Depressive disorder in childhood and adolescence is a highly prevalent mood disorder that tends to recur throughout life. Untreated mood disorders can adversely impact a patient's quality of life and cause socioeconomic loss. Thus, an accurate diagnosis and appropriate treatment is crucial. However, until now, diagnoses and treatments are conducted according to clinical symptoms. Objective and biological validation are lacking. This may result in a poor outcome for patients with depressive disorder. Research has been conducted to identify the biomarkers that are related to depressive disorder. Cumulative evidence has revealed that certain immunologic biomarkers in-cluding brain-derived neurotrophic factor (BDNF) and cytokines, gastrointestinal biomarkers, hormones, oxidative stress, and certain hypothalamus-pituitary axis biomarkers are associated with depressive disorder. This article reviews the biomarkers related to the diagnosis and treatment of pediatric depressive disorders. To date, clinical biomarker tests are not yet available for diagnosis or for the prediction of treatment prognosis. However, cytokines such as Interleukin-2, interfer-on-gamma, tumor necrosis factor-alpha, and BDNF have shown significant results in previous studies of pediatric depressive disorder. These biomarkers have the potential to be used for diagnosis, prognostic assessment, and group screening for those at high risk.

Keywords: mHealth; peritoneal dialysis; functionality; usability

We study the generalized χ and η cross helicities for non-ideal non-barotropic magnetohydrodynamics (MHD). χ and η, the additional label translation symmetry group, have been used to generalize the cross helicity in ideal flows. Both new helicities are additional topological invariants of ideal MHD. To study there behaviour in non-ideal MHD, we derive the time derivative of both helicities using the non-ideal MHD equations taking viscosity, finite resistivity and heat conduction into account. Physical variables are divided into ideal and non-ideal quantities separately during the mathematical analysis for simplification. The analytical results indicate that χ and η cross helicities are not strict constant of motion in the non-ideal MHD and show the rate of dissipation which is compared to the dissipation of other topological constants of motion.

In urban area, one of the great problem is the rise of temperature, which leads to form the urban heat island effect. This paper refers to the trend of the urban surface temperature extracted from the Landsat images from which to consider changes in the formation of surface urban heat island for the north of Ho Chi Minh city in period 1995-2015. Research has identified land surface temperature from thermal infrared band, according to the ability of the surface emission based on characteristics of normalized difference vegetation index NDVI. The results showed that temperature fluctuated over the city with a growing trend and the gradual expansion of the area of the high-temperature zone towards the suburbs. Within 20 years, the trend of the formation of surface urban heat island with two typical locations showed a clear difference between the surface temperature of urban areas and rural areas with space expansion of heat island in 4 times in 2015 compared to 1995. An extreme heat island located in the inner city has an area of approximately 18% compared to the total area of the region. Since then, the solution to reduce the impact of urban heat island has been proposed, in order to protect the urban environment and the lives of residents in Ho Chi Minh City becoming better

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

In recent years, governments applied intelligent transportation system (ITS) technique to provide several convenience services (e.g., garbage truck app) for residents. This study proposes a garbage truck fleet management system (GTFMS) and data feature selection and data clustering methods for travel time prediction. A GTFMS includes mobile devices (MD), on-board units, fleet management server, and data analysis server (DAS). When user uses MD to request the arrival time of garbage truck, DAS can perform the procedure of data feature selection and data clustering methods to analyses travel time of garbage truck. The proposed methods can cluster the records of travel time and reduce variation for the improvement of travel time prediction. After predicting travel time and arrival time, the predicted information can be sent to user’s MD. In experimental environment, the results showed that the accuracies of previous method and proposed method are 16.73% and 85.97%, respectively. Therefore, the proposed data feature selection and data clustering methods can be used to predict stop-to-stop travel time of garbage truck.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aim to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior of harvest at the province scale for fourteen provinces in Thailand's Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and variance inflation factor (VIF). Temperature condition index (TCI), normalized difference vegetation index (NDVI), land surface temperature (LSTnight), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root mean square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region would be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation.

Regulatory T cells are key immune cells to initiate tolerable immune responses. TGFβ is the key cytokine of regulatory T cells. TGFβ is also the master cytokine to stimulate tissue fibrosis. Systemic sclerosis is an autoimmune disease related to overt organ fibrosis. Thus, systemic sclerosis is a disease related to regulatory T cell over-representation. Besides, many end stage organ failures including heart failure, liver cirrhosis, uremia, and pulmonary fibrosis also related to tissue fibrosis. Thus, TGFβ producing regulatory T cells are also involved in the pathogenesis of these end stage organ failure. Once we know the significance of TGFβ and it induced tolerable immune reactions in the pathophysiology of end organ failure, we can develop better prevention or therapeutic strategies to combat these detrimental disorders.

Background: The link between nocturia and aging male symptoms (AMS) has not been scientifically established. This study aimed to measure the degree of severity of AMS that impact on the health-related quality-of-life (HRQoL) in adult males living with nocturia, and to determine the predictive values of nocturnal factors on AMS. Methods: It is an extended analysis of new data collected by using the Hong Kong Traditional AMS (HK-AMS) scale and Cantonese version of the Pittsburgh Sleep Quality Index (PSQI) in a recently published cross-sectional population-based survey. Results: Of the 781 respondents that have completed the set of questionnaires, 68% and 61% of men living with nocturia reported clinically significant (at moderate-to-severe levels) somato-vegetative and sexual AMS, whereas the prevalence and severity were increased with advancing nighttime voiding frequency. The nocturia-specific QoL (NQoL) score and nocturnal frequency were found to be significant predictive factors for composite, somato-vegetative and sexual AMS, in addition to age, global OSQI score, and certain metabolic diseases. Conclusions: Current findings suggested the inclusion of nocturia when measuring the male-specific HRQoL related to aging.

A simple bounded stochastic motion is employed for modelling equity price dynamics under shocks, where the prices are constrained to lie between two positive bounds. While the stochastic process has an inaccessible boundary, the other boundary is quasi-bounded, implying that the boundary can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus provide an indicator of possible risk of equities under price shocks. Empirical calibration of model parameters of the proposed process for equities can be performed easily due to the availability of an analytically tractable probability density function which generates fat-tailed distributions consistent with empirical observations. The volatility and mean-reversion of the S&P500 dynamics calibrated by the process is positively and negatively co-integrated respectively with the VIX index representing the level of market distress. The process captures the high likelihood of Hertz’s default about two months earlier, using only information until that point, and before the firm filed for Chapter 11 bankruptcy in May 2020 as a result of the Covid-19 pandemic. Empirical calibration of the process for GameStop’s stock price shows that the short squeeze of the stock occurred when the condition for breaching an upper boundary was met on 14 January 2021, i.e., about two weeks before major short sellers closed out their positions with significant losses. The trading volume of the stock was positively co-integrated with the probability leakage ratio.

The purpose of this study is to apply the convergence-confinement method (CCM) based on the assumption of hydrostatic stress to the surrounding rock excavated in a circular tunnel whose rock mass characteristics meet the requirements of the non-linear Hoek-Brown failure criterion. Based on a consistent and rigorous method of theoretical analysis, a closed-form analytical solution for the stress/displacement of the ground reaction induced by the advancing excavation of the tunnel, especially in the elastic and plastic regions, is derived and investigated under the condition of an isotropic stress state. By taking the value of a confinement loss as an explicit analysis process in an incremental manner, not only is the computationally executable analytical solution achieved, but it can also be performed using a simple spreadsheet. The validity of the analytical solution implemented by the incremental procedure was examined with published data to study the effect of nonlinear failure criteria on the ground reaction curve; in particular the mechanical behavior at the intrados of the tunnel, and the distribution of stress/displacement on the cross-section of the tunnel. A comparison between the published results and the solution proposed in this study, which interprets the closed-form by the incremental procedure, shows consistent and favorable trends.

The mechanism of immune activation and de-activation is very important. Host should trigger effective immunity against pathogen infection. And, host cannot trigger immunity against self-antigens that will lead to detrimental autoimmune disorders. Spleen and liver have opposite roles in mediating immune responses. Spleen is the organ involving in immune activation, and liver is the organ involving in immune de-activation. Sympathetic nerve system and parasympathetic nerve system have opposite roles in mediating immune responses. Sympathetic nerve system is involving in pro-inflammatory, and parasympathetic nerve system is involving in anti-inflammatory. ACTH and glucocorticosteroid have opposite roles in mediating immune responses. ACTH is involving in activating adaptive immunity with inhibiting innate immunity, and glucocorticosteroid is involving in activating innate immunity with inhibiting adaptive immunity. The molecular chaperons, heat shock proteins, which are induced by fever, have the vital roles in immune activation. On the contrary, IgD B cells and gamma-delta T cells are causing immune de-activation via the mechanism of clonal anergy. These mechanism can help us the know the work of immunological pathways better for better control of infectious diseases and autoimmune disorders.

To study the influence of Latin dance and Tai Chi on knee joint proprioception and balance. Method: All experiments were performed in the Beijing Normal University School of Physical Education and Sport Biomechanics Laboratory. An isokinetic test system (Biodex system 4, BS4) and a balance testing system (Biodex Balance System, BBS)from the United States Biodex medical system were used to test related indexes. Results: In the LOS test, the overall score of Latin group is much better than sedentary group. In the ASL test, Latin group have a better balance ability in anterior and posterior direction and overall score than sedentary group(p＜0.05); TAI CHI group have a better balance ability than sedentary group in anterior and posterior direction(p＜0.05)and right and left direction (p＜0.01). When the knee joint was at 15, 30, and 45degree positions, the Tai Chi exercise group was much better than sedentary group (P < 0.05), and the 45 degree position in the Tai Chi group was much better than that in the Latin dance group (P < 0.01).When the knee joint was at the 30 degree position, the quadriceps force sense was significantly better in the Tai Chi group than in the Latin dance and sedentary groups. In a test of the four angles, the hamstring force sense in the Tai Chi exercise group was obviously better than that in the sedentary group (P < 0.05); at the 15 degree position, the Tai Chi group was significantly better than the Latin group (P < 0.05); and at 30 degree position, the hamstring force sense of the Latin group was obviously better than that of the sedentary group (P < 0.05).

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli, an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluid (MRF), magnetorheological elastomer (MRE) and magnetorheological plastomer (MRP). To easily understand the history of sensing research using the two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss on the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensor, tactile device, strain sensor, wearable bending sensor, magnetometer, display device and flux measurement sensor. After briefly describing what have been reviewed in a conclusion, several challenging future works, which should be done for practical applications as sensors or sensing devices, are remarked in terms of new technologies such as artificial intelligence neural network in which several parameters affecting the sensor signals can be precisely tuned or controlled. It is sure that this review article is very helpful to make creative sensors using not only the proposed smart materials but also several different types of smart materials including shape memory alloys and active polymers.

Rose (Rosa spp.), especially R. hybrida, is one of the most popular ornamental plants in the world and the third largest cut flower crop in Taiwan. Rose mosaic disease (RMD), showing mosaic, line patterns and ringspots on leaves, is a common rose disease caused by complex infection of various viruses. Due to pests and diseases, the rose planting area in Taiwan has been decreasing since 2008; however, no rose virus disease has been reported in the past five decades. In the spring of 2020, rose samples showing RMD-like symptoms were observed at an organic farm in Chiayi, central Taiwan. The virome in the farm was analyzed by RNA-seq. Rose genomic sequences were filtered from the obtained reads. The remaining reads were de novo assembled to generate 294 contigs, 50 of which were annotated as viral sequences corresponding to 10 viruses. Through reverse transcription-polymerase chain reaction validation, a total of seven viruses were detected, including six known rose viruses, namely apple mosaic virus, prunus necrotic ringspot virus, rose partitivirus, apple stem grooving virus, rose spring dwarf-associated virus and rose cryptic virus 1, and a novel ilarvirus. After completing the whole genome sequencing and sequence analysis, the unknown ilarvirus was demonstrated as a new species, tentatively named rose ilarvirus 2. This is the first report of the rose viruses in Taiwan.

Insulated Gate Bipolar Transistor (IGBT) may convey high current and sustain high breakdown voltages; it may provide high power control. In a sense, how IGBT demonstrates the capability of power control was not specifically described, and the equivalent bipolar junction transistors (BJT) activated by an applied bias to the insulated gate is intriguing. As seen in Figure 1, IGBT shows an equivalent circuit structure containing two types of BJT, (NPN-type Bipolar transistor and PNP-type Bipolar transistor), which can be modeled as diode-associated transistor. Apparently, as a bias applies to the controlling gate, the channel beneath the gate oxide gets strongly inversed and turns out to be conductive just like what happens on metal-oxide-silicon field effect transistor (MOSFET), which provides necessary igniting currents to both BJTs and thus causes high current gain flowing through them-selves from the bottom. A proposed and renewed formula provides addressing the above scenario and successfully fitting the measured data that forms the characteristic curves. Some parameters in the proposed formula, which includes size and mobility associated constant, threshold voltage, and Early-Voltage-related lambda for MOSFET, explain and explore the underlying physics. IGBT thus enjoy high input impedance and high current gain due to MOSFET (Insulated Gate) and BJT, respectively. This formula combines both merits of MOSFET and BJT in order to fit the measured data. Indeed, the formula intimately fits IGBT electrical characteristic curves, and thus may be qualified and served as a model. The new model is then advisable for designing power integrated circuit to exactly achieve power saving, power control, and power efficiency.

Current stock market forecasting methods encompass fundamental, technical, emotional, and bargaining factors. Predominantly, price prediction hinges on order volume and price, although correlating these two within existing models proves challenging. This study employs Cycle Generative Adversarial Network (Cycle GAN) to unravel the intricate price-volume relationship, combining it with Bollinger Bands for trading signal analysis, overcoming hurdles in short-term forecasting prevalent in numerical analysis and AI. Focusing on TSMC (2330.TW) stock price, the research leverages Cycle GAN in deep learning to master the price-volume nexus, juxtaposed with LSTM and RNN. Historical TSMC closing prices and transaction counts are model inputs, scrutinizing their interconnectedness for predictions. This innovative approach aligns stock price, volume, market value, taxes, and prior changes via system engineering. By intertwining Bollinger Bands with stock price forecasts, trading signals are distilled, factoring in extended index %b for a comprehensive market picture.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

As governments in various countries and regions issue tourism-friendly policies in the post-pandemic period, the voices for tourism consumption recovery and innovative transformation have attracted great attention. Many scholars, experts and industry professionals in the field of festivals are actively exploring efficient solutions for festival marketing and sustainability. It is worth rethinking tourists’ consumption psychology and perceived image toward exiting festival products as well as the antecedents that affect festival loyalty. The Qingdao International Beer Festival, as one of the most influential festivals in China, was selected as the research case for this study. The main goal of this study was to investigate festival consumption promotion mechanism by proposing a conceptual framework in which the associations from destination familiarity and product familiarity to loyal intentions (re-patronage and recommendation) were examined, with mediators of overall festival image, perceived value and overall satisfaction. Furthermore, the moderating role of visitor involvement in the proposed framework was also adequately demonstrated. The research findings are expected to provide a significant reference for the recovery and competitiveness improvement of the entire festival industry.

Systemic lupus erythematosus (SLE) is a prevalent autoimmune condition, yet its alignment with a specific host immunological pathway remains unclear. The THαβ host immunological pathway, recognized for its defense against viruses and prions, has recently emerged as a response to DNA, RNA, and protein pathogens. SLE patients often produce anti-double strand DNA antibodies and anti-nuclear antibodies, suggesting a potential association with the THαβ host immune reaction. Throughout the course of SLE, elevated levels of type 1 interferons, type 3 interferons, interleukin-10, IgG1, and IgA1 are commonly observed. These cytokines and antibody isotypes are indicative of the THαβ host immunological pathway. Similarly, Myasthenia gravis, Grave’s disease, Graft versus host disease, autoimmune hemolytic anemia, immune thrombocytopenia, dermatomyositis, and Sjogren’s syndrome are also linked to THαβ-related type 2 hypersensitivities. Considering the potential association of these diseases with dysregulated THαβ immune responses, therapeutic strategies such as anti-interleukin-10 or anti-interferons α/β could be explored to manage these disorders effectively.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

Many theoretical models of iron-based superconductors have been proposed but Tc calculations based on the models are usually missing. We have chosen two models of iron-based superconductors in the literature and then compute the Tc values accordingly: Recently two models have been announced which suggest that superconducting electron concentration involved in the pairing mechanism of iron-based superconductors may have been underestimated, and that the antiferromagnetism and the induced xy potential may even have a dramatic amplification effect on electron-phonon coupling. We use bulk FeSe, LiFeAs and NaFeAs data to calculate the Tc based on these models and test if the combined model can predict the superconducting transition temperature (Tc) of the nanostructured FeSe monolayer well. To substantiate the recently announced xy potential in the literature, we create a two-channel model to separately superimpose the dynamics of the electron in the upper and lower tetrahedral plane. The results of our two-channel model support the literature data. While scientists are still searching for a universal DFT functional that can describe the pairing mechanism of all iron-based superconductors, we base on the ARPES data to propose an empirical combination of DFT functional for revising the electron-phonon scattering matrix in the superconducting state, which ensures that all electrons involved in iron-based superconductivity are included in the computation. Our computational model takes into account this amplifying effect of antiferromagnetism and the correction of the electron-phonon scattering matrix together with the abnormal soft out-of-plane lattice vibration of the layered structure, which allows us to calculate theoretical Tc values of LiFeAs, NaFeAs and FeSe as a function of pressure that correspond reasonably well to the experimental values. More importantly, by taking into account the interfacial effect between an FeSe monolayer and its SrTiO3 substrate as an additional gain factor, our calculated Tc value is up to 91 K high, and provides evidence that the strong Tc enhancement recently observed in such monolayers with Tc reaching 100 K may be contributed from the electrons within the ARPES range.

Three new secoiridoid constituents Gonocarin A-C (1-3) and a new derivative Gonocarin A monoacetate (4), along with two known lignins pinoresinol (5) and paulownin (6) were isolated from the seed of Gonocaryum calleryanum (Baill.) Becc. The structures of the new metabolites were determined on the basis of extensive spectroscopic analysis, particularly mass spectroscopy and 2D NMR (1H–1H COSY, HMQC, HMBC, and NOESY) spectroscopy. When mouse macrophages RAW264.7 were treated with compounds 1-6 together with LPS -stimulated, a concentration-dependent inhibition of nitric oxide (NO) and tumor necrosis factor (TNF-α) productions were detected. The results confirmed that the Gonocaryum calleryanumrrg could be a potential anti-inflammatory agent.

In Taiwan, colorectal cancer is ranked second and third in terms of mortality and cancer incidence, respectively. In addition, medical expenditures related to colorectal cancer are considered to be the third highest. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five data mining classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. In total, 4,287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.

Due to their excellent physicochemical properties, fluorinated greenhouse gases (F-gases), including hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), are used in a variety of applications, but they are potent greenhouse gases. Therefore, they have been blanketed into the list of phase-out under the international protocols or treaties. In this work, the updated statistics of the Taiwan’s national inventory report (NIR) were used to analyze the trends of F-gases (i.e., HFCs, PFCs, SF6 and NF3) emissions during the period of 2000-2020. Furthermore, the regulatory strategies and measures for reducing the emissions of the four F-gases will be summarized to be in accordance with the national and international regulations. With the progressive efforts by the regulatory requirements and the industry’s voluntary reduction, the total F-gases emissions indicated a significant increase from 2,462 kilotons of carbon dioxide equivalents (CO2eq) in 2000 to the peak value (i.e., 12,643 kilotons) of CO2eq in 2004, but sharply decreased from 10,284 kilotons of CO2eq in 2005 to 3,906 kilotons of CO2eq in 2020. It was also found that the most commonly used method for controlling the emissions of F-gases from the semiconductor and optoelectronic industries was based on the thermal destruction-local scrubbing technology.

Blockchain technology can achieve decentralization, multi-party verification, anti-tampering, anonymity, traceability of transactions, and the application of distributed ledger. Countries around the world continue to seek the blockchain business models, technologies and applications, and have different visions and policies for the development of blockchain. This study conducts a comparative policy framework of theoretical analysis of the blockchain technology between the USA and China. Using the innovative policy tools proposed by Rothwell and Zegveld, the above mentioned governments are analyzed from the viewpoint of twelve policy tools. The results show that the USA and China all prefer to use “Environmental-side” policy. The USA has paid more attention to “Legal and regulatory”, “Public services” and “Procurement”. China has the highest proportion of policies in “Political tools”, followed by “Legal & regulatory”, while “Scientific and technical”, “Education” and “Overseas agent” come in third . The blockchain technology has developed vigorously among industries and its applications have gradually diversified. The results are provided to various stakeholders as a reference for policy planning.

In Taiwan, farmers often apply excess compost to ensure adequate crop yield in highly frequent tillage, highly weathered, and lower fertility soils. The potential of biochar (BC) for diminishing soil C mineralization, and improving soil nutrient availability in compost over-fertilized soil is promising, but the study is still under-examined. To test the hypothesis, 434 days in vitro C mineralization kinetics of incubation experiment were conducted. Woody BC 0%, 0.5%, 1.0% and 2.0% (w/w) made of lead tree (Leucaena leucocephala (Lam.) de. Wit) were added to an Oxisols, and two Inceptisols of Taiwan. In each treatment, 5% swine manure compost (2 times recommended amount) was added and served as the over-fertilized soil. The results indicated that soil type strongly influenced the impact of BC addition on soil carbon mineralization potential. Respiration per unit of total organic carbon (total mineralization coefficient, TMC) of three studied soils significantly decreased with BC addition increased. Principal component analysis (PCA) suggested that for retaining more plant nutrients in addition to the effects of carbon sequestration, it is recommended that farmer could use locally produced biochars and composts in highly weathered and highly frequent tillage soil. Adding 0.5%-1% woody BC in soil should be reasonable and appropriate.

In Taiwan, farmers often apply excess compost to ensure adequate crop yield in highly frequent tillage, highly weathered, and lower fertility soils. The potential of biochar (BC) for diminishing soil C mineralization, and improving soil nutrient availability in compost over-fertilized soil is promising, but the study is still under-examined. To test the hypothesis, 434 days in vitro C mineralization kinetics of incubation experiment were conducted. Woody BC 0%, 0.5%, 1.0% and 2.0% (w/w) made of lead tree (Leucaena leucocephala (Lam.) de. Wit) were added to an Oxisols, and two Inceptisols of Taiwan. In each treatment, 5% swine manure compost (2 times recommended amount) was added and served as the over-fertilized soil. The results indicated that soil type strongly influenced the impact of BC addition on soil carbon mineralization potential. Respiration per unit of total organic carbon (total mineralization coefficient, TMC) of three studied soils significantly decreased with BC addition increased. Principal component analysis (PCA) suggested that for retaining more plant nutrients in addition to the effects of carbon sequestration, it is recommended that farmer could use locally produced biochars and composts in highly weathered and highly frequent tillage soil. Adding 0.5%-1% woody BC in soil should be reasonable and appropriate.

Background: Most prevalence surveys on nocturia have focused on older populations. This study aimed to measure the nocturia prevalence across the full spectrum of male adults living in Hong Kong, where severity and associated quality-of-life (QoL) were also explored. Methods: A cross-sectional population-based survey was conducted in men aged 18 or above using the ICIQ-NQoL Questionnaire. Results: With 1,239 respondents at age ranged 18-99, the overall nocturia prevalence were found to be 63.0% (ranged 41.6-84.6% at different age groups) and 31.2% (ranged 13.0-56.3% at different age groups), for ≥1 and ≥2 bedtime voiding episodes, respectively. The chance of nocturia was dramatically increased at age 60 or above while both prevalence and voiding frequency were increased with advancing age. About 83% of the nocturia men experienced 1-2 voiding episodes per night, but many of them had self-rated their QoL poor or very poor and indicated moderate-to-high bothersome. Younger men at age 18-39 were found to have high prevalence as 41.6% and almost 30% of them rated poor or very poor QoL. Conclusions: Nocturia was not only affecting the older populations but also caused significant bothersome and negative impacts on QoL in younger males.

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, we found that in the short-term, oil price volatility has negative asymmetric effects on the market returns. Specifically, in the short-term, a one percent increase in oil price volatility immediately leads to a 2,6868 percent decrease in the market returns while a similar magnitude decrease in oil price volatility is associated with 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the long-term, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that 98.21 percent of the disequilibria from the previous week are converged and corrected back to the long-term equilibrium in the current week.

A sensitive electrochemical sensor based on AuPtPd trimetallic nanoparticles functionalized multi-walled carbon nanotubes coupled with chitosan modified glassy carbon electrode (GCE/CS/MWCNTs-AuPtPd) for the rapid detection of bisphenol A (BPA) was proposed. AuPtPd trimetallic nanoparticles were first assembled on MWCNTs to obtain MWCNTs-AuPtPd nanocomposite. Then the MWCNTs-AuPtPd was further dispersed on the surface of chitosan modified electrode surface to fabricate the GCE/CS/MWCNTs-AuPtPd sensor. Due to the superior catalytic properties of MWCNTs-AuPtPd and the good film formation of chitosan, the constructed sensor displayed good performances for BPA detection. The structural morphology of CS/MWCNTs-AuPtPd was characterized by many methods, such as SEM, UV-vis and TEM. Under the optimal conditions, the designed sensor showed a linear range from 0.05 to 100 µM for BPA detecting with a low detection limit of 1.4 nM. The designed sensor was further used to realize the determination of BPA in real samples and the recoveries ranged from 94.4% to 103.6%. The results demonstrated that the electrochemical sensor designed by CS/MWCNTs-AuPtPd nanocomposites was reliable and could be a useful tool for the monitoring of food safety.

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman Filter to enhance the perception performance. In addition, a back-end UAV path planning for surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

Paraphrase generation is an important yet challenging task in NLP. Neural network-based approaches have achieved remarkable success in sequence-to-sequence(seq2seq) learning. Previous paraphrase generation work generally ignores syntactic information regardless of its availability, with the assumption that neural nets could learn such linguistic knowledge implicitly. In this work we make an endeavor to probe into the efficacy of explicit syntactic information for the task of paraphrase generation. Syntactic information can appear in the form of dependency trees which could be easily acquired from off-the-shelf syntactic parsers. Such tree structures could be conveniently encoded via graph convolutional networks(GCNs) to obtain more meaningful sentence representations, which could improve generated paraphrases. Through extensive experiments on four paraphrase datasets with different sizes and genres, we demonstrate the utility of syntactic information in neural paraphrase generation under the framework of seq2seq modeling. Specifically, our GCN-enhanced models consistently outperform their syntax-agnostic counterparts in multiple evaluation metrics.

The potential adverse effects of coronavirus disease 2019 (COVID-19) vaccinations raise public concerns. Data from Taiwan’s Vaccine Injury Compensation Program (VICP) can provide valuable insights. This study analyzed preliminary application data for COVID-19 vaccine compensation in Taiwan’s VICP, focusing on applicants receiving vaccines between March 2021 and June 2022. Among the 2941 adverse events, 113 cases (3.8%) were deemed causally associated with vaccination, 313 (10.6%) were indeterminate, and 2515 (85.5%) had no causal association. Nearly half (47.6%) of applicants were over 60 years old, and 76.6% had a history of preexisting chronic dis-eases. Among the 426 vaccine-associated or indeterminate cases, the most common causes were hematological diseases and thrombosis. There were 920 mortality cases reported, and 97.4% were unassociated with vaccination. Only five deaths were judged to be associated with the COVID-19 vaccination, all involving the adenovirus vector vaccine and thrombosis with thrombocytopenia syndrome. In conclusion, most compensation applications were not causally linked to vaccination. Compared to other countries, the number of applications in Taiwan’s VICP is relatively high. These findings may indicate a need to adjust the application requirements for compensation in Taiwan’s program.

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.

High-methoxyl apple pectin (AP) derived from apple was employed as the main ingredient facilitating rheological modification features for developing dysphagia-friendly fluidized alimentary matrices. Xanthan gum (XG) was also included as a composite counterpart to modify the viscoelastic properties of the thickened system under different thermal processes. The results indicate that AP is extremely sensitive to thermal processing, and the viscosity is greatly depleted under a neutral pH level. Moreover, the inclusion of calcium ions echoed a modification effect on the rheological properties of AP, and both the elastic property and viscosity value are promoted after thermal processing. The modification effect of viscoelastic properties (Gʹ and Gʹʹ) is observed while XG is incorporated into the composite formula. By promoting the XG ratio from 7: 3 to 6: 4 (AP: XG), it triggers the rheological transformation from a liquid-like form to a solid-like state, and the viscosity value shows that the AP-XG composite system exhibits better thermal stability after thermal processing. The ambient modifiers of pH (pH &lt; 4) and calcium chloride concentration (7.5%) with an optimal AP-XG ratio of 7:3 lead to weak-gel-like behavior (Gʹʹ &lt; Gʹ), helping to maintain the texture properties of dysphagia-friendly features similar to those prior to the thermal processing.

A fast and effective determination method of different species of vegetable seeds oil is vital in the plant oil industry. The near-infrared reflectance spectroscopy (NIRS) method was developed in this study to massively analyze the oil and moisture contents of Camellia gauchowensis Chang and C. semiserrata Chi seeds kernels. In the prediction models of NIRS, the levels of accuracy obtained were sufficient for C. gauchowensis Chang and C. semiserrata Chi, the correlation coefficient of which oil were 0.983 and 0.962, respectively, while which of moisture were 0.937 and 0.907, respectively. The near infrared spectrum of crush seeds kernels was more precise compared to intact kernels. Based on the calibration models of the two Camellia species, the NIRS predictive oil contents of C. gauchowensis Chang and C. semiserrata Chi seeds kernels were 48.71 ± 8.94% and 31.71 ± 7.39%, respectively, and the NIRS predictive moisture contents were 4.39 ± 1.08% and 3.49 ± 0.71%, respectively. Compared with the traditional chemical measurement, the rapid, precise measurement of oil and moisture of C. gauchowensis Chang and C. semiserrata Chi seeds kernels can be actualized by NIRS method.

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

A recent study found that a 3ppb increase in O₃ ambient concentration was associated with an increased progression of 0.18 percentage points in percent emphysema and that such increase was equal to smoking 20 cigarettes per day for 29 years. A simple estimation of population attributable fraction shows that COPD diagnoses due to smoking are actually 30 times more than those attributed to a 3 ppb increment in O₃ concentration. Labelling ozone pollution as the new smoking may distort perception of the risks and hinder proper response to real life threatening risk such as smoking.

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

Climate and land use changes are major factors affecting runoff in regional basins. Understanding variation by considering interactions among hydrological components is an important process for water resource management. This study aimed to assess the variation of future runoff in the Upper Chi Basin, Northeastern Thailand. QSWAT hydrological model was integrated to 3 CMIP6 GCMs including ACCESS-CM2, MIROC6, and MPI-ESM1-2-LR under SSP245 and SSP585 scenarios during 2023 – 2100. Land Change Modeler (LCM) was also used for future land use simulation. The results revealed that future average of long-term precipitation and temperature tended to increase while forest land tended to decrease and be replaced by sugarcane plantations. The accuracy assessment of baseline year runoff calculation by QSWAT during 1997 – 2022 showed acceptable result as can be seen from R2, NSE, RSR, and PBIAS indices. This result could lead to temporal and spatial simulation of future runoff. Likewise, runoff of 2 SSPs scenarios tended to increase consecutively, especially in SSP585 scenario. In addition, in case of long-term spatial changes in the subbasins scale, over 90% of the area, from upstream to outlet point, tended to get higher due to 2 major factors including future increased precipitation and changes in cultivation, which would be influential to groundwater and interflow components respectively. Methodology and result of this study can be useful to stakeholders in understanding changes in hydrological system so that they can apply it to develop a strategy for water resource management and handling factors affecting different dimensions properly and sustainably.

COS production is carried out enzymatically using chitosanase, but this process is expensive. Production costs can be reduced by using crude enzyme chitosanase, and the resulting COS has depolymerization &lt; 20 and molecular weight &lt; 3900 Da. Sources of crude enzyme chitosanase come from the soil, sediment, and fishery waste. Chitosanase is produced from bacteria and molds isolated from the source or direct isolation process for extracellular enzymes, especially from fishery waste. Microorganisms in the soil produce chitosanase as a biocontrol and self-defense agent. Bacteria and molds have been isolated, including Bacillus cereus, Bacillus mycoides, Paenibacillus sp., and Penicillium janthinellum. Fishery waste, especially parts of the digestive tract, is a source of chitosanase because of its relation to its nature and food, such as small fish, frogs, and several types of crustaceans. In addition, fishery waste can be used as a carbon and nitrogen source (C/N) in microorganism culture media. The chitin content becomes a stimulant for the synthesis of chitosanase by microorganisms. Crude enzyme chitosanase has an active site that hydrolyzes chitosan by breaking glycosidic bonds and producing glucosamine units (GlcN) and N-acetyl glucosamine (GlcNAc). This mechanism can take place through two events, namely inverting and retaining. Crude enzyme chitosanase can break glycosidic bonds, especially with the same groups, for example, bonds between glucosamine or N-acetyl glucosamine. The COS produced is of the partially acetylated COS (paCOS) type, which still contains an acetyl group. The degree of deacetylation is a significant factor in the resulting COS product, COS with the fully deacetylated COS (fdCOS) type of chitosan with a high degree of deacetylation. Other factors to consider are the raw material of chitosan, the incubation time, the screening process, and the production of microorganism cultures.

There is a noticeable absence of health education among college students. This study aimed to evaluate the extent of general nutrition knowledge among Chinese university students and explore its association with eating attitudes. Data were collected from a group of 273 students in Spring of 2023, using a valid and reliable research instrument consisting of three sections: demographic variables, the General Nutrition Knowledge Questionnaire (GNKQ), and the Eating Attitudes Test (EAT-26). The results were analyzed using SPSS, with correlations and t-tests to examine the relationships between nutritional knowledge and dietary attitudes. Furthermore, the present study employed the Random Forest (RF) algorithm, a machine learning technique, utilizing the Mean Decrease Impurity (MDI) method to investigate the influence of various features on participants' eating attitudes. The findings revealed that Chinese university students had an average accuracy of over 60% in their nutritional knowledge, but their understanding of the relationship between diet and disease still needs improvement. Moreover, male students had significantly lower nutritional knowledge than female students, and there was a positive correlation between nutritional knowledge and parents’ income. The study also found a significant correlation between the level of nutritional knowledge and eating attitudes. RF results indicated that family income level exhibited the most substantial impact on the eating attitudes of the participants. The study highlights the need for nutrition education curriculum developers to focus more on improving students' nutritional knowledge, with particular attention given to male students, low-income individuals, and those with an abnormal BMI.

Suitable soil structure and nutrient security are important for plant growth and development, characteristics of soil fractal dimension and distribution of physical and chemical properties and their interactions play an important role in studying the stability of soil structure and water and fertilizer cycles. As a sustainable management model, intercropping has positive benefits for erosion control, spatial optimization of resources, as well as improving system productivity. The effects of four intercropping methods on soil fractal dimension and physicochemical properties were investigated by intercropping Salvia miltiorrhiza with forage and S. miltiorrhiza with forest under typical karst rock desertification habitats in Guizhou. The results showed that soil nutrient content of intercropping was significantly higher than that of monoculture, the organic carbon content of soil grown under forest is higher than other treatments, and there was a non-significant change in soil water content of intercropping compared with monoculture. The soil fine-grained matter of intercropping was significantly higher than that of monoculture, while the soil fractal dimension showed a tendency to become larger with the increase of fine-grained matter. The intercropping planting, due to its component types and spatial and temporal configurations, leads to differences in soil water and fertilizer interactions, which can be combined with other ecological restoration measures to optimize the composite model and jointly promote the restoration and development of ecologically fragile areas.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

Current-voltage characteristics curves of NFinFET are presented and are fitted with the modi-fied current-voltage (I-V) formulas, where the modified term in the triode region is demon-strated to be indispensable. In the as-known I-V formula, important parameters need determin-ing to make both the measured data and the fitting data as close as possible. Those parameters include kN (associated with the sizes of the transistor and mobility), (associated with Early Voltage), and Vth (the threshold voltage). The differences between the measured data and the fit-ting data vary as the applied source-drain bias, proving that the mobility of the carriers is not consistently constant. On the other hand, a modified formula, called kink effect factor, is nega-tively or positively added simulating solitary heat waves, which disturb the propagation of car-riers and thus influence the Source-Drain current (IDS). The new statistical standard deviations () are then found to be effectively suppressed as the kink effect is taken into account.

Building sustainable campuses has become a priority for various higher education institutes (HEIs). Internalization is one of the important tasks in the progress. Therefore, attracting and maintaining international faculty has converted to action for various internationalized campuses. During the COVID-19 pandemic, it has impacted most of the citizens’ life, while the international faculty members’ life might be tight during this period. The purpose of this study is to explore the perspectives of international faculty members on their life in higher education institutes by using partial least square structural equation modeling (PLS-SEM). Using a self-compiled online survey, we invited 80 international faculty members of HEIs in Taiwan to investigate this issue. The survey covers career and professional status, teaching and research status, and demographics of the faculty. This study proposes a novel conceptual framework for addressing international faculty’s campus life, the design will examine the relationships among working conditions, views of institutions, views of government’s measures, and levels of satisfaction through PLS-SEM. The demographic profile of the participants revealed (a) most international faculty are employed full-time as lecturers or assistant professors in most public universities; (b) most international faculty members earned their first degree outside of Taiwan; however, 66% of them earned their post-doctoral degrees in Taiwan. The results of PLS-SEM suggest that the international faculty perspective of government is more directly related to their current work satisfaction and overall satisfaction. The designed model has shown work well to assess this issue. This can be extended to other higher education settings to tackle similar issues.

The pharmacological effects of the fruiting body of Ophiocordyceps sinensis (Os) exerted antioxidant, anti-virus, and immunomodulatory, whereas the anti-inflammatory effects and active components of Os mycelial submerged culture (OMSC) are not known. This study aimed to investigate the active compounds in the fermented liquid (FLOS), hot water (WEOS), and 50-95% (EEOS-50, EEOS-95) ethanol extracts of OMSC and their anti-inflammatory effects and potential mechanisms in LPS-stimulated microglial BV2 cells. The results demonstrated that all of the OMSC extracts could inhibit NO production in BV2 cells. EEOS-95 exhibited the strongest inhibitory effects (41 % inhibitory ability at 500 µg/mL), and its ergosterol, GABA, total phenolic, and total flavonoid contents were significantly higher than those of the other extracts (18.60, 18.60, 2.28, and 2.14 mg/g, p &lt; 0.05, respectively). EEOS-95 also has a strong inhibitory ability against IL-6, IL-1β and TNF-α with IC50 of 617, 277 and 507 µg/mL, respectively, which is higher than that of 1 mM melatonin. The anti-inflammatory mechanism of EEOS-95 seems to be associated with up-regulation of PPAR-γ/Nrf-2/HO-1 antioxidant-related expression and down-regulation of NF-κB/COX-2/iNOS pro-inflammatory expression signaling. Therefore, we hypothesized that EEOS-95 can prevent neurodegenerative diseases, but the effect on the application in vivo should be further investigated.

This study explores the significance of active suspension systems for vehicles with lower chassis compared to conventional ones, aiming at the development of future automobiles. Conventional linear MR (Magneto-Rheological) dampers were found inadequate in ensuring sufficient vibration control because the vehicle's chassis becomes lowered in the unmanned vehicles or purposed based vehicles. As an alternative, a rotary type of MR damper is proposed in this work. The proposed damper is designed based on pre-specified design parameters through mathematical modeling and magnetic field analyses. Subsequently, a prototype of the rotary MR damper identical to the design is fabricated, and effectiveness is shown through experimental investigations. In configuring the experiments, a proportional-integral (PI) controller is employed for current control to reduce the response time of the damper. The results presented in this work provides useful guidelines to develop a new type of MR damper applicable to various types of future vehicles’ suspension systems with low distance from the tire to the body floor.

There has been a growing awareness of the need to implement environmentally friendly operations in the hotel industry, but most studies focus on guest behaviors. Only a few studies investigate employees’ willingness to comply with hotels’ green practices. This study seeks to further the investigation from the human resource perspective. Using alumni from a tourism and hospitality programs, this study collected 233 responses. The study hypothesizes that the employees’ green ability consisting of environmental awareness, environmental knowledge, and environmental skill creates a positive impact on hotels’ green ability. The results indicate that employees contribute approximately a fifth of hotels’ ability to implement greener practices.

Recently, various applications of indoor location-based services using Wi-Fi fingerprint data have been studied. However, since fingerprint data collected at a specific location does not have continuous information between signals and correlation information between signals, path data cannot exist in principle. Therefore, most indoor positioning technologies are based on predicting the position of a stationary positioner at a specific location based on signal data collected at a specific location in the indoor space. Due to the discontinuous nature of these signals, they are unable to account for user movement. Therefore, there is a need for techniques to improve the accuracy of indoor positioning in moving situations. This paper proposes a method for enriching all data points with relevant information to obtain improved indoor positioning results and an improved technique for generating movement path data. The proposed technique generates relevant information with continuity by expressing data points as Bounding Boxes (BB) and subsequently clustering them using grid cells. Through the proposed method, we represented and expanded the indoor location data, which consists of data points, as BB. However, data points represented by BB do not have any adjacency information with each other, and as a result, it is not possible to create a movement path between neighboring BB. To solve the problem, we used clustering, a machine learning technique that categorizes data points into groups, to group BBs. This was done by constructing a YOLO (You Only Look Once) grid cell that included all BBs and clustered them into grid cells that were divided into specific regions through the suggestion phase. The clustering results align with the indoor structure of the building, indicating that the data points have been appropriately clustered. BBs inside a grid cell are contained within a moveable area and have related information. We also generated association information between neighboring grid cells through the proposal technique, and then generated path information that can be traveled between grid cells. In this paper, we use the movement path information generated from a dataset as training data for machine learning, and through this, we propose an enhanced indoor positioning technology.

An oceanic radar altimeter such as TOPEX/Poseidon (T/P) is typically for observing elevation changes over the open oceans or large inland lakes/rivers, with limited applications over solid earth due to its large footprint and susceptibility to waveform contamination and slope effect. Here we demonstrate that it is possible to construct a long-term time series of glacier elevation change from T/P-series radar altimeters over two flat surfaces near a glacier terminus and an icefield (Sites A and B, with slopes of 2° and 0.8°) in Mt. Tanggula, Tibet, at elevations over 5400 m. We retracked radar waveforms using the subwaveform threshold algorithm, selected quality altimeter data (1/4 of the original) with nearly the same slope and adjusted the original elevations by fitting with a time-varying, 2nd order surface. The glacier elevation changes at the two sites from T/P (1993–2002) show seasonal elevation oscillations with linear rates at about −3 m/year and abnormal seasonal changes around the 1997–98 El Niño. Site A is over a deep valley in southern Tanggula. Its elevation dropped about 30 m over 1993–2002 (from T/P) and the glacier almost disappeared by 2016 (from altimeters and satellite images). Despite the sporadic Jason-2 and Jason-3 altimeter data, we also derived long-term rates of glacier elevation change over 1993–2017. Landsat-derived glacier area and elevation changes near the two sites confirm the rapid glacier thinning from the altimeters. The glacier meltwater near Site A supplied increasing source water to Chibuzhang Co west of Mt. Tanggula, contributing partially to its accelerated rising lake level. The glacier thinning at Site B (icefield) was correlated with the increased discharge of the Tuotuo River in eastern Mt. Tanggula, a source region of the Yangtze River. The successful detection of glacier thinning at the two sites shows that T/P-series altimeters can serve as a virtual station at a flat glacier spot to monitor long-term glacier elevation changes in connection to climate change. This virtual station concept is particularly useful for inaccessible glaciers, but its implementation faces two challenging issues: increasing the volume of quality altimeter data and improving the ranging accuracy over a targeted mountain glacier spot.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Lung cancer is almost the most common cause of cancer death in the world. Clinically, the conventional therapy to eradicate the cancer cells is chemotherapy but a better drug remains required. In this study, the effects of three bufadienolides, kalantuboside B, kalantuboside A and bryotoxin C, isolated from Kalanchoe tubiflora (Harvey) were evaluated and characterized in CL1-5 highly metastatic human lung cancer cells. Contrary to the apoptosis-promoting activity in other cancer cells, these three bufadienolides did not induce apoptosis in CL1-5 cancer cells. Instead, they activated an autophagy pathway, as indicated by the increase of autophagosomes formation. The induction of autophagy by these three bufadienolides was demonstrated to link to down-regulation of p-mTOR as well as up-regulation of LC3-II, ATG5, ATG7, Beclin-1. Moreover, among these three compounds, kalantuboside B in which a monosaccharide is attached at the bufadienolide aglycon, exhibited a better autophagy induction. Our findings revealed an alternative mechanism of drug action by bufadienolides in lung cancer cells and provided evidence for the possibility of treating highly metastatic human lung cancer through an autophagy pathway.

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

This study presents an innovative approach for the utilization of industrial by-products and municipal waste in the production of sustainable and environmentally friendly cement mortar. We explored stabilized stainless-steel reduced slag (SSRS) and polyethylene (PE) plastic waste as partial replacements for aggregates. Various engineering properties of the resulting cement mortar specimens, including slump, slump flow, compressive strength, flexural strength, tensile strength, water absorption, and ultrasonic pulse velocity (UPV), were investigated through comprehensive experimental tests. The influence of different water-cement (w/c) ratios and substitution amounts on the engineering properties of the cement mortar samples was thoroughly examined. The findings revealed that an increase in PE substitution adversely affected the overall workability of the cement mortar mixtures, whereas an increase in SSRS amount contributed to enhanced workability. As for the hardened properties, a consistent trend was observed for both cases, with higher w/c ratios and substitution amounts leading to reduced mechanical properties. Water absorption and UPV test results validated the increased formation of porosity with higher w/c ratios and substitution amounts. This study proposes a promising method to effectively repurpose industrial by-products and municipal wastes, transforming them into sustainable construction and building materials. Additionally, a comparative analysis of transportation costs and carbon footprint emissions between SSRS-cement mortar and PE-cement mortar was conducted to assess their environmental impact and sustainability. Generally, higher w/c ratios and replacement levels corresponded to reduced carbon footprint. The geographical location of the source of SSRS and PE remains a challenge and studies to overcome this challenge must be further explored.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

The prediction of stock prices holds significant implications for researchers and investors in evaluating stock value and risk. In recent years, researchers have increasingly replaced traditional machine learning methods with deep learning approaches in this domain. However, the application of deep learning in forecasting stock prices is confronted with the challenge of overfitting. To address the issue of overfitting and enhance predictive accuracy, this study proposes a stock prediction model based on GRU (Gated Recurrent Unit) with reconstructed datasets. This model integrates data from other stocks within the same industry, thereby enriching the extracted features and mitigating the risk of overfitting. Additionally, an auxiliary module is employed to augment the volume of data through dataset reconstruction, thereby enhancing the model’s training comprehensiveness and generalization capabilities. Experimental results demonstrate a substantial improvement in prediction accuracy across various industries.

With the fast development of the mobile Internet, the online platforms of social networks have rapidly been developing for the purpose of making friends, sharing information, etc. In these online platforms, users being related to each other forms social networks. Literature reviews have shown that social networks have community structure. Through the studies of community structure, the characteristics and functions of networks structure and the dynamical evolution mechanism of networks can be used for predicting user behaviours and controlling information dissemination. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.

This study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile statioThis study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4,525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., 2 timestamps and 3 timestamps); the experimental results can be observed that the average error of location estimation was 9.19 meters by the proposed mobile positioning method with 2 timestamps.

The survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method to discover the gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for the patients' dichotomization in the continuous gene expression. There is some optimization software for cutoff determination. However, the software's predetermined cutoffs are usually set at the median or quantiles of gene expression value to perform the analyses. There are also few clinicopathological features available on their pre-processed data sets. We applied an in-house workflow, including data retrieving and pre-processing, feature selection, sliding-window cutoff selection, Kaplan-Meier survival analysis, and Cox proportional hazard modeling for biomarker discovery. In our approach for the TCGA HNSCC cohort, we scanned human protein-coding genes to find optimal cutoff values. After adjustment with confounders, the clinical tumor stage and the surgical margin involvement are independent risk factors for patients' prognosis. According to the resulting tables with Bonferroni-adjusted P value under the optimal cutoff and the hazard ratio, three biomarker candidates, CAMK2N1, CALML5, and FCGBP, are significantly associated with the patients' overall survival. We validated this discovery by using the other independent HNSCC dataset (GSE65858). Thus, we suggest the transcriptomic analysis could help for biomarker discovery.

Abstract: NFinFET transistors with fin channel length 90nm and a planar MOSFET transistor with channel length 180nm and 90nm are presented with characteristic curves at various gate biases. A finalized algorithm with kink effects is effectively responsible for addressing the field effect transistors. The algorithm includes the modified conventional current-voltage formula and a nonlinear heat-associated kink solution which is simplified as a Gaussian form. Three parameters in modified model includes kN (related with channel width, channel length, and gate oxide capacitor, and proportional to the mobility of carriers)Vth(Threshold Voltage), and (the inverse of Early Voltage). Those parameters are determined to minimize the discrepancies between the measured data and the fitting values, but leave kinks located at around (VGS－Vth), which are deliberately eliminated by the Gaussian form because of the agitation of thermal kink effects. The whole fitting is made to be as close as the as-measured IDS-VDS. In the mean time, those determined parameters are physically meaningful after the analysis has been done.

The paper presents a new type of hydraulic clutch operating with magnetorheological (MR) fluids and the results achieved from both theoretical analysis and experimental measurement. The hy-draulic clutch system of its own design with MR working fluid and a rotating magnetic field lo-cated is designed. The principle of this clutch is to use a rotating magnetic field created by an al-ternating current electromagnet to set the MR fluid in motion. In the tests of the hydraulic clutch with a rotating magnetic field, MR fluid produced on their own, consisting of solid iron particles of various diameters mixed with a silicone oil, are used as working fluids. The rheological properties of MR fluids are assessed on the basis of tests carried out with a Brookfield DV2T rheometer equipped with a magnetic device for generating the magnetic field of its own design. The char-acteristics of the hydraulic clutch with MR working fluid and the rotating magnetic field are tested on a specially built test stand. As a result of the research, it has been found that the torque transmitted by the clutch is the greater, the higher the rotational speed of the magnetic field and the lower the rotational speed of the beaker in which the working fluid is placed, and that the greatest torque occurs for the working fluid with the highest iron content. Based on the analysis of the structure and characteristics of the clutch in which the magnetic field is used, it has been shown that the design of the developed clutch is similar to that of an induction clutch, and its characteristics correspond to the characteristics of the eddy current clutch. Therefore, the proposed new clutch working with MR fluid and the rotating magnetic field can be applied to stationary power transmission system similarly to an eddy current clutch.

Neuronal cell death in the central nervous system has always been a challenging process to decipher. In physiological condition, neuronal cell death is restricted in the adult brain even as people ages. However, in pathological conditions of various neurodegenerative diseases, the cell death and shrinkage of a specific brain region represent a fundamental pathological feature across different neurodegenerative diseases. In this review, we will briefly go through the general pathways of cell death and describe the evidence of the cell deaths in the context of common neurodegenerative diseases individually, discussing our current understandings of cell death in connecting with the renowned pathogenic proteins, including tau, amyloid-beta, alpha-synuclein, huntingtin, and TDP-43.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

Land-use planning plays an important role in agricultural development, however it lacks tools to support planner in proposing land-use planning solutions especially, under saltwater intrusion condition in the coastal regions. This study aims to apply optimization in analyzing land-use solutions for agricultural land-use planning. In which a multi-objective optimization model is developed to optimize land-use area including land-use allocation taking into account socio-economic and environmental factors. The model has been applied in three districts of Soc Trang province, Vietnam (Long Phu, My Xuyen and Tran De) representing three ecological regions including salt water, brackish water and freshwater in the Mekong Delta of Vietnam. The results showed that two multi-objective optimization scenarios (in terms of profit, labor, environment benefits and risk reduction) have been implemented: (i) Multi-objective optimization of agricultural land-use until 2030 under normal conditions; (ii) Optimizing agricultural land-use until 2030 under climate change conditions similar to the 2016 drought and saltwater intrusion phenomenon in the Mekong Delta. The results showed that the second scenario is the option considered for implementing land-use plan thanks to the balance between good profits and minimizing economic and environmental risk. The land allocation was carried out taking into account household economic, the influence of adjacent production types, local traffic and canal systems factors to allocate optimal land-use areas. The combination of land-use optimization and spatial allocation process can help planners improve the quality of agricultural land-use planning.

The solar cell industry is a part of the green energy sector, and effectively combining the patents of various manufacturers within the supply chain to collectively create sustainable patent market value is of paramount importance. This study aimed to establish a business strategy framework for Project-based licensing through the analysis of solar cell patents and the integration of supply chain manufacturers' patents, with the goal of collectively creating market value for patents sustainably. The research methodology included literature analysis and focus group discussions. Initially, the study analyzed solar cell industry patents from the past decade using the WIPS Global Patent database to identify projects with the highest patent count. Subsequently, through the use of focus group discussions, the feasibility of a patent pool strategy within the supply chain was explored. Finally, a strategy framework was proposed for Project-based patent pool licensing, focusing on the top 15 projects with the highest count of 5,579 patents. The results indicated that adopting this strategy framework for supply chain manufacturer patents can result in higher commercial patent value.

In this study, a simple, easy and convenient fluorescent sensing system for detection of vascular endothelial growth factor (VEGF) based on VEGF aptamers, aptamer-complementary fluorescence-labeled probe and streptavidin magnetic beads was developed in only one single tube. The VEGF is the most important biomarker in cancer angiogenesis, and is investigated that serum VEGF level was various according to the different type and course of cancers. Hence, efficient quantification of VEGF is able to improve the accuracy of cancer diagnosis and precision of disease surveillance. In this research, the VEGF aptamer was designed to be able to bind with the VEGF by forming G-quadruplex secondary structures, and then the magnetic beads would capture the none-binding aptamers due to none steric interference, and finally, the fluorescence-labeled probes were hybridized with the aptamers captured by the magnetic beads. Therefore, the fluorescent intensity in the supernatant would specifically reflect the present of VEGF. After an overall optimization, the optimal conditions for detection of VEGF were as followed, KCl, 50 μM; pH 7.0; aptamer, 0.1 μM and magnetic beads, 10 μL (4 μg/μL). The VEGF could be well quantified within a range of 0.2-2.0 ng/ml in plasma, and the calibration curve possessed a good linearity (y = 1.0391x + 0.5471, r = 0.998). The detection limit (LOD) was calculated to be 0.0445 ng/mL according to the formula (LOD = 3.3×σ/S). The specificity of this method was also investigated under the appearance of many other serum proteins, and the data showed the good specificity in this aptasensor-based magnetic sensing system. This strategy provided a simple, sensitive and selective biosensing platform for detection of serum VEGF. Finally, it was expected that this detection technique can be promoted to more clinical applications.

Civic engagement refers to the ways in which citizens participate in the life of a community to help shape its future or improve conditions for others. While it might have been shaped by the COVID-19 pandemic recovery, this study explored how college students perceive civic engagement on selected campuses that were partially locked down. We sampled 1036 student volunteers from six universities in Taiwan by using a self-designed module of civic engagement and transferable capabilities. The Student’s t-test, ANOVA, regression, factor analysis, and structural equation modeling (SEM) were used to interpret the differences and relationships among these variables. This study provides a more detailed example of the current status of civic engagement and its relative to transferable capabilities in a higher education setting. The related programs and institutes should take responsibility for enhancing students’ civic engagement and transferable capabilities during the pandemic. How to ameliorate the situation? The findings suggest that it should consider a student’s academic major, learning experiences in the department, and time spent on related activities during the pandemic recovery. The findings might prove useful to various campuses for enhancing ongoing practices.

In the field of machine learning, cluster analysis has always been a very important technology for determining useful or implicit characteristics in the data. However, the current mainstream cluster analysis algorithms require comprehensive analysis of the overall data to obtain the best parameters in the algorithm. As a result, handling large-scale datasets would be difficult. This research proposes a distributed related clustering mechanism for Unsupervised Learning, which assumes that if adjacent data are similar, a group can be formed by relating to more data points. Therefore, when processing data, large-scale datasets can be distributed to multiple computers, and the correlation of any two datasets in each computer can be calculated simultaneously. Later, results are processed through aggregation and filtering before assembled into groups. This method would greatly reduce the pre-processing and execution time of the dataset; in practical application, it only needs to focus on how the relevance of the data is designed. In addition, the experimental results show the accuracy, applicability, and ease of use of this method.

The corrosion of prestressing steel in prestressed concrete bridges is a critical issue for bridge maintenance. To assess structures with corroded strands, it is necessary to define the mechanical properties of the strands and their influence on the structural behavior. In this study, corroded strands are taken from external tendons in existing bridges and tested to define the effects of corrosion on the tensile properties of the strand. Empirical equations for the tensile strength and ductility of the corroded strand are proposed using test results. The most corroded wire governs the mechanical properties of the strand. Experiments on prestressed concrete beams with a single corroded strand are conducted to investigate the structural behavior. A reduction in the flexural strength and maximum deformation is observed from the experiment. According to the section loss of a wire in a strand and its location in a beam, the flexural capacity can be evaluated using the proposed equation. The reduced ultimate strain of the corroded strand can be the governing factor of the flexural strength.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

Neuronal cells are believed to rely on autophagy to maintain cellular homeostasis. Nevertheless, autophagy may be a therapeutic target for neurodegenerative diseases because of the role it plays in neuronal cell death. Despite Beclin 1 association with autophagy and apoptosis, the molecular mechanisms that underlie Tau-induced vesicular pathology and Tau protein aggregates in humans remain poorly understood. Here, we set out to examine the role of three genes-ATG14L, Beclin 1 and VPS34- in autophagy nucleation failure-associated Tau disorders in fruit fly models. We found that either reduced ATG14L levels improve neuronal outcomes, or transgenic expression of ATG6/Beclin 1 greatly reduced age-dependent neurodegeneration and Tau pathology; whereas, the loss of Beclin 1 exacerbated these effects. Prior to p-Tau accumulation, the neuronal Beclin 1 network is critical for reducing p-Tau accumulation, indicating that the loss of Beclin 1 causes Tau pathology and neurodegeneration. As neurodegeneration progressed, glial cells showed an upregulation of vesicular Beclin 1 immunoreactivities. The reduced VPS34 also resulted in p-Tau accumulation, suggesting that neurons require both Beclin 1 and VPS34 to reduce p-Tau accumulation. Our results provide mechanistic insight into the molecular pathways that cause age-dependent Tau pathology and neurodegeneration due to an excess of autophagy nucleation processes and autophagy activity, and we derive novel inferences on the therapeutic possibilities of rebalanced Beclin 1 network by reducing autophagic specific nucleation component of ATG14L.

Interleukin-33 (IL-33), a member of the IL-1 cytokine family has been recently associated with the development of autoimmune diseases, including SLE. IL-33 is an alarmin and a pleiotropic cytokine that affects various types of immune cells via binding to its receptor, ST2. In this study, we determine the impact of intraperitoneal IL-33 treatments in young lupus, NZB/W F1 mice. Mice were treated from the age of 6 to 11 weeks. We then assessed the proteinuria level, renal damage, survival rate, and anti-dsDNA antibodies. The induction of regulatory B (Breg) cells and changes in gene expression were also examined. In comparison to the control group, young NZB/W F1 mice administered with IL-33 had a better survival rate as well as reduced proteinuria level and lupus nephritis. IL-33 treatments significantly induced IgM anti-dsDNA antibody, IL-10 expressing Breg cells, and alternatively induced M2 macrophage gene signatures. These results imply that IL-33 exhibit regulatory roles during lupus onset via the expansion of protective IgM anti-dsDNA as well as regulatory cells such as Bregs and M2 macrophages.

Butyric acid as a histone deacetylase (HDAC) inhibitor was produced by a number of periodontal and root canal microorganisms (such as Porphyromonas, Fusobacterium etc.). Butyric acid may affect the biological activities of periodontal/periapical cells such as osteoblasts, periodontal ligament cells etc., and thus affect periodontal/periapical tissue destruction and healing. The purposes of this study were to study the toxic effects of butyrate on matrix and mineralization markers’ expression of MG-63 osteoblasts. Cell viability and proliferation were determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. Cellular apoptosis and necrosis were analyzed by propidium iodide/Annexin V flow cytometry. Protein and mRNA expression of OPG, and RANKL were analyzed by western blotting and RT-PCR. OPG, soluble RANKL (sRANKL), 8-isoprostane, pro-collagen I, MMP-2, osteonectin (SPARC), osteocalcin and osteopontin secretion into culture medium were measured by enzyme-linked immunosorbant assay. Histone H3 acetylation levels were evaluated by immunofluorescent staining (IF) and western blot. We found that butyrate induced morphologic changes of growing MG-63 cells, with bigger and flattened in appearance. Butyrate activated histone H3 acetylation of MG-63 cells. Exposure of MG-63 cells to butyrate partly decreased cell number with no marked increase in apoptosis and necrosis. Butyrate stimulated RANKL protein expression, whereas it inhibited OPG protein expression. Butyrate also inhibited the secretion of OPG in MG-63 cells, whereas sRANKL level was below detection limit. Butyrate stimulated 8-isoprostane, MMP-2 and osteopontin secretion, but not procollagen I, osteonectin, osteocalcin in MG-63 cells. In conclusion, butyric acid generated by periodontal and root canal microorganisms may potentially induce bony destruction and impair bone repair by alteration of OPG/RANKL expression/secretion, 8-isoprostane, MMP-2, and osteopontin secretion, and affect cell proliferation. These effects are possibly related to increased histone acetylation. These events are important in the pathogenesis of periodontal and periapical destruction.

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.

To study the anti-cracking performance of cast-in-place concrete inner shaft lining structure under the combined effects of temperature and constraint, this paper first developed a novel temperature stress test machine suitable for the shape of the inner shaft lining structure and with adjustable endpoint constraint, providing specialized experimental equipment for subsequent cast-in-place concrete inner shaft lining temperature stress simulation experiments. Then, this paper preliminarily studied the temperature, strain, and stress changes of the cast-in-place inner shaft lining under the condition of 100% endpoint constraint by simulating the internal hydration heating and cooling process of the cast-in-place inner shaft lining and using the temperature stress test machine to accurately control the endpoint constraint conditions of the inner shaft lining scaled model.

Melon pest management relies on the excessive application of pesticides. Reducing pesticide spraying has become a global issue for environmental sustainability and human health. Therefore, developing a new cropping system that is sustainable and eco-friendly is important. This study found that melon seedlings irrigated with ultrafine water containing H2 and O2 (UFW) produced more root hairs, increased shoot height, and produced more flowers than the control irrigated with reverse osmosis (RO) water. Surprisingly, we also discovered that UFW irrigation significantly reduced aphid infestation in melon. Based on cryo-scanning electron microscope (cryo-SEM) observations, UFW treatment enhanced trichome development and prevented aphid infestation. To investigate whether it was H2 or O2 that helped to deter insect infestation, we prepared UF water enrichment of H2 (UF+H2) and O2 (UF+O2) separately and irrigated melons. Cryo-SEM results indicated that both UF+H2 and UF+O2 can increase the density of trichomes in melon leaves and petioles. RT-qPCR showed that UF+H2 significantly increased the gene expression level of the trichome-related gene GLABRA2 (GL2). We planted melons in a plastic greenhouse and irrigated them with ultrafine water enrichment of hydrogen (UF+H2) and oxygen (UF+O2). The SPAD value, photosynthetic parameters, root weight, fruit weight, and fruit sweetness were all better than the control without ultrafine water irrigation. UFW significantly increased trichome development, enhanced insect resistance, and improved fruit traits. This system thus provides useful water management for pest control and sustainable agricultural production.

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural network (CNN) and LSTM to capture spatial-temporal patterns from the data and improve the model ability to recognize dynamic features of the disease. In addition, we introduced an attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epochs) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the incidence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers made accurate management decisions and reduced disease impacts through optimal fungicide application timing.

During the coronavirus disease 2019 (COVID-19) pandemic, individuals with immune-mediated inflammatory diseases (IMIDs), such as systemic lupus erythematosus and rheumatoid arthritis, face a heightened risk of infection and severe outcomes due to immunological alterations resulting from their underlying conditions and immunosuppressive treatments. Even as the pandemic has transitioned to an endemic state, it remains crucial to recognize that these patients continue to be at risk. In this narrative review, we analyzed existing literature to explore the impact of IMIDs, clinical risk factors, and the influence of immunosuppressive therapies on COVID-19-related risks and outcomes. Notably, certain disease-modifying antirheumatic drugs (DMARDs), like rituximab, are associated with worse COVID-19 outcomes, and rituximab-treated patients show impaired immune responses to COVID-19 vaccination. Additionally, we outline the diverse effects of glucocorticoids on COVID-19 outcomes and management. To highlight real-life challenges faced by clinicians caring for patients with IMIDs, we present an illustrative scenario that underscores the importance of effective vaccination, timely boosting, and additional mitigation strategies against COVID-19. Given the clinical heterogeneity and diverse disease states within IMIDs, it is crucial to understand the ongoing implications and risks associated with COVID-19 in these patients, to guide the implementation of appropriate measures and optimize care and outcomes in the current endemic era.

Federated learning trains a neural network model using the client's data to maintain the benefits of centralized model training, while maintaining the privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client's data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments.

Mechanical ventilation (MV), used in patients with acute lung injury (ALI), induces diaphragmatic myofiber atrophy and contractile inactivity, termed ventilator-induced diaphragm dysfunction. Phosphoinositide 3-kinase- (PI3K-) is crucial in modulating fibrogenesis during the reparative phase of ALI; however, the mechanisms regulating the interactions among MV, myofiber fibrosis, and PI3K- remain unclear. We hypothesized MV with or without bleomycin treatment would increase diaphragm muscle fibrosis through the PI3K- pathway. Five days after receiving a single bolus of 0.075 units bleomycin intratracheally, C57BL/6 mice were exposed to 6 or 10 mL/kg MV for 8 h after receiving 5 mg/kg of AS605240 intraperitoneally. In wild-type mice, bleomycin exposure followed by MV 10 mL/kg prompted significant increases in disruptions of diaphragmatic myofibrillar organization, transforming growth factor-1, oxidative loads, Masson’s trichrome staining, extracellular collagen levels, positive staining of -smooth muscle actin, PI3K- expression, and myonuclear apoptosis (P &lt; 0.05). Decreased diaphragm contractility and peroxisome proliferator activated receptor- coactivator-1 levels were also observed (P &lt; 0.05). MV-augmented bleomycin-induced diaphragm fibrosis and myonuclear apoptosis were attenuated in PI3K-γ-deficient mice and through AS605240-induced inhibition of PI3K- activity (P &lt; 0.05). MV-augmented diaphragm fibrosis after bleomycin-induced ALI is partially mediated through PI3K-γ. Therapy targeting PI3K-γ may ameliorate MV-associated diaphragm fibrosis.

This study integrated coastal-watershed models and combined a risk assessment method to develop a methodology to investigate the impact resulting from coastal disasters under climate change. The mid-western coast of Taiwan suffering from land subsidence was selected as the demonstrative area for the vulnerability analysis based on prediction of sea level rise (SLR), wave run-up, overtopping, and coastal flooding under the scenarios of 2020 to 2039. Database from tidal gauges and satellite images were used to analyze sea level rise using EEMD (Ensemble Empirical Mode Decomposition). Extreme wave condition and storm surge were estimated by numerical simulation using WWM (Wind Wave Model) and POM (Princeton Ocean Model). Coastal inundation was then simulated via WASH123D watershed model. The risk map of study areas based on the analyses of vulnerability and disaster were established using the AHP (Analytic Hierarchy Process) technique. Predictions of sea level rise, the maximum wave condition and storm surge under the scenarios of 2020 to 2039 are presented. The results indicate that the sea level at the mid-western coast of Taiwan will rise in an average of 5.8 cm, equivalent to a rising velocity of 2.8 mm/year. The analysis indicates that Wuqi, Lukang, Mailiao, and Taixi townships are susceptive, low resistant and low resilient, and reaches the high risk level. The assessment provides that important information for making adaption policy in the mid-western coast of Taiwan.

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level in Drinking Water Distribution Systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in-situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced-detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large scale distributed fiber-optic turbidity monitoring networks.

Circadian rhythm disruptions are a hallmark feature of mood disorders. Patients experiencing acute depressive episodes report noticeable changes in their sleep-wake cycles. This research explains the association between depression and various circadian rhythm metrics, explicitly focusing on adolescents diagnosed with depressive disorders. Adolescence is a critical period marked by significant physiological and psychological changes, making it imperative to understand how mood disorders manifest during this phase. However, there have been minimal specific studies in pediatric populations to determine whether circadian rhythm changes differ between adolescents with first- and multiple-recurrent depressive episodes. Our study involved a group of 61 adolescents aged between 13 and 18. We performed a cross-sectional study of a clinical popu-lation of patients presenting to a child and adolescent psychiatry clinic diagnosed with depression. Participants were asked to complete self-report evaluations using several tools: The Korean ver-sion of the Biological Rhythms Interview of Assessment in Neuropsychiatry (K-BRIAN), the Korean Translation of Composite Scale to Measure Morningness-Eveningness (KtCS), and the Seasonal Pattern Assessment Questionnaire (SPAQ). Tools such as the Children's Depression In-ventory (CDI), State-Trait Anxiety Inventory (STAI), and K-Mood Disorder Questionnaire (K-MDQ) were employed for the assessment of clinical characteristics of depression. Based on the frequency of their depressive episodes, participants were bifurcated into two distinct groups: those experi-encing their first episode (n=22, mean age: 15.09±1.44 years) and those with recurrent episodes (n=39, mean age: 15.95±1.26 years). At first, the two groups' data revealed no significant differences regarding mood or circadian rhythm metrics (CDI: first-episode 26.18±10.54 and recurrent episode 25.90±10.59, STAI-S: first-episode 56.91±12.12 and recurrent episode 57.49±11.93, STAI-T: first-episode 60.36±11.63 and recurrent episode 59.09±12.10, SPAQ-total: first-episode 6.59±4.86 and recurrent episode 6.77±5.23, KtCS: first-episode 30.32±5.83 and recurrent episode 28.13±7.36). However, we observed significant correlations between circadian rhythm disruptions and depression scales (CDI with SPAQ-weight (r=0.26), KtCS (r=-0.48), K-BRIAN-sleep (r=0.58), K-BRIAN-activity (r=0.64), K-BRIAN-social (r=0.71) and K-BRIAN-eating (r=0.40)). These correlations were especially pronounced in the recurrent episode group, suggesting that with the progression and chronicity of depression, the relationship between circadian rhythms and depression becomes more intertwined and evident. In conclusion, especially in adolescents, as the severity and chronicity of depression increase, the interplay between circadian rhythms and mood disorders becomes more pronounced, warranting further research and clinical attention.

Ti17 alloy is mainly used to manufacture aero-engine discs due to its excellent properties such as high strength, toughness and hardenability. It is often subjected to creep-fatigue cyclic loading in service environments. Shakedown theory describes the state in which the accumulated plastic strain of the material stabilizes after several cycles of cyclic loading, without affecting its initial function and leading to failure. This theory includes three behaviors: elastic shakedown, plastic shakedown, and ratcheting. In this paper, the creep-fatigue tests (CF) were conducted on Ti17 alloy at 300℃ to study its shakedown behavior under creep-fatigue cyclic loading. Based on the plasticity-creep superposition model, a theory model that accurately describes the shakedown behavior of Ti17 alloy was constructed, and ABAQUS finite element software was used to validate the accuracy of the model. TEM analysis was performed to observe the micro-mechanisms of shakedown in Ti17 alloy. The results reveal that the Ti17 alloy specimens exhibit plastic shakedown behavior after three cycles of creep-fatigue loading. The established finite element model can effectively predict the plastic shakedown process of Ti17 alloy, with a relative error between the experimental and simulation results within 4%. TEM results reveal that anelastic recovery controlled by dislocation bending and back stress hardening caused by inhomogeneous deformation are the main mechanisms for the plastic shakedown behavior of Ti17 alloy.

Feruloylacetone (FER) is a natural degradant of curcumin after heating, which structurally reserves some functional groups of curcumin. It is not as widely discussed as its original counterpart in previous studies and, therefore, its anti-cancer efficacy is investigated herein. This study focuses on the suppressive effect of FER on colon cancer, as the efficacious effect of curcumin on this typical cancer type has been well evidenced. In addition, demethoxy-feruloylacetone (DFER) was applied to compare the effect that might be brought on by the structural differences of the methoxy group. It was revealed that both FER and DFER inhibited the proliferation of HCT116 cells, possibly via suppression of the phosphorylated mTOR/STAT3 pathway. Notably, FER could significantly repress both the STAT3 phosphorylation and protein levels. Furthermore, both samples showed the capability of arresting HCT116 cells at the G2/M phase via the activation of p53/p21 and upregulation of cyclin-B. In addition, ROS elevation and changes in mitochondrial membrane potential were revealed, as indicated by p-atm elevation. The apoptotic rate rised to 36.9% and 32.2% after being treated by FER and DFER, respectively. In summary, both compounds exhibited an anti-cancer effect, and FER showed a greater pro-apoptotic effect, possibly due to the presence of the methoxy group on the aromatic ring.

Aim: Aging is companied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging on different phases of insulin secretion (ISEC); i.e., the first- and second-phase of insulin secretion (FPIS, SPIS) and glucose effectiveness (GE). Methods: Totally 169 individuals (43 men and 126 women) with the similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS and GE). The equations that are used to calculate the DF in the present study were built and published by our group. Results: The age of the participants ranged from 18- to 78-year-old. Men had higher FPIS but lower HDL-C levels than women (2.067±0.159, 1.950±0.186 μU/min and 1.130±0.306, 1.348±0.357 mmol/dl, accordingly). The results of Pearson correlation revealed that age was negatively related to IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). In the meanwhile, FPIS was also only found to be negatively correlated with age in female (r = -0.238, p = 0.003). But there was no different with SPIS and age in both of genders. Conclusions: We have found that in Chinese with normal FPG level (90 mg/dl) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to IR and GE in both genders. At the same time, only FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest are, GE, FPIS and IR accordingly. The importance of GE should be re-evaluated. These results should be interpreted with caution because of the small sample size.

To generate a probability density function (PDF) for fitting probability distributions of real data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be adopted as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of trained deep learning model can be used to estimate the PDF. To evaluate the proposed method, numerical experiments with single and mixed distributions are performed. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method.

The present paper provides a new definition for dual spline curves in a geometric intuitive way based on adaptive curve refinement techniques. The dual spline is an implementation of the interpolatory subdivision scheme for curve modeling, which is comprised of polynomial segments of different degrees. Specially, the dual spline curves are mainly aims to solve the difficult geometry defeaturing problems in the existing computer-aided technology. Dual spline curves maintain various desirable properties of conventional curve modeling methods, such as local adaptive subdivision, high interpolation accuracy and convergence, continuous and discontinuous boundary approximation. By adding fictitious and intrinsic nodes inside or at the vertices of interpolation elements, the dual spline is flexible and convenient for approximating a set of ordered points or discrete segments. Combined with the Lagrange interpolation polynomial and meshless method, the proposed approach is capable of approximating the non-smooth boundary for geometry defeaturing. Experimental results are given to verify the validity, robustness, and accuracy of the proposed method.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

Direct Energy Deposition (DED) is a versatile and efficient method in metal additive manufacturing. However, humping, caused by abnormal dynamics in the melt pool (MP), poses a significant threat to the geometric integrity of manufactured products. Current state-of-the-art (SOTA) methods primarily detect humping by analyzing late-stage spatial abnormalities, such as MP detachment. This approach is fundamentally reactive, leading to a tendency to miss early humping spatiotemporal dynamics, like cyclic elongation of the MP. This study introduces a novel, proactive indicator named VIMPS (Variability of Instantaneous MP Solidification-Front Speed), a physics-based tool designed to quantify early abnormal fluctuations in MP solidification speed. The experiments demonstrate VIMPS correlation with humping-induced geometric inaccuracies. By capturing early spatiotemporal dynamics of the MP, VIMPS reduces detection latency by 30 seconds compared to existing SOTAs that focus solely on spatial abnormalities. This significant improvement transforms detection from reactive to proactive, providing the time needed for corrective actions to enhance the overall productivity and quality of the DED process.

Vaccination represents the historical milestone in medical sciences, which has been saving millions of lives. Nonetheless, continued outbreaks of emergent infectious diseases are swamping from every coast to every border all over the world. Modern vaccines are our most powerful arsenals to defend the public health of a global population ever since 1796. As unexpected, modern vaccination approaches have brought out all sorts of unwelcome pathogenic disorders to varying extents. Pathogenic viruses have also continued to evolve to defeat elaborate arsenals devised by human intelligence. In this review, we have covered a variety of vaccination approaches along with their sophisticated materials from the past to the most recent development. Furthermore, we discuss varied serious pathogenic outcomes elicited by contemporary vaccination programmes involving novel materials in real clinical trials. The complex immune mediators will be discussed in the context to tackle the complication of vaccine-induced pathogenesis. We will offer the insight into the intractable discrepancy of vaccine-associated pathology. The precision approach bestowed with evolutionary concepts will be considered as a feasible option to confront various issues of pathogenic complicacy.

For esophageal cancer patients treated with neoadjuvant chemoradiotherapy (nCRT), restaging using F-18-fluorodeoxyglucose (18F-FDG) positron emission tomography computed tomography (PET-CT) following nCRT can detect interval metastases, including liver metastases, in almost 10% of patients. However, in clinical practice, focal FDG liver uptake, unrelated to liver metastases, is observed after chemoradiotherapy. This radiation-induced liver injury (RILI) can potentially lead to overstaging. Liver radiation damage is also a concern during distal esophageal cancer radiotherapy due to proximity to the left liver lobe, typically included in the radiation field. Post-CRT, if FDG activity appears in the left or caudate liver lobes, thorough investigation is needed to confirm or rule out distant metastases. The increased FDG uptake in liver lobes post-CRT often presents a diagnostic dilemma. Distinguishing between radiation-induced liver disease and metastasis is vital for appropriate patient management, necessitating a combination of imaging techniques and an understanding of the factors influencing radiation response. Diagnosis involves identifying new foci of hepatic FDG avidity on PET/CT scans. Geographic regions of hypoattenuation on CT and well-demarcated regions with specific enhancement patterns on contrast-enhanced CT scan and MRI are characteristic of radiation-induced liver disease (RILD). Lack of mass effect on all three modalities (CT, MRI, PET) indicates RILD. Resolution of abnormalities on subsequent examinations also helps in diagnosing RILD. Moreover, it can also help to rule out occult metastases, thereby excluding those patients from further surgery who will not benefit from esophagectomy with curative intent.

Information is uploaded and downloaded through the Internet day in and day out ever since we immersed ourselves in the Internet. Data security becomes an area demanding high attention and one of the efficient techniques to protect data is data hiding. Among various data hiding schemes, more applications prefer the schemes with reversibility. In recent reversible data hiding studies, the indices of a codebook can be reordered to hide secret bits. The hiding capacity of the codeword index reordering scheme increases when the size of the codebook gets larger. Since the codewords in the codebook are not modified, the visual performance of compressed images is retained. We propose a novel scheme making use of the fundamental principle of codeword index reordering technique to hide secret data in encrypted images. By observing our experimental results, the embedding capacity is significantly larger. Secret data can be extracted when a receiver owns a data hiding key and image can be recovered when a receiver owns an encryption key.

Helicobacter pylori (H. pylori) is known to play a significant role in the development of peptic ulcer diseases and gastric cancer. Previous studies have reported the existence of polymorphism in the ureC gene of H. pylori. However, the relationship between genetic sequence variations in ureC and the pathogenesis of peptic ulcer diseases remains unclear. We aimed to investigate the associations between ureC sequence variations and the development of gastric ulcer and gastric cancer. PCR amplification and DNA sequencing to analyze ureC sequences, and conducted phylogenetic analysis using MEGA X software. Samples from Taiwanese patients with gastric ulcer and gastric cancer were included in this study. PCR was used to amplify the ureC gene from these samples, followed by DNA sequencing. The obtained sequences were then subjected to phylogenetic analysis using MEGA X software. In addition, ureC sequences from different geographical regions, including China and other countries, were included in the analysis for comparison. Analysis of the ureC se-quences from Taiwanese samples revealed that 6 out of 7 gastric ulcer samples clustered together in one group, while the sequences from 7 out of 8 gastric cancer samples were distributed across other groups. Phylogenetic analysis incorporating ureC sequences from different geographical regions showed that Taiwanese ureC sequences could be divided into two distinct groups, although the bootstrap values supporting this separation were low. Notably, the ureC sequences from China formed a distinct group with a high bootstrap value, separate from sequences from all other countries. There are two genotypes of ureC sequences present in Taiwanese samples, with one genotype showing a closer association with gastric cancer. Additionally, the ureC sequences from China appear to be unique and separate from those obtained from other countries, indicating the presence of genetic diversity in ureC sequences among H. pylori strains from different regions, potentially contributing to differences in disease outcomes. Further research is needed to elucidate the specific mechanisms by which ureC sequence variations may influence the pathogenesis of peptic ulcer diseases and gastric cancer.

Electro-mechanical braking(EMB) which represents development direction of autonomous and intelligent braking plays very useful role on enhancing the braking response performance and intelligence level for mine underground electric trackless rubber-tired vehicle (ETRV). However,there is no Electro-Mechanical Brake system applied practically in coal mine UTRV until now.The accurate control of braking clamping force can determine the length and precision of braking distance of mine ETRV.In addition,because of poor working conditions in underground coal mine tunnel ,braking clamping force sensor may be easy to occur failure,which may cause vital accident.Therefore,A cascaded three-closed-loop EMB system with a positive clamping force sensor or force estimator built for ETRV is established to achieve autonomous elimination and reset of braking clearance and reliable braking force tracking ability via utilizing the electro motor rotor angle displacement and hysteresis effect of mechanical components in this EMB.The results of simulation and experiment indicate that clamping force response is faster than traditional hydraulic disk braking system and proposed force estimator presents good fault-tolerant ability when sensor is fault.This EMB can replace the current hydraulic brake system to enhance automation level of braking control for ETRVs and braking force response performance, reduce oil pollution and cost of maintenance, which is also key technology support for really precise motion control of mine autonomous driving vehicles.

Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

Air pollution is related to the development of people's livelihood. As one of the main pollutants in the air, formaldehyde affects people's physical and mental health. In order to remove formaldehyde and better protect the health of residents, it is very necessary to develop efficient adsorption materials. In this study, a cellulose composite aerogel Microcrystalline Cellulose/ N-(2- aminoethyl) -3- aminopropyl methyl dimethoxysilane (MCC/APMDS) modified by APMDS was explored. The pore volume and specific surface area of the modified aerogel decreased, the average pore diameter increased, and the adsorption capacity of formaldehyde was stronger. The adsorption experiment shows that the sample prepared under the conditions of APMDS dosage of 6 wt%, reaction time of 4 h and reaction temperature of 90℃ has the largest average pore size (14.56 nm) and the largest formaldehyde adsorption capacity (9.52 mg/g). The excellent adsorption of the composite aerogel is due to high porosity and rich active amino groups on the surface. This study provides valuable information for the preparation of adsorption materials with high formaldehyde adsorption capacity for air purification.