In 2019, Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China, achieved a prominent price reduction of 52% on average for 25 bidding winning products. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, and applied single-group Interruption Time Series (ITS) design to examine the impact of "4+7" policy on the drug price index (DPI) of policy-related drugs. The ITS analysis showed that the DPI of winning (-0.183 per month, p<0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p<0.0001) after "4+7" policy. These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

Three new cembranoids (1–3) and a new casbanoid (4), along with three known analogues (5–7), have been isolated from the soft coral Sinularia nanolobata collected off Ximao Island. The structures including the absolute configurations of new compounds were established by extensive spectro-scopic data analysis, time-dependent density functional theory/electronic circular dichroism (TDDFT-ECD) calculations and the comparison with spectroscopic data of known compounds. In in vitro bioassay, compounds 1 and 5 exhibited moderate cytotoxic activities against human erythroleukemia (HEL) cells line with IC50 values of 37.1 and 42.4 uM, respectively.

Although a lot of research has been carried out on hand sanitizer as an effort to prevent Covid-19, there is still limited research on non-alcoholic hand sanitizer lotion from the brown algae Scytosiphon lomentaria (S. lomentaria) as a raw material that provides antibacterial effects and maintains healthy skin. Therefore, this study aimed to analyze microbial inhibitory ability, physical and chemical characteristics of hand sanitizer lotion made from S. lomentaria algae. S. lomentaria powder was tested for phytochemicals, total phenolic content, free radical scavenging activity (IC50), and antibacterial tests. S. lomentaria slurry was prepared by adding distilled water (1:1) for lotion preparations in four treatment groups, such as normal control (lotion base without adding algae), lotion with the addition of algae 20%, 30%, and 40%. The lotion preparations for each treatment group were tested for antibacterial activity of Staphylococcus aureus and Escherichia coli, phytochemicals, pH, adhesion, spreadability, homogeneity, emulsion type, and hedonic. S. lomentaria powder contains bioactive polyphenolic compounds with a total phenolic content of 117.70±1.40 mg GAE/g dry weight and an IC50 value of 455±3.4 ppm, while lotion with the addition of S. lomentaria contains polyphenols and saponins The best treatment was shown by 40% S. lomentaria hand sanitizer lotion with inhibition of 6.30 ± 0.64 mm E. coli, 6.85 ± 1.12 mm S. aureus, hedonic test for color, texture and absorption with likes to likes very much level, neutral pH, greenish-white color, O/W emulsion type, high adhesion, homogeneous, and easily absorbed by the skin. This research can be an alternative reference in making non-alcoholic hand sanitizer lotion that is safe for the skin.

Determining the general-name-derived semantic relationships between geographic names often requires consulting many geographic name documents for verification. In geographic name translation and geographic name knowledge graphs, this method cannot meet the needs of large-scale extraction of general-name-derived semantic relationships between geographic entities. To solve this problem, this paper proposes a general-name derivation relation recognition method based on Prompt learning and Semantic Spatiotemporal Distribution Attention Feature Fusion (SSD-AFF). This method adopts a pipe-based relationship recognition strategy. First, the Prompt learning method is adopted to form a general-name derivation pattern with the general-name-derived geographical name category and the original geographical name category as the prompt word, and then the derivation pattern is combined with the geographical name as the input of the model so that the recognition problem of the general-name derivation of the geographical name can be transformed into a classification problem using the bidirectional language model. Then, by analyzing the characteristics of the semantic association and geographical spatio-temporal distribution between the general-name-derived geographical name and the original geographical name, the semantic spatio-temporal distribution characteristics of the general-name-derived geographical name are constructed. Finally, the network model based on the semantic spatio-temporal distribution Attention Feature Fusion (SSD-AFF) is used to distinguish the general-name-derived semantic relationships. The experimental results show that the proposed method has good performance. It can effectively mine the general-name-derived semantic relations between the vector data of geographical entities and has important application value in geographical name translation and geographical name knowledge maps.

Although agricultural crops production systems in subtropical plateau regions currently pose high environmental risks, especially in developing countries, their environmental impacts and emission-reduction potentials remain unclear, and the development potential of such systems is likely huge. We conducted a case study in Yunnan Province, China, to quantify the environmental impacts from crop production during 2002–2021. The life cycle assessment method was employed to identify factors driving the environmental impacts, and potential mitigation strategies were proposed. The yield and total nutrient input of grain crops in Yunnan Province increased over the 20-year period, and environmental footprint of crops production in Yunnan Province was higher than that of other regions. Average annual mean greenhouse gas (GHG) emissions and soil acidification potentials (AP) and water eutrophication potentials (EP) of crops production during 2002–2021 were 732 kg CO2-eq·Mg-1, 15.8 kg SO2-eq·Mg-1, and 2.7 kg PO4-eq Mg-1, respectively. GHG emissions from g crops mainly originated from applying agricultural materials during the crop life cycle. There was a significant correlation between surplus nitrogen and environmental impacts. Scenario testing showed that through crop sowing, nutrient regulation, and crop management, the crop yield could be increased and environmental costs could be reduced. The greatest reductions were found in wheat production: 53.1%, 67.9%, and 67.7% for GHG emissions, AP, and EP, respectively. Adopting comprehensive agricultural management measures could reduce the negative impact of crops production on the environment in subtropical plateau areas and achieve sustainable agricultural development.

Based on research data from the renovation of old residential areas in Chengdu, China, this study provides empirical evidence regarding the causal relationship between urban renewal and the transformation of residents' pro-environmental behaviors. The research findings demonstrate a significant enhancement in residents' engagement in pro-environmental behaviors due to the renovation of old residential areas. Robustness tests based on replacement samples and two-stage difference-in-difference models also confirm the reliability of the conclusions. The preliminary mechanism test indicates that the mechanism of the impact of renovation of old residential areas on the transformation of residents' pro-environmental behaviors can be attributed to two main factors. Firstly, the improvement of environmental infrastructure provides residents with better conditions for participating in pro-environmental activities. Secondly, there is an elevation in residents' environmental protection awareness, prompting a more voluntary engagement in pro-environmental behavior. This study suggests that decision-makers should fully recognize that urban renewal activities nationwide are not only a process of improving urban hardware and functionality, but also a realistic and vivid "environmental education" course for the whole nation. Taking urban renewal as an opportunity to enhance citizens' awareness of environmental protection will effectively promote the transformation of pro-environmental behaviors among residents.

This research delves into librarians' perceptions of artificial intelligence (AI) support in learning experiences, lifelong learning, and digital literacy within the library context, undertaking a comparative analysis between Malaysia and Indonesia. Employing a survey methodology, a self-developed questionnaire was administered to 59 librarians in Malaysia and 85 in Indonesia. The questionnaire, utilizing a Likert scale, systematically explored subjective judgments on AI's impact across various dimensions. A noteworthy finding is the consistent and harmonized perception among librarians in both countries, underscoring a universal recognition of AI's perceived support for learning experiences, lifelong learning, and digital literacy. Of particular interest is the emphasis on sustainability in librarians' perspectives, as they recognize the enduring value of AI in fostering educational growth. Despite the limitations inherent in perceptual measures and a relatively modest sample size, the empirical and practical contributions of this study are significant. It offers a nuanced exploration of librarians' viewpoints, providing valuable insights for future research endeavors. Moreover, the study's findings have immediate practical implications for the integration of AI tools in library services, policy-making, and professional development, with a focus on ensuring sustainable implementation for long-term benefits.

Water depth estimation holds paramount importance in various domains including navigation, environmental monitoring, and resource management. Traditional depth measurement methods such as bathymetry can often be prohibitively expensive and time-consuming, especially in remote or inaccessible areas. This study delves into the application of machine learning techniques, with a specific focus on the Baidu Easy DL model, for water depth estimation leveraging satellite imagery. Utilizing Sentinel-2 satellite data over Rushikonda Beach in India and processing it into remote sensing reflectance using the ACOLITE software, the research compares the performance of several machine learning algorithms, including the Stumpf Model, Log-Linear Model, and the Baidu Easy DL Model, for accurate depth estimation. The results indicate that the Easy-DL model outperforms traditional methods, particularly excelling in the 0-11 meter depth range. This study showcases the substantial potential of machine learning in the realm of remote sensing, offering robust water depth estimates, even in complex coastal environments. Furthermore, it underscores the critical role of comprehensive training datasets and ensemble learning techniques in enhancing accuracy. This research not only opens avenues for further exploration of machine learning applications in remote sensing but also highlights the promising prospects of online model APIs in streamlining remote sensing data processing.

Excitotoxicity related to the dysfunction of the N-methyl-d-aspartate receptor (NMDAR) has been indicated to play an integral role in the pathophysiology of multiple disease states, including neurodegenerative disorders such as Parkinson’s disease. There is a notable gap in the market for novel NMDAR antagonists, however current methods to analyze potential antagonists rely on indirect measurements of calcium flux and hazardous radioligand binding assays. Recently, a fluorescent NMDAR ligand, N-adamantan-1-yl-dimethylamino-1-naphthalenesulfonic acid, known as AM-DAN was developed by our group. Additional studies on this ligand is necessary to evaluate its potential as a biological tool in NMDAR research. Therefore, this study was aimed at conducting structural analyses, fluorescence experiments, high-accuracy NMDAR molecular modelling and NMDAR phencyclidine (PCP) site competition binding studies using AM-DAN. Results revealed that AM-DAN has appropriate structural properties, significant fluorescent ability in various solvents and is able to bind selectively and compete for the PCP-binding site of the NMDAR. Therefore, AM-DAN holds promise as a novel fluorescent ligand to measure the affinity of prospective drugs binding at the NMDAR PCP-site and may circumvent the use of radioligands.

In the last decade, a higher level of reliability became a compulsory demand when it comes to modern DC-DC converters. This work addresses the main reliability metrics: the failure rate λλ and MTBF of an output capacitor bank used within a high current low voltage buck converter due to many studies have shown that the output capacitor bank was demonstrated being the most critical component within the converter. Many authors treated this issue, usually by doing reliability predictions. The majority of the papers use only one specific Standard Prediction to solve the problem. Herein calculation was done using both the older standard MIL-HDBK-217 and the latest one Telcordia SR-332, providing a benchmark comparison between the two which is a helpful tool for the output capacitor selection in the early stage design. Military standard was well accepted for decades in order to reliability prediction even on industrial electronics and is still used today under a critical manner because no more update after the latest version MIL-HDBK-217F - Notice 2 released in 1995. Since then, newer prediction standards had appeared in the electronics reliability market. Over time, this standard was mostly used but it does not accurately model the reliability because lack of taking account of mission profile. The above-mentioned newer standard – i.e., Telcordia SR-332 is trying to compensate also the lack of the newest component technology in the older standard (which is the first standard released on the market) supplying useful design data for design engineers who use the so-called “design with the reliability in mind” concept. Within the paper were established the environmental condition for the passive components by means of a PoL (Point of load) buck converter that is used for both calculation methods. The influence of temperature and several specific concepts like reference conditions, operating conditions, ripple, and internal self-heating were taking account in order to display the results. The temperature for the capacitor’s capsule needed in π_T stress factor calculation was derived by PSPICE simulation. High fidelity and dedicated SPICE models provided by the manufacturer were used for MOSFETs, polymer electrolytic, and MLCC capacitors that compose the converter.

With the prevalence of smartphones, the scope of mobile applications has been significantly broadened in the past few years including almost every aspect of our daily life. Among the applications, message dissemination services play a significant role. Traditionally, users send messages to the servers, which forward them according to friend lists. This client-server architecture is efficient with the support of infrastructures, however, it restricts the application scope and may hinder the wide deployment of location-based mobile applications. This paper proposes a novel communication model called passive broadcast for local message dissemination. In this new model, each node does not maintain a connection state and data delivery is initialized by a receiver via a ’scan’ operation. The representative carriers of passive broadcast include Bluetooth and WiFi-Direct, both of which define a mandatory ‘peer discovery’ function. Passive broadcast features negligible cost for establishing and maintaining direct links and is extremely suitable for short message dissemination in proximity. Furthermore, we study the operation management in the proposed communication model with complete protocols and in-depth analysis for optimization. We prototyped our solution on commercial phones and evaluated it with extensive experiments and simulation.

In the last decades, we have witnessed a spectacular information explosion over the Internet. Millions of users are consuming the Internet through various services, such as mobile applications, and online games. The service providers, at the back-end side, are supported by state-of-art infrastructures. Targeting on providing the services at scale, virtualization is one of the emerging technologies used in data centers and cloud environments to improve the quality of services. In this project, we aim to develop a dynamic resource management scheme based on virtual containers. It collects the runtime job progress from the running tasks and allocates the resources dynamically to improve the overall system performance.

Smartphone has become one of the most revolutionary devices in the history of computing. With various kinds of applications, the scope of smartphone has been significantly broadened in the past few years including almost every aspect in our daily life. However, due to the limited on-board resources such as CPU, storage, network bandwidth and battery power, smartphones and the mobile network serving them bring new challenges that have not been encountered in the traditional computing and networking environments. This dissertation focuses on the research areas of improving the network architecture and enhancing the current applications on smartphones. It mainly investigates the areas in the following two directions for three representative categories of mobile services. - In the first direction, the dissertation aims to develop new communication models for smartphone Ad-Hoc networks to achieve efficient communication in the proximity. It is motivated by the fact that smartphone Ad-Hoc networks can help improve the current location-based services and propel new applications. Moreover, the new communication models provide complementary alternatives to the traditional infrastructure-based wireless networks. - In the second direction, the dissertation focuses on improving the other two categories of services, cloud storage services and real-time video streaming services for mobile devices. In the field of cloud storage, we introduce a cloud-assisted approach to provide a set of advanced file operations, such as encryption, decryption and compression, on smartphones. Furthermore, by utilizing the on-board Near Field Communication(NFC) module, we develop an algorithm to securely share the files between mobile devices. For the services of real-time video streaming, we propose approaches that identify a user’s status by analyzing the accelerometer data and then, dynamically adjust the buffer mechanism to save network bandwidth on smartphones.

This study explores the potential of natural compounds, including Daidzin, Luteolin, and Silibinin, as inhibitors of human monoamine oxidase B (MAO-B) for the treatment of Parkinson's disease. Docking simulations were performed using Autodock Vina with Pyrx program to evaluate the binding affinities of these compounds with MAO-B. Results indicate that Daidzin, Luteolin, and Silibinin exhibit promising binding affinities with MAO-B, with Daidzin demonstrating the highest affinity. Toxicity parameters were further predicted using the PkCSM Server, confirming the low toxicity of these compounds. Overall, the findings suggest that Daidzin, Luteolin, and Silibinin have the potential to be effective and safe treatments for Parkinson's disease through their inhibition of MAO-B. Further experimental validation is warranted to support their therapeutic efficacy in clinical settings.

The multitarget therapeutic strategy, as opposed to the more traditional ‘one disease-one tar-get-one drug’, may hold promise in treating multifactorial neurodegenerative syndromes, such as Alzheimer’s disease (AD) and related dementias. Recently, combining a photopharmacology approach with the multitarget-directed ligand (MTDL) design strategy, we disclosed a novel donepezil-like compound, namely 2-(4-((diethylamino)methyl)benzylidene)-5-methoxy- 2,3-dihydro-1H-inden-1-one (1a), which in the E diastereomeric form (and about tenfold less in the UV-B photo-induced isomer Z) showed the best activity as dual inhibitor of the AD-related targets acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B). Herein, we investigated further photoisomerizable 2-benzylideneindan-1-one analogs 1b-h with the unconjugated tertiary amino moiety bearing alkyls of different bulkiness and lipophilicity. For each compound the thermal stable E diastereoisomer, along with the E/Z mixture as produced by UV-B light irradia-tion in the photostationary state (PSS, 75% Z), was investigated for the inhibition of human ChEs and MAOs. The pure E-diastereoisomer of the N-benzyl(ethyl)amino analog 1h achieved low nanomolar AChE and high nanomolar MAO-B inhibition potencies (IC50s 39 and 355 nM, re-spectively), whereas photoisomerization to the Z isomer (75% Z in the PSS mixture) resulted in a decrease (about 30%) of AChE inhibitory potency, and not in the MAO-B one. Molecular docking studies were performed to rationalize the different E/Z diastereoselectivity of 1h toward the two target enzymes.

Many Western studies have indicated that older women are generally more vulnerable in terms of mobility compared to older men, particularly regarding driving. However, the situation may differ in the context of China. This study, based on activity diaries and semi-structured inter-views, focuses on the spatiotemporal behavior of older adults in Tianjin and explores how the constraints posed by activity companions (in terms of type, size, and composition) shape the mo-bilities of older men and women, including activity locations, travel distances, and transporta-tion modes. The key findings are as follows: First, older women are more engaged with their families due to a higher percentage and longer duration of activities spent with family members. Second, older men tend to have more concentrated travel distances near their homes compared to older women. Third, older women exhibit a broader range of activities in different locations and engage in longer-distance leisure travel with family members when compared to older men. In the context of Western literature, this study discusses older women's enhanced social interactions, their earlier retirement in China, and the impact of COVID-19 as factors that help explain these findings. This study contributes to a deeper understanding of accompanied mobilities among Chinese older adults using geographical theory and methods, emphasizing the importance of flexible work schedules for the workforce and the organization of community-based activities to promote the social interactions and mobilities of older adults.

Based on the two-flavors Polyakov quark-meson model, we study the bubble dynamics of a first-order phase transition in chiral phase diagram at high density but low temperature in the framework of the homogeneous thermal nucleation theory. Under the mean-field approximation, after obtaining the effective potential with inclusion of the fermionic vacuum term, we build a geometric approach to probe two possibly existing minima, which can actually tunnel through a bounce interpolated between a metastable vacuum to an adjacent true vacuum. By separating our discussions into a weak first-order hadron quark phase transition and strong ones, the bubble profiles, the surface tension, the typical radius of the bounce and the saddle point action in the presence of a nucleation bubble as a function of temperature are numerically calculated in detail as the chemical potentials are fixed at $\mu=306 \mathrm{MeV}$ and $\mu=310 \mathrm{MeV}$. our results show that the surface tension remains a small value even when the density is very high and phase boundaries for hadron and quark matter should be resized properly according to the saddle point action evaluated on the bounce. It is also noticed that the inclusion of the deconfinement phase transition in term of the Polyakov loop does not change chiral phase transition dramatically for light quarks.

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival and other prognostic endpoints in multiple cancer types, including Lower-Grade Gliomas (LGG), Breast Invasive Carcinoma (BRCA) and many others. We also showed significant association of MNAI with genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Futhermore, we found that patients with high MNAI benefit more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated on LGG and BRCA that the multimodal integration of MNAI and the AI-empowerd Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

Sediment transport is highly sensitive to flow conditions, showing significant increase during flood events. Based on this principle, this study set out to rank flood events occurring along river Trotuș (Romania) based on the amount of transported sediment and event duration. The 77 flood events recorded from 2000 to 2017 were ranked into 4 classes: type A (4%); type B (16%), type C (14%) and type D (66%). The sediment transport specific for the 4 types of flood events was related to the flow discharge (sediment rating curve and hysteresis effect), the specific stream power and the energy expenditure of these events. More than 60% of the hysteresis loops typical for flood events were clockwise, thus singling out the channel as the main sediment source. Ca. 74% of the total sediment yield was transported at stream power values higher than the 300 Wm-2 threshold, which was exceeded in less than 1% of the investigated timeframe. The changes occurring in the sediment transport rates after major floods show that these events are significant thresholds in the hydrogeomorphic evolution of river channels.

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected nine months of real-time Wuhan traffic monitoring data from Amap. We proposed Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long and short-term memory networks (LSTM) and convolutional neural networks (CNN) to effectively achieve long-term anomaly detection at the city level. First, we convert the traffic flow data into image representation, which enables the capture of the spatial correlation between traffic flow and roads and the correlation between traffic flow and roads, as well as the surrounding environment. Second, The model utilizes convolution kernels of different sizes to extract spatial features at road-level, regional-level, and city-level scales, while incorporating temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic control strategies.

We have recently reported that a series of 1,3,4-oxadiazol-2-ylbenzenesulfonamides acts as potent and specific monoamine oxidase B (MAO-B) inhibitors with some compounds possessing potencies in the nanomolar range. While sulfonamide compounds (e.g., zonisamide) have been reported to inhibit the MAO enzymes, nanomolar MAO inhibition potencies for this class are not frequently observed. The present study expanded on these findings by replacing the 1,3,4-oxazole moiety with a 1,3-thiazole heterocycle. A series of 23 primary sulfonamides were synthesized by reaction of an α-haloketone with a substituted carbothioamide. The results of the MAO inhibition studies showed that the 1,3-thiazolylbenzenesulfonamides were specific inhibitors of human MAO-B. The most potent MAO-B inhibitor exhibited an in vitro IC50 value of 0.103 µM (3j). The derivatives were, however, weaker inhibitors of human MAO-A with the most potent compound exhibiting an IC50 value of 11.9 µM (3a). Examination of the structure-activity relationships (SARs) showed that the most potent MAO-B inhibition was obtained with substitution of the sulfonamide on the meta position of the phenyl rather than the para position. This study concludes that 1,3-thiazolylbenzenesulfonamides represent a class of potent and specific MAO-B inhibitors, which may serve as leads for the development of new treatments for disease states such as Parkinson’s disease.

Background: Ultraviolet (UV) radiation from the sunlight can harm skin cells, causing short-term effects, such as sunburns and long-term effects, such as pigmentation changes, changes in the skin's immune responses, and increased risk of skin cancer. These conditions can be largely prevented with proper sun protection measures and behavioural changes. This study aims to assess the knowledge on sun protection behaviour and baseline knowledge of medical students from the “Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania. Methods: Data in this cross-sectional study were collected through a Google Forms questionnaire, which was distributed from November 2023 to December 2023 via digital communication channels to students across all academic years. STROBE guidelines were consulted for data synthesis; Results: A total of 668 stu-dents responded. The student cohort demonstrated a commendable level of awareness of the risks associated with sun exposure and the advantages of employing sun protection factor (SPF); how-ever, the incidence of sunburns remained notably elevated (72.3%). Further analysis revealed cor-relations among gender, academic year, presence of photosensitizing conditions, and adherence to SPF application. Additionally, there is a need to improve understanding of the appropriate appli-cation techniques for SPF, as only 11.5% of the students exhibited proficiency in this regard; Con-clusions: As future healthcare providers, medical students hold significance in promoting healthy behaviours in the broader community. This study indicates that while their knowledge about sun protection is satisfactory, there is scope for enhancement.

Background: Coronavirus disease (COVID-19) related pneumonia is leaded by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which infects host cells through receptors named Angiotensin-converting enzyme 2 (ACE2). Smoking is thought to be related to poor disease prognosis, as a large amount of evidence highlights the negative effects of smoking on lung health and its causal relationship with various respiratory diseases. Methods: We first evaluated the role of ACE2 expression level with tobacco effect. And correlation analyses were used to identify the related genes of ACE2 both in smoker trait and human normal multi-tissues. After intersections, hub genes were later used to further GSEA and co-regulated mechanisms were explored by GeneMANIA. We used UALCAN to perform survival curves of pan-cancers included 33 types of cancers. New clinical model of top co-occurrence cancer type was constructed and validated. Results: Effected by cigarette smoking, the expression level of ACE2 expressed statistically upwards in current smokers compared with never smokers, upwards in groups after acute smoke exposed compared with normal control. But no strong evidence detected in third-hand smoke, as poor amounts of samples, only 4. A little trend of expressing upwards became in groups after third-hand smoke compared with groups after clean air exposing. 4 genes included PIR, ADH7, AKR1C2 and AKR1C3 were identified as ACE2 related genes in smoker trait and human normal multi-tissues. Then, we made the survival curves of pan-cancers in 4 genes. Brain lower grade glioma was the co-occurrence type as both ACE2, PIR and AKR1C3 had the significantly prognostic situation. Later, we made the new clinical prediction model as the C-index was 0.827 and the Area Under Curves (AUCs) of 1-year survival, 2-year survival and 3-year survival were 0.921(95%CI, 0.882-0.961), 0.911(95%CI, 0.860-0.962) and 0.878(95%CI, 0.818-0.939). In inner validation, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.777(95%CI, 0.640-0.914), 0.916(95%CI, 0.842-0.990) and 0.888(95%CI, 0.768-1.000). In outer validation one, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.764(95%CI, 0.672-0.856), 0.848(95%CI, 0.783-0.913) and 0.748(95%CI, 0.656-0.841). And in outer validation two the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.740(95%CI, 0.654-0.826), 0.766(95%CI, 0.688-0.843) and 0.794(95%CI, 0.721-0.866). Conclusions: In our study, we compared the ACE2 expression level between smokers and non-smokers crowds and identified associated hub genes. Then we explored the further role of hub genes in tumor fields to identify the correlation and influence between the avoidable host factors such as smoking on COVID-19 contamination and tumor. ACE2 and AKR1C3 are potential prognostic genes for brain lower grade glioma, and we created the web dynamic nomogram and encapsulation app through validations.

Expression of the breast cancer resistance protein (BCRP/ ABCG2) transporter is downregulated in placentas from women with preeclampsia (PE) and in an immunological rat model of PE, which closely resembles human PE. While many drugs are substrates of this important efflux transporter, the impact of BCRP downregulation on maternal and fetal drug exposure has not been investigated. Using the PE rat model, we performed a pharmacokinetic study with rosuvastatin (RSV), a BCRP substrate, to investigate this impact. PE was induced in rats during gestational days (GD) 13 to 16 with daily low-dose endotoxin. On GD18, RSV (3 mg/kg) was administrated intravenously, and rats were sacrificed at time intervals between 0.5-6 hours. Placental expression of Bcrp and Oatp2b1 significantly decreased in PE rats. A corresponding increase in RSV levels was seen in fetal tissues and amniotic fluid of the PE group (p<0.05), while maternal plasma concentrations remained unchanged. An increase in Bcrp expression and decreased RSV concentration, was seen in the livers of PE dams. This suggests that PE-mediated transporter dysregulation leads to significant changes in the maternal and fetal RSV disposition. Overall, our findings demonstrate that altered placental expression of transporters in PE can increase fetal accumulation of their substrates.

Genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only consid-ers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experi-mentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineer-ing targets. Here we present the first genome-scale enzyme-constrained model (eciCW773) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of pheno-types and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used eciCW773 to identify several gene modifica-tion targets for L-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

Wearable exoskeleton solutions for upper limb rehabilitation or assistance, particularly for the hand area, have become increasingly attractive to researchers, proving to be effective over time in treating hand movement impairments following various neurological diseases. In the present work, the development of a wearable exoskeleton-type device for active hand rehabilitation has been addressed, based on fluid actuators made of elastomeric material and on myoelectric control by capturing myoelectric signals from the forearm area. The flexion movement of the fingers (phalanges) is actively assisted by pneumatic actuation of the built-in actuators and the extension movement is assisted by a depressurization control of the actuator. The device has been designed to be as easy to use as possible, light in weight, and to provide a high degree of comfort when used by the patient for rehabilitation training or daily life activities (ADL), which can be performed in the comfort of one's own home.

This study presented a novel and sustainable method for producing chitin nanocrystals (PA-ChNCs) using phosphoric acid hydrolysis. The produced PA-ChNCs were used as stabilizers for Pickering emulsions and their physicochemical properties as well as stabilization mechanisms were systematically investigated. The PA-ChNCs exhibited small particle sizes and high surface areas, enabling an effective adsorption at the oil/water interface, which prevented coalescence in Pickering emulsions. The particle size and surface charge PA-ChNCs were strongly pH-dependent, which were critical factors for stabilizing Pickering emulsions. The study showed that emulsions with higher O:W ratios (50%) exhibited larger droplets but greater viscosity, forming a stable gel-like matrix that was more stable than emulsions with other O:W ratios after storage for 30 days. Emulsions with higher PA-ChNCs concentrations (2.0 wt%) and higher pH values (pH 10) showed small droplets and high viscosity, leading to stable emulsions. This research contributes to the development of sustainable and effective emulsifiers for use in the food industry, making utilization of seafood waste in an eco-friendly manner possible. Overall, this study highlights the potential of using newly developed PA-ChNCs as stabilizers for Pickering emulsions in food products.

In order to study the petrogenesis and tectonic setting of Permian A-type granites and their relationships with hydrothermal mineralization along the Hegenshan-Heihe suture zone (HHSZ) in northeastern China, we select the newly discovered Hongyan Cu-polymetallic deposit in the northeastern part of the HHSZ that develops three stages of mineralization associated with the Shanshenfu alkali-feldspar granite (SAFG). The zircon U-Pb dating and whole rock geochemistry suggest that the SAFG is a typical A-type granite formed in the Early Permian. The zircon Hf isotopes and trace elements suggest that the SAFG has high Ti-in-zircon temperature (721–990℃), high magmatic oxygen fugacity and largely positive εHf(t) (+6.0 to +9.9). Therefore, we propose that the SAFG was derived from the crustal assimilation and fractional crystallization of the charnockitized juvenile crust. The high oxygen fugacity favors the chalcophile elements (e.g., Cu, Au, Ag) of the source region enriched in the fluid phases after magmatic fractional crystallization, consequently facilitating subsequent hydrothermal mineralization, which is also consistent with the characteristics of ore-forming fluids that changed from the initial high temperature, high salinity, high fO2 and CO2-rich magmatic-hydrothermal fluids of stage I to CO2-poor, dilute, and cooling meteoric fluids of stage III. Combined with regional geological background, the Permian A2-type granites along the HHSZ can be formed in post-collisional slab break-off process. In subsequent exploration for hydrothermal deposits along the HHSZ, the Permian A-type granites with arc-related juvenile crustal source and high fO2 have great potential and need more attention.

The loss or dysfunction of skeletal tissue often necessitates surgical intervention, particularly in cases involving trauma, tumors, or abnormal development leading to critical-sized defects. Despite autografts being considered the gold standard for bone grafts, their limitations and complications prompt the exploration of alternative approaches for bone repair and regeneration. Current advancements in bone tissue engineering have led to innovative strategies aiming to regenerate injured bone structures, providing viable alternatives to traditional autografts or allografts. Among these strategies, Hydrogel Microparticles (HMPs) have emerged as promising scaffolds due to their notable characteristics, including high porosity and mechanical tunability. HMPs play an important role in facilitating vasculature formation, mineral deposition, and overall bone tissue regeneration. HMPs, fabricated through various techniques, exhibit versatility in functions such as drug and cell delivery, structural scaffolding, and bioinks for 3D printing. In addition, these microgels can be injected for minimally invasive delivery and can display modular properties with different designs and configurations. This review exclusively focuses on the advancements in HMPs for bone regeneration, delving into synthesis and functionalization techniques while highlighting their diverse applications documented in the literature. Our aim is to shed light on the unique benefits and potential of HMPs in the intricate field of engineering bone tissue.

With the rapid development of urbanization and industrialization in China, environmental issues have become a knotty problem, especially issues related to air, water, and solid-waste pollution. These pollutants pose threats to the health of the population and to that of communities and have a vicious influence on the healthcare system. Additionally, pollution also exhibits spill-over effects, which means that pollution in the local region could affect the healthcare services in a neighbouring region. Therefore, it is necessary to explore the relationship between pollution and healthcare. A spatial autocorrelation analysis was conducted and spatial panel econometric models were constructed to explore the characteristics of pollution and healthcare services in China and the relationship between them using data on all 31 provinces over twelve consecutive years (2006-2017). The results showed that the utilization of healthcare services and environmental pollution were not randomly distributed; unsurprisingly, air pollution and solid-waste pollution were mainly found in parts of northern China, while water pollution was highest in southern and coastal China. In addition, environmental pollution exhibited spill-over effects on healthcare services. For example, a 1% increase in solid waste in one specific geographical unit was estimated to increase the inpatient visits per capita in adjacent counties by 0.559%. Specifically, pollution showed different degrees of influence on healthcare services, which means that the impact of environmental pollution on the number of outpatient visits is greater than on the number of inpatient visits. Our results provide the government with evidence for effectively formulating and promulgating policies, especially policies aimed at tackling spill-over effects among different regions.

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

Under the dual background of underemployment and health inequality, this paper empirically analyzes the impact of education level on underemployed workers’ health based on CLDS2016 data. The results show that underemployment is significantly related to the decline of self-rated health, increased depression tendency, and morbidity in a certain period. The results indicate that underemployment can significantly reduce the health level of workers in the low education level group and the high education level group. However, it has no significant impact on workers’ health in the middle education level group; even if we change the measurement method of indicators and consider endogeneity, the research conclusion is still robust. Moreover, this kind of health inequality mainly comes from the difference in economic effect and leisure effect of underemployment to workers with different educational levels. This paper provides empirical support for increasing the labor protection mechanism of underemployed people and reducing the health inequality caused by educational level differences.

Iron overnutrition has been implicated with higher risk of developing metabolic and cardiovascular diseases, including metabolic syndrome (MetS), while iron deficiency anemia exacerbates many underlying chronic conditions. Hemoglobin (Hb) concentration in the blood, which reflects a major functional iron (i.e., heme iron) in the body, may serve as a surrogate of iron nutrition status. We conducted sex-specific observational association studies where we carefully titrated the association between Hb deciles and MetS and its components among the Taiwanese Han Chinese (HC) from the Taiwan Biobank and Europeans of White ancestry from the UK Biobank, representing two large ethnicities. Our data show that at higher-than-normal levels of Hb, increasing deciles of Hb concentration were significantly associated with MetS across all sex subgroups in both ethnicities, with the highest deciles resulting in up to three times greater risks than the reference group [Taiwanese HC: OR=3.17 (95% CI, 2.75-3.67) for Hb >16.5 g/dL in men, OR=3.11 (2.78-3.47) for Hb >14.5 g/dL in women; European Whites: OR=1.89 (1.80-1.98) for Hb >16.24 g/dL in men, OR=2.35 (2.24-2.47) for Hb >14.68 g/dL in women]. The association between stronger risks and increasing Hb deciles was similarly observed with all metabolic components except diabetes. Here we found that both the highest Hb decile groups and contrarily the lowest ones, with respect to the reference, were associated with higher odds of diabetes in both ethnic groups [e.g., Taiwanese HC men: OR=1.64 (1.33-2.02) for Hb >16.5 g/dL, OR=1.71 (1.39-2.10) for Hb <13.5 g/dL; European Whites women: OR=1.39 (1.26-1.45) for Hb >14.68 g/dL, OR=1.81 (1.63-2.01) for Hb <12.39 g/dL]. These findings confirm that elevated Hb concentrations, a potential indicator of iron overnutrition, may play a role in the pathophysiology of MetS and metabolic components.

Social and spatial characteristics of a population often interact to influence health outcomes, suggesting a need to jointly analyze both to offer useful insights in community health. However, researchers have used either social or spatial analyses to examine community-based health issues and inform intervention programs. We propose a combined socio-spatial analytic approach to develop a social network with spatial weights and a spatial statistic with social weights, and apply them to an ongoing study of mental and physical well-being of rural Latino immigrants in North Florida, USA. We demonstrate how this approach can be used to calculate measures, such as social network centrality, support contact dyads, and spatial kernel density based on a health survey data. Findings reveal that the integrated approach accurately reflected interactions between social and spatial elements, and identified community members (who) and locations (where) that should be prioritized for community-based health interventions.

Fully mechanized mining face production involves complex geological conditions; mining pressure; gas, water, fire and other mining environments. The working face needs a shearer, hydraulic support, a scraper conveyor and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for coal mining robot technology systems, including single robots, robotized equipment, robot clusters and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve customized research and development and standardized production of various types of robots. A specific analysis was conducted on the research progress of common key technologies such as explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mine robot research and development is proposed. Moreover, robotic mining systems should be promoted to enhance the overall intelligence level and mining efficiency of mining equipment. To develop human‒machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots. The digital twinning of the entire mine robot system should be accelerated, the normalized operation level of coal mine robots should be improved, research on coal mining robots, support robots, and transportation robots should be carried out, intelligence should be achieved in fully mechanized mining faces, and equipment support for fewer and unmanned fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization has been summarized, and the technical and engineering feasibility of the coal mining machine population has been verified.

Incident rates of neurodegenerative diseases have steadily increased globally, but there is no therapeutic access available. We newly prescribed medicinal herbal remedy including five different herbal plants called, Chen-Ma-Dan-Sam-Ga-Mi-Bang (CMST), purposed to prove for pharmacological properties and corresponded actions on hippocampus neuronal cell injury by hypoxia-induced mice model. Mice were adapted to normoxia or hypoxia with or without CMST for 5 days. We gathered pharmacological effects of CMST on cell injury by enhancement of dihydroethidium and 4-hydroxynonenal signals which were correlated with abnormal redox status in the protein or gene expression levels (abnormal elevations of nitric oxide, reactive oxygen species, lipid peroxidation and deteriorations of total glutathione, total antioxidant capacity, and activities of superoxide dismutase and catalase) due to hypoxia. CMST also notably exerted to attenuates molecules for neuronal cell injury markers such as p-tau, cleaved caspase-3 due to DNA oxidations (53bp1and phosphor-histone H2AX), inflammatory cytokines, and hemeoxigenase-1. We further figured out the underlying actions of CMST by in vitro experiment through inactivation of microglial cell which can mediate neuronal cell injury. Collectively, CMST prevented from hippocampal neuronal cells via inactivation of microglial cell with normalization of redox status on hypoxia-induced hippocampus neuronal cell injury.

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of carbon peak and carbon neutrality, green building evaluation system (GBRS) has become a research hotspot in the field of green building. This paper systematically summarizes the current research progress based on GBRS, focuses on the current weight and indicators setting, analyzes it with Cite Space software, and puts forward its future development direction. The results show that the weight setting method of green building evaluation system is determined by multidimensional and multi-method. From the perspective of environment, society and economy, the scope of indicators setting is expanding. The green building evaluation system will play a guiding role in green building design, and the evaluation process will continue to be optimized and developed in the direction of intelligence and automation.

Traditional discrimination techniques for Euler deconvolution use only the color spectrums of structural indexes, without considering the spatial distribution characteristics and inherent relationships among the Euler solutions to separate adjacent causative sources. In the present study, a new approach was developed for discriminating uncorrelated Euler solutions from coherent solutions based on the focusing levels indicated by the probability density distributions generated using multivariate kernel density estimations (KDE). A novel multiple coverage technique was proposed by using a series of different sized moving windows over gridded gravity data, which formed tight clusters of Euler solutions for different sized causative sources. The results of the probability density distributions were obtained using a 3-D KDE method for the Euler solution subsets {x, y, z} of synthetical models, and real data from a survey conducted in British Columbia (Canada) which had successfully established more credible and meaningful geological models when compared with three other subsets.

This work presents the mathematical framework of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-CASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters, which are considerably more than the number of features, being incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities (of any order) with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces. The application of the 2nd-CASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.

Work on callings has burgeoned in the past 20 years, yet recent reviews exposed a lack of conceptual clarity and disagreements around its definition, components and measures. One lingering point of contention revolves around the element of prosociality: Is a calling orientation primarily motivated by self-interest, prosocially orientated, or a mix of both? This conceptual paper reviews and examines the pro-self and prosocial component of a calling outlook, by examining and comparing the ways in which they feature in different calling subtypes: Classic, neoclassic, and modern callings. Our analysis suggests that these subtypes vary in where they are located on a pro-self – prosocial continuum: Classic callings are located on the prosocial side of the axis, modern callings are located on pro-self side of the axis, and neoclassic callings can be situated in the middle of the con-tinuum, integrating self-orientated and other-orientated motivations. Our analysis further suggests that these calling subtypes draw on divergent value systems: Classic callings are propelled by self-transcendent values, modern callings are driven by self-actualization motivations, and neoclassic callings integrate both value systems. We therefore argue that the subjective experiences of pursuing a calling within each subtype pathway differ, alt-hough they may culminate in similar outcomes. The paper offers a novel framework for analyzing people’s calling that draws on their values.

In South Sudan, three case-control studies found a positive association between Nodding Syn-drome (NS) and a Mansonella perstans infection. To investigate whether M. perstans could play the primary and sole causal role in the development of NS, we examined blood films of persons with and without epilepsy in rural villages affected by NS in Mahenge, Tanzania, to identify M. perstans infections. Study participants were also examined for onchocercal nodules and skin lesions and tested for the presence of anti-O. volvulus antibodies (Ov16 ELISA). One hundred and thirteen epilepsy cases and 132 controls were enrolled, with a mean age of 28.3 years (interquartile range 22 – 34 years); 125 (51%) were females. Of the persons with epilepsy, 43 (38.1%) met the probable NS criteria. M. perstans infection was absent in all participants, but Ov16 prevalence and oncho-cerciasis-associated skin manifestations were positively associated with epilepsy, including probable NS. Onchocercal nodules in the mothers of cases were also positively associated with epilepsy. In conclusion, in contrast with O. volvulus, M. perstans is likely not endemic in Mahenge and therefore cannot be a co-factor for NS in the area. Hence, this filaria cannot be the primary and sole causal factor in the development of NS.

EGFR tyrosine kinase inhibitors (TKIs) are the preferred initial treatment for non-small cell lung cancer (NSCLC) patients harboring EGFR mutations. However, remission is transient, and no further effective treatment options are available for EGFR-TKI-advanced EGFR-mutant NSCLCs. Immunotherapy with immune checkpoint inhibitors (ICIs) induces sustained cancer remission in a subset of NSCLCs. However, ICI therapy exhibits limited activity in most EGFR-mutant NSCLCs. Mechanistically, the strong oncogenic EGFR signaling in EGFR-mutant NSCLCs contributes to the non-inflamed tumor immune microenvironment (TIME), characterized by a limited number of CD8+ T cell infiltration, a high number of regulatory CD4+ T cells and a high number of inactivated infiltrated T cells. Besides, EGFR-mutant NSCLC patients are generally non-smokers with low levels of PD-L1 expression and tumor mutation burden. However, current understanding only partially explains why a small population of EGFR-mutant NSCLCs durably respond to ICI therapy, resulting in many researchers actively working in this field. This review reviews the hope seen from pre-clinical studies and clinical trials which may be adopted to improve the outcome of ICI therapy in EGFR-mutant NSCLCs. Besides, the underlying mechanisms leading to the inferior clinical outcome of ICI therapy in EGFR-mutant NSCLCs are discussed.

In intermediary metabolism, enzymes are mainly for the specific provision of one small molecule from another, B from A, for its further use. But there are also decades of work relating to another evolved purpose, which is to prevent untoward effects of odd compounds of various origins, and of ordinary intermediates, whose improper concentrations may be avoided by hydrolases and other activities of metabolic correction.

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

This work introduces the mathematical framework of the novel “First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (1st-CASAM-NODE)” which yields exact expressions for the first-order sensitivities of NODE decoder-responses to the NODE parameters, including encoder initial conditions, while enabling the most efficient computation of these sensitivities. The application of the 1st-CASAM-NODE is illustrated by using the Nordheim-Fuchs reactor dynamics/safety phenomenological model, which is representative of physical systems that would be modeled by NODE while admitting exact analytical solutions for all quantities of interest (hidden states, decoder outputs, sensitivities with respect to all parameters and initial conditions, etc.). This work also lays the foundation for the ongoing work on conceiving the “Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-CASAM-NODE)” which aims at yielding exact expressions for the second-order sensitivities of NODE decoder-responses to the NODE parameters and initial conditions while enabling the most efficient computation of these sensitivities.

This paper utilizes dynamic panel threshold technology to conduct a nonlinear test on the direct effect between environmental regulation and economic fluctuations and the effect of industrial structure upgrading by taking 35 industrial sectors in China from 2003 to 2016 . The research has found that there is an inverted "U" relationship between environmental regulation and economic fluctuations, with the enhancement of environmental regulations, the economic fluctuation increases first and then decreases. The cross-terms of environmental regulation and industrial structure rationalization or industrial structure upgrading are significantly negative, which indicates that the enhancement of environmental regulation is conducive to promoting industrial structure upgrading and reducing the economic fluctuations. While the rationalization factors of industrial structure and advanced industrial structure are significantly negative, indicating that both forms of industrial structure upgrading are conducive to reducing the economic fluctuations. Environmental regulation has technical innovation thresholds for industrial structure upgrading and economic fluctuations, but there are no human capital or FDI thresholds. In the rationalization model of industrial structure, there is a nonlinear "U" relationship between environmental regulation and economic fluctuations when the proportion of scientific research expenditure is more than 1.35%. With the enhancement of environmental regulation, the economic fluctuation reduces first and then increases, and the corresponding inflection point value is 2.398% of the environmental regulation level. At the same time, the environmental regulation can indirectly reduce economic fluctuations by pushing down the industrial structure upgrades. In the advanced model of industrial structure, there is a “U” relationship between environmental regulation and economic fluctuation when the proportion of scientific research expenditure is greater than 1.26%. With continuous enhancement of environmental regulation, the economic fluctuation reduces first and then increases. The corresponding inflection point value is 1.78% of the environmental regulation level, and environmental regulation can indirectly reduce economic fluctuations by promoting the industrial structure at the same time.

The performance of the absolute atom gravimeters used on moving platforms, such as vehicles, ships and aircrafts, is strongly affected by the vibration noise. To suppress its influence, we summarize a vibration compensation method utilizing data measured by a classical accelerometer. The measurements with the accelerometer show that the vibration noise in the vehicle can be 2 order of magnitude greater than that in the lab during daytime, and can induce an interferometric phase fluctuation with a standard deviation of 16.70π. With the compensation method, our vehicle-mounted atom gravimeter can work normally in these harsh conditions. Comparing the Allan standard deviations before and after the vibration noise correction, we find a suppression factor of 22.74 can be achieved in static condition with an interrogation time of T = 20 ms, resulting a sensitivity of 1.35 mGal/Hz1/2, and a standard deviation of 0.5 mGal with an average time of 10 s. We also demonstrate the first test of an atom gravimeter in a moving vehicle, in which a suppression factor of 50.85 and a sensitivity of 60.88 mGal/Hz1/2 were realized with T = 5 ms.

The article discusses early diagnosis and intervention for developmental delays in children, emphasizing the role of specialized services and personnel in supporting recovery and development. It presents findings from a pilot project initiated in Bucharest, aiming to assess the incidence of developmental delays in children aged 1-3 years in selected kindergartens, including ASD. The study involved children, parents, and specialists, utilizing systematic behavior observation (SBO) and semi-structured interviews. Results indicate that out of 613 evaluated children, 81 received specialized control recommendations, with 4 cases showing potential risk of ASD. Additionally, 8 children were diagnosed with autism. The incidence of autism in children aged 1-3 years was 1 in 51. Although exploratory, these findings suggest a model of good practice for future early diagnosis campaigns nationally. Conclusively, the study underscores the importance of early diagnosis and intervention for developmental delays, advocating for a responsive community and emphasizing the significance of national-level efforts to address these issues effectively.

Hyaluronic acid, in the form of a gel or viscoelastic colloidal solution, is currently used for viscosupplementation of joints affected by osteoarthritis, but its effectiveness is under debate in relation to newer alternatives. Based on meta-analytical arguments, the present article reinforces the opinion that there are still no decisive arguments for its complete replacement, but for its use adapted to the peculiarities of the disease manifestation and of the patients. A “broad” comparison is first made with almost all alternatives studied in the last decade, and then a meta-regression study is performed to compare and predict the effect size induced by viscosupplementation and platelet-rich plasma therapies. If they are computerized, the developed models can represent tools for clinicians in determining the appropriateness of the option or not for viscosupplementation, in a manner adapted to the pain felt by the patients, to their age, or to other clinical circumstances. The models were generated using the latest algorithms for meta-analysis and meta-regression implemented in the R language and assembled in different R packages. All primary data and necessary R scripts are provided in accordance with the philosophy of reproducible research. Finally, HA-based products, currently under circumspection, are still clinically useful.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

Moral identity can promote people’s well-being, but existing research has paid little attention to the mechanism of the link between the two. The current study proposed that the eudaimonic identity function is a critical mechanism that links moral identity and well-being. Specifically, the quality of identity commitment mediates the link between subjective well-being and the two dimensions of moral identity, namely, internalization and symbolization. We examined these hypotheses in 419 participants, who completed the Self-importance of Moral Identity Questionnaire, Satisfaction with Life Scale, Scale of Positive and Negative Experience, and Questionnaire for Eudaimonic Well-being. Results from the obtained data confirmed our hypotheses: There is a significant correlation between moral identity and subjective well-being. Both the internalization and symbolization dimensions of moral identity promote subjective well-being through the mediating role of identity commitment quality. We discussed these findings with respect to implications and proposed research suggestions for future studies.

This study presents the development of a number of finite element (FE) models of the pelvis using different continuum and structural modelling approaches. Four FE models were developed using different modelling approaches: continuum isotropic, continuum orthotropic, hybrid isotropic and hybrid orthotropic. The models were subjected to an iterative adaptation process based on the Mechanostat principle. Each model was adapted to a number of common daily living activities (walking, stair ascent, stair descent, sit-to-stand and stand-to-sit) by applying onto it joint and muscle loads derived using a musculoskeletal modelling framework. The resulting models, along with a structural model previously developed by the authors, were compared visually in terms of bone architecture, and their response to a single load case was compared to a continuum FE model derived from CT imaging data. The main findings of this study were that the continuum orthotropic model was the closest to the CT derived model in terms of load response albeit having less total bone volume, suggesting that the role of material directionality in influencing the maximum orthotropic Young's modulus should be included in continuum bone adaptation models. In addition, the hybrid models, where trabecular and cortical bone were distinguished, had similar outcomes, suggesting that the approach to modelling trabecular bone is less influential when the cortex is modelled separately.

Artificial intelligence (AI) systems are poised to become deeply integrated into society. If developed responsibly, AI has potential to benefit humanity immensely. However, it also poses a range of risks, including risks of catastrophic accidents. It is crucial that we develop oversight mechanisms that prevent harm. This article outlines a framework for evaluating and auditing AI to provide assurance of responsible development and deployment, focusing on catastrophic risks. We argue that responsible AI development requires comprehensive auditing that is proportional to AI systems’ capabilities and available affordances. This framework offers recommendations toward that goal and may be useful in the design of AI auditing and governance regimes.

To solve the benefit allocation problem between the subjects of the supply chain of general contracting projects. The Cobb-Douglas function is applied to establish the benefit distribution model when the two cooperate in the supply chain system, analyze the optimal benefit situation of one-time cooperation between the two parties under two modes of egoistic decision and collectivistic decision as well as the benefit distribution problem of multiple cooperation, and conduct numerical simulation to calculate the functional relationship between each decision variable and the contribution degree of the general contractor under different effort cost coefficients. The results show that：In the case of one-time cooperation between general contractors and subcontractors, regardless of the decision mode, the degree of effort of both parties is positively related to their own contributions and negatively related to each other's contributions; the overall benefit of the supply chain system is positively proportional to the degree of contribution of the general contractor and inversely proportional to the share of benefit distribution of the subcontractor; in multiple cooperation, the equilibrium result of both parties achieving Pareto optimality at each stage is that both parties choose to cooperate.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

The COVID-19 pandemic has created huge damage to society and brought panics around the world. Such panics can be ascribed to the seemingly deceptive features of the COVID-19: compared to other deadly viral outspreads, it has medium transmission and mortality rates. As a result, the severity of the causative coronavirus, SARS-CoV-2, was deeply underestimated by the society at the beginning of the COVID-19 outbreak. Based on this, in this review, we define the viruses with features similar to those of SARS-CoV-2 as the Panic Zone viruses. To contain those viruses, accurate and fast diagnosis followed by effective isolation and treatment of patients are pivotal at the early stage of virus breakouts. This is especially true when there is no cure or vaccine available for a transmissible disease, which is the case for current COVID-19 pandemic. As of January 2021, more than two hundred kits for the COVID-19 diagnosis on the market are surveyed in this review, while emerging sensing techniques for SARS-CoV-2 are also discussed. It is of critical importance to rationally use these kits for the efficient management and control of the Panic Zone viruses. Therefore, we discuss guidelines to select diagnostic kits at different outbreak stages of the Panic Zone viruses, SARS-CoV-2 in particular. While it is of utmost importance to use nucleic acid-based detection kits with low false negativity (high sensitivity) at the early stage of an outbreak, the low false positivity (high specificity) gains its importance at later stages of the outbreak. When a society is set to reopen from the lock-down stage of the COVID-19 pandemic, it becomes critical to have antibody based immunoassay kits with high specificity to identify people who can safely return to the society after their recovery of SARS-CoV-2 infections. Given that the emergence of mutant viruses at the beginning of 2021 has complicated current battle against the COVID-19, we also discussed approaches and guidelines to detect viral mutants in the middle of the second wave of the pandemic that started at the end of 2020. Finally, since a massive attack from a viral pandemic requires a massive defense from the whole society, we urge both government and private sectors to research and develop more affordable and reliable point-of-care testing (POCT) kits, which can be used massively by the general public (and therefore called as massive POCT) to contain Panic Zone viruses in future.

Transition between free-surface and pressurized flows is an crucial phenomenon in many hydraulic systems, including water distribution systems, urban drainage systems, etc. During the transition, the force exerted on the structures changes drastically, thus it is meaningful to simulate this process. However, severe numerical oscillations are widely observed behind filling-bores, causing unphysical pressure variations and even computation failure. In this paper, some oscillation-suppressing approaches are reviewed and evaluated on a benchmark model. Then a new oscillation-suppressing approach is proposed to admit numerical viscosity when the water surface is at proximity of conduct roof which has first order accuracy. This approach adds numerical viscosity when water surface is at the proximity of conduct roof. It can sufficiently suppress numerical oscillations under an acoustic wave speed of 1000m/s and is simple to apply. In comparison with two experiments, the simulation results of this method show good agreement and little numerical oscillations. The results in this paper can help readers to choose an appropriate oscillation-suppressing method to improve the robustness and accuracy of flow regime transition simulations.

A lack of trust in Energy Service Company (ESCo) is the most critical factor affecting the development of Energy Performance Contracting (EPC) in China compared with other constraints. One cannot easily estimate the energy-saving performance of an EPC project. Under that condition, lack of trust may cause the Energy-Consuming Unit (ECU) to suspect the energy-saving performance promised by the ESCo, thus leaving potentially profitable projects without necessary funding. Currently, specific studies taking an across-projects viewpoint on annual energy-saving performance of EPC projects in multiple subsectors objectively and quantitatively are lacking. This paper studies the regression relationships of annual energy-saving quantity in terms of revamping cost and the regression relationships of annual cost saving in terms of revamping cost. The regression results show that there are statistically significant correlations in the above relationships in the nine subsectors investigated. This is significant for ESCos and ECUs because knowledge on energy-saving performance could contribute to EPC investment decisions and trust relationships between ESCos and ECUs. Then a multiple linear regression model of revamping cost is set up to analyze its influencing factors. The model indicates that the subsector the sample belongs to, financing, registered capital of the ESCo, and contract period have significant effects on revamping cost. Thus, policy implications regarding innovation of EE promotion technology, clarifying ESCos’ exit mechanism, innovation of financing mechanism, and improving the market credit environment for promoting investment in EPC projects are provided.

In this paper, we synthesized MC(BeA-co-MMA) copolymer microcapsules through suspension polymerization. The pendent n-behenyl group of BeA is highly crystalline, and it acts as the side-chain in the structure of BeA-co-MMA copolymer. The highly crystalline n-behenyl side-chain provides BeA-co-MMA copolymer thermal-energy-storage capacity. In order to investigate the correlation between thermal properties and crystal structure of BeA-co-MMA copolymer, the effects of monomer ratio, temperature changing and changing rate, as well as synthesis method were discussed. The monomer ratio influenced crystal transition behavior and thermal properties greatly. The DSC results proved that when the monomer ratio of BeA and MMA was 3:1, MC(BeA-co-MMA)3 showed the highest average phase change enthalpy ΔH (105.1 J·g–1). It indicated that the n-behenyl side-chain formed relatively perfect crystal region, which ensured a high energy storage capacity of copolymer. All the DSC and SAXS results proved that the amount of BeA had a strong effect on the thermal-energy-storage capacity of copolymer and the long spacing of crystals, but barely on the crystal lamella. It was found that MMA units worked like defects in the n-behenyl side-chain crystal structure of BeA-co-MMA copolymer. Therefore, a lower fraction of MMA, that is, a higher fraction of BeA contributed to a higher crystallinity of BeA-co-MMA copolymer for providing a better energy storage capacity and thermoregulation property. ST(BeA-co-MMA) copolymer sheets with the same ingredients as microcapsules were also prepared through light-induced polymerization aiming at clarifying the effect of synthesis method. The results proved that synthesis method mainly influenced the copolymer chemical component, but lightly on the crystal packing of n-behenyl side-chain.

This work presents a new scheme for autonomous control and obstacle detection of a catamaran unmanned surface vessel. Based on the Newtonian mechanics, hydrodynamics, and maneuvering modelling group modelling principles in the attached coordinate system, a three-degree-of-freedom motion model of the catamaran unmanned surface vessel is constructed. The fuzzy inference method is used to overcome the drawback that the traditional PID control cannot tune the PID parameters online, and the trajectory controller is designed. Meanwhile, 3D LIDAR is used to achieve obstacle detection. The results of the trajectory tracking simulations and experiments verify the accuracy of the model design and the effectiveness of the autonomous control design.

This paper examines the use of business maturity models as a tool for achieving business maturity and improving organizational performance in a rapidly changing business environment. Specifi-cally, the paper evaluates existing maturity models and methodologies, analyzes their effective-ness, and develops a novel and enhanced model tailored to the oil and gas sector. The authors highlight the importance of considering fundamental factors that underlie the implementation of maturity models and the need to evaluate their fit with organizational goals and values. The pa-per concludes by emphasizing the ongoing nature of business maturity and the effectiveness of theproposed Business Maturity Framework (BMF) model in enhancing operational excellence, competitiveness, and sustainable growth in the oil and gas industry. The paper makes a notable contribution to the specialized literature precisely through the differ-ent approaches to the business maturity process and through the proposed business maturity model itself as a base of strategy formulation, which includes a new dimension. This dimension analyzes whether the market and customers are ready for the level of digitization that the com-pany wants to achieve in a future state.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

B lymphocyte development has two DNA recombination processes: V(D)J recombination of the immunoglobulin (Igh) gene variable region and class switching of the Igh constant regions from IgM to IgG, IgA, or IgE. V(D)J recombination is required for successful maturation of B cells from pro-B to pre-B to immature-B and then to mature B cells in the bone marrow. CSR occurs outside of the bone marrow when mature B cells migrate to peripheral lymphoid organs, such as spleen and lymph nodes. Both V(D)J recombination and CSR depend on an “open chromatin” state that makes DNA accessible to specific enzymes, recombination activating gene (RAG), and activation-induced cytidine deaminase (AID). Acetyltransferases GCN5 and PCAF possess redundant functions acetylating histone H3 lysine 9 (H3K9). Here, we generated a mouse model that lacks both GCN5 and PCAF in B cells. We found that double-deficient mice possess low levels of mature B cells in the bone marrow and peripheral organs, an accumulation of pro-B cells in bone marrow, and reduced CSR levels. We conclude that both GCN5 and PCAF are required for B cell development in vivo.

In the paper, a fully discrete compact difference scheme with $O(\tau^{2}+h^{4})$ precision is established by considering the numerical approximation of the one-dimensional Allen-Cahn equation. The numerical solutions satisfy discrete maximum principle under reasonable step ratio and time step constraint is proved. And the energy stability for the fully discrete scheme is investigated. An example is finally presented to show the effectiveness of scheme.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, in-cluding fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters, in the combined phase-space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21976 im-precisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “medium-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st- through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems mod-eled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

China is in the climbing phase of the "Inverted U" environmental Kuznets curve, from excessive environmental consumption to the development stage of governance and protection, and green innovation. It is the most complex period of environmental quality. Environmental and policy regulation are very important. Select panel data of 29 provinces of China (excluding Hong Kong, Macau, Taiwan, Hainan and Tibet) from 2008 to 2017, construct a nonlinear two-threshold regression model, empirically analyse the impact of environmental tax and government innovation subsidies to green processes innovation, and attempt to explore whether the implementation of these two policies at the same time can make enterprises more inclined to green process innovation and whether there is an optimal space for policy implementation. It is proposed that the synergy effect of environmental tax and government innovation subsidy policies on enterprise green process innovation should be fully utilized, and the enterprise green process innovation environment and incentive mechanism should be improved to promote the green transformation and sustainable development of enterprises.

Due to the long duration and high cost of traditional pumping tests, the response of well water level to seismic wave, earth tide, and barometric pressure provides a feasible method for determining continuous changes in aquifer hydraulic parameters. Aiming at the problem that the response phase shift of well BB water level to tidal tide M2 is greater than that of tide O1, this paper preferentially calculated the time series changes of S, Be, n and BKu based on the response mechanism of well water level to barometric pressure and earth tide and with the help of smooth moving method. Then by using the mixed flow model, the variation of transmissivity and leakage coefficient over time were simultaneously obtained, and the evolution process and characteristics of aquifer parameters near well BB caused by the Wenchuan earthquake were ultimately discussed. The calculation results are basically consistent with the previous pumping test, and have been verified and compared by using other scholars' methods. This solving process avoids the problems such as excessive dependence on initial values, multiple solutions, and unstable tide O1, which has a promoting effect on the study of the impact of seismic activity on aquifer systems.

Aberration in microRNA (miRNA) expression or DNA methylation is a causal factor for polycystic ovarian syndrome (PCOS), a common endocrine disorder and leading cause of infertility. However, the epigenetic interactions between miRNA and DNA methylation remain unexplored in PCOS. In this study, we conducted an integrated analysis of RNA-seq, miRNA-seq and MBD-seq on ovarian granulosa cells of PCOS and control groups to reveal the epigenetic interactions involved in the pathogenesis of PCOS. Firstly, we identified 830 genes and 30 miRNAs that were expressed differently in PCOS, and seven miRNAs were found to negatively regulate targeted mRNA expression. Next, in total, 130 miRNAs were found to be significantly differently methylated in promoter regions, while 13 were found to be associated with miRNA expression. Furthermore, the promoter hypermethylation of miR-429, miR-141-3p, and miR-126-3p was proven to suppress miRNA expression and therefore upregulate their corresponding genes, including XIAP, BRD3, MAPK14 and SLC7A5. Our results demonstrate that DNA methylation regulates miRNA expression and therefore controls its corresponding gene expression. The reactivation of the transcription of epigenetically silenced genes may be one of the key elements in PCOS pathogenesis. Meanwhile, the epigenetic mechanisms underlying the regulation of miRNA expression can provide a potential therapeutic target for PCOS in the future.

MIGAL – Galilee Research Institute is a regional R&amp;D center in the northeast peripheral region of Israel. An internationally recognized applied research institute, MIGAL specializes in biotechnology and computational sciences, plant sciences, precision agriculture and environmental sciences, as well as food, nutrition and health. Most of MIGAL’s researchers serve as the core faculty at Tel Hai Academic College (TH). Despite the country’s small surface area, socioeconomic inequality in Israel is high by OECD standards, with wage differences between rich and poor regions reaching up to 400%. The aims of this study are to identify possible socioeconomic impacts of MIGAL–TH on the peripheral northeast’s development. We discuss the effects of academic research and institutions of higher education on mitigating differences between the center and periphery of the country. Data for MIGAL, TH and the northeastern peripheral region were collected from the yearly reports of the two institutions and the Israel Central Bureau of Statistics. MIGAL was found to serve as a link between research, academic teaching and socioeconomic development in the northeast periphery. Several variables related to this link and describing MIGAL–TH and northeastern periphery development were analyzed over time: MIGAL’s budget, total number of employees and number of employees with PhDs; number of TH graduate students; socioeconomic index (SEI) of the northeastern periphery and its position on the Israeli list of regional SEIs. The signs and significance levels of their trends indicate a potential socioeconomic impact of academic research and higher education on peripheral development in the northeast of the country. Research budgets and the creation of jobs for academics living in the region are just a few examples of this impact.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

Post-training quantization is pivotal in deploying convolutional neural networks for mobile applications. Block-wise reconstruction with adaptive rounding, as employed in prior works like BrecQ and Qdrop, facilitates acceptable 4-bit quantization accuracy. However, adaptive rounding is time-intensive, and its constraint on weight optimization space curtails the potential for quantization performance. The optimality of block-wise reconstruction hinges on the quantization status of subsequent network blocks. In this investigation, we delve into the theoretical underpinnings of the limitations inherent in adaptive rounding and block-wise reconstruction. Our exploration leads to the development of a post-training quantization methodology, designated as AE-Qdrop. This algorithm operates in two distinct phases: block-wise reconstruction and global fine-tuning. The block-wise reconstruction phase introduces a progressive optimization strategy, superseding adaptive rounding, which not only augments quantization precision but also significantly improves quantization efficiency. To mitigate the risk of overfitting, we introduce a random weighted quantized activation mechanism. During the global fine-tuning phase, we account for interdependencies among quantized network blocks. The weight of each network block will be corrected with logit matching and feature matching. Extensive experiments validate that AE-Qdrop achieves high-precision and efficient quantization. For instance, in the case of 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop by achieving a 6.26% enhancement in quantization accuracy and quintupling the quantization efficiency.

Although the concept of additive manufacturing has been proposed for several decades, momentum of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization in SLM to obtain high relative density and low surface roughness simultaneously in the previous literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. The statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. It is noted that the effects of the laser power and scanning speed on the above objective quality show highly significant, while hatch space behaves an insignificant impact. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

Lipid nanoparticles (LNPs) are promising carriers for constructing drug delivery systems (DDSs) in disease treatments. Previous studies have suggested that lipid composition and biophysical properties of LNPs can significantly impact their interaction with cells and tissues, allowing for the development of suitable LNPs for precise drug delivery. Our previous study proposed the concept and facile preparation of reconstituted lipid nanoparticles (rLNPs), which not only have the advantages of traditional LNPs but also contain the lipids of mother cell/tissue. In this study, we have found that brain-derived rLNPs (B-rLNPs) can have much better accumulation to the ischemic area of the ischemic stroke (IS) model than liver-derived rLNPs (L-rLNPs). This homing effect hopefully makes rLNPs a useful tool for developing highly accessible devices with homologous targeting ability for precise drug delivery.

The best business relationships are built as relationships between people and organizations that work together to achieve common goals. In the current digital era, B2B collaboration relationships are essential for business success. The response time to customer requests can be a key factor in maintaining these relationships and obtaining a competitive advantage. The use of VBA automation for repetitive tasks can help speed up this process, but it is important that computer operators have medium knowledge about using VBA macros. This work deals with issues related to improving response times to requests received by email, using a macro program for repetitive tasks. The empirical study was conducted on an average of 50 daily emails for order confirmation, with an average writing time of 68 s per email, resulting in a potential reduction in working time for the operator of approximately 1 h/day, only for order confirmation. The results show a significant improvement in working time for computer operators, using VBA buttons for repetitive tasks.

Supervised machine learning (SML) is a powerful method for predicting a small number of well-defined output groups (e.g., potential buyers of a certain product) by taking as input a large number of known well-defined measurements (e.g., past purchases, income, ethnicity, gender, credit record, age, favorite color, favorite chewing gum). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known to be true. SML has had enormous success in the world of commerce, and this success may have prompted a few scientists to employ it in the study of molecular and genome evolution. Here, we list the properties of SML that make it an unsuitable tool in certain evolutionary studies. In particular, we argue that SML cannot be used in an evolutionary exploratory context for the simple reason that training datasets that are known to be a priori true do not exist. As a case study, we use an SML study in which it was concluded that most human genomes evolve by positive selection through soft selective sweeps (Schrider and Kern 2017). We show that in the absence of legitimate training datasets, Schrider and Kern (2017) used (1) simulations that employ many manipulatable variables and (2) a system of cherry-picking data that would put to shame most modern evangelical exegeses of the Bible. These two factors, in addition to the lack of methodological detail and negative controls, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S-HIC) should be taken with a huge shovel of salt.

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.

Tulou dwellings in Southeast China have captivated global interest due to their distinctive appearance, sustainable construction materials and technologies, and their defensive and collective housing functions. Despite several recognized as World Cultural Heritage sites, the vast majority of tulou buildings are undergoing irreversible decline and destruction, necessitating a comprehensive and systematic study. Taking 83 Tulou buildings in Raoping County, Chaozhou City, Guangdong Province as the research object, this study reconstructs the historical scenes and systematically reveals the emergence, popularity, and consolidation process of tulou dwellings as integrated defensive and residential buildings for ordinary people by conducting a comprehensive analysis of historical documents and local chronicles. Based on an extensive field investigation, the study systematically analyzes the geospatial distribution and the spatial characteristics of Raoping tulou and its residential unit. The results demonstrate the adaptability and flexibility of tulou dwellings, showcasing their developmental process and revealing the inclusive nature of these traditional residences, as well as the initiative of those who reside within them. The research findings contribute to a more dynamic, comprehensive, and authentic understanding of tulou and Chinese traditional residences, providing valuable references for the preservation and sustainable development of tulou architectural heritage.

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human-machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices' responsiveness and adaptability. The exploration begins with an overview of TENGs' operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs' potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

In this paper, the research progress on dynamic behaviors of RC structural members is reviewed from experimental, theoretical and numerical perspectives. Firstly, the basic overview, measurement methods, main conclusions and current limitations of available dynamic loading tests are presented. Then the theoretical studies on the dynamic constitutive models of RC materials, the dynamic increase factor (DIF) model for concrete and reinforcing steel and the proposed modified models of dynamic behavior parameter at the structural member level are summarized. Finally, the available modelling approach and the method for incorporating the dynamic effect in numerical simulation of RC structures are reviewed. Moreover, a brief introduction is made to the dynamic hysteretic model established on the experimental data, which provides an alternative approach to consider the dynamic effect beside the commonly used DIF method. This paper provides valuable reference for experimental studies and numerical simulations on the dynamic behaviors of RC structures, and also puts forward some issues that need to be solved in the future research works..

Aspartame is the methyl-ester of the aspartate-phenylalanine dipeptide. Over time it became a very popular artificial sweetener. However, since its approval by the main food safety agencies, several concerns have been raised related to neuropsychiatric effects and neurotoxicity, due to its ability to activate glutamate receptors, as well as carcinogenic risks by increased production of reactive oxygen species. Within this review we critically evaluated reports concerning the safety of aspartame. Some studies evidenced subtle mood and behavioral changes upon daily high-dose intake below the admitted limit. Epidemiology studies also evidenced associations between daily aspartame intake and higher predisposition for malignant diseases like non-Hodgkin lymphomas and multiple myelomas, particularly in males, but association by chance still could not be excluded. While the debate over the carcinogenic risk of aspartame is ongoing, it is clear that its use may pose some dangers in peculiar cases, such as patients with seizures or other neurological diseases; it should be totally forbidden for patients with phenylketonuria, and reduced doses or complete avoidance are advisable during pregnancy. It would be also highly desirable for every product containing aspartame to clearly indicate on the label the exact amount of the substance and some risk warnings.

The present paper proposes a fractal analysis of the Covid-19 dynamics in 45 European countries. We introduce a new idea of using the box-counting dimension of the epidemiologic curves as a means of classifying the Covid-19 pandemic in the countries taken into consideration. The classification can be a useful tool in deciding upon the quality and accuracy of the data available. We also investigated the reproduction rate, which proves to have significant fractal features, thus enabling another perspective on this epidemic characteristic. Moreover, we studied the correlation between two meteorological parameters: global radiation and daily mean temperature and two Covid-19 indicators: daily new cases and reproduction rate. The fractal dimension differences between the analysed time series graphs could represent a preliminary analysis criterion, increasing research efficiency. Daily global radiation was found to be stronger linked with Covid-19 new cases than air temperature (with a greater correlation coefficient -0.386, as compared with -0.318), and consequently it is recommended as the first-choice meteorological variable for prediction models.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

Diffuse large B cell lymphoma (DLBCL), the most frequent type of non-Hodgkin lymphoma in adulthood, remains challenging clinical issues. Despite enhanced remission rates can be achieved, there are one-third of patients who will not respond to current treatment or will relapse with resistant disease, necessitating ongoing efforts on effective treatment strategies and agents. The vacuole H+-ATPase inhibitor bafilomycin A1 is broadly used to block late stage of autophagy flux at high concentration. In this study, we show that, to our surprise, bafilomycin A1 effectively inhibited and killed DLBCL cells at nanomolar concentrations (5nM). Bafilomycin A1 targeted cell cycle regulators cyclin D1 and cyclin E2 to induce cell cycle arrest in G0/G1 phase. Meanwhile, it induced caspase-dependent apoptosis with concomitant cleaved caspase-3 and Parp. Furthermore, we found that bafilomycin A1 inhibited autophagy flux at both early and late stages of the autophagy flux through activating ERK and mammalian target of rapamycin signaling, as well as by inhibiting the degradation of autolysosomes. We speculated that bafilomycin A1 as autophagy inhibitor might enhance the effect of DLBCL chemotherapeutic drug rituximab. Accordingly, our results provided evidence that the combination of bafilomycin A1 with rituximab enhanced the inhibition of DLBCL cells notably. Taken together, our data suggest that bafilomycin A1 may be a promising candidate drug in the therapy of diffuse large B cell lymphoma.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

Background: Physical and mental health of corporate employees is equally important, especially in international salespeople in the in vitro diagnostic (IVD) medical device industry. The rapid growth of the IVD market is driven by the increasing prevalence of chronic and infectious diseases. This study aims to identify the association of socio-demographic, occupational, organizational, and psychosocial factors with mental health outcomes for depression, anxiety, and somatic symptoms in Chinese IVD international salespeople. Methods: The study was a cross-sectional survey of ISs in IVD companies officially registered in China. An online survey was designed to collect data through email contact with IVD companies and social media between August 2022 and March 2023. Measured factors included effort-reward imbalance (ERI), health-promoting leadership (HPL), health climate (HC), inner strength (IS), and perceived social support (PSS). Mental health outcomes assessed using the Core Symptom Index (CSI) were depression, anxiety, and somatic symptoms. Results: A total of 244 salespeople responded to the survey. CSI scores indicated that 18.4% (n = 45) and 10.2% (n = 25) of the respondents had symptoms of major depression and anxiety, respectively. ERI was positively correlated, while the IS and PSS were negatively correlated with major depression, anxiety, and somatic symptoms (p < .01). Health climate was negatively correlated with major depression (p < .05). Education background was associated with somatic symptoms (p < .05). ERI, IS, and gender were significant predictors of major depression, anxiety, and somatic symptoms (p < .05). Conclusion: The prevalence of depression and anxiety in China’s IVD international salespeople was considered low compared with prevalence in Chinese populations during COVID-19, but higher than those before the pandemic. Effort–reward imbalance, inner strength, and gender were significant factors in major depression, anxiety, and somatic symptoms among IVD international salespeople.

In response to the fire protection design challenges faced by the integrated station-city engineering under the new model of station-city fusion, such as large building volume, diverse traffic types, functional integration, spatial complexity, and inapplicable standards, this paper selects Chongqing East Station as a typical engineering case. The fire safety engineering methods and performance-based fire protection design ideas were used. The paper summarized the fire protection difficulties, such as multifunctional combination construction, large fire protection distance, large fire protection zoning area, long evacuation distance, and design of evacuation (quasi) safety zones. Combining with the actual situation of the engineering, this paper analyzed the basic technical measures taken to address these design challenges. By adopting the method of fire safety engineering, the safety of the design scheme is verified, which can provide a solution approach for the current fire protection design challenges in the integrated station-city engineering.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Then, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

This paper presents an original method of additive manufacturing of cylindrical parts with variable circumference thickness, which allows the control of the deposition of molten material using an algorithm for decomposing the part geometry into volumetric elements with known dimensional configuration. In the absence of a post-processor capable of controlling additive manufacturing on a 5-axis numerical control machine, control of the deposition of molten material is done using parameterized programs, which can control both the feed speeds of the machine tool axes and the specific functions of the printing equipment. Additive manufacturing can make a positive contribution to sustainable development compared to traditional manufacturing technologies, thus making a positive contribution for a sustainable future. The aim of the work is to make it possible to 3D print parts with variable wall thickness using a CNC machining centre. To obtain the variable thickness layer we have implemented an original method of deposition with molten material (FDM), the coordination of the system composed of physical elements respectively programmable elements, is realized through control functions materialized in parameterized part programs, the generated outputs being the variable speed of the machine axes on a circular trajectory, the angular positioning, the filament advance.

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

The fusion of global contextual information with local cropped block details is crucial for segmenting ultra-high resolution images. In this study, we introduce a novel fusion mechanism termed Global-Local Deep Fusion (GL-Deep Fusion) based on an enhanced transformer architecture, which efficiently integrates global contextual information and local details. Specifically, we propose the Global-Local Synthesis Networks (GLSNet), a dual-branch network where one branch processes the entire original image, while the other branch handles cropped local patches as input. The feature fusion of different branches in GLSNet is achieved through GL-Deep Fusion, significantly enhancing the accuracy of ultra-high resolution image segmentation. Particularly effective is GLSNet in identifying tiny overlapping items. To optimize GPU memory utilization, we meticulously design a dual-branch architecture that proficiently leverages the features it extracts, seamlessly integrating them into the enhanced transformer framework of GL-Deep Fusion. Extensive experiments conducted on challenging benchmarks, including DeepGlobe and Vaihingen datasets, demonstrate that GLSNet achieves a new state-of-the-art performance in terms of GPU memory utilization and segmentation accuracy tradeoff.

The heat generated during the startup process of dry gas seals, such as the friction heat of the asperities, viscous shear heat, and the expansion heat of the gas film, easily leads to seal failure and endangers the stable operation of turbomachinery. This study combines the statistically modified contact model and the average Reynolds equation by considering the adiabatic index and the microscale effects of dry gas seals to explore the heat generation characteristics of dry gas seals formed in series at multiple times. In particular, the change laws and influencing factors among the components, including the friction heat of asperities and the shear heat and expansion heat of the gas film and total heat, are investigated. A comparative analysis indicates that the slip flow effect increases the total heat during the startup process. The friction heat is much greater than expansion heat and shear heat. Despite constant acceleration, exponential acceleration, or Harrision acceleration, the heat changes are uneven during the startup process. In particular, the heat changes sharply in the early stage and gently in the later stage. The Harrision acceleration mode is the most conducive to sealing stability.

Based on multi-source remote sensing data, scenario analysis, and model simulation, the Pareto optimal solutions for water supply, water purification (N retention), and carbon storage and sequestration services under different scenarios were sought by adjusting its land use structure. The results showed that. In Scenario 1(S1), the water supply service needs to increase by 86.820 to 11.211 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 to 11,700 tons, and the carbon storage and sequestration service need to decrease by 2.070 to 2.487 billion tons. In Scenario 2(S2), the water supply service needs to increase by 8.243–10.666 billion cubic metres, the water purification (N retention) service needs to decrease by 11,300–1.10 million tons, and the carbon storage and sequestration service needs to decrease by 2.033 to 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 7.832–11.437 billion cubic metres, the water purification (N retention) service needs to decrease by 1.16–10,800 tons, and the carbon storage and sequestration service needs to decrease by 19.220 to 2.380 billion tons. After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. After optimising the S3 ecosystem service supply pattern, the water supply, water purification (N retention), and carbon storage and sequestration services could connect the western and eastern ecosystem service supply areas, balance the overall ecosystem service supply pattern of the study area and meet the demand for ecosystem services. The results can guide regional land planning and ecosystem service management optimisation.

The corrected mechanism model of battery voltammetric function is helpful to guide the development and application of battery. There are two scientific issues that need to be answered: First, how many mechanisms do batteries have; Second, how to establish the mechanism model separately under the overlapping of these mechanisms. Volt-ampere characteristics of both linear state and nonlinear state exist; the monotonic decreasing of volt-ampere characteristics indicates that the battery have only three kinds of mechanisms. Without changing the basic form of the function and under the principle of the mechanism function’s working region considered, we propose a mechanism function which satisfies the monotonic decreasing characteristic of the voltammetric curve of battery, via the derivative law of each mechanism function in the voltammetric function of battery. By using the voltammetric data, the obtained cell mechanism function can accurately predict the potential (current or voltage) when the independent variable of the cell is zero, and provide the theoretical basis for the internal working mechanism of the cell, which can guide the practice.

Leucine-rich repeat receptor-like kinases (LRR-RLKs) have been reported to play important roles in plant growth, development and stress responses. However, no comprehensive analysis of this family has been performed in Gossypium, which are important economic crop and suffer various stresses in growth and development. Here we conducted a comprehensive analysis of LRR-RLK family in four Gossypium species (G. arboreum, G. barbadense, G. hirsutum and G. raimondii). A total of 1641 LRR-RLK genes were identified in the four Gossypium species involved in our study. Maximum-likelihood phylogenetic tree revealed that all the LRR-RLK genes were divided into 21 subgroups. Exon-intron organization structure of LRR-RLK genes kept relative conserved in subfamilies and between Arabidopsis and Gossypium. Subfamilies XI and XII were found dramatically expanded in Gossypium. Tandem duplication acted as an important mechanism in expansion of Gossypium LRR-RLK gene family. Function analysis suggested that plant hormone signaling and plant-pathogen interaction pathway were enriched in Gossypium LRR-RLK genes. Promoters analysis and expression profiles analysis revealed that Gossypium LRR-RLK genes were extensively regulated by TFs, phytohormone and various environmental stimuli, and play key roles in stress defense and diverse development processes. Our study provided valuable information for further function study of Gossypium LRR-RLK genes.

The objective of this study was to investigate the impact of adding fermented herbal extracts (FHE) derived from mulberry leaf (Morus alba), Javanese turmeric (Curcuma xanthorrhiza), and fingerroot (Boesenbergia rotunda) to the diet of tiger shrimp (Penaeus monodon) on their growth, moulting performance, feed efficiency, and nutrient retention. The main feed used in this trial was a commercially manufactured pellet, then five different doses of FHE supplementation were used: 0 mg/kg feed (P0, control), 50 mg/kg feed (P1), 100 mg/kg feed (P2), 150 mg/kg feed (P3), and 200 mg/kg feed (P4). Weight gain, average daily gain, and length gain of shrimps fed P2 was significantly higher than that of those fed the control diet. Similar result was observed in moulting performance. The application of P2 showed superior results in enhancing the FE of cultured shrimp. Thus, the protein and energy retention of P. monodon was significantly better in P2 treatment groups. P0 had the lowest crude protein, while shrimp given P2 and P3 diets had the highest crude protein content of any treatment group. Based on the findings, it is recommended that the juvenile tiger shrimp diet contain 100 mg/kg of FHE for the best effects.

Strategic intelligence covers the examination of what impacts and influences the business and the related SWOT analysis, (strengths, weaknesses, opportunities, and threats mapping). Through this research, leaders will be able to properly allocate and re-route resources, identify areas of improvement, and ensure that strategic initiatives are aligned with the organization's mission. Strategic intelligence is, therefore, effective through the use of critical thought and data-driven decisions and seeing the outcomes of different positioned alternatives. It must also form and support a culture of cooperation and open communication within the organizations, stimulating employees' inputs and learning curves. The article's subject is the necessity for strategic intelligence, a crucial competence for leaders, managers, and companies striving to remain competitive, to be utilized and best leveraged in today's volatile and dynamic business contexts. It helps the leadership turn proactive, foresee trends, and make correct, agile decisions that enforce companies' long-term growth and success. This study follows a research design that includes a thorough literature review and bibliometric research, executed via VoSViewer, illustrating the concept of strategic intelligence from a leadership point of view as a driver of competitive advantage. Further, the article points out that the limitations are acknowledged because the literature on the matter is limited, as strategic intelligence is relatively new to the business sector; moreover, more research is required to fully understand and use the potential of strategic intelligence in business growth. By overcoming these limitations and continuing to study this concept, businesses can gain a competitive edge and make informed decisions that fuel their success in an ever-evolving market. As a result, this study contributes to developing strategic intelligence in leadership and business and paves the road for further research and applications of strategic intelligence theories in organizational contexts.

Fifteen new diterpenoids, namely xishaklyanes A-O (1-15), along with three known related ones (16-18), were isolated from the soft coral Klyxum molle collected from Xisha Islands, South China Sea. The stereochemistry of the new compounds were elucidated by a combination of detailed spectroscopic analyses, chemical syntheses, quantum chemical calculations and comparison with the reported data. The absolute configuration of compound 18 was established by the modified Mosher's method for the first time. In bioassay, some of these compounds exhibited considerable antibacterial activities on fish pathogenic bacteria, and compound 4 showed the most effective activity with MIC of 0.225 g/mL against Lactococcus garvieae.

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument's spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the spectral response function (SRF) is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which looks like a similar distribution to the flat-topped multi-Gaussian function, is used to fit the measured data in a spectral calibration. According to the results, the SRF’s shapes, due to the Earth port, resemble a Gaussian curve with a flatted top could be concluded, which contains an FWHM of 1.78–1.82 nm for the AAS. The results show that the correlation coefficients are about 0.99, which proves that the fitting function could better characterize the SRF of the instrument.