Search Results (20,109)

Szechwan lovage rhizome (SLR, the rhizome of Ligusticum chuanxiong Hort., Chuanxiong in Chinese transliteration) is one Chinese materia medica (CMM) commonly used to activate blood circulation and remove blood stasis. SLR is applicable to most blood stasis syndromes. It has significant clinical efficacy in relation to human diseases of the cardiocerebrovascular system, nervous system, respiratory system, digestive system, urinary system, etc. Apart from China, SLR is also used in Singapore, Malaysia, the European Union, and the United States of America. However, the current chemical markers in pharmacopeia or monography for the quality assessment of SLR are not well characterized or specifically characterized, nor do they fully reflect the medicinal efficacy of SLR, resulting in the quality of SLR not being effectively controlled. CMM can only have medicinal efficacy when they are applied in vivo to an organism. The intensity of their pharmaceutical activities can more directly represent the quality of CMM. Therefore, the chemical constituents and pharmacological actions of SLR are reviewed in this paper. In order to demonstrate the medicinal efficacy of SLR in promoting blood circulation and removing blood stasis, bioassay methods are put forward to evaluate the pharmaceutical activities of SLR to improve hemorheology, hemodynamics, and vascular microcirculation, as well as its anti-platelet aggregation and anticoagulation properties. Through comprehensive analyses of these pharmaceutical properties, the quality and therapeutic value of SLR are ascertained. Full article

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network. Full article

In the glycerolysis process for diacylglycerol (DAG) preparation, free lipases suffer from poor stability and the inability to be reused. To address this, a cost-effective immobilized lipase preparation was developed by cross-linking macroporous resin with poly (ethylene glycol) diglycidyl ether (PEGDGE) followed by lipase adsorption. The selected immobilization conditions were identified as pH 7.0, 35 °C, cross-linking agent concentration 2.0%, cross-linking time 4 h, lipase amount 5 mg/g of support, and adsorption time 4 h. Enzymatic properties of the immobilized lipase were analyzed, revealing enhanced pH stability, thermal stability, storage stability, and operational stability post-immobilization. The conditions for immobilized enzyme-catalyzed glycerolysis to produce DAG were selected, demonstrating the broad applicability of the immobilized lipase. The immobilized lipase catalyzed glycerolysis reactions using various oils as substrates, with DAG content in the products ranging between 35 and 45%, demonstrating broad applicability. Additionally, the changes during the repeated use of the immobilized lipase were characterized, showing that mechanical damage, lipase leakage, and alterations in the secondary structure of the lipase protein contributed to the decline in catalytic activity over time. These findings provide valuable insights for the industrial application of lipase. Full article

This paper presents a novel circular-ESG framework integrating circular economy (CE) principles with environmental, social, and governance (ESG) criteria to address the lack of uniform sustainability measures. We introduce normalized sustainability coordinates (NSCs) as a comprehensive metric for sustainability performance, reconciling economic development with environmental balance. The circular-ESG model employs a four-quadrant Cartesian system to map business model impacts on natural and socio-economic systems, ranging from linear open-loop to circular closed-loop ESG models. This framework enables empirical analysis through data-driven NSCs (−1 to 1) and establishes temporal key performance indicators. By incorporating the Human Development Index within ecological limits, the model promotes regenerative development aligned with planetary boundaries. The circular-ESG approach offers a practical tool for businesses, households, organizations, and policymakers to navigate sustainable development complexities. This integrated framework fosters innovation and supports a just transition towards regenerative practices, providing a roadmap for high human development within ecological limits. The circular-ESG model advances sustainability science and management, contributing to the discourse on measuring and implementing sustainable practices across sectors and scales. The model is currently conceptual; we encourage empirical validation and further research to explore its practical applications and effectiveness in real-world scenarios. While the provided examples of use cases serve as conceptual demonstrations, future research could empirically apply the model to real-world data. Full article

In the context of green building and sustainable urban development, understanding the mechanical behavior of structural components like concrete-filled steel tube (CFST) columns is crucial due to their improved load-bearing capacity, energy efficiency, and optimized material usage, which enhance structural resilience and sustainability. This research addresses the complex development of confining stress and its impact on the concrete core (CC) behavior within these columns, which are essential for urban infrastructure. Through extensive numerical studies, this study proposes a model to estimate the confining stress in axially loaded CFST short columns. Study findings reveal that the confinement effectiveness is influenced by variables such as compressive strength (CS) of the concrete, cross-sectional shape, and depth-to-wall thickness percentage. Additionally, the confinement is also significantly affected by the yield strain of steel ε_y/ε_c to the peak strain of unconfined concrete ε_c. A three-dimensional finite element model (FEM) was built for the simulation of the columns’ nonlinear behavior and was rigorously validated against experimental data. This model aids in the design and implementation of more efficient and resilient urban structures, supporting the principles of sustainable construction. The study underscores the importance of structural integrity in sustainable urban development and provides valuable insights for improving the design of green building materials. Full article

Digital technology has the function of information governance, and digital transformation of enterprises may be the key way to identify and restrain ESG greenwashing. Based on the theory of digital empowerment, this study analyzes the influence and mechanism of digital transformation on restraining corporate green washing behavior from the perspective of internal and external factors. This study takes A-share listed companies in 2012–2022 as research samples and tests the effectiveness of digital transformation. Research has found that (1) digital transformation can significantly suppress corporate greenwashing behavior, and this conclusion still holds after a series of endogeneity and robustness tests. (2) In the context of high environmental awareness among executives, the inhibitory effect of digital transformation on corporate ESG greenwashing is more pronounced. (3) Mechanism analysis shows that digital transformation has inhibited the company’s greenwashing behavior by increasing the attention of investors. (4) Heterogeneity analysis shows that in state-owned enterprises, non-heavily polluting industries, high-tech industries, and enterprises located in the eastern region digital transformation has a more effective inhibitory effect on corporate greenwashing behavior. This study examines the impact of digital transformation on corporate ESG greenwashing, expands the research on the non-economic effects of digital transformation, and may provide empirical evidence for improving the quality of ESG information disclosure and sustainable development of enterprises. Full article

The use of geopolymer as a cementitious material for geopolymer concrete (GPC) is beneficial for the sustainable development and green transformation of the construction industry. Geopolymer concrete has many advantages, such as high strength, heat and corrosion resistance, low hydration heat, and carbon emissions. This paper adopted the water–binder ratio, alkaline activator modulus, and slag replacement as the influencing factors, and used the 28-day compressive strength and flexural strength of geopolymer concrete as the response values to seek a reasonable mix design. In addition, through microstructure analysis, the mechanism of geopolymer on concrete was studied. The results indicated that as the water–binder ratio increased, the compressive strength first increased and then decreased, while the flexural strength continuously decreased. As the amount of slag replacement increased, the compressive strength and flexural strength would also increase. The effect of alkaline activator modulus on compressive strength and flexural strength was not significant. Through response surface methodology (RSM) analysis, the optimal design of geopolymer concrete was determined to have a water–cement ratio of 3.50, a modulus of 1.54 for alkaline activator solution, and a slag substitution rate of 47%. The microstructure analysis showed that the water–binder ratio and slag replacement improved the pore structure and density of concrete, thereby enhancing the macroscopic mechanical properties of concrete. This paper can provide a theoretical basis for the application of geopolymer concrete in engineering. Full article

A geographic information system (GIS) is a tool and technology capable of addressing the effects and challenges of natural disasters, particularly flash floods. GIS applications are used to generate flood risk maps to tackle flood issues. However, various challenges and problems arise when employing GIS to manage flash flood disasters in Shah Alam, Malaysia. Hence, this study aims to identify these challenges and gaps in GIS utilisation by Malaysian agencies for flash flood management in Shah Alam. Using the quadruple helix model technique, informal interviews were conducted as part of the study’s qualitative methodology. Five respondents were chosen from each of the four main sectors for primary data collection: government, academia, business, and community/NGO. The data were analysed using Taguette qualitative theme analysis. The findings reveal that the primary challenges lie in government management, particularly in providing equipment and access to GIS for all stakeholders, including the public. This challenge is attributed to the high costs and complexity associated with GIS data usage, limiting accessibility. Furthermore, there is a lack of expertise and research on GIS in Malaysian universities concerning flash flood management. The government should take proactive steps to improve flash flood management in Shah Alam, Malaysia, in order to solve these issues. Specifically, GIS training should be given to stakeholders, particularly those in the government and academic sectors, in order to develop GIS specialists who will be necessary for efficient flood management in Malaysia. Full article

Cone-beam computed tomography (CBCT) has emerged as a promising tool for the analysis of the upper airway, leveraging on its ability to provide three-dimensional information, minimal radiation exposure, affordability, and widespread accessibility. The integration of artificial intelligence (AI) in CBCT for airway analysis has shown improvements in the accuracy and efficiency of diagnosing and managing airway-related conditions. This review aims to explore the current applications of AI in CBCT for airway analysis, highlighting its components and processes, applications, benefits, challenges, and potential future directions. A comprehensive literature review was conducted, focusing on studies published in the last decade that discuss AI applications in CBCT airway analysis. Many studies reported the significant improvement in segmentation and measurement of airway volumes from CBCT using AI, thereby facilitating accurate diagnosis of airway-related conditions. In addition, these AI models demonstrated high accuracy and consistency in their application for airway analysis through automated segmentation tasks, volume measurement, and 3D reconstruction, which enhanced the diagnostic accuracy and allowed predictive treatment outcomes. Despite these advancements, challenges remain in the integration of AI into clinical workflows. Furthermore, variability in AI performance across different populations and imaging settings necessitates further validation studies. Continued research and development are essential to overcome current challenges and fully realize the potential of AI in airway analysis. Full article

The transformative integration of artificial intelligence (AI) into educational settings, exemplified by ChatGPT, presents a myriad of ethical considerations that extend beyond conventional risk assessments. This study employs a pioneering framework encapsulating risk, reward, and resilience (RRR) dynamics to explore the ethical landscape of ChatGPT utilization in education. Drawing on an extensive literature review and a robust conceptual framework, the research identifies and categorizes ethical concerns associated with ChatGPT, offering decision-makers a structured approach to navigate this intricate terrain. Through the Analytic Hierarchy Process (AHP), the study prioritizes ethical themes based on global weights. The findings underscore the paramount importance of resilience elements such as solidifying ethical values, higher-level reasoning skills, and transforming educative systems. Privacy and confidentiality emerge as critical risk concerns, along with safety and security concerns. This work also highlights reward elements, including increasing productivity, personalized learning, and streamlining workflows. This study not only addresses immediate practical implications but also establishes a theoretical foundation for future AI ethics research in education. Full article

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling. Full article

The objectives of this study were to evaluate the bonding durability of four different self-etch adhesives to dentin after 24 h and thermal cycling (TC) and to measure the degree of polymerization conversion (DC) in situ. Two-step self-etch adhesives, Clearfil SE Bond 2 (SE2, Kuraray Noritake Dental) and G2-Bond Universal (G2B, GC), and one-step self-etch adhesives, Scotchbond™ Universal Plus Adhesive (SBU, 3M ESPE) and Clearfil Universal Bond Quick (UBQ, Kuraray Noritake Dental), were used. The labial surfaces of bovine teeth were ground to create flat dentin surfaces. The adhesives were applied according to the manufacturers’ instructions. After resin composite buildup and 24 h water storage, the specimens were sectioned into beams and all groups were subjected to thermal stress for 0, 10,000 (10k), or 20,000 (20k) cycles followed by micro-tensile bond strength (µTBS) testing. In situ DC was investigated with a laser Raman microscope. The µTBS data were statistically analyzed and subjected to a Weibull analysis. The different groups were compared at the characteristic strength (63.2% probability of failure) (α = 0.05). Two-Way ANOVA was used to show the effect of different adhesives and thermal cycling on the mean DC% followed by Tukey’s multiple comparison post hoc test. G2B/TC10k resulted in a significant increase in the µTBS compared to TC0. SBU/TC20k showed significantly higher µTBS compared to TC0. For comparison between different tested adhesives, SBU showed a significantly lower µTBS compared to G2B after TC10k. G2B and SBU showed a greater number of adhesive failures after TC. Mean DC% was different for each adhesive. The newly developed MDP- and HEMA-free 2-SEA showed similar bonding performance with the gold-standard 2-SEA. However, there is still room for further improvement in terms of SEAs. Full article

Landscape character assessment (LCA) is a crucial tool for conserving an area’s unique character. However, in our literature review, we found no data linking LCA to historic coal mining settings. This systematic review explores the ways in which the landscape character assessment (LCA) methodology has been applied, as well as the factors that influence it, in the conservation of historic coal mine landscapes. It focuses on three areas: analyzing the ways in which LCA has been applied in landscape conservation, proposing recommendations for the application of LCA in historic coal mine setting landscapes, and summarizing the factors that influence LCA in landscape conservation in historic coal mine settings. Methods: This study used the Meta-Analyses (PRISMA) method to perform the systematic review. The whole review was selected from 2030 potential articles; a total of 21 articles were included. Results: This study demonstrates that the LCA approach can be operationalized in the conservation of environmental landscapes in historic coal mines by combining cluster analysis and multi-scale assessment and incorporating other theories. The quality of the results can be affected by factors such as the accuracy and completeness of the data and the complexity and tractability of the model. Conclusions: Future research should focus on improving the data capture technology, model complexity, and design of actionable models. Additionally, we recommend the strategies of enhancing stakeholder engagement and raising public awareness. Full article

Human parainfluenza viruses (HPIVs) are the leading causes of acute respiratory tract infections (ARTIs), particularly in children. During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) significantly influenced the epidemiology of respiratory viruses. This study analyzed 19,339 respiratory specimens from pediatric patients with ARTIs to detect HPIVs using PCR or tNGS, focusing on the period from 2021 to 2023. HPIVs were identified in 1395 patients (7.21%, 1395/19,339), with annual detection rates of 6.86% (303/4419) in 2021, 6.38% (331/5188) in 2022, and 7.82% (761/9732) in 2023. Notably, both the total number of tests and HPIV-positive cases increased in 2023 compared to 2021 and 2022. Seasonal analysis revealed a shift in HPIV prevalence from winter and spring in 2021–2022 to spring and summer in 2023. Most HPIV-positive cases were in children aged 0–7 years, with fewer infections among those aged 7–18 years. Since June 2022, HPIV-3 has been the most prevalent serotype (59.55%, 524/880), whereas HPIV-2 had the lowest proportion (0.80%, 7/880). The proportions of HPIV-1 (24.89%, 219/880) and HPIV-4 (15.45%, 136/880) were similar. Additionally, the incidence of co-infections with other common respiratory pathogens has increased since 2021. This study highlights rising HPIV detection rates post-COVID-19 and underscores the need for continuous surveillance of HPIVs to inform public health strategies for future epidemic seasons. Full article

Plant-based waste biomass with lignocellulose as an important component is produced in large quantities worldwide every year. The components of lignocellulose that typically exhibit high utilization value include cellulose and hemicellulose, as well as pentoses and hexoses derived from their hydrolysis. As a pretreatment for the hydrolysis process, delignification is a pivotal step to enhance cellulose/hemicellulose accessibility and achieve high yields of fermentable sugars. Additionally, deep eutectic solvents (DESs) are the most widely used solvents for delignification during biomass fractionation due to their clean and environmentally friendly attributes. DESs dissolve lignin by inducing a large amount of β-O-4 bond cleavage and partial carbon–carbon bond cleavage, retaining cellulose in the solid residue, while most of the hemicellulose is hydrolyzed in DES pretreatment. This article provides a comprehensive review of the influence of DESs in the lignocellulose separation process. Key factors such as lignin removal rate, sugar conversion rate, and product chemical structure are critically reviewed to assess the feasibility of employing DESs for lignocellulose separation. Full article