revealing that while natural dietary antioxidants support health, high-dose supplements could potentially hinder positive adaptations to exercise. Recommendations for athletes and the general active population highlight the importance of balancing exercise and dietary intake to optimize health benefits. The review concludes with a call for more detailed research on tailored exercise and nutrition plans to dissect these intricate relationships further.

Assessing the quality of corn seeds necessitates evaluating their content of water, fat, protein, and starch. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using both the original spectra and the extracted characteristic wavelengths, four content detection models were constructed: PLSR, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Least Squares Support Vector Machine (LSSVM). The experimental results demonstrate that the DT-CARS-LSSVM model exhibits exceptional accuracy and stability in predicting these four key components, with determination coefficients of 0.9877, 0.9344, 0.9827, and 0.9592, respectively. This study not only provides a scientific basis for evaluating the quality of corn seeds but also opens up new avenues for the development of non-invasive detection technology in related fields.

The shape of the knee is a chain-like structure of an ellipsoid. This suggests that, in the latest study, in addition to rolling, there is slippage in the motion of the knee. This paper selects a mechanical braking model for cattle based on two mechanical structures in different directions at the joints. The experimental results show that the modified dynamical system has strong chaotic properties. This can be one of the basis for judging various health states.

Enterovirus 71 (EV71) infection hand-foot-mouth disease (HFMD), meningoencephalitis, neonatal sepsis, and even fatal encephalitis in children, thereby representing a serious public health hazard. It is important to determine the mechanisms underlying the regulation of EV71 infection. In this study, we initially reveal that the interleukin enhancer binding factor 2 (ILF2) down-regulates EV71 50% tissue culture infective dose (TCID50), attenuates EV71 plaque formation unit (PFU), thereby repressing EV71 infection. Moreover, we reveal a distinct mechanism by which EV71 antagonizes ILF2-mediated antiviral effects. Chip data analyses show that ILF2 mRNA is reduced upon EV71 infection. Cellular studies indicate that EV71 infection represses ILF2 mRNA expression and protein production in human leukemic monocytes (THP-1) differentiated macrophages and in human rhabdomyosarcoma (RD) cells. Additionally, EV71 non-structural protein 2B interacts with ILF2 in human embryonic kidney (HEK293T) cells. Interestingly, in the presence of EV71 2B, ILF2 is translocated from the nucleus to the cytoplasm and co-localizes with 2B in the cytoplasm. Therefore, we reveal a distinct mechanism by which EV71 antagonizes ILF2-mediated antiviral effects by inhibiting ILF2 expression and promoting ILF2 translocation from the nucleus to cytoplasm through its 2B protein.

Electrocatalytic reduction of CO2 to valuable chemicals can alleviate the energy crisis, and solve the greenhouse effect. The key is to develop non-noble metal electrocatalysts with high activity, selectivity and stability. Herein, bimetallic MOF materials (BiZn-MOF, BiSn-MOF and BiIn-MOF) were constructed by coordinating the P-zone transition metals Zn, In, Sn and Bi with the organic ligand 3-amino-1H-1,2,4-triazole-5-carboxylic acid (H2atzc) through a rapid microwave synthesis approach. The coordination centers in bimetallic MOF Catalyst were regulated to optimize the catalytic performance to CO2RR. The optimized catalyst BiZn-MOF exhibited high catalytic activity than those of Bi-MOF, BiSn-MOF and BiIn-MOF. The BiZn-MOF exhibited a higher selectivity for formate production with a Faradic efficiency (FE=92%) at a potential of -0.9 V (vs. RHE) with a current density of 13 mA/cm2. The current density maintained for 13 h continuous electrolysis. The electrochemical conversion of CO2 to formic acid mainly follows the *OCHO pathway. The good catalytic performance of BiZn-MOF may be attributed to the Bi-Zn bimetallic coordination centers in the MOF, which can reduce the binding energies of the reaction intermediates by tuning the electronic structure and atomic arrangement. This study provides a feasible strategy for performance optimization of bismuth-based catalysts.

The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak processing capabilities for the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation when processing Chinese texts, resulting in performance loss. To address these issues, this paper introduces part-of-speech (POS) and pinyin features to aid the model in learning semantic features that are more contextually appropriate. A Chinese Joint Entity and Relation Extraction Model based on Progressive Contrastive Learning and Heterogeneous Feature Fusion is proposed (CJE-PCHF). During model training, an interactive fusion network based on progressive contrastive learning is employed to learn the dependencies between pinyin, POS, and semantic features. This guides the model in heterogeneous feature fusion, capturing higher-order semantic associations between heterogeneous features. On the commonly used DuIE evaluation dataset for joint extraction, this model achieved a significant improvement, with the F1 score increasing by 5.4% compared to the benchmark model CasRel.

North and West Africa are the most vulnerable regions to drought, due to the high variation in monthly precipitation. An accurate and efficient monitoring of drought is essential. In this study, we use TRMM data with remote sensing tools for effective monitoring of drought. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), Normalized Difference Vegetation Index (NDVI), and Normalized Vegetation Supply Water Index (NVSWI) are more useful for monitoring the drought over North and West Africa. To classify the areas affected by drought, we used the TRMM spatial maps to verify the TVDI, DSI and NVSWI indexes derived from MODIS. The DSI, TVDI, NVSWI and Monthly Precipitation Anomaly (NPA) indexes with the employ of MODIS-derived ET/PET and NDVI were chosen for monitoring the drought in the study area. The seasonal spatial correlation between the DSI, NPA, NVWSI, NDVI, TVDI and TCI indicates that NVSWI, NDVI and DSI present an excellent monitor of drought indexes. The change trend of drought from 2002 to 2018 was also characterized. The frequency of drought showed a decrease during this period.

Spider silk has excellent strength and elasticity in natural, researchers have been working for decades try to achieve natural spider silk outstanding mechanical properties using recombinant spider silk protein (spidroin) through artificial spinning. In this work, we chose wet spinning method to explore the relationship between concentration of coagulation bath and fiber performance. It was found that the concentration of methanol has important effect on fiber continuity, diameter and mechanical properties. Lower concentration favors spinning continuous thinner, fibers with high strain. Secondary stretching benefits spinning silk fibers with stable mechanical properties, and thermal stability. Through applying different methanol concentration and additional stretching, we obtained silk fibers with Young’s modulus of 3.052± 2.626 GPa, stress of 25.3944 ± 17.48 MPa, and strain of 140 ± 95.4%.

Based on the major, trace element, and isotope data of the Cenozoic basalts in the study area, it is indicated that the basalts in this region can be classified into three distinct magma systems: alka-li basalts, tholeiite basalts, and Nushan alkali basalts. The geochemical characteristics observed in the studied basalts indicate that the mantle source in this region might have experienced met-asomatic processes prior to partial melting, which could be linked to an ancient subduction pro-cess. Combining the geochemical characteristics in the studied basalts with previous research, we propose that the underlying mantle source in Anhui province may have undergone metaso-matism due to the carbonatitic melts. These melts are believed to have originated from car-bonated eclogite, derived from subducted Pacific slab materials present in the deeper mantle. According to the Sr-Nd isotope data, it is suggested that the mantle source in the Nushan region could be derived from a depleted asthenospheric mantle, while the basaltic composition in the Niugang area may be representative of the mantle's end components associated with the EM-I enriched mantle source present in this study area. Therefore, we propose that the Anhui basalts may have originated from the partial melting of a mantle source representing a mixing of two end-members: DMM (Depleted MORB Mantle) and EM-Ⅰ (Enriched Mantle 1). The Anhui basal-tic magmas may have originated from the partial melting of EM-Ⅰ, potentially influenced by upwelling asthenospheric mantle or asthenospheric diapirism processes. The mantle source in the study area experiences partial melting at 1-5%, 15%, and 1-10%, resulting in the formation of parent magmas for alkali basalt, tholeiitic basalt, and Nushan alkali basalt. Subsequently, the parent magmas experienced fractional crystallization processes, which included minerals like olivine, pyroxene, and plagioclase. This led to the formation of various types of basalt in the study area.

Astragalus membranaceus (AM) and Panax ginseng (PG) are two herbal products with a long history of clinical usage in traditional Chinese medicine (TCM), used in treating a variety of diseases especially in stimulating or inhibiting the immune system. To elucidate the immunity effect of these two traditional Chinese medicine on animal model, four pharmacodynamic indexes (spleen index, thymus index, splenic lymphocyte proliferation and cytotoxic activity of natural killer (NK) cells) were observed on mice. Furthermore, metabolic profiles of plasma were also analyzed by ultra-performance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-Q/TOF-MS, LC-MS) method. All mice were intragastric administrated at three doses (low dose, moderate dose and high dose) once daily for 30 days. Principal components analysis (PCA) and orthogonal projection to latent structure discriminant analysis (OPLS-DA) were performed on LC-MS spectra of plasma, showing that all administration groups developed the disturbance of internal milieu, compared to the blank control (BC) group. Besides, correlation analysis was conducted between pharmacodynamic index and metabolic index. It indicated that uracil, lysoPC(18:3(6Z,9Z,12Z)), sphinganine, LPA(0:0/16:0), UDP-glucuronate, PC(14:0/18:0) were five main endogenous substances, much closely related to four immunological indexes. Glycerophospholipid metabolism was found in both AM and PG groups. Pyrimidine metabolism and sphingolipid metabolism were closely regulated in AM groups. Energy metabolism (starch and sucrose metabolism, Pentose and glucuronate interconversions, together with glycerolipid metabolism) and glycerolipid metabolism were found in PG groups. These findings could contribute to the understanding of mice plasmatic metabolic profiling after long-term administration. Comparative immune-related metabolomic analysis of AM and PG was obtained on the base of pathway analysis of immune-related biomarkers. PG groups trended to have effect on cytotoxic activity of NK cells. AM groups trended to effect thymus index. Our work provides a detailed interpretation of immunological characteristics in different traditional Chinese medicine on metabonomic level.

General Linear Modelling (GLM) has been widely employed to estimate the hemodynamic changes evoked by cognitive processing, which are more likely to be nonlinear than linear. First, this study re-analyzed the fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected in the Mixed-Order Design Dimensional Change Card Sort (DCCS) task. The results indicated that the quadratic equation was better than GLM to model HbO changes in this task. Second, analysis of a new set of data indicated that the Habit-DisHabit design of DCCS was more effective in identifying the neural correlates of cognitive shifting than the Mixed-Order Design. Third, this study found that the Non-users were more attentive and engaged than the Heavy-users, with a slower but more steady increase of brain activation in BA8 and BA9.

This study aims to examine the impact of tablet use on preschoolers’ executive function during the Dimensional Change Card Sort Task (DCCS) task using the functional near-infrared spectroscopy (fNIRS). Altogether 38 Chinese preschoolers (Mage = 5.0 years, SD = 0.69 years, 17 girls) completed the tasks before the COVID-19 lockdown. Eight children never used tablets, while 16 children were diagnosed as the ‘heavy-user'. The results indicated that: (1) the 'Non-user' outperformed the 'Heavy-user' with a significantly higher correct rate in the DCCS task; (2) the two groups differed significantly in the activation of the prefrontal cortex (BA 9): the 'Non-user' pattern is normal and healthy, whereas the 'Heavy-user' pattern is not normal and needs further exploration.

The development of new diabetes drugs continues to be explored. Loureirin B, a flavonoid, extracted from Dracaena cochinchinensis, has been confirmed to increase insulin secretion and decrease blood glucose levels. For understanding the mechanism, a series of experiments had been employed based on computational methods and cell experiments. The insulin secretion significantly increased with the incubation of 0.01μM loureirin B for 4 hours. The viability of Ins-1 cells showed no significant difference with the treatment of loureirin B. Through computational methods, we hypothesized that loureirin B could interacts with K_ATP channels to promote insulin secretion. In cell experiments, it could be found that the current of K_ATP channel of Ins-1 cells was inhibited by the effect of loureirin B. After then, the voltage-dependent calcium channels were activated, the increase of Cx43 protein expression might mediate the Ca²⁺ to the intracellular. In summary, it could be concluded that loureirin B promoted insulin secretion mainly through inhibiting K_ATP current, the influx of Ca²⁺ to the Intracellular and the expression of Cx43.

Orchids are often a mystery because of their close and complex relationships with various microorganisms in the natural environment. Orchids rely on microorganisms to obtain nutrients, affecting their seed germination, protocorm, and adult plant growth. Currently, the majority of relevant research is concentrated on isolating and identifying environmental microorganisms that support orchid development and growth. With the development of metagenomic technology, our understanding of orchid mycorrhizal fungi (OMF) and root-associated bacteria (RAB) has been expanded. New research results and discoveries have emerged, which require a comprehensive assessment to provide a reference for studying microorganisms related to orchids. Therefore, we present a comprehensive summary, identifying significant inadequacies of present methodologies while providing ideas for further research.

Glaucoma, a global leading cause of blindness, necessitates accurate segmentation of the optic disc (OD) and optic cup (OC) for effective screening. However, existing segmentation methods based on convolutional neural networks (CNNs) suffer from high computational complexity and long inference times. In this paper, we propose an end-to-end segmentation method for the OD and OC using a lightweight MobileNetv3 as the feature extraction network. Our approach incorporates boundary branches and an adversarial learning network to achieve multi-label segmentation of the OD and OC. We evaluate the proposed method on three publicly available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental results demonstrate segmentation accuracies of 0.974/0.900, 0.966/0.875, and 0.962/0.880 for the OD and OC on the respective datasets, while significantly reducing inference time.

The iron roughneck is an automated equipment utilized for the connection and removal of drilling tools. This paper presents the design of an integrated iron roughneck, providing a detailed introduction to its clamp body structure, along with an analysis of its structural characteristics and performance requirements. The study delves into the integration mode and working characteristics of the clamping mechanism and spin buckle mechanism for the integrated upper clamp body structure of the iron roughneck. Additionally, this paper conducts an in-depth theoretical study on the spin buckle mechanism. Firstly, it analyzes the actual working condition of the spin buckle roller from two perspectives: contact theory and rolling friction theory, determining the structural form of the spin buckle roller. Secondly, it investigates the relative displacement between the spin buckle mechanism and the drilling tool, proposing a design method for the floating device mounted on the spin buckle roller and establishing the kinematic equation of the spin buckle roller under the influence of the floating device. Furthermore, the kinematic equations of the spin buckle roller under the influence of the floating device are established. Finally, a dynamics simulation experiment is performed to simulate the working process of the spin buckle mechanism under actual working conditions, analyzing the dynamics and kinematics of the spin buckle mechanism and obtaining the relevant parameter curves of the spin buckle mechanism and drilling tools. Through data comparison and analysis, the correctness of the theoretical analysis results and the rationality of the performance and structure of the spin buckle mechanism are verified.

Molybdenum tailings powder (MTs) is an industrial waste that contains more than 85% contents of SiO2, Al2O3, CaO, and Fe2O3. Therefore, MTs has the potential pozzolanic activity and can be used as a mineral admixture in cementitious materials. In order to understand the influence of MTs as an admixture on the cement hydration process, the hydration heat and hydration kinetics of the composite cementitious materials (CCMs) were investigated by an isothermal calorimeter and the Krstulovic-Dabic model. Furthermore, the influences of fly ash (FA), slag (SL) and MTs on hydration heat at the same content were compared and analyzed. Results show that proper amount of MTs can promote the hydration of CCMs. When the content of MTs is not more than 15%, the second exothermic peak of CCMs appears earlier and the peak heat release increases. However, when the content of MTs exceeds 15%, the second exothermic peak of CCMs is delayed and the peak heat release decreases. The cumulative heat release of CCMs gradually decreases with an increasing content of MTs. When the replacement of MTs, FA, and SL is 15% respectively, the addition of MTs enhances the second exothermic peak of CCMs compared to FA and SL. The final heat release of MTs is higher than that of FA, but lower than that of SL. The hydration process of CCMs undergoes three stages: nucleation and crystal growth (NG), interactions at phase boundaries (I), and diffusion (D). The incorporation of MTs shortens the NG stage and I stage.

To clarify effects of weathered coal humic acid on water-stable aggregates and pore characteristics of reclaimed cambisol, this research analyzed the evolution characteristics of soil aggregates and pores. Effects of different humic acid dosages (0, 1%, 3%, and 5% by weight) and application period (1 year, 2 years, and 3 years) on soil aggregates and organic carbon components in soil water-stable aggregates were investigated. The results showed that water-stable macroaggregates (>0.25 mm) had the highest content in the first year; but in the third year, water-stable microaggregates (0.053–0.25 mm) had the highest content, and silt and clay (<0.053 mm) were at a maximum. In the third year of 5% treatment, mean weight and geometric mean diameters were the largest, soil structure was the most stable, and TOC and water-stable microaggregates (0.053–0.25 mm) in soil had the highest proportions. For 5% treatment, the majority of irregular pores were independent in the first year, and number of pores decreased in the second year, but average pore volume increased, forming robust connected pores. In the third year, the number of elongated and irregular pores increased. In the third year of 5% treatment, most pores were irregular and of size >100 μm, and soil structure was the most stable.

Late onset Pompe disease is a recessive lysosomal storage disease. Clinical features include skeletal muscles deficiency and diaphragm weakness. Clinical management relies on supportive treatment and mechanical ventilation in patents with chronic respiratory failure. M mode ultrasound and tissue Doppler imaging can be used to assess and to follow up diaphragm function.

Fabry-Pérto (FP) cavity is the essential component of ultra-stable laser (USL) for gravitational wave detection, which couples multi-physic (Optical/Thermal/Mechanics) fields and requires ultra-high precision. To satisfy the requirements of precise and efficient design, a multi-physic coupling optimization method for fixing cubic FP cavity based on data learning is proposed. A multi-physic model for the cubic FP cavity is established and the performance is obtained by finite element analysis. The key performance indices (V, wF, wF) and key design variables (d, l, F) are determined considering the features of the FP cavity. The 49 sets of data by orthogonal experiment are acquired for the establishment and comparison of different data learning models (NN, RSF, KRG). The result turns out that the neural network has the best performance. Based on NSGA-II, the Pareto optimal front is obtained and the optimal combination of design variables is finally determined as {5,32,250}. The performance after optimization has proven to be a great improvement, of which the displacement under the fixing force and vibration test are decreased by more than 60%. The optimization strategy can not only help the design of the FP cavity but also enlighten other optimization fields.

Utilizing ecological engineering to manage pests by enhancing the biological control function of natural predators through the selection of high-efficiency bank plants and the establishment of bank plant systems within agricultural ecosystems is an optimal strategy. While bank plant systems have found widespread use in greenhouse crop cultivation, their application in rice fields remains limited. This study aimed to assess the efficacy of Leersia sayanuka as a bank plant for controlling planthoppers and to explore the potential of sesame as a complementary plant to enhance the effectiveness of the bank plant system through field trials. The findings revealed that planting a strip of L. sayanuka, at least 50 cm wide and extending over 1 m in length, had a notable impact on planthopper control in paddy fields. Moreover, the incorporation of Sesamum indicum, a nectar plant, into the bank plant system consisting of L. sayanuka, Nilaparvata muiri, and Anagrus nilaparvatae, led to a significant increase in the population of Anagrus spp. in rice fields. This increase was accompanied by a rise in the egg parasitism rate targeting rice planthoppers, resulting in a marked improvement in overall rice planthopper control in paddy fields. Thus, the combined system demonstrated efficiency as an effective bank plant system for managing rice planthoppers.

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, extracting the surface area affected by precipitation using a threshold method constrained by confidence intervals derived from microwave remote sensing images; Second, collecting disaster information from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms was proposed (TextCNN-Attention Disaster Classification); Finally, matching traffic road information on social media with basic geographic data, visualizing the classification of traffic road disaster risk levels, and completing the assessment of traffic road disaster levels based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this study categorizes the disaster impact on traffic roads into five levels: particularly severe, severe, moderate, mild, and affected, as interpreted from remote sensing images. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative Spatial Augmented Reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It employs a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: 1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects; 2) evaluating the system's fluidity and stability in performing complex interactive tasks; 3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrate that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

This study investigated the efficiency of scale-propellers, typically used on small drones. A scale-propeller is accepted as having a diameter of 7 to 21 inches. The Ukraine War has demonstrated the utility of relatively small, low-cost first-person view (FPV) drones, which are attritable. This investigation outlines the development of a MATLAB optimisation code, based on minimum induced loss propeller theory, which calculates the optimal chord and twist distribution for a chosen propeller operating in known flight conditions. The MATLAB code includes a minimum Reynolds number functionality, which provides the option to alter the chord distribution to ensure the entire propeller is operating above a set threshold value of Reynolds (>100,000), as this has been found to be a transition point between low and high section lift-to-drag ratios. Additional functions allow plotting of torque and thrust distributions along the blade. The results have been validated on experimental data taken from an APC ‘Thin Electric’ 10” x 7” propeller, where it was found that both the chord and twist distributions were accurately modelled. The MATLAB code resulted in a 16% increase in the maximum propulsive efficiency. Further work will investigate a direct interface to SolidWorks to aid rapid propeller manufacturing capability.

Wildfires are important natural drivers of forest stands dynamics, strongly influencing on their natural regeneration and ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns over the period 2000–2022 in the Middle Volga region of the Russian Federation on the base of remote sensing time series, considering the impact of cli-matic factors on forest fires. The temporal trends were assessed with the Mann-Kendall nonpara-metric statistical test and Theil-Sen’s slope estimator using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment indicated a high overall accuracy (> 84%) and F-score value (> 82%) for forest burnt area detection as evaluated against 581 test sites of ref-erence data. The results revealed that the fire occurrences in the region were mainly irregular with the highest frequency of 7.3 over a 22-year period. The total forest BA was about 280 thousand ha, which equals to 1.7% of the land surface area or 4.0% of the total forested area under study in the Middle Volga region. The coniferous forest stands are the most fire-prone ecosystems accounting for 59.0 % of the total BA; deciduous stands accounts for 25.1%; and insignificant fire occurrences were registered in young forests and shrub lands. On a seasonal scale, temperature generally has a greater impact on the BA than precipitation and wind speed.

The color of carcass fat is an important factor in meat quality. To investigate the characteristics and changes in lipid metabolism of SAT in Mongolian cattle at 34 M of age compared with the control group, we evaluated the color, relative expression of genes associated with browning, and profiles of lipid metabolites by using qPCR and non-targeted lipidomics techniques. Overall, the yellowness value and relative expression of mitochondrial biogenesis–specific markers, thermogenic, UCP2, and beige adipocyte marker genes in the SAT of 34 M cattle were higher than those control (p < 0.05). Using multivariate analysis, 172 significantly different lipids (SDLs) were identified. Triacylglycerols (TGs) and phosphatidylcholines (PCs) had high relative contents with abundant unsaturated fatty acids. The upregulation of TGs observed in the SAT of 34 M cattle mainly consisted of medium- and long-chain fatty acids. Pathway analysis demonstrated that 172 SDLs were primarily involved in glycerophospholipid, glycerolipid, ether lipid, and sphingolipid metabolism. In summary, yellow fat of naturally grazed cattle was impacted by many factors such as lipid metabolism, browning genes expression and age. These findings provide new insight into the characteristics and metabolic pathways of adipose tissue in natural grazing Mongolian cattle.

This paper extends the concept of weighted graphs to matrix weighted graphs. The consensus algorithms dictate that all agents reach consensus when the weighted graph is connected. However, it is not always the case for matrix weighted graphs. The conditions leading to different types of consensus have been extensively analysed based on the properties of matrix-weighted Laplacians and graph theoretic methods. However, in practice, there is concern on how to pick matrix-weights to achieve some desired consensus, or how the change of elements in matrix weights affects the consensus algorithm. By selecting the elements in the matrix weights, different clusters may be possible. In this paper, we map the roles of the elements of the matrix weights in the systems consensus algorithm. We explore the choice of matrix weights to achieve different types of consensus and clustering. Our results are demonstrated on a network of three agents where each agent has three states.

Wear-resistant steel/carbon steel composite plate not only has the double performance advantage of high strength and wear-resistance, but also can reduce energy consumption and production cost. Based on 50% reduction rate, the wear resistance of BTW1/Q345 composite was studied at different annealing temperatures, and the dry friction and wear tests of BTW1/Q345 composite at different annealing temperatures were carried out by using RETC MFT-5000. The macro-micro morphology and wear mechanism of wear marks at different annealing temperatures were analyzed by using white light interference three-dimensional surface profiler, Scanning electron microscope scanning electron microscope (SEM) and backscatter electron diffraction (EBSD) . The results show that with the increase of annealing temperature, the grain size near the scratch of BTW1 in the wear-resistant layer of the composite plate is refined, and the wear-resistant property is higher than that of the original sample.When the annealing temperature is 860℃,the wear resistance is the best.

Abstract Background: Parkinson's disease (PD) is a neurodegenerative disorder featured with motor and non-motor deficits. Using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced dopamine neuron degeneration has been widely used to produce reliable animal models of the PD. However, most of previous preclinical studies focused on the motor dysfunctions, few non-motor symptoms were evaluated. So far there is a lack of comprehensive investigation of the non-motor symptoms in animal models. Objectives: In this study, we aim to use a battery of behavioral methods to evaluate the non-motor symptoms in MPTP-induced non-human primate PD models. Methods: Cognitive functions, sleep and psychiatric behaviors were evaluated in MPTP-treated cynomolgus monkeys. The tests consisted of delayed matching-to-sample (DMTS), physical activity monitor (PAM), apathy feeding task (AFT), human intruder test (HIT), novel fruit test (NFT) and predator confrontation test (PCT). In addition, we tested whether the dopamine receptor agonist pramipexole (PPX) will improve these non-motor symptoms. Results: The present results show that the MPTP-treated monkeys exhibited cognitive deficits, abnormal sleep, and anxiety-like behaviors as compared to the control monkeys. These symptoms were relieved partially by PPX. Conclusions: These results suggest that MPTP-induced PD monkeys displayed non-motor symptoms that were similar to those found in PD patients. PPX treatment showed moderate therapeutic effects on these non-motor symptoms. This battery of behavioral tests may provide a valuable model for future preclinical research.

Driven by the rapidly growing demand for information security, covert wireless communication conceived as one of the essential technology has attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations that challenge deploying in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS) assisted covert communication system (CCS) with a friendly UAV, where the UAV generates artificial noise (AN) to interfere with the monitors. Furthermore, we model the power of AN emitted by UAV as an uncertainty model, with which the closed-form detection error probability (DEP) on the covert wireless communication for the monitor is derived. Under the derived DEP, we formulate the optimization problem to maximize the covert rate, and an iterative algorithm is designed to solve the optimization problem to obtain the optimal covert rate by using Dinkelbach method. Simulation results show that the proposed system achieves the maximum covert rate when the phase of the IRS units, the trajectory, and the transmit power of the UAV are jointly optimized.

A novel metal-doped carbon quantum dot, zinc-chlorine co-doped carbon quantum dots (Zn/Cl-CQDs), has been developed for the fluorescent probe detection of vitamin B2 and the analysis of the correlation properties of this carbon quantum dot and vitamin B2. Stability experiments demonstrate that Zn/Cl-CQDs possess good fluorescence properties under alkaline conditions. However, when vitamin B2 is added into Zn/Cl-CQDs, the fluorescence intensity decreases sharply, indicating that a fluorescence quenching phenomenon occurs between vitamin B2 and Zn/Cl-CQDs. Lastly, the use of Zn/Cl-CQDs in the detection of vitamin B2 tablets and vitamin B2-rich fruits resulted in recovery rates of 98.2% and 100.6%, respectively. Therefore, this method can be well applied to the detection and analysis of vitamin B2, and has great development prospects in the pharmaceutical industry and food monitoring fields.

Panax ginseng C. A. Meyer (P. ginseng), has been widely used in traditional Chinese medicine (TCM). It contains a number of chemical components and possesses a variety of pharmacological activities. Alzheimer's disease (AD) is neurodegenerative disease that creates a huge burden on the lives and health of individuals. In recent years, studies have indicated that the chemical components of P. ginseng, especially ginsenosides, play a pronounced positive role in the prevention and treatment of neurological diseases. This review aimed to summarize currently studies on chemical components and the mechanisms action in AD intervention treatment of P. ginseng, especially ginsenosides. In this review, the components of P. ginseng and their respective active effects were first introduced. Then the key molecular mechanisms and signaling pathways of P. ginseng were introduced, and its different active ingredients in the prevention and treatment of Alzheimer's disease-related pathogenesis were also summarized from the pathogenesis of AD Aβ generation and aggregation, hyperphosphorylation of tau protein，oxidant stress，neuroinflammation，mitochondrial damage，disorder of neurotransmitter and gut microbiota. Signaling pathway networks related to the action of ginseng active ingredients was constructed, which could serve as a therapeutic target for Alzheimer's disease. In addition to a detailed report on P. ginseng in improving Alzheimer's disease-related pathogenesis, the application of the current technology, spatial metabonomics in AD therapeutics and diagnostics were later discussed. Spatial metabonomics could be applied to investigate the multi-target intervention of P. ginseng on Alzheimer's disease. Research perspectives for the study of P. ginseng in the treatment of Alzheimer's disease were provided for further studies.

With the increase of global human activities, a large number of technogenic magnetic particles (TMPs) generated by industrial activities such as coal combustion, traffic and so on enter sediments on continental shelves. However, there is currently a lack of understanding of the spatiotemporal distribution of TMPs on continental shelves, as well as the sedimentary effects they cause. This review work comprehensively analyzes the source, morphology, composition, and spatiotemporal distribution characteristics of TMPs. Then it elucidates their magnetic contribution to sedimentary environments in continental shelf seas, as well as the influence of heavy metals and active iron. At the same time, the existing problems and future development directions of TMPs on continental shelves are summarized.

Soil extracellular enzyme activity (EEAs) and enzymatic stoichiometry (ES) can provide a crucial indication of changes in soil ecosystem's nutrient availability and the microbial resource limitations. However, the changing characteristics of soil EEAs and ES at different stages of the native succession process and their key drivers are unclear. In order to investigate the soil EEAs, ES and driving factors of soil under vegetation at different succession stages, we adopted the "spatio-temporal substitution" method to collect the surface soil of bryophyte community, herbaceous community, shrub community and tree community in the new volcanic lava platform of Wudalianchi Volcanic Nature Reserve. We measured seven soil EEA, including carbon(C)-acquiring enzyme (β-1,4-glucosidase (BG)), N-acquiring enzymes (β-N-acetyl-glucosaminidase (NAG) and leucine aminopeptidase (LAP)) and phosphorus (P)-acquiring enzyme (acid phosphatase (AP)) activities. The length and angle of vectors defined by ratios of enzyme activities (BG/(NAG + LAP) vs. BG/AP) were used to indicate relative microbial investments in C- (length), and N- and P- (angle) acquiring enzymes. Our results showed that the contents of TC, TN, TP, MBC, DOC and NO3-N in shrub community soil were significantly higher than those in bryophyte, herb and tree communities, and increased by 441%, 246%, 137%, 5570%, 12% and 484%, respectively. The highest soil EEA of C-, N- and P-acquiring were found in shrub community, and the soil EEAs/MBC of C-, N- and P-acquiring were the highest in bryophyte community. Enzyme C:N, C:P and N:P ratios increased progressively in the order of bryophyte, herb and shrub community, but the enzyme C:N, C:P and N:P ratios of tree community were both far less than shrub community. Vector lengths increased progressively in the order of bryophyte (1.16), herb (1.27), tree (1.29) and shrub (1.40), and Vector angles decreased progressively in the order of bryophyte (49.15°), herb (45.65°), Tree (45.31°) and shurb (44.54°), suggested that as succession progresses, soil microbial nutrients transforms from P limitation (angle＞45°) to N limitation (angle＜45°). Redundancy analysis showed that TC, TN, EC and C:N were important drivers of variation in soil EEAs and ES in vegetation at different succession stages. Our findings highlight that the primary succession process cause nutrient limitation transformation. Soil ES might be a sensitive indicator mediated by soil microorganisms to the relative resource limitation at different stages of the primary succession process.

The porous structure is an important three-dimensional morphological feature of the peripheral nerve guidance conduit (NGC), which permits infiltration of cells, nutrients, and molecular signals, and the discharge of metabolic waste. Porous structures with precisely customised pore sizes, porosities, and connectivities were used to construct fully permeable, semi-permeable, and asymmetric peripheral NGCs to replace traditional nerve autografts in treatment of long-segment peripheral nerve injury. In this review, the features of porous structures and classification of NGCs based on these characteristics are discussed. Common methods for constructing 3D porous NGCs in current research are described, as well as the pore characteristics and the parameters used to tune the pores. The effects of the porous structure on the physical properties of NGCs including biodegradation, mechanical performance, and permeability were analysed. Pore structure affects the biological behaviour of Schwann cells, macrophages, fibroblasts, and vascular endothelial cells during peripheral nerve regeneration. Construction of ideal porous structures is a significant advancement in the regeneration of peripheral nerve tissue engineering materials. The purpose of this review is to generalise, summarise, and analyse the preparation methods for porous NGCs and their biological functions in promoting peripheral nerve regeneration to guide development of medical nerve repair materials.

Bipolar disorder is a severe mood disorder and is one of the top 20 reasons of disability in the world. It causes a huge burden on society. In this study, the prediction models of bipolar disorder were constructed based on the concept of knowledge distillation. The input data consisted of patients of bipolar disorder and matched controls, all of which were selected from the open database MIMIC. The method of kernel density estimation (KDE) was exploited to generate probability density functions (PDF) which identify distributions of input data. The PDF values referred to as the soft labels were combined with the input data to construct the prediction models of bipolar disorder using decision tree and artificial neural network respectively. According to the evaluation results, indicators for identifying positive samples of bipolar disorder were improved. Meanwhile, the indicators for identifying negative samples have also been advanced. In addition, the branching attributes selected by the decision trees can be mapped back to specific disease diagnoses, which are all associated with bipolar disorder. In conclusion, using KDE to generate the soft label information of the input data can make knowledge distillation work and has improved the performances of prediction models for bipolar disorder.

We present a method to study the interannual variability of the annual cycle. The method consists of first determining the amplitude and phase of segments of 12 monthly means at all spatial points, resulting in one complex number per grid point and per year. The complex fields, once per year, are then subject to a complex EOF (CEOF) analysis. We consider as an example the barotropic stream function in the South China Sea as simulated with an ocean general circulation model across 6 decades of years, driven my realistic (NCEP) weather forcing. We find 3 to 4 to “significant” CEOFs, which account for about 53 to 62% of variance. These CEOFs go with large-scale patterns. Their time coefficients are mostly stationary, but point to some inhomogeneities related to instationarities in the forcing. In particular, the simulation since 1950-1958 deviates from the remainder of the simulation. The first CEOF describes variations in the center of the South China Sea. Its principal component describes a systematic, albeit noisy shift by almost 180o from 1960 to about the year 2000. When overlaid the long-term mean annual mean, the overall change consists of an amplification of the annual cycle in the 1960s and 1990s, whereas In the 1970s, the amplitude was reduced. Phase shifts in the anomaly (given by the CEOFs) have a small effect, because of the dominance of the mean annual cycle. These variations are not related to ENSO variability but may origin in variations of the Southeast monsoon. The second EOF represents strong changes, both in terms of intensity and phase, in the Luzon strait.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Alzheimer’s disease and related dementias (ADRD) remain a public health challenge in developing counties. We developed a needs-based essential care service package (ECSP) for care planning of persons living with dementia (PLWD) using a cross-sectional survey among PLWD in institutions in six cities in China (n= 1,299). Face-to-face interviews were conducted with caregivers of PLWD by trained staff between 2018 and 2019. Care service needs and utilization by the level of cognitive impairment were summarized. The average age of PLWD was 80.7 years. 76% of participants had severe cognitive impairment. The needs-based ECSP with 30 service items would be sufficient in supporting care services of PLWD in China, of which seven items are core care. The selection plan for ECSP at different levels is designed as “General Care Services + Selective Care Services”, in which service items for low-, mid-and high-level care for PLWD are 7+3, 7+6, and 7+10, respectively. The findings provide the first large-scale data on service needs and utilization of PLWD in mainland China. The ECSP for PLWD advanced in the paper was a practicable and effective quantitative management means. It is deserved to application in a large scale.

Background Hokkaido is the northernmost, least populous, and coldest of the Japanese islands. It was the first prefecture to be affected by COVID-19, while Kanagawa is home to one of the most populous areas of Japan, namely the Tokyo metro area. The Japanese government responded early during the pandemic by identifying infected patients, contact tracing, and performing PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. The government has also been publishing information about each individual who tested positive for the virus. Both Hokkaido and Kanagawa started recording COVID-19 cases in the winter of 2020 and have detailed records of thousands of patients, thus providing an invaluable resource for the transmission and behavior of the virus. Methods The current study analyzed the COVID-19 registry data from the Hokkaido and Kanagawa prefectures. The Hokkaido registry contained 1,269 cases (674 (53%) females and 595 (47%) males) recorded between February 14 and July 22, 2020. The Kanagawa registry had 3,123 cases (1,346 (43%) females and 1,777 (57%) males. The final data contained a total of 4,392 cases (2,020 (46%) females and 2,372 (54%) males). By leveraging the information on viral transmission paths available in the registry data, we performed exponential random graph model (ERGM) network analysis to examine demographic and symptomological homophilies of the SARS-CoV-2 viral transmission networks. Results We observed age, symptomatic, and asymptomatic homophilies in both prefectures. Furthermore, those patients who contracted the virus through secondary or tertiary contacts were more likely to be asymptomatic than those who contracted it from primary infection cases. The transmission networks showed that transmission occurred significantly in healthcare settings, as well as in families, although the size of the networks was small in the latter. Most of the transmissions stopped at the primary and secondary levels and no transmission beyond quaternary was observed. We also observed a higher level of asymptomatic transmission in Kanagawa than in Hokkaido. Conclusions Symptom homophilies are an important component of COVID-19 and suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can thus result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Moreover, environmental variables such as temperature and humidity may also be playing an important role in the overall pathogenesis of the virus.

The construction industry is an important part of the national economic market of various countries; since 2013, the construction industry's added value in the gross domestic product has been more than 6%, reaching 6.89% in 2022, and is a pillar industry of the national economy. Intelligent construction is the realistic demand to promote the high-quality development of China's construction industry. It is the key focus of transforming and upgrading the traditional construction industry to information, digital, and intelligent. As a new production factor, construction robots have become the key to promoting intelligent construction. Under the guidance of various national and industry policies, thanks to China's huge construction market volume and rich application scenarios, many innovative and entrepreneurial entities have entered the field of construction robots. Architectural visualization is a crucial aspect of architectural design and communication. With the development of science and technology, artificial intelligence (AI) technology is increasingly becoming an essential tool for architectural visualization and communication. The emergence of AI technology has provided architects with more flexible and creative ways to present design ideas. All along, designers who love architecture have been passionate about exploring better solutions for architecture, but there is never an optimal solution for architectural design; AI is the fire of the future era; it brings us more opportunities but also forces us to face new challenges from the future.

Livestock farming plays a crucial role in agriculture and is important in meeting people's needs for a better life in the new era. However, due to China's "double carbon" target, the development of animal husbandry has been affected by the level of carbon emissions. To study the spatial dis-tribution and dynamic evolution of livestock carbon emissions in Shandong Province, we used panel data from 16 prefecture-level cities in Shandong Province from 2001 to 2022 to determine the spatial differentiation of livestock carbon emissions in Shandong Province and their dynamic evolution by combining spatial and non-parametric estimation methods based on livestock carbon emissions and livestock emission intensity measured by city. The results of the study show that the distribution of carbon emissions from animal husbandry in Shandong Province is in a "dis-persed-aggregated" pattern, and the intensity of carbon emissions shows a gradual downward trend, with a ladder-like distribution characterized by high in the west and low in the east. Through the analysis of the dynamic evolution of nuclear density, we observe that the regional differences of animal husbandry carbon emissions in Shandong Province are expanding, showing the characteristics of polarization. The difference of carbon emissions in the peninsula is gradually narrowing, and the phenomenon of polarization is weakening. The carbon emissions of animal husbandry in the central and western regions of Shandong are polarized.

The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges such as the need for sophisticated decision-making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

With the development of artificial intelligence, the ability of capturing the background characteristics of hyperspectral imagery (HSI) is improved and promising performance in hyperspectral anomaly detection (HAD) tasks is yielded. However, existing methods proposed in recent years still suffer from certain limitations: 1) constraints are lacking in the deep feature learning process in terms of the issue for absence of prior background and anomaly information. 2) hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. 3) architectures of fully connected deep network in hyperspectral anomaly detectors lead to low utilization of spatial information and destruction for original spatial relationship of hyperspectral imagery, and disregard spectral correlation between adjacent pixels. 4) hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. With the consideration of above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD which is carried out in a self-supervised learning-based processing. Firstly, the density-based spatial clustering of applications with noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with spatial-spectral joint attention (SSJA) is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network is proposed to achieve pure background reconstruction with the ability of learning actual background distribution in hyperspectral imagery. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method on seven groups of real-world hyperspectral datasets, and the experimental results confirmed the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

The present study examined and compared the effects of high- and low-molecular weight (MW) chitosan, a nutraceutical, on intestinal and liver lipid metabolism in rats fed with high-fat diet. Both high- and low-MW chitosan decreased liver weight, elongated small intestine, improved the dysregulation of blood lipids and liver fat accumulation, and increased fecal lipid excretion in high-fat diet-fed rats. Supplementation of both high- and low-MW chitosan significantly inhibited the decreased phosphorylated AMP-activated protein kinase (AMPK)α and peroxisome proliferator-activated receptor (PPAR)α protein expressions and the increased lipogenesis/cholesterogenesis-associated protein expressions (sterol regulatory element binding protein (SREBP)1c, SREBP2, and PPARγ) and the decreased apolipoprotein (Apo)E and microsomal triglyceride transfer protein (MTTP) protein expressions in the livers of high-fat diet-fed rats. Both high and low-MW chitosan supplementation could also suppress the increased MTTP protein expression and the decreased angiopoietin-like protein (Angptl)4 protein expression in the intestines of high-fat diet-fed rats. Comparison between high and low-MW chitosan, high-MW chitosan has a higher efficiency than low-MW chitosan on the inhibition of intestinal lipid absorption and the increase of hepatic fatty acid oxidation, which can improve liver lipid biosynthesis and accumulation.

The paper is concerned with a class of stochastic differential equations in infinite dimensional Hilbert space with random coefficients driven by Teugel's martingales which are more general processes. and its optimal control problem. Here Teugels martingales are a family of pairwise strongly orthonormal martingales associated with L\'{e}vy processes (see Nualart and Schoutens). There are three major ingredients. The first is to prove the existence and uniqueness of the solutions by continuous dependence theorem of solutions combining with the parameter extension method. The second is to establish the stochastic maximum principle and verification theorem for our optimal control problem by the classicconvex variation method and dual technique.The third is to represent an example of a Cauchy problem for a controlled stochastic partial differential equation driven by Teugels martingales which our theoretical results can solve.

The application of AI technology in urban planning covers multiple levels, such as data analysis, decision support, and automated planning. Urban research relies on AI technology to understand and summarize the law of urban growth and improve the analysis of the evolution trend of urban space. Planning and design use AI technology to explore the relevant factors affecting urban development and their weights and discuss the critical role of green building technology in the sustainable development of the construction industry. With the increase in global energy consumption and carbon emissions, traditional building methods can no longer meet environmental protection requirements and efficient use of resources. As a sustainable development solution, green building technology has been paid more and more attention to and adopted by people. These technologies focus not only on the energy efficiency and environmental impact of buildings but also on the resource utilization and environmental load of green buildings over their entire life cycle driven by machine learning. This paper details the basic principles and applications of green building technologies, including AI-driven reduction of negative environmental impacts, improvement of occupant health, efficient use of resources, and optimization of indoor environmental quality. This paper focuses on the critical role of the LEED assessment system developed by the U.S. Green Building Council in advancing green building practices. In addition, the paper analyzes vital points such as water use in green building design, machine learning-driven wind environment optimization, solar technology application, and practical application cases of these technologies on a global scale.

This study examined the relationship among the three domains of executive function (EF; cognitive shifting, inhibitory control, and working memory) to test the applicability of the unity-diversity model in preschoolers using both behavioral and fNIRS approaches. Altogether 58 Chinese preschoolers (34 boys, 24 girls, Mage = 5.86 years, SD = 0.53, Age range = 4.83-6.67 years) were administered the Dimensional Card Change Sort (DCCS), go/no-go, and missing scan task. Their brain activations in the prefrontal cortex during the tasks were examined using fNIRS. First, the behavioral results indicated that the missing scan task scores (working memory) correlated with the DCCS (cognitive shifting) and go/no-go tasks (inhibitory control). But the latter two did not correlate with each other. Second, the fNIRS results demonstrated that the prefrontal activations during the working memory task correlated with those in the same regions during the cognitive shifting and inhibitory control tasks. Still, the latter two did not correlate. The behavioral and neuroimaging evidence jointly indicates that the unity-diversity model of EF does apply to Chinese preschoolers.

The assessment of concrete structures' durability in chlorine environments is significantly impacted by the uncertainty inherent in existing durability models. It introduce an integrated approach for updating these models, based on detection information of existing structures. This approach narrows the gap between theoretical predictions and observed structural durability. Specifically, we refine the probability model of critical chloride content by analyzing steel bar corrosion sample proportions, using Bayesian theory for greater accuracy. The enhanced model enables more reliable life expectancy prediction, forming a solid foundation for maintaining and strengthening existing structures.This method was demonstrated through a case study of a reinforced concrete industrial building with a service life of 12 years.

Evapotranspiration is a critical component of the hydrological cycle, and it has a decisive impact on the ecosystem balance in arid and semi-arid regions. The Yinchuan Plain, located in the Gobi of Northwest China, has a strong surface ET. In order to assess the ET pattern in this region, we obtained the ETa of Yinchuan Plain between 1987 and 2020 using the GEE platform. We employed the Surface Energy Balance Model (SEBAL), specifically geeSEBAL, to analyze the change pattern of ET over different seasons, years, and land types. The results show that: ① In spring, the daily ETa of the Yinchuan Plain is highest in the central lake wetland area, with a maximum value of 4.32 mm day-1. In summer, it is concentrated around the croplands and water bodies, with a maximum value of 6.90 mm day-1; In Autumn and winter, it is mainly concentrated around the water bodies and impervious areas, with maximum values of 3.93 and 1.56 mm day-1, respectively. ② From 1987 to 2020, the ET of the Yinchuan Plain showed an obvious upward and downward trend in some areas with significant land use changes, but the overall ET of the region remained relatively stable without dramatic fluctuations. ③ The evapotranspiration of different underlying land use types in the Yinchuan Plain region is as follows: water body>cultivated land>impervious>grassland>bare land. Our results showed that geeSEBAL is highly applicable in the Yinchuan Plain area. It allows for the accurate and detailed inversion of ET in the study area, which facilitates the study of regional hydrological cycle and water governance.

The fluctuations in both time and space of the fish larvae community in relation to hydrographic characteristics in the waters surrounding the Taiwan Bank were studied in October 2021 (autumn) and March 2022 (spring). Throughout the study period, we identified a total of 149 taxa of fish larvae, encompassing 96 genera and 71 families. Engraulis japonicus, Diaphus slender type, unidentified Gobiidae, Apogon sp., unidentified Clupeidae, and Benthosema pterotum were the six most dominant taxa, together constituted 47.39% of the total catch. No significant temporal difference in abundance of fish larvae was found, but the species number of fish larvae was more diverse in spring than in autumn. There was a notable difference in species composition between the cruises, and the cluster analysis unveiled a distinct temporal structure in the assemblage of fish larvae. The dynamics of prevailing currents induced by seasonal monsoons play an important role in the transport of fish larvae. The distribution of fish larvae was closely linked to hydrographic features, where seawater temperature and salinity emerged as the primary explanatory factors influencing the composition of fish larvae assemblages in the waters surrounding the Taiwan Bank. While the increased influx of nutrients from upwelling ensures abundant food availability, the hydrographic conditions may not be suitable for every fish larva.

The extant Keteleeria is endemic to East and Southeast Asia, while it is widely distributed in the northern hemisphere in Earth’s history. In this paper, we reported a novel wood fossil of Keteleerioxylon changchunense Shi, Sun, Meng et Yu sp. nov. collected from the middle member of Yingcheng Formation, Yingcheng Coal Mine, Changchun City, Jilin Province, Northeast China. The quantitative growth-ring analyses of K. changchunense indicate that it was evergreen and its leaf longevity was 1-3 years, which is consistent with the foliar retention of extant Keteleeria. Its high ring markedness index (RMI) indicates that the climate seasonality was pronounced during the early Albian in Songliao Basin, Northeast China. The fossil records of Keteleeria and closely related taxa indicate that this group might originate in Northeast China, spread and migrated northward during the Cretaceous, gradually decreased in the Cenozoic, and so far only survives in East and Southeast Asia.

Dye/salt separation has gained increasing attention in recent years, prompting the quest for cost-effective and environmentally-friendly raw materials for synthesis high performance nanofiltration (NF) membrane for effective dye/salt separation. Herein, a high-performance loose structured NF membrane was fabricated by via a simple vacuum filtration method using a green nanomaterial, 2,2,6,6-tetramethylpiperidine-1-oxide radical (TEMPO)-oxidized cellulose nanofiber (TOCNF), by sequentially filtrating larger-size and finer-size TOCNFs on a microporous substrate, followed by cross-linked with trimesoyl chloride. The resultant TCM membrane possesses a separating layer composed entirely of pure TOCNF, eliminating the need for other polymer or nanomaterials additives. TCM membranes exhibits high performance and effective dye/salt selectivity. Scanning Electron Microscope (SEM) analysis shows that the TCM membrane with Fine-TOCNF layer has a tight layered structure. Further characterizations via Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) confirm the presence of functional groups and chemical bonds of the crosslinked membrane. Notably, the optimized TCM-5 membrane exhibits a rejection rate of over 99% for various dyes (Congo red, orange yellow) and 14.2% for NaCl, showcasing a potential candidate for efficient dye wastewater treatment.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

The use of spherical radial basis functions (SRBFs) in regional gravity field modeling has become popular in recent years. However, to our knowledge, their potential for combining gravity data from multiple sources, particularly data with different spectrum information in the frequency domain, has not been extensively explored. Therefore, band-limited SRBFs, which have good lo-calization characteristics in the frequency domain, are the main tool in this study. We propose a residual and a-prior accuracy comparative analysis method to determine the optimal degree of expansion of SRBFs for gravity data. Using this methodology, we constructed a high-resolution geoid model called ColSRBF2023 in Colorado. The degrees of expansion for terrestrial and airborne data were set to 5200 and 1840, respectively. Results indicate that ColSRBF2023 has a standard deviation (STD) value of 2.3 cm compared to the GSVS17 validate data. This value is 2-6 mm lower than models obtained using different degrees of expansion for gravity data and models from other institutions considered in this study. Additionally, at the 1'×1' grid in the entire target area, ColSRBF2023 has an STD value of 2.4 cm when compared to the validation model. This value is also the best among the options examined in this study. These findings highlight the importance of determining the optimal expansion degree of gravity data, particularly when constructing high-resolution gravity field models in rugged mountainous regions.

Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time–frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods.

Several specific types of ordinary and generalized separation axioms of a generalized topological space have been defined and investigated for various purposes from time to time in the literature of topological spaces. Our recent research in the field of a new class of generalized separation axioms of a generalized topological space is reported herein as a starting point for more generalized classes.

As an important carrier of urban residents' public life, urban public open space plays an indispensable role in improving the quality of life of urban residents. Accessibility is an important indicator to characterize the publicity and service level of a public open space. This study optimized the GIS-based, Gaussian two-part mobile search method to quantitatively calculate and evaluate the accessibility of public open space within a 5-minute walking distance for residents in Nanshan District, Shenzhen. On this basis, social survey methods, including the questionnaire survey method via a field survey and the interview method, were used to conduct field research on residents' accessibility satisfaction. Comparing the results of the survey on residents' accessibility satisfaction to the GIS quantitative evaluation results of accessibility, the results of the field research show that current residents of Nanshan are more satisfied with the accessibility of public open space. In view of the similarities and differences between the two sets of results, the influencing factors of public open space accessibility were analyzed, corresponding countermeasures were proposed for the construction of public open spaces in the future.

Human monkeypox, caused by monkeypox virus, has spread unprecedentedly to more than 100 countries since May 2022. Here we summarized the epidemiology of monkeypox through a literature review and elucidated the risks and elimination strategies of this outbreak mainly based on the summarized epidemiology. We demonstrated that monkeypox virus became more contagious and less virulent in 2022, which could result from the fact that the virus entered a special transmission network favoring close contacts (i.e., sexual behaviors of men who have sex with men outside Africa) and the possibility that the virus accumulated a few adaptive mutations. We gave the reasons to investigate whether cattle, goats, sheep, and pigs are susceptible to monkeypox virus and whether infection with monkeypox virus could be latent in some primates. We listed six potential scenarios for the future of the outbreak (e.g., the outbreak could lead to endemicity outside Africa with increased transmissibility or virulence). We also listed multiple factors aiding or impeding the elimination of the outbreak. We showed that the control measures strengthened worldwide after the World Health Organization declared the outbreak a public health emergency of international concern (PHEIC) could eliminate the outbreak in 2022. We clarified eight strategies, i.e., publicity and education, case isolation, vaccine stockpiling, risk-based vaccination or ring vaccination, importation quarantine, international collaboration, and laboratory management, for the elimination of the outbreak.

Viral pathogens pose a substantial threat to public health and necessitate the development of effective remediation and antiviral strategies. This short communication aimed to investigate the antiviral efficacy of disinfectants on the surface proteins of human pathogenic viruses. Using in silico modeling, ligand binding energies (LBEs) of selected disinfectants were predicted and combined with their environmental impacts and costs through eco-pharmaco-economic analysis (EPEA). Results revealed the binding affinities of chemical disinfectants to viral proteins varied significantly (p

Owing to the reduction of rupture instability and the avoidance of wrinkle defect, hydrodynamic deep drawing (HDD) process is gradually becoming attractive for fabricating lightweight and complicated products. Meanwhile, since metallic material presents anisotropic deformation behavior, it is necessary to select an appropriate constitutive model for the prediction of plastic deformation behavior of applied material with high precision. In the present research, several anisotropic yield criteria namely, Hill’48, Yld2000-2d and BBC2005 are implemented to investigate the effect of yield functions on the prediction accuracy of the critical process window and deformation behavior for HDD process of 2024 and 5754 aluminum alloys. Material constants in the yield criteria are determined by applying uniaxial and equi-biaxial tension tests and optimizing an error-function by using the Levenberg-Marquardt algorithm. Furthermore, the process window diagram is computed utilizing the stress analytical model combined material properties with workpiece geometrical features. Numerical simulation results of predicted material anisotropic parameters, process window and HDD deformation for aluminum alloys are compared with the experimental data. Through the comparison of diverse yield criteria based on materials anisotropic coefficients, critical process window prediction, earing profile, and thickness distribution, it is revealed that the Yld2000-2d and the BBC2005 yield criteria can offer more precise models of material behavior in planar anisotropy properties for HDD process of 2024 and 5754 aluminum alloys.

Through the geometric relationships and force analysis of the main components of the panto-graph on high-speed trains, coefficients of aerodynamic forces and lift transmission between the pantograph and main components under crosswind conditions were derived. Based on the aer-odynamic forces acting on the pantograph at different crosswind speeds, wind angles, and oper-ating speeds, the aerodynamic lift of the pantograph and main components was ultimately de-termined. The results indicate that the aerodynamic lift of the pantograph is mainly distributed on the bow structure, with the aerodynamic lift of the upper frame all being negative values, while the absolute value of the aerodynamic lift of the lower arm rod is the smallest. The oper-ating speed of the pantograph and the wind angle of the crosswind have a significant impact on the aerodynamic lift of the main components, while the impact of the crosswind speed is rela-tively small. At the same operating speed of the pantograph, the lower the corresponding crosswind speed, the smaller the aerodynamic lift of the pantograph. The aerodynamic lift of the pantograph tends to decrease gradually with the increase of crosswind speed, and the impact of crosswind speed decreases gradually with the increase of pantograph operating speed. A com-prehensive relationship formula between the aerodynamic lift of the pantograph and the oper-ating speed, crosswind speed, and wind angle is obtained, and the empirical formula for the contact force of the bow net and train operating speed is modified. The research results are of great significance and value for the study and application of lift forces on pantographs under crosswind conditions.

The device and method in accordance with the constant pressure regulation of micro droplet PCR in microfluidic chips are developed to explore the optimization way for the micro droplet movement, fragmentation, and bubble generation in microfluidic chips in existing digital PCR technology. In the developed device, an air source device is adopted to regulate the pressure in the chip, such that micro droplet generation and PCR amplification without bubbles can be achieved. In 3 min, the sample in 20ul will be distributed into nearly 50,000 water-in-oil droplets exhibiting a diameter of about 87μm, and the micro droplet will be subjected to a close arrangement in the chip without air bubbles. The device and chip are adopted to quantitatively detect the human gene . As indicated by the experimental results, a good linear relationship exists between detection signal and DNA concentration ranging from 101~ 105 copies /μL (R2=0.999). The micro droplet PCR devices based on constant pressure regulation chips exhibit a wide variety of advantages (e.g., achieving high pollution resistance, micro droplet fragmentation and integration avoidance, reducing human interference, and standardizing results). Thus, micro droplet PCR devices based on constant pressure regulation chips have promising applications in nucleic acid quantification.

We aimed to evaluate the protective effects of acetyl-xylogalactan on the activity of RAW264.7 macrophages against heat stress. To this end, we assessed cell survival, phagocytic activity, in-tracellular Ca2+ level, mitochondria potential exchange, apoptotic assay findings, galactosidase activity and the RNA-seq by NGS and real-time polymerase chain reaction (PCR) expression. In our evaluation of macrophage morphology at 37°C and 41°C, the macrophages showed an oval shape at 41°C , unlike the spindle shape at 37°C. Therefore, 41°C was chosen as the heat stress condition. Subsequently, we designed an experiment to evaluate changes in the RAW264.7 macrophages after acetyl-xylogalactan treatment under heat stress. The survival of RAW264.7 macrophages treated with acetyl-xylogalactan was higher than that of controls that were not treated with acetyl-xylogalactan. Moreover, on the basis of the results of the annexin-V detection assay, the apoptotic activity of macrophages appeared to have reduced after treatment with ac-etyl-xylogalactan. Moreover, treatment with acetyl-xylogalactan resulted in a stronger recovery trend in the intracellular Ca2+ and mitochondrial membrane potential after heat stress. RNA sequencing and real-time polymerase chain reaction (PCR) illustrated that Sarcodia suieae acetyl-xylogalactan could upregulate the expression of the anti-apoptosis Cflar gene and down-regulate the expression of the apoptosis factors Ddit3, and Hyou1 to protect macrophages under heat stress.

Monitoring the dynamic responses of bridge structures has received considerable attention. It is important to synchronously measure both the quasi-static and dynamic displacements of bridge structures. However, the traditional accelerometer method cannot capture the quasi-static displacement component, although it can detect the dynamic displacement component. To this end, a novel composite instrument of a smartstation was proposed to monitor vibration displacements of footbridges. Full-scale experiments were conducted on a footbridge to validate the feasibility of the composite instrument-based monitoring method. A Chebyshev filter and wavelet algorithms were developed to process the composite instrument measurements. It was concluded that the measurement noise of the composite instrument was mainly distributed in a frequency range of 0–0.1 Hz. In two case studies with displacement peaks of 5.7–10.0 mm and 1.3– 2.5 mm, the composite instrument accurately identified the quasi-static and dynamic displacements. The composite instrument will be a potential tool for monitoring structural dynamics because of its enhanced overall performance.

This narrative review critically evaluates the multidimensional impacts of team sports on student wellness through the Wellness Wheel framework, encompassing physical, emotional, intellectual, social, spiritual, occupational, and environmental well-being. Methodologically, the review synthesizes empirical studies, systematic reviews, and meta-analyses from the last 20 years, extracted from databases such as PubMed, PsycINFO, Scopus, Web of Science, and Google Scholar, to construct a comprehensive understanding of how team sports contribute to holistic student development. Our findings highlight team sports as a vital component of educational and wellness programs, significantly enhancing not just physical health but also emotional stability, cognitive performance, social skills, spiritual enrichment, and environmental awareness. The review underscores the necessity of integrating team sports into educational curricula to promote a balanced and comprehensive approach to student well-being. It also identifies existing gaps in literature, particularly the need for further research on enhancing inclusivity and accessibility in team sports, ensuring that all students can benefit from their wide-ranging impacts. This study advocates for strategic initiatives to incorporate team sports in educational settings, emphasizing their essential role in cultivating a well-rounded, healthy, and inclusive student body.

Celiac disease is an autoimmune disease triggered by oral ingestion of gluten, with certain gluten residues resistant to digestive tract enzymes. Within the duodenum, the remaining peptides incite immunogenic responses, including the generation of autoantibodies and inflammation, leading to irreversible damage. Our previous exploration unveiled a glutenase called Bga1903 derived from the Gram-negative bacterium Burkholderia gladioli. The cleavage pattern of Bga1903 indicated its moderate ability to mitigate the toxicity of pro-immunogenic peptides. The crystal structure of Bga1903, along with the identification of subsites within its active site, was determined. To improve its substrate specificity toward prevalent motifs like QPQ within gluten peptides, the active site of Bga1903 underwent site-directed mutagenesis according to structural insights and enzymatic kinetics. Among the double-site mutants, E380Q/S387L exhibits an approximately 38-fold increase in its specificity constant toward the QPQ sequence, favoring glutamines at the P1 and P3 positions compared to the wild type. Not only does the heightened specificity of E380Q/S387L improve the hydrolytic activity against pro-immunogenic peptides, but also holds this version of the enzyme as a promising candidate as an oral therapy enzyme for celiac disease.