The WHO recently declared that COVID-19 no longer constitutes a public health emergency of international concern; however, lessons learned through the pandemic should not be left behind. Lung ultrasound was largely utilized as a diagnostic tool thanks to its feasibility, easy application, and the possibility to reduce the source of infection for health personnel. Lung ultrasound scores consist of grading systems used to guide diagnosis and medical decisions, owning a good prognostic value. In the emergency context of the pandemic, several lung ultrasound scores emerged either as new scores or as modifications of pre-existing ones. Our aim is to clarify the key aspects of lung ultrasound and lung ultrasound scores to standardize their clinical use in a non-pandemic context. The authors searched on PubMed for articles related to “COVID-19”, “ultrasound”, and “Score” until 5 May 2023; other keywords were “thoracic”, “lung”, “echography”, and “diaphragm”. A narrative summary of the results was made. Lung ultrasound scores are demonstrated to be an important tool for triage, prediction of severity, and aid in medical decisions. Ultimately, the existence of numerous scores leads to a lack of clarity, confusion, and an absence of standardization. Full article

For the last two decades, artificial neural networks (ANNs) of the third generation, also known as spiking neural networks (SNN), have remained a subject of interest for researchers. A significant difficulty for the practical application of SNNs is their poor suitability for von Neumann computer architecture, so many researchers are currently focusing on the development of alternative hardware. Nevertheless, today several experimental libraries implementing SNNs for conventional computers are available. In this paper, using the RCNet library, we compare the performance of reservoir computing architectures based on artificial and spiking neural networks. We explicitly show that, despite the higher execution time, SNNs can demonstrate outstanding classification accuracy in the case of complicated datasets, such as data from industrial sensors used for the fault detection of bearings and gears. For one of the test problems, namely, ball bearing diagnosis using an accelerometer, the accuracy of the classification using reservoir SNN almost reached 100%, while the reservoir ANN was able to achieve recognition accuracy up to only 61%. The results of the study clearly demonstrate the superiority and benefits of SNN classificators. Full article

Community awareness and participation in mass screening is critical for schistosomiasis control. This study assessed the impact of sharing anonymized image-based positive test results on the uptake of screening during community mobilization outreach. We conducted an observational study to compare the population response to standard and image-based strategies in 14 communities in Abuja, Nigeria. Six hundred and ninety-one (341 females, 350 males) individuals participated in this study. We analyzed the response ratio, relative increase, and sample collection time. The potential treatment uptake and change in social behavior were determined based on a semi-structured questionnaire. The mean response ratio of the image-based strategy was 89.7% representing a significantly higher ratio than the 27.8%, which was observed under the standard mobilization approach (p ≤ 0.001). The image-based method was associated with 100% of the participants agreeing to provide urine samples, 94% willing to be treated, 89% claiming to have been invited to participate in the study by a friend, and 91% desiring to change a predisposing behavioral habit. These findings indicate that image-based community awareness campaigns may increase the population’s perception about schistosomiasis transmission and treatment. This raises new possibilities for local resource mobilization to expand services in reaching the last mile in schistosomiasis control. Full article

Today’s dynamic business environment has pushed service-oriented firms such as banks to collaborate with external partners through open innovation (OI) to address issues of service differentiation, optimize customer experience, and create effective open innovation strategies (OIS). However, the essential elements required to design OIS and the methods to manage these strategies are missing. Therefore, this study aims to investigate the strategic resources essential to creating OIS and identify the tools to manage these resources. Following the fundamentals of the resource-based view (RBV), bank openness (BOP), selection of external partners (SEP), open innovation methods (OIM), formalizing collaboration processes (FCP), and banks’ internal practices (BIP) are identified as the strategic elements required for creating OIS, and the role of big data analytics (BDA) in these strategic resources is examined. The data were collected through a survey questionnaire from 425 bank executives employed at different digital banks located in Malaysia. To achieve our research objectives, a quantitative deductive research design was employed and the collected data were processed in WarPLS using the structural equation modeling (SEM) technique to test the research hypotheses of this study. The empirical results reveal that BDA has a significant positive impact on BOP, SEP, and FCP, whereas OIM and BIP have an insignificant positive impact. The findings of this study contribute to designing a robust digital strategy to enhance the banking sector’s contribution to the development of financial industries in developing countries by employing BDA as a major strategic policy tool of OIS Full article

Advances in applied mechanics have facilitated a better understanding of the recycling of heat and work in the troposphere. This goal is important to meet practical needs for better management of climate science. Achieving this objective may require the application of quantum principles in action mechanics, recently employed to analyze the reversible thermodynamics of Carnot’s heat engine cycle. The testable proposals suggested here seek to solve several problems including (i) the phenomena of decreasing temperature and molecular entropy but increasing Gibbs energy with altitude in the troposphere; (ii) a reversible system storing thermal energy to drive vortical wind flow in anticyclones while frictionally warming the Earth’s surface by heat release from turbulence; (iii) vortical generation of electrical power from translational momentum in airflow in wind farms; and (iv) vortical energy in the destructive power of tropical cyclones. The scalar property of molecular action (@_t ≡ ∫mvds, J-sec) is used to show how equilibrium temperatures are achieved from statistical equality of mechanical torques (mv² or mr²ω²); these are exerted by Gibbs field quanta for each kind of gas phase molecule as rates of translational action (d@_t/dt ≡ ∫mr²ωdϕ/dt ≡ mv²). These torques result from the impulsive density of resonant quantum or Gibbs fields with molecules, configuring the trajectories of gas molecules while balancing molecular pressure against the density of field energy (J/m³). Gibbs energy fields contain no resonant quanta at zero Kelvin, with this chemical potential diminishing in magnitude as the translational action of vapor molecules and quantum field energy content increases with temperature. These cases distinguish symmetrically between causal fields of impulsive quanta (Σhν) that energize the action of matter and the resultant kinetic torques of molecular mechanics (mv²). The quanta of these different fields display mean wavelengths from 10⁻⁴ m to 10¹² m, with radial mechanical advantages many orders of magnitude greater than the corresponding translational actions, though with mean quantum frequencies (v) similar to those of radial Brownian movement for independent particles (ω). Widespread neglect of the Gibbs field energy component of natural systems may be preventing advances in tropospheric mechanics. A better understanding of these vortical Gibbs energy fields as thermodynamically reversible reservoirs for heat can help optimize work processes on Earth, delaying the achievement of maximum entropy production from short-wave solar radiation being converted to outgoing long-wave radiation to space. This understanding may improve strategies for management of global changes in climate. Full article

This paper is dedicated to identifying stable equilibrium positions of the tether systems attached to the L₁ or L₂ libration points of the Mars–Phobos system. The orbiting spacecraft deploying the tether is at the L₁ or L₂ libration point and is held at one of these unstable points by the low thrust of its engines. In this paper, the analysis is performed assuming that the tether length is constant. The equation of motion for the system in the polar reference frame is obtained. The stable equilibrium positions are found and the dependence of the tether angular oscillation period on the tether length is determined. An analytical solution in the vicinity of the stable equilibrium positions for small angles of deflection of the tether from the local vertical is obtained in Jacobi elliptic functions. The comparison of the numerical and analytical solutions for small angles of deflection is performed. The results show that the dependencies of the oscillation period on the length of the tether are fundamentally different for L₁ and L₂ points. Analytical expressions for the tether tension are derived, and the influence of system parameters on this force is investigated for static and dynamic cases. Full article

Artificial intelligence (AI) will play an important role in realizing maritime autonomous surface ships (MASSs). However, as a double-edged sword, this new technology brings forth new threats. The purpose of this study is to raise awareness among stakeholders regarding the potential security threats posed by AI in MASSs. To achieve this, we propose a hypothetical attack scenario in which a clean-label poisoning attack was executed on an object detection model, which resulted in boats being misclassified as ferries, thus preventing the detection of pirates approaching a boat. We used the poison frog algorithm to generate poisoning instances, and trained a YOLOv5 model with both clean and poisoned data. Despite the high accuracy of the model, it misclassified boats as ferries owing to the poisoning of the target instance. Although the experiment was conducted under limited conditions, we confirmed vulnerabilities in the object detection algorithm. This misclassification could lead to inaccurate AI decision making and accidents. The hypothetical scenario proposed in this study emphasizes the vulnerability of object detection models to clean-label poisoning attacks, and the need for mitigation strategies against security threats posed by AI in the maritime industry. Full article

Sapindus mukorossi seeds are commonly drilled and processed into Buddha beads and other craft products. To improve the machining qualities of the micro-drilling of S. mukorossi seeds, the effects of drilling parameters (feed rate, drilling speed, and drill diameter) on the drilling force, hole diameter and hole morphology were found. The mechanism behind these changes was further analyzed. The drilling parameters and machining process for the micro-drilling of S. mukorossi seeds were optimized. Results indicated that each drilling parameter influenced the drilling force and hole-machining quality by affecting the amount of cutting, the working conditions of the drill bit, chip formation, and the generation of drilling heat. Moreover, the feed rate and drill diameter were the main factors that influenced the drilling force, and the drilling speed played a leading role that influenced hole-machining quality. Interestingly, for the drilling of S. mukorossi seeds, a feed rate of 0.03 mm/r, a drilling speed of 6 m/min, and a drill-bit diameter of 0.9 or 1.5 mm resulted in a hole with high accuracy, good hole morphology, and minimal wear on the drill bit. This study facilitates the selection of suitable drilling parameters and processing technology for the micro-drilling of S. mukorossi seeds to improve processing accuracies and qualities, and provides a theoretical basis for the improvement of related drilling processing equipment. Full article

Due to the rarity and complexity of treatment for Ewing sarcoma and rhabdomyosarcoma, studies demonstrate improved patient outcomes when managed by a multidisciplinary team at high-volume centres (HVCs). Our study explores the difference in outcomes of Ewing sarcoma and rhabdomyosarcoma patients based on the centre of initial consultation in British Columbia, Canada. This retrospective study assessed adults diagnosed with Ewing sarcoma and rhabdomyosarcoma between 1 January 2000 and 31 December 2020 undergoing curative intent therapy in one of five cancer centres across the province. Seventy-seven patients were included, 46 seen at HVCs and 31 at low-volume centres (LVCs). Patients at HVCs were younger (32.1 vs. 40.8 years, p = 0.020) and more likely to receive curative intent radiation (88% vs. 67%, p = 0.047). The time from diagnosis to first chemotherapy was 24 days shorter at HVCs (26 vs. 50 days, p = 0.120). There was no significant difference in overall survival by treatment centre (HR 0.850, 95% CI 0.448–1.614). Variations in care exist amongst patients treated at HVCs vs. LVCs, which may reflect differences in access to resources, clinical specialists, and varying practice patterns across centres. This study can be used to inform decisions regarding triaging and centralization of Ewing sarcoma and rhabdomyosarcoma patient treatment. Full article

Beech wood is a material commonly used for furniture, and cutting performance is the key to improving product quality and enterprise benefits. In this work, beech milling experiments using diamond cutters were carried out, and the changes in cutting power, temperature, and surface roughness were examined using the factor analysis method. The main results of this work are listed as follows: Firstly, a higher cutting speed and depth led to higher cutting power, temperature, and surface roughness. Meanwhile, cutting power and surface roughness were negatively related to the rake angle; however, cutting temperature first increased and then decreased with the increase in rake angle. Furthermore, cutting depth had greatest impact on the cutting power and surface roughness, followed by rake angle and cutting speed. Cutting speed had the greatest contribution to the cutting temperature, followed by cutting depth and rake angle. Only the cutting depth had a significant contribution to both cutting power, temperature, and surface roughness. Finally, optimal cutting parameters were determined to be a rake angle of 15°, cutting speed of 54 m/s, and depth of 0.5 mm. These values best meet the multiple objectives of lower cutting power, temperature, and surface roughness, which relate to superior product quality and enterprise benefits. Full article

Background: In Chile, the consumption of legumes at least two times per week is promoted. However, there is a low consumption of legumes. Therefore, our objective is to describe legume consumption in two different seasonal periods. Methods: Serial cross-sectional study: surveys were distributed during summer and winter using different digital platforms. Frequency of consumption, purchase access, and preparation type were investigated. Results: In total, 3280 adults were surveyed in summer and 3339 in winter. The mean age was 33 years. Totals of 97.7% and 97.5% of the population reported consuming legumes in both periods; consumption increased to 3 times per week during winter. In both periods, the main reason for their preference is that they are delicious and nutritious, followed by their use as a meat substitute; the main barriers to their consumption in both periods are that they are expensive (29% in summer and 27.8% in winter) and difficult to prepare. Conclusion: A good consumption of legumes was observed, but with a higher frequency of consumption during winter, with an intake of ≥1 per day; additionally, differences were found in purchases according to season, although no differences were found in the method of preparation. Full article

It is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results. Only a minuscule amount of work has been conducted under RSS with missing data. This paper makes a modest attempt to provide some efficient difference- and ratio-type imputation methods in the presence of missing values under RSS. The envisaged imputation methods are demonstrated to provide better results than the existing imputation methods. The theoretical results are enhanced by a computational analysis using real and hypothetically generated symmetric (Normal) and asymmetric (Gamma and Weibull) populations. The computational results show that the proposed imputation method outperforms the existing imputation methods in terms of its higher percent relative efficiency. Additionally, the impact of skewness and kurtosis on the efficiency of the suggested imputation methods has also been calculated. Full article

Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human–computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain–computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software. Full article

Background: The occurrence of accidental nerve damage during surgery and the increasing application of image guidance during head-and-neck surgery have highlighted the need for molecular targeted nerve-sparing interventions. The implementation of such interventions relies on the availability of nerve-specific tracers. In this paper, we describe the development of a truncated peptide that has an optimized affinity for protein zero (P0), the most abundant protein in myelin. Methods and Materials: Further C- and N-terminal truncation was performed on the lead peptide Cy5-P0_101–125. The resulting nine Cy5-labelled peptides were characterized based on their photophysical properties, P0 affinity, and in vitro staining. These characterizations were combined with evaluation of the crystal structure of P0, which resulted in the selection of the optimized tracer Cy5-P0_112–125. A near-infrared Cy7-functionalized derivative (Cy7-P0_112–125) was used to perform an initial evaluation of fluorescence-guided surgery in a porcine model. Results: Methodological truncation of the 26-amino-acid lead compound Cy5-P0_101–125 resulted in a size reduction of 53.8% for the optimized peptide Cy5-P0_112–125. The peptide design and the 1.5-fold affinity gain obtained after truncation could be linked to interactions observed in the crystal structure of the extracellular portion of P0. The near-infrared analogue Cy7-P0_112–125 supported nerve illumination during fluorescence-guided surgery in the head-and-neck region in a porcine model. Conclusions: Methodological truncation yielded a second-generation P0-specific peptide. Initial surgical evaluation suggests that the peptide can support molecular targeted nerve imaging. Full article

Studying the impact of various factors on environmental perception is crucial because humans live in an environment where these factors interact and blend. The thermal-acoustic environment is the major factor that affects the overall perception of urban parks. This study focuses on urban parks in the subtropical region, with Xihu Park in Fuzhou, China, as the research area. Through measurements and questionnaires, this study explores the effects of the thermal-acoustic environment in urban parks on subjective evaluation (thermal assessment, acoustic assessment, and overall environmental assessment). The results reveal that: (1) a higher temperature significantly increases the sensation of heat and lowers thermal comfort, heat acceptance, and overall thermal environment evaluation scores. The type of sound source has a significant positive impact on thermal assessment, and the higher the ranking of the sound source type, the greater its positive impact on thermal assessment. (2) Regarding acoustic evaluation, higher sound pressure level is associated with more negative subjective ratings of loudness, harshness, intensity, and excitement. In contrast, positive sound sources can enhance comfort, preference, disorder, coordination, and overall soundscape evaluation. Additionally, temperature increases tend to result in more negative harshness, intensity, and coordination ratings. The interaction between temperature and sound pressure level also significantly affects subjective loudness, harshness, and intensity. (3) Overall environmental evaluation is also affected by temperature, with increasing temperatures leading to decreased comfort and satisfaction while increasing irritation. High sound pressure environments result in worse overall irritation ratings, while positive sound sources can significantly enhance overall comfort, irritation, and satisfaction ratings. Furthermore, the interaction between temperature and sound pressure level significantly impacts overall irritation and satisfaction ratings. These findings are significant for managing and improving the park’s thermal environment and soundscape, providing a practical framework for landscape architects. Full article

Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry. Full article

Deep learning, with continuous development, has achieved relatively good results in the field of left atrial segmentation, and numerous semi-supervised methods in this field have been implemented based on consistency regularization to obtain high-performance 3D models by training. However, most semi-supervised methods focus on inter-model consistency and ignore inter-model discrepancy. Therefore, we designed an improved double-teacher framework with discrepancy information. Herein, one teacher learns 2D information, another learns both 2D and 3D information, and the two models jointly guide the student model for learning. Simultaneously, we extract the isomorphic/heterogeneous discrepancy information between the predictions of the student and teacher model to optimize the whole framework. Unlike other semi-supervised methods based on 3D models, ours only uses 3D information to assist 2D models, and does not have a fully 3D model, thus addressing the large memory consumption and limited training data of 3D models to some extent. Our approach shows excellent performance on the left atrium (LA) dataset, similar to that of the best performing 3D semi-supervised methods available, compared to existing techniques. Full article

Soft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-redundant robotics systems in industrial and medical technology is the development of suitable modeling and control approaches. Due to the complex non-linear behavior of soft materials and the unpredictable motion of actuators, the task of modeling complex soft actuators is very time-consuming. As a result, earlier studies have undertaken research into model-free methods for controlling soft actuators. In recent years, machine learning (ML) methods have become widely popular in research. The adaptability of an ML model to a non-linear soft drive system alongside the varying actuation behavior of soft drives over time as a result of material characteristics and performance requirements is the key rationale for including an ML model. The system requires the online updating of the ML model in order to work with the non-linear system. Sequential data collected from the test bench and converted into a hypothesis are used to perform incremental learning. These methods are called lifelong learning and progressive learning. Real-time data flow training is combined with incremental learning (IL), and a neural network model is tuned sequentially for each data input. In this article, a method for the intelligent control of soft pneumatic actuators based on an incremental learning algorithm is proposed. A soft pneumatic actuator was subjected to three distinct test conditions in a controlled test environment for a specified duration of data gathering. Additionally, data were collected through finite element method simulations. The collected data were used to incrementally train a neural network, and the resulting model was analyzed for errors with both training and test data. The training and testing errors were compared for different incremental learning (IL) algorithms, including K-nearest neighbors, a decision tree, linear regression, and a neural network. The feasibility of the modulo-free intelligent control of soft pneumatic actuators based on an incremental learning algorithm was verified, solving the problem of the control of software actuators. Full article

There is an increasing trend toward the application of bioactive glasses in different areas of biomedicine, including tissue engineering and oncology. The reason for this increase is mostly attributed to the inherent properties of BGs, such as excellent biocompatibility, and the ease of tailoring their properties by changing, for example, the chemical composition. Previous experiments have demonstrated that the interactions between BGs and their ionic dissolution products, and mammalian cells, can affect and change cellular behaviors, and thereby govern the performance of living tissues. However, limited research exists on their critical role in the production and secretion of extracellular vesicles (EVs) such as exosomes. Exosomes are nanosized membrane vesicles that carry various therapeutic cargoes such as DNA, RNA, proteins, and lipids, and thereby can govern cell–cell communication and subsequent tissue responses. The use of exosomes is currently considered a cell-free approach in tissue engineering strategies, due to their positive roles in accelerating wound healing. On the other hand, exosomes are known as key players in cancer biology (e.g., progression and metastasis), due to their capability to carry bioactive molecules between tumor cells and normal cells. Recent studies have demonstrated that the biological performance of BGs, including their proangiogenic activity, is accomplished with the help of exosomes. Indeed, therapeutic cargos (e.g., proteins) produced in BG-treated cells are transferred by a specific subset of exosomes toward target cells and tissues, and lead to a biological phenomenon. On the other hand, BGs are suitable delivery vehicles that can be utilized for the targeted delivery of exosomes to cells and tissues of interest. Therefore, it seems necessary to have a deeper understanding of the potential effects of BGs in the production of exosomes in cells that are involved in tissue repair and regeneration (mostly mesenchymal stem cells), as well as in those that play roles in cancer progression (e.g., cancer stem cells). This review aims to present an updated report on this critical issue, to provide a roadmap for future research in the fields of tissue engineering and regenerative medicine. Full article

Mycobacterium kansasii infections predominantly manifest in immunocompromised people and are primarily responsible for lung disease and systemic disseminated infection. Osteopathy is a rare consequence of M. kansasii infection. Here, we present imaging data from a 44-year-old immunocompetent Chinese woman diagnosed with multiple bone destruction, particularly of the spine, secondary to M. kansasii pulmonary disease, which is easily misdiagnosed. The patient underwent an emergency operation after experiencing unexpected incomplete paraplegia during hospitalization, indicating an aggravation of bone destruction. Preoperative sputum testing and next-generation sequencing of DNA and RNA of intraoperative samples confirmed the diagnosis of M. kansasii infection. Treatment with anti-tuberculosis therapy and the subsequent patient response supported our diagnosis. Given the rarity of osteopathy secondary to M. kansasii infection in immunocompetent individuals, our case offers some insight into this diagnosis. Full article

Accurate hydrological simulations are crucial for managing water resources and promoting sustainable agriculture in submerged paddy agricultural watersheds. The SWAT-MODFLOW, which couples the Soil and Water Assessment Tool (SWAT) and the Modular Groundwater Flow (MODFLOW) model, is a widely used tool for hydrologic simulations that consider surface water and groundwater (SW-GW) interactions. However, it falls short of effectively simulating the hydrological processes of submerged rice paddy field areas. To address this, we developed the hourly SWAT-MODFLOW-PADDY model, which enables integrated surface and groundwater simulations and effectively represents the hydrological responses of submerged paddy fields to high-resolution rainfall data. Our findings demonstrated that the hourly SWAT-MODFLOW-PADDY model could dynamically simulate soil moisture and runoff patterns in submerged paddy fields. Notably, the developed model showed enhanced performance throughout the entire period for hourly flow in the watershed, with an average coefficient of determination (R²) of 0.75, Nash and Sutcliffe efficiency (NSE) of 0.76, and percent bias (PBIAS) of 13.22 compared to the original model (R² = 0.62, NSE = 0.70, PBIAS = 48.21). The model’s performance in predicting water quality was improved, and it highlighted the significant impact of complex hydrological mechanisms within submerged paddy fields on the spatial distribution of groundwater recharge and stream water volumes exchanged through SW-GW interactions. Given these promising results, the SWAT-MODFLOW-PADDY model could be a valuable resource for managing submerged paddy-dominated agricultural watersheds across various climates and regions. Full article

This research paper investigates the evolving defense cooperation between India and Japan in the context of the strategic landscape in the Indo-Pacific region, particularly considering China’s rise. The existing literature recognizes China’s increasing influence as a significant factor in India-Japanese security cooperation, but tends to overlook the dynamics behind India’s shifting stance. The study reveals that India initially hesitated to actively engage in anti-China security cooperation with Japan until the mid-2010s, but later adopted a more proactive partnership. An analysis of policy documents, political speeches, and government records attributes India’s initial reluctance to its commitment to non-alignment and strategic autonomy, its desire to balance relations with both China and Japan, and its domestic political priorities favoring economic development over military expansion or strategic alliances. However, growing concerns over China’s assertiveness, military modernization, a change in political leadership, and the Quad’s development as a regional security platform have prompted India’s attitude shift. The research’s recommendations not only offer a roadmap for India, Japan, and other Indo-Pacific countries with deep economic ties to China, but also help combat China’s military threats to contribute to regional stability and security, address common challenges, and foster a peaceful and prosperous Indo-Pacific region. Full article

Modern Nanjing experienced a boom in the construction industry. As an advanced material and construction system, reinforced concrete structure was also imported during this period. This paper presents the brief historical background and structural adaptability of reinforced concrete buildings in modern Nanjing. Reinforced concrete was used in industrial buildings in the districts along the Yangtze River in the early 20th century, and in public buildings along Zhongshan Road after 1927. Western-style reinforced concrete buildings used both pure frame structure system and mixed structure with a reinforced concrete frame, brick masonry and timber roof trusses. The Chinese Renaissance buildings used roof structures with different shapes and materials on the main reinforced concrete structure. Based on the study of existing modern reinforced concrete buildings in Nanjing, this paper classifies and compares different types of structures, and indicates that the selection of structures was the adaptive result of comprehensive trade-offs between structural performance, space availability and economy. This research implies the transplantation and adaptation of reinforced concrete structures in modern Nanjing and lays a fundamental structural foundation for the conservation of this architectural heritage. Full article

Artificial intelligence (AI) is increasingly being utilized in cybersecurity, particularly for detecting malicious applications. However, the black-box nature of AI models presents a significant challenge. This lack of transparency makes it difficult to understand and trust the results. In order to address this, it is necessary to incorporate explainability into the detection model. There is insufficient research to provide reasons why applications are detected as malicious or explain their behavior. In this paper, we propose a method of a Vision Transformer(ViT)-based malware detection model and malicious behavior extraction using an attention map to achieve high detection accuracy and high interpretability. Malware detection uses a ViT-based model, which takes an image as input. ViT offers a significant advantage for image detection tasks by leveraging attention mechanisms, enabling robust interpretation and understanding of the intricate patterns within the images. The image is converted from an application. An attention map is generated with attention values generated during the detection process. The attention map is used to identify factors that the model deems important. Class and method names are extracted and provided based on the identified factors. The performance of the detection was validated using real-world datasets. The malware detection accuracy was 80.27%, which is a high level of accuracy compared to other models used for image-based malware detection. The interpretability was measured in the same way as the F1-score, resulting in an interpretability score of 0.70. This score is superior to existing interpretable machine learning (ML)-based methods, such as Drebin, LIME, and XMal. By analyzing malicious applications, we also confirmed that the extracted classes and methods are related to malicious behavior. With the proposed method, security experts can understand the reason behind the model’s detection and the behavior of malicious applications. Given the growing importance of explainable artificial intelligence in cybersecurity, this method is expected to make a significant contribution to this field. Full article

Waning vaccine-induced immunity, coupled with the emergence of SARS-CoV-2 variants, has inspired the widespread implementation of COVID-19 booster vaccinations. Here, we evaluated the potential of the GX-19N DNA vaccine as a heterologous booster to enhance the protective immune response to SARS-CoV-2 in mice primed with either an inactivated virus particle (VP) or an mRNA vaccine. We found that in the VP-primed condition, GX-19N enhanced the response of both vaccine-specific antibodies and cross-reactive T Cells to the SARS-CoV-2 variant of concern (VOC), compared to the homologous VP vaccine prime-boost. Under the mRNA-primed condition, GX-19N induced higher vaccine-induced T Cell responses but lower antibody responses than the homologous mRNA vaccine prime-boost. Furthermore, the heterologous GX-19N boost induced higher S-specific polyfunctional CD4⁺ and CD8⁺ T cell responses than the homologous VP or mRNA prime-boost vaccinations. Our results provide new insights into booster vaccination strategies for the management of novel COVID-19 variants. Full article