In the process of expanding the application of electronic licenses in the Chinese agricultural sector, challenges such as inconsistent information standards, poor interoperability, significant data barriers, and lack of regulatory basis are encountered. By examining online information and practical data of agricultural electronic licenses in the 31 provincial-level administrative units in mainland China, variations in the reasons for promoting the expanded application of electronic licenses in different regions were identified. It is believed that strategies should be proposed around key factors such as government capacity, economic development, social needs, and intergovernmental relations. Suggestions include enhancing the intensity of information aggregation, diversifying the management entities, advancing the intelligence of underlying technology, and improving the completeness of legal rules. New conceptual proposals, such as constructing a database for agricultural information collection, establishing a multi-entity management authorization mechanism, implementing intelligent applications of electronic licenses at the grassroots level, and developing specialized legal rules, require further validation.

The flowering of Amorphophallus bulbifer plays an important role in its reproduction. The flowers and leaves of A. bulbifer cannot grow at the same time. However, the physiological and molecular mechanisms involved in flower bud and leaf bud formation are still unclear. In this study, the flower buds and leaf buds of A. bulbifer in the early stage of growth were used as research materials, transcriptome and metabolome analyses were carried out, and the soluble sugar and starch contents of A. bulbifer corms were determined. Transcriptome analysis revealed 5 542 differentially expressed genes (DEG) between flower buds and leaf buds, 3 107 of which were upregulated and 2 435 of which were downregulated. Enrichment analysis of the KEGG pathway showed that these differential genes were enriched mainly in the plant hormone signal transduction, DNA replication and fatty acid elongation pathways. Plant hormone signal transduction, DNA replication and fatty acid elongation pathways. A total of 5 296 significant differentially abundant metabolites were screened out by nontargeted metabolomics analysis. The differentially abundant metabolites were functionally classified in the HMDB, and 118 were successfully matched, including 17 that were highly expressed in flower buds. The differentially abundant metabolites in the flower buds were mainly enriched in pathways such as amino acid metabolism, isoquinoline alkaloid biosynthesis and pyrimidine metabolism. Targeted metabolomics analysis revealed that the contents of ABA, ZT and iPA in flower buds were significantly greater than those in leaf buds, while the opposite trend was observed for IAA. The analysis of soluble sugar and starch contents showed that the starch and soluble sugar contents in flower buds were significantly greater than those in leaf buds. The results of this study showed that flower bud development in A. bulbifer was regulated by amino acids, starch, ABA, ZT, iPA, IAA and other hormones. These findings could lead to valuable genetic resources for further study of A. bulbifer flowering and provide a deeper understanding of the molecular basis of A. bulbifer flowering.

Transcription factors (TFs) in the homeodomain-leucine zipper (HD-ZIP) family serve as essential regulators of plant development and control responses to environmental stimuli. To date, however, the HD-ZIP gene family in Prunus nana remains to be fully characterized. Accordingly, a genome-wide analysis of P. nana HD-ZIP family genes was performed using the most recent genomic data available, leading to the classification of 30 HD-ZIP TFs. These genes were annotated and subjected to systematic analyses of phylogenetic relationships, protein physicochemical properties, sequence-based structural characteristics, chromosomal distributions, and associated cis-acting regulatory elements. High levels of diversity were observed with respect to the gene structures for these PnaHD-Zip genes and the cis-regulatory elements found in the promoter regions upstream of these genes, suggesting that they play diverse roles in a range of biological contexts. These 30 PnaHD-Zip genes were further classified into four subgroups based on the results of phylogenetic, gene structure, and conserved motif analyses. Subsequent qPCR analyses indicated that PnaHD-Zip1 and PnaHD-Zip7 expression levels tended to increase was continuously inhibited in response to cold stress, suggesting that proteins in this HD-ZIP gene family may exhibit distinct responses to low-temperature stress exposure. Overall, these results offer a robust foundation for future studies seeking to explore the functional roles that HD-ZIP TFs play as regulators of cold stress tolerance in P. nana, in addition to offering more general insight regarding the regulatory functions and characteristics of these different HD-ZIP genes in P. nana.

conclusions. The blends of ultrahigh molecular weight polyethylene/polypropylene (UHMWPE/PP) possess excellent processability, but limited by its reduced mechnical properties. Herein, the high mechanical properties and wear—resistance UHMWPE/PP composites were fabricated by the addition of silicon carbide (SiC) nanoparticles under a consecutive elongational flow without any other additives or solvents. The morphology and thermal properties, mechanical properties, wear resistance as well as scratch resistance of UHMWPE/PP/SiC composites were investigated . The mechanical properties and friction resistance of composites increase initially then decrease with the increase content of SiC. The optimum mechanical properties of UHMWPE/PP/SiC composites are achieved when the content of SiC is 3 phr. In addition, both the hardness and scratch resistance of composites improved upon increasing the content of SiC. These improved mechanical properties and wear resistance originate from the well disperison of SiC nanoparticles along with favourable interfacial adhesive under a consecutive elongational flow.

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

Due to its high power, high efficiency, low pollution, and compact size, permanent magnet synchronous motors (PMSMs) have been widely used in a variety of fields, including electric vehicles, aerospace, wind turbines, and marine devices which are used in renewable, sustainable, and environmentally friendly energy resources. However, in these practical scenarios, the motor operating conditions are complex and variable. Under high temperatures and high current conditions, PMSMs may experience demagnetization failures, not only leading to performance degradation but also inducing unexpected failures of the motors. To reduce the risk of unexpected losses caused by demagnetization faults and improve the safety and reliability of motor systems, it is necessary to apply automated monitoring of the magnet flux of the motor's permanent magnets and achieve real-time diagnosis of early demagnetization faults, ensuring the safe operation of the motor. This review article tries to summarize the current detecting methods of the automated monitoring of demagnetization faults in PMSMs. The main online monitoring technologies from both practical and academic perspectives were summarized and their benefits and challenges have been reviewed. Finally, the research trends and suggestions for future improvements are provided. This review article will not only shed light on the origins of the automated monitoring of demagnetization faults but also help to design highly effective and sustainable permanent magnet synchronous motors.

Effective biomarkers are essential to the early diagnosis of non-small cell lung cancer (NSCLC). Herein, a retrospective study of 49 newly diagnosed and recurrent NSCLC patients, 31 patients with benign pulmonary disease and 24 healthy volunteers was conducted, to evaluate the diagnostic value of circulating rare cells for NSCLC. The expression of circulating tumor cells (CTCs) and circulating tumor-derived endothelial cells (CTECs) in peripheral blood were measured by subtraction enrichment-immunostaining-fluorescence in situ hybridization (SE-iFISH). The level of CTCs (P＜0.001) and CTECs (P＜0.001) was significantly higher in NSCLC group than that in benign pulmonary disease group. The proportion of small CTCs (P＜0.001) and CTECs (P＜0.0001) significantly increased from benign lung disease individuals to NSCLC patients. The AUC of ROC curves of total CTCs and CTECs were 0.815 (95%CI: 0.722~0.907), 0.739 (95%CI: 0.618~0.860), respectively. The cut-off values for discriminating NSCLC with benign lung disease patients were total CTCs 11.5 units/6ml and total CTECs 10.5 units/6ml, with sensitivity and specificity being 67.3% and 83.9%, 77.6% and 77.4%, respectively. When CTCs and CTECs were combined, predictive value significantly increased to 82.6% as measured by the area under the curve. Small CTCs and triploid CTCs had high positive predictive value (PPV) and positive likelihood ratio (LR+) of the diagnosis of NSCLC in early stage. CTCs and CTECs can not only be used as new biomarkers for the diagnosis of NSCLC, but can also improve diagnostic performance of the early stage NSCLC. Moreover, the combined examination of CTCs and CTECs is be superior to the single.

We report a prebiotically relevant solution to the N1-ribosylation of pyrimidine nucleobases, a well-known challenge in the RNA World hypothesis. It is found that the presence of metal cations and clay mineral enables the previously unachievable direct ribosylation of uracil, providing by far the highest yield. Spectroscopy and chromatography analyses confirmed the formation of ribosylated uracil. The method can also be extended to the ribosylation of 2-pyrimidinone. These findings are also compatible with the metal-doped-clay model developed by our lab for the unified route of the selection of ribose and subsequent syntheses of nucleotide and RNA.

The nonlinear dynamics exhibited by aircraft flying at high angle of attack (AOA) pose great challenges to the design of flight control. The control design based on nonlinear dynamic inversion (NDI) is to reversely find the required control input based on the current nonlinear dynamics of the aircraft and the control objectives to be achieved. Since it is derived from the nonlinear dynamics of the aircraft, a single NDI controller can cover the entire flight envelope without the need for controller switching or gain scheduling. This paper employs the layered structure of the flight control loop to implement the NDI controller through the combination of aerodynamic control and thrust vectoring control (TVC). The resulting NDI controller is applied to an experimental F-16 installed with TVC to verify its high AOA maneuverability in the post-stall region. The verification simulation shows that the experimental F-16 under the NDI control can effectively track the high AOA command and successfully demonstrate the Herbst maneuver by performing the heading reversal with 70° AOA.

Real estate has the dual characteristics of consumption and investment. Under the inherent concept of "where there is land, there is wealth", real estate has become the investment target of the main asset. In addition to the supply and demand of the market will affect the price of real estate, many general economic factors will also have a certain impact on the price of real estate. This paper pays a special focus on the effects of MRT on housing prices, finding the closer a house is to an MRT station, the higher its price. In addition, convenience stores are very popularized, and this research report is also interested in whether the number of convenience stores within walking distance will affect the house price. In a word, these two factors do affect housing prices significantly.

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

In order to make full use of the complementarity of existing navigation systems to improve the navigation and positioning accuracy, this paper investigates the visual/INS integrated navigation system based on the factor graph optimization (FGO) algorithm. In the traditional visual/INS integrated navigation system, there is often the problem of image blurring due to the rapid change of light and shadow captured by the vision sensor and the attitude change of the carrier, which leads to the decrease of the positioning accuracy of the combined navigation system. Therefore, this paper firstly introduces Retinex algorithm and BID algorithm to carry out the design of image de-blurring method; then implements the visual/INS integrated navigation algorithm based on the factor graph optimization method; finally, the experimental scheme is designed, comparative experiments are carried out, and the researched method is verified by using the public dataset. The experimental results show that the combined navigation algorithm realized in this paper has high accuracy, and its positioning accuracy is improved by 40% compared with visual positioning.

Maxwell's equations accurately describe the propagation of electromagnetic (EM) waves. However, the conventional formulation implies invariance to observers, posing challenges in explaining how the wave is perceived by different observers. For example, Doppler effect shows that observers perceive the same EM wave differently.By employing mathematical transformations, we derive a general form of Maxwell’s wave equations by incorporating a "time scaling" factor to account for observer perception. The original form is shown to be a special case when static. The Doppler effect and Sagnac effect are directly derived from Maxwell’s equations. All the results are consistent with established experiments.Our findings offer a fresh perspective, promising a deeper understanding and unification of EM phenomena. We extend Maxwell’s equations to describe not only the propagation of an EM wave, but also how it will be measured by different observers, say, antennas.

The black hole (BH) phenomenon is an intraluminal restenotic lesion. It was identified by intravascular ultrasound (IVUS) and optical coherence tomography (OCT) after intracoronary brachytherapy and drug-eluting stent implantation. Despite the similarity in the mode of action of brachytherapy and drug-eluting stent implantation, the BH phenomenon appears to be uncommon after drug-eluting stent implantation. Specifically, the BH phenomenon is better identified by OCT than by IVUS. Herein, we present a case of in-stent restenosis with suspected BH phenomenon on IVUS confirmed by OCT.

On 17 October 1947, Niels Bohr was made a knight of the Order of the Elephant by the King of Denmark in view of his outstanding achievements and contributions to science. Bohr designed his own coat of arms that featured a pattern of Yin and Yang (Tai Chi symbol) to symbolize the wave-particle complementarity. However, Bohr’s Yin-Yang diagram (YYD) was neither drawn based on the principles of quantum mechanics, nor did it originate from the traditional Taoist YYD. Scientists still have doubts about the legitimacy of using YYD as the icon of the wave-particle complementarity, because the YYD belonging to quantum mechanics itself is unknown so far. This article reports the YYDs existing in quantum mechanics and justifies the role of YYD in the wave-particle duality by showing that any system, whether classical or quantum, has an ideal YYD as long as it satisfies Bohr’s principle of complementarity (BPC). The deviation of a deformed YYD from the ideal YYD indicates the extent to which a real system satisfies BPC. This article constructs the quantum YYD by the complex quantum trajectory of a particle tunneling via a step barrier, which displays the continuous transition between the wave behavior and the particle behavior. It appears that the YYD designed by Bohr in his coat of arms resembles the YYD generated by tunneling motion, not only in appearance but also in the governing equation.

We explored the dynamics of the two interacting information systems. We show that for the Markovian marginal systems the driving force for information dynamics is determined by both the information landscape and information flux. While the information landscape can be used to construct the driving force to describe the equilibrium time reversible information system dynamics, the information flux can be used to describe the nonequilibrium time-irreversible behaviours of the information system dynamics. The information flux explicitly breaks the detailed balance and is a direct measure of the degree of the nonequilibriumness or time irreversibility. We further demonstrate that the mutual information rate between the two subsystems can be decomposed into the equilibrium time-reversible and nonequilibrium time-irreversible parts respectively. This decomposition of the mutual information rate (MIR) corresponds to the information landscape-flux decomposition explicitly when the two subsystems behave as Markov chains. Finally, we uncover the intimate relationship between the nonequilibrium thermodynamics in terms of the entropy production rates and the time-irreversible part of the mutual information rate. We found that this relationship and MIR decomposition still hold for the more general stationary and ergodic cases. We demonstrate the above features with two examples of the bivariate Markov chains.

The proliferation of ransomware poses a significant threat to global cybersecurity. This study presents a comprehensive review of the methodologies employed in the detection and analysis of ransomware, emphasizing the dichotomy between static and dynamic analysis approaches. It introduces the historical context and the necessity for robust cybersecurity measures, followed by an outline of the methodological framework used to evaluate existing ransomware analysis techniques. The results detail the effectiveness and limitations of various analysis strategies, identifying key features and patterns that aid in the detection and classification of ransomware threats. The study concludes by summarizing the primary achievements, including the identification of gaps in current research and proposing future research directions aimed at enhancing ransomware detection and mitigation strategies. The synthesis provided in this survey offers a consolidated view of the state-of-the-art in ransomware threat analysis and serves as a resource for cybersecurity professionals and researchers.

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

For an enterprise, the most critical aspect of development is resource management, which has a significant impact on all aspects of the enterprise. Therefore, enterprises must pay attention to resource management allocation, which can better promote the sustainable development of the enterprise and obtain optimal benefits. In the production and development of modern enterprises, the acquisition of benefits also involves the allocation of resources in enterprise management. This paper proposed a benefit optimization scheduling algorithm based on a macroeconomic prediction model under auction mechanism and a grid resource scheduling algorithm driven by a benefit function to allocate resources reasonably. This article used macroeconomic forecasting models to fully understand the resource needs of various departments and the resources held by enterprises, and rationally allocate various resources in various departments. This can improve the work efficiency of various departments, thereby reducing the cost of the enterprise, and achieving optimal benefits for the enterprise. The experimental results in this paper showed that the cost of resource management allocation for the benefit optimization scheduling algorithm and the grid resource scheduling algorithm based on the benefit function driven under the auction mechanism was 105.6 yuan and 46.8 yuan respectively when the task volume was 125 under the multi user environment. The time allocated for resource management was 36.6s and 18.9s respectively. It can be seen that the efficiency function driven grid resource scheduling algorithm had a lower cost and time for resource management allocation, so the efficiency function driven grid resource scheduling algorithm can achieve enterprise efficiency optimization.

The principle of the constancy of the velocity of light, which stated that the light velocity is invariant to the motion of the emitter, was well established and directly proven by experiments. Interestingly, the further assumption that the light velocity is independent of the motion of the observer was, arguably, never directly proven by any experiment for a century. This paper proposed the design of two experiments to directly test this assumption, which tried to address some perceived technical difficulty in such experiments. One is to directly measure the light speed as to moving sensors. The experiment setup is designed in such a way that the concerns of time synchronization and dilation can be avoided. Another experiment is to test the isotropy of the light speed to a moving particle in the electromagnetic accelerator by measuring the momentum to acceleration ratio. The experiment result, if positive, will provide direct and solid proof of the assumption. Otherwise, it may imply a need for further investigation. Since the light speed invariance to moving observers is a key assumption of some fundamental physical theory, either way, the experiments will have significant meanings.

This study examines the unique circumstances surrounding state-owned equity participation in non-state-controlled enterprises in China. Specifically, this study examines the impact of state-owned equity participation on the environmental, social, and governance (ESG) performance of non-state-controlled enterprises. Focusing on A-share listed firms on the Shanghai and Shenzhen Stock Exchanges and using data from 2013 to 2021, empirical testing shows that state-owned equity participation can significantly improve the ESG performance of non-state-controlled enterprises, with this conclusion remaining reliable after a series of robustness tests. Top management incentives is a mediating mechanism for state-owned equity participation in enhancing ESG performance. This study also finds that when state-owned equity participates in large enterprises or companies with a high degree of digital transformation, the effect on ESG performance is greater than that of small to medium-sized enterprises (SMEs) or enterprises with a low level of digital transformation. The findings of this study add to the current body of the existing field of research on the factors influencing corporate ESG performance and the impact of state-owned equity on corporate non-financial performance.

The study of co-fusion was one of the essential topics in the history of metallurgy in China. Simulation experiments had been an essential concept in the study of the co-fusion steelmaking process. This paper mainly studied the simulation experiments of co-fusion from two aspects: the replication of co-fusion swords by three different methods, and the micro-analysis of the co-fusion samples. The experimental results indicated that several co-fusion swords could be made by different processes, but the carbon content and surface hardness were quite different. During repeated forging welded, the microstructure of the samples transformed from laminated to homogenized, finally the steel with a uniform carbon content was obtained. It was challenging to find the characteristics of co-fusion from the homogenized samples. The results prompted people to rethink the microstructure characteristics of ancient co-fusion artifacts.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

Saccharomyces cerevisiae fermentation products (SCFP) can potentially promote gastrointestinal growth and immunity in young livestock. However, reports within the existing literature on the effects of SCFP supplements on calves have been inconsistent; therefore, we perform the following experiments to resolve the inconsistencies. A total of 22 Holstein calves [10 d after birth, BW = 48.93 ± 3.99 kg (mean ± SD)] were assigned randomly into two groups, namely the control group (CON) and SCFP group, each having 11 replicas. The calves in the CON were fed a basal diet, while the SCFP group was fed the basal diet supplemented with 5g/head/d SCFP (NutriTek, Diamond V, Cedar Rapids, IA52404, United States) incorporated into feed. All the calves were regularly fed thrice daily at 08:00, 14:30, and 21:00 and had free access to water. A 5-day adaptation phase was followed by a 45-day experimental period. The results showed that compared to the CON, at the end of the d-45 trial, the body weight was significantly greater in the SCFP group (p < 0.05), and during the 1-45 days, the ADG was higher (p < 0.05). The FCR in the 30-45 days SCFP group was higher (p < 0.05). Furthermore, the apparent digestibility of DM, CP, EE, ADF, Ca, and P were significantly increased in the SCFP group, except for NDF (p < 0.05). The concentration of GH and IGF-1 in serum showed a tremendous increase (p < 0.05) with SCFP supplementation on d 15 and d 45. On d 15, SCFP supplementation significantly increased the serum IgA contents (p < 0.05). Notably, on d 15 and d 45, the serum concentrations of IL-1β, IL-6, and TNF-α reduced (p < 0.001). Moreover, the Actinobacteriota in the SCFP group were significantly lower than those in the CON group (p = 0.034). SCFP significantly increased the abundance of Butyricimonas, Parabacteroides, and Ruminococcus. The differences among sob, Chao1, and PD-tree groups were statistically significant (p < 0.05). In conclusion, these results demonstrate that SCFP supplementation improved ADG, apparent digestibility and serum hormone, enhanced immunity, and regulated gut microbiota, thereby jointly promoting the growth of pre-weaning calves.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

The fluorine-less noble-gas containing anions OBONgO- and OCNNgO-have been studied by correlated electronic structure calculation and density functional theory. The obtained energetics indicates that for Ng = Kr and Xe, these anions should be kinetically stable at low temperature. The molecular structures and electron density distribution suggests that these anions are stabilized by ion-induced dipole interactions with charges concentrated on the electronegative OBO and OCN groups. The current study shows that in additional to the fluoride ion, polyatomic groups with strong electronic affinities can also form stable noble-gas containing anions of the type X-...NgO.

The Paeonia suffruticosa, called as 'Feng Dan', has been used for thousands of years in traditional Chinese medicine. In our chemical investigation on the root bark of the plant, five new phenolic dimers, namely paeobenzofuranone A‒E (1‒5), have been characterized. Their structures were determined by spectroscopic analysis including 1D and 2D NMR, HRESIMS, UV, and IR, as well as ECD calculations. Compounds 2, 4, and 5 showed cytotoxicity against three human cancer cell lines with IC50 values ranging from 6.7 to 25.1 μM. Compounds 1 and 2 showed certain inhibitory activity on NO production. To the best of our knowledge, the benzofuranone dimers and their cytotoxicity of P. suffruticosa are reported for the first time.

Scaffoldings play a critical role as temporary structures in supporting construction processes. Accidents at construction sites frequently stem from issues related to scaffoldings, including insufficient support caused by deviations from the construction design, insecure rod connections, or absence of cross bracing, which result in uneven loading and potential collapse, leading to casualties. In this study, we introduce a deep-learning-based, augmented reality (AR)-enabled system called scaffolding assembly deficiency detection system (SADDS) to assist field inspectors in identifying deficiencies in scaffolding assemblies. Inspectors have the flexibility to utilize SADDS through various devices, such as video cameras, mobile phones, or AR goggles (i.e., Microsoft HoloLens 2), for automated detection of deficiencies in scaffolding assemblies. The training test yielded satisfactory results, with a mean average precision of 0.89 and individual precision values of 0.96, 0.82, 0.90, and 0.89 for “qualified” frames and frames with the “missing cross-tie rod,” “missing lower-tie rod,” and “missing footboard” deficiencies, respectively. Field tests conducted at two construction sites demonstrated improved system performance compared to that in the training test. However, these field tests also revealed certain limitations of the SADDS system.

The recurrent coronavirus outbreaks in China (SARS-CoV and its relative, SARS-CoV-2) have raised speculations that perhaps Asians are somehow more susceptible to these coronaviruses. Here, we test this possibility based on an analysis of the lung-specific expression of ACE2, which encodes the known cell-entry receptor of both SARS-CoV and SARS-CoV-2. We show that ACE2 expression is not affected during tumorigenesis, supporting that the abundant transcriptomes in cancer genomic studies can be informatively used to study ACE2 expression among diverse individuals without cancer. We find that ACE2 expression in the lung increases with age, but is not associated with sex. Further, Asians do not differ from other populations for ACE2 expression and do not harbor unique genetic polymorphisms in the ACE2 locus. Thus, beyond illustrating an innovative method for assessing the potential impacts of demographic factors for non-cancer diseases from large-scale cancer sample datasets, our statistically robust findings emphasize that individuals of all races require the same level of personal protection against SARS-CoV-2.

Contaminants of Emerging Concern (CECs), a new category of contaminants in limelight, is a major issue of global concern. The pervasive nature of CECs and its harmful effects, such as cancer, reproductive disorders, neurotoxicity etc., make the situation alarming. The perilous nature of CECs lies in the fact that even very small concentration of CECs can cause great impacts on living beings. It also has a nature of bioaccumulation. Thus, it is the ultimate need to have efficient sensors for detection of CECs to ensure safe living environment. Electrochemical sensors are an efficient platform for CEC detection as they are highly selective, sensitive, stable, reproducible, and prompt, and can detect very low concentrations of analyte. Major classes of CECs are pharmaceuticals, illicit drugs, personal care products, endocrine disruptors, newly registered pesticides, and disinfection by-products. This review focusses on CECs, including their sources and pathways, health effects caused by them, and electrochemical sensors as reported in literatures under each category for the detection of major CECs.

Introduction: Treating elbow fracture-dislocation is a challenge, especially in the situation of basal-coronoid trans-ulnar fracture. The study ams to explain whether or not the minimal invasive surgery (MIS) would be feasible in elbow dislocation with coronoid fracture. Materials & Methods: At Taipei Veterans General Hospital, patients who had elbow dislocations with coronoid process fractures underwent single one surgeon’s MIS techniques which included the fluoroscopy-guided ulnar anteromedial (FGUAM) approach in the stage of reducing coronoid process. When there is a proximal ulnar fracture, the posterior incision should be necessary, followed by the incision over the lateral or medial elbow in treating radial fractures or ligament injuries. Results: The Flow Diagram about approach recommendation was established in base of defining MIS as not to do cross-plane dissection. Anteriorly rigid fixation for the coronoid process was also emphasized. Conclusions: MIS can be achieved by multiple limited surgical incisions. However, if there is an unstable ulnar metaphysis or ligament avulsion fracture, the posterior approach has to be extended and the so-called MIS fails to be established.

GaN ultraviolet photodetector with metal-semiconductor-metal structure is achieved by growing on a periodic trapezoid column-shape patterned sapphire substrate using metalorganic chemical vapor deposition. Under 5-V reverse bias, the photodetector fabricated on such patterned sapphire substrate exhibits a lower dark current, a higher photocurrent, and a 476 % enhancement in the maximum responsivity as compare with those of the photodetector fabricated on conventional flat sapphire substrate. It is also found that the much larger UV-to-visible rejection ratio and the fact that responsivity drops in a smaller cut-off region are observed from photodetector fabricated by using a periodic trapezoid column-shape patterned sapphire substrate. These phenomena may all be attributed to the reduction of threading dislocation density and the improved quality of GaN film, as well as the internal reflection and/or scattering effect on the interface between GaN film and the periodic trapezoid column-shape pattern of the substrate.

In order to study the effect of explosion load on the flexural performance of RC beams, two RC beams of the same size were carried out explosion test and bearing capacity test successively. Piezoelectric intelligent aggregate was embedded in the beams in advance to monitor the damage state of the beams, which realized the whole process of damage monitoring of the concrete inside the structure from explosion damage to compression damage.Based on wavelet packet analysis and residual bearing capacity damage evaluation criteria, the beam damage was quantitatively evaluated, and the bending bearing capacity of RC beam after explosion damage was compared and analyzed.The results show that the change of concrete strain gauge signal, the phenomenon of crack development and the increase of internal crack development index indicate that the RC beam is damaged by explosion load.After explosion damage, the damage index of the beam increases and the residual bending capacity decreases, which proves that the piezoelectric intelligent aggregate sensor can effectively monitor the bending capacity change of the component after explosion damage.

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.

Trehalose is a non-reducing disaccharide and participates in physiological activities such as or-gan formation, energy metabolism, and stress resistance of insects. In the present study, phylo-genetic analysis divided 21 Bmtret1 orthologs into three clades. These genes are equally distrib-uted on the nine chromosomes. Cis-elements in the promoter regions of Bmtret1s indicated the possible function of Bmtret1s in response to hormones and environmental stimulus. The qPCR analysis showed the significantly different expression levels of Bmtret1s in different tissues and organs, indicating possible functional divergence. In addition, most Bmtret1s showed disturbed expression levels in response to BmNPV stresses. Our results provide a foundation for further functional dissection of Tret1s in Bombyx mori and implicate them as potential regulators for an-tiviral responses.

Applied talent is not only a builder of human civilization but also a promoter of economic development. They not only need systematic and solid engineering theoretical knowledge but also rich engineering practical experience. Applied undergraduate colleges are the cradle for cultivating applied talent and shoulder the important mission of cultivating various types of applied talent for society. With the rapid development of higher education, the number of applied undergraduate institutions has sharply increased, but the quality of applied talent cultivated by them has shown a significant decline, which greatly restricts the pace of industrial development in the world. This article proposes a new concept of promoting the "three certificates" (graduation certificate, degree certificate, and skill certificate) education model in applied undergraduate colleges, which is of great significance for the country to cultivate composite talents with the ability of "product design, production management, and product processing" at the same time.

The potential effect of implementing environmental regulations on economic growth is a controversial issue for a long time. As portrayed by Porter hypothesis, environmental regulations may impact on economic growth by reconstructing the economic structure. A number of previous literature proved the connections between the above three parties. With the intention of exploring the nature of this mechanism, it is important to understand the internal inter-relation of the three parties, requiring data from a large economy experiencing the transition in economic structure. By constructing a panel containing data from 30 Chinese provinces over 10 years (2008-2019), this paper investigate the dynamic inter-relation of the three parties by introducing PVAR model with Granger Test. Results reveal that environmental regulations give a “U” shaped impact to economic growth. In return, economic growth promotes the development of economy with a weakening strength. In addition, results also support the hypothesis that economic structure is the intermediate of economic growth and environmental regulations. There is a rooftop for the effect of economic structure to environmental regulations. The rooftop may signify the best optimization of primary structure.

Heavy metals contamination in groundwater often occurs in various industrial processes. Stud-ies have confirmed that polysulfide could reduce hexavalent chromium to trivalent chromium, achieving the effect of in-situ stabilization. For other heavy metals contamination in groundwa-ter, whether polysulfide also had a stabilizing ability to achieve in-situ remediation. This re-search focused on heavy metals except for chromium that often contaminated in groundwater, including lead, nickel, zinc, copper, and cadmium to explore the feasibility of using calcium polysulfide (CaSx) as an in-situ stabilization technology for these heavy metals contamination groundwater. Results showed that CaSx had a great removal efficiency for heavy metals lead, nickel, zinc, copper, and cadmium. However, for nickel, zinc, copper and cadmium, when CaSx was added excessively, complexes would be formed, causing the result of re-dissolve and this would also reduce the removal efficiency. Since it is difficult to accurately control the dosage of agents for in-situ groundwater remediation, the concentration of re-dissolved nickel, zinc, cop-per, and cadmium may not be able to meet the groundwater control standards. CaSx had high lead removal efficiency, and it would not cause re-dissolution due to excessive CaSx dosing. CaSx can be used as an in-situ stabilization technique for lead contaminated groundwater.

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

Ransomware constitutes a distinctive category of pernicious software that sequesters a user's digital assets by encryption, holding them hostage until a sum is extorted from the victim. These incursions have escalated to become among the most prevalent and significant threats confronting both individuals and corporate entities. In combatting this virulent program, dynamic analysis has been established as the favored detection modality. Such analyses typically hinge on Windows API calls, the conduits through which programs requisition services from the operating system. Yet, the superfluous and unrelated Windows API calls interjected by adversaries into the execution stream of suspect binaries precipitate an excessively noisy behavioral sequence, which impairs the performance of counter-ransomware mechanisms. The research outlined herein introduces a novel non-signature-based detection paradigm that harnesses efficacious Windows API call sequences through supervised machine learning strategies. An innovative Enhanced Min Max (EmRmR) filter technique is proposed, aiming to purge noisy features and isolate the most indicative feature subset that encapsulates the ransomware's true behavior. The EmRmR method, diverging from the traditional Min Max approach, circumvents the superfluous calculations that are a hallmark of the conventional algorithms, thereby necessitating a reduced number of evaluations. Additionally, a refinement procedure has been integrated to contract the program's call trace volume by discarding those Windows API calls lacking a robust correlation with ransomware's pivotal behavior. Subsequent to rigorous experimental analyses and juxtaposition with extant behavior-based detection methodologies, the proposed strategy has demonstrated its efficacy in differentiating ransomware behavior, delivering high detection precision alongside a diminution in false-positive occurrences.

Automatic Voice Query Service can extremely reduce the artificial cost, which could improve the response efficiency for users. The automatic speech recognition (ASR) is one of the important component in AVQS. However, many dialect areas in China make the AVQS have to response the multi-accented Mandarin users by single acoustic model in ASR. This problem severely limits the accuracy of ASR for multi-accented speech in the AVQS. In this paper, a new framework for AVQS is proposed to improve the accuracy of response. Firstly, the fusion feature including iVector and filterbank acoustic features is used to train the Transformer-CTC model. Secondly, the transformer-CTC model is used to construct the end-to-end ASR. Finally, key words matching algorithm for AVQS based on fuzzy mathematic theory is proposed to further improve the accuracy of response. The results show that the final accuracy in our proposed framework for AVQS arrives at 91.5%. The proposed framework for AVQS can satisfy the service requirement of different areas in mainland of China. This research has a great significance for exploring the application value of artificial intelligence in the real scene.

This paper considers a non-mixture cure model for right censored data. It utilizes the maximum likelihood method to estimate model parameters in the non-mixture cure model. The simulation study is based on Fréchet susceptible distribution to evaluate the performance of the method. Comparing with Weibull and exponentiated exponential distributions, the non-mixture Fréchet distribution is shown to be the best in modeling a real data on allogeneic marrow HLA-matched donors and ECOG phase III clinical trial e1684 data.

There always exist steel cuttings, holes and cracks at the interfaces in the explosive composite plate. Multi-pass friction stir processing (M-FSP) is proposed in this research to optimize the interface microstructure and the interface connection for 1060Al/Q235 explosive composite plate. Results show that the microstructures of 1060Al after M-FSP are fine and uniform owing to the strong stirring effect and recrystallization. Micro-defects formed by the explosive welding can be repaired by the M-FSP. However, M-FSP can also form tunnel defects in the aluminum, especially when the passes are one and two. The melting block and the melting lump in the composite plates are easy to become source of crack. The shear strengths and the bending properties for the 1060Al/Q235 explosive composite plate after M-FSP are the best when the passes are three, with the tool rotation speed of 1200rpm and the forward speed of 60mm/min. The optimized interfaces for the explosive composite plate after M-FSP are mainly by the metallurgical bondings, with a certain thickness and are discontinuous. Therefore, the crack extension stress is the largest and the mechanical properties are the best.

In the rapidly evolving landscape of genetic engineering, the advent of CRISPR-Cas technologies has catalyzed a paradigm shift, empowering scientists to manipulate the genetic code with unprece-dented accuracy and efficiency. Despite the remarkable capabilities inherent to CRISPR-Cas sys-tems, recent advancements have witnessed the integration of small molecule to augment their functionality, introducing new dimensions to the precision and versatility of gene editing applica-tions. This review delves into the synergy between CRISPR-Cas technologies and small molecule drugs, elucidating the pivotal role of chemicals in optimizing target specificity and editing efficiency. By examining a diverse array of applications, ranging from therapeutic interventions to agricultural advancements, we explore how the judicious use of chemicals enhances the precision of CRISPR-Cas-mediated genetic modifications. In this review we emphasize the significance of small molecule drugs in fine-tuning the CRISPR-Cas machinery, which allows researchers to exert meticulous con-trol over the editing process. We delve into the mechanisms through which these chemicals bolster target specificity, mitigate off-target effects, and contribute to the overall refinement of gene edit-ing outcomes. Additionally, we discuss the potential of chemical integration in expanding the scope of CRISPR-Cas technologies, enabling tailored solutions for diverse genetic manipulation challenges. As CRISPR-Cas technologies continue to evolve, the integration of small molecule drugs emerges as a crucial avenue for advancing the precision and applicability of gene editing techniques. This re-view not only synthesizes current knowledge but also highlights future prospects, paving the way for a deeper understanding of the synergistic interplay between CRISPR-Cas systems and chemical modulators in the pursuit of more controlled and efficient genetic modifications.

The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.

AI advances integrate generative design tools in architecture, providing architects with sophisticated design options. It enables the creation of intricate, high-performing projects by exploring diverse design possibilities with AI and algorithms. Generative AI and generative design empower architects to create better-performing, sustainable, and efficient design solutions and explore diverse design possibilities. This paper leverages multimodal generative AI to enhance design creativity by combining textual and visual inputs. Blockchain technology converts design metadata into NFTs, ensuring secure, authentic, and traceable data storage. The framework addresses data ownership, legal adherence, and client-architect collaboration and is entirely scalable for digital design authentication. This research exemplifies the pragmatic fusion of Generative AI and blockchain technology applied in architectural design for more transparent, secure, and effective results. This study provides a strategy that uses generative AI technologies to achieve an efficient and creative workflow in the early stages of architectural design.

In the TANGO study, the long-term noninferior efficacy of switching to dolutegravir/lamivudine (DTG/3TC) compared to continuing tenofovir alafenamide–based regimens was demonstrated in treatment-experienced adults with HIV. However, there is limited real-world data comparing the effectiveness and safety of bictegravir/emtricitabine/tenofovir alafenamide fumarate (BIC/FTC/TAF) and DTG/3TC in Asia. This retrospective observational study, conducted at a designated HIV-care hospital from March 2019 to January 2023, aimed to address this gap. Demographic, clinical, and laboratory data, including HIV viral loads and lipid profiles, were collected and analyzed. The primary outcome was the proportion of patients with HIV RNA <50 copies/mL at week 48 in both groups, utilizing a snapshot, per-protocol–exposed population. A total of 1086 patients were included, with 511 in the DTG/3TC group and 575 in the BIC/FTC/TAF group. At week 48, 98% of patients in the DTG/3TC group and 94% in the BIC/FTC/TAF group achieved HIV RNA <50 copies/mL (p <0.01). Logistic regression analysis identified risk factors for detectable viral loads at week 48, including HIV RNA >100,000 copies/mL (AOR 5.13, 95% CI 1.42–18.53), low-level viremia (AOR 3.91, 95% CI 1.07–14.25), and record of virologic failure ≥2 times (AOR 4.21, 95% CI 1.64–10.85) before the switch. Furthermore, no significant difference was found between the DTG/3TC and BIC/FTC/TAF regimens (AOR: 1.51, 95% CI 0.70–3.24). In conclusion, the study demonstrates that the effectiveness and safety profiles of DTG/3TC and BIC/FTC/TAF were comparable in treatment-experienced adults with HIV, particularly among those with undetectable viral loads at baseline.

Post-evaluation of ecological redevelopment is a good method for its achievements. The eco-engineering technologies and achievements of landscape water reconstructed from aquaculture ponds in Shanghai Chenshan Botanical Garden have been introduced in this study. The sediments and water quality were also sampled and tested for basic physicochemical parameters and heavy metal concentration. The ecological redevelopment of landscape water reconstructed from aquaculture ponds was evaluated using the Nemero comprehensive pollution index method. The results shown that nutrients including organic matter, organic nitrogen and their ratio of sediment were found to be in a state of moderate pollution, while their ecological risk of heavy metals was low. Although total nitrogen and total phosphorus of water quality was really higher than that of other indexes, the decline trends of ammonia nitrogen, total nitrogen and total phosphorus was obvious presented over time. In general, this is a good example that redevelopment of water ecosystems from aquaculture ponds using eco-engineering technologies.

Purification of biologically-derived therapeutics is a major cost contributor to the production of this rapidly growing class of pharmaceuticals. Monoclonal antibodies comprise a large percentage of these products therefore new antibody purification tools are needed. Small peptides, as opposed to traditional antibody affinity ligands such as Protein A, may have advantages in stability and production costs. Multiple heptapeptides that demonstrate Fc binding behavior that have been identified from a combinatorial peptide library using M13 Phage Display are presented herein. Seven unique peptide sequences of diverse hydrophobicity and charge were identified. All seven peptides showed strong binding to the four major human IgG isotypes, human IgM, as well as binding to canine, rat, and mouse IgG. These seven peptides were also shown to bind human IgG4 from DMEM cell culture media with 5% FCS and 5 g/L ovalbumin present. These peptides may be useful as surface ligands for antibody detection and purification purposes. Molecular docking and classical molecular dynamics (MD) simulations were conducted to elucidate the mechanisms and energetics for the binding of these peptides to the Fc region. The binding site was found to be located between the two glycan chains inside the Fc fragment. Both hydrogen bonding and hydrophobic interactions were found to be crucial for the binding interactions. Excellent agreement for the binding strength was obtained between experimental results and simulations.

Ganoderic Acid (GA), a major bioactive compound isolated from an oriental medicinal mushroom, i.e. Ganoderma tsugae, has traditionally been found to have significant medicinal properties. As a result of GA's poor solubility in water, formulation poses several challenges. Additionally, the traditional method of administering it via alcohol always results in unfavorable patient side effects. In order to overcome these problems, Ganoderma Tsugae extracts obtained by ethanol extraction were encapsulated in nanodispersions by ultrasonic cavitation and solvent evaporation to maximise bioavailability. Resultant nanodispersions contained Ganoderic acid in a hydrophobic core with a mean particle size of no more than 200 nm and a very narrow particle distribution. An analysis of the interactions between the control variables and the process optimisation was conducted using the Response Surface Methodology (RSM). Based on the results of the experiment, it was found that the particle size of nanodispersions was influenced by the Hydrophilic-Lipophilic Balance (HLB) number and the evaporation temperature. Moreover, the physical stability of micellar systems is generally determined by the organic phase within them. Regarding formulation stability, the zeta average of the nanodispersions remained virtually unchanged in solutions with different pH levels and short-term storage (14 days) at room temperature. Overall, it has been demonstrated that Ganoderic acid nanodispersions can be efficiently generated by combining ultrasound cavitation and solvent evaporation.

This paper presents a comprehensive study on the application and optimization of automotive thermoelectric generators (ATEGs), focusing on the crucial role of heat exchangers in enhancing energy conversion efficiency. Recognizing the substantial waste of thermal energy in internal combustion engines, our research delves into the potential of TEGs to convert engine waste heat into electrical energy, thereby improving fuel efficiency and reducing environmental impact. We meticulously analyze various heat exchanger designs, assessing their influence on the TEG's output power under different exhaust gas flow rates and temperatures. Furthermore, we explore the impact of TEG material properties on the overall energy conversion effectiveness. Our findings reveal that specific heat exchanger designs significantly enhance the efficiency of waste gas heat exchange, leading to an improved performance of the TEG system. We also highlight the importance of thermal insulation in maximizing TEG output. This study not only contributes to the ongoing efforts to develop more sustainable and efficient vehicles but also provides valuable insights into the practical application of thermoelectric technology in automotive engineering. Through this research, we aim to pave the way for more environmentally friendly transportation solutions, aligning with global efforts to reduce fossil fuel dependence and mitigate environmental pollution.

As an important part of urban terminal delivery, automated guided vehicle (AGV) has been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding stochastic programming model into rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, To handle real-time orders under dynamic demand, an optimization mechanism based on rolling scheduling framework is proposed, which allows the AGV route to be continuously updated. Different from most VRP models, open chain structure is used to describe the dynamic delivery path of AGV. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expectation of order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm.

Due to the fact that abrasive media can deform flexibly in abrasive flow machining (AFM) making this method easily to polish the complex holes and the curved surface of the hard machining shapes. Although abrasive media dominate the polishing behavior in AFM process, the mechanism of the abrasive media are not easy to understand because of the high viscous gels, therefore, many finishing works need lots of time to design AFM process. Power laws of the abrasive gels is studied here to evaluate the non-Newtonian flow of abrasive gels in complex holes polishing firstly, at the mean time different abrasive gels are utilized to finish the complex holes to follow the results by non-Newtonian flow calculation. Moreover, traditional AFM has difficulty to achieve a uniform roughness of radial distribution in the complex holes polishing because of the non-uniform abrasive forces. So a helical passageway is proposed to create a multiple motion of abrasive medium and to obtain the even surface of the complex holes in AFM process.

The controllability and consistency in the fabrication of micro-textures on large-scale remains a challenge for existing production processes. Mask electrolyte jet machining (MEJM) is an alternative to Jet-ECM for controllable and high-throughput surface microfabrication with more consistency of dimensional tolerances. This hybrid configuration combines the high-throughput of masked-ECM and the adjustable flow-field of jet-ECM. In this work, a duckbill jet nozzle was introduced to make MEJM more capable of batch micro-structuring. A multiphysics model was built to simulate the distribution of electrochemical reaction ions, the cur- rent density distribution and the evolution of the shape of the machined cavity. Experimental investigations are presented showing the influence of the machining voltage and nozzle moving speed on the micro cavity. Several 35 ×35 micro cavity arrays with a diameter of 24.92 − 11.73 µm and depth of 15.86 − 7.24 µm are generated on 304 stainless steel.

Background: It remains unclear whether transfer RNA-derived small RNAs (tsRNAs) play a role in pathological cardiac hypertrophy (PCH). We aimed to clarify the expression profile of tsRNAs and disclose their relationship to the clinical phenotype of PCH and the putative role. Methods: Small RNA sequencing was performed in the plasma of PCH patients and healthy volunteers. In a larger sample size and angiotensin Ⅱ (Ang II)-stimulated H9c2 cells, the data were validated by real-time qPCR. The atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) were examined in Ang II-stimulated H9c2 cells. The role of tsRNAs in the pathogenesis of PCH was explored by bioinformatics analysis. Results: A total of 4185 differentially expressed tsRNAs were identified, of which 4 and 5 tsRNAs were observed to be significantly differentially upregulated and downregulated expressed. Of the 5 down-regulated tsRNAs, 4 of them were verified to be significantly down-regulated in the larger sample group, among which tRF-30-3JVIJMRPFQ5D, tRF-16-R29P4PE, tRF-21-NB8PLML3E, and tRF-21-SWRYVMMV0 had areas under the curve to diagnose concentric hypertrophy. The 4 down-regulated tsRNAs were negatively correlated with left ventricular posterior wall dimensions in PCH patients (r=-0.4227; r=-0.4517; r=-0.5567; r=-0.4223). The levels of ANP and BNP as well as cell size were decreased in Ang II-stimulated H9c2 cells with 21-NB8PLML3E mimic transfection. Bioinformatics analysis revealed that the target genes of tRF-21-NB8PLML3E were mainly enriched in the metabolic pathway and involved in the regulation of ribosomes. Conclusion: The plasma tsRNAs tRF-21-NB8PLML3E might be considered biomarkers in patients with PCH with early screening potential.

The integration of robotic devices into healthcare settings for rehabilitation purposes represents a rapidly advancing field with significant benefits for both therapists and patients. However, standalone robot systems face limitations such as inefficient processing of patient data and logistical challenges for patients, such as travel time to rehabilitation centers. To address these shortcomings, this paper proposes the integration of cyber-physical systems into rehabilitation robotics. Cyber-physical systems leverage network connectivity and real-time data processing to enhance the efficiency and convenience of robot-based rehabilitation. By seamlessly integrating sensors, actuators, and computational algorithms, cyber-physical rehabilitation systems empower therapists to more effectively treat patients by providing actionable insights derived from comprehensive data analysis. This enables therapists to tailor treatment plans precisely to each patient’s unique needs, ultimately leading to improved outcomes. Additionally, cyber-physical rehabilitation systems facilitate remote monitoring and intervention, allowing therapists to oversee patient progress and make timely adjustments to treatment protocols without the need for in-person visits. This not only saves patients valuable time and resources but also ensures continuity of care, particularly for individuals residing in remote or underserved areas. Furthermore, cyber-physical rehabilitation systems enable collaborative decision-making by facilitating communication and information sharing among healthcare professionals, further enhancing the quality and efficacy of rehabilitation services. In summary, integrating cyber-physical systems into rehabilitation robotics represents a significant advancement in leveraging technology to optimize patient care and outcomes, promising to revolutionize rehabilitation therapy.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

The UHPC grouted bellow is an innovative technology for connecting assembled buildings, where the anchorage performance of the rebar and UHPC filled in bellows plays a critical role in determining the overall connection effectiveness. To ascertain a dependable anchorage length and establish a bond-slip relationship between the rebar and UHPC within the bellow, we conducted tests on 8 groups of 16 specimens of metal corrugated pipes filled with UHPC and rebar under tension. The tests considered varying parameters such as the aperture ratio D/d and anchorage length L. By analyzing the failure modes, load versus deflection curves, and steel strain data, we gained valuable insights into the influence of the aperture ratio and anchorage length on the anchorage performance. Furthermore, we calculated the experimental curve of the bond-slip relationship between the rebar and the UHPC interface grouted in the bellows and derived a bond-slip model through fitting. These findings serve as a crucial foundation for the force analysis of assembled structures connected using UHPC grouted bellows.

Obesity is a highly prevalent chronic disease characterized by excessive weight gain and fat accumulation. There is growing evidence that Lactiplantibacillus plantarum strains with bile salt hydrolase (BSH) activity are effective in preventing and alleviating obesity. However, few of them has confirmed the anti-obesity effect of BSH by gene deletion. In this study, 23 BSH-positive Lp. plantarum strains were screened by bile salt precipitation halo assay. Among these strains, Lp. plantarum Lp20 showed the highest BSH activity with a hydrolysis rate to glycodeoxycholic acid (GDCA) by 91.62%. Genomic sequencing and bioinformatics analysis indicated that gene bsh1 was associated with BSH activity of Lp. plantarum Lp20 and gene deletion of bsh1 confirmed the role of Bsh1 in GDCA hydrolysis. To investigate if the Bsh1 from Lp. plantarum Lp20 could alleviate obesity, the wild type and bsh1 deletion mutant strains were administered daily for 8 weeks to diet-induced obese mice, respectively. Compared with mutant strain, the wild type strain Lp20 significantly reduced body weight gain, inguinal white adipose tissue and mesenteric fat mass in obese mice. In addition, Lp20 also reduced serum total cholesterol levels and LDL-C by 20.8% and 33.3%, respectively. Furthermore, the recovered liver steatosis was only observed in Lp. plantarum Lp20 treated mice. Taken together, the bile salt hydrolase Bsh1 from Lp. plantarum Lp20 was a key functional factor on ameliorating HFD-induced obesity by reducing cholesterol levels and suppressing adipogenesis in mice.

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.

Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.

Oil-Gan, also known as emblica, is the fruit of the genus Phyllanthus emblica L. The fruits of these trees are high in nutrients and have excellent effects on health care and development values. There are many methods for the management of diabetic nephropathy (DN). However, there is a lack of effective drugs for treating DN from throughout the disease course. The primary aim of this study was to examine the protective effects (including urine analyses, the measurement of inflammatory cytokine levels, and histological examination) and mechanisms of action of an ethyl acetate extract of P. emblica (EPE) on db/db mice, an animal model of diabetic nephropathy, as well as a secondary aim of examining the expression levels of protein kinase Cα (PKCα) in the kidney and its downregulation of vascular endothelial growth factor (VEGF) and fibrosis gene transforming growth factor-β1 (TGF-β1) by Western blot analyses. Eight db/m C57BLKS/J mice were used as the control group. Forty db/db mice were randomly divided into five groups. Treatments included vehicle, EPE1, EPE2, EPE3 (at doses of 100, 200, or 400 mg/kg EPE), or the comparative drug aminoguanidine for 8 weeks. After 8 weeks of treatment, blood samples were collected for analyses of blood glucose, insulin, and HbA1c levels, and pancreatic and kidney tissues were dissected for histological analyses. The present study indicated that the administration of EPE to db/db mice effectively controlled hyperglycemia and hyperinsulinemia by markedly lowering blood glucose, insulin, and glycosylated HbA1c levels. Administration of EPE to db/db mice decreased the levels of BUN and creatinine both in blood and urine and reduced urinary albumin excretion and the albumin creatine ratio (UACR) in urine. Moreover, EPE treatment decreased the blood levels of inflammatory cytokines, including kidney injury molecule-1 (KIM-1), C-reactive protein (CRP), and NLR family pyrin domain containing (NLRP3). Our findings showed that EPE not only had antihyperglycemic effects but also improved renal function in db/db mice. Histological examination of the kidney by immunohistochemistry indicated that EPE can improve kidney function by ameliorating glomerular morphological damage following glomerular injury; alleviating proteinuria by upregulating the expression of nephrin, a biomarker of early glomerular damage; and inhibiting glomerular expansion and tubular fibrosis. Moreover, the administration of EPE to db/db mice increased the expression levels of p- PKCα/t-PKCα but decreased the expression levels of VEGF and renal fibrosis biomarkers (TGF-β1, collagen IV, p-Smad2, p-Smad3, and Smad4), as shown by Western blot analyses. These results implied that EPE as a supplement has a protective effect against renal dysfunction through the amelioration of insulin resistance as well as the suppression of nephritis and fibrosis in a DN model.

In this work, optimal consumption and investment strategies are derived for risk-averse investors under the 4/2 stochastic volatility class of models. We work under an expected utility (EUT) framework and consider a Constant Relative Risk Aversion (CRRA) investor, who might also be ambiguity-averse. The corresponding Hamilton-Jacobi-Bellman (HJB) and HJB-Isaacs (HJBI) equations are solved in closed-form for a subset of the parametric space and under some restrictions on the portfolio setting, for complete markets. Conditions for proper changes of measure and well defined solutions are provided. These are the first analytical solutions for the 4/2 stochastic volatility model and the embedded 3/2 model for the type of excess returns established in .

The collaboration of multimodal transport documents is the foundation for conducting multimodal transport operations. Blockchain technology can effectively address trust issues and information sharing difficulties in current multimodal transport document collaboration. However, in current blockchain-based electronic document research, the bottleneck problem lies in the collaboration of multiparty in multimodal transport under the "one-bill coverage system". We design a blockchain platform architecture for multimodal transport, reshaping collaboration processes for different modes of transport based on the blockchain platform. It proposes a collaboration model for multimodal transport under the "one-bill coverage system" and performs model solving and case analysis. The obtained collaboration strategies are embedded into the platform's application layer. Finally, using Solidity language, it writes smart contracts related to multimodal transport under the "one-bill coverage system " and deploys and executes them on the Remix platform.

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.

Questions：SAP in mountainous cities of China serve as crucial carriers for species and wild food, constituting potential green infrastructure. These patches should be recognized as locally specific resources. This study investigates the transformation characteristics of the landscape patterns of SAP, along with their elements, across urban-rural gradients, providing foundational support for the planning of conservation and utilization strategies. Location: Yongchuan area of Chongqing in southwest China. Methods: The study investigated urban-rural gradient-based transformation of SAP. A four-direction transect space was selected and divided into uniform sample squares. Fragstats 4.3 calculated the landscape index of SAP, and Spearman correlation coefficients in SPSS were used to evaluate the influence of landform and artificial factors on agroforestry patch transformation. Results: 1) Patch area (PA), Plaque density (PD), and CLUMPY of SAP systematically transform, showing increased SAP areas and higher connectivity with greater distance from the city, resulting in reduced patch fragmentation. However, Edge density (ED) and Largest plaque index (LPI) exhibit irregular transformation patterns. 2) With increased distance from the city, woodland patch area and connectivity increase, leading to decreased fragmentation, dispersal, and lower fractal dimensions. Conversely, Cultivated land follows the opposite trend. garden land patch area, fractal dimension, fragmentation, and dispersal all increase, while connectivity decreases. 3) Landscape pattern transformations of SPA are significantly influenced by artificial factors. Woodland patches are influenced similarly by landform and artificial factors. In contrast, cultivated land and garden land patches are more significantly impacted by artificial factors. Conclusions: SAP exhibit differentiated conservation and utilization measures across urban-rural gradients. Small patches located far from urban centers may benefit from systematic conservation measures to prevent further fragmentation and erosion in peri-urban areas.

To decrease privacy risks, consumers may choose to misrepresent when they are asked to offer personal information. This paper examines the impact of consumer misrepresentation on a monopolistic firm and consumers with an economic model. The results show that consumer misrepresentation may benefit the firm but hurt consumers. In addition, consumers misrepresentation may encourage the firm to provide higher personalized service level in certain scenarios, such as, when the unit cost of personalized service is low. Thirdly, when consumers misrepresent, and the firm only covers part of the market, a greater unit value of consumer’s privacy information will reduce the firm’s profit, but a greater unit cost of personalized service increases the firm’s profit.

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, \eg{} low accuracy, slow speed, \etc The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Luoyang Pedestrian Dataset \footnote{The dataset can be downloaded through this anonymous link: \url{https://drive.google.com/drive/folders/13po1FX7Qk5RDgb60-dzOi74cq_kqDJtM?usp=sharing} }, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Luoyang Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperform the most commonly used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

Excretory-secretory products (ESPs) are the main research targets for investigating the hosts and helminths interaction. Parasitic worms can migrate to parasitic sites and avoid the host immune response by secreting this product. Angiostrongylus cantonensisis an important food-borne zoonotic parasite that causes severe neuropathological damage and symptoms, including eosinophilic meningitis or meningoencephalitis in humans. Benzaldehydes are organic compounds composed of a benzene ring and formyl substituents. This compound has anti-inflammatory and antioxidation properties. Previous studies showed that 3-hydroxybenzaldehyde (3-HBA) and 4-hydroxybenzaldehyde (4-HBA) can reduce apoptosis in A. cantonensis ESPs treated astrocytes. These results on the protective effect underlying benzaldehyde have primarily focused on cell survival. The study was designed to investigate the molecular mechanisms of endoplasmic reticulum stress (ER stress) and oxidative stress in astrocytes in A. cantonensis ESPs treated astrocytes and to evaluate the therapeutic consequent of 3-HBA and 4-HBA. First, we initially established the RNA-seq dataset in each group, including Normal, ESPs, ESPs+3-HBA, and ESPs+4-HBA. We also found that benzaldehyde (3-HBA and 4-HBA) can stimulate astrocytes to express ER stress-related molecules after ESP treatment. The level of oxidative stress could also be decreased in astrocytes by elevating antioxidant activity and reducing ROS generation. These results suggested that benzaldehyde may be a potential therapeutic compound for human angiostrongyliasis to support brain cell survival by inducing the expression levels of ER stress- and oxidative stress-related pathway.

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Natural killer cells are associated with the pathogenesis of ulcerative colitis (UC), but their precise contributions remain unclear. The present study sought to investigate the diagnostic value of activated NK-associated genes (ANAGs) in UC. Bulk RNA-seq and scRNA-seq datasets were obtained from the Gene Expression Omnibus (GEO) and Single Cell Portal (SCP) databases. In the bulk RNA-seq, 92 differentially expressed genes (DEGs) were screened out by the “Batch correction” and “Robust rank aggregation” (RRA) methods. The immune infiltration landscape was estimated by single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT, which revealed a higher abundance of activated NK cells in noninflamed UC tissues. 54 DEGs correlated with activated NK cells were identified as ANAGs. Protein-protein interaction (PPI) analysis and least absolute shrinkage and selection operator (LASSO) regression were utilized to screen out 4 key ANAGs (SELP, TIMP1, MMP7, and ABCG2) and establish an activated NK-associated gene score (ANAG score). The ANAG score demonstrated excellent diagnostic value and was validated in three external datasets. The expression of the 4 key ANAGs was validated in UC patients and healthy controls (HC) samples. Through scRNA-seq data analysis, higher expression levels of SELP, TIMP1, MMP7, and ABCG2 were observed in post-capillary venules, inflammatory fibroblasts, enterocytes, and immature enterocytes. The cell scores based on the ANAGs showed enrichment in endothelial cells and fibroblasts. In conclusion, we established and validated an ANAG score with the ability to precisely diagnose UC. The 4 key ANAGs have the potential to serve as therapeutic targets in UC.

Three kinds of Ni based alloy cladding coatings were prepared on 316L stainless steel at different power. The microstructure of the cladding layer was observed and analyzed by XRD, metallographic microscope and SEM. The hardness of the cladding layer was measured, and the wear resistance of the cladding layer was tested by friction instrument. The results show that the effect of laser cladding is good and the cladding layer has a good metallurgical bonding with the substrate. Different microstructures such as dendritic and equiaxed grains can be observed in the cladding layer. With the increase of laser power, more equiaxed and columnar dendrites can be observed. The phase composition of the cladding layer is mainly composed of γ - Ni solid solution and some intermetallic compounds such as Ni3B, Cr5B3 and Ni17Si3. The results of EDS show that there are some differences in the distribution of C and Si between dendrites. The hardness of the cladding layer is about 600 HV0.2, which is about three times of the substrate (~ 200 HV0.2). Through the analysis of the wear morphology, the substrate wear is serious, there are serious shedding, mainly adhesive wear and abrasive wear. However, the wear of cladding layer is slight, which is abrasive wear, and there are some grooves on the surface.

Inflammation is the response of the immune system to infection and injury, with complex interactions with the body, tissues and cells, and plays an important role in the initiation and progression of many diseases such as cancer and cardiovascular disease. Traditional excipients functioning as drug delivery systems can reduce the side effects of systemic distribution of inflammatory drugs. Oil phase, a common excipient in nanoemulsions (NEs), has the advantages of assisting the formation of emulsions, loading lipophilic drugs, thermodynamic stability, wide sources, low price and easy availability. In particular, some of them, like linolenic acid and medium chain triglyceride (MCT), also function as inhibitors of systemic inflammation marker and vascular inflammation marker. However, the influences of different oil phases on the construction of NEs and their distribution in vivo have not been fully investigated. Herein, Tween 80 and ethyl alcohol were screened out as emulsifiers and co-emulsifiers, and three different oil phases used in clinical practice widely, including MCT, oleic acid (OA), and ethyl oleate (EO), were selected as the oil phase to prepare NEs delivery platform. Three NEs had consistent size, potential, morphology, excellent stability and mitochondrial localization function. Importantly, they exhibited superior cellular uptake capacity in inflammatory-injuried human umbilical vein endothelial cells and significantly targeted the brain, lung, heart and thoracic aorta in the lipopolysaccharide-induced mouse and zebrafish larvae inflammation models. Meanwhile, the fluorescence intensities of three NEs were different in these organs, suggesting that their differential enrichment might provide alternative delivery platform for disease treatment in the corresponding organs.

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.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

This study presents a novel approach to enhance Large Language Models (LLMs) like Alpaca by dynamically integrating real-time information. This method addresses the issue of content hallucination and data relevancy by automatically collecting and integrating current data from credible sources into model prompts. Experiments show a significant improvement in accuracy and a decrease in content hallucination, with a manageable increase in response time. The research underscores the potential of real-time data integration in making LLMs more accurate and contextually relevant, setting a foundation for future advancements in dynamic data processing in AI.

Time series data having low signal-to-noise ratio, non-stationarity and non-linearity are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time series data. In this paper, we develop an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future time, we use only the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing involves updating only the current data into the process. We evaluate the performance of O-LGT by analyzing the high-frequency Limit Order Book (LOB) data from the China market. It shows that our model in most cases achieves similar speed with much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 40 times faster than the existing high-accuracy neural network models for the LOB data in China market.

Many countries have implemented free early childhood education to solve the affordability and accessibility problems without considering the sustainability of policy; thus, they have to discontinue the policy. The city of Nanjing, China, is no exception. This case study adopted a mixed-methods approach to understanding how and why the policy was formulated, implemented, and failed in the city, using the “policy cycle” framework. Altogether 232 kindergarten principals and teaching/research staff were randomly sampled and surveyed, and 5 kindergarten principals and 5 teaching/researcher staff were interviewed. The results indicated that there were many obstacles to the policy’s sustainability, including the conflict of interest, the inefficient policy implementation, the insufficient funding, and more social inequality issues. And the policymakers made mistakes in the five domains of the policy cycle: the context of influence, the context of policy text production, the context of practice, the context of outcomes, and the context of political strategy. Finally, the lessons for a more sustainable policy decision process are discussed.

HIV-associated neurocognitive disorder (HAND) is an array of neurocognitive changes associated with HIV infection, and the roles of microRNAs in HAND are not completely revealed yet. Based on published data and publicly available databases, we constructed an integrated miRNA-mRNA network involved in HAND. Bioinformatics analyses, including gene ontology, network analysis, and KEGG pathway analysis, were applied for further study of the network and the genes of the network. The axon guidance KEGG pathway, three genes NTNG1, EFNB2, CXCL12, and 17 miRNAs which regulates them, are spotlighted in our study. This study provides new perspectives to the knowledge of miRNAs’ roles in the process of HAND, and our findings provided potential therapeutic targets and clues of HAND.

Climate change and environmental degradation are major threats to sustainable agricultural development in Southern Africa. Thus, the concept of sustainable intensification (SI) i.e. getting more output from less input using certain practices such as agroforestry, organic fertilizer, sustainable water management etc. has become an important topic among researchers and policy makers in the region in the last three decades. A comprehensive review of literatures on the adoption of SI in the region identify nine relevant drivers of adoption of SI among (smallholder) farmers. These drivers include (i) age, (ii) size of arable land, (iii) education, (iv) extension services, (v) gender, (vi) household size, (vii) income, (viii) membership in farming organization and (ix) access to credit. We present the results of a meta-analysis of 21 papers on the impact of these determinants on SI adoption among (smallholder) farmers in Southern African Development Community (SADC) using random-effects estimation techniques for the true effect size. While our result suggests that variables such as extension services, education, age, and household size may influence the adoption of SI in SADC, factors such as access to credit is also of great importance. Decision-makers should therefore concentrate efforts on these factors in promoting SI across the SADC. This includes increasing the efficiency of public extension service as well as involvement of private sector in extension service. Furthermore, both public and private agriculture financing models should consider sustainability indicators in their assessment process.

The purpose of this paper was to analyze the development trend of hazardous chemical packaging towards low carbon economy from both qualitative and quantitative perspectives. Four types of relatively small volume packaging with volume/weight less than 450L/400kg, respectively, and three intermediate bulk containers (IBCs), which are widely used for hazardous chemicals were studied to calculate the carbon footprint (CF) from cradle to grave using life cycle assessment (LCA) method and to predict the future carbon emission of hazardous chemical packaging in the next five years (2016-2020), based on the export data of Tianjin Port in China. Grey model (GM) was adopted in the prediction. The results showed that majority of IBCs have lower carbon footprint than other types when the packaging contained same amount of same hazardous chemical. With the development of international trading, the demand of hazardous chemicals will increase as well. As the result, carbon emission generated by hazardous chemical packaging will increase accordingly. However, based on GM simulation result, increasing the amount of IBC use will effectively reduce the relative amount of carbon emission.

Under the development mode of deep blending of emerging technologies, major projects are important for enhancing China's comprehensive national strength and independent innovation capability. Based on the social benefits of major projects, and considering the influence of reputation factors, an evolutionary game model composed of a general contractor and subcontractor of major projects is established. This paper studies the influence of various factors including the income distribution coefficient of collaborative innovation, and MATLAB simulation software is analyzed the influence of factors on an evolutionary path. The results show that a reasonable income distribution coefficient can promote the game behavior of both parties to the direction of active collaborative innovation; The reduction of innovation cost and the improvement of spillover technology absorption capacity can make innovation subjects tend to choose active collaborative innovation strategies. In addition, reputation loss above a specific threshold can effectively inhibit free-riding behavior and encourage innovation subjects to choose active collaborative innovation strategies. The research results provide reference for promoting major projects' technology innovation.

There is a current worldwide outbreak of a new type of coronavirus (2019-nCoV), which originated from China and has now spread to 17 other countries. Governments are under increase pressure to stop the outbreak spiraling into a global health emergency. At this stage, preparedness, transparency and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak sites, and from laboratories supporting the investigation. This paper aggregates and consolidates the virology, epidemiology, clinical management strategies from both English and Chinese literature, official news channels and other official government documents. In addition, by fitting the number of infections with a single-term exponential model, we report that the infection is spreading at an exponential rate, with a doubling period of 1.8 days.

This paper reviews the requirements for the production, quality, and quality assurance of solid recovered fuels (SRF) that are increasingly used for the substitution of fossil fuels in the indus-trial utilities. The economical, technological and environmental aspects should be considered before using SRF as an alternative fuel. In general, the most important factors determining the commercial production of SRF referred to the calorific values and their available amounts adopted in the starting materials. Furthermore, the low-melting impurities and undesired constituents/elements, including chlorine, sulfur, potassium and sodium, may pose negative impacts on air pollutant emission and slagging & fouling in the process utilities. In this regard, the vent emitted from the industrial utilities must comply with the effluent standards of station-ary sources like particulate, sulfur oxides, nitrogen oxides, heavy metals (e.g., lead, cadmium, and mercury) and dioxins. For these reasons, this work will summarize the legal or regulatory requirements under development in Asian countries (i.e., Japan, South Korea, and Taiwan) and European Union (EU). Finally, the outlook of solid recovered fuel (SRF) in the waste manage-ment and industrial sectors will be addressed to echo the sustainable development goals (SDGs).

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

[Introduction]: In the contemporary international financial environment, marked by intricate complexities and increasing uncertainties, the impact of exchange rate fluctuations on national economies has become increasingly significant. In this context, this study uses the Chinese economy as a case study to delve deeply into the interaction effects between macroprudential and monetary policies, analyzing how these policies jointly address economic instability. The aim is to offer new perspectives and strategies for stabilizing the global economic and financial landscape. [Methods]: This paper constructs an open-economy Dynamic Stochastic General Equilibrium (DSGE) model specifically tailored for China, incorporating Bayesian estimation and numerical simulation techniques. The study meticulously examines how macroprudential and monetary policies can be coordinated to achieve economic stability in response to exchange rate shocks. [Results]: The research finds that (1) targeted coordination of macroprudential and monetary policies towards asset prices and price levels can maintain stability under exchange rate shocks; (2) the effective implementation of such coordinated policies not only mitigates the fluctuations in asset prices due to exchange rate shocks but also enhances the level of societal welfare; (3) in terms of choosing an exchange rate regime, a managed floating rate system can prevent policy overlaps and conflicts that might occur in a floating rate system during the coordination of macroprudential and monetary policies, thus offering comparative advantages. [Discussion]: The findings underscore the importance of policy integration in a dynamic and complex global economic environment. They provide policy recommendations for China and other emerging market countries on how to effectively maintain economic stability through the synergistic interplay of macroprudential and monetary policies amid fluctuations in exchange rates and asset prices.

The morphological and compositional complexities of keratinized components make feathers ingenious skin appendages adapted to diverse ecological needs. Frizzling feathers, characterized by their distinct curling phenotypes, offer a unique model to explore the intricate morphogenesis in developing a keratin-based bioarchitecture over a wide range of morphospace. Here, we investigated the heterogeneous allocation of α- and β-keratins in flight feather shafts of homozygous and heterozygous frizzle chickens by analyzing the medulla-cortex integrations using quantitative morphology characterizations across scales. Our results reveal intriguing construction of frizzling feather shaft through modified medulla development, leading to a perturbed balance of the internal biomechanics and, therefore, introducing the inherent natural frizzling compared to those from wild-type chickens. We elucidate how the localized developmental suppression of the α-keratin in the medulla interferes with the growth of the hierarchical keratin organization by changing the internal stress in the frizzling feather shaft. This research not only offers insights into the morphogenetic origin of the inherent bending of frizzling feathers but also facilitates our in-depth understanding of the developmental strategies toward the diverse integuments adapted for ecological needs.

Isokinetic testing of the knee joint is the “gold standard” in strength assessment in sport especially in soccer, where it is part of pre-season screening to establish norms in case of injuries. There are many devices on the market that make it difficult to use of normative data. Isokinetics literature in females’ soccer players is scarce in comparison to males. Furthermore, evaluation protocols strongly differ from each other, using different ranges of speeds, movement patterns and output data. Thus, it is difficult for the researcher and the clinician to choose a correct protocol for isokinetic evaluation in female soccer players. The indexes of isokinetic performance that are widely accepted are the hamstring/quadriceps (H/Q) ratio and dominant/non-dominant (D/ND) ratio as reference. In this review are highlighted some common features of isokinetic testing in female soccer players of different levels of qualification. From this study, it emerges that, for a reliable test, 3 reps at the speed of 60°/sec in knee flexion/extension is preferable due to the high reliability and that an optimal H/Q ratio in healthy female soccer players of different level of qualification and age is 50%, while the interlimb differences (D/ND) should be below 10%.

We firstly present two popular space discretization models of the Nonlinear Schrödinger Equation in dark solitons motion : the Direct-Discrete model and the Ablowitz-Ladik model. Applying the midpoint scheme to the space discretization models, we get two time-space discretization models: the Crank-Nicolson method and the New-Difference method. Secondly, we demonstrate that the solutions of the two space discretization models converge to the solution of the Nonlinear Schrödinger Equation. Also, we prove that the convergence order of the two time-space discretization models are O(h2+τ2) in discrete L2-norm error estimates. Finally, the numerical experiments agree well with the proven theoretical results.

With the rapid development of Internet of Vehicles (IoV), particularly the introduction of mobile edge computing (MEC), vehicles can efficiently share data with one another. However, edge computing nodes are vulnerable to various network attacks, posing security risks to data storage and sharing. Moreover, the presence of abnormal vehicles during the sharing process poses significant security threats to the entire network. To address these issues, this paper proposes a novel reputation management scheme, which proposes an improved multi-source multi-weight subjective logic algorithm. This algorithm fuses direct and indirect opinion feedback of nodes through the subjective logic trust model while considering factors such as event validity, familiarity, timeliness, and trajectory similarity. Vehicle reputation values are periodically updated, and abnormal vehicles are identified through reputation thresholds. Finally, blockchain technology is employed to ensure the security of data storage and sharing. By analyzing real vehicle trajectory datasets, the algorithm is proven to effectively improve the differentiation and detection rate of abnormal vehicles.

Acute ischemic strokes are the third leading cause of death and the leading cause of neurological disability worldwide. The oxygen and glucose deprivation associated with ischemic strokes not only leads to neuronal cell death, but also increases the inflammatory response and decreases functional output of the brain. The only intervention approved by US Federal Drug and Food Administration for treatment of ischemic strokes is tissue plasminogen activator (tPA), however, such treatment can only be given within 4.5 hours of the onset of stroke-like symptoms. This narrow time-range limits its application, and it also might induce detrimental rather than beneficial effects to stroke patients by treatment of the tPA. In order to reduce the infarct volume of an acute ischemic stroke while increasing the time period for treatment, emerging therapies reveal great potential by targeting inflammation, growth factors, ion channels, and neurotransmitter receptors with monoclonal antibody (MAB). With successfully application in the treatment of cancer patient by MAB, in this review, we will focus on recent advances on stroke therapy by using MAB on the treatment of stroke by targeting inflammation, growth factors, ion channels, and neurotransmitter receptors. Therefore, developing specific MAB targeting the signaling pathway of stroke will contribute to stroke therapy.

The deepening of electricity reform results in increasingly frequent auctions and the surge of generators, it becomes difficult to analyze generators’ behaviors. Since it’s hard to find analytical market equilibriums, approximate equilibriums were obtained instead in previous studies by market simulations, which are strict to initial estimations and simulation results are chaotic in some cases. In this paper, a multi-unit power bidding model is proposed to reveal the bidding mechanism under clearing pricing rule by employing auction approach, for which initial estimations are non-essential. Normalized bidding price is introduced to construct generator's price-related bidding strategy. Nash equilibriums are derived depend on the marginal cost and the winning probability which are computed from bidding quantity, transmission cost and demand distribution. Furthermore, we propose a comparative analysis to explore the impact of uncertain elastic demand on the performance of the electricity market. The result indicates that, there exists market power among generators leading to social welfare decreases even under competitive conditions but elastic demand is an effective way to restrain generators’ market power. The feasibility of the models is verified by a case study. Our work provides decision support for generators and a direction for improving market efficiency.

Abstract: Currently, thermal power is the largest source of power in the world. Although the impacts of climate change on cooling water sufficiency in thermal power plants have been extensively assessed globally and regionally, their economic consequences have seldom been evaluated. In this study, the Asia-Pacific Integrated Model Computable General Equilibrium model (AIM/CGE) was used to evaluate the economic consequences of projected future cooling water insufficiency on a global basis, which was simulated using the H08 global hydrological model. This approach enabled us to investigate how the physical impacts of climate change on thermal power generation influence economic activities in regions and industrial sectors. To account for the uncertainty of climate change projections, five global climate models and two representative concentration pathways (RCPs 2.6 and 8.5) were used. The ensemble-mean results showed that the global gross domestic product (GDP) loss in 2070–2095 due to cooling water insufficiency in the thermal power sector was −0.21% (−0.12%) in RCP8.5 (RCP2.6). Among the five regions, the largest GDP loss of −0.57% (−0.27%) was observed in the Middle East and Africa. Medium-scale losses of −0.18% (−0.12%) and −0.14% (−0.12%) were found in OECD90 (the member countries of the Organization for Economic Co-operation and Development as of 1990) and Eastern Europe and the Former Soviet Union, respectively. The smallest losses of −0.05% (−0.06%) and −0.09% (−0.08%) were found in Latin America and Asia, respectively. The economic impact of cooling water insufficiency was non-negligible and should be considered as one of the threats induced by climate change.

Microvascular self-healing composite materials have significant potential for application and their mechanical properties need in-depth investigation. In this paper, tensile and compressive properties of woven fabric carbon fiber reinforced polymers (CFRP) laminates containing three-dimensional microvascular channels were investigated experimentally. Several detailed finite element (FE) models were established to simulate the mechanical behavior of the laminate and the effectiveness of different models were examined. The damage propagation process of the microvascular laminates and the influence of microvascular parameters were studied by the validated models. The results show that microvascular channels arranged along the thickness direction (z-direction) of the laminates are critical locations under the loads. The channels have minimal effect on the stiffness of the laminates but causing a certain reduction in strength, which varies approximately linearly with the z-direction channel diameter within its common design range of 0.1~1mm. It is necessary to consider the resin-rich region formed around microvascular channels in the warp and weft fiber yarns of the woven fabric composite when establishing the FE model. And the layers in model should be assigned with equivalent unidirectional ply material in order to calculate the mechanical properties of laminates correctly.

The development of saltiness enhancement peptides is important in reducing salt intake and promoting dietary health. Our study aimed to analyze and identify straw mushroom enzymatic peptides with salty taste enhancing effects. The taste-related peptides within the straw mush-room extract were isolated via ultrafiltration and identified using UPLC-Q-TOF-MS/MS. Sub-sequent, E-tongue and sensory analyses unveiled that the ultrafiltration fraction (500-2000 Da) of straw mushroom peptides exhibited a saltiness enhancement effect. The UPLC-Q-TOF-MS/MS analysis identified 220 peptides within the ultrafiltration fractions (500-2000 Da), which were then assessed for their interaction with the T1R1/T1R3 receptor. The investigation underscored the significant involvement of Asp223, Gln243, Leu232, Asp251, and Pro254 in the binding of peptides from triple enzymatically hydrolyzed straw mushrooms to T1R1/T1R3. Based on the binding energy and active site analysis, three peptides were selected for synthesis: DFNALPFK (-9.2 kcal/mol), YNEDNGIVK (-8.8 kcal/mol), and VPGGQEIKDR (-8.9 kcal/mol). Notably, 3.2 mmol of VPGGQEIKDR enhanced a 0.05% NaCl solution to the saltiness level of a 0.15% NaCl solution. Also, 0.8 mmol of YNEDNGIVK elevated a 0.05% NaCl solution to the saltiness of a 0.1% NaCl solution.

In this paper we overview all analytical engineering solutions for corrosion problems in pressure vessels delivered over the past 60 years. We briefly detail strengths and weaknesses of existing approaches, thus demonstrating the need for novel uniform corrosion analysis methods for both current and new applications. To complement the review, we also present a new analytical model allowing to estimate the lifetime of pressured elastic vessels with mechanically assisted corrosion. Both internal/external pressure and internal/external corrosion are captured and dissolution driven corrosion is also considered. The readily implemented method improves existing analytical approaches and is shown to be effective for thin and thick shells, as well as various loading and corrosion intensity and geometrical conditions.

We present three reduced integrable hierarchies of nonlocal integrable NLS type equations from a vector integrable hierarchy associated with a matrix Lie algebra, not being A type. Three similarity transformations are taken to keep the invariance of the transformed zero curvature equations. The key step is to formulate a solution to a reduced stationary zero curvature equation so that the zero curvature formulation works for a reduced case.

We employed the Riemannian Face Manifold (RFM) as an alternative approach to the conventional linear Euclidean space for explaining the atypicality bias in face likeliness judgments. The RFM posits that the mental representation of faces is better captured as a manifold of stable states, accounting for the nonlinearity in the physical properties of faces. To examine the relationship between physical and psychological distance of morph and typical/atypical faces, we manipulated the parameter k and incorporated a weight function in the Riemannian metric. Our results indicate that the psychological distance between the morph and typical face was longer than that between the morph and atypical face, consistent with prior research on the atypicality bias in perceptual similarity. The RFM approach provides mathematical support and is a powerful tool for studying face perception and recognition, offering potential implications for explaining the other-race effect. Future research can utilize the RFM to investigate individual differences in face recognition abilities and expertise, and how they impact psychological distance and face processing.

Intensive care nurses are highly prone to occupational stress and burnout, affecting their physical and mental health. The occurrence of the pandemic and related events increased nurses’ workload and exacerbated stress and burnout. We conducted a prospective longitudinal mixed-methods study with a cohort of nurses working in a medical ICU (COVID unit; n = 14) and cardiovascular ICU (non-COVID unit; n = 5). Each participant was followed for six 12-hour shifts. Validated questionnaires measured occupational stress and burnout prevalence. Wrist-worn wearable technologies recorded physiological indices of stress. Participants elaborated on the contributors to stress via post-study questionnaire. Data were analyzed using statistical and qualitative methods. Participants who cared for COVID patients at the COVID unit were 3.71 times more likely to experience stress (p &lt; .001) in comparison to non-COVID unit participants. No differences in stress levels were found when the same participants worked with COVID and non-COVID patients at different shifts at the COVID unit. The cohorts expressed similar contributors to stress including communication tasks, patient acuity, clinical procedures, admission processes, proning, labs, and assisting coworkers. Nurses in COVID units, irrespective of whether they care for a COVID patient, may experience high occupational stress and burnout.

In this paper we present the exploration of indoor positioning technologies for UAVs, and navigation techniques for path planning and obstacle avoidance. The objective is to perform warehouse inventory tasks using a drone to search for barcodes or markers to identify the objects. For the indoor positioning techniques, we employ Visual-Inertial Odometry (VIO), Ultra-Wideband (UWB), AprilTag fiducial markers, and Simultaneous Localization and Mapping (SLAM). These algorithms encompass global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. In UAV navigation, we combine SLAM-based RTAB-Map indoor mapping and navigation path planning of the ROS for indoor environments. This system enables precise drone positioning indoors and utilizes global and local path planners to generate flight paths that avoid dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve the warehouse inventory inspection, a reinforcement learning approach is proposed to recognize the markers by adjusting the UAV's viewpoint. We address several main problems in inventory management, including efficiently planning paths while ensuring a certain level of detection rate. Two reinforcement learning techniques, AC (Actor-Critic) and PPO (Proximal Policy Optimization), are implemented based on AprilTag identification. Testing is performed in both simulated and real-world environments, and the effectiveness of the proposed method is validated.Code is available at https://github.com/kellen080/Navigation and https://github.com/kellen080/Indoor\_Positioning.