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.

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.

STAT3 is an oncoprotein which has been shown to contribute to drug resistance in multiple myeloma (MM). Nonetheless, the clinical utility of STAT3 inhibitors in treating MM has been limited, partly related to some of their pharmacologic properties. To overcome these challenges, our group had previously packaged STAT3 inhibitors using a novel formulation of nanoparticles (NP) and found encouraging results. In this study, we aimed to further improve the pharmacologic properties of these NP by decorating them with monoclonal anti-CD38 antibodies. NP loaded with S3I-1757 (a STAT3 inhibitor), labeled as S3I-NP, were generated. S3I-NP decorated with anti-CD38 (labeled as CD38-S3I-NP) were found to have a similar nanoparticular size, drug encapsulation and loading as S3I-NP. The release of S3I-1757 at 24 hours was also similar between the two formulations. Using Cy5.5 labeling of the NP, we found that the decoration of anti-CD38 on these NP significantly increased the cellular uptake by two MM cell lines (p<0.001). Accordingly, CD38-S3I-NP showed a significantly lower inhibitory concentration at 50% (IC50) compared to S3I-NP in two IL6-stimulated MM cell lines (p<0.001). In a xenograft mouse model, CD38-S3I-NP significantly reduced the tumor size by 4 folds compared to S3I-NP on day 12 after drug administration (p=0.006). The efficacy of CD38-S3I-NP in suppressing STAT3 phosphorylation in the xenografts was confirmed by using immunocytochemistry and western blot analysis. In conclusion, our study suggests that the decoration of anti-CD38 on NP loaded with STAT3 inhibitors can further improve their therapeutic effects against MM.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

This paper contributes to the debate on the impact of economic diversity and the resource curse on economic growth. We use dynamic panel data models on data on Canadian and US sub-national jurisdictions. We find evidence for a positive relationship between diversity and growth. Based on the Krugman Specialization Index, our analysis shows that the required threshold for not having the resource curse is 0.209. Above this threshold, the marginal contribution of natural resources to economic growth is lower for a more diversified regional economy than a less diversified one. We highlight the policy implications of these findings.

Using lightweight fire-rated board (LFRB) presents cost-effective opportunities for various passive fire protection measures. The aim of the project is to develop a LFRB with enhanced fire resistance, acoustic properties, and mechanical properties. These properties were determined using the Bunsen burner, furnace, energy dispersive X-Ray, impedance tube instrument, and Instron universal testing machine. To fabricate the LFRBs, vermiculite and perlite were blended with flame-retardant bind-ers, and four types of LFRBs were produced. A fire test was conducted to compare the fire-resistance performance of the LFRBs with a commercially available flame retardant board. The B2 prototype showed exceptional fire-resistant properties, with a temperature reduction of up to 73.0 °C as compared to the commercially available fire-rated magnesium board. Incorporating nano chicken eggshell into the specially formulated flame-retardant binder preserved the LFRBs’ structural integrity, enabling them to withstand fire for up to 120 minutes with an equilibrium temperature of 92.6 °C. This approach also provided an absorption coefficient of α = 2.0, a high flexural strength of 3.54 MPa, and effective flame retardancy properties with a low oxygen/carbon ratio of 2.60. These results make the LFRBs valuable for passive fire protection applications in the construction and building materials industry.

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper to build such a surrogate model, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9,000 test samples used for building the surrogate model, and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.

Background: The pathogenesis of duodenal ulcer has never been explained although the first description of this disease in medical literature appeared in 1817. Marshall et al. concluded that Helicobacter pylori was the most important etiological factor for duodenal ulcer in 1988, but the etiology based on this bacterium is controversial and how the bacterial infection leads to ulceration is presently unknown. Objectives: This study aims to identify the cause of duodenal ulcer, address the controversial issues surrounding Helicobacter pylori, elucidate the roles of gastric acid, and describe the pathological process of duodenal ulceration. Methods: First, a comprehensive systematic review on peptic ulcers (including gastric ulcer and duodenal ulcer) was conducted and the results were summarized. Second, a recently published causal relationship was employed to identify the etiology of peptic ulcers. Third, novel concepts and methods were applied to analyze the existing data on duodenal ulcer. Results: The etiology of duodenal ulcer and the roles of Helicobacter pylori and gastric acid in this disease were identified. The controversies surrounding Helicobacter pylori were addressed, and many characteristics and phenomena/observations of duodenal ulcer were elucidated. The pathological process of duodenal ulceration was described. Conclusion: Existing data accumulated over the past 300 years was sufficient, when analyzed using novel concepts, to understand the pathogenesis of duodenal ulcer. Duodenal ulcer is not an infectious disease caused by the infection of Helicobacter pylori, but a psychosomatic disease triggered by psychological stress. Helicobacter pylori plays a secondary role in only the late phase of duodenal ulceration.

This paper investigates the hydroelastic responses of offshore floating solar photovoltaic farms (OFPVs) to minimize structural motion. The OFPV usually occupies a large sea space in the order of hectares and due to its huge structural length-to-thickness ratio, the structural deformation under wave action has to be taken into consideration. The flexible deformation of the structure under hydrodynamic loading is termed the hydroelastic response. Due to the relatively long structural length with respect to the encountering wavelength, the diffraction and radiation of waves have to be taken into account to accurately represent the hydrodynamic loadings on the floating platform. The numerical model is first validated by comparing the eigenvalues and eigenvectors of the OFPV obtained from the proposed numerical scheme with their counterparts obtained from an established finite element software. This is followed by the investigation of the hydroelastic response of various OFPVs designed in varying layout configurations. The various layout configurations are obtained by altering the floating modular units’ dimensions as well as the spacing of the OFPVs when deployed adjacent to each other. The optimal configuration that gives the best performance in terms of the overall smallest response, known as compliance, is then suggested. The results suggest that a longish OFPV layout has a lower hydroelastic response and the motion could be further reduced by increasing the global flexural stiffness via layout arrangement.

Many parents have difficulty managing childhood asthma. In Hong Kong (HK), while medication is the primary form of treatment, traditional Chinese medicine is another favored option. In addition, HK follows a dual-track healthcare system, which may pose unique experiences for Chinese parents in childhood asthma management. This qualitative descriptive study aimed to explore the experiences of HK Chinese parents in caring for their children with asthma. Methods: Fourteen HK Chinese mothers of children (aged 3-10) suffering from asthma were purposively sampled to participate in individual, semi-structured interviews. A realist approach following conventional content analysis was used to interpret the interviews. Results: The mothers expressed feelings of uncertainty, fear of asthma crises, and searching for ways to cope. These feelings triggered various strategies to control their child’s asthma. As long as the child’s asthma symptoms recurred, the mothers’ distress continued. Their distress was sometimes exacerbated by self-doubt and worries about whether they would receive adequate support from their family and healthcare professionals. Conclusion: Helping parents to understand their limits may help them be more open to varied aspects of their caregiving experiences, and thus to cope better. Psychological interventions together traditional educational training may help to alleviate parents’ psychological difficulties.

Cashless payments are so common and popular that people are gradually not taking cash out of the house. But people still need to carry some cash and a couple of debit cards, which increases the risk of people having their cash or cards lost or stolen. The mobile payment method based on scanning codes solves the problem that you don't need to carry cash or cards when you go out, but you still need to enter the APP to scan the code to complete the payment. This report proposes a POS system with NFC technology, enabling people to complete transactions with new payment methods and reducing carrying other payment tools. Therefore, this paper explores the understanding of the proposed system and improves its functionality. The project aims to facilitate quickness for users to complete transactions, that is, near-field communication, using POS systems with received signals for transactions. Data is collected via triangulated research methods from various respondents across Malaysia with statistics and analysis on the proposed solution. The contactless NFC POS solution allows users to interact with the system via smartphone host card emulation application technology to complete transactions and allow people to reduce carrying other payment tools.

In the fast-paced internet world that we live in today, internet technologies and tools that enable us to connect online are rapidly changing and improving. To adapt to the changing environment, many game developers are changing their business models to adjust to this online environment by developing games that are centred around online gaming. As such, the internet demands of consumers such as gamers are increasing because of the need for quality internet connection for optimal gameplay. There are too many factors that can affect online gameplay. Moreover, not all gamers are equipped with the network knowledge and skills to diagnose and troubleshoot any internet issues that may arise during their gameplay session. The main contribution of this research was to gather insights from Malaysian broadband internet consumers, mainly from gamers and general users regarding their opinions of their ISP service experience in gaming, general use and service quality. A 1-click solution is presented as a smart autonomous network monitoring tool that can autonomously monitor, diagnose, and troubleshoot network problems with minimal input from the user. This research was conducted using a mixed-mode research methodology where the participants were asked to fill up a questionnaire regarding the user experience of their Internet Service Provider (ISP), and their perception of the 1 click-automated networking tool for their personal use. It is shown that there is a market demand for a 1 click-automated networking tool as gamers and general users both agree that such a tool would be beneficial to them by improving the internet quality of a network.

Reducing sedentary behaviours can help prevent non-communicable diseases, particularly among young adolescents with long term illnesses or disabilities (LTID). Much of young people’s voluntary sedentary time is related to screen-time behaviours (STBs) such as TV viewing, playing computer games, and using the computer for other activities. Although public health data on adolescents’ STB is growing, information about adolescents with LTID is currently lacking in a European context. The purpose of this study is to compare time on STBs between adolescents with and without LTID in European Countries through the HBSC 2013/14 study. Young adolescents (n = 61,329; boys 47.8%) from 15 European countries reported the time spent on TV viewing, playing computer games, and using the computer for other purposes on weekdays and the weekend. STBs were dichotomised based on international recommendations of less than 2 h per day, and Chi-square tests of independence were performed to investigate differences. STB time was combined to produce a sum score as dependent variable in multiple analysis of covariance with age and family affluence as covariates. There were statistically significant differences in computer gaming among boys and other computer use among girls for both weekdays and weekends, whereby adolescents with LTID reported higher use. In addition, both boys and girls with LTID spent more time on STBs than their same sex peers without LTID (Boys, F = 28.17, p < 0.001; Girls, F = 9.60, p = 0.002). The results of this study indicate a need for preventive strategies to address high levels of STB among young adolescents with LTID and reduce the risk of poor health outcomes associated with higher levels of sedentary behaviour.

Since sensor-based perception systems are used in autonomous vehicle applications, validating such systems is imperative to guarantee the robustness of the systems before they are being put to use. In this study, a comprehensive corruption-related simulation-based robustness verification and enhancement process for sensor-based perception systems is proposed. Firstly, we present a methodology and scenario-based corruption generation tools for creating diverse simulated test scenarios that can analogously represent real-world traffic environments, especially considering corruption types related to safety concern. Then, an effective corruption similarity filtering algorithm is proposed to remove corruption types with high similarity and identify the representative corruption types to represent all considered corruption types. As a result, we can generate efficient corruption-related robustness test scenarios with less testing time and good scenario coverage. Subsequently, we perform the vulnerability analysis of object detection models to identify model weaknesses and construct an effective training dataset for model vulnerability enhancement. This enhances the tolerance of object detection models to weather and noise-related corruptions, ultimately improving the robustness of the perception system. We employ case studies to demonstrate the feasibility and effectiveness of the proposed robustness verification and enhancement procedures. Additionally, we explore the impact of different "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

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

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear because abnormality is often absent in structural imaging findings. The purpose of the study was to determine whether machine learning-based models using functional magnetic resonance imaging (fMRI) biomarkers and demographic or neuropsychological measures at baseline could effectively predict 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically-matched healthy controls enrolled between September 2015 to August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to the significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after concussion. Approximately half of the patients experienced prolonged cognitive impairment at 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy=87.5%), 6 months (accuracy=83.3%), 1 year (accuracy=83.3%), and performed worse at 1-year follow-up compared to baseline assessment (accuracy=83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.

Abstract Background The number of immune-related endocrine dysfunctions (irEDs) has concurrently increased with the widespread use of immunotherapy in clinical practice and further expansion of the approved indications for immune checkpoint inhibitor (ICI) combinations using different modalities of anti-cancer treatment. Method A retrospective analysis was conducted on consecutive patients >18 years of age with advanced solid malignancies who had received at least one dose of anti-programmed cell death protein 1 (anti-PD-1) and/or anti-CTLA4 antibodies between January 2014 and December 2019 at a Hong Kong university hospital. Patients were reviewed for up to two months after the last administration of an ICI. The types, onset times and grades of irEDs, including hypothyroidism, hyperthyroidism, adrenal insufficiency and immune-related diabetes mellitus, were recorded. Factors associated with irEDs were identified using multivariate analysis. Result A total of 953 patients (male: 603, 64.0%; median age: 62.0 years) received ICIs during the study period. Of these, 580 patients (60.9%) used ICI-alone, 132 (13.9%) used dual-ICI, 187 (19.6%) used an ICI combined with chemotherapy (chemo+ICI), and 54 (5.70%) used immunotherapy with a targeted agent (targeted+ICI). A significantly higher proportion of patients using targeted+ICI had irEDs and hypothyroidism; in contrast, a higher proportion of patients using dual-ICI had adrenal insufficiency. There was no significant difference in the incidence of irED between the younger (<65 years) and older (>65 years) patients. Using logistic regression, only treatment type was significantly associated with irEDs. Notably, older patients had a higher risk of having immune-related diabetes mellitus. Conclusions This large, real-world cohort demonstrates that combining ICI with targeted therapy has a higher risk of overall irED and hypothyroidism. Immunotherapy is safe and well-tolerated regardless of age, but close monitoring of fasting glucose is needed in older populations.

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near-surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess the suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near-surface wind speeds to those of wind speed at turbine hub height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide a detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 is a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plans and constructions.

As mental health problems tend to increase during adolescence and is a serious public health issue in the Republic of Kazakhstan. Early detection is necessary and monitoring at the population level can be used to evaluate the progress of national programmes promoting positive well-being. Physical activity (PA) can be protective whereas increased screen time behaviours (STB) can be a risk for low levels of well-being. A national representative sample (n=4,731) of young adolescents aged 11y, 13y, and 15y from the Republic of Kazakhstan took part in the WHO collaborative Health Behaviour in School-aged Children (HBSC) study. Respondents completed the WHO-5 Well-being scale, and items in on PA and STB. Internationally recognised, recommended cut-offs were used for analyses. Two models of binary logistic regressions were performed to examine the associations with PA (Model 1) and PA with STB (Model 2) after stratification by gender and controlling for age, locality and family affluence. Three quarters of young adolescents in the Republic of Kazakhstan have good overall well-being, despite the proportion reduces as adolescents age from 11y to 15y (boys, OR=0.66 CI=0.49-0.80; girls, OR=0.55, CI=0.43-0.71). The odds ratio for positive well-being were more than twice for boys and more than 3.5 for girls who reported daily PA than not being active daily. Spending less time on STB for girls was associated with positive well-being than spending more STB time (OR=1.28, CI=1.04-1.59). Well-being among young adolescents drops dramatically between the ages of 11y and 15y and is higher among rural schools attendees than in urban schools. The recommended amounts of PA can be protective of low well-being for both boys and girls. However, meeting reporting STB recommendations was only protective for girls and not boys. Designing and implementing positive well-being programmes require consideration of locality and amounts of PA and STB

Flash flooding, a hazard which is triggered by heavy rainfall is a major concern in many regions of the world often with devastating results in mountainous elevated regions. We adapted remote sensing modelling methods to analyse one flood in July 2015, and believe the process can be applicable to other regions in the world. The isolated thunderstorm rainfall occurred in the Chitral River Basin (CRB), which is fed by melting glaciers and snow from the highly elevated Hindu Kush Mountains (Tirick Mir peak’s elevation is 7708 m). The devastating cascade, or domino effect, resulted in a flash flood which destroyed many houses, roads, and bridges and washed out agricultural land. CRB had experienced devastating flood events in the past, but there was no hydraulic modelling and mapping zones available for the entire CRB region. That is why modelling analyses and predictions are important for disaster mitigation activities. For this flash flood event, we developed an integrated methodology for a regional scale flood model that integrates the Tropical Rainfall Measuring Mission (TRMM) satellite, Geographic Information System (GIS), hydrological (HEC-HMS) and hydraulic (HEC-RAS) modelling tools. We collected and use driver discharge and flood depth observation data for five river sub-stream areas, which were acquired in cooperation with the Aga Khan Rural Support Program (AKRSP) organization. This data was used for the model’s calibration and verification. This modelling methodology is applicable for other regional studies especially for rough mountainous areas which lack local observations and river discharge gauges. The results of flood modelling are useful for the development of a regional early flood warning system and flood mitigation in hazardous flood risk areas. The flood simulations and prepared connected video visualization can be used for local communities. This approach is applicable for flood mitigation strategies in other regions.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

High Efficiency Video Coding (HEVC) uses an 8-point filter and a 7-point filter, which are based on the discrete cosine transform (DCT), for the 1/2-pixel and 1/4-pixel interpolations, respectively. In this paper, discrete sine transform (DST)-based interpolation filters (IF) are proposed. The first proposed DST-based IFs (DST-IFs) use 8-point and 7-point filters for the 1/2-pixel and 1/4-pixel interpolations, respectively. The final proposed DST-IFs use 12-point and 11-point filters for the 1/2-pixel and 1/4-pixel interpolations, respectively. These DST-IF methods are proposed to improve the motion-compensated prediction in HEVC. The 8-point and 7-point DST-IF methods showed average BD-rate reductions of 0.7% and 0.3% in the random access (RA) and low delay B (LDB) configurations, respectively. The 12-point and 11-point DST-IF methods showed average BD-rate reductions of 1.4% and 1.2% in the RA and LDB configurations for the Luma component, respectively.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

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

Corrosive burns lead to progressive esophageal stricture and dysphagia. There are many trials to prevent Esophageal stricture formation after corrosive burn. This study aimed to access the effects of EW-7197 on prevention for esophageal stricture formation after corrosive esophageal burn. animal study were classified divided into three groups: a healthy group, a control group (corrosive burn without EW-7197), and a treatment group (corrosive burn with EW-7197). Corrosive esophageal burns were produced using 30% NaOH on the lower esophagus. For 3 weeks, the control group received vehicle and the treatment group received 20 mg/kg/day EW-7197. Treatment efficacy was assessed by measuring the stenosis ratio by esophagogram with contrast media on day 21. histologic staining was performed to evaluate the fibrosis area ratio, and western blotting was performed to evaluate fibrotic markers. Among 20 rats that underwent surgery, 14 survived. Three in the treatment group died because of esophageal perforation, and three in the control group died due to their debilitating status. The esophageal stenosis ratio was significantly lower in the treatment group than in the control group (12.1 ± 9.5% and 42.2 ± 8.3%, respectively; p = 0.001). The histologic fibrosis area ratio was also significantly lower in the treatment group (12.5 ± 3.0% and 21.6 ± 2.1%, respectively; p = 0.001). The treatment group showed lower expressions of profibrogenic proteins such as TGF-β1, pSmad3, and α-SMA. EW-7197 may be a good alternative for the prevention esophageal stricture formation after corrosive burn.

Many corporations have now revised their value statements to showcase their commitment to sustainable development and green business due to the pressing issues of climate and environmental damage. Despite the importance of value statements that can direct companies to achieve sustainable goals, what kind of values can be perceived as organizational orientations to sustainability remains unknown. This study explores how the general public views the inclusion of sustainable components in value statements from the listed companies in Hong Kong by using Q-sort methodology (n = 30). Analyzing the final sample of 40 value statements extracted from 27 listed companies, we identified three factors related to sustainability, namely, Empowering business process management, Empowering customer-centric excellence, and Empowering sustainable progress. This study provides an additional component to the traditional triple bottom line by suggesting that internal operation of a company is an emerging idea to understand corporate sustainability.

Postoperative adhesive arachnoiditis is an inflammatory response of the spinal leptomeninges that occurs after surgery, resulting in scar formation in the avascular nature of the arachnoid layer. Clinical manifestations of this condition include pain, sensory deficits, motor dysfunction, reflex abnormalities, and bladder/bowel impairments. In MRI scans, the signs of postoperative adhesive arachnoiditis can vary, but some specific indicators can assist surgeons in locating the lesion and planning surgical interventions more accurately. In this report, we reported a 37-year-old man with postoperative adhesive arachnoiditis after two surgeries for Chiari I malformation, demonstrating that the gradually-forming the delta cord sign with sequential syringomyelia. During the intraoperative examination, we identified the presence of the delta cord sign, which was formed by an arachnoid scar that was tethering the dorsal spinal cord to the dura. This discovery allowed us to precisely pinpoint the location of the arachnoid scar, thereby providing valuable guidance to avoid any unnecessary exploration of innocent structures during the procedure. Other localization signs were also reviewed.

Polluted rivers are a primary problem in Changhua County, Taiwan, due to rapid and massive developments in agriculture and industry. In this study, samples consisted of water influent from the Yangzi-Cuo River, Ci-Tong Jiao, Changhua County. Ecology technology, a combination of gravel contact oxidation and surface flow constructed wetlands, was employed to enhance the water quality. Ecology technology is a useful and ordinary process that focuses on original treatment of pollution through chemical, physical, and biological procedures based on the mediums of soil, plant, water, and microorganisms from pure environments. Moreover, this study aimed to minimize the river pollution index (RPI) of the Yangzi-Cuo River by using combinations of gravel contact oxidation and surface flow constructed wetlands. The inflow volume of the water samples in situ was found to be 1534–2261 cubic meters per day (CMD). The pollution in the water sample mostly involved suspended solid (SS), ammoniacal nitrogen (NH₃–N), total phosphorus (TP), total nitrogen (TN), and biochemical oxygen demand (BOD), and the values of the above indexes were found to be 10.0–26.7 mg/L, 0.9–14.0 mg/L, 1.2–14.1 mg/L, 11.1–18.2 mg/L, and 0.5–3.6 mg/L, respectively. Efficiencies of pollution elimination in SS, NH₃–N, TP, TN, and BOD were found to be 24.2–93.1%, 58.3–86.2%, 5.2–85.0%, 59.4–77.2%, and 46.3–76.4%, correspondingly, after purification treatment via ecology technology. Thus, ecology technology is a valid means of purification treatment for polluted rivers.

Red djulis (Chenopodium formosanum) is a native cereal plant in Taiwan; it contains abundant polyphenols, betalian and dietary fiber. The appearance of red djulis is bright red. Therefore, it is also called the “ruby of cereals”. The antioxidative activity of red djulis extract is well-understood. However, the antiaging function still remains unclear. This study examined the potential of red djulis extract for enhancing collagen secretion and preventing cutaneous aging using red djulis extracts. The red djulis extracts are comprised of an abundant active component that can effectively enhance the ability of collagen secretion of dermal fibroblasts, prevent the glycation of collagen and resist the damage of ultraviolet light exposure. After fibroblast treatment with red djulis extracts, TGM1, KRT1, KRT10 and SOD2 genes were up-regulated significantly by 2.3, 4.3, 4.4 and 27.3 times, respectively, compared to those of the control group. Additionally, it can increase COL1A2 gene expression by 43% and decrease MMP9 gene expression 33%. Therefore, it was demonstrated that red djulis extracts affect gene expressions related to the skin barrier, antioxidation and collagen. Moreover, we found positive effects on skin barrier integrity, endogenous antioxidant activity and skin collagen-preservation. The preparation of the red djulis extracts is environmental friendly and can promote the economic value of Chenopodium formosanum; thus, the proposed extract is suitable for applications in the development of food products, especially beverages, skin care and cosmetic products.

Determining disability prevalence is an important area for population statistics, especially among young adolescents. The Washington Group on Disability Statistics is one source of reporting disabilities through functional difficulties. However, young adolescents self-reporting this measure is in its infancy. The purpose of this study was to carry out an intra-rater test-retest reliability study on a modified set of items for self-reporting functional difficulties. Young adolescents (n=74; boys=64%; age m=13.7, SD=1.8) completed a self-reported version of the child functioning module in a supervised classroom. The second administration took place two weeks later. Intraclass correlation coefficient (ICC) and Kappa (k) statistics were used to test reliability of the items, and interpretation through Landis &amp; Koch, and Cohen, respectively. The majority of items had substantial or moderate agreement, although there was only fair agreement for self-care (ICC=0.59), concentration (ICC=0.50), and routine (ICC=0.54). Kappa statistics of behaviour were interpreted to be large (k=0.65), and seeing (k=0.49), walking (k=0.49), and speaking (k=0.49) difficulties were moderate. The majority of the items in the self-reported version of the child functioning module can be used in a scale format, although some caution may be required on items of self-care and concentration when used as a dichotomous variable.

Background: A prospective study was conducted to follow-up how the living lifestyle of hikikomori could change the social, mental and physical health profile. Methods: A cohort consisted of 104 young people living as Hikikomori were interviewed at baseline, 6 months and 12 months by using the same set of questionnaires and anthropometric measurements. Results: Besides the high attrition of 30% in wave 2 and 25% in wave 3, almost half of the participants have recovered from hikikomori by returning to the workforce in society. The three domains of health profile of hikikomori were significantly improved over the follow-up period by: 1) increasing the social network scores from 2.791.80 to 3.091.87, 2) decreasing the perceived stress scores from 21.185.87 to 20.115.79, and 3) reducing blood pressure levels from 118/75 to 115/71 and waist-to-hip ratios. Those variables were also predictable by lifestyle living with the hikikomori according to the Generalized Estimating Equation analysis, whereas the participants of current study had increased the practice of moderate-intensity exercises. Conclusion: Social work intervention was effective in helping the recovery of hikikomori while physical assessments followed by encouragement from social workers to do more exercises might enhance their awareness in health modification towards a better health.

Several sun models suggest the radioactive balance where the concentration of greenhouse gases and the albedo effect are related to the Earth's surface temperature. There is a considerable increment of greenhouse gases due to anthropogenic activities. Climate change correlates with this alteration in the atmosphere and an increase in surface temperature. Efficient forecasting of climate change and its impacts of 1.5°C global warming above pre-industrial levels could be helpful to respond to the threat of c.c. and develop sustainably. Many studies have predicted the temperature change in the coming years. The global community has to create a model that can realize good predictions to ensure the best way to deal with the warming. Thus, we propose a finite-time thermodynamic (FTT) approach in the present work. The FTT can solve problems such as the faint young sun paradox. In addition, we use different machine learning models to evaluate our method and compare the experimental prediction and results.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

The kinetics of biogas production from biomass depends on several factors such as: carbon to nitrogen ratio (C/N), reactor temperature (T), and retention time (RT). The purpose of this study was to obtain a new model for predicting biogas production. Spent Mushroom Compost (SMC) was used to produce biogas in a batch type reactor. The experiments were carried out with different C/N ratios (12.1, 20, 30 and 40) and in both mesophilic (35°C) and thermophilic (55°C) temperatures. The results showed that the Maximum biogas production at 35°C, C/N=20 was equal to 41.9 mL/gVS and 55°C, C/N=30 was equal to 51.6 mL/gVS. By using experimental data, a new kinetic model was proposed to predict biogas production. Comparing the values of the results indicate that the total values of RMSE for Logistics, Gampartz and new kinetic models was 0.1906, 0.1830 and 0.1617, respectively. Therefore, the process of anaerobic digestion of biomass can be assumed to be just a chemical reaction, and the new kinetic model is an appropriate alternative to microbial growth models.

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

Ensuring a protection scheme in DC distribution is more difficult to achieve against pole-to-ground fault than in AC distribution system because of the absence of zero crossing points and low line impedance. To complement the major obstacle of limiting the fault current, several compositions have been proposed related to mechanical switching and solid-state switching. Among them, solid-state circuit breakers(SSCBs) are considered a possible solution to limit fast fault current. However, they may cause problems in circuit complexity, reliability and cost-related troubles due to the use of multiple power semiconductor devices and additional circuit configuration to commutate current. This paper proposes the SSCB with a coupled inductor(SSCB-CI) which has symmetrical configuration. The circuit is comprised of passive components like commutation capacitors, a CI and damping resistors. Thus, proposed SSCB-CI offers the advantages of simple circuit configuration and fewer utilized power semiconductor devices than another typical SSCBs in LVDC microgrid. For analysis, six operation states are described for the voltage across main switches and fault current. The effectiveness of the SSCB-CI against a short-circuit fault is proved via simulation and experimental results in a lab-scale prototype.

Cold brew coffee has emerged as a new trend during the last decade. However, “cold brew” is an extraction style of ground roasted coffee with water at less than body temperature (typically 8°C or room temperature) rather than a beverage per se. Cold brew extraction poses several challenges including the need of specific optimization depending on the multivariate influences of coffee variety and processing, roast degree, grinding, dosage, water composition, turbulence, brew system (drip, immersion etc.), time and temperature. While cold brew is typically characterized by a floral sweetness, over-extraction may lead to abundant acidity and bitterness. To avoid this, an extraction degree of 70% was suggested using rather shorter times (i.e. 2 h at 15°C with 80 g/L coffee with optimized medium roast profiles). Due to the lack of sterilizing temperatures during preparation, cold brew is special in the coffee sector because hygiene and food safety aspects pose specific challenges for food industry. To avoid microbiological contamination and spoilage, cold brew should be as freshly prepared as possible and shelf-life should be minimized.

The feasibility of recovering bioactive antioxidants from Phyllantus emblica leaves using ethanol -water mixture (0 – 100%) and heat-assisted extraction technology (HAE-T) was investigated in this present study. To this effect, the process parameter of operating temperature (OT: 30 -50 oC), solid -to-liquid ratio (S:L: 1:20 - 1:60 g/mL) and extraction time (ET: 45 - 180 min) were investigated on the extract total phenolic content (TPC, mg GAE/g d.w), yield (EY, %) and antioxidant activity (μM AAE/g d.w) by employing the Box-Behnken experimental design (BBD) available in response surface methodology (RSM). Multi-objective process optimization using the desirability function algorithm to determine set of process variables that simultaneously optimize process responses of TPC, EY and AA was also investigated. The recovery process was thereafter modeled with BBD-RSM and multi gene genetic programming (MGGP) algorithm and model reliability assessment via Monte Carlo simulation were conducted for the best performing set of models for predicting TPC, EY and AA values. The HPLC characterization of the recovered extract was carried out to ascertain the phenolic compound constituents of the Phyllantus emblica leaf extract. The 50% ethanol solution was found to be the best for the optimal extraction of antioxidants from Phyllantus emblica leaves. The HAE-T was suitable for recovering bioactive extract from Phyllantus emblica leaves and investigated process parameters have impacts on the recovery process. The process parameters that simultaneously gave the optimum EY of 21.6565%, TPC of 67.116 mg GAE/g and AA of 3.68583 µM AAE/g were OT of 41.61 oC, S:L of 1:60 g/mL and ET of 180 min. The HPLC extract profiling revealed that the bioactive constituents present in the recovered extract were betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid. Both the developed BBD-RSM and evolved MGGP-based models predicted the observed process response of TPC, EY and AA satisfactorily with BBD-RSM models marginally better in their predictions. The reliability results showed that the BBD-RSM models predicted TPC, EY and AA values with high certainty of 99.985%, 97.569% and 98.661%, respectively.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

In recent years, land supply in Hong Kong has fallen sharply and fails to keep pace with the growth of population and number of households. Land shortage becomes urgent and dire. With the inadequate provision of residential land, housing supply is also adversely affected.Insufficient housing supply leads to surging property and rental price in the private market. According to the Rating and Valuation Department (RVD), the overall private domestic price index peaked in June 2017 at 336.1. Keen housing demand is also observed in the private rental market. RVD statistics showed that the domestic rental index also reached the peak at 183.6 in June 2017 and the vacancy rate of about 3.8% as at the end-2016, which is much lower than the average vacancy rate of 5% from 1996 to 2015 (Development Bureau, 2017).In addition, there is soaring housing demand in public housing. As at the end-December 2021, the average waiting time for general applicants was 5.7 years, which is nearly double of the Housing Authority’s target for providing the first flat offer to general applicants at around three years on average (Housing Authority, 2021). Identifying and securing sufficient land to meet both current and anticipated pressing housing demand becomes a key challenge of the government. In the light of this, the government has established a dedicated task force (Task Force on Land Supply) in September 2017 and appointed 30 professionals from different disciplines, such as planning, engineering, academia and think tank, to take a Marco-review on the land supply sources, look for feasible options as well as consider the pros and cons of various options. 18 land supply options were worked out for the public to make choices in the public engagement exercise. Among the 18 choices, the development area on the Periphery of Country Parks was regarded as one of the land supplies sources. This option does not only include the Hong Kong Housing Society’s studies on developing Two Pilot Areas on the Periphery of Tai Lam and Man On Shan Country Parks, but also include developing more areas on the periphery of Country Parks. The proposed option received overwhelming attention from the public and aroused debates in various perspectives. Its ecological value, recreational value, financial viability and even legitimacy were discussed. However, spatial analysis on the suitability of having residential development in Country Parks has not been conducted. Therefore, in this study, multi criteria GIS analysis is conducted to evaluate the geographic suitability of Hong Kong Country Parks for residential purpose with the consideration of various criteria.

As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100^th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

Immune escape is observed with SARS-CoV-2 Omicron (Pango lineage B.1.1.529), the predominant circulating strain worldwide. Booster dose was shown to restore immunity against Omicron infection, however, real world data comparing mRNA (BNT162b2; Comirnaty) and inactivated vaccine (CoronaVac; Sinovac) homologous and heterologous boosting is lacking. A retrospective study was performed to compare the rate and outcome of COVID-19 in healthcare workers (HCWs) with various vaccination regime during a territory-wide Omicron outbreak in Hong Kong. During the study period 1 Feb – 31 Mar 2022, 3167 HCWs were recruited, 871 HCWs reported 746 and 183 episodes of significant household and non-household close contact. 737 HCWs acquired COVID-19 which were all clinically mild. Time dependent Cox regression showed that, comparing with 2-dose vaccination, 3-dose vaccination reduced infection risk by 31.7% and 89.3% in household contact and non-household close contact respectively. Using 2-dose BNT162b2 as reference, 2-dose CoronaVac recipient had significantly higher risk of being infected (HR 1.69 P<0.0001). Three-dose BNT162b2 (HR 0.4778 P<0.0001) and 2-dose CoronaVac + BNT162b2 booster (HR 0.4862 P=0.0157) were associated with lower risk of infection. Three-dose CoronaVac and 2-dose BNT162b2 + CoronaVac booster were not significantly different from 2-dose BNT162b2. The mean time to achieve negative RT-PCR or E gene cycle threshold 31 or above was not affected by age, number of vaccine dose taken, vaccine type and timing of the last dose. In summary, we have demonstrated lower risk of breakthrough SARS-CoV-2 infection in HCWs given BNT162b2 as booster after 2 doses of BNT162b2 or CoronaVac.

The anti-inflammatory effect of ginsenoside Rh2 (GRh2) is one of the most important ginsenosides. The purpose of this study is to identify the anti-inflammatory and antioxidant effects of GRh2 after LPS challenge lung injury animal model. GRh2 reduced LPS-induced NO, TNF-α, IL-1, IL-4, IL-6 and IL-10 productions in lung tissues. GRh2 treatment decreased the histological alterations in the lung tissues and BALF protein content and total cells number also diminished in LPS-induced lung injury mice. Moreover, GRh2 blocked iNOS, COX-2, the phosphorylation of IκB-α, ERK, JNK, p38, Raf-1 and MEK protein expression which is corresponded to the growth of HO-1, Nrf-2, catalase, SOD and GPx expressions in LPS-induce lung injury. An experimental study has suggested that GRh2 has provided with anti-inflammatory effects in vivo, and its potential therapeutic efficacy in major anterior segment lung diseases.

Importance: Because of the high prevalence of myopia in Taiwan, understanding the risk factors for its development and progression is important to public health. Background: This study investigated the risk factors for myopia and their influence on the progression of myopia in schoolchildren in Taiwan. Design: Patients’ clinical records were obtained retrospectively from ophthalmologists. Questionnaires were given to collect demographic information, family background, hours spent on daily activities, myopia progression, and treatment methods. Participants: A total of 522 schoolchildren with myopia from a regional medical hospital in northern Taiwan participated the study. Written informed consent was obtained from the participants of legal age or the parents or legal guardians. Methods: Multivariable regression analyses were performed. Myopia measured in dioptres was analysed, controlling for patients’ family and demographic information as well as their daily behaviours. Main Outcome Results: Children with high myopic parents were more myopic. Earlier onset age of myopia was associated with a higher level of myopia and greater annual myopic progression. Children reporting more near work activities had higher levels of myopia and greater progression of myopia. Lower levels of myopia were associated with more exercise, longer periods of sleep, and better vision care knowledge in children and parents. Intake of food supplements had no effect on myopia. Conclusions and Relevance: In addition to genetics, education, environment, and near work activity can influence the development of myopia. Health policies for schoolchildren should promote protective activities and vision care knowledge in order to protect the eyesight of schoolchildren.

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.

Streptococcal infection is a main infectious diseases for farmed grey mullet (Mugil cephalus). This study were to identify spreptococcal species in diseased farmed grey mullet and to investigate differences in susceptibility to 13 antibiotics and in genotypes between the stains from the grey mullet and non-grey mullet. 170 samples from diseased farmed grey mullet were collected from three county in 2013 -2016. Multiplex PCR identified L. garviea (146) as the main pathogen, S. agalactia (9), S. dysgalactiae (19), and double infection (5), but no S. iniae. The prevalence changed annually and differed among three counties. Pulsed-field gel electrophoresis (PFGE) analysis demonstrated identical genotype with an ApaI-digested DNA pattern. Disc diffusion results demonstrated differences in antibiotic susceptibility between the strains from grey mullet (146) and non-grey mullet (30). Almost all strains resisted to clindamycin and all strains were susceptible to six antibiotic in grey mullet and 4 antibiotics in non-grey mullet. The reduced susceptible strains was more in non-grey mullet than grey mullet group. The reduced susceptible strains were observed the highest in 2014 and in Chiayi county and decreased from 2014 to 2016. However, the strains with reduced susceptibility to ceftriaxone, cirpofoxacin, moxifloxacin, tetracycline for human treatment were observed.

In this study, an optimization of the first blade in new test rig presented. Blade tuning is conducted using 3D geometrical parameters. Sweep and dihedral play an essential role in this study. Compressor characteristics and blades vibrational behavior are the main objective of the evaluation. Here, the attachments are designed to isolate blade dynamics from Disk. So, the Vibrational behaviors of the one's blade are tuned based on the self-excited and forced vibration phenomenon. Using a semi analytical MATLAB code instability conditions are satisfied. The code takes advantages of whitehead and force response theory to predict classically and stall flutter speeds. Beside, Forced vibrations instability is controlled using a theory presented by Campbell. Aerodynamics of new blade geometry determined using multistage simulations Computational fluid dynamics (CFD) software. Numerical results show increasing performance near the surge line and working interval along with increasing mass flow.

This paper shares our work in developing and implementing an immersive gamification training platform for students who undergo manufacturing shopfloor training at the School of Engineering, Nanyang Polytechnic, Singapore. In this gamification training platform, we developed a virtual manufacturing shopfloor that is identical to the actual shopfloor located in the school. Students have the freedom to learn the manufacturing shopfloor operations and safety acts through the various game scenarios and training tasks which include workshop safety, CNC machine introduction, CNC machining dynamics, MES, etc. In addition, the assessment feature with immediate feedback were embedded within the gamification platform, which aim to help students to assess their level of understanding and help teachers to monitor the learning progress of their students. To investigate the impact of this gamification training platform on students’ learning outcome and motivation in manufacturing shopfloor technologies and safety acts, a pilot study was conducted in AY2018 semester 2 for a total 134 students from 4 classes of digital & precision engineering diploma. It is found that gamification can be integrated effectively into manufacturing education to motivate students and enhance their learning effectiveness. Based on the collected data from the technical quizzes and satisfactory survey, the results showed that the integration of gamification into the classroom learning not only added a stimulating and captivating game-like layer to the learning experience of the students, but also provided a safe environment for students to learn without fear of making errors. Challenges faced in implementing this gamification training platform will also be discussed in this paper.

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil - water systems is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on two-phase oil - water relative permeability is still a major challenge for precise studying and evaluation of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable, accurate, simple and quick to use paradigms to predict the temperature dependency of relative permeability in oil - water systems (K_rw and K_ro). To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, including oil and water viscosities, absolute permeability and water saturation, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP based correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for K_rw and K_ro, respectively. Lastly, the comparison of the performances of our correlations against those of the preexisting ones indicated the large superiority of the introduced correlations compared to previously published methods. The findings of this study can help for better understanding and studying the temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes.

The once-held wisdom of the supreme efficiency of one-way streets has been gradually sup-planted by the perceived sustainability of two-way streets in the design of livable cities that prioritizes the safety of pedestrians and thriving of local businesses. However, it is rarely dis-cussed on whether one-way street conversions have truly improved the long-term traffic effi-ciencies on urban street networks, as conflating socioeconomic factors such as vehicular popula-tion growth and induced travel demand may render empirical analysis inconclusive. In this study, microscopic traffic simulations implemented on SUMO platform was performed to ana-lyze the effect of street conversion in Downtown Brickfields, Kuala Lumpur. This approach can control and standardize travel demand in both one-way and two-way street networks, and would therefore give a fairer evaluation by precluding all socioeconomic factors. It was found that one-way streets do not necessarily improve the traffic efficiency of the network, as it is very dependent on the traffic scenario evolution over time. One-way streets perform better at the on-set of traffic congestion due to its higher capacity, but on average, the 4-fold longer travel times that made it harder to clear traffic by getting vehicles to their destinations compared to two-way streets. As time progresses, congestion in one-way streets may become twice as worse compared to two-way streets. This study may contribute to a more holistic assessment of traffic circulation plan designed for smart and livable cities

The key goal of the current study was to determine suitable areas of water pumping for drinking and agricultural harvest in Tabriz aquifer, located in East Azerbaijan province, northwest Iran. In the study area, groundwater is the key foundation of water for drinking and farming requirements. Groundwater compatibility study was conducted by analyzing Electrical conductivity (EC), Total dissolved solids (TDS), Chloride (Cl), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Sulfate (SO4), Total hardness (TH), Bicarbonate (HCO3), pH, carbonate (CO3) and Sodium Adsorption Ratio (SAR) obtained from 39 wells in the period of 2003 to 2014. For this purpose, the Water Quality Index (WQI) and irrigation water quality (IWQ) index were utilized. The WQI index zoning exposed that the groundwater of the study area for drinking purposes is categorized as excellent, good and poor water. Most drinking water harvested for urban and rural areas are in the class of 'excellent water'. The results revealed that about 37 percent (296 km2) of groundwater has high compatibility, and 63 percent of the study area (495 km2) has average compatibility for agricultural purposes. The trend of IWQ and WQI indexes demonstrates that the groundwater is getting worse over the time.

Hyperuricemia (HC) was one of the important risk factors for gout, arteriosclerosis, and cardiovascular disease. Animal studies have shown that Lactobacillus plantarum can improve the microbiota, immune regulation, and inhibit the uric acid production. However, it was not clear whether L. plantarum can improve HC and intestinal microbiota. We used potassium oxonate (PO) to induce HC in male SD rats, and then treated L. plantarum TCI227 in a dose dependent (HC+LD, HC+MD, HC+HD) for 4 weeks, and examined weight organs, biochemical examination of blood and urine, and analyzed the intestinal microbiota in feces by 16s rDNA sequence analysis. In this study, TCI227 improved body weight, decreased creatinine, serum uric acid, and increased urine uric acid compared to the HC group. Furthermore, TCI227 increased short-chain fatty acids (SCFAs). In fecal microbiota (family), TCI227 increased the level of Lactobacillaceae, and then decreased the level of Deferribacteres and Prevotellaceae compared to the HC group. Finally, in fecal microbiota (genus), TCI227 decreased the level of Prevotella, and then increased the level of Lactobacillus and Ruminococcus compared to the HC group. This study suggested that TCI227 can improve HC and change the compositions of the intestinal microbiota in PO induced male HC SD rats.

The increased consumption of high fat-containing foods has been linked to the prevalence of obesity and abnormal metabolic syndromes. Rhizopus oligosporus, a fungus in the family Mucoraceae, is widely used as a starter for homemade tempeh. Although R. oligosporus can prevent the growth of other microorganisms, it grows well with lactic acid bacteria (LAB). Lactobacillus plantarum can produce β-glucosidase, which catalyzes the hydrolysis of glucoside isoflavones into aglycones (with greater bioavailability). Therefore, the development of a soybean-based functional food by the co-inoculation of R. oligosporus and L. plantarum is a promising approach to increase the bioactivity of tempeh. In this study, the ameliorative effect of L. plantarum in soy tempeh on abnormal carbohydrate metabolism in high-fat diet (HFD)-induced hyperglycemic rats was evaluated. The co-incubation of L. plantarum with R. oligosporus during soy tempeh fermentation reduced the homeostatic model assessment of insulin resistance, HbA1c, serum glucose, total cholesterol, triglyceride, free fatty acid, insulin, and low-density lipoprotein contents and significantly increased the high-density lipoprotein content in HFD rats. It also increased the LAB counts as well as the bile acid, cholesterol, triglyceride, and short-chain fatty acid contents in the feces of HFD rats. Our results suggested that the modulation of serum glucose and lipid levels by LAB occurs via alterations in the internal microbiota, leading to the inhibition of cholesterol synthesis and promotion of lipolysis. Tempeh, produced with both L. plantarum and R. oligosporus, may be a beneficial dietary supplement for individuals with abnormal carbohydrate metabolism.

This study aims to develop an elderly care system for improving the interpersonal relationship of the elderly with mild cognitive impairment (MCI) by employing the speaker recognition technique and association functionality of social network platforms. Firstly, the speaker recognition units based on the Gaussian Mixture Model (GMM) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) are implemented to identify the visitor via individual input utterance. After the visitor is identified, the proposed system will be linked to the private database and social network platforms to extract the associated message of two parties. Experimental results indicate that the speaker recognition unit based on GMM-UBM achieves the best performance. Finally, five elderly persons are invited to measure the usability of the proposed system. A questionnaire is used to survey the five elderly persons, and the result indicates that the proposed system is highly potentially applicable in improving the interpersonal relationship of the elderly with MCI.

This study aimed to use biomineralization technology to improve the bond strength of fiber-reinforced lightweight aggregate concrete (LWAC) after exposure to temperatures of 300 °C and 500 °C. The specimens of a control group (Group A) and two experimental groups (Group B and Group C) were prepared. These specimens healed themselves in different ways after exposure to high temperatures and pull-out tests. The self-healing method of Group A involved placing the specimen into an incubator. The self-healing method of Group B was the same as that of Group A. The self-healing method of Group C involved immersing the specimen in a mixed solution of urea and calcium acetate for two days, then taking it out and placing it in an incubator for two days. A cycle of four days was undertaken until the desired age was reached. After the specimens were healed to the planned age, the first and secondary pull-out tests and repair compound identification were carried out. After exposure to a temperature of 300 °C and self-healing for 90 days, compared with the specimens in Group A, the relative bond strength ratios of the secondary pull-out tests of the specimens in Group C increased by 20.3%. This indicates that biomineralization can effectively improve the bond strength of LWAC after high-temperature damage.

Due to the recent trend of unmanned economy, retail stores have gradually reduced their service and cashier manpower. The retail product recognition becomes one of key issues for unmanned shopping. Although the success of deep neural networks has made object recognition feasible in a variety of applications, it still struggles to perform well on a large number of classes of retail products. In this paper, we propose an improved ensemble learning-based approach for retail product recognition. In the proposed approach, the object classification network is first improved through feature extraction and block attention. An ensemble model is then built by integrating multiple network models and using loss selection as model weights. In the experiments, the feasibility of our ensemble learning-based approach has been evaluated through many production items. The results demonstrate the effectiveness of the proposed approach compared with previous retail product recognition methods.

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.

Background and Aims: Of all primary liver tumors, primary hepatic angiosarcoma (PHA) is a rare and aggressive malignant vascular tumor. The standard therapeutic care for hepatic angiosarcoma remains unclear. This study compared the survival outcomes of aggressive treatment (resection and liver transplant) and nonaggressive treatment (chemotherapy, transarterial chemoembolization [TACE], and conservative treatments) for patients with PHA and analyzed the prognostic factors influencing survival. Materials and Methods: Data of patients diagnosed as having PHA at our facility were retrospectively reviewed. The primary outcome was survival time. The secondary outcome was calculated baseline characteristics. Results: We included a total of 19 patients, who were divided into 2 treatment groups: aggressive (8 patients had undergone resection or transplants) and nonaggressive (11 patients had undergone TACE, chemotherapy, or conservative treatment). The mean survival time was 233.1 ± 189.7 days in the aggressive treatment group and 146.5 ± 115.8 days in the nonaggressive treatment group. A Kaplan-Meier plot revealed no significant difference in survival time between the 2 treatment groups (P = .3256). Conclusions: The survival time of patients receiving aggressive treatment was longer than that of those receiving nonaggressive treatment. The long term survival time in some selective cases of aggressive treatment will be achieved. Thought a difference was not significant between the groups. Because the number of patients was limited, more cases are required to confirm these findings.

When a microgrid is grid-tied to a distribution system, it can provide surplus power generation to the distribution system, if any abnormality or interruption occurs in the distribution system, the microgrid can operate in standalone mode to isolate the impact of the abnormality or interruption. However, if the microgrid can not collect enough information from the distribution system, it may cause the failure of generation transferring of distribution feeders, or even further influence the stability of the distribution system. In this paper, a strategy for the resilient control of a microgrid is proposed. It can solve the above-mentioned problem, reduce the duration of the outage of loads. This strategy is experimented in the microgrid in the Institute of Nuclear Energy Research (INER), the reliability is also analyzed to evaluate the unavailability of the microgrid in INER, and it is verified that the proposed strategy can reduce the duration of the outage of loads, and hence the reliability of a microgrid can be upgraded.

To understand the health impacts of “hikikomori” lifestyle and to establish its first comprehensive health profile, a cross-sectional study was designed to measure how well the cases of hikikomori youths of Hong Kong were living, in terms of social, mental and physical aspects. This study involved 104 eligible participants at age 19.02 year-old who had completed the set of questionnaires and a series of anthropometric and physical health measurements. Despite SF36 score of 84.0 indicated good physical functioning in general, participants were lived sedentarily with high incidence of hypertension at 15.4% and prehypertension at 31.7%. Occurrence of hypertension in cases living as hikikomori >6 months was 3-times higher than those newly onset cases. The blood pressure levels were correlated with age and all obesity index parameters measured including waist circumference and body mass index. Half of the hypertensive cases involved the elevation of systolic blood pressure, which suggested higher odds of cardiovascular complications. Participants were mentally stable living with moderate levels of perceived stress and state anxiety, but borderline clinical depression. In conclusion, the hikikomori lifestyle could be a risk behavior that may harm the younger generation physically by promoting obesity and hypertension and probably other chronic illnesses.

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.

Among the different applicable irrigants for root canal disinfection, sodium hypochlorite 5.25% is one of the most attractive ones. The quality of root canal disinfection is dependent on some factors such as the employed approach, type of flow rate of irrigant and the size of needle. The majority of studies in the field of root canal disinfection are experimentally carried out. In the current article, Computation Fluid Dynamic (CFD) is used for modeling the antimicrobial liquid flow in the root canal and evaluate the effects of needle size and flow rate. Two needles, G28 and G30, are used for irrigation in three volumetric rates of flow including 0.10 mL⁄s , 0.20 mL⁄s and 0.30 mL⁄s. The results of numerical simulations revealed the improved quality of root canal disinfection by augmentation in the rate of flow and decrease in the inner diameter of the needle. According to the outcomes of the modeling, the highest average wall shear stress obtained in the case of using G28 needle and 30 mL⁄s flow rate, which was approximately 10.21 Pa.

A case-crossover study examined how PM_2.5 from Asian Dust Storms (ADS) affects the number of emergency room (ER) admissions for cardiovascular diseases (CVDs) and respiratory diseases (RDs). Our data indicated that PM_2.5 concentration from ADS was highly correlated with ER visits for CVDs and RDs. The odds ratios (OR) increased by 2.92 (95% CI: 1.22-5.08) and 1.86 (95% CI: 1.30-2.91) per increase 10 µg/m³ in PM_2.5 levels, for CVDs and RDs, respectively. A 10 µg/m³ increase in PM_2.5 from ADSs was significantly associated with increase in ER visits for CVDs among those 65 years of age and older (an increase of 2.77 in OR) and for females (an increase of 3.09 in OR). In contrast, PM_2.5 levels had a significant impact on RD ER visits among those under 65 years of age (OR=1.77). The risk of ER visits for CVDs increased on the day when the ADS occurred in Taiwan and the day after (lag 0 and lag 1); the corresponding risk increase for RDs only increased on the fifth day after the ADS (lag 5). In Taiwan’s late winter and spring, the severity of ER visits for CVDs and RDs increases. Environmental protection agencies should employ an early warning system for ADS to reduce high-risk groups’ exposure to PM_2.5.

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare but severe adverse event that was first observed during the global vaccination campaign against SARS-CoV-2 infection, specifically in those receiving adenoviral vector-based vaccines for Coronavirus disease 2019 (COVID-19). VITT develops 4 to 42 days post-vaccination and is characterized by the development of platelet-activating anti-platelet factor 4 (PF4) antibodies, leading to thrombocytopenia and thrombosis at unusual sites. The rise in awareness and subsequent prompt recognition of VITT was paramount in reducing mortality. Moreover, as vaccination campaigns around the world continue, a better understanding of VITT not only has important clinical implications but is also crucial for future vaccine development. In this review, we summarize the clinical features, pathophysiology, and incidence rates of VITT as well as highlight other anti-PF4 antibody-mediated disorders of growing clinical significance.

Upper mesophilic temperature acclimation of halophilic, hydrogen-producing bacteria from salt fields was investigated in this study, along with the changes in microbial abundance during an-aerobic digestion (AD) process. Genomic approaches such as PCR-denaturing gradient gel elec-trophoresis (DGGE) and next-generation sequencing (NGS) were performed to profile the mi-crobial communities. During AD, there was a significant abundance of Halanaerobacter lacunarum at 48°C followed by the increase in hydrogen yield, signifying potential contribution from the halophile in the hydrogen production. A decrease in the dominance of H. lacunarum and Halan-aerobium fermentans at 42°C, likely due to an increase in other bacterial species, was noted but their dominance significantly increased at temperatures of 45°C and 48°C. This investigation provides valuable insights in highlighting the potential of Halanaerobium sp. and the other halo-philic bacteria to adapt under upper mesophilic temperature conditions and synthesizing hy-drogen. The findings in the present study also underscore the importance of optimizing temper-ature and pH conditions to maximize hydrogen yield during high-salt anaerobic digestion.

Objective: We tested a model of individual health literacy information sharing with family members, personal preventive behaviours and family well-being during the Coronavirus Disease 2019 (COVID-19) pandemic in Hong Kong. Methods: We analysed data of 1501 randomly selected Chinese adults from a cross-sectional survey in Hong Kong from 9 to 23 April, 2020. Individual health literacy about COVID-19 with the items extracted from the questionnaire in World Health Organization Risk Communication and Community Engagement (RCCE) Action Plan Guidance for COVID-19 preparedness and response, COVID-19 information sharing with family members, preventive behaviours against COVID-19 and family well-being were measured. Structural equation modelling analysis tested the proposed model. Findings: COVID-19 information sharing with family members partially mediated the association between individual health literacy and personal preventive behaviours. The direct effect of 0.24 was shown, and the indirect effect through COVID-19 information sharing with family members was small at 0.03 (Z = 3.66, p < 0.001). Family well-being was associated with personal preventive behaviours against COVID-19. The model was adjusted for sex, age, and socioeconomic status factors and had good fit with RMSEA = 0.04, CFI = 0.98, TLI = 0.96, and SRMR = 0.02. Conclusion: COVID-19 information sharing with family members was a partial mediator between individual health literacy and personal preventive behaviours against COVID-19. Strategies for enhancing health literacy and preventive measures against COVID-19 are needed to promote family well-being in the pandemic.

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

This purpose of the study presented in this article is to comparing different service quality measurements between Kano and TRIZ that plays the critical roles in the catering industrial. Data collected from a DINESERV questionnaire comprises service-quality standards to increase customer satisfaction of mobile dining car. Finally, the TRIZ is standardized measure designed to improve the idealization of strategy for selecting the most appropriate service quality model. In addition, the preferences of more than one decision maker are internally aggregated into the TOPSIS procedure. All these things provide several important theoretical and practical implications for developing a successful mobile catering app.

The diffusion of molecules in aqueous solutions in the domain of membrane technology is very critical in the efficiency of chemical engineering and purification processes. In this study, the diffusion in high and low concentration regions is simulated with finite difference method (FDM), and then the results of numerical computations are coupled with adaptive network-based fuzzy inference system (ANFIS) and bilayered neural network method (BNNM). Machine learning approach can individually predict diffusion phenomena across the domain based on understanding of the machine instead of the discretization of an ordinary differential equation (ODE). The findings of the machine learning method are in good agreement with those of FDM at different times of the simulation. In addition to numerical computation, the error of the system is computed for different iterations. The results show that by increasing the number of iterations and training datasets, all errors reduce significantly for both training and testing. BNN method is also used to train the prediction process of diffusion for a more accurate comparison. This technique is similar to ANFIS method in terms of prediction capability. According to the findings, ANFIS approach predicts diffusion slightly better than BNN method. In this regard, ANFIS technique produces R>0.99 while BNN method produces R around 0.98. Both machine learning methods are accurate enough to predict diffusion throughout the domain for different time steps. The computational time for both algorithms is less than that of FDM method to predict low and high concentrations in the domain. Besides, based on the results, artificial intelligence (AI) can find the relationship between inputs and outputs and determine which input has the main influence on the output in this study to optimize the process. As such, future studies can be focused on AI and other methods for faster prediction and optimization processes.

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

There is limited evidence for the association between malnutrition and hospital mortality as well as Intensive Care Unit length-of-stay (ICU-LOS) in critically ill patients. We aimed to examine the aforementioned associations by conducting a prospective cohort study in an ICU of a Singapore tertiary hospital. Between August 2015 and October 2016, all adult patients with ≥24 h of ICU-LOS were included. The 7-point Subjective Global Assessment (7-point SGA) was used to determine patients’ nutritional status within 48 hours of ICU admission. Multivariate analyses were conducted in two ways: 1) presence versus absence of malnutrition, and 2) dose-dependent association for each 1-point decrease in the 7-point SGA. There were 439 patients of which 28.0% were malnourished, and 29.6% died before hospital discharge. Malnutrition was associated with an increased risk of hospital mortality [adjusted-RR 1.39 (95%CI: 1.10–1.76)], and this risk increased with a greater degree of malnutrition [adjusted-RR 1.09 (95%CI: 1.01–1.18) for each 1-point decrease in the 7-point SGA]. No significant association was found between malnutrition and ICU-LOS. Conclusion: There was a clear association between malnutrition and higher hospital mortality in critically ill patients. The association between malnutrition and ICU-LOS could not be replicated and hence requires further evaluation.

The bimodal grain-size distribution 0.9KNbO₃-0.1BaTiO₃ ceramics, with a typical perovskite structure in tetragonal phase at room temperature, were successfully prepared by an induced abnormal grain growth (IAGG) method at a relatively low sintering temperature. In this bimodal grain-sized distribution structure, the extra-large grains (about 10‒50 μm) were evolved from the micron-sized filler powders and the fine grains (about 0.05‒0.35 μm) were derived from the sol precursor matrix. The 0.9KNbO₃-0.1BaTiO₃ ceramics exhibit relaxor-like behavior with the diffused phase transition near room temperature, and confirmed by the existence of the polar nanodomain regions (PNRs) using the HRTEM images. The large room-temperature electrocaloric (EC) effect was found, characterized by an adiabatic temperature drop of 1.5 K, an isothermal entropy change of 2.48 J kg^-1 K^-1, and high EC strengths of ïDT/DEï = 1.50×10^-6 KmV^-1 and DS/DE = 2.48×10^-6 Jmkg^-1K^-1V^-1directly measured under E = 1.0 MV/m. These excellent ECEs demonstrate that this simple IAGG method is highly appreciated for synthesizing high-performance EC materials for efficient cooling devices.

In the last three decades the presence of phospholipids in the nucleus has been shown and thoroughly investigated. A great interest raised about nuclear inositol lipids mainly because of their role in signalling acting. Here we review the main issues of nuclear phospholipid localization and the role of nuclear inositol lipids and their related enzymes in cellular signaling both in physiological and pathological conditions.

1) Background: A population-based cross-sectional study was conducted to understand how green cover and accessibility of common public open spaces in compact urban areas affect physical activeness of resident. 2) Methods: A total of 554 residents completed a structured questionnaire on quality-of-life, physical activity level, and healthy eating practice. 3) Results: The sampled population lived with green cover averaged 10.11 ± 7.95% (ranged 1.56–9.88), whereas majority (90%) of the residents performed physical activities at medium and high levels. Metabolic Equivalent of Task (MET)-minutes/week was associated with the green cover percentage (Pearson r = 0.092; p < 0.05). Irrespective of age and physical activity level, active residents commonly used public open spaces within district for performing exercise, especially parks and promenade were mostly used by older residents while sports facilities by the younger groups. 4) Conclusions: Current findings suggested promotion of exercise could be achieved by the design or redesign of built environment to include more parks accessible to the residents with the increase of vegetation.

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the SST turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.

This work focuses on addressing the dynamic positioning and trajectory tracking problem for a 4 degree-of-freedom (DOF) unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. To tackle this problem, a disturbance observer-based control (DOBC) scheme is proposed. The scheme is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the EAOB has the unique ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC (DOBMPC) is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms a robust adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and Robot Operating System (ROS) environment. The results show that it can effectively reject unpredictable disturbances and improve the UUV’s control performance.

The emergence of SARS-CoV-2 mutations pose significant challenges to diagnostic tests as these mutations can reduce the sensitivity of commonly used RT-PCR assays. Therefore, there is a need to design diagnostic assays with multiple targets to enhance sensitivity. In this study, we identified a novel diagnostic target, nsp10 gene, using nanopore sequencing. Firstly, we determined the analytical sensitivity and specificity of our COVID-19-nsp10 assay. The COVID-19-nsp10 assay had the limit of detection of 74 copies/mL (95% confidence interval: 48-299 copies/mL) and did not show cross-reactivity with other respiratory viruses. Next, we determined the diagnostic performance of the COVID-19-nsp10 assay using 261 respiratory specimens, including 147 SARS-CoV-2-positive specimens belonging to ancestral strain and Alpha, Beta, Gamma, Delta, Mu, Eta, Kappa, Theta and Omicron lineages. Using the LightMix E-gene RT-PCR assay as the reference method, the diagnostic sensitivity and specificity of the COVID-19-nsp10 assay were 100%. The median Cp values for the LightMix E-gene RT-PCR and our COVID-19-nsp10 RT-PCR were 22.48 (range: 12.95-36.60) and 25.94 (range 16.37-36.87), respectively. The Cp values of the COVID-19-nsp10 RT-PCR assay correlated well with those of the LightMix E-gene RT-PCR assay (Spearman’s ρ = 0.968; P&lt;0.0001). In conclusion, nsp10 is a suitable target for SARS-CoV-2 RT-PCR assay.

Photoacoustic technology is a promising tool to provide morphological and functional information in biomedical research. To enhance the imaging efficiency, the reported photoacoustic probes have been designed coaxially involving complicated optical/acoustic prisms to bypass the opaque piezoelectric layer of ultrasound transducers, but leading to bulky probes and hindering the applications in limited space. Though the emergence of transparent piezoelectric materials helps to save the effort on the coaxial design, the reported transparent ultrasound transducers were still bulky. In this work, a miniature photoacoustic probe with an outer diameter of 4 mm was developed, in which an acoustic stack was made of the combination of transparent piezoelectric material and gradient-index lens as a backing layer. The transparent ultrasound transducer exhibited a high center frequency of ~47 MHz and a -6 dB bandwidth of 29.4%, which could be easily assembled with a pigtailed ferrule of a single-mode fiber. The multi-functional capability of the probe was successfully validated through the experiments of fluid flow sensing and photoacoustic imaging.

The buildup dynamics of diode-pumped passively mode-locked solid-state laser is thoroughly explored by the real-time measurement with temporal sampling rate up to 40 GHz. A concise cavity is developed to ensure the transient dynamics purely arising from the gain medium and saturable absorber. Experimental results reveal that the laser output in the buildup process exhibits numerous passively Q-switched pulses followed with a damped relaxation oscillation prior to the stable mode locking. Furthermore, it is confirmed that the laser output has already displayed single clean mode-locked pulses inside the first several Q-switched envelopes before stepping into the stage of relaxation oscillation. The present real-time exploration is expected to provide important information for practical applications with temporal modulation of the pump intensity.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

This research uses ARM-based FPGA to implement the computation of a virtual-real fusion hardware system. It also evaluates the acceleration effect of virtual-real fusion execution speed after connecting related hardware acceleration and provides a reference for real-time information fusion system design. The hardware uses Xilinx Zynq UltraScale+ MPSoC ZCU102 Evaluation Kit. It also obtains color and depth images with Intel® RealsenseTM D435i depth cameras. This system receives image data through the Linux system built on the ARM and fed to the FPGA for object detection using CNN models. In addition, it develops a module for rapidly performing registration between the color images and the depth images. It can calculate the size and position of the display information on the transparent display according to the pixel coordinates and depth values of the human eye and the object. The fusion took about 47ms on a personal computer with a GPU RTX2060 and 25ms on the ZCU102. The acceleration is nearly 168%. Finally, the results were successfully transplanted into the retail display system for demonstration.

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.

Traditional edible barbecue products use with lemon juice not only make the barbecue more delicious but also reduce the risk of PAHs in the barbecue products. One of the major economics crops in Taiwan, the waste from citrus fruits was very tremendous mass. However, the peelings of citrus fruits are rich in essential oil, especially, the limonene is the major. Whether the anti-carcinogenesis activities of terpene, such as limonene, in citrus fruits essential oil extraction. This study to demonstrate the PAHs content in fish skin increased markedly after being roasted at 210℃ for 20 minutes and greater mutagenicity risk of roasted fish skin was observed by Ame's test. The reduction of mutagenicity risk of roasted fish skin, which the antimutagenic abilities of substances in descending order were limonene > cold pressure oil > lemon >grapefruit. The antimutagenicity rate and ability of the three extracts were limonene: 18–23%; cold-pressed lemon oil: 18–22%; and steam distilled lemon essential oil: 8–16%. The obvious anti- mutagenicity effects against the PAHs mutagenicity of roasted fish skins can be found in citrus fruits essential oil extraction.

Objective: Scaphoid fractures, particularly occult and non-displaced fractures, are difficult to detect using traditional X-ray methods due to their subtle appearance and variability in bone density. The study proposes a two-stage CNN approach to detect and classify scaphoid fractures using anterior-posterior (AP) and lateral (LA) X-ray views for more accurate diagnosis. Methods: The study emphasizes the use of multi-view X-ray images (AP and LA views) to improve fracture detection and classification. The multi-view fusion module helps integrate information from both views to enhance detection accuracy, particularly for occult fractures that may not be visible in a single view. The proposed method includes two stages that are stage 1: Detect the scaphoid bone using Faster R-CNN and Feature Pyramid Network (FPN) for region proposal and small object detection. The detection accuracy for scaphoid localization is 100% with Intersection over Union (IoU) scores of 0.8662 for AP views and 0.8478 for LA views. And stage 2: Perform fracture classification using a ResNet backbone and FPN combined with a multi-view fusion module to combine features from both AP and LA views. This stage achieves a classification accuracy of 89.94%, recall of 87.33%, and precision of 90.36%. Results: The proposed model performs well in both scaphoid bone detection and fracture classification. The multi-view fusion approach significantly improves recall and accuracy in detecting fractures compared to single-view approaches. In scaphoid detection, both AP and LA views achieved 100% detection accuracy. In fracture detection, using multi-view fusion, the accuracy for AP views reached 87.16%, and for LA views, it reached 83.83%. Conclusion: The multi-view fusion model effectively improves the detection of scaphoid fractures, particularly in cases of occult and non-displaced fractures. The model provides a reliable, automated approach to assist clinicians in detecting and diagnosing scaphoid fractures more efficiently.

The Korean water deer (WD), the dominant wildlife species in South Korea, is classified as vul-nerable on the IUCN Red List. Beside their several unique characteristics, they have a lower in-cidence of tick bites compared to other Cervidae species, particularly roe deer (RD). In natural environments, ticks exhibit questing behavior by waiting on grasses to climb onto host's hair coat. They use sensory organs to detect signals such as body temperature and chemicals from the host's skin, then move to preferred feeding location. Therefore, we conducted a comparative analysis of the skin tissue and hair features of both species to compare the factors leading to lower incidence of tick bites in WD. Notably, WD had significantly larger blood vessels, sebaceous glands, and sweat glands which maintain the barrier functions of the skin (p≤0.004), irregular scale surfaces in hair cuticles, and stiff and 2.83-times thicker primary hair diameter (p<0.001). Each parameters made the ticks difficult to move onto the epidermis of WD. To our knowledge, this is the first report which is focused on the comparative analysis for skin and hair feature in WD and RD. We suggest that these differences may attribute to evade tick bites in WD.

In order to perceive the behavior presented by the multiphase chemical reactors, the ant colony optimization algorithm was combined with computational fluid dynamics (CFD) data. This intelligent algorithm creates a probabilistic technique for computing flow and it can predict various levels of three-dimensional bubble column reactor (BCR). This artificial ant algorithm is mimicking real ant behavior. This method can anticipate the flow characteristics in the reactor using almost 30 % of the whole data in the domain. Following discovering the suitable parameters, the method is used for predicting the points not being simulated with CFD, which represent mesh refinement of Ant colony method. In addition, it is possible to anticipate the bubble-column reactors in the absence of numerical results or training of exact values of evaluated data. The major benefits include reduced computational costs and time savings. The results show a great agreement between ant colony prediction and CFD outputs in different sections of the BCR. The combination of ant colony system and neural network framework can provide the smart structure to estimate biological and nature physics base phenomena. The ant colony optimization algorithm (ACO) framework based on ant behavior can solve all local mathematical answers throughout 3D bubble column reactor. The integration of all local answers can provide the overall solution in the reactor for different characteristics. This new overview of modelling can illustrate new sight into biological behavior in nature.

ST-segment elevation myocardial infarction (STEMI), a major cause of mortality worldwide, has been shown through epidemiological studies to have a relationship with short-term exposure to air pollutants; however, the association between air pollutants and the outcome of STEMI has not been well studied. The aim of this study was to estimate the impact of air pollutants on the outcomes of STEMI. Data on particulate matter <2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) at each of the 11 air monitoring stations in Kaohsiung City were collected between January 1, 2012, and December 31, 2017. Medical records of non-trauma patients aged > 20 years who had visited the ED with a principal diagnosis of STEMI were extracted. The primary outcome measure was in-hospital mortality. After adjusting for potential confounders and meteorological variables, we found that an increase in the interquartile range (IQR) in NO2 was associated with an elevated risk of in-hospital mortality in patients with STEMI. Moreover, during the warm season, an IQR increase in NO2 corresponded to a higher risk of in-hospital mortality in patients with STEMI on lag 3 (OR=3.266; 95% CI:1.203–8.864, p=0.02). Conversely, an IQR increase in PM10 was associated with an increased risk of in-hospital mortality in patients with STEMI on lag 3 (OR=2.792; 95%CI:1.115–6.993, p=0.028) during the cold season. Our study suggests that exposure to NO2 (during warm season) and PM10 (during cold season) may contribute to a higher risk of poor prognosis in patients with STEMI.

With the increasing demand for energy worldwide, researchers from different fields have been striving to improve the sustainability and proper utilization of energy resources. Passive radiative cooling, as a natural energy transport method, can achieve cooling without additional external energy input. This review provides a comprehensive examination of passive radiative cooling, including its fundamental theories and latest development. A particular emphasis is placed on the diverse range of fields where passive radiative cooling has been applied, notably including but not limited to construction and architecture. The current state of applications, potential challenges that may arise with wider adaption and promising research directions for each field are thoroughly discussed. This review underscores the universal applicability and feasibility of passive radiative cooling and contributes to its future development by identifying promising areas for further exploration.

Introduction: The treatment of peritoneal dialysis in end-stage renal disease is increasing in clinical practice. The main purpose of this study was to evaluate the effect of far-infrared radiation therapy on inflammation and the cellular immunity of patients undergoing peritoneal dialysis. Materials and Methods: We recruited 56 patients undergoing peritoneal dialysis, and we included 32 patients from the experimental group and 24 patients from the control group in the final analysis. The experimental evaluation in our study was as follows: (1) We used abdominal computed tomography to measure the diameter and degree of hardening of the abdominal veins and large blood vessels to explore the changes in abdominal blood vessels. (2) The study compared the effects of peritoneal dialysis using albumin, blood urea nitrogen, creatinine, white blood cell, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, erythrocyte sedimentation rate, Hs-CRP clearance rate, and HBAlC. (3) We compared the cytokine concentration of blood between the two groups while controlling for GM-CSF, IL-2, IFN-γ, IL-6, IL-18, IL-4, IL-5, IL-13, IL-12p70, TNF-α, and IL-1β. Results and Discussion: (1) There was no significant difference in the abdominal blood vessels of the experimental group relative to the control group according to abdominal CT measured over 6 months. (2) Our study demonstrates the statistically significant effect of FIR therapy on the following parameters: albumin (p = 0.048*), creatinine (p = 0.039*), and Hs-CRP (p < 0.001**) decreased significantly, and glomerular filtration rate (eGFR, p = 0.043*) and glucose (p < 0.001**) increased significantly. Our study found that, in the experimental group, albumin and creatinine decreased significantly due to FIR therapy for 6 months. However, our study also found that, in the experimental group, glucose (p < 0.001**) increased significantly due to FIR therapy for 6 months. Peritoneal dialysis combined with FIR can reduce the side effects of glucose in dialysis buffer, which interferes with peritoneal inflammation and peritoneal mesothelial cell fibrosis. In addition, we also found that, in the experimental group, Hs-CRP (p < 0.001**) decreased significantly due to FIR therapy for 6 months. (3) No statistical significance in the inflammatory cytokines related to FIR therapy differences was observed in our study. IFN-γ (p = 0.124), IL-12p70 (p = 0.093), IL-18 (p = 0.213), and TNF-α (p = 0.254) did not exhibit significant improvements in peritoneal dialysis with FIR treatment over 6 months. IFN-γ and IL-18 in the plasma of patients in the experimental group and the control group were higher in the third month than in the first month. Conclusion: We found that the effect of peritoneal dialysis improved significantly with FIR therapy, and significant improvements in the peritoneal permeability and inflammatory response were observed. end-stage renal disease; peritoneal dialysis; far-infrared therapy (FIR)

Sweet pepper is an important vegetable in the world. Bacterial leaf spot caused by the pathogen Xanthomonas perforans, is a limiting factor that significantly reduces the quality and yield of sweet pepper To control this disease, the use of chemical fungicides is currently the main disease control method. Thus, we want to develop an alternative method by using antagonistic microorganisms. Under this demand, Bacillus amyloliquefaciens PMB04 has strong antagonistic effects against pathogens and can inhibit the occurrence of diseases. B. amyloliquefaciens PMB04 has the potential for the development of a disease control product. Primarily, PMB04 revealed to contain a strong inhibitory effect against all isolated X. perforans strains. In the inoculation assay, the severity of bacterial spot disease on sweet peppers was reduced by PMB04 bacterial suspensions. To increase the convenience of field application for future prospects, the development of PMB04 fermentation liquid was carried out with different ratios of brown sugar and yeast extract in a 30-liter fermentation tank subsequently. Results exhibited that the fermentation liquid of 3-1 formula obtained the highest bacterial population in a 30-liter fermentation tank. The fermentation liquid of 0.5-0.5 formula was the most stable formula under two different conditions in terms of consistent bacterial population and sporulation. In addition, the 200-fold dilution of 3-1 and 0.5-0.5 fermentation liquids revealed best control efficacy on bacterial leaf spot of sweet pepper. Additionally, the results of the 0.5-0.5 fermentation liquid (PMB4FL) with different dilution concentrations also showed that the 200- and 500-fold dilutions had the best control efficacy. To understand the effect of commonly used copper-containing fungicides on sweet peppers on the application of microbial agent PMB4FL, the effects of copper hydroxide and tribasic copper sulfate on the growth of X. perforans strains and B. amyloliquefaciens PMB04 were assayed. The results exhibited that the above two fungicides did not have any inhibitory effect on the growth of PMB04, but had a strong inhibitory effect on the X. perforans strain. In the follow-up control experiment, the treatment of copper hydroxide had no synergistic effect with PMB4FL to control bacterial leaf spot. We concluded that the use of PMB4FL fermentation liquid alone on the leaves can effectively control the occurrence of bacterial leaf spot in sweet pepper crops.

Dynamic visualizations have been developed to exchange information that transforms over time across a broad range of professional and academic contexts. However, these visual tools may impose substantial demands on the learner’s cognitive resources that are very limited in current knowledge. Cognitive load theory has been used to improve learning from dynamic visualizations by providing certain design techniques to manage learner cognitive load without adding any oral/written explanations. This systematic review examined a series of experimental studies assessing the roles of these design techniques in learning tactical scenes of play through dynamic visualizations. Electronic databases PubMed and Google Scholar were used to search relevant articles. Eleven studies were eventually included for the systematic review based on the eligibility criteria. The present review revealed that adapting design techniques to the level of learners’ expertise, type of depicted knowledge, and level of content complexity is a crucial part of effective learning.

Owing to the time-varying characteristics and nonlinearities of industrial processes, control has higher difficulties and results in challenges for advanced technology. In this paper, a self-tuning controller that includes a nonlinear proportional-integral-derivative (NPID) control function as well as a self-tuning function is proposed for first-order plus time delay (FOPTD) process control. The NPID control function is implemented using the nonlinear PID controller whose optimum parameters are adapted by a neural network (NN). The self-tuning function is able to identify the process dynamics using a short period of process behavior and tune NPID parameters based on the identified parameters. The advantage of the proposed method is validated with a set of simulation works on three processes and the comparison results are presented.

This study critically reviews the situation of Language teachers in China, who transited from on-campus face-to-face teaching to online teaching within one week of the official school opening dates, which were postponed due to the outbreak of the COVID-19 pandemic. Presently, the study is located in an interpretive research paradigm, and hence, it takes the human experiences within educational setups to be subjective and complex. In so doing, to elude this educational transition and its complex dynamics, the research uses such qualitative research methods as document analysis and interviews. Painting a picture of great variety in individual experiences and challenges, strategies, and outcomes not only across these two specific institutions but, certainly, more broadly through the lens of previous case studies. First, it values depth over breadth and tries to unearth the varied experiences, challenges, innovations, and achievements faculty made during their transition into online Language Teaching. The results of the study are believed to be able to contribute quite a rich, detailed, and multi-faceted perspective to the pool of knowledge for the complexities of online Language education in China, which may be of interest to educators, policymakers, and researchers.

This randomized control trial aims to test the hypothesis that twice-daily nurse-administered saline enema(SE) is comparable to physician-administrated SE in very low birth weight infants (VLBW) with meconium obstruction of prematurity (MOP). MOP is defined as no bowel opening for 48 h, and treatment failure is defined as the need for additional intervention and safety. Twice-daily SE was administered by accredited nurses, surgeons/neonatologists using standardized protocols. The outcomes were comparability of nurse to physician-administered SE in terms of frequency of administration, catheter insertion length and volume of enema, daily maximum meconium output, and treatment failure, defined as the need for additional intervention. Safety outcomes were evaluated. Twenty-eight infants were managed with SE. 389 SE were administered, 96 and 293 in 750-999g and 1000-1500g birth weight groups, respectively. Nurses administered 72.9% and 81.5% of SE in 750-999g and 1000-1500g birth weight groups, respectively. Physicians performed the rest of the SE. Daily meconium output was comparable in nurse and physician-administered SE in both groups. There were no treatment failures or adverse events in both SE group. SE administered by nurses was found to be comparable in terms of safety, efficacy, and frequency of administration to that of physician-administered SE.

Licochalcone H (LCH), a regioisomer of licochalcone C derived from the root of Glycyrrhiza inflata. CRC treatment has always been challenged by the development of resistance. We investigated the antiproliferative activity of LCH in oxaliplatin (Ox)-sensitive and -resistant CRC cells. LCH significantly inhibited cell viability and colony growth in both Ox-sensitive and Ox-resistant CRC cells. We found that LCH decreased epidermal growth factor receptor (EGFR) and AKT kinase activities and related activating signaling proteins including phospho (p)-EGFR and pAKT. A computational docking model indicated that LCH may interact with EGFR, AKT1, and AKT2 at the ATP-binding sites. LCH induced ROS generation, as verified by N-acetyl-L-cysteine (NAC) treatment, and increased the expression of the ER stress markers. LCH treatment of CRC cells induced depolarization of MMP and increased. Multi-caspase activity was induced by LCH treatment and confirmed by Z-VAD-FMK treatment. LCH increased the number of subG1 cells and arrested the cell cycle at the G1 phase. LCH inhibits the growth of Ox-sensitive and Ox-resistant CRC cells by targeting EGFR and AKT, and inducing ROS generation and ER stress-mediated apoptosis. Therefore, LCH could be a potential therapeutic agent for improving not only Ox-sensitive but also Ox-resistant CRC treatment.

Cervical cancer, primarily caused by high-risk human papillomavirus (HR-HPV) types 16 and 18, is a major global health concern. Persistent HR-HPV infection can progress from reversible pre-cancerous lesions to invasive cervical cancer, which is driven by the oncogenic activity of human papillomavirus (HPV) genes, particularly E6 and E7. Traditional screening methods, including cytology and HPV testing, have limited sensitivity and specificity. This review explores the ap-plication of p16/Ki-67 dual-staining cytology for cervical cancer screening. This advanced im-munocytochemical method allows for simultaneously detecting p16 and Ki-67 proteins within cervical epithelial cells, offering a more specific approach for triaging HPV-positive women. Dual staining and traditional methods are compared, demonstrating their high sensitivity and negative predictive value but low specificity. The increased sensitivity of dual staining results in higher detection rates of CIN2+ lesions, which is crucial for preventing cervical cancer progression. However, its low specificity may lead to increased false-positive results and unnecessary biopsies. The implications of integrating dual staining into contemporary screening strategies, particularly considering the evolving landscape of HPV vaccination and changes in HPV genotype prevalence are also discussed. New guidelines and further research are necessary to elucidate the long-term effects of integrating dual staining into screening protocols.