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Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.

A β-Li3PS4 solid electrolyte was prepared via liquid-phase synthesis using ethyl isobutyrate as a synthetic medium. The precursor and solid electrolyte structures were characterized using thermogravimetry–differential thermal analysis and X-ray diffraction techniques. The dielectric relaxation analysis showed two relaxation regions, which revealed a bulk and grain boundary ionic migration process. At a temperature lower than 90°C, the frequency dependence of the dielectric constant of the prepared sample was different from that of glassy Li3PS4, indicating that the motion of the PS4 unit enhances the ionic conductivity of the Li3PS4 solid electrolyte. The ionic conductivities of the cold-pressed and warm-pressed pellets at 25°C were 6.8 × 10−5 Scm−1 and 3.6 × 10−4 Scm−1, respectively.

Zinc–air batteries are promising candidates as stationary power sources because of their high specific energy density, high volumetric energy density, environmental friendliness, and low cost. The oxygen-related reactions at the air electrode are kinetically slow; thus, the air electrode integrated with an oxygen electrocatalyst is the most critical component, and inevitably determines the performance of a Zn–air battery. The aim of this paper was to document progress in researching bifunctional catalysts for Zn–air batteries. The catalysts are divided into several categories: noble metal, metal nanoparticle (single and bimetallic), multicomponent nanoparticle, metal chalcogenide, metal oxide, layered double hydroxide, and non-metal materials. Finally, the battery performance is compared and discussed.

Export market orientation has always been a central issue in the study of international business and marketing. Therefore, it is essential to put effort into understanding the concept and its application in real-life business. Following in-depth studies and critical review papers in previous years, this research paper focuses on synthesizing and analyzing empirical studies in the period from 2015 to 2021, revolving around theoretical issues, context, the main features of the object, and the methodology. Within a predetermined time frame, twenty critical papers on export market orientation were selected and analyzed. The result reveals in detail that there are sixteen antecedents and five outcomes of export market orientation, as well as a total of eight moderators and seven mediators influencing their relationships. Findings have indicated that in recent years, models and theories of earlier eras have been developed and many of them have reached maturity.

ImidazoleTriflate (ITF)—SiO2 composites xITF-(100-x)SiO2 were prepared by planetary ball-milling method. The structure of the composites was studied by TG-DTA, XRD and FT-IR methods. The ionic conductivity was assessed by alternative current (AC) electrochemical impedance measurement. From XRD results, the crystal size of imidazole triflate became smaller as its loading in xITF-(100-x)SiO2 decreased. With x = 40 weight percent, the calculated crystal size was about 7.3 nm. FT-IR spectra showed the existence of CF3SO3-···H+ ion aggregates and imidazole—proton bonds. The electrical conductivity of sample x = 50 was about 6.3 × 10−3 Scm−1 at 130 °C under anhydrous condition and about 30 times higher than that of sample x = 100.

Influenza burden estimates are essential to informing prevention and control policies. To complement recent influenza vaccine production capacity in Vietnam, we used acute respiratory infection (ARI) hospitalization data, severe acute respiratory infection (SARI) surveillance data, and provincial population data from 4 provinces representing Vietnam’s major regions during 2014–2016 to calculate provincial and national influenza-associated ARI and SARI hospitalization rates. We determined the proportion of ARI admissions meeting the World Health Organization SARI case definition through medical record review. The mean influenza-associated hospitalization rates per 100,000 population were 218 (95% uncertainty interval [UI] 197–238) for ARI and 134 (95% UI 119–149) for SARI. Influenza-associated SARI hospitalization rates per 100,000 population were highest among children 65 years of age (207; 95% UI 186–227), underscoring the need for prevention and control measures, such as vaccination, in these at-risk populations.

Novel (80Li2S − 20AlI3)·yLiI composite solid electrolytes (y = 5, 10, 15) were prepared by mechannochemical synthesis. XRD results showed that the pattern of 80Li2S − 20AlI3 was similar to that of AlI3, which means that Li2S was dissolved in AlI3 matrix during preparation. This structure was still maintained after LiI addition. The current measured at constant applied DC voltage indicated that (80Li2S − 20AlI3)·yLiI composites are intrinsically pure Li-ion conductors. The ionic conductivity at 25 °C of y = 10 was about 2.3 × 10−4 Scm−1, which was about three times higher than that of y = 0. The conductivity of y = 10 increased 20 times to 2.2 × 10−3 Scm−1 at 70 °C. These values were highest among those observed from Li2S-based materials. It was revealed that Li-ion moves in 80Li2S − 20AlI3 by a hoping mechanism, while the lattice dipoles are the origin of Li-ion movement in (80Li2S − 20AlI3)·yLiI. The polarization measurements using Liǀ90 (80Li2S − 20AlI3)·10LiI ǀLi and LiǀLi6PS5Clǀ90 (80Li2S − 20AlI3)·10LiIǀLi6PS5ClǀLi cells proved that 90 (80Li2S − 20AlI3)·10LiI reacts with Li metal, but it is relatively stable at a low voltage. Sample y = 10 was also employed as a solid electrolyte in the positive electrode of a solid-state Li-S battery to study its stability in the voltage range of the positive electrode. CuS and Li4.4Si were the electrode-active materials. The cell was cycled in CC-CV mode at 1.0 mA cm−2 (CC) with a cut-off voltage of 1.0–2.3 V. The cell delivered a stable capacity of about 400 mAh g−1CuS after 40 cycles.

Introduction: Chaotic resonance is similar to stochastic resonance, which emerges from chaos as an internal dynamical fluctuation. In chaotic resonance, chaos-chaos intermittency (CCI), in which the chaotic orbits shift between the separated attractor regions, synchronizes with a weak input signal. Chaotic resonance exhibits higher sensitivity than stochastic resonance. However, engineering applications are difficult because adjusting the internal system parameters, especially of biological systems, to induce chaotic resonance from the outside environment is challenging. Moreover, several studies reported abnormal neural activity caused by CCI. Recently, our study proposed that the double-Gaussian-filtered reduced region of orbit (RRO) method (abbreviated as DG-RRO), using external feedback signals to generate chaotic resonance, could control CCI with a lower perturbation strength than the conventional RRO method. Method: This study applied the DG-RRO method to a model which includes excitatory and inhibitory neuron populations in the frontal cortex as typical neural systems with CCI behavior. Results and discussion: Our results reveal that DG-RRO can be applied to neural systems with extremely low perturbation but still maintain robust effectiveness compared to conventional RRO, even in noisy environments. [ABSTRACT FROM AUTHOR]

To assess the role of in-flight transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we investigated a cluster of cases among passengers on a 10-hour commercial flight. Affected persons were passengers, crew, and their close contacts. We traced 217 passengers and crew to their final destinations and interviewed, tested, and quarantined them. Among the 16 persons in whom SARS-CoV-2 infection was detected, 12 (75%) were passengers seated in business class along with the only symptomatic person (attack rate 62%). Seating proximity was strongly associated with increased infection risk (risk ratio 7.3, 95% CI 1.2–46.2). We found no strong evidence supporting alternative transmission scenarios. In-flight transmission that probably originated from 1 symptomatic passenger caused a large cluster of cases during a long flight. Guidelines for preventing SARS-CoV-2 infection among air passengers should consider individual passengers’ risk for infection, the number of passengers traveling, and flight duration.

A cluster of severe acute respiratory syndrome coronavirus 2 infections in Danang, Vietnam, began July 25, 2020, and resulted in 551 confirmed cases and 35 deaths as of February 2021. We analyzed 26 sequences from this cluster and identified a novel shared mutation in nonstructural protein 9, suggesting a single introduction into Vietnam.

BACKGROUND:Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001-2012 to determine seasonal trends, develop risk maps and an incidence forecasting model. METHODS:The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001-2009) and validation (2010-2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil's coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010-2012 were also used to generate risk maps. RESULTS:The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil's coefficient of inequality of 0.22 was generated. CONCLUSIONS:The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil's coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.

Breast cancer is a common cancer that affects women all over the world. Therefore, detection at an early stage is crucial for reducing the mortality rate linked to this disease. Mammography is the primary screening method for breast cancer. However, it has drawbacks, including high rates of false-positive and negative results, inter-observer variability, and limited sensitivity with dense breast tissue. To solve such problems, breast cancer was analyzed and classified using mammography images and deep learning models from the Radiological Society of North America (RSNA) database. This database contains processed and raw images from the RSNA that consist of annotated malignancies and clinical data. Using deep learning models based on convolutional neural network (CNN) models such as visual geometry group (VGG), Googlenet, EfficientNet, and Residual Networks, mammograms were classified into cancer or non-cancer categories. In this study, a novel architecture was proposed by combining CNNs and attention mechanisms, which extracted and highlighted the relevant features. A dataset of 8000 patients with 47,000 photographs was used to train and assess the model via 5-fold cross-validation. The results outperformed prior methods using the same database and reached an average accuracy rate of 95%. The results showed that mammography with deep learning methods considerably improved breast cancer detection and diagnosis. [ABSTRACT FROM AUTHOR]

As an alternate non-destructive analytical modality for monitoring from pre-harvest to post-storage, optical imaging with near-infrared wavelength is used to forecast the quality of numerous fruits. In the near-infrared spectrum, bio-chemicals are identified and measured with light by penetrating deeply into food components. In addition, apples and other fruits with a high water content benefit from water absorption capabilities. The optical approaches are efficient, inexpensive, and environmentally beneficial. This study is performed to examine the setup of reflection imaging to pick the near-infrared wavelength and optimize the distance between the detector and the light source. Molecular Optical Simulation Environment (MOSE) and Monte Carlo multi-layered programs (MCML) were used to simulate the light propagation in a model of apple tissue to select the appropriate wavelength for evaluating food quality in experiments and optimize the position of the reflected signal receiver. As a consequence, the 700–900 nm wavelength has great promise for use in assessing food quality, particularly apple quality. One centimeter is the optimal distance between the detector and the light source. The data may be used to organize an experiment and create an evaluation tool for determining the quality of fruits using optical methods, particularly apples. [ABSTRACT FROM AUTHOR]