Differences in birch tar composition are explained by adhesive function in the central European Iron Age.

Birch bark tar is the most widely documented adhesive in prehistoric Europe. More recent periods attest to a diversification in terms of the materials used as adhesives and their application. Some studies have shown that conifer resins and beeswax were added to produce compound adhesives. For the Iron Age, no comparative large-scale studies have been conducted to provide a wider perspective on adhesive technologies. To address this issue, we identify adhesive substances from the Iron Age in north-eastern France. We applied organic residue analysis to 65 samples from 16 archaeological sites. This included residues adhering to ceramics, from vessel surface coatings, repaired ceramics, vessel contents, and adhesive lumps. Our findings show that, even during the Iron Age in north-eastern France, birch bark tar is one of the best-preserved adhesive substances, used for at least 400 years. To a lesser extent, Pinaceae resin and beeswax were also identified. Through statistical analyses, we show that molecular composition differs in samples, correlating with adhesive function. This has implications for our understanding of birch bark tar production, processing and mode of use during the Iron Age in France and beyond.

Automotive Technology Revolutionized Restorative Dentistry Discovery of Alpha-diketone/Amine Catalyst: Part II -Reflections on Photopolymerization from Conception to Development of Composite Resin and Activator Light.

A full account of the preceded early research tribulation that led to the development of the first visible light activated composite resin and the first visible light activator source is documented. The events took place over four years since early 1974 when a raw industrial composite resin that was radiolucent, stiff paste, universal optically opaque white color, polymerized by exposure to a prototype visible light for 120 seconds was given to developed. Four years later, the developed restorative composite resin ended up as a radiopaque, optically translucent, universal color with an additional three shades that possessed the biologic, esthetic, mechanical and physical characteristics with proven efficacy be used for esthetic zone. This conservative account of early development of a technology that must count as one of a small number of inventions during the seventies, over the years has revolutionized restorative dental practice. We were privileged and very lucky to be associated with developmental stages, stumbling blocks, and final success of the first LC Composite resin that ignited the thought process worldwide and laid the foundation for modern esthetic restoratives practiced today.

Green supply chain management practices and triple bottom line performance: Insights from an emerging economy with a mediating and moderating model.

The current study explores the dynamics of business practices in Pakistan's Small and Medium Enterprises (SMEs). This research focusing on how "green supply chain management" (GSCM), Industry 4.0 technologies (IND), total quality management (TQM) and sustainable performance (SP) are interconnected. The study collected data from 382 SMEs owners and managers and data analysis was conducted utilizing by SPSS and SmartPLS. The results indicate that employ GSCM pattern has constructive impact on SMEs overall performance in terms of their social and financial aspects. Interestingly IND act as intermediaries among GSCM and sustainable performance underscoring their potential to translate sustainability efforts into outcomes. Moreover, our survey reveals that TQM have a crucial function in enriching the relationship between Industry 4.0 technologies and sustainable performance by executing as a moderator. It further highlighting the value of adopting a quality focused approach to maximize the causal factor of advancements on sustainability output. The findings of this survey research offering insights for practitioners, SMEs, and policy makers alike by highlighting the significance of integrating practices such as GSCM, IND and effective quality management to enhance SME efficiency. These outcomes further contribute to an understanding of the mechanics at play, within SMEs while offering guidance for organizations maneuvering the changing landscape of sustainable business practices and IND.

Towards secure and efficient integration of blockchain and 6G networks.

The future of communication systems is undergoing a transformative shift towards intelligence, efficiency, and flexibility. Presently, the amalgamation of blockchain technology and the sixth-generation mobile communication network (6G) has garnered significant attention, as their fusion is poised to profoundly impact the digital economy and society at large. However, the convergence of blockchain and 6G networks poses challenges pertaining to security and performance. In this article, we propose an approach based on the design of secure mechanisms and performance optimization to delve into the key issues surrounding the integration of blockchain and 6G networks from both security and performance perspectives. Specifically, we first introduce the application scenarios of 6G networks and blockchain's empowerment of them to highlight the necessity of combining blockchain technology with 6G. Subsequently, in order to ensure the security of communication and data transmission between blockchain and 6G networks, we have investigated the design requirements for security mechanisms. Furthermore, we discuss the efficient realization of the amalgamation between blockchain and 6G networks by proposing a solution based on Directed Acyclic Graph (DAG) for blockchain's asynchronous consensus protocol, alongside optimization strategies for storage and communication to meet the desired characteristics and requirements of 6G networks. Lastly, we provide valuable research directions that serve as references and guidance for the future development of the integration between blockchain and 6G networks.

Conversational technology and reactions to withheld information.

People frequently face decisions that require making inferences about withheld information. The advent of large language models coupled with conversational technology, e.g., Alexa, Siri, Cortana, and the Google Assistant, is changing the mode in which people make these inferences. We demonstrate that conversational modes of information provision, relative to traditional digital media, result in more critical responses to withheld information, including: (1) a reduction in evaluations of a product or service for which information is withheld and (2) an increased likelihood of recalling that information was withheld. These effects are robust across multiple conversational modes: a recorded phone conversation, an unfolding chat conversation, and a conversation script. We provide further evidence that these effects hold for conversations with the Google Assistant, a prominent conversational technology. The experimental results point to participants' intuitions about why the information was withheld as the driver of the effect.

Methodological Frameworks and Dimensions to Be Considered in Digital Health Technology Assessment: Scoping Review and Thematic Analysis.

BACKGROUND: Digital health technologies (dHTs) offer a unique opportunity to address some of the major challenges facing health care systems worldwide. However, the implementation of dHTs raises some concerns, such as the limited understanding of their real impact on health systems and people's well-being or the potential risks derived from their use. In this context, health technology assessment (HTA) is 1 of the main tools that health systems can use to appraise evidence and determine the value of a given dHT. Nevertheless, due to the nature of dHTs, experts highlight the need to reconsider the frameworks used in traditional HTA. OBJECTIVE: This scoping review (ScR) aimed to identify the methodological frameworks used worldwide for digital health technology assessment (dHTA); determine what domains are being considered; and generate, through a thematic analysis, a proposal for a methodological framework based on the most frequently described domains in the literature. METHODS: The ScR was performed in accordance with the guidelines established in the PRISMA-ScR guidelines. We searched 7 databases for peer reviews and gray literature published between January 2011 and December 2021. The retrieved studies were screened using Rayyan in a single-blind manner by 2 independent authors, and data were extracted using ATLAS.ti software. The same software was used for thematic analysis. RESULTS: The systematic search retrieved 3061 studies (n=2238, 73.1%, unique), of which 26 (0.8%) studies were included. From these, we identified 102 methodological frameworks designed for dHTA. These frameworks revealed great heterogeneity between them due to their different structures, approaches, and items to be considered in dHTA. In addition, we identified different wording used to refer to similar concepts. Through thematic analysis, we reduced this heterogeneity. In the first phase of the analysis, 176 provisional codes related to different assessment items emerged. In the second phase, these codes were clustered into 86 descriptive themes, which, in turn, were grouped in the third phase into 61 analytical themes and organized through a vertical hierarchy of 3 levels: level 1 formed by 13 domains, level 2 formed by 38 dimensions, and level 3 formed by 11 subdimensions. From these 61 analytical themes, we developed a proposal for a methodological framework for dHTA. CONCLUSIONS: There is a need to adapt the existing frameworks used for dHTA or create new ones to more comprehensively assess different kinds of dHTs. Through this ScR, we identified 26 studies including 102 methodological frameworks and tools for dHTA. The thematic analysis of those 26 studies led to the definition of 12 domains, 38 dimensions, and 11 subdimensions that should be considered in dHTA.

[Essentials for high-quality development of fangjiomics].

The development of modern omics technology has facilitated in-depth research on many disciplines in the field of medicine. For instance, the introduction of omics-related technology has facilitated research on the mechanism of formula's action and led to the innovative development of sophisticated pharmacological analysis methods. However, in general, the previous ideas are only limited to the application level, failing to integrate with the discipline connotation of formulaology. Furthermore, they are unable to fulfill their potential role in the future evolution of formulaology and steer comprehensive research on the clinical efficacy and safety of formulas. We should not forget our origins, which are compatible with other countries, and embrace the future. Therefore, this paper proposes the three essentials of "holism, macro, and practice" to investigate the future direction of high-quality development in fangjiomics.

[Element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine].

This study aims to construct the element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine, providing basic technical support for the formation and transformation of the evidence chain of Chinese patent medicine and providing collection, induction, and summary schemes for massive and disorganized clinical data. Based on the elements of evidence-based PICOS, the conventional construction methods of knowledge graph were collected and summarized. Firstly, the data entities related to Chinese patent medicine were classified, and entity linking was performed(disambiguation). Secondly, the study associated and classified the attribute information of the data entity. Finally, the logical relationship between entities was constructed, and then the element relationship and extension path of the knowledge map conforming to the characteristics of clinical evidence of Chinese patent medicine were summarized. The construction of the clinical evidence knowledge map of Chinese patent medicine was mainly based on process design and logical structure, and the element relationship of the knowledge map was expressed according to the PICOS principle and evidence level. The extension path crossed three levels(model layer, data layer application, and new evidence application), and the study gradually explored the path from disease, core evaluation indicators, Chinese patent medicine, core prescriptions, syndrome and treatment rules, and medical case comparison(evolution law) to new drug research and development. In this study, the top-level design of the construction of the clinical evidence knowledge map of Chinese patent medicine has been clarified, but it still needs the joint efforts of interdisciplinary disciplines. With the continuous improvement of the map construction technology in line with the characteristics of TCM, the study can provide necessary basic technical support and reference for the development of the TCM discipline.

[Methods for traditional Chinese medicine syndrome-based efficacy evaluation: a review].

Traditional Chinese medicine(TCM) syndrome-based efficacy is an evaluation index which is unique to TCM and can reflect the advantages of TCM. The development of the methods and measurement tools for evaluating TCM syndrome-based efficacy can provide objective and quantitative evidence for the clinical efficacy evaluation of TCM and the development of new Chinese medicine preparations, being the exploration direction of innovative methods and technologies for evaluating TCM efficacy. The conventional evaluation methods are subjective and limited to the mitigation of symptoms and the improvement of physical signs, which make it difficult to form a unified evaluation standard. In addition, the evaluation methods lack unity, objectivity, and quantitative research. The scientific connotation, evaluation ideas and methods, and key technologies of the evaluation for the therapeutic effect on syndromes remain unclear, which leads to diverse evaluation modes, methods, and indexes. The syndrome-based efficacy scale provides a new idea for the objective quantification and standardization of TCM syndromes. This review systematically summarizes the methods and problems, introduces the research progress in the evaluation scales, and puts forward some thoughts on the characteristics of TCM syndrome-based efficacy evaluation, aiming to provide insights for the research in this field.

[Scientific connotation of temperature control in processing of Chinese medicinal materials based on theory of quality evaluation through morphological identification].

Processing of Chinese medicinal materials is an important part in the Chinese medicine heritage, and the temperature control in the processing has a direct impact on the quality and efficacy of traditional Chinese medicines. However, the processing of Chinese medicinal materials has the problems of subjective temperature judgement, determination of the end point based on experience, unclear processing mechanism, unstable quality of products, and inconsistent processing standards. The temperature control in the processing is reflected in the appearance and internal quality of Chinese medicinal materials. The theory of quality evaluation through morphological identification is developed based on the comprehensive evaluation of the shape, color, taste, and components, which is associated with the temperature control in the processing. To solve the problems above, this paper puts forward the following solutions. The first is literature mining. By review of the ancient medical works and pharmaceutical experience, the temperature control in processing and the evolution of processing methods can be revealed. Second, according to the ancient method, the processing principle can be explored, on the basis of which the processing technology can be innovated. Third, the standard operating procedure(SOP) should be established to quantify the fire temperature, providing a theoretical basis for the formulation of Chinese medicinal material processing standards. Moreover, it provides a basis for improving the quality of processed products and increasing the safety and effectiveness of traditional Chinese medicines.

Does the opening of producer services promote the wage growth of the downstream manufacturing industry?-Empirical evidence from Chinese manufacturing listed companies.

Based on the vertical connection between upstream and downstream industries, a unique theoretical model is constructed to analyse the impact mechanism of the opening of producer services on downstream manufacturing wage growth. The empirical tests are carried out using the data of China's manufacturing listed companies from 1999 to 2020. Our findings indicate that the opening of producer services has an inverted-U-shaped impact on downstream manufacturing wage growth, and the average level of the opening of producer services in the sample period is lower than the corresponding threshold. Overall, it is in the stage of promoting the wage growth of the downstream manufacturing industry. The opening of producer services mainly affects the wage growth of the downstream manufacturing industry through two channels: labour productivity and labour income share. The results of heterogeneity analysis show that the wages of capital and technology-intensive and low-competitive manufacturing industries are relatively strongly promoted by the opening of producer services. Therefore, promoting the orderly opening of producer services and strengthening the technological links between industries will help promote the wage growth of downstream manufacturing industries.

Can digital economy compensate the effect of aging on total factor productivity?

In China, the number of senior citizens has grown, along with the burden of old age, and aging has hampered economic growth. The advent of the digital age has led to the emergence of the digital economy as a new engine for economic growth. This paper uses DEA-Malmquist index model to measure the total factor productivity growth rate of 31 provinces in China from 2011 to 2021, and uses the moderating effects model to empirically investigate the relationship between the digital economy, aging and total factor productivity, and to verify whether the development of the digital economy can mitigate the negative impact of aging on total factor productivity. The results show that aging inhibits total factor productivity growth, and the digital economy can promote total factor productivity growth. Digital economy can alleviate the negative impact of aging on total factor productivity growth, and has a moderating effect. Digital economy plays a moderating role by improving the level of human capital and facilitating technological progress. The regional heterogeneity analysis shows that the moderating effect of the digital economy exists in the eastern and western regions and the southern region, but not in the central region and the northern region. Furthermore, the digital economy has a moderating effect on both the high and low aging groups. The research in this paper not only helps to evaluate the productivity effects of the digital economy, but also has important implications for finding ways to mitigate the negative effects of aging.

Novel genetic variants of HLA gene associated with Thai Behcet's disease (BD) patients using next generation sequencing technology.

Behçet's disease (BD) manifests as an autoimmune disorder featuring recurrent ulcers and multi-organ involvement, influenced by genetic factors associated with both HLA and non-HLA genes, including TNF-α and ERAP1. The study investigated the susceptible alleles of both Class I and II molecules of the HLA gene in 56 Thai BD patients and 192 healthy controls through next-generation sequencing using a PacBio kit. The study assessed 56 BD patients, primarily females (58.9%), revealing diverse manifestations including ocular (41.1%), vascular (35.7%), skin (55.4%), CNS (5.4%), and GI system (10.7%) involvement. This study found associations between BD and HLA-A*26:01:01 (OR 3.285, 95% CI 1.135-9.504, P-value 0.028), HLA-B*39:01:01 (OR 6.176, 95% CI 1.428-26.712, P-value 0.015), HLA-B*51:01:01 (OR 3.033, 95% CI 1.135-8.103, P-value 0.027), HLA-B*51:01:02 (OR 6.176, 95% CI 1.428-26.712, P-value 0.015), HLA-C*14:02:01 (OR 3.485, 95% CI 1.339-9.065, P-value 0.01), HLA-DRB1*14:54:01 (OR 1.924, 95% CI 1.051-3.522, P-value 0.034), and HLA-DQB1*05:03:01 (OR 3.00, 95% CI 1.323-6.798, P-value 0.008). However, after Bonferroni correction none of these alleles were found to be associated with BD. In haplotype analysis, we found a strong linkage disequilibrium in HLA-B*51:01:01, HLA-C*14:02:01 (P-value 0.0, Pc-value 0.02). Regarding the phenotype, a significant association was found between HLA-DRB1*14:54:01 (OR 11.67, 95% CI 2.86-47.57, P-value 0.001) and BD with ocular involvement, apart from this, no distinct phenotype-HLA association was documented. In summary, our study identifies specific HLA associations in BD. Although limited by a small sample size, we acknowledge the need for further investigation into HLA relationships with CNS, GI, and neurological phenotypes in the Thai population.

Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer.

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.

Modeling and simulating the multi-generation product sales, production and inventory system within the context of quality upgrades.

The rapid development of science and technology has led to an increasing number of high-tech enterprises offering new products through successive generations of product upgrades. This trend presents a new challenge for the sustainable operations of enterprises. Based on the Norton-Bass model, this study begins by constructing a multi-generation product diffusion model within a single enterprise in the context of a monopoly under the quality upgrade scenario. Subsequently, a supply model is established based on this foundation, and these two models are seamlessly integrated using product sales volume as an interface, culminating in a comprehensive sales-supply system. This study analyzes the effects of new-product pricing, quality levels, initial stock, and production capacity on the performance of this system. The system dynamics (SD) method was used to simulate and solve the system in the decentralized and centralized decision-making modes, and the two decision-making modes were compared and analyzed. The research reveals several key findings. i) Comprehensive decision optimization yields enhanced profitability through joint optimization calculation of the multi-generation product diffusion system and the supply adjustment system. ii) consumer price sensitivity significantly affects product quality upgrades and profits. A negative correlation exists between consumer price sensitivity and both factors. The upgrades of product quality should be carefully traded off with consideration of pricing and quality costs. iii) Maximizing profits by maintaining a certain order level of backlog or stock shortage is beneficial for overall enterprise profitability. Additionally, optimal production capacity has been identified as a crucial element in efficient operational inventory management. This study expands the multi-generation product diffusion operational theory and provides valuable theoretical support and decision-making foundations for the sustainable management of enterprises.

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles.

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

Rapid Nucleic Acid Diagnostic Technology for Pandemic Diseases.

The recent global pandemic of coronavirus disease 2019 (COVID-19) has enormously promoted the development of diagnostic technology. To control the spread of pandemic diseases and achieve rapid screening of the population, ensuring that patients receive timely treatment, rapid diagnosis has become the top priority in the development of clinical technology. This review article aims to summarize the current rapid nucleic acid diagnostic technologies applied to pandemic disease diagnosis, from rapid extraction and rapid amplification to rapid detection. We also discuss future prospects in the development of rapid nucleic acid diagnostic technologies.

Gold Nanoparticles (AuNPs)-Toxicity, Safety and Green Synthesis: A Critical Review.

In recent years, the extensive exploration of Gold Nanoparticles (AuNPs) has captivated the scientific community due to their versatile applications across various industries. With sizes typically ranging from 1 to 100 nm, AuNPs have emerged as promising entities for innovative technologies. This article comprehensively reviews recent advancements in AuNPs research, encompassing synthesis methodologies, diverse applications, and crucial insights into their toxicological profiles. Synthesis techniques for AuNPs span physical, chemical, and biological routes, focusing on eco-friendly "green synthesis" approaches. A critical examination of physical and chemical methods reveals their limitations, including high costs and the potential toxicity associated with using chemicals. Moreover, this article investigates the biosafety implications of AuNPs, shedding light on their potential toxic effects on cellular, tissue, and organ levels. By synthesizing key findings, this review underscores the pressing need for a thorough understanding of AuNPs toxicities, providing essential insights for safety assessment and advancing green toxicology principles.

Harnessing technology to improve sleep in frontline healthcare workers: A pilot study of electronic noise-masking earbuds on subjective and objective sleep measures.

Objective: This pilot study assessed the effects of electronic noise-masking earbuds on subjective sleep perception and objective sleep parameters among healthcare workers (HCWs) reporting sleep difficulties during the COVID-19 pandemic. Methods: Using a pre-post design, 77 HCWs underwent 3 nights of baseline assessment followed by a 7-night intervention period. Participants wore an at-home sleep monitoring headband to assess objective sleep measures and completed subjective self-report assessments. The difference in mean sleep measures from baseline to intervention was estimated in linear mixed models. Results: Compared to baseline assessments, HCWs reported significant improvements in sleep quality as measured by the Insomnia Severity Index (ISI) (Cohen's d = 1.74, p < 0.001) and a significant reduction in perceived sleep onset latency (SOL) during the intervention (M = 17.2 minutes, SD = 7.7) compared to baseline (M = 24.7 minutes, SD = 16.1), (Cohen's d = -0.42, p = 0.001). There were no significant changes in objective SOL (p = 0.703). However, there was a significant interaction between baseline objective SOL (<20 minutes vs >20 minutes) and condition (baseline vs intervention) (p = 0.002), such that individuals with objective SOL >20 minutes experienced a significant decrease in objective SOL during the intervention period compared to baseline (p = 0.015). Conclusions: HCWs experienced a significant improvement in perceived SOL and ISI scores after using the electronic noise-masking earbuds. Our data provide preliminary evidence for a nonpharmacological intervention to improve the sleep quality of HCWs which should be confirmed by future controlled studies.