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A wireless sensor network (WSN) consists of a very large number of sensors which are deployed in the specific area of interest. A sensor is an electronic device equipped with a small processor and has a small-capacity memory. The WSN has the functions of low cost, easy deployment, and random reconfiguration. In this paper, an energy-efficient load balancing tree-based data aggregation scheme (LB-TBDAS) for grid-based WSNs is proposed. In this scheme, the sensing area is partitioned into many cells of a grid and then the sensor node with the maximum residual energy is elected to be the cell head in each cell. Then, the tree-like path is established by using the minimum spanning tree algorithm. In the tree construction, it must meet the three constraints, which are the minimum energy consumption spanning tree, the network depth, and the maximum number of child nodes. In the data transmission process, the cell head is responsible for collecting the sensing data in each cell, and the collected data are transmitted along the tree-like path to the base station (BS). Simulation results show that the total energy consumption of LB-TBDAS is significantly less than that of GB-PEDAP and PEDAP. Compared to GB-PEDAP and PEDAP, the proposed LB-TBDAS extends the network lifetime by more than 100%. The proposed LB-TBDAS can avoid excessive energy consumption of sensor nodes during multi-hop data transmission and can also avoid the hotspot problem of WSNs.

Computation offloading for wireless sensor devices is critical to improve energy efficiency and maintain service delay requirements. However, simultaneous offloadings may cause high interferences to decrease the upload rate and cause additional transmission delay. It is thus intuitive to distribute wireless sensor devices in different channels, but the problem of multi-channel computation offloading is NP-hard. In order to solve this problem efficiently, we formulate the computation offloading decision problem as a decision-making game. Then, we apply the game theory to address the problem of allowing wireless sensor devices to make offloading decisions based on their own interests. In the game theory, not only are the data size of wireless sensor devices and their computation capability considered but the channel gain of each wireless sensor device is also included to improve the transmission rate. The consideration could evenly distribute wireless sensor devices to different channels. We prove that the proposed offloading game is a potential game, where the Nash equilibrium exists in each game after all device states converge. Finally, we extensively evaluate the performance of the proposed algorithm based on simulations. The simulation results demonstrate that our algorithm can reduce the number of iterations to achieve Nash equilibrium by 16%. Moreover, it improves the utilization of each channel to effectively increase the number of successful offloadings and lower the energy consumption of wireless sensor devices.

A novel nitrated [6,6,6]tricycles-derived compound containing nitro, methoxy, and ispropyloxy groups, namely SK1, was developed in our previous report. However, the anticancer effects of SK1 were not assessed. Moreover, SK1 contains two nitro groups (NO2) and one nitrogen-oxygen (N-O) bond exhibiting the potential for oxidative stress generation, but this was not examined. The present study aimed to evaluate the antiproliferation effects and oxidative stress and its associated responses between oral cancer and normal cells. Based on the MTS assay, SK1 demonstrated more antiproliferation ability in oral cancer cells than normal cells, reversed by N-acetylcysteine. This suggests that SK1 causes antiproliferation effects preferentially in an oxidative stress-dependent manner. The oxidative stress-associated responses were further validated, showing higher ROS/MitoSOX burst, MMP, and GSH depletion in oral cancer cells than in normal cells. Meanwhile, SK1 caused oxidative stress-causing apoptosis, such as caspases 3/8/9, and DNA damages, such as γH2AX and 8-OHdG, to a greater extent in oral cancer cells than in normal cells. Siilar to cell viability, these oxidative stress responses were partially diminished by NAC, indicating that SK1 promoted oxidative stress-dependent responses. In conclusion, SK1 exerts oxidative stress, apoptosis, and DNA damage to a greater extent to oral cancer cells than in normal cells.

Introduction: Bacteremia is a common but life-threatening infectious disease. However, a well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is lacking. The aim of this study is to establish a predictive model for bacteremia in septic patients using available big data in the emergency department (ED) through logistic regression and other machine learning (ML) methods. Material and Methods: We conducted a retrospective cohort study at the ED of National Cheng Kung University Hospital in Taiwan from January 2015 to December 2019. ED adults (≥18 years old) with systemic inflammatory response syndrome and receiving blood cultures during the ED stay were included. Models I and II were established based on logistic regression, both of which were derived from support vector machine (SVM) and random forest (RF). Net reclassification index was used to determine which model was superior. Results: During the study period, 437,969 patients visited the study ED, and 40,395 patients were enrolled. Patients diagnosed with bacteremia accounted for 7.7% of the cohort. The area under the receiver operating curve (AUROC) in models I and II was 0.729 (95% CI, 0.718–0.740) and 0.731 (95% CI, 0.721–0.742), with Akaike information criterion (AIC) of 16,840 and 16,803, respectively. The performance of model II was superior to that of model I. The AUROC values of models III and IV in the validation dataset were 0.730 (95% CI, 0.713–0.747) and 0.705 (0.688–0.722), respectively. There is no statistical evidence to support that the performance of the model created with logistic regression is superior to those created by SVM and RF. Discussion: The advantage of the SVM or RF model is that the prediction model is more elastic and not limited to a linear relationship. The advantage of the LR model is that it is easy to explain the influence of the independent variable on the response variable. These models could help medical staff identify high-risk patients and prevent unnecessary antibiotic use. The performance of SVM and RF was not inferior to that of logistic regression. Conclusions: We established models that provide discrimination in predicting bacteremia among patients with sepsis. The reported results could inspire researchers to adopt ML in their development of prediction algorithms.

Intraoperative facial nerve monitoring (FNM) has been widely accepted as an adjunct during parotid surgery to facilitate identification of the facial nerve (FN) main trunk, dissection of FN branches, confirmation of FN function integrity, detection of FN injury and prognostication of facial expression after tumor resection. Although the use of FNM in parotidectomy is increasing, little uniformity exists in its application from the literature. Thus, not only are the results of many studies difficult to compare but the value of FNM technology is also limited. The article reviews the current literature and proposes our standardized FNM procedures during parotid surgery, such as standards in FNM setup, standards in general anesthesia, standards in FNM procedures and application of stimulus currents, interpretation of electrophysiologic signals and prediction of the facial expression outcome and pre-/post-operative assessment of facial expressions. We hope that the FNM standardized procedures will provide greater uniformity, improve the quality of applications and contribute to future research.

A wireless sensor network (WSN) is made up of multiple sensors deployed in a specific sensing area to identify the occurrence of events and quickly transmit useful information back to the base station (BS). In WSNs, schemes to reduce energy consumption are an important topic of research. A well-designed data transmission scheme can effectively extend the lifetime of a network. In this paper, we propose an energy aware grid-based clustering power efficient data aggregation protocol (GB-PEDAP) for WSNs. The proposed scheme has a two-layer architecture: the inner layer and the outer layer. The inner layer uses direct transmission to collect the data of the cluster (cell), and the outer layer uses a tree structure transmission to collect the data of the cluster head (cell head). In our simulations, the number of rounds executed by GB-PEDAP was approximately 1.2 rounds of TBEEP, 1.3 rounds of GSTEB, and 1.5 rounds of PEDAP. With the initial energy, 0.25 J, the execution rounds of the first node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP were 751, 572, 486, and 339, respectively. The proposed GB-PEDAP can evenly disperse the energy consumption of sensors to avoid the rapid death of sensors, extending the lifetime of a WSN.

In this study, we investigated the molecular evolution and phylodynamics of respiratory syncytial virus (RSV) over 10 consecutive seasons (2008–2017) and the genetic variability of the RSV genotypes ON1 and BA in central Taiwan. The ectodomain region of the G gene was sequenced for genotyping. The nucleotide and deduced amino acid sequences of the second hypervariable region of the G protein in RSV ON1 and BA were analyzed. A total of 132 RSV-A and 81 RSV-B isolates were obtained. Phylogenetic analysis revealed that the NA1, ON1, and BA9 genotypes were responsible for the RSV epidemics in central Taiwan in the study period. For RSV-A, the NA1 genotype predominated during the 2008–2011 seasons. The ON1 genotype was first detected in 2011 and replaced NA1 after 2012. For RSV-B, the BA9 and BA10 genotypes cocirculated from 2008 to 2010, but the BA9 genotype has predominated since 2012. Amino acid sequence alignments revealed the continuous evolution of the G gene in the ectodomain region. The predicted N-glycosylation sites were relatively conserved in the ON1 (site 237 and 318) and BA9 (site 296 and 310) genotype strains. Our results contribute to the understanding and prediction of the temporal evolution of RSV at the local level.

Conventional dark-field digital holographic microscopy (DHM) techniques require the use of specialized optics, and, thus, obtaining dark-field images with high contrast has a high cost. Herein, we propose a DHM system that uses an interference-dark-field technique for improving image contrast. Unlike conventional dark-field DHM, the proposed technique does not require expensive and specialized optical elements, or a complicated optical setup, to obtain dark-field images. The proposed technique employs a pure optical basis method to suppress scattering noise—namely, interference-dark-field—and mainly adopts an arbitrary micro-phase shifting method to achieve destructive interference for obtaining holograms. Under the framework of the proposed technique and through the observation of the USAF 1951 resolution target, the reconstructed image can retain the high contrast of the interference-dark-field DHM. The image contrast is enhanced by at least 43% compared to that which is obtained by conventional dark-field DHM. The resolution of the system can be as high as 0.87 μm. The proposed technique can switch between bright-field and dark-field DHM and prevents damage to the sample, which results from high-intensity illumination in conventional techniques.

There are increasing incidences of elderly patients with recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC). However, the treatment is not yet established. We conducted a propensity score matching analysis to evaluate the efficacy and safety of tegafur–uracil versus 5-fluorouracil in combination with cisplatin plus cetuximab in elderly patients with R/M HNSCC. Elderly patients with R/M HNSCC treated with cetuximab-containing chemotherapy were recruited into this study. In order to reduce the selection bias, propensity score matching was performed. Kaplan–Meier curves were plotted for progression-free survival (PFS) and overall survival (OS). Toxicities were graded according to the National Cancer Institute’s Common Terminology Criteria V3.0. After propensity sore matching, 54 patients with tegafur–uracil, cisplatin plus cetuximab (UPEx), and 54 patients with 5-fluorouracil, cisplatin plus cetuximab (EXTREME) were identified. The median PFS was 5.4 months in UPEx and 5.8 months in EXTREME (p = 0.451). The median OS was 10.8 months in UPEx and 10.2 months in EXTREME (p = 0.807). The overall response rate (ORR) and disease control rate (DCR) were insignificant in both arms, accounting for 61% versus 59% (p = 0.680) and 72% versus 70% (p = 0.732) in the UPEx arm and the EXTREME arm, respectively. A multivariate analysis showed that age and ECOG PS were, independently, predictors. Grade 3/4 adverse events were much fewer in UPEx than in EXTREME (p < 0.001). Both cetuximab-containing chemotherapies are effective in elderly patients with R/M HNSCC. Safety profiles are improved when tegafur–uracil is substituted for 5-fluorouracil. Further prospective studies are warranted to validate our conclusions.

We aimed to determine the incidence of bacteremia and prognostic effects of prompt administration of appropriate antimicrobial therapy (AAT) on nontraumatic out-of-hospital cardiac arrest (OHCA) patients achieving a sustained return of spontaneous circulation (sROSC), compared with non-OHCA patients. In the multicenter case-control study, nontraumatic OHCA adults with bacteremia episodes after achieving sROSC were defined as case patients, and non-OHCA patients with community-onset bacteremia in the emergency department were regarded as control patients. Initially, case patients had a higher bacteremia incidence than non-OHCA visits (231/2171, 10.6% vs. 10,430/314,620, 3.3%; p < 0.001). Compared with the matched control (2288) patients, case (231) patients experienced more bacteremic episodes due to low respiratory tract infections, fewer urosepsis events, fewer Escherichia coli bacteremia, and more streptococcal and anaerobes bacteremia. Antimicrobial-resistant organisms, such as methicillin-resistant Staphylococcus aureus and extended-spectrum beta-lactamase-producing Enterobacteriaceae, were frequently evident in case patients. Notably, each hour delay in AAT administration was associated with an average increase of 10.6% in crude 30-day mortality rates in case patients, 0.7% in critically ill control patients, and 0.3% in less critically ill control patients. Conclusively, the incidence and characteristics of bacteremia differed between the nontraumatic OHCA and non-OHCA patients. The incorporation of blood culture samplings and rapid AAT administration as first-aids is essential for nontraumatic OHCA patients after achieving sROSC.

Glioblastoma, World Health Organization—grade IV, is the most malignant glioma type and it is still an incurable tumor due to the high level of heterogeneity and uncontrolled metastatic nature. In addition to the tumorigenicity-suppressing activity, bone morphogenetic protein 7 (BMP7) has recently been found for its invasion-promoting role in glioblastoma. However, the detailed and precise mechanism in this issue should have more elucidation. Thus, in this study, we determined the BMP7 effect on glioblastoma transmigration and migration regulations and the underlying mechanisms. Human LN18/LN229 glioblastoma cells were used in this study. Our results showed a higher BMP7/pSmad5 level in human malignant glioma tissues compared to healthy brain tissues. In addition, it was demonstrated that endogenous and exogenous BMP7 stimulation could increase the transmigration and migration capabilities of human LN18/LN229 glioblastoma cells. Moreover, this event is regulated by Smad5 and p75 neurotrophin receptor (p75NTR) signaling. Furthermore, unexpected data are that the Smad1 gene knockdown could lead to the cell death of human LN18 glioblastoma cells. Overall, the present study finds that the invasion-promoting activity of BMP7 might be an autocrine stimulation of glioblastoma and this effect could be regulated by Smad5-p75NTR signaling.

The diagnosis of primary hepatocellular carcinoma (HCC) in high risk patients, such as those with chronic hepatitis B or C with/without cirrhosis of the liver, can be made with relevant image studies alone; however, morphologic confirmation by cytology or histopathology is essential to the diagnosis of metastatic HCC because the treatment of the latter differs from that of primary extrahepatic malignancy. Although both cytology and histopathology are important tools in the diagnosis of malignancy, histopathology almost always plays a confirmatory and pivotal role, and also a gold standard in cyto-histological correlation. We present two patients of metastatic HCC to rare sites; endobronchus in one, and neck lymph nodes, face and the abdominal wall in the other. Repeated bronchial biopsy in the first patient and excisional biopsy from the metastatic sites of the second patient failed to make a conclusive diagnosis of metastatic HCC. However, bronchial brushing in the first patient and fine needle aspiration cytology from the respective metastatic sites of the second patient consistently showed characteristic HCC features and provided clinicians with the rationale for ongoing treatment. The diagnostic issues and possible pitfalls are discussed. Cytology and histopathology appear equipollent; sometimes cytology can play a diagnostic role when histopathology fails its expected role in this scenario.

Anti-cancer activity of catechin nanoemulsions prepared from Oolong tea leaf waste was studied on prostate cancer cells DU-145 and DU-145-induced tumors in mice. Catechin nanoemulsions composed of lecithin, Tween-80 and water in an appropriate proportion was prepared with high stability, particle size of 11.3 nm, zeta potential of −67.2 mV and encapsulation efficiency of 83.4%. Catechin nanoemulsions were more effective than extracts in inhibiting DU-145 cell growth, with the IC50 being 13.52 and 214.6 μg/mL, respectively, after 48 h incubation. Furthermore, both catechin nanoemulsions and extracts could raise caspase-8, caspase-9 and caspase-3 activities for DU-145 cell apoptosis, arresting the cell cycle at S and G2/M phases. Compared to control, catechin nanoemulsion at 20 μg/mL and paclitaxel at 10 μg/mL were the most effective in reducing tumor volume by 41.3% and 52.5% and tumor weight by 77.5% and 90.6% in mice, respectively, through a decrease in EGF and VEGF levels in serum.

To exploit the ability of vegetation to capture particulate matter (dust) from the air and improve air quality, 546 clean air zones (CAZs) consisting of various types of urban green space have been established in Taiwan. This study systematically assessed the pollutant filtering efficiency of tree species planted in these green spaces. This research aims to provide quantitative data on individual trees’ dust retention functions for future green space planning in urban areas. Field surveys were conducted in 98 CAZs throughout Taiwan. The vegetation composition of approximately 14,000 woody trees, consisting of 210 species, was surveyed. The vegetation surveyed showed that the dominant species in many CAZs in southern Taiwan were introduced species. The dust capturing capacity of the tree species was found to be positively correlated with leaf size. However, the amount of dust retention was affected mainly by the surface structure and morphological characteristics of the leaves, such as a rough, hairy surface. Among the tree species, Spathodea campanulata, Pterocarpus indicus, and Delonix regia exhibited the best dust capture and retention capacity in southern Taiwan, and Ficus macrocarpa, Alstonia scholaris, and Melia azedarach were the most desirable dust retention species. The results suggest that native evergreen species are suitable for dust retention in urban green spaces.

In this study, an adaptive driving method for synchronous rectification in bidirectional full-bridge LLC resonant converters used in railway applications is proposed. The drain to source voltage of the synchronous rectifier is utilized to detect the conduction of the body diode, and a suitable driving signal for synchronous rectification is generated accordingly. The proposed driving scheme is simple and can be realized using a low-cost digital signal processor (DSP). According to the experimental results, which averaged 0.4625% and 1.097%, improvement can be observed under charging and discharging mode, respectively.

High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.

Methods for direct dehydrative glycosylations of carbohydrate hemiacetals catalyzed by diphenylammonium triflate under microwave irradiation are described. Both armed and disarmed glycosyl-C1-hemiacetal donors were efficiently glycosylated in moderate to excellent yields without the need for any drying agents and stoichiometric additives. This method has been successfully applied to a solid-phase glycosylation.

In a wireless sensor network (WSN), a great number of sensor nodes are deployed to gather sensed data. These sensor nodes are typically powered by batteries so their energy is restricted. Sensor nodes mainly consume energy consumption in data transmission, especially for a long distance. Since the location of the base station (BS) is remote, the energy consumed by each node to directly transmit its data to the BS is considerable and the node will die very soon. A well-designed routing protocol is thus essential to reduce the energy consumption. In this paper, we propose a Cycle-Based Data Aggregation Scheme (CBDAS) for grid-based WSNs. In CBDAS, the whole sensor field is divided into a grid of cells, each with a head. We prolong the network lifetime by linking all cell heads together to form a cyclic chain so that the gathered data can move in two directions. For data gathering in each round, the gathered data moves from node to node along the chain, getting aggregated. Finally, a designated cell head, the cycle leader, directly transmits to the BS. CBDAS performs data aggregation at every cell head so as to substantially reduce the amount of data that must be transmitted to the BS. Only cell heads need disseminate data so that the number of data transmissions is greatly diminished. Sensor nodes of each cell take turns as the cell head, and all cell heads on the cyclic chain also take turns being cycle leader. The energy depletion is evenly distributed so that the nodes’ lifetime is extended. As a result, the lifetime of the whole sensor network is extended. Simulation results show that CBDAS outperforms protocols like Direct, PEGASIS, and PBDAS.

The Phase-Locked Loop (PLL) is a key component of modern electronic communication and control systems. PLL is designed to extract signals from transmission channels. It plays an important role in systems where it is required to estimate the phase of a received signal, such as carrier tracking from global positioning system satellites. In order to robustly provide centimeter-level accuracy, it is crucial for the PLL to estimate the instantaneous phase of an incoming signal which is usually buried in random noise or some type of interference. This paper presents an approach that utilizes the recent development in the semi-definite programming and sum-of-squares field. A Lyapunov function will be searched as the certificate of the pull-in range of the PLL system. Moreover, a polynomial design procedure is proposed to further refine the controller parameters for system response away from the equilibrium point. Several simulation results as well as an experiment result are provided to show the effectiveness of this approach.

In this paper, a fast equalizer for series-connected battery packs with adaptive balancing current control is proposed. As the duty cycle of the power switch in conventional equalizers is kept constant during the equalization process, smaller voltage difference between cells will decrease balancing current and consequently result in extended balancing time, especially in the later phase of equalization. To deal with this problem and take the battery nonlinearity and circuit parameter non-ideality into consideration, an adaptive balancing current control based on a fuzzy logic inference is proposed. The presented approach can adjust the duty ratio adaptively, according to voltages of individual cells and pack, to keep the balancing current nearly constant: Therefore, the balancing time can be shortened and the balancing efficiency can be improved. Finally, experimental results of three compared methods will be given and discussed to validate the feasibility, effectiveness, and performance improvement of the studied method.

Deployment of wireless sensor networks (WSNs) has drawn much attention in recent years. Given the limited energy for sensor nodes, it is critical to implement WSNs with energy efficiency designs. Sensing coverage in networks, on the other hand, may degrade gradually over time after WSNs are activated. For mission-critical applications, therefore, energy-efficient coverage control should be taken into consideration to support the quality of service (QoS) of WSNs. Usually, coverage-controlling strategies present some challenging problems: (1) resolving the conflicts while determining which nodes should be turned off to conserve energy; (2) designing an optimal wake-up scheme that avoids awakening more nodes than necessary. In this paper, we implement an energy-efficient coverage control in cluster-based WSNs using a Memetic Algorithm (MA)-based approach, entitled CoCMA, to resolve the challenging problems. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme, which are responsible to prolong the network lifetime while maintaining coverage preservation. The MA-based schedule is applied to a given WSN to avoid unnecessary energy consumption caused by the redundant nodes. During the network operation, the wake-up scheme awakens sleeping sensor nodes to recover coverage hole caused by dead nodes. The performance evaluation of the proposed CoCMA was conducted on a cluster-based WSN (CWSN) under either a random or a uniform deployment of sensor nodes. Simulation results show that the performance yielded by the combination of MA and wake-up scheme is better than that in some existing approaches. Furthermore, CoCMA is able to activate fewer sensor nodes to monitor the required sensing area.

Astragalus polysaccharides (APSs), the compounds extracted from the common herb Astragalus membranaceus, have been extensively studied for their antitumor properties. In this study, we investigated the effect of APS on lung adenocarcinoma A549 cells. The effects of APS and the anti-diabetic drug metformin on apoptosis and ferroptosis were compared. Furthermore, the combination treatment of APS and metformin was also investigated. We found that APS not only reduced the growth of lung cancer cells but also had a synergistic effect with metformin on A549 cells. The study results showed that it may be promising to use APS and metformin as a combination therapy for the treatment of lung adenocarcinoma.

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

This study uses deep learning methods to explore the Internet of Things (IoT) network intrusion detection method based on the CIC-IoT-2023 dataset. This dataset contains extensive data on real-life IoT environments. Based on this, this study proposes an effective intrusion detection method. Apply seven deep learning models, including Transformer, to analyze network traffic characteristics and identify abnormal behavior and potential intrusions through binary and multivariate classifications. Compared with other papers, we not only use a Transformer model, but we also consider the model’s performance in the multi-class classification. Although the accuracy of the Transformer model used in the binary classification is lower than that of DNN and CNN + LSTM hybrid models, it achieves better results in the multi-class classification. The accuracy of binary classification of our model is 0.74% higher than that of papers that also use Transformer on TON-IOT. In the multi-class classification, our best-performing model combination is Transformer, which reaches 99.40% accuracy. Its accuracy is 3.8%, 0.65%, and 0.29% higher than the 95.60%, 98.75%, and 99.11% figures recorded in papers using the same dataset, respectively.

The first reported infections from COVID-19 were in 2019 and, since then, an outbreak has spread rapidly to other parts of the world, resulting in many deaths. As a result, governments began to implement border restrictions and quarantine measures, bringing the travel industry to a halt and plunging the global economy into a severe contraction. Many regions chose to coexist with COVID-19 and gradually eased their border restrictions with certain conditions, such as using personal health status certificates, vaccination certificates, etc. Digital certificates are becoming a global trend, and Taiwan has invested in developing related tools. This paper presents a technical evaluation from the government’s point of view. Taiwan uses the European Union (EU) Digital COVID Certificate as a basis to build a digital certificate that can fully meet the residents’ current international business and tourism needs. The government hopes that this digital proof will promote the public’s return to normal life and overcome the inconveniences brought about by the COVID-19 pandemic. In the post-pandemic era, finding a way to coexist with the virus while gradually relaxing border and community epidemic-prevention policies without impacting our Taiwan’s medical capacity is a significant challenge. Providing key technological solutions to assist in risk stratification is essential in addressing this issue.

Compared with the conventional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method offers advantages such as rapid charging speed and high charging efficiency. However, MSCC must find the optimal charging current profile (OCCP) in order to achieve the aforementioned benefits. Hence, in this paper, five bio-inspired optimization algorithms (BIOAs), including particle swarm optimization (PSO), modified PSO (MPSO), grey wolf optimization (GWO), modified GWO (MGWO), and the jellyfish search algorithm (JSA), are applied to solve the problem of searching for the OCCP of the MSCC. The best solution-finding procedure is run on the MATLAB platform developed based on minimizing the objective function of combining charging time (CT) and energy loss (EL) with a proportional weight. Without requiring numerous and time-consuming actual charge-and-discharge experiments, a wide range of searches can be quickly achieved only through the battery equivalent circuit model (ECM) established. The theoretical derivation and correctness are confirmed via the simulation and experimental results, which demonstrate that the OCCPs obtained by using the devised charging strategies possess the shortest CT and the best charging efficiency (CE), and among them, MPSO has the best fitness value (FV). Compared with the traditional CC-CV method, the experimental results show that the maximum improvement rates (IRs) of the studied approaches in terms of six charging performance evaluation indicators (CPEIs), including CT, charging capacity (CHC), CE, charging energy (CWh), average temperature rise (ATR), and FV, are 21.10%, 0.40%, 0.24%, 2.85%, 18.86%, and 68.99%, respectively. Furthermore, according to the comprehensive evaluation with CPEIs, the top three with the best overall performance are the JSA, MPSO, and GWO methods, respectively.