In this paper, we design two inertial-type subgradient extragradient algorithms with line-search process for solving the pseudomonotone variational inequality problems (VIPs) and common fixed-point problem (CFPP) of finite Bregman relatively nonexpansive mapping and a Bregman relatively demicontractive mapping in p-uniformly convex and uniformly smooth Banach spaces, which are more general than Hilbert spaces. Under mild conditions, we derive weak and strong convergence of the suggested algorithms to a common solution of the VIPs and CFPP, respectively. Additionally, an illustrated example is furnished to back up the feasibility and implementability of our proposed methods.

Though the energy density of lithium-ion batteries continues to increase, safety issues related with the internal short-circuit and the resulting combustion of highly flammable electrolyte impede the further development of lithium-ion batteries. It has been well-accepted that a thermal stable separator is important to postpone the entire battery short-circuit and thermal-runaway. Traditional methods to improve the thermal stability of separators includes surface modification and/or developing alternate material systems for separators which may always affect the battery performance negatively. Herein, a thermostable and shrink-free separator with little compromise in battery performance is prepared by coaxial electrospinning and tested. The separator consists of core-shell fiber networks where poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) layer serves as shell and polyacrylonitrile (PAN) as the core. This core-shell fiber network exhibits little or even no shrinking/melting at elevated temperature over 250 °C. Meanwhile, it shows excellent electrolyte wettability and can take large amount of liquid electrolyte three times more than that of conventional Celgard 2400 separator. In addition, the half-cell using LiNi1/3Co1/3Mn1/3O2 as cathode and the aforementioned electrospun core-shell fiber network as separator demonstrates superior electrochemical behavior, stably cycling for 200 cycles at 1 C with a reversible capacity of 130 mAh g-1 and little capacity decay.

The scalar meson f0(980) has been a long-standing puzzle in light hadron physics. The mass and width of the f0(980) in normal decay processes are estimated to be M=990±20 MeV/c2 and Γ=40−100 MeV, respectively. Theoretically, the internal structure of the f0(980) is proposed to be a conventional quark-antiquark meson, tetraquarks state, KK¯ molecule, or quark-antiquark gluon hybrid. So far, explanations about the nature of f0(980) have been controversial for a long time. Recently, anomalously narrow width f0(980) were observed by the BESIII experiment in five independent isospin-violating decay channels. Based on the experimental data, we performed a simultaneous fit to ππ invariant mass distributions, and the mass and width in isospin-violating decays are determined to be M=990.0±0.4 MeV/c2 and Γ=11.4±1.1 MeV, respectively. In addition, we use the parameterized Flatté formula to simultaneously fit the same ππ invariant mass distributions, the two coupling constants of gfππ and gfKK¯ are measured to be gfππ=0.46±0.03 and gfKK¯=1.24±0.32, respectively. According to the joint confidence regions of gfππ and gfKK¯, we conclude that the experimental data tend to support the KK¯ molecule model and the quark-antiquark (qq¯) model, but tend not to support the tetraquarks (q2q¯2) model and the quark-antiquark gluon (qq¯g) hybrid model.

Lithium-ion batteries have attracted enormous interests recently as promising power sources. However, the safety issue associated with the employment of highly flammable liquid electrolyte impedes the further development of next-generation lithium-ion batteries. Recently, researchers reported the use of electrospun core-shell fiber as the battery separator consisting of polymer layer as protective shell and flame retardants loaded inside as core. In case of a typical battery shorting, the protective polymer shell melts during thermal-runaway and the flame retardants inside would be released to suppress the combustion of the electrolyte. Due to the use of a single precursor solution for electrospinning containing both polymer and flame retardants, the weight ratio of flame retardants is limited and dependent. Herein, we developed a dual-nozzle, coaxial electrospinning approach to fabricate the core-shell nanofiber with a greatly enhanced flame retardants weight percentage in the final fibers. The weight ratio of flame retardants of triphenyl phosphate in the final composite reaches over 60 wt.%. The LiFePO4-based cell using this composite nanofiber as battery separator exhibits excellent flame-retardant property without compromising the cycling stability or rate performances. In addition, this functional nanofiber can also be coated onto commercial separators instead of being used directly as separators.

Arc sensors have been used in seam tracking and widely studied since the 80s; commercial arc sensing products for T and V shaped grooves have been developed. However, it is difficult to use these arc sensors in narrow gap welding because arc stability and sensing accuracy are not satisfactory. Pulse gas melting arc welding (P-GMAW) has been successfully applied in narrow gap welding and all position welding processes, so it is worthwhile to research P-GMAW arc sensing technology. In this paper, we derived a linear mathematical P-GMAW model for arc sensing, and the assumptions for the model are verified through experiments and finite element methods. Finally, the linear characteristics of the mathematical model were investigated. In torch height changing experiments, uphill experiments, and groove angle changing experiments the P-GMAW arc signals all satisfied the linear rules. In addition, the faster the welding speed, the higher the arc signal sensitivities; the smaller the groove angle, the greater the arc sensitivities. The arc signal variation rate needs to be modified according to the welding power, groove angles, and swing or rotate speed.

To accurately identify high-quality functional wood with superior mechanical properties and enhance its durability, it is imperative to understand the relationships between the physical and mechanical attributes of diverse wood types. Presently, there exist issues related to accuracy, efficiency, and differentiation in studying these performance correlations. The Liang Kleeman (L-K) information flow theory, a causative analytical approach rooted in fundamental physics principles, offers swift computational capabilities. Furthermore, the coefficient of variation in L-K information flow quantitatively gauges the intensity of causal effects. Therefore, this article adopts a causal artificial intelligence perspective and introduces a wood performance analysis method based on L-K information flow.This study focuses on four Chinese fir varieties, including fast-growing Chinese fir (YKS), red heart Chinese fir (CSH, XXH), and iron heart Chinese fir (XXT). By computing and comparing variation coefficients of L-K information flow for various physical properties and their impact on mechanical properties, causal relationships between these properties are elucidated. These findings are then applied to predict wood mechanical properties, achieving a remarkable maximum accuracy of 90%.In the future, these findings will prove invaluable for the selection of high-quality wood and for improving the manufacturing process to prolong the service life of functionalized wood.

Objective: This study investigated the effects of an easily implemented 8-week program centered around jump rope exercise (JRE) on executive function among children with ADHD. Methods: Using the Stroop Color Word Test to assess inhibitory control, the n-back task (1- and 2-back) to assess working memory, and task switching to assess cognitive flexibility. Results: After the intervention, there were within-group effects only for the experimental group, with higher accuracy on the 1-back working memory task (t= -2.79，p= 0.011< 0.05，Cohen’s d= 0.60) and on task switching after compared with before the JRE intervention (t= -4.00，p=0.01<0.05，Cohen’s d= 0.85), but with no change in reaction time. There was no significant within-group difference on the Stroop test (P> 0.05). Additionally, there were no between-group effects as assessed by one-way analyses of variance. Conclusion: 8-week program focused on JRE improved two aspects of executive function, working memory and cognitive flexibility, with no significant effect on a third aspect, inhibitory control, among children with ADHD aged 7 to 12 years.

Taking Shanxi Province, a typical energy region in China, as the research object, data on economic development quality and environmental pollution intensity from 2001 to 2021 were selected. Linear regression, numerical simulation, and Pearson correlation coefficient were used to analyze the evolution characteristics of economic development quality and environmental pollution intensity in Shanxi Province in the past 20 years, and to explore the relationship between economic development and environmental pollution. The results indicate that since 2001, Shanxi Province has made long-term progress in economic development, with GDP increasing by nearly 10 times and an average annual growth rate of around 7%. In the past 20 years, the main pollutants have shown a trend of increasing first and then decreasing steadily, with a turning point occurring during the "12th Five Year Plan" period. This shows that the environmental policies and investments of the Chinese and Shanxi provincial governments in the past 10 years have been effective in the new era. The numerical simulation curve results show that the per capita GDP exhibits a classic inverted "U" curve relationship with wastewater and SO2 emissions, with a turning point occurring at around 20000 yuan per capita GDP; However, there is a monotonic decreasing trend with Chemical oxygen demand and ammonia nitrogen emissions, and a monotonic increasing trend with solid waste generation. There is no turning point yet. The correlation analysis results further support the fitting curve conclusion.

Group-IV GeSn photodetectors (PDs) compatible with standard complementary metal-oxide-semiconductor (CMOS) processing have emerged as a new and non-toxic infrared detection technology to enable a wide range of infrared applications. The performance of GeSn PDs is highly dependent on the Sn composition and operation temperature. Here, we develop theoretical models to establish a simple rule of thumb, namely “GeSn-rule 23”, to describe GeSn PDs’ dark current density in terms of operation temperature, cutoff wavelength, and Sn composition. In addition, analysis of GeSn PDs’ performance shows that the responsivity, detectivity, and bandwidth are highly dependent on operation temperature. This rule provides a simple and convenient indicator for device developers to estimate the device performance at various conditions for practical applications.

The realization of value in eco-products represents a rapidly advancing research theme. However, the clarity of relevant concepts, types and operational mechanisms remains elusive. A comprehensive review of current-stage research outcomes in eco-product value realization not only provides a novel approach to rejuvenating regional economies but also proactively contributes to the enhancement of local ecological environments. This article conducts a systematic literature review on the fundamental concepts, research scope and pathways for realizing the value of eco-products. The conclusions are as follows: (1) To clarify the basic concepts of ecosystem services and eco-products, the interrelationship between the two and the research scope of eco-products. (2) To elaborate the method for accounting the value of eco-products. (3) To demonstrate governmental path, market path and collaborative path for realizing the value of eco-products. (4)To review cases pertaining to the realization of value in public eco-products, quasi-public eco-products and operational eco-products both domestically and internationally. Building on this research foundation, the article identifies shortcomings in past research results and proposes key directions for future research progress.

Detecting, especially locating local degradations at an incipient stage, is very important for mission-critical high voltage rotating machines. One particular challenge of the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially made defects. These defects include turn-to-turn short, surface creepage, loose coils, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that the FDR can identify and locate both structure and insulation degradations of both shielded and unshielded objects with good sensitivity. This makes the FDR a comprehensive tool for fault diagnosis and aging assessment.

Plant phenotype plays an important role in crop breeding and planting. Leaf phenotype is an important part of plant phenotype. In order to analyze the leaf phenotype, the target leaf is required to be segmented from the complex background image. In this paper, an automatic soybean leaf segmentation method based on object detection and interactive segmentation models is proposed. Firstly, the Libra R-CNN object detection algorithm is used to detect all soybean leaves in the image. Then, based on the idea that the target soybean leaf is located in the center of the image and the area is large, the detection bounding box of the target leaf is selected. In order not to destroy the segmentation result, the bounding box is optimized to completely enclose the whole leaf. Finally, according to the optimized bounding box, the prior channels of foreground and background are constructed using Gaussian model. The two channels together with the original image are as the input of the interactive object segmentation with inside-outside guidance model to segment the target soybean leaf. A large number of qualitative and quantitative experimental results show that the method has high segmentation accuracy and strong generalization capacity.

Marine soft corals are prolific sources of various natural products that have served as a wealthy reservoir of diverse chemical scaffolds for new drug leads. The genus Litophyton contains almost 100 species but only a small part of them had been classified and chemically investigated, which calls for more attentions from the global researchers. In the current work, 109 secondary metabolites have been discussed based on published data from 1975 to July, 2023, covering a period of near five decades. The studied species of the genus Litophyton inhabited various tropical and temperate regions and afforded a variety of biologically active natural products such as terpenes, steroids, alkaloids, and lipids. A wide spectrum of pharmacological effects of these compounds had been evaluated, including cytotoxic, anti-HIV, antibacterial, antifungal, anti-malarial, antifeedant, molluscicidal, PTP1B inhibitory, and insect growth inhibitory activities. This review aims to provide an up-to-date literature survey and comprehensive insight into chemical structures, taxonomical distributions, and biological activities of the reported metabolites from the title genus whenever available.

Novel Ca3(BTC)2 metal-organic frameworks with different sizes and shapes constructed by Ca2+ and trimesic acid as metal center and ligands respectively were synthesized by a surfactant-assisted method. The relationship between the quantity of surfactant and the particle morphology of the product have been further studied. The samples were characterized by scanning electron microscope, powder X-ray diffraction and Inductively coupled plasma mass spectrometry. The results indicated that the size and shape of Ca3(BTC)2 could be regulated by changing the quantity of surfactant. Rod- and sheet-like Ca3(BTC)2 MOFs have been readily availabled. The removal kinetics of heavy metals of Pb(II) were also studied by rod-like Ca3(BTC)2. The results showed that as-synthesized Ca3(BTC)2 could effectively remove heavy metals of Pb(II) from the sewage.

We experimentally study the supercontinuum (SC) induced by femtosecond filament in different liquid media, and determine the relative measurement value of filamentation threshold Pth for these media by using a Mach-Zehnder interferometer (MZI). It is found that the value of Pth is higher than the critical power for self-focusing Pcr, which can be attributed to the strong dispersion effect. Changing the focal length of the focusing lens affect the measured results of the filamentation threshold: In the case of shorter focal length, linear focusing (i.e., geometrical focusing) regime dominates, and the measured values of Pth for different media are almost the same; as the focal length becomes larger, self-focusing starts to play a role, making the values of Pth for different media different from each other. This work provides an effective approach to study the femtosecond filamentation process in liquid media, and helpful to further understand the physical process of supercontinuum induced by femtosecond filament in liquid media.

To prevent CO2 leakage and ensure the safety of long-term CO2 storage, it is essential to investigate the flow mechanism of CO2 in complex pore structures at the pore scale. This study focuses on reviewing the experimental, theoretical, and numerical simulation studies on the microscopic flow of CO2 in complex pore structures during the last decade. For example, advanced imaging techniques, such as X-ray computed tomography (CT) and nuclear magnetic resonance (NMR), are used to reconstruct the complex pore structures of rocks. Mathematical methods, such as Darcy's law, Young–Laplace’s law, and the Navier-Stokes equation, are used to describe the microscopic flow of CO2. Numerical methods, such as the lattice Boltzmann method (LBM) and pore network (PN) model, are used for numerical simulation. The application of these experimental and theoretical models and numerical simulation studies is discussed, considering the effect of complex pore structures. Finally, future research is suggested to focus on: (1) Conducting real-time CT scanning experiments of CO2 displacement combined with the developed real-time CT scanning clamping device to realize real-time visualization and provide quantitative description of the flow behavior of CO2 in complex pore structures; (2) The effect of pore structures change on the CO2 flow mechanism caused by the chemical reaction between CO2 and the pore surface, the flow theory of CO2 considering wettability and damage theory in a complex pore structures; (3) The flow mechanism of multi-phase CO2 in complex pore structures; (4) The flow mechanism of CO2 in the pore structures at multiscale and the scale upgrade from microscopic to mesoscopic to macroscopic. Generally, this study focuses on reviewing the research progress of CO2 flow mechanisms in complex pore structures at the pore scale and affords an overview of potential advanced developments to enhance the current understanding of CO2 microscopic flow mechanisms.

Bending waveguide is one of the building blocks of on-chip photonic integrated circuits (PICs). Previous bending waveguides (BWs) usually suffer from larger footprint size in the bending region, which is are not conducive to develop large-scale and high-density PICs. To this end, we propose and numerically investigate an ultra-compact silicon-on-insulator 180-degree waveguide bend with a bending radius of 2 µm. Being different from the previous BWs, the design is based on topologically optimized digital metamaterials, which is implemented by using intelligent genetic algorithms. Simulated results show that the proposed structure has a -1.36 dB bend efficiency per 180-degree at a wavelength of 1.55 µm and occupies a footprint of only 4 × 4 µm2. Moreover, the crosstalk is <−20 dB and the loss is <2.6 dB over a >100 nm operating bandwidth (1.6-1.7 µm) for TE0 modes. With the wide operating bandwidth and ultra-compact footprint, the proposed silicon-based 180-degree waveguide bend shows a potential application as the fundamental component for optical communication interconnect in the ultra-highly integrated photonic circuit.

Air filtration mechanisms in the composite filter media used in practical applications are important and challenging to understand because the component fibers could have various size scales and morphologies. In this work, a three-dimensional digital model of nanofiber-based filter media was reconstructed for the first time based on the X-ray tomography data for the cellulose substrate and the Focused Ion Beam-Scanning Electron Microscope (FIB-SEM) image analysis for the several microns thick (3.82-7.90 μm) electrospun polyvinylidene fluoride (PVDF) nanofiber membrane. Besides the high-resolution model where the details of the fibrous structures were fully resolved, another low-resolution model with approximated unresolved structures was also established. Filtration simulations utilizing these models were conducted considering the drag force, Brownian diffusion and aerodynamic slip. The simulated filtration efficiencies agreed well with the experiments for particles of 70-400 nm, including the most penetrating particle size (MPPS, 100-200 nm). Moreover, the structure-resolved models had higher accuracy but higher computational costs, while the unresolved simulations saved much running time but over-predicted the filtration efficiency, especially for smaller particles (<100 nm). Our study presents a comprehensive strategy for investigating the composite filter media with multiscale complex structures using a combination of advanced characterization technologies and modular simulation models.

Porous MnO/C microspheres have been successfully fabricated by a fast co-precipitation method in a T-shaped microchannel reactor. The structures, compositions and electrochemical performances of the obtained MnO/C microspheres are characterized by X-ray diffraction, emission scanning electron microscopy, transmission electron microscopy (HRTEM), Brunauer–Emmett–Teller analysis, charge-discharge testing, cyclic voltammograms, and electrochemical impedance spectra. Experimental results reveal that the as-prepared MnO/C, with a specific surface area of 96.66 m²·g^–1 and average pore size of 24.37 nm, exhibits excellent electrochemical performance, with a discharge capacity of 655.4 mAh·g^–1 after cycling 50 times at 1 C and capacities of 808.3, 743.7, 642.6, 450.1, and 803.1 mAh·g^–1 at 0.2, 0.5, 1, 2, and 0.2 C, respectively. Moreover, the controlled method of using a micro-channel reactor, which can produce larger specific surface area porous MnO/C with improved cycling performance by shortening lithium-ion diffusion distances, can be easily applied in real production on a large-scale.

Based on the dynamic strength variation mechanism of concrete under complex loading conditions, two approaches were devised: numerical testing and CT scanning, guided by the principle of minimizing energy dissipation. A methodology for calculating concrete's failure area and measuring its strength was developed. The analysis delves into the mechanisms behind failure patterns and variations in concrete specimen strength, focusing on crack initiation, development, and the resultant failure surface formed by crack propagation, along with the stress distribution in concrete specimens subjected to diverse loading conditions. Through evaluating the strength and computed failure area of concrete specimens, it is evident that the loading method influences both the strength and failure area of concrete specimens. In general, a larger failure area correlates with a higher measured strength.

The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of B factor at the regional scale is fundamental in predicting regional soil erosion and the dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B factor from Sentinel-2 data and proposed a new method, namely trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat-7, Landsat-8). We then calculated and mapped the B-factor in Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall erosivity and rainfall, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10-m resolution land use data, enhancing the B factor calculation accuracy from the region to the spot level. The B factor computation procedure developed in this study is applicable to various river basin and regional scales for regional soil erosion monitoring.

Residual stress (RS) is the most challenging problem in metal additive manufacturing (AM) since the build-up of high tensile RS may influence the fatigue life, corrosion resistance, crack initiation, and failure of the additively manufactured components. While tensile RS is inherent in all the AM processes, fast and accurate prediction of stress state within the part is extremely valuable and would result in optimization of the process parameters in achieving a desired RS and control of the build process. This paper proposes a physics-based analytical model to rapidly and accurately predict the RS within the additively manufactured part. In this model, a transient moving point heat source (HS) is utilized to determine the temperature field. Due to the high temperature gradient within the proximity of the melt pool area, material experience high thermal stress. Thermal stress is calculated by combining three sources of stresses known as stresses due to the body forces, normal tension, and hydrostatic stress in a homogeneous semi-infinite medium. The thermal stress determines the RS state within the part. Consequently, by taking the thermal stress history as an input, both the in-plane and out of plane RS distributions are found from incremental plasticity and kinematic hardening behavior of the metal by considering volume conservation in plastic deformation in coupling with the equilibrium and compatibility conditions. In this modeling, material properties are temperature-sensitive since the steep temperature gradient varies the properties significantly. Moreover, the energy needed for the solid-state phase transition is reflected by modifying the specific heat employing the latent heat of fusion. Furthermore, the multi-layer and multi-scan aspects of metal AM are considered by including the temperature history from previous layers and scans. Results from the analytical RS model presented excellent agreement with XRD measurements employed to determine the RS in the Ti-6Al-4V specimens.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Nature-based solutions (NbS) rapidly develop globally to address societal challenges and provide human well-being and biodiversity. Watershed restoration plays an essential role in enhancing the ecological and socio-economic benefits of the region. The design and implementation of watershed restoration projects play a crucial role in their effectiveness, and NbS has been applied as a concept in ecosystem-related projects. This paper provides an idea to evaluate the design or implementation of watershed restoration projects based on the eight criteria proposed by the IUCN Global Standard for Nature-Based Solutions and to adjust the projects based on the evaluation results. The aim is to standardize the implementation process of watershed restoration projects to achieve more significant benefits and practically apply the concept of NbS in watershed restoration. implementation scheme of the Shan-Shui Initiative in the Jiulong River Watershed and concludes that the method is feasible in evaluating and improving the design and implementation scheme of ecological restoration projects in the actual basin. According to the assessment results, the degree of matching based on NbS for the implementation scheme of the Jiulong River Watershed Shan-Shui Initiative is 73%, which meets the criteria of NbS but needs to be improved in terms of monitoring and assessment, synergistic management, and benefit trade-offs.

The modeling of flood-induced bend scour near embankment toes can provide important information for river engineering, embankment safety warnings, and emergency action management. During the rainy seasons, short-term general scour and bend scour are the most common causes for the failure of reinforced concrete embankments in Taiwan. To gain a deeper understanding of the scouring process near levee foundations, this study proposed a straightforward and practical method for bend scour simulation. The proposed simulation method is subdivided into three stages: two-dimensional flow simulation, general scour estimation, and bend scour estimation. A new bend-scour computation equation is proposed and incorporated into a two-dimensional hydraulic finite-volume model for simulating the evolution of bend scour depth around embankment toes. The proposed method is applied to simulate the temporal evolution of bend scouring near the Shuideliaw Embankment on the Cho-Shui River in Taiwan, where serious failure occurred during the June 2012 monsoon. Field data were gathered using the numbered-brick technique at the Shuideliaw Embankment to demonstrate the accuracy of the proposed method. The results of the bend scour simulations compared reasonably well with field measurements, indicating close agreement in terms of water levels and bend scour depths near the Shuideliaw Embankment. The proposed method was found to quickly estimate the maximum short-term general scour and bend scour depths for further enhancement of the safety of the embankment toe.

This study aimed to investigate how transplanting date affects the agronomic and grain quality traits of two early maturing rice varieties. The experiment was conducted in the rice research field of Chungnam Agricultural Research and Extension Services, South Korea and rice materials were transplanted at intervals of approximately 15 days from April 16 to July 16 in 2019 and 2020. Results showed that agronomic and grain quality traits varied according to the transplanting date, and earlier transplanting resulted in the longer period of days from transplanting to heading (DTH). The spikelet number m-2 was highly correlated with the milled rice yield (r = 0.963** for Jinbuol, r = 0.909** for Yeoreumi) and it significantly decreased as the transplanting date was delayed, which was leading to lower yield. Environmental factors, the mean temperature during the grain filling stage, had a negative correlation with head rice rate (r2 = 0.825** for Jinbuol, r2 = 0.803** for Yeoreumi), and the number of days from transplanting to heading showed negative correlation with protein content (r2 = 0.777** for Jinbuol, r2 = 0.833** for Yeoreumi). Therefore, increasing the number of days from transplanting to heading date can lead to higher milled rice yield and lower protein content, and avoiding heading dates on July 17 can improve the appearance traits. As a results, it is suggested that early transplanting is advantageous to increase the milled rice yield and grain quality of early maturing rice.

L-Theanine, a unique non-protein amino acid, is an important bioactive component of green tea. Previous studies have shown that L-theanine has many potent health benefits, such as anti-anxiety effect, regulation of immune response, relaxing neural tension, reducing oxidative damage. However, little is known concerning whether L-theanine can improve the clearance of mitochondrial DNA (mtDNA) damage in organisms. Here, we reported that L-theanine treatment increased ATP production and improved mitochondrial morphology to extend the lifespan of UVC-exposed nematodes. Mechanistic investigations showed that L-theanine treatment enhanced the removal of mtDNA damage and extended lifespan by activating autophagy, mitophagy, mitochondrial dynamics, and mitochondrial unfolded protein response (UPRmt) in UVC-exposed nematodes. In addition, L-theanine treatment also up-regulated the expression of genes related to mitochondrial energy metabolism in UVC-exposed nematodes. Our study provided a theoretical basis for the possibility that tea drinking may prevent mitochondrial related diseases.

Background: Recently we demonstrated that Astragalus polysaccharide (PG2), the active ingredient in dried roots of astragalus membranaceus, ameliorates cancer symptom clusters and improve quality of life (QoL) in patients with metastatic disease by modulating inflammatory cascade, against the background that inflammatory cells including macrophages, dendritic cells (DCs), and cytotoxic T lymphocytes (CTLs) in tumor initiation, metastasis, and progression. Nevertheless, the role of PG2 in the modulation of anticancer immunogenicity and therapeutic response remains relatively underexplored and unclear. Purpose: The present study investigates how and to what extent PG2 modulates cellular and biochemical components of the inflammatory cascade, enhance anticancer immunity, and the therapeutic implication of same in patients with lung cancer. Methods and Results: Herein, we demonstrated that PG2 significantly increased the M1/M2 macrophage polarization ratio in non-small cell carcinoma (NSCLC) H441 and H1299 cells. This PG2-induced preferential pharmacologic up-regulation of tumoral M1 population in vitro, positively correlated with the downregulation of tumor-promoting IL-6 and IL-10 expression in NSCLC cell-conditioned medium, with concomitant marked inhibition of cell proliferation, clonogenicity and tumorsphere formation. Our ex vivo results, using clinical sample from our NSCLC cohort, demonstrated that PG2 also promoted the functional maturation of DCs with consequent enhancement of T cell-mediated anticancer immune responses. Consistent with the in vitro and ex vivo results, our in vivo studies showed that mice treated with PG2 exhibited significant time-dependent depletion of the tumor-associated M2 population, synergistically enhanced the anti-M2-based anticancer effect of cisplatin, and inhibition of xenograft tumor growth in the NSCLC mice models. Moreover, in the presence of PG2, cisplatin-associated dyscresia and weight-loss was markedly suppressed. Conclusion: These results do indicate a therapeutically-relevant role for PG2 in modulating the M1/M2 macrophage pool, facilitating DC maturation, and synergistically enhancing the anticancer effect of conventional chemotherapeutic agent, cisplatin; thus laying the foundation for further exploration of the curative relevance of PG2 as surrogate immunotherapy and/or clinical feasibility of its use for maintenance therapy in patients with lung cancer.

We constrain two dynamical dark energy models that are parametrized by the logarithm form of $w(z)=w_{0}+w_{1}\left(\frac{\ln (2+z)}{1+z}-\ln 2\right)$ and the oscillating form of $w(z)=w_{0}+w_{1}\left(\frac{\sin(1+z)}{1+z}-\sin(1)\right)$. Comparing with the Chevallier-Polarski-Linder (CPL) model, the two parametrizations for dark energy can explore the whole evolution history of the universe properly. Using the current mainstream observational data including the cosmic microwave background data and the baryon acoustic oscillation data as well as the type Ia supernovae data, we perform the $\chi^2$ statistic analysis to global fit these models, finding that the logarithm parametrization and the oscillating parametrization are almost as well as the CPL scenario in fitting these data. We make a comparison for the impacts of the dynamical dark energy on the cosmological constraints on the total mass of active neutrinos. We find that the dark energy properties could significantly change the fitting results of neutrino mass. Looser constraints on $\sum m_{\nu}$ are obtained in the logarithm and oscillating models than those derived in the CPL model. Consideration of the possible mass ordering of neutrinos reveals that the most stringent constraint on $\sum m_{\nu}$ appears in the degenerate hierarchy case.

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

Background: Accurate prediction of patients’ response to therapy is clinically indispensable, howbeit challenging. With increased understanding of the human genome and malignancies, there is the renaissance of in silico pharmacogenomics with renewed interest in drug response predictability based on gene-drug interaction. Objective: Evidence-based transcript-proteome profiling is essential for synthesizing clinically applicable algorithms for predicting response to anticancer therapy, including immune checkpoint blockade (ICBT); thus, saving physicians’ time, reducing polypharmacy, and curtailing unnecessary treatment expense. In this study, we tested and validated the hypothesis that a selected proteomic signature in ICBT-naïve patients is sufficient for the prediction of response to ICBT. Methods: Using a multimodal approach consisting of computational pharmacogenomics, transcript-proteome analytics, mathematical modeling, and machine learning systems; we delineated therapy-sensitivity and stratified patients into graduated response groups based on their proteomic profile. Protein expression levels in our cohort tissue specimens were evaluated based on T cell- and non-T cell- inflamed phenotypes by immunohistochemistry. Results: We established β-catenin, PDL1, CD3 and CD8 expression-based ICBT response model. Statistical regression models validated the predictive association between our predefined algorithms and therapeutic outcome. Interestingly, our 4-gene prediction classifier was constitutively independent of tumor tissue origin, correctly stratified patients into high-, low-, and non- responders pre-treatment, with high prediction accuracy, and exhibited good association with patients’ performance status and prognosis (p < 0.01). Conclusion: Our findings demonstrate the possibility of accurate proteomics based ICBT response prediction and provide a putative basis for drug response prediction based on selective proteome profile in untreated cancer patients.

Transcutaneous injection laryngoplasty is a well-known procedure for treating paralyzed vocal fold by injecting augmentation material into it. Hence, vocal fold localization plays a vital role in the preoperative planning as the fold location is required to determine the optimal injection route. In this communication, we propose a mirror environment based reinforcement learning (RL) algorithm for localizing the right and left vocal folds in preoperative neck CT. RL-based methods commonly showed noteworthy outcome in general anatomic landmark localization problem in the recent years. However, such methods suggest training individual agent for localizing each fold, though the right and left vocal folds are located in close proximity and have high feature-similarity. Utilizing the lateral symmetry between the right and left vocal folds, the proposed mirror environment allows for a single agent for localizing both the folds by treating the left fold as a flipped version of the right fold. Thus, localization of both folds can be trained using a single training session which utilizes the inter-fold correlation and avoids redundant feature learning. Experiment with 120 CT volumes showed improved localization performance and training efficiency of the proposed method compared with the standard RL method.

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or system. Recent advancements have extended the concept of digital twins to humans, incorporating complex biological data such as DNA (Predictiv Care, Inc.) and immune system profiles. These sophisticated models go beyond mere pictorial representations, offering a more holistic digital reflection. However, a significant gap remains. The current human digital twin models are not capable of learning one’s preferences. In this pilot study, we introduce Diginality, a chatbot powered by HyperCLOVA X, a Large Language Model (LLM) developed by NAVER, Inc. Diginality, learns one’s preferences with custom training from data collected by interview-style questions on the user’s topic of interest. Our findings demonstrate that Diginality successfully answers one’s preferences, thereby adding a new dimension to the concept of human digital twins. This work represents a pioneering step towards creating a more comprehensive and psychologically nuanced human digital twin.

This study uses a mass-spring-damping system to simulate the repeated strain of liquefaction cyclic triaxial tests. The two results are compared in this paper to understand the feasibility of the mass-spring-damping system developed in this paper and the feasibility of future research on this related topic and development potential. The main factors affecting this mode's repeated strain are the spring coefficient k and the external force Q0. The spring coefficient has an inverse relationship but does not increase in multiples. The external force has a direct proportional relationship but does not increase the result- a non-multiple increase. When the pore water pressure of the liquefied cyclic triaxial test specimen rises, it will cause the specimen to liquefy, decreasing effective stress, shear modulus G, and damping ratio D, causing an increase in strain. The shear modulus and damping ratio are related to the spring coefficients k and c. Both are variables that change with time. This article takes an original thin-tube specimen at point A in the Yunlin area in Taiwan as a testing example. Through Mathematica software, it can be obtained that the mass m=1kg, the spring coefficient k=244e-0.1t kgf/cm, and the damping coefficient c=0.739e-0.257t kgf-s/cm and external force Q0=10.1sin2πt kg; Finally, this study selected the original thin-tube specimens from four locations in the Yunlin area, and simulated the repeated strain amount of the cyclic triaxial test specimens through a spring-damping system. The results show that the spring-damping system is feasible for simulated cyclic triaxial tests because the model is simple and the parameters are easy to understand and obtain, which also shows the extensibility of this model. Preliminary results of the research show that this model can further simulate the repeated strain obtained by cyclic triaxial tests without considering the increased trend in pore water pressure and the decrease in effective stress during cyclic loading.

The three-pronged sliding universal coupling is a new kind of three-pronged coupling. To study its kinematic and dynamic characteristics, directional cosine matrices are utilized as tools to analyze the coordinate systems by establishing simplified geometric models and corresponding motions. Through the analysis of the motions of the input and output shafts and employing the method of single-force element, a set of equilibrium equations for the forces acting on the output shaft and the input shaft are formulated for solution analysis. The research indicates that during the rotation process, there exists a small angular difference between the input shaft and the intermediate shaft, demonstrating the quasi-constant angular velocity characteristics of the new tripod sliding universal coupling. The curves of the forces and torques acting on each component of the coupling approximate sinusoidal curves. The optimal operating angle range for the new tripod universal coupling is at angle , during which the system exhibits good transmission and mechanical performance.

This study presented a novel and sustainable method for producing chitin nanocrystals (PA-ChNCs) using phosphoric acid hydrolysis. The produced PA-ChNCs were used as stabilizers for Pickering emulsions and their physicochemical properties as well as stabilization mechanisms were systematically investigated. The PA-ChNCs exhibited small particle sizes and high surface areas, enabling an effective adsorption at the oil/water interface, which prevented coalescence in Pickering emulsions. The particle size and surface charge PA-ChNCs were strongly pH-dependent, which were critical factors for stabilizing Pickering emulsions. The study showed that emulsions with higher O:W ratios (50%) exhibited larger droplets but greater viscosity, forming a stable gel-like matrix that was more stable than emulsions with other O:W ratios after storage for 30 days. Emulsions with higher PA-ChNCs concentrations (2.0 wt%) and higher pH values (pH 10) showed small droplets and high viscosity, leading to stable emulsions. This research contributes to the development of sustainable and effective emulsifiers for use in the food industry, making utilization of seafood waste in an eco-friendly manner possible. Overall, this study highlights the potential of using newly developed PA-ChNCs as stabilizers for Pickering emulsions in food products.

This study evaluated the effects of high-temperature and high-pressure treatment on the antioxidant and anti-inflammatory activities of carrot extracts. The total polyphenol and flavonoid content of heated carrot extract increased as the heating temperature increased, with 403.93 mg GAE/100 g and 53.14 mg CE/100 g at 150°C. DPPH and ABTs radical scavenging activity (IC50) of heated carrot extract also increased as the heating temperature increased, with 4.65 mg/mL and 7.31 mg/mL, respectively, at 150°C. The reducing power increased with the heating temperature. Heated carrot extract did not exhibit any temperature- or concentration-dependent cytotoxicity. Nitric oxide (NO) generation was suppressed as the heating temperature and NO production increased. The heated carrot extract showed a decrease in the production of dose-dependent TNF-α, IL-1β, and IL-6 compared to the control group. The above results show that heated carrot extract had effective antioxidant and anti-inflammatory activities.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

Considering the aging society and budget constraints, a strategy for reforming public transportation welfare policies is essential for sustainability. Among the various transportation welfare policies, fare discount policies are provided to specific age groups, and most countries offer benefits to several public transportation. South Korea provides a fare-free policy limited to subways, and the need for policy modification is emerging due to sustainability problems caused by an aging society. Hence, the scope of the policy should be expanded to all travel modes, but the discount rate should be reduced, or the target group should be reduced in consideration of the budget. This study proposes a travel mode to focus on by city through the preference of the elderly. The latent class model is employed to classfy stated preference data collected from the survey. The estimation results show a significant preference heterogeneity depending on the level of subway supply by region, and a policy focused on subways would be more reasonable in cities with sufficient subway infrastructure. Hence, regarding sustainability, it may be more appropriate to focus on specific means than to provide the same discount policy for all public transport.

The advent of deep learning has completely reshaped our world. Now, our daily life is fulfilled with many well-known applications that adopt deep learning techniques, such as self-driving cars and face recognition. Furthermore, robotics developed more forms of technology which share the same principle with face recognition, such as hand pose recognition and fingerprint recognition. Image recognition technology requires a huge database and various learning algorithms, such as convolutional neural network and recurrent neural network, that requires lots of computational power, such as CPUs and GPUs. Thus, clients could not be satisfied with the computational resource of the local machine. The cloud resource platform emerged at a historic moment. Docker containers play a significant role of microservices-based applications in the next generation. However, it could not guarantee the quality of service. From clients’ perspective, they have to balance the budget and quality of experiences (e.g. response time). The budget leans on individual business owners and the required Quality of Experience (QoE) depends on usage scenarios of different applications, for instance, an autonomous vehicle requires real-time response, but, unlocking your smartphone can tolerate delays. Plenty of on-going projects developed user-oriented optimization resource allocation to improve the quality of the service. Considering the users’ specifications, including accelerating the training process and specifying the quality of experience, this thesis proposes two differentiate containers scheduling for deep learning applications: TRADL and DQoES .

Panax notoginseng is a traditional Chinese medicine, and saponins are its main active ingredients. The content of saponins in flower of P. notoginseng is the highest in the whole plant. Minor ginsenosides have stronger pharmacological activity than main ginsenosides, but its content in nature is very low. We can use extracted total saponins from P. notoginseng flower as the substrate to produce minor ginsenosides by biotransformation method. In this study, we used the fungus Talaromyces flavus and its crude enzymes to transform the extracted total saponins of P. notoginseng flower. Through the qualitative and quantitative analysis of the transformation substrates and products, it was found that minor ginsenosides Gyp-XVII, ginsenosides C-Mc1, C-Mx1, F2, Rg3, 20(R)-Rg3, Rk1 and Rg5 were found in the products and the conversion rate of the major saponins in P. notoginseng flower ranged from 44.16 to 55.22%, the yields of the minor ginsenosides ranged from 0.05 to 3.24%. This study provided a good experimental idea, greatly increased the utilization rate of total saponins in P. notoginseng flower, and laid a foundation for promoting the wide application of minor saponins.

Background: Colorectal cancer (CRC) is one of the most prevalent diseases and the second leading cause of death worldwide. However, the relationship between CRC and cerebrovascular-specific mortality (CVSM) remains elusive and less is known about the influencing factors associated with CVSM in CRC. Here, we aimed to analyze the incidence as well as the risk factors of CVSM in CRC. Methods: Patients with a primary CRC diagnosed between 1973 and 2015 were identified from Surveillance Epidemiology and End Results database with follow-up data available until 31 December 2016. Conditional standardized mortality ratios were calculated to compare the incidence of CVSM between CRC patients and the general US population. Univariate and multivariate survival analyses with a competing risk model were used to interrogate the risk factors for CVSM. Results: A total of 563298 CRC individuals were included. The CVSM in CRC patients was significantly higher than the general population in all age subgroups. Among competing causes of death in patients, the cumulative mortality caused by cerebrovascular-specific diseases steadily increased during study period. While age and surgery positively influenced CVSM on both univariate and multivariate analyses, male patients and those who had radiotherapy, chemotherapy, more recent year (2001-2015) of diagnosis as well as multiple primary or distant tumors experienced a lower risk of CVSM. Interpretation: Our data suggest a potential role for CRC in the incidence of CVSM and also identify several significant predictors of CVSM, which may be helpful for risk stratification and therapeutic optimization of cerebrovascular-specific diseases in CRC patients.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

Polypyrrole (PPy)-capped caped silver nanowire (Ag NW) nanomaterials (core–shell rod-shaped Ag NW@PPy) were synthesized suing a one–port suspension polymerization technique. The thickness of PPy layer on the 50-nm-thickness/15-μm-length Ag NW was effectively controlled to 10, 40, 50, and 60 nm. Thin films cast from one-dimensional conductive Ag NW@PPy formed a three-dimensional (3-D) conductive porous network structure and provided excellent electrochemical performance. The 3-D Ag NW@PPy network can significantly reduce the internal resistance of the electrode and maintain structural stability. As a result, a high specific capacitance of 625 F/g at a scan rate of 1 mV/s was obtained from the 3-D porous Ag NW@PPy composite film. The cycling performance over a long period exceeding 10,000 cycles was also evaluated. We expect that our core–shell-structured Ag NW@PPy composites and their 3-D porous structure network films can be applied as electrochemical materials for the design and manufacture of supercapacitors and other energy storage devices.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

Background: Improving patients’ quality of life (QoL) is a principal objective of all treatment in any clinical setting, including oncology practice. Cancer-associated inflammation is implicated in disease progression and worsening of patients’ QoL. Conventional anticancer therapeutics while selectively eliminating cancerous cells, are evaded by stem cell-like cells, and associated with varying degrees of adverse effects, thus reducing patients’ QoL. This necessitates novel therapeutic approaches with enhanced efficacy, minimal or no treatment-related adverse effects, and improved QoL in patients with cancer, especially those with metastatic/advance stage disease. Methods: Sequel to our team’s previous publication, the present study explores probable effects of Astragalus polysaccharides (PG2) on cancer-related inflammatory landscape and known determinants of QoL, as well as the probable link between the two to provide mechanistic insight. In exploratory double blind randomized controlled trial using patients with metastatic disease (n=23), we comparatively evaluated the therapeutic efficacy of high (500mg) or low (250mg) dose PG2 administered intravenously (i.v.), with particular focus on its suggested anti-inflammatory function and the probable effect of same on QoL indices at baseline, then at weeks 4 and 8 post-PG2 treatment. Results: All 23 patients with metastatic disease treated with either low or high PG2 experienced reduced pain, nausea, vomiting and fatigue, as well as better appetite and sleep, culminating in improved global QoL. This was most apparent in the high dose group, with significant co-suppression of pro-inflammatory interleukin (IL)-1β, IL-4, IL-6, IL-13, IL-17, monocytes chemotactic protein (MCP)1, granulocyte-macrophage colony-stimulating factor (GM-CSF), vascular endothelial growth factor (VEGF), tumor growth factor (TGF)-β1, interferon (IFN)-γ, and immune suppressors IL-10, and IL-12. Univariate and multivariate analyses revealed that IL-1β, IL-13 and GM-CSF are independent prognosticators of improved QoL. Conclusion: This proof-of-concept study provides premier evidence of functional association between PG2 anti-inflammatory effects and improved QoL in patients with advanced stage cancers, laying the groundwork for future larger cohort blinded controlled trials to establish the efficacy of PG2 as adjuvant anticancer therapy in metastatic or advanced stage clinical settings.

The research examines the relationship between self-rated health situation and personal percep-tion of heat waves, and how social linkage of communities would be a moderator variable in residents’ perception of heat waves in Taiwan. This study uses the questionnaire conducted by Sinica “Responsive Capacity under Heat Wave: The Perspectives of the Locals”(2019), using OLS method for estimating the unknown parameters in multiple regression model. The author finds that the correlation of self-rated health situation and perception toward heat is significantly posi-tive. Also, social linkage in communities affects strongly as a moderator variable: While the sat-isfaction to with their community could reduce negative reaction to heat, contacts with neighbors could increase possibility people feel uncomfortable in high-temperature situation. This study ex-hibits the effects of social environment on community, and expects further related researches or practices to strengthen capability to resist heat wavesƒ for Taiwanese residents.

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.

Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A number of deep learning approaches have been developed in recent years to improve the accuracy of such affinity prediction. While the predicting power of these systems have advanced to some degrees depending on the dataset used for model training and testing, the effects of the quality and quantity of the underlying data have not been thoroughly examined. In this study, we employed erroneous datasets and data subsets of different sizes, created from one of the largest databases of experimental binding affinities, to train and evaluate a deep learning system based on convolutional neural networks. Our results show that data quality and quantity do have significant impacts on the prediction performance of trained models. Depending on the variations in data quality and quantity, the performance discrepancies could be comparable to or even larger than those observed among different deep learning approaches. In particular, the presence of proteins during model training leads to a dramatic increase in prediction accuracy. This implies that continued accumulation of high-quality affinity data, especially for new protein targets, is indispensable for improving deep learning models to better predict protein-ligand binding affinities.

To clarify whether cryptochrome contributes to stem elongation and flowering promoted by blue lights associated with low phytochrome activity, wild-type Arabidopsis was compared with its cryptochrome-deficient mutants and cryptochrome-overexpressing transgenic plants. Results indicated that the promotion effects were mainly related to low CRY1 activity, despite partial involvement of high-activity CRY2.

In this paper, we investigate the new degenerate Daehee polynomials and numbers which are called the degenerate Daehee polynomials of the second kind, and derive some new and interesting identities and properties of those polynomials.

Aging is associated with muscle atrophy and fatty infiltration of skeletal muscle. The multifidus muscle stabilizes the lumbar spine and undergoes adipose accumulation with age, leading to functional decline in the elderly. Therefore, quantitative assessment of the multifidus muscle can be beneficial for the elderly when formulating treatment strategies and reducing future complications. Fifty-seven patients (mean age, 73.89 ± 6.09; 23 male patients) who underwent lumbar Magnetic resonance imaging (MRI) were prospectively recruited. The cross-sectional area (CSA) of the multifidus from the L2-S1 level and the CSA of the L4-5 level psoas muscle were measured. The functional CSA (fCSA) of the multifidus muscle was measured by excluding the fat infiltration area from the multifidus CSA. The CSA to fCSA ratio was obtained by multiplying 100 by the value obtained by dividing CSA by the fCSA. Pfrrmann classification was used to evaluate the degree of disc degeneration. The functional disability measurements were the Short Physical Performance Battery (SPPB), Berg Balance Scale (BBS), grip strength, and functional reach test (FRT). Pearson's correlation analysis was used to examine the relationship between the functional disability measurements and the multifidus muscle. The CSA to fCSA ratio value was relatively constant at each spine level and showed a significant correlation with the SPPB, grip strength, FRT, and psoas index (p&lt;0.05). However, degree of disc and multifidus muscle degeneration were not statistically significant. So, age-related changes play a significant role in developing back muscle fatty infiltration than disc dgeneration. Moreover, Grip strength showed a stronger relationship with the quality of the multifidus muscle than other functional disability measurements.

The betel nut chewing habit of taxi drivers has shown that can promote companionship, relaxation, and emotional stability at work. 19.3% of betel nut behaviors are instigated by an invitation among drivers to engage in the habit. The objective of this study was to explore the impact of social learning, subjective norm, and self-efficacy on betel nut chewing in taxi drivers in Taiwan. We conducted a cross-sectional study on taxis’ driver of betel nut behaviour and measured their evaluations of its consequences using a self-report questionnaire. Other variables include socio-demographic characteristics and chewing-related experience. The overall variance is 44.5% in socio-demographic variable, social learning, self-efficacy, and subjective norm. Results show that the variables of affected betel nut chewing behavior, driving years has the strongest influence, followed by learning about betel nut from the media, relatives, and friends, and education level, which positively affect betel nut chewing behavior. Findings indicated that education related to quitting betel nut chewing, significant others, and rules of the taxi fleet are adopted to design a plan for quitting betel chewing to reduce the positive expectations of taxi drivers for betel nut chewing and decrease their betel nut chewing behavior.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

Embracing innovative digital technologies within educational systems is essential for overcoming challenges in modern learning environments, especially in the post-COVID-19 era. Teaching sustainability management in higher education institutions with innovative digital tools plays a fundamental role in the transition toward sustainable societies. Suitable game design elements play a significant role in facilitating sustainable learning. This study explored the effectiveness of incorporating business simulation games with project-based learning (PBL) in a flipped classroom setting. This approach was adopted within the context of a university cross-border e-commerce course to prepare students for acquiring 21st -century skills as higher-order thinking skills in a rapidly changing educational landscape. A quasi-experimental method was employed, involving a total of 60 university students from China's Zhejiang Province. Participants completed an online questionnaire designed to assess their learning engagement across three dimensions (cognitive, emotional, and behavioral) as well as their higher-order thinking skills (problem-solving, critical thinking, and creativity). The results show that the business simulation games combined with flipped classroom learning had a significantly positive impact on students’ learning outcomes, enhancing their problem-solving, critical thinking, and creative capabilities. Importantly, this approach also improved student engagement and promoted sustainable practices by applying real-life scenarios in an interactive environment. We conclude that business simulation games integrated with project-based learning (PBL) in flipped classroom settings represent a valuable educational approach. This approach not only enhances learning engagement but also fosters the development of higher-order thinking skills, encouraging students to adopt sustainable learning practices.

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main QTLs that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map quantitative trait loci (QTLs) for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as re-combination-rich areas in all chromosomes except chromosome 1B. The middle area of the chro-mosomes was identified as the recombination-barren area. Both the genetic map and the physical map showed a significant correlation when p=0.001, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11mm. A total of 43 QTLs were identified, including 8 QTLs for Plant height(PH), 11 QTLs thousand grain weight(TGW), 15 QTLs for grain length(GL),and 9 QTLs for grain width(GW), which explained 1.36%–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci Qph.nwafu-4B and Qph.nwafu-4D that determined plant height. The explanation rates of phenotypic variation were 7.39%-12.26% and 20.11%-27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43%-6.85% for phenotypic variation, two co-segregating KASP markers were developed, the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43%-6.85%. Three loci that affected the grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2 and Qgl.nwafu-6B, illustrating the explana-tion rates of phenotypic variation as 6.72%-9.59%, 5.62%-7.75%, and 6.68%-10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

The evaporation of polymer solution droplet is an important in solution-based polymer film fabrications, such as inkjet print, spray coatings, and droplet casting etc. In this work, we investigated the effect of droplet size, solvent evaporation rate, and concentration on “coffee-ring” effect, crystal nucleation, polymorphism, and morphology of dried poly (vinylidene fluoride) (PVDF) solution droplets with atomic force microscopy (AFM) method. We found that the crystal structure, morphology and crystal distribution in the center and edge regions of dried PVDF droplets were different due to “coffee-ring” effect. The “coffee-ring” effect of dried PVDF droplets was mainly composited of accumulated crystals at the edge region of droplet, which was mainly made by crystallization of migrated chains. The interplay between migration of chains, crystallization and solidification of PVDF droplets significantly influenced the formation of “coffee-ring”. In addition, our results showed that the decrease of droplet size and controlling solvent evaporation rate were effective ways to improve the PVDF β-phase nucleation and decrease crystal size.

This study was conducted to examine the moderating and mediating effect of ego - resilience, parenting attitude, and peer attachment in the relation between smartphone dependency and aggression. Participants were 1,863 youths using a smartphone among the first middle school students responded in the 7th Korean Children and Youth Panel Survey (KCYPS) conducted by the National Youth Policy Institute in Korea. The data were analyzed by descriptive statistics, a correlation, and a hierarchical regression analysis. First, ego-resilience showed a partial mediating effect on the relationship between smartphone dependency, aggression and significant moderating effects were revealed. Second, parenting behavior showed a partial mediating effect on the relationship between smartphone dependency and aggression, with no moderating effects seen. Third, peer attachment had a partial mediating effect on the relationship between smartphone dependency and aggression, with no moderating effects seen. The research suggested the mental health and growth of students could be improved by applying various nursing and health care programs to improve ego-resilience, parenting behavior and peer attachment as they grow into adulthood.

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low field NMR (LF-NMR) relaxation characteristic data of various sample oils were analyzed. We also suggest a new method for prediction size of edible oil molecules using LF-NMR relaxation time distributions. According to the relative molecular mass, carbon chain length, transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid are displayed, reflecting the composition information of different oils. To study the correlation between the rotation correlation time and molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation, and molecular dynamics of complex fluids. Measurements of grease in the free fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured by low-field nuclear magnetic resonance.

The energy transfer process of laser selective melting is very complex. To study the effect of la-ser selective melting on the microstructure and properties of 18Ni-300 martensitic steel, ABAQUS was used to simulate the temperature of laser cladding 18Ni-300 martensitic steel at different time points and different laser power. The results show that the cross-section shape of the molten pool changes from round to oval With the increase of laser power, the higher the peak value of temperature time curve, the greater the temperature gradient; and the laser clad-ding experiment of 18Ni-300 martensitic steel was carried out, and the microstructure and me-chanical properties of the samples under different laser power were analyzed. The results show that with the increase of laser power, the grain size of the cladding layer becomes smaller and the microstructure becomes more compact; the hardness of the side surface of the sample is higher than that of the upper surface, and the tensile strength and elongation show a trend of first increasing and then decreasing.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

Drugs-drugs interactions(DDI) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is considered the center of a substructure, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differ- ences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. To address these issues, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

The practice of quantified-self sleep tracking is increasingly common nowadays among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization, and are incapable of providing actionable recommendations that are tailored to users' physical, behavioral and environmental context. Here we coined the term context-aware sleep health recommender system (CASHRS) as an emerging multidisciplinary research field that bridges ubiquitous sleep computing and context-aware recommender systems. In this paper, we presented a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges in peer-reviewed publications that meet the characteristics of CASHRS. Analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.

Phase synchronization is one of the key issues that must be addressed in the bistatic synthetic aperture radar (BiSAR) system. LuTan-1 is an innovative L-band spaceborne BiSAR mission of China. An advanced synchronization scheme is used in the LuTan-1 system. The synchronization pulses are exchanged immediately after the ending time of the radar echo receiving window and before the starting time of the next pulse repetition interval. Therefore, it can not interrupt the normal SAR data acquisition, further improving synchronization frequency and avoiding the data missing effect. In order to accurately monitor system malfunctions and compensate for the phase synchronization errors introduced by hardware system, an innovative internal calibration strategy is designed and employed in the LuTan-1 mission. The test data acquired from the ground validation system of LuTan-1 are used to demonstrate the feasibility of the proposed scheme. The results validate the effectiveness of the proposed scheme and prove the promise for its future application in LuTan-1.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

Silicon carbide single crystal (SiC) has been widely used in the field of power devices, while it is difficult to fabricate ultra-flat surface by traditional manufacturing technologies due to its difficult-to-machine property. This paper investigates the interaction process during femtosecond laser ablation of crystalline silicon carbide based on the two-temperature model simulation and experimental study. The findings reveal that parameters such as laser pulse energy, scanning interval, and defocus amount exert significant influence on ablation depth, roughness, and surface morphology. The femtosecond laser proves effective in removing surface defects from cut SiC substrates, albeit resulting in increased surface roughness. However, subsequent polishing treatment of the laser-ablated silicon carbide surface can mitigate this roughness, underscoring the feasibility of laser surface ablation for silicon carbide single crystals.

Coal gasification with carbon dioxide is a process for generating clean gaseous fuels and relieving greenhouse effect. Zhundong coal has high alkali and alkali earth metals (AAEMs) content, medium volatile and low ash in nature. Isothermal CO₂ gasification of char derived from Zhundong coal (R-char) and char from acid washing R-char (AR-char) are performed in thermo-gravimetric analyzer (TGA). The effect of AAEMs is investigated on the gasification behavior in the range of temperatures 1073 K to 1273 K. The carbon conversion increases rapidly with increasing reaction temperature and CO₂ concentration. R-char has high gasification rate and carbon conversion compared with AR-char. The accuracy of the free-model approach for calculating activation energy at different conversions is validated by compared with different kinetic models (volume reaction model, distributed activation energy model). Moreover, R-char gasification with CO₂ shows a compensation effect as the Arrhenius parameters (EA and k0) increase or decrease simultaneously.

Fractures of the talar neck and body are associated with spine fractures and scoliosis deformity, which affect cosmetic appearance and cause difficulty in ambulation. The implant design for talus surgery is thriving as a functional alternative in case of severe talar destruction, focusing on segmentation and reconstruction of the talus’s shape. However, manual segmentation of the talus is time-consuming and subjective. In this study we exploited the automatic segmentation framework to efficiently train a deep learning-based model to accurately segment the talus based on computed tomography imaging. We developed three model configurations with nnU-Net and investigated their Dice similarity coefficients (DSC) and 95% Hausdorff distances (HD95) for talus segmentation on a CT image dataset. The three configurations performed well (DSC > 0.95, HD95 < 0.6). When tested on the same samples, one of the configurations was more efficient while ensuring higher accuracy. We propose to focus on talus anatomic variations with increasing age based on this framework and apply it to clinical trials at the next stage.

Multi-view stereo (MVS) employs multi-point photography for image point positioning and three-dimensional reconstruction technology. Recently, this technology has been introduced into the monitoring of road slopes due to advances in photography and computing technology. In general, the various phases of post-image processing procedures are applied to various photographic data. In this study a novel, automated image-monitoring system is proposed to improve the ability of automatic processing. First, an Internet of things (IoT)-based digital photography system architecture was constructed to provide automatic control of camera photography and real-time transmission of image data. In addition, a visual SfM-MVS 3D reconstruction technique was used to develop related software and hardware interfaces based on the built-in Python computing framework of Photoscan Pro. The software integrates fully automatic photography, image transmission, monitoring of data processing and product release programs. The experimental results show that the system architecture can be applied to fully automatic three-dimensional monitoring of road slopes.

A challenge in building large-scale superconducting quantum processors is the precise control and manipulation of the qubit state. However, the crosstalk between the microwave control lines impedes the parallel execution of high-fidelity digital and analog quantum operations. Here, we propose and demonstrate a universal compensation protocol for calibrating the microwave signal crosstalk. We also introduce amplified error sequences to optimize the accuracy. Furthermore, we show a definitive improvement in parallel gate operations with crosstalk cancellation, demonstrating the technique's effectiveness. This work paves the way for superconducting hardware that features automated calibration of microwave crosstalk, leading to enhanced fidelities in multiqubit circuits.

In recent years, there has been significant growth in the ubiquity and popularity of three dimensional(3D) point clouds, with an increasing focus on the classification of 3D point clouds. To extract richer features from point clouds, many researchers have turned their attention to various point set regions and channels within irregular point clouds. However, this approach has limited capability in attending to crucial regions of interest in 3D point clouds and may overlook valuable information from neighboring features during feature aggregation. Therefore, this paper proposes a novel 3D point cloud classification method based on global attention and adaptive graph convolution. The method consists of two main branches: the first branch com-putes attention masks for each point, while the second branch employs adaptive graph convolu-tion to extract global features from the point set. It dynamically learns features based on point interactions, generating adaptive kernels to effectively and precisely capture diverse relationships among points from different semantic parts. Experimental results demonstrate that the proposed model achieves 93.8% in overall accuracy and 90.8% in average accuracy on the ModeNet40 dataset.

The security of several full homomorphic encryption (FHE) schemes depends on the hardness of the approximate common divisor (ACD) problem. The analysis of attack and defense against the system is one of the frontiers of cryptography research. In this paper, the performance of existing algorithms, including orthogonal lattice, simultaneous diophantine approximation, multivariate polynomial and sample pre-processing are reviewed and analyzed for solving the ACD problem. Orthogonal lattice (OL) algorithms are divided into two categories (OL-$\land$ and OL-$\vee$) for the first time. And an improved algorithm of OL-$\vee$ is presented to solve the GACD problem. This new algorithm works well in polynomial time if the parameter satisfies certain conditions. Compared with Ding and Tao's OL algorithm, the lattice reduction algorithm is used only once, and when the error vector $\mathbf{r}$ is recovered in Ding et al.'s OL algorithm, the possible difference between the restored and the true value of $p$ is given. It is helpful to expand the scope of OL attacks.

Miniature sensors are key components for the applications in the Internet of Things (IoT), wireless sensor networks, autonomous vehicles, smart cities and smart manufacturing. As a miniature and self-powered magnetic sensor, Wiegand sensor possesses the advantageous traits including changing-rate-independent output, low cost, and remarkable repeatability and reliability. Typical Wiegand sensor requires hard magnetic pole pieces that provide external fields for triggering voltage outputs that are called Wiegand pulses. However, the wire-shaped sensing element of Wiegand sensor is the critical issue that limits the design, selection, and adoption of the external triggering magnets. Currently, the widely used pole piece materials are rare-earth magnets. However, adopting rare-earth magnets brings strong stray fields, causing electromagnetic interference (EMI) problem. In this study, patterned CoNiP hard magnets were electrodeposited on flexible substrates through microfabrication. Origami magnetization was utilized to control the resultant stray fields, and hence the pole piece of CoNiP magnets can successfully trigger the output of Wiegand pulse. In comparison, the output voltage of the triggered pulse acquired through the patterned CoNiP magnets is comparable to that by using the rare-earth magnets. Furthermore, both the volume (meanwhile the weight) of the Wiegand sensor and the EMI issue can be significantly reduced and mitigated by the CoNiP magnets.

This study aims to understand the contents of debriefing performance in simulation education and its results by comprehensively examining the learning performance of the education ac-cording to the difference in the debriefing methods employed in domestic and overseas nursing simulation training. This is a literature review conducted to identify the effect of debriefing of simulation nursing practice education. The existing literature was found in electronic databases using Pubmed, Embase, MEDLINE complete, PsycINFO, Web of Science, CINAHL, the Cochrane Library, KoreaMed, National Discovery for Science Leaders, and Research Information Sharing Service and the key words were “nurse,” “nursing student,” “simulation,” “simulator,” “stand-ardized patient,” “debriefing.” Finally, 32 studies were analyzed. All the studies were conducted from 2012 to 2021. A total of 11 RCT, 17 quasi-experimental studies, 3 mixed method studies and 1 pilot study were identified. The debriefing process used media, structured questionnaires, and a method of teaching or peer-led debriefing. The outcome variables that were statistically significant were skill, performance, knowledge, problem-solving competency, critical thinking disposition, clinical judgement, self-confidence, satisfaction, and debriefing quality evaluation. It is necessary to educate the debriefers who are responsible for strategy development and meeting effective debriefing goals.

A large amount of concrete waste is generated around the world and its recycling is an urgent issue. In this research, a new approach to recycle concrete waste with wooden waste was studied. Concrete and wooden wastes were crushed, mixed, and heat compacted to produce plates with different water contents and mix proportions at various temperatures, pressures, and durations of compaction. The bending strength of the plates was measured after compaction. The result indicated that with an increase in the percentage of wooden waste in the mixture, pressure, or temperature improved the bending strength. The increase in water content reduced the bending strength. Most of the products exhibited higher bending strength than that of ordinary concrete.

Surface initiated atom transfer radical polymerization (SI-ATRP) is one of the most versatile technique to modify the surface properties of material. Recent developed metal free SI-ATRP makes such technique more widely applicable. Herein photo-induced metal-free SI-ATRP of methacrylates, such as methyl methacrylate, N-isopropanyl acrylamide, and N,N- dimethylaminoethyl methacrylate, on the surface of SBA-15 was reported to fabricate organic-inorganic hybrid materials. SBA-15 based polymeric composite with adjustable graft ratio was obtained. The structure evolution during the SI-ATRP modification of SBA-15 was monitored and verified by FT-IR, XPS, TGA, BET, and TEM. The obtained polymeric composite showed enhanced adsorption ability for the model compound toluene in aqueous. This procedure provides a low cost, ready availability, and facile modification way to synthesize the polymeric composites without the contamination of metal.

The one-dimensional MoS₂/carbon nanofibers (1D MoS₂/CNFs) are synthesized by electrospinning using exfoliated MoS₂ nanosheets and polyacrylonitrile as raw materials. The exfoliated MoS₂ nanosheets with size of about 150 nm are encapsulated in carbon nanofibers, and the free-standing MoS₂/CNFs can be easily cut into flexible tablet and directly used as binder-free anode for lithium storage. The resultant 1D MoS₂/CNFs exhibit a very high reversible capacity of 700 mAh g^-1 at 100 mA g^-1 after 50 cycles, high rate capacity (450 mAh g^-1 at 1000 mA g^-1 after 200 cycles) and good cycle stability.

The Connor–Davidson Resilience Scale (CD-RISC) and the Brief Resilience Scale (BRS) are two widely used scales to measure resilience. Although both scales seek to assess an individual’s ability to recover from and adapt to disruptions or stressful events, they may capture different aspects of resilience. While the CD-RISC focuses on resources that can help individuals recover from and adapt to disruptions or stressful events, the BRS directly measures one’s ability to bounce back or be resilient. The aim of this study is to empirically examine the differences between the CD-RISC and the BRS. Two samples (n = 202, 246) consisting of undergraduate students from Taiwan were used. The results of confirmatory factor analysis show that the CD-RISC and the BRS are highly correlated but still distinct. The results of regression analyses show that the CD-RISC and the BRS have unique predictive effects on depression and life satisfaction. The research findings suggest that the CD-RISC and the BRS capture different aspects of resilience. For future research on resilience, researchers should pay attention to the differences between these scales and choose the one that most closely fits their research purpose.

Skeletal muscle is a specialized tissue consisting of nondividing and multinucleated muscle fibers. Skeletal muscle-specific myostatin (MSTN) is a secreted protein that acts as a negative regulator of muscle mass by inhibiting proliferation and differentiation of myoblasts. In this study, we established a screening platform to monitor myostatin promoter activity in rat L8 myoblasts. Extract of Glycyrrhiza uralensis (GUE), an oriental herbal medicine, was identified through this screening platform and, the active fractions of GUE were identified by process-scale liquid column chromatography system. For in vivo study, the GUE as a feed additive was investigated in growth-finishing pigs. The results showed that GUE significantly increased body weight, carcass weight and lean content in pigs. Microbiota analysis indicated that GUE did not affect the composition of gut microbiota in pigs. In summary, this established rodent myoblast screening platform was used to identify a myogenesis-related phytogenic, GUE, and further demonstrated that the active fractions inhibited MSTN expression. These finding suggest a novel application for GUE in growth performance enhancement through modulation of MSTN expression. Furthermore, this established screening platform has great potential for identification and evaluation of a wide variety of phytogenics involving in myogenesis.

Objective: To evaluate the effect of care bundles on treatment compliance and intestinal barrier function in patients with refractory septic shock in the intensive care units (ICUs). Methods: In this retrospective study, the clinical data of 94 patients with refractory septic shock admitted to our hospital between June 2020 and April 2022 were collected. Patients with routine nursing were included in the routine group, and those with care bundles were assigned to the care bundles group, with 47 cases in each group. Outcome measures included nursing efficiency, treatment compliance, sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation (APACHE II) score, intestinal barrier function, inflammation factor level, treatment outcome and complications. Results: Care bundles resulted in significantly higher nursing efficiency and treatment compliance versus routine care (P<0.05). Patients receiving care bundles showed significantly higher SOFA scores and APACHE II scores than those receiving routine care (P<0.05). Intestinal fatty-acid binding protein (I-FABP), diamine oxidase (DAO), lactate, endotoxin, and intestinal dysfunction scores in the care bundles group were significantly lower than those in the conventional group after treatment (P<0.05). Care bundles were associated with significantly lower levels of procalcitonin (PCT) and hypersensitive C-reactive protein (hsCRP), shorter time to symptom relief, ICU treatment time, and duration of mechanical ventilation, and lower 28-d morbidity and mortality versus routine care (P<0.05). Patients in the care bundles group had a significantly lower incidence of complications than those in the routine group (P<0.05). Conclusion: Care bundles effectively enhance treatment compliance, improve intestinal barrier function and treatment outcomes, reduce the inflammatory response, and decrease the risk of SOFA score, APACHE II score, and complications in patients with refractory septic shock.

Objective: To evaluate the effect of care bundles on treatment compliance and intestinal barrier function in patients with refractory septic shock in the intensive care units (ICUs). Methods: In this retrospective study, the clinical data of 94 patients with refractory septic shock admitted to our hospital between June 2020 and April 2022 were collected. Patients with routine nursing were included in the routine group, and those with care bundles were assigned to the care bundles group, with 47 cases in each group. Outcome measures included nursing efficiency, treatment compliance, sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation (APACHE II) score, intestinal barrier function, inflammation factor level, treatment outcome and complications. Results: Care bundles resulted in significantly higher nursing efficiency and treatment compliance versus routine care (P<0.05). Patients receiving care bundles showed significantly higher SOFA scores and APACHE II scores than those receiving routine care (P<0.05). Intestinal fatty-acid binding protein (I-FABP), diamine oxidase (DAO), lactate, endotoxin, and intestinal dysfunction scores in the care bundles group were significantly lower than those in the conventional group after treatment (P<0.05). Care bundles were associated with significantly lower levels of procalcitonin (PCT) and hypersensitive C-reactive protein (hsCRP), shorter time to symptom relief, ICU treatment time, and duration of mechanical ventilation, and lower 28-d morbidity and mortality versus routine care (P<0.05). Patients in the care bundles group had a significantly lower incidence of complications than those in the routine group (P<0.05). Conclusion: Care bundles effectively enhance treatment compliance, improve intestinal barrier function and treatment outcomes, reduce the inflammatory response, and decrease the risk of SOFA score, APACHE II score, and complications in patients with refractory septic shock.

Taxanes, particularly docetaxel (DTX), has been widely used for combination therapy of head and neck squamous cell carcinoma (HNSCC). For locally advanced unresectable HNSCC, DTX combined with cisplatin and 5-fluorouracil as a revolutionary treatment revealed an advantage in improvement of patient outcome. In addition. DTX plus immune check inhibitors (ICIs) showed low toxicity and increased response of patients with recurrent or metastatic HNSCC (R/M HNSCC). Accumulating data indicate that taxanes not only function as antimitotics but also impair diverse oncogenic signalings including angiogenesis, inflammatory response, ROS production, and apoptosis induction. However, despite an initial response, development of resistance remains a major obstacle to treatment response. Taxane resistance could result from intrinsic mechanisms such as enhanced DNA/RNA damage repair, increased drug efflux, and apoptosis inhibition, and extrinsic effects such as angiogenesis and interactions between tumor cells and immune cells. This review provides an overview of taxanes therapy applied in different stages of HNSCC and describe the mechanisms of taxane resistance in HNSCC. Through a detailed understanding the mechanisms of resistance may help developing the potential therapeutic methods and the effective combination strategies to overcome drug resistance.

Background: Diffuse large B-cell lymphoma is the most common type of non-Hodgkin's lymphoma. Due to the variety of clinical manifestations, accurate diagnosis can be challenging. Here, we present a case of DLBCL that was initially misdiagnosed as a hematoma, highlighting the importance of considering malignancy in the setting of non-responsive soft tissue swelling. Methods: A 76-year-old man presented to the emergency department with right periorbital swelling and ecchymosis. The symptoms first appeared after an injury and did not resolve spontaneously. The patient was taking warfarin for atrial fibrillation. A facial bone CT revealed a large irregular homogeneous mass. An extraconal hematoma was suspected based on clinical history and radiologic findings. Incision and drainage were performed, resulting in evacuation of old blood. Despite continued drainage, the patient's symptoms worsened. A comparison with the previous CT revealed an increase in the size of the lesion. As a result, a surgical excisional biopsy was performed. Results: Biopsy of the mass showed a diffuse infiltrate of lymphocytes around the tissue. The diagnosis of diffuse large B-cell lymphoma was confirmed pathologically. The patient was referred to hematology for further management. Conclusions: DLBCL is a rare disease associated with a poor prognosis. Due to the previous injury and the use of anticoagulants, the patient was initially misdiagnosed as having a hematoma. This delayed the prompt diagnosis and affected the timing of treatment initiation. We must be aware that a malignant mass is a potential cause of unresponsive soft tissue swelling.

Fragment-based drug discovery (FBDD) has become a major strategy to derive novel lead candidates for various therapeutic targets, as it promises efficient exploration of chemical space by employing fragment-sized (MW < 300) compounds. One of the first challenges in implementing a FBDD approach is the design of a fragment library, and more specifically, the choice of its size and individual members. A diverse set of fragments is required to maximise the chances of discovering novel hit compounds. However, the exact diversity of a certain collection of fragments remains underdefined, which hinders direct comparisons among different selections of fragments. Based on structural fingerprints, we herein introduced quantitative metrics for the structural diversity of fragment libraries. Structures of commercially available fragments were retrieved from the ZINC database, from which libraries with sizes ranging from 100 to 100,000 compounds were selected. The selected libraries were evaluated and compared quantitatively, resulting in interesting size-diversity relationships. Our results demonstrated that while library size does matter for its diversity, there exists an optimal size for structural diversity. It is also suggested that such quantitative measures can guide the design of diverse fragment libraries under different circumstances.

The move to a low carbon economy is very important for enhancing international competitiveness. The eco-innovation is the critical factor of the green paradigm. This study is designed to investigate deeply the determinants of eco-innovation of manufacturing firms in Korea by suggesting anticipated regulations, self-regulations, and industry-specific characteristics as external factors and open information sources as internal factors. The data used in the analysis is 1946 sample firms from Korean Innovation Survey 2010 based on the Oslo Manual. Using the multi-variate probit analysis and the zero-inflated negative binomial (ZINB) regression analysis, we have found out that the anticipated regulations and self-regulations have significant influences both on eco-process innovation and eco-product innovation, while industrial characteristics have no effects. The empirical results also show that the breadth of information sources has a positive effect on businesses in implementing eco-innovations. Our findings show that the Korean government should provide a good platform where firms can better understand the future trends of environmental policies, particularly policies on anticipated and self-regulations. At the same time, Korean firms should establish a voluntary system to control environmental activities so that they can improve eco-innovations through integrating external information.

Recently L-shaped tunneling field-effect-transistor (LTFET) has been introduced to overcome the thermal subthreshold limit of conventional metal-oxide-semiconductor field-effect-transistors (MOSFET). In this work, shortcoming of LTFET was investigated. It was found that corner effect present in LTFET effectively degrades its subthreshold slope. To get rid of corner effect a new type of device with dual material gates is presented. The new device termed as DG-LTFET gets rid of the corner effect and results in a significantly improved subthreshold slope of less than 10 mV/dec, and an improved ON/OFF current ratio over LTFET. In this work DG-LTFET was evaluated for different device parameters, and bench-marked against LTFET. This work presents an optimum configuration of DG-LTFET in terms of device dimensions and doping levels, to get the best subthreshold, ON current and ambipolar performance from the DG-LTFET.

This study investigated and classified typical structures in rural village and analyzed the vulnerability of various typical types of structures. Based on the statistics of earthquake damages with magnitudes above 5 from 1996 to 2013 in China, the damage matrixes of different types of structures in rural village are obtained. And The vulnerability index and the vulnerability equation of structure are crucial to assess the earthquake losses of typical structures under different magnitudes earthquakes. According to the seismic loss of different types of structures under different earthquake magnitudes, there are possible to improve the seismic resilience of the buildings in rural village. Moreover, the regional vulnerability is analyzed by β probability distribution function, and the comprehensive seismic performance index of different types of agricultural buildings in the region is obtained. The main research is to predict the loss of different types of structures under different earthquake magnitudes in the future, and to provide technical support for different types of building in rural village reinforcement.

This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.

This is the first study to provide a systematic monetary benefit matrix, including greenhouse gas (GHG) emissions reduction benefits and air pollution reduction health co-benefits, for a change in on-the-road transport to low-carbon types. The benefit transfer method is employed to estimate the social cost of carbon and the health co-benefits via impact pathway analysis in Taiwan. Specifically, the total emissions reduction benefits from changing all internal combustion vehicles to either hybrid electric vehicles, plug-in hybrid electric vehicles, or electric vehicles would generate an average of US$760 million from GHG emissions reduction and US$2,091 million from health co-benefits based on air pollution reduction, for a total benefit of US$2,851 million annually. For a change from combustion scooters to light- or heavy-duty electric scooters, the average GHG emissions reduction benefits would be US$96.02 million, and the health co-benefits from air pollution reduction would be US$1,008.83 million, for total benefits of US$1,104.85 million annually.

Change orders have received considerable attention from researchers thus far, but none have considered pricing strategies of change orders through the interaction between general contractors and subcontractors. Previous studies found that contractors’ opportunistic bidding considering beyond-contractual reward (BCR) in the execution stage can be reduced by improving the construction management system and strengthening the supervision of contractors’ performance. However, the BCR remains in ecology of construction engineering. This study proposes an integrated evolutionary game theory-system dynamics model (ET-SD model) and simulates the pricing strategy of change orders between general contractors and subcontractors to explore the root cause of BCR phenomenon. Sensitivity analysis on the evolutionary dynamics of payoff is explored. Results reveal that change orders with BCR maintain Nash equilibrium and evolutionary stable strategy (ESS) unless changing the payoff structure between general and subcontractors’ pricing strategies. This study presents important managerial insights from the evolutionary game perspectives, nature of change orders, and payoff of the alternative.

In this research, catalytic steam reforming acetic acid derived from the aqueous portion of bio-oil for hydrogen production was investigated by using different Ni/ATC (Attapulgite Clay) catalysts prepared by precipitation, impregnation and mechanical blending methods. The fresh and reduced catalysts were characterized by XRD, N₂ adsorption-desorption, TEM and H₂-TPR. The comprehensive results demonstrated that the interaction between active metallic Ni and ATC carrier was significantly improved in Ni/ATC catalyst prepared by precipitation method, and in which the mean Ni particle size was the smallest (~13 nm) resulted in the highest metal dispersion (7.5%). The catalytic performance of the three catalysts was evaluated through the process of steam reforming of acetic acid in a fixed-bed reactor under atmospheric pressure at two different temperatures, such as 550 ℃ and 650 ℃. Results showed that the Ni/ATC (PM-N/ATC) prepared by precipitation method, achieved the highest H₂ yield of ~82% and little lower acetic acid conversion efficiency of ~85% than that (~95%) of Ni/ATC (IM-NATC) prepared by impregnation method. In addition, the deactivation catalysts after reaction for 4 h were analyzed by XRD, TGA-DTG and TEM, which demonstrated that the catalyst deactivation was not caused by the amount of carbon deposition, but owed to the significant agglomeration and sintering of Ni particles in the carrier.

The chitin synthase B gene is a key enzyme in the chitin synthesis of insect peritrophic matrix (PM), affecting insects' feeding behavior. The chitin synthase B gene has been cloned from the transcription library of Heortia vitessoides Moore. RT-qPCR showed that HvChsb was highly expressed in the larval stage of H. vitessoides, especially on the first day of the prepupal stage, and in the midgut of larvae and the abdomen of adults. After starvation treatment, HvChsb was found to be significantly inhibited with time. After 48 h of starvation, the feeding experiment showed that HvChsb increased with the prolongation of the refeeding time. The experimental data showed that feeding affected the expression of HvChsb. HvChsb was effectively silenced by RNA interference, so its function was lost, significantly decreasing the survival rate of H. vitessoides. The survival rate from larval-to-pupal stages was only 43.33%, accompanied by abnormal phenotypes. It can be seen that HvChsb plays a key role in the average growth and development of H. vitessoides.

Currently, active and intelligent packaging have been developed to solve the spoilage problem for rich protein foods during storage, especially by adding anthocyanin extracts. In such film system, the antioxidant and antibacterial properties were dramatically increased by adding anthocyanins. The physicochemical properties were enhanced through interactions between active groups in anthocyanins and reactive groups in polymer chains. Additionally, active and intelligent film could monitor the spoilage of rich protein foods in response to pH change. Therefore, this film could monitor the sensory acceptance, and extending the shelf life of rich protein foods simultaneously. In this paper, the structure and functional properties of anthocyanins, the composite actions of anthocyanin extracts and biomass materials, and their reinforced properties of active and intelligent film were discussed. Also, the applications of this film in quality maintenance, shelf-life extension and quality monitoring for fresh meat, aquatic product and milk were summarized. The film that achieves high stability and continuous release of anthocyanins on demand will become an underlying trend in packaging applications for rich protein foods.

Various vaccines have been developed in response to the SARS-CoV-2 pandemic, and the safety of vaccines has become an important issue. COVID-19 vaccines-related central nervous system inflammatory demyelinating diseases (CNS IDDs) have been reported recently. We present one case of AstraZeneca vaccine-related myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease and literature review another 78 patients published from January 2020 to October 2022. Patients were divided into three vaccine groups (viral vector, mRNA and inactivated vaccines) for further analyses. Among 79 patients with COVID-19 vaccines-related CNS IDDs, 49 (62%) cases received viral vector vaccines, 20 (25.3%) received mRNA vaccines and 10 (12.7%) received inactivated vaccines. Twenty-seven cases (34.2%) were confirmed with autoantibodies, including 15 patients (19%) with anti-MOG, 11 (13.9%) with anti-aquaporin 4 (AQP4), and one (1.3%) with both antibodies. Significantly, more males developed CNS IDDs post viral vector vaccines compared to mRNA and inactivated vaccines. Patients receiving mRNA vaccines were older than other groups. Furthermore, mRNA and inactivated vaccines correlated more with anti-AQP4 antibodies, while viral vector vaccines showed higher MOG positivity. The research suggests potential associations between COVID-19 vaccines-related CNS IDDs and gender, age, and autoantibodies, contingent on vaccine types. Protein sequence analysis implies similarities between the S protein and AQP4/MOG. Further studies may elucidate the mechanisms of CNS IDDs, aiding vaccine selection for specific groups.

The processes of soliton generation and interference focus on the complex nonlinear soliton dynamics resembling matter particles. In order to further understand the dynamic process of solitons from a multidimensional perspective, here we report the vectorial solitons nature under two-sets pulse splitting in a single cavity induced by dual-core fiber assisted ultrafast fiber lasers. Owing to the weakly coupled cores in symmetrical dual core fiber (SDCF), two pulse groups interaction are formed in a cavity. By using the dispersive Fourier transformation technique (DFT), it was found that the four-component polarized rotation vector solitons (PRVS) generate. Moreover, gradually increasing the power can obtain the locked soliton bound state in two core space, and the corresponding evolution is similar to that of non-degenerate bright solitons in Bose Einstein condensates (BEC). In addition, by properly controlling the soliton phase offset in SDCF, the soliton rain state of multi pulse evolution can be obtained. The related experimental results would be fruitful to the communities interested in soliton dynamics and frequency comb lasers.

Efficiency serves as a crucial metric in assessing the advancement of scientific research for universities. Reasonable inputs of human resources, finances, materials, and other resources, along with high-quality research output, are important guarantees for the research efficiency of Chinese universities. This study delves into the patterns of changes of research efficiency among universities across the 31 provinces and municipalities of China from 2012 to 2022. By scrutinizing input-output perspectives and utilizing data sourced from the China Statistical Yearbook, the analysis unveils prevailing scenarios of input redundancy and output insufficiency in research efficiency across universities. The static and dynamic trends in research efficiency were further investigated by employing a static non-parametric Data Envelopment Analysis (DEA)-BCC model and a dynamic Malmquist index. Key findings from the analysis from 2012 to 2022 include: (1) notable growth in investments in teaching, research equipment assets, and research and development funds among nationwide universities; (2) a steady increase in the mean value of technical efficiency (TE) from 0.97 to 0.99, indicating continuous enhancement in overall efficiency in research input resource allocation across the provinces; (3) the mean Malmquist productivity index of 1.285, with an average annual increase of 28.5%, signifying a consistent improvement in university research efficiency throughout the study period; and (4) the provinces experiencing varying degrees of input redundancy or output insufficiency were selected to suggest the need for tailored strategies in resource allocation. The study underscores the importance of a holistic evaluation system involving stakeholders from government, universities, and society to optimize research efficiency and promote scientific innovation and social development.

Although health policy resides mainly with the government, industry can play an important role in building a health ecosystem. From March to May, we administered questionnaires to 1,200 individuals from the general Korean population asking about their perception of health-friendly labels, and if they would purchase such labeled products (foods, pharmaceuticals, etc.) and services (purifying water, preventing air pollution, etc.) at extra cost. The participants placed a high value on the importance of mental, social, spiritual, and physical health factors in terms of company’s products and services with a score of about 8 out of 10 (range, 7.74-8.33). Most respondents (72.4%) said they were interested in adopting health-friendly labels. When a health-friendly label is introduced (such as one by the Business for Social Responsiveness), 65.1% of the respondents said they intended to purchase the product or service, while 6.8% said they did not, and 75.0% said they were willing to pay extra for the health-friendly product or service. Multivariate logistic regression models showed urban residence, high education level, and good social health to be significantly associated with positive attitudes toward health-friendly labels. People with high income, no religion, or normal weight were more likely to say they intend to purchase products and services with health-friendly labels. They also had a more positive attitude toward paying more for such products and services, as did people with good spiritual health. This study provides data that illustrate the importance of health-friendly products and services to the general population and companies.

Background and Objectives: The ingestion of anhydrous calcium chloride (CaCl2) causes direct injury to the gastrointestinal wall via a thermal burn. Severe CaCl2 intoxication can induce a hypercalcemic crisis, presenting with arrhythmia, acute pancreatitis, and acute kidney injury. This case report details a patient with hematemesis and hypercalcemia following the ingestion of a commercial desiccant. We aimed to report the progression of the case and focused on the elec-trocardiographic manifestations. Case presentation: A 39-year-old female presented at a regional emergency center with blood in her vomit after the ingestion of a commercial desiccant. Bloody emesis was the initial symptom, and various electrolyte imbalances developed during admission. Electrocardiogram (ECG) changes occurred early after hospitalization and disappeared before the electrolyte levels normalized. The patient was maintained in an NPO state throughout her hospital stay. Bloody emesis and abdominal pain resolved rather early, despite her minimal mention of symptoms, possibly due to her suspected negative psychiatric symptoms. Conclusions: The rise in suicidal attempts has led to an increase in unusual intoxication cases. Previous reports on CaCl2 ingestion primarily considered the gastrointestinal injury. In this case, we observed the dynamic and prolonged multiple electrolyte imbalances along with the early-phase ECG changes, all of which responded well to supportive care. This report adds to the understanding of the diverse manifestations and management of CaCl2 intoxication.

In the last two decades, our knowledge of synaptic proteomes and their relationship to normal brain function and neuropsychiatric disorders has been expanding rapidly through the use of more powerful neuroproteomic approaches. However, mass spectrometry (MS) based neuroproteomic studies of synapses still need cell-type, spatial, and temporal proteome information. With the advancement of sample preparation and MS techniques, we have just begun to identify and understand proteomes within a given cell type, subcellular compartment, and cell-type-specific synapse. Here, we review the progress and limitations of MS-based neuroproteomics of synapses in the mammalian CNS and highlight the recent applications of these approaches in studying neuropsychiatric disorders such as major depressive disorder and substance use disorders. Combining neuroproteomic findings with other omics studies can generate an in-depth, comprehensive map of synaptic proteomes and possibly identify new therapeutic targets and biomarkers of several central nervous system disorders.

Aluminum alloy (Al6061) sheet micro-hole processing is extensively used in smartphones, tablet PC, and smart wearable devices. The micro-hole processing is commonly performed using laser, micro drilling, microstamping, micro discharge, and chemical etching technologies. Micro-stamping technology is characterized by precision and rapid processing, but the precision positioning of the punch head and lower die is one of the major difficulties in micro-hole stamping, especially in manufacturing array micro-holes. This study used stamping without die technology. This technology uses an array punch head to punch the lower die holes on the base, then performs array micro-hole stamping. The experimental results show that a micro-hole array with 37 micro-holes were successfully manufactured and can be reproduced many times. Tapered array micro-holes with a high aspect ratio can be manufactured using this stamping without die technology; the micro-hole depth can be 260μm, the inlet diameter is 116μm, and the outlet diameter is 25μm. This study has successfully developed the feasibility of array micro-hole stamping technology.