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The southwestern region of China is a global biodiversity hotspot. Understanding the environmental mechanisms behind treeline formation in high-altitude areas is crucial for predicting ecosystem changes, such as the upward movement of the treeline due to climate warming and the disappearance of high-altitude rocky beach and shrub ecosystems. Globally, observations show that growing seasonal temperatures at treelines are typically 6–7 °C, but trees do not always reach the predicted elevations. Spatial heterogeneity exists in the deviation (Dtreeline) between actual treeline elevation and the thermal treeline; however, the main driving factors for Dtreeline in many areas remain unclear. This study uses Yulong Snow Mountain as an example, employing machine learning methods like Support Vector Machine (SVM) to precisely identify actual treeline elevation and Extreme Gradient Boosting Tree (XGBoost) to explore the main environmental factors driving the spatial heterogeneity of Dtreeline. Our research found that (1) more than half of the treelines deviated from the thermal treeline, with the average elevation of the thermal treeline (3924 ± 391 m) being about 56 m higher than the actual treeline (3863 ± 223 m); (2) Dtreeline has a complex relationship with environmental factors. In addition to being highly correlated with temperature, precipitation and wind speed also significantly influence the treeline in this region; and (3) the influence of individual variables such as precipitation and wind speed on the spatial variation of Dtreeline is limited, often nonlinear, and involves threshold effects. This knowledge is essential for developing comprehensive protection strategies for Yunnan's high-altitude ecological systems in response to climate warming. Furthermore, it plays a significant role in understanding the changes in biological communities and the response of high-altitude areas to climate change. [ABSTRACT FROM AUTHOR]

Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical simulations stand out due to their well-defined physical and mechanical concepts, and their ability to make effective predictions with limited monitoring data. The predictive accuracy of hybrid models is closely tied to the precise determination of rock mass mechanical parameters in structural numerical simulations. However, rock masses in rock slopes are characterized by intersecting geological structural planes, resulting in reduced strength and the creation of multiple fracture flow channels. These factors contribute to the heterogeneous, anisotropic, and size-dependent properties of the macroscopic deformation parameters of the rock mass, influenced by the coupling of seepage and stress. To improve the predictive accuracy of the hybrid model, this study introduces the theory of equivalent continuous media. It proposes a method for determining the equivalent deformation parameters of fractured rock mass considering the coupling of seepage and stress. This method, based on a discrete fracture network (DFN) model, is integrated into the hybrid prediction model for rock slope deformation. Engineering case studies demonstrate that this approach achieves a high level of prediction accuracy and holds significant practical value. [ABSTRACT FROM AUTHOR]

Urban land use provides essential information about how land is utilized within cities, which is critical for land planning, urban renewal, and early warnings for natural disasters. Although existing studies have utilized multi-source perception data to acquire land use information quickly and at low cost, and some have integrated urban morphological indicators to aid in land use identification, there is still a lack of systematic discussion in the literature regarding the potential of three-dimensional urban morphology to enhance identification effectiveness. Therefore, this paper aims to explore how urban three-dimensional morphology can be used to improve the identification of urban land use types. This study presents an innovative approach called the UMH–LUC model to enhance the accuracy of urban land use identification. The model first conducts a preliminary classification using points of interest (POI) data. It then improves the results with a dynamic reclassification based on floor area ratio (FAR) measurements and a variance reclassification using area and perimeter metrics. These methodologies leverage key urban morphological features to distinguish land use types more precisely. The model was validated in the Pearl River Delta urban agglomeration using random sampling, comparative analysis and case studies. Results demonstrate that the UMH–LUC model achieved an identification accuracy of 81.7% and a Kappa coefficient of 77.6%, representing an 11.9% improvement over a non-morphology-based approach. Moreover, the overall disagreement for UMH–LUC is 0.183, a reduction of 0.099 compared to LUC without urban morphology and 0.19 compared to EULUC-China. The model performed particularly well in identifying residential land, mixed-use areas and marginal lands. This confirms urban morphology's value in supporting low-cost, efficient land use mapping with applications for sustainable planning and management. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article published in the journal Remote Sensing. The correction addresses mistakes in the legend for Figure 5b,c and Figure 9, which occurred during the proofreading stage. The authors confirm that these corrections do not affect the figure captions or the scientific conclusions of the article. The corrected legend for Figure 5 provides information on the seasonal variation of the joint probability distribution of liquid water clouds over the Indochina Peninsula, while the corrected legend for Figure 9 presents the marginal probabilities of cloud top pressure with respect to cloud optical thickness in different regions. [Extracted from the article]

The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam's structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement. [ABSTRACT FROM AUTHOR]

Skeletal muscle growth in livestock impacts meat quantity and quality. Concerns arise because certain feed additives, like beta-agonists, may affect food safety. Skeletal muscle is a specialized tissue consisting of nondividing and multinucleated muscle fibers. Myostatin (MSTN), a protein specific to skeletal muscle, is secreted and functions as a negative regulator of muscle mass by inhibiting the proliferation and differentiation of myoblasts. To enhance livestock muscle growth, phytogenic feed additives could be an alternative as they inhibit MSTN activity. The objective of this study was to establish a systematic screening platform using MSTN activity to evaluate phytogenics, providing scientific evidence of their assessment and potency. In this study, we established a screening platform to monitor myostatin promoter activity in rat L8 myoblasts. Extract of Glycyrrhiza uralensis (GUE), an oriental herbal medicine, was identified through this screening platform, and the active fractions of GUE were identified using a process-scale liquid column chromatography system. For in vivo study, GUE as a feed additive was investigated in growth-finishing pigs. The results showed that GUE significantly increased body weight, carcass weight, and lean content in pigs. Microbiota analysis indicated that GUE did not affect the composition of gut microbiota in pigs. In summary, this established rodent myoblast screening platform was used to identify a myogenesis-related phytogenic, GUE, and further demonstrated that the active fractions and compounds inhibited MSTN expression. These findings suggest a novel application for GUE in growth performance enhancement through modulation of MSTN expression. Moreover, this well-established screening platform holds significant potential for identifying and assessing a diverse range of phytogenics that contribute to the process of myogenesis. [ABSTRACT FROM AUTHOR]

An efficient and more accurate millimeter-wave imaging algorithm, applied to a close-range monostatic personnel screening system, with consideration of dual path propagation loss, is presented in this paper. The algorithm is developed in accordance with a more rigorous physical model for the monostatic system. The physical model treats incident waves and scattered waves as spherical waves with a more rigorous amplitude term as per electromagnetic theory. As a result, the proposed method can achieve a better focusing effect for multiple targets in different range planes. Since the mathematical methods in classical algorithms, such as spherical wave decomposition and Weyl identity, cannot handle the corresponding mathematical model, the proposed algorithm is derived through the method of stationary phase (MSP). The algorithm has been validated by numerical simulations and laboratory experiments. Good performance in terms of computational efficiency and accuracy has been observed. The synthetic reconstruction results show that the proposed algorithm has significant advantages compared with the classical algorithms, and the reconstruction by using full-wave data generated by FEKO further verifies the validity of the proposed algorithm. Finally, the proposed algorithm performs as expected over real data acquired by our laboratory prototype. [ABSTRACT FROM AUTHOR]

One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population's variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO's accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN's coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively. [ABSTRACT FROM AUTHOR]

Numerous companies create innovative software systems using Web APIs (Application Programming Interfaces). API search engines and API directory services, such as ProgrammableWeb, Rapid API Hub, APIs.guru, and API Harmony, have been developed to facilitate the utilization of various APIs. Unfortunately, most API systems provide only superficial support, with no assistance in obtaining relevant APIs or examples of code usage. To better realize the "FAIR" (Findability, Accessibility, Interoperability, and Reusability) features for the usage of Web APIs, in this study, we developed an API inspection system (referred to as API Prober) to provide a new API directory service with multiple supplemental functionalities. To facilitate the findability and accessibility of APIs, API Prober transforms OAS (OpenAPI Specifications) into a graph structure and automatically annotates the semantic concepts using LDA (Latent Dirichlet Allocation) and WordNet. To enhance interoperability, API Prober also classifies APIs by clustering OAS documents and recommends alternative services to be substituted or merged with the target service. Finally, to support reusability, API Prober makes it possible to retrieve examples of API utilization code in Java by parsing source code in GitHub. The experimental results demonstrate the effectiveness of the API Prober in recommending relevant services and providing usage examples based on real-world client code. This research contributes to providing viable methods to appropriately analyze and cluster Web APIs, and recommend APIs and client code examples. [ABSTRACT FROM AUTHOR]

China is the world's primary energy consumer. In order to address global warming, China has proposed a strategic goal of "reaching peak carbon and carbon neutrality", which is related to a balance between human and natural life and has vital strategic significance for accelerating the construction of a sustainable society and achieving high-quality development. The energy sector is the main battlefield upon which the country will strive to achieve the "double carbon" goal, and power systems take the hierarchical first place in the current carbon emissions structure in China. Thermal power enterprises are facing severe challenges, such as low-carbon development, transformation, and upgrading. Therefore, it is crucial to study the thermal power industry's carbon footprint. A scenario prediction method for estimating the carbon footprint of the thermal power industry in Zhejiang Province based on stacking integrated learning—i.e., the STIRPAT model—is proposed in this study. Using this model, to identify the main influencing factors, one can take the coefficient of determination (R2) and mean absolute percentage error (MAPE) as evaluation indicators, building a fusion advantage model to predict the carbon footprint. Four carbon peak action scenarios are set up to determine the thermal power industry's carbon peak in 2021–2035, taking Zhejiang Province as an example. The findings indicate that the proposed method can accurately predict the carbon footprint of the thermal power industry, with the prediction coefficient (R2) being higher than 0.98 and the error (MAPE) being lower than 0.01. The carbon emission peaks of the thermal power industry under different carbon peak action scenarios are calculated, verifying that Zhejiang Province can reach the goal of a carbon peak; however, the low-carbon development model is too extreme and needs to be revised in combination with more reasonable improvement methods. Therefore, Zhejiang Province must be restructured industrially, the construction of high-tech industries must be encouraged, the energy consumption structure must be optimized, energy efficiency must be boosted, and energy use must be reduced. Relevant research offers a theoretical foundation and benchmark for China's thermal power industry to promote industrial restructuring and low-carbon transformation by means of comprehensive governance. [ABSTRACT FROM AUTHOR]

Isorhamnetin (IRh), which has a wide range of pharmacological effects, is one of the most significant active components in the fruits of Hippophae rhamnoides L. and the leaves of Ginkgo biloba L. It protects the heart and brain, in addition to possessing anti-tumor, anti-inflammatory, antioxidant, organ protection, and anti-obesity properties. We sought to assess IRh's anti-psoriatic activity, explore its immunomodulatory properties in reducing the severity of psoriatic symptoms, and evaluate its potential immunotherapeutic effects. We used IRh to treat imiquimod (IMQ)-induced psoriasis in BALB/C mice and examined the underlying mechanisms. The outcomes demonstrated that IRh reduced epidermal hyperplasia, lowered PASI scores, and improved histopathological psoriasiform lesions in IMQ-induced mice. IRh attenuated the accumulation of malondialdehyde (MDA), and also reversed the reduction caused by IMQ of superoxide dismutase (SOD) and catalase (CAT) in skin tissues. Additionally, IRh effectively inhibited IMQ's ability to increase proinflammatory cytokines such as TNF-α, IL-6, IL-17A, and transcription factor NF-κB. Furthermore, IRh significantly reduced the percentage of Th1 and Th17 in the spleens of mice treated with IMQ and suppressed the maturation of splenic dendritic cells. Overall, our research suggests that IRh protects against oxidative stress and inflammation in the pathogenesis of psoriasis, with potential for the development of new and potent medication for the treatment of psoriasis. [ABSTRACT FROM AUTHOR]

In response to sustainable development goals, the architectural industry aims to decrease the high proportion of emissions and energy use in the construction sector. Therefore, the design method of building performance optimization (BPO) has been advocated in recent studies as a method for accomplishing high-performance building design. However, BPO remains difficult to implement in practice due to the lack of a definite process and supporting tools for architects/designers in the early design process. The purpose of this paper is to propose a BPO framework and integrated design decision support (DDS) interface to provide a visual and science-based analysis and assist designers working with high-performance building façade designs. The framework and DDS tool are then tested by designers through a practice design of the headquarters façade. All the designers started and implemented the facade optimization design in a short training session, although they reported that the developed support tools still needed to be improved in terms of also integrating optimization tools. The characteristics of the user interface help considerably with comparing and making decisions in optimal solutions. The results emphasize the importance of developing design support tools for practical adoption from practical designers' perspectives. [ABSTRACT FROM AUTHOR]

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network–gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009–2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R² differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering. [ABSTRACT FROM AUTHOR]

This paper takes the industrial poverty alleviation of local state-owned enterprises in China as the research object, and takes the local state-owned enterprises listed in Shanghai and Shenzhen A shares in China from 2016 to 2020 as the sample to empirically test the impact of industrial poverty alleviation on the sustainable economic growth of the region and consider the regulatory effect of digital innovation. This study found that China's local industrial poverty reduction behavior in state-owned enterprises can effectively promote regional economic growth. Moreover, the digital innovation produced a regulating effect; that is, if enterprises carry out digital innovation and have a higher degree of digital innovation, their industrial poverty alleviation behavior will have a stronger role in promoting regional economic growth. This conclusion still holds even after controlling for factors of robustness and endogeneity. In addition, the study of influence mechanisms found that the proportion of primary industry in GDP was the mediating effect of industrial poverty alleviation on regional economic growth, and the proportion of primary industry in GDP had a partial mediating effect. Further heterogeneous group testing shows that the impact of industrial poverty alleviation on regional economically sustainable growth is more obvious in agriculture-related, local state-owned enterprises; non-high-tech, local state-owned enterprises; and local state-owned enterprises with subsequent poverty alleviation plans. The empirical evidence in this paper verifies the role of local state-owned enterprises' participation in industrial poverty alleviation in promoting regional economic growth. It is a useful supplement to the research literature on the economic consequences of Chinese enterprises' participation in targeted poverty alleviation, which helps to better understand such a phenomenon and also provides a powerful explanation for China's poverty alleviation achievements. [ABSTRACT FROM AUTHOR]

Sesquiterpenoid tagitinin C, present in Tithonia diversifolia leaves, has been known to have anti-hepatoma properties. Therefore, we investigated the anti-metastatic potential of tagitinin C in xenograft models of hepatocellular carcinoma (HCC). We isolated tagitinin C from a methanolic extract of the leaves of T. diversifolia. HepG-2 and Huh 7 hepatoma cells were treated with tagitinin C, and cell viability, migration, and matrix metalloproteinase (MPP) activity were assessed using the 3-(4,5-dimethylthiozol-2-yl)-2,5-diphenyltetrazolium bromide assay, scratch migration assay, and MMP activity assay, respectively. We used magnetic resonance spectroscopy to determine the tumorigenicity of xenografts inoculated with Hep-G2 and Huh 7 cells. Tagitinin C was cytotoxic against Hep-G2 and Huh 7 cells, with IC50 values of 2.0 ± 0.1 µg/mL and 1.2 ± 0.1 µg/mL, respectively, and it showed an anti-metastatic effect in vitro. Additionally, MRS assays revealed that tagitinin C (15 g/mouse/day) reduced the tumorigenicity of Hep-G2 and Huh 7 cell xenografts. Tagitinin C demonstrated significant antitumor and anti-metastatic activity in the two human hepatoma cell lines. Tagitinin C might be used as an alternative or auxiliary therapy for the treatment of HCC, and its effect should be further investigated in clinical settings. [ABSTRACT FROM AUTHOR]

In addition to the Taiwan Strait, the geographical channel effect (GCE) has been observed in the Taipei Basin during winters. This study explored the potential links amongst GCE phenomena in the basin, fine particulate matter (PM2.5) events, and position of the continental high-pressure system (CHPS). Principal component analysis was applied to classify the CHPS distribution patterns influencing PM2.5 concentrations and meteorological parameters in the Taipei metropolis. In addition, non-parametric Kruskal–Wallis and Dwass–Steel–Critchlow–Fligner tests were applied to compare the parameters amongst distinct groups. Moreover, track analysis was utilised to trace the trajectories of air masses. The merged Dark Target Aerosol Angstrom Exponent (Ocean), Deep Blue Aerosol Angstrom Exponent (Land), and Aerosol Optical Depth (AOD) 3 km (Land and Ocean) of Terra/MODIS and Aqua/MODIS were used to confirm the connection of long-range PM2.5 transport. Two features in the atmospheric environment induce double GCEs: (1) wind direction below 90° in most of the upwind area of the Taisumi and Xindian River Basins; (2) CHPS centres moved across 110° E. Double GCEs can reduce the magnitude of PM2.5 air pollution and shape the distribution of PM2.5; however, they cannot prevent PM2.5 events. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) systems are crucial tools in atmospheric science education and weather forecasting, and high-performance computing (HPC) is essential for achieving such science. The goals of NWP systems are to simulate different scales of weather systems for educational purposes or to provide future weather information for operational purposes. Supercomputers have traditionally been used for NWP systems; however, supercomputers are expensive, have high power consumption, and are difficult to maintain and operate. In this study, the Raspberry Pi platform was used to develop an easily maintained high-performance NWP system with low cost and power consumption—the Improved Raspberry Pi WRF (IRPW). With 316 cores, the IRPW had a power consumption of 466 W and a performance of 200 Gflops at full load. IRPW successfully simulated a 48-h forecast with a resolution of 1 km and a domain of 32,000 km2 in 1.6 h. Thus, IRPW could be used in atmospheric science education or for local weather forecasting applications. Moreover, due to its small volume and low power consumption, it could be mounted to a portable weather observation system. [ABSTRACT FROM AUTHOR]

An exacerbated precipitation–temperature relationship can lead to compound extremes. The role of clouds in such a relationship is relatively uncertain. Here, we investigate the cloud–precipitation–temperature relationships over the Indochina Peninsula during the summer monsoon transition. The negative correlation between cloudiness/precipitation and surface maximum temperature is valid on seasonal and interannual timescales. The near-surface temperature exhibits interdecadal variability and a long-term warming trend. The warming trend has accelerated in the past two decades. In the anomalous warm years, the remarkably strong western Pacific subtropical high inhibits the development of clouds, especially the middle and high cloud-top regimes, leading to the suppression of deep convection and precipitation. There are more optically thin (moderate to thick) clouds with smaller (larger) effective radii in the high cloud-top regime for the warm (cold) years. The dominance of shallow cumulus is a distinct feature in the warm years. The daytime heating of enhanced surface insolation due to decreased cloudiness is worsened by the dry condition of the precipitation deficit. The water vapor warming effect can prevent an efficient drop in nighttime temperature, thereby exacerbating the warm condition under the warming trend. The cloud–precipitation–temperature relationships coupling with the monsoon development can be used to diagnose the regional scale cloud–climate interactions in climate models. [ABSTRACT FROM AUTHOR]

In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, personnel characteristics, ship characteristics, pilotage characteristics, wharf characteristics and other factors on the safety risk of maritime navigation are discussed. Based on the official data of Zhejiang Maritime Bureau, the extreme gradient boosting (XGBoost) algorithm was used to construct a maritime accident classification prediction model, and the explainable machine learning framework SHAP was used to analyze the causal factors of accident risk and the contribution of each feature to the occurrence of maritime accidents. The results show that the XGBoost algorithm can accurately predict the accident types of maritime accidents with an accuracy, precision and recall rate of 97.14%. The crew factor is an important factor affecting the safety risk of maritime navigation, whereas maintaining the equipment and facilities in good condition and improving the management level of shipping companies have positive effects on improving maritime safety. By explaining the correlation between maritime accident characteristics and maritime accidents, this study can provide scientific guidance for maritime management departments and ship companies regarding the control or management of maritime accident prevention. [ABSTRACT FROM AUTHOR]

Invasive deep brain stimulation has proven to be clinically therapeutic for patients with drug-refractory epilepsy. The aim of this study was to develop a novel transcranial electrical device as a noninvasive stimulation modality for seizure treatment. We fabricated a novel transcranial electrical device and tested it in four swine brains with depth electrodes surgically implanted under neuro-navigation. Stimulation with two high-frequency alternating currents was used to cause an interference envelope. Acute focal epilepsy was induced by a subcortical injection of penicillin and specific anesthesia protocol. The frequency and electric field of the stimulation in the hippocampus were investigated. The two frequencies (2 k and 2.14 kHz) of stimulation successfully caused an envelope of 140 Hz. With 1 mA stimulation, the electric field degraded gradually and induced an in situ electric field of 0.68 mV/mm in the hippocampi. The interference mode transcranial electric stimulation attenuated the originally induced epileptic form discharges. No neuronal or axonal injuries were noted histopathologically after the stimulation. The feasibility and biosafety of our proposed device were preliminarily verified. Future translational research should focus on the electrode deposition and stimulation parameters for a quantitative therapeutic effect. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the impact of digital transformation on corporate international strategy. With a dataset of the Chinese stock market from 2014 to 2020, our empirical results reveal that digital transformation has a positive impact on the international strategy of Chinese enterprises. More specifically, firms with digital transformation are more likely to implement international strategy, and firms with a higher degree of digital transformation are associated with a higher level of internationalization. In addition, our empirical results reveal that corporate innovation exhibits the mediation effect. Moreover, our findings show that the impact of digital transformation is more pronounced for private firms and non-high-tech enterprises, and this impact is also moderated by high institutional development in eastern China. Our findings survive numerous robustness checks. [ABSTRACT FROM AUTHOR]

On 4 June 2021, short-duration extreme precipitation occurred in Taipei. Within 2 h, over 200 mm of rainfall accumulated in the Xinyi district. In this study, advanced data assimilation technology (e.g., hybrid data and 3D variations) was incorporated to develop a high-resolution, small-scale (e.g., northern Taiwan) data assimilation forecast system, namely the weather research and forecast-grid statistical interpolation (WRF-GSI) model. The 3D wind field data recorded by the Doppler wind lidar system of Taipei Songshan Airport were assimilated for effective simulation of the extreme precipitation. The results revealed that the extreme rainfall was caused by the interaction between the northeast wind incurred by a front to the north of Taiwan, a humid southerly wind generated by Typhoon Choi-wan, and the regional sea–land breeze circulation. For the Xinyi district, the WRF-GSI_lidar model reported accumulated rainfall 30 mm higher than that in the non-assimilated experiment (WRF-GSI_noDA), indicating that the WRF-GSI model with lidar observation was improved 15% more than the nonassimilated run. [ABSTRACT FROM AUTHOR]

Long-range transport (LRT) can carry air pollutants to downwind areas. However, studies about the impacts of LRT on bacterial communities are few. This study investigated the influence of Asian dust storms (ADS) and frontal pollution (FP) on bacterial communities in ambient air using next-generation sequencing (NGS) and Terminal Restriction Fragment Length Polymorphism (T-RFLP). Air samples were collected at Cape Fugui (CF) and National Taiwan University (NTU) in northern Taiwan before (or background days), during, and after LRTs from November 2013 to March 2015. The richness, H index, and evenness increased during FPs and then decreased after FPs. During and after ADS and FP, the prevalence of the phylum Proteobacteria decreased, but that of Firmicutes increased. The dominant class of Proteobacteria changed from Alphaproteobacteria on background days to Betaproteobacteria during LRTs. At the genus level, the high abundance of Ralstonia and Bacillus during FP and Clostridium during ADS were detected at both locations. Additionally, Ralstonia was dominant at CF during ADS. In conclusion, FP and ADS both changed the bacterial community. The indicator genus was Clostridium and Ralstonia for ADS as well as Bacillus and Ralstonia for FP. Given the potential health threats posed by the bioaerosols transported, people should avoid outdoor activities during LRTs. [ABSTRACT FROM AUTHOR]

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts' expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410 × 10−2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors. [ABSTRACT FROM AUTHOR]

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management. [ABSTRACT FROM AUTHOR]

This purpose of this study is to investigate the efficacy of shoulder joint mobilization generated by an innovative servomotor joint mobilization apparatus that was developed in this research for patients suffering from frozen shoulder. Forty-eight patients with frozen shoulder were recruited and stratified randomly assigned into one of two groups: joint mobilization apparatus (posterior and inferior gliding, 80 N, 5 Hz, 30 min) combined with regular therapy (experimental group; EG) versus a regular therapy alone group (control group; CG), three times a week for eight weeks. The visual analogue scale (VAS) for pain and shoulder range of motion (ROM) were measured before, during, and the end of the treatment. Results showed that the shoulder flexion, abduction, internal rotation, and external rotation of the EG improved by 36%, 51%, 81%, and 88%, respectively, while VAS pain scores decreased by 62% when compared with the baseline. Furthermore, the shoulder flexion, abduction, internal rotation, external rotation, and pain score of the EG was 11%, 25%, 41%, 24%, and 34% better than those of the CG, respectively. No complaint as well as no side effects were found during or after usage of the joint mobilization apparatus in EG. This study suggests that the joint mobilization apparatus operated by a very small amount of professional manpower and combined with physical therapy further improves shoulder ROM and pain in patients with frozen shoulder compared to regular physical therapy alone and could be one of the new therapeutic regimens in the future. [ABSTRACT FROM AUTHOR]

The synchronization and control of chaos have been under extensive study by researchers in recent years. In this study, an adaptive Takagi–Sugeno–Kang (TSK) fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed, which is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing CMAC (SOCMAC), and a compensation controller. Specifically, SOCMAC, RCMAC, and adaptive laws are adopted so that the association memory layers of ATFSORC can be modulated in accordance with the layer decision-making mechanism in order to reduce the structure complexity and improve the control performance of ATFSORC. Moreover, the Takagi–Sugeno–Kang fuzzy rules are introduced to increase the learning speed of ATFSORC, and the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the proposed ATFSORC is applied to chaotic systems in order to validate its performance and feasibility. Several simulation schemes are demonstrated to show the effectiveness of the proposed method. Simulation results show that the proposed ATFSORC can obtain a favorable control performance when the chaotic systems are operated at different parameters. Specifically, ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC. [ABSTRACT FROM AUTHOR]

Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 × 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement. [ABSTRACT FROM AUTHOR]

In recent years, the Taiwan government has been calling for the use of public transportation and has been popularizing pollution-reducing green vehicles. Passenger transport operators are being encouraged to replace traditional buses with electric buses, to increase their use in urban transportation. Reduced energy consumption and operating costs are important operational benefits for passenger transport operators, and driving behavior has a significant impact on fuel consumption. Although many literatures or real-world systems have addressed the issues related to reducing energy consumption with electric buses, these works do not involve the records collected from an on-vehicle battery management system (BMS). Accordingly, the results of analyses of existing works lack in-depth discussions, and therefore the applicability of existing works is insignificant. Therefore, in this study, driving data were collected using a battery management system (BMS), and vehicular power consumption was classified according to energy efficiency. Then, decision trees and random forest were applied to construct energy consumption analytical models. Finally, the driving behaviors that influence energy consumption were investigated. A case study was conducted in which a Taichung passenger transport operator's electric bus driving data on urban routes were collected to construct energy consumption analytical models. The data consisted of two parts, i.e., vehicle records and route records. On the basis of these records, we considered the practicability and applicability of the analytical models by transforming the unstructured records into raw data. Passenger transport operators and drivers can leverage the obtained eco-driving indicators for different bus routes for energy savings and carbon reduction. [ABSTRACT FROM AUTHOR]

This study presents a numerical tool for calculating storm surges from offshore, nearshore, and coastal regions using the finite-difference method, two-way grid-nesting function in time and space, and a moving boundary scheme without any numerical filter adopted. The validation of the solitary wave runup on a circular island showed the perfect matches between the model results and measurements for the free surface elevations and runup heights. After the benchmark problem validation, the 2013 Super Typhoon Haiyan event was selected to showcase the storm surge calculations with coastal inundation and flood depths in Tacloban. The catastrophic storm surges of about 8 m and wider, storm-induced inundation due to the Super Typhoon Haiyan were found in the Tacloban Airport, corresponding to the findings from the field survey. In addition, the anti-clockwise, storm-induced currents were explored inside of Cancabato Bay. Moreover, the effect of the nonlinear advection terms with the fixed and moving shoreline and the parallel efficiency were investigated. By presenting a storm surge model for calculating storm surges, inundation areas, and flood depths with the model validation and case study, this study hopes to provide a convenient and efficient numerical tool for forecasting and disaster assessment under a potential severe tropical storm with climate change. [ABSTRACT FROM AUTHOR]

In this study, we report a perovskite solar cell (PSC) can be benefited from the high quality of inorganic nickel oxide (NiOx) as a hole transport layer (HTL) film fabricated from the physical vapor deposition (PVD) process. The power conversion efficiency (PCE) of PSC is found to depend on the thickness of NiOx HTL. The NiOx thickness is optimized via quantitative investigation of the structure, optical and electrical properties. With an active area of 11.25 cm2, a PSC module (25 cm2) with a PCE of 15.1% is demonstrated, while statistically averaged PCE = 18.30% with an open voltage (Voc) 1.05 V, short-circuit current density (Jsc) 23.89 mA/cm2, and fill factor (FF) 72.87% can be achieved from 36 devices with smaller active areas of 0.16 cm2. After the stability test at 40% relative humidity (RH) and 25 °C for 1200 h, the highest performance NiOx-based PSC is shown to be about 1.2–1.8 times superior to PEDOT:PSS organic HTL based PSC at the same environment. [ABSTRACT FROM AUTHOR]

In a subtropical climate like that of Taiwan, the high temperature and humid environmental conditions often result in discomfort and health effects for building occupants. With regard to building geometry, the wind environment and thermal comfort assessment, which can enhance energy efficiency and the comfort and health of occupants, both ought to be considered as soon as possible in the design process. In view of the limited comprehensive design evaluation methods and design workflows regarding wind and thermal performance currently available, this research aims to develop an early decision support workflow that includes suggested performance evaluation methods and design optimization processes. The results of our case study show that the building had clear performance results using the proposed evaluation methods, making it easier for architects to understand and compare alternatives. Appropriate analysis and visualization of the results also effectively assisted architects in determining design solutions and making relevant decisions. The methods and results in this article can facilitate performance-based buildings for healthy and energy-efficient built environments. [ABSTRACT FROM AUTHOR]

This research investigates the impact of higher education expansion on the educational wage premium from a long-term perspective in Taiwan. By using 1985 to 2015 Manpower Utilization Survey (MUS) data with the difference-in-difference-in-differences model (DDD), this study analyzes the change of the wage premium of university educated versus lower-than-university educated counterparts across the expansion of higher education since 1995. The number of universities in Taiwan tripled between 1995 and 2005, from 50 to more than 150, with upgrading of about 100 technology colleges and vocational schools additionally. Dramatic expansion of universities as well as the number of university graduates will shrink the university wage premium for the young generation who entered into the labor market after year 2000, but the older generation will be less affected. The empirical results show that the wages premium of university graduates of the younger generation is 12% to 21% lower than their older generation counterparts due to the higher-education expansion. [ABSTRACT FROM AUTHOR]

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark. [ABSTRACT FROM AUTHOR]

This study explores the discrepancies of storm surge predictions driven by the parametric wind model and the numerical weather prediction model. Serving as a leading-order storm wind predictive tool, the parametric Holland wind model provides the frictional-free, steady-state, and geostrophic-balancing solutions. On the other hand, WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) provides the results solving the 3D time-integrated, compressible, and non-hydrostatic Euler equations, but time-consuming. To shed light on their discrepancies for storm surge predictions, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay are selected. The Holland wind model predicts strong southeastern winds in the San Pedro Bay after Haiyan makes landfall at the Leyte Island than WRF-ARW 3 km and WRF-ARW 1 km. The storm surge simulation driven by the Holland wind model finds that the water piles up in the San Pedro Bay and its maximum computed storm surges are almost twice than those driven by WRF-ARW. This study also finds that the storm surge prediction in the San Pedro Bay is sensitive to winds, which can be affected by the landfall location, the storm intensity, and the storm forward speed. The numerical experiment points out that the maximum storm surges can be amplified by more 5–6% inside the San Pedro Bay if Haiyan's forward speed is increased by 10%. [ABSTRACT FROM AUTHOR]

Millimeter wave (MMW) technology is expanding rapidly into security screening for dangerous items concealed under clothing. It uses safe non-ionizing radiation and penetrates clothing well. We present a new planar system at Ka band for the three-dimensional simultaneous imaging of both sides of an inspected person where the images are produced in real time by a recently proposed generalized holographic reconstruction algorithm. Low-cost linear frequency modulation (LFM) radar technology is used along with a simple but efficient method for system calibration. Experimental characterization of the spatial resolution and the sensitivity of the system prototype has been carried out. It is established that the achieved spatial resolution is 6 mm or better if the item is not obscured by clothing and it may deteriorate to 7 mm depending on the clothing hiding the item. The spatial sensitivity is confirmed to be at least 2 mm. [ABSTRACT FROM AUTHOR]

This study proposed a model for highway accident detection that combines the You Only Look Once v3 (YOLOv3) object detection algorithm and Canny edge detection algorithm. It not only detects whether an accident has occurred in front of a vehicle, but further performs a preliminary classification of the accident to determine its severity. First, this study established a dataset consisting of around 4500 images mainly taken from the angle of view of dashcams from an open-source online platform. The dataset was named the Highway Dashcam Car Accident for Classification System (HDCA-CS) and was developed with the aim of conforming to the setting of this study. The HDCA-CS not only considers weather conditions (rainy days, foggy days, nighttime settings, and other low-visibility conditions), but also various types of accidents, thus increasing the diversity of the dataset. In addition, we proposed two types of accidents—accidents involving damaged cars and accidents involving overturned cars—and developed three different design methods for comparing vehicles involved in accidents involving damaged cars. Canny edge detection algorithm processed single high-resolution images of accidents were also added to compensate for the low volume of accident data, thereby addressing the problem of data imbalance for training purposes. Lastly, the results showed that the proposed model achieved a mean average precision (mAP) of 62.60% when applied to the HDCA-CS testing dataset. When comparing the proposed model with a benchmark model, two abovementioned accident types were combined to allow the proposed model to produce binary classification outputs (i.e., non-occurrence and occurrence of an accident). The HDCA-CS was then applied to the two models, and testing was conducted using single high-resolution images. At 76.42%, the mAP of the proposed model outperformed the benchmark model's 75.18%; and if we were to apply the proposed model to only test scenarios in which an accident has occurred, its performance would be even better relative to the benchmark. Therefore, our findings demonstrate that our proposed model is superior to other existing models. [ABSTRACT FROM AUTHOR]

Infectious diseases, such as COVID-19, SARS, MERS, etc., have seriously endangered human safety, economy, and education. During the spread of epidemics, restricting the range of activities of personnel is one of the options for the prevention and treatment of infectious diseases. A global navigation satellite system (GNSS), it can provide accurate coordinates of latitude and longitude to targets with GNSS receivers. However, it is not common to use GNSS coordinates to represent positions in social life. For epidemic management, it is important to know the locations (and addresses) of targets, especially in social life. When there are many targets, it is not easy to efficiently map these coordinates to locations. Therefore, we propose a GNSS-based crowd-sensing strategy for specific geographical areas that can be used to calculate how many targets are in specific geographical areas or whether a target is in a specific area. This strategy is based on the coordinates of latitude and longitude provided by GNSS to find the locations of these coordinates. As simulated data, the data records containing latitude and longitude in a well-known social networking service platform are used. The strategy is also available for mining hot spots or hot areas. [ABSTRACT FROM AUTHOR]

As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the "last mile" of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted. [ABSTRACT FROM AUTHOR]

As the network technology continues to grow at a high rate of speed, the traditional network topology is improved with novel distributed topologies such as the Cloud computing network. A cloud computing environment consists of a huge number of processors and memories, high-speed networks, and various application services to provide a lot of services over the Internet for users. However, many services need to search for suitable service nodes, and the workload of each node can be unbalanced. Based on the reason above, the Reference Queue based Cloud Service Architecture (RQCSA) and Fitness Service Queue Selection Mechanism (FSQSM) are proposed to handle more tasks, lower the makespan and queue waiting time, and improve efficiency. Moreover, the tasks can be distributed more evenly to avoid overloading cluster managers and lower the efficiency of the system. [ABSTRACT FROM AUTHOR]

Simple Summary: Antibiotics have been commonly used worldwide as growth promoters and for prophylactic treatment of diarrhea in weaning piglets. However, The European Union has banned the use of antibiotic growth promoters in animal production. Therefore, finding alternative solutions for preventing diarrhea in weaning piglets is urgent. Modulation of gut microbiota composition by probiotics has a beneficial effect on animal health. In this study, we assessed the effects of Bacillus licheniformis-fermented products on diarrhea incidence and the fecal microbiome composition in weaning piglets. Results showed that B. licheniformis-fermented products could improve diarrhea incidence and the fecal microbiota community in weaning piglets. These findings indicate that B. licheniformis-fermented products have the potential for development as feed additives and use as possible substitutes for antibiotics to prevent postweaning diarrhea in the pig industry. Prophylactic use of antibiotics in-feed has been effective in decreasing the incidence of diarrhea in weaning piglets. However, the overuse of antibiotics as prophylactic or therapeutic agents in animal feed leads to the evolution of drug-resistant bacteria and antibiotic residues in pigs. This study investigated the effects of Bacillus licheniformis-fermented products on diarrhea incidence and the fecal microbial community in weaning piglets. A total of 120 crossbred piglets with an average initial body weight of 9.87 ± 1.43 kg were randomly allotted to four dietary treatments consisting of three replicate stalls with 10 piglets in each. The dietary treatments comprised a basal diet as control, control plus 1 g/kg or 4.5 g/kg of B. licheniformis-fermented products, and control plus 30 mg/kg antibiotics (bacitracin methylene disalicylate). Results showed that 4.5 g/kg of B. licheniformis-fermented product supplementation significantly reduced diarrhea incidence in weaning piglets. Principal coordinate analysis and a heatmap of species abundance indicated distinct clusters between the groups treated with antibiotics and B. licheniformis-fermented products. The bacterial richness and evenness in the feces decreased in weaning piglets fed 1 g/kg of B. licheniformis-fermented products and antibiotics. The abundance of the genera [Ruminococcus] gauvreauii group, Ruminococcaceae UCG-005, and Ruminococcaceae UCG-008 in the feces decreased in weaning piglets fed B. licheniformis-fermented products or antibiotics. The average abundance of the genus Prevotella 9 in the feces was positively correlated with the concentration of B. licheniformis-fermented products and negatively correlated with the diarrhea incidence in weaning piglets. Furthermore, the average abundance of the genus Prevotella 9 in the feces was positively correlated with the growth performance of weaning piglets. These results demonstrate that B. licheniformis-fermented products can improve diarrhea incidence and fecal microflora composition in weaning piglets. [ABSTRACT FROM AUTHOR]

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants' muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising. [ABSTRACT FROM AUTHOR]

As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection. [ABSTRACT FROM AUTHOR]

Liposomes are considered one of the most successful drug delivery systems (DDS) given their established utility and success in the clinic. In the past 40–50 years, Canadian scientists have made ground-breaking discoveries, many of which were successfully translated to the clinic, leading to the formation of biotech companies, the creation of research tools, such as the Lipex Extruder and the NanoAssemblr™, as well as contributing significantly to the development of pharmaceutical products, such as Abelcet®, MyoCet®, Marqibo®, Vyxeos®, and Onpattro™, which are making positive impacts on patients' health. This review highlights the Canadian contribution to the development of these and other important liposomal technologies that have touched patients. In this review, we try to address the question of what drives innovation: Is it the individual, the teams, the funding, and/or an entrepreneurial spirit that leads to success? From this perspective, it is possible to define how innovation will translate to meaningful commercial ventures and products with impact in the future. We begin with a brief history followed by descriptions of drug delivery technologies influenced by Canadian researchers. We will discuss recent advances in liposomal technologies, including the Metaplex technology from the author's lab. The latter exemplifies how a nanotechnology platform can be designed based on multidisciplinary groups with expertise in coordination chemistry, nanomedicines, disease, and business to create new therapeutics that can effect better outcomes in patient populations. We conclude that the team is central to the effort; arguing if the team is entrepreneurial and well positioned, the funds needed will be found, but likely not solely in Canada. [ABSTRACT FROM AUTHOR]

The persistent transmission of Japanese encephalitis virus (JEV) in Taiwan necessitates exploring the risk factors of occurrence of Japanese encephalitis (JE). The purpose of this study was to assess the relationship between meteorological factors and the incidence of JE in Taiwan. We collected data for cases of JE reported to the Taiwan Centers for Disease Control (Taiwan CDC) from 2000 to 2014. Meteorological data were obtained from the Taiwan Central Weather Bureau. The relationships between weather variability and the incidence of JE in Taiwan were determined via Poisson regression analysis and a case-crossover methodology. During the 15-year study period, a total of 379 cases of JE were reported. The incidence of JE showed significant seasonality, with the majority of cases occurring in summertime (for oscillation, p < 0.001). The number of JE cases started to increase at temperatures of 22 °C (r² = 0.88, p < 0.001). Similarly, the number of JE cases began to increase at a relative humidity of 70-74% (r² = 0.75, p < 0.005). The number of JE cases was positively associated with mean temperature and relative humidity in the period preceding the infection. In conclusion, the occurrence of JE is significantly associated with increasing temperature and relative humidity in Taiwan. Therefore, these factors could be regarded as warning signals indicating the need to implement preventive measures., Competing Interests: The authors declare no conflict of interest.

The choice of a pure cohesive or a pure frictional viscoplastic model to represent the rheological behaviour of a flowslide is of paramount importance in order to obtain accurate results for real cases. The principal goal of the present work is to clarify the influence of the type of viscous model-pure cohesive versus pure frictional-with the numerical reproduction of two different real flowslides that occurred in 1966: the Aberfan flowslide and the Gypsum tailings impoundment flowslide. In the present work, a depth-integrated model based on the v - p w Biot-Zienkiewicz formulation, enhanced with a diffusion-like equation to account for the pore pressure evolution within the soil mass, is applied to both 1966 cases. For the Aberfan flowslide, a frictional viscous model based on Perzyna viscoplasticity is considered, while a pure cohesive viscous model (Bingham model) is considered for the case of the Gypsum flowslide. The numerical approach followed is the SPH method, which has been enriched by adding a 1D finite difference grid to each SPH node in order to improve the description of the pore water evolution in the propagating mixture. The results obtained by the performed simulations are in agreement with the documentation obtained through the UK National Archive (Aberfan flowslide) and the International Commission of large Dams (Gypsum flowslide)., Competing Interests: The authors declare no conflict of interest.