Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spectral bands than a general image, thereby permitting the multi-spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-spectral analysis and explores the middle-level statistics of spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-spectral analysis firstly learns the multi-spectral prototypes (codebook) for representing any pixel-wise spectral data, and then based on the learned codebook, a sparse coded spectral vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded spectral vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is much more accurate than even the deep learning framework for spatial analysis.

A novel binary toxin-positive non-027, non-078 Clostridium difficile strain designated LC693 whose sequence type was ST201 was isolated from the fecal sample of a patient with severe diarrhea in China. To understand the pathogenesis basis of C. difficile ST201, this recently recovered isolate LC693 was then chosen for whole genome sequencing. The project finally generated an estimated genome size of approximately 4.07 Mbp. The genome sequence was then analyzed together with the other two ST201 strains VL-0104 and VL-0391 and compared to the epidemic 027/ST1 and 078/ST11 strains. Phylogenetic analysis demonstrated that the ST201 strains belonged to clade 3. Genome size of the three ST201 strains ranged from 4.07 Mb~4.16 Mb, with an average GC content between 28.5%~28.9%. The ST201 genomes contained more than 40 antibiotic resistance genes and 15 of them were predicted to be associated with vancomycin-resistance, suggesting that they may have a strong antibiotic resistance. The ST201 strains contained a typical clade 3 specific PaLoc with a Tn6218 element inserted, and those genes harbored on their PaLoc that participated in the toxin expression and regulation were highly homologues to the epidemic 027 and 078 strains, with the exception of tcdC. A truncated TcdC was found in the ST201 strains, which is suggestive to have a contribution to the toxin production of the ST201 strains. In addition, the ST201 strains contained intact binary toxin coding and regulation genes, which is also proposed to contribute to the virulence. Genome comparison of the ST201 strains with the epidemic 027 and 078 strain identified 641 genes specific for the C. difficile ST201, and a number of them were predicted as fitness and virulence associated genes. The identification of those genes also contributes to the pathogenesis of the ST201 strain. To our knowledge, this is the first study that the genome sequence of C. difficile ST201 was discussed in detail, and the present study would have a contribution to understanding the pathogenesis basis of C. difficile ST201.

Emerging countries in Southeast Asia are facing considerable challenges in addressing rising motorisation and its negative impact on air quality, traffic, energy security, liveability, and greenhouse gas emissions. Indeed, even as initial policies to address these issues are being agreed and implemented, current trends are incompatible with sustainable development and long-term climate change targets. This paper presents a comparative analysis of the approach and status of sustainable, low-carbon transport policy in ASEAN countries and identifies differences and similarities, with the aim of helping assessment of feasibility of future policies and informing future studies on policy innovations and cross-country learning. The methodology is based on the taxonomy of policy components developed by Howlett and Cashore, and our data on comprehensive country studies for Indonesia, the Philippines, Thailand and Vietnam and interviews. We find that each country has a specific set of goals, objectives and targets that support sustainable transport, and, directly or indirectly, climate change mitigation. In terms of specific instruments and calibrations, which we analyse based on the Avoid-Shift-Improve approach, there are notable differences between the countries, for example in fuel economy policy.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

In this article, we estimate the value of Government Expenditure on Education-GEE in the context of Environmental, Social and Governance-ESG dataset of the World Bank. We use data from 193 countries in the period 2011-2020. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we apply the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of the future value of GEE. We found that the Polynomial Regression is the best predictive algorithm. The Polynomial Regression predicts an increase in GEE of 7.09% on average for the analysed countries.

In this article, we estimate the role of Political Stability and Absence of Violence and Terrorism-PS in the context of Environmental, Social and Governance-ESG data at world level. We analyse data from 193 countries in the period 2011-2020. We apply Panel Data with Fixed Effects, Panel Data with Random Effects and Pooled Ordinary Least Square-OLS. We found that PS is positively associated, among others, to Population Density and Government Effectiveness, and negatively associated, among others, to Research and Development Expenditure and Maximum 5-day Rainfall. Furthermore, we apply the k-Means algorithm optimized with the application of the Elbow Method and we find the presence of four clusters. Finally, we propose a confrontation among eight different machine-learning algorithms for the prediction of PS and we find that the Polynomial Regression shows the higher performance. The Polynomial Regression predicts an increase in the level of PS of 0.25% on average for the analysed countries.

In this article, I estimate the value of “Individual Using Internet”-IUI in the context of Environmental, Social and Governance-ESG database of the World Bank. I use data from 193 countries for the period 2011-2020. I found that among others the value of IUI is positively associated to “Methane Emissions” and “People Using Safely Managed Sanitation Services” and negatively associated among others to “Fossil Fuel Energy Consumption” and “Renewable Energy Consumption”. I apply the k-Means algorithm for the clusterization optimized with the Elbow Method and we find the presence of three clusters. Finally, I confront eight machine-learning algorithms to predict the future value of IUI. I found that the best predictive algorithm is Linear Regression and that the value of IUI is expected to decrease on average of 0.30% for the analysed countries.

We analyze the question of GDP Growth-GDPG rate in the context of Environmental, Social and Governance-ESG framework. We use World Bank data for 193 countries in the period 2011-2020 using different econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS. We found that GDPG rate is positively associated, among others, to “Government Effectiveness” and “Prevalence of Undernourishment” and negatively associated among others to “Unemployment” and “Research and Development Expenditure”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters in the sense of GDPG rate. Finally, we confront eight machine learning algorithms to predict the value of GDPG rate and we found that the Polynomial Regression is the best predictor. The Polynomial Regression predicts an increase of GDPG rate equal to 2.88% on average for the analysed countries.

We estimate the determinants of Emissions-COE in the context of Environmental, Social and Governance-ESG model at world level. We use data of the World Bank for 193 countries in the period 2011-2020. We found that the level of COE is positively associated, among others to “Methane Emissions”, “Research and Development Expenditures”, and negatively associated among others to “Renewable Energy Consumption” and “Mean Drought Index”. Furthermore, we have applied a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four cluster. Finally, we apply eight machine-learning algorithms for the prediction of the future value of COE and we find that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predicts a reduction in the level of COE equal to 5.69% on average for the analysed countries.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.

This paper presents a comprehensive analysis of the financial factors and risk management strategies essential for optimizing disaster resilience within the Serbian defence industry's critical infrastructure. The significance of this sector is multi-faceted, impacting national security, economic stability, and technological advancement. Primarily, the Serbian defence industry ensures the preservation of vital defence interests, maintaining Serbia's independence from foreign sources for weapons and military equipment in both peacetime and wartime. Economically, it is a significant employer of the working-age population, directly affecting local employment rates, fostering economic development, and ensuring the sustainable growth of this crucial sector. This, in turn, stimulates broader economic activity and enhances social cohesion while strengthening the national balance of payments through increased export potential. From a technological perspective, the defence industry drives scientific, technological, and industrial development, reinforcing Serbia's global political and military standing within the Western Balkans and on the international stage. Consequently, the paper aims to examine the risk management and protection of the Serbian defence industry's critical infrastructure, offering concrete and actionable measures to improve and develop these systems with a particular emphasis on security. The research's utility and contribution lie in identifying similarities and differences in the operational performance of defence industry companies, a vital segment of the national economy. The presentation of these findings focuses on the protection of critical infrastructure. The results will form the basis for further investigation into the underlying causes of business performance and the effective management of critical infrastructure security.

This study investigates the environmental parameters contributing to the Urban Heat Island (UHI) effect in historically built environments, focusing on Matera's Street Canyon. The primary objective is to enhance understanding of heritage centers' ecological sustainability and management frameworks. The study involves two main phases: the initial research and development phase, including a comprehensive literature review and methodology development, followed by the empirical data gathering and analysis phase, utilizing advanced techniques such as 3D laser scanning, satellite imagery, and meteorological data integration. Key findings reveal significant correlations between urban morphology, material properties, and UHI intensity, suggesting stra-tegic urban planning interventions to mitigate UHI effects. This research provides actionable in-sights for urban planners and policymakers, emphasizing the importance of balancing heritage preservation with modern sustainability needs.

Hypophthalmichthys nobilis are widely distributed in the Yangtze River basin and its related lakes. They are an important economic fish species and are a famous cultured species known as the "Four Famous Domestic Fishes" in China. Currently, with the fishing ban in the Yangtze River basin, fishing for H. nobilis in natural water bodies of the Yangtze River basin has been completely prohibited. In order to identify the sources of H. nobilis appearing in the market, further control and accountability, it is necessary to trace the sources of H. nobilis in the Yangtze River basin and its related water bodies. Therefore, this study identified and traced different sources of H. nobilis through muscle element fingerprint analysis (EFA). The results show that H. nobilis from different stations have characteristic element compositions. The characteristic element of H. nobilis from Wuhan (WH) is Pb, which is significantly higher than that in other stations; the characteristic element from Anqing (AQ) is Hg, which is significantly higher than that in other stations; and the characteristic element from Taihu (TH) is Al, which is significantly higher than that in other water areas. Multivariate analysis selected different spatial distribution patterns of 4 discriminant elements ratios (Pb/Ca, Cr/Ca, Na/Ca, Al/Ca) in the muscle of H. nobilis in the Yangtze River basin and its related lakes. The study suggests that the screened discriminative elements can be used to visually distinguish different sources of H. nobilis, and to quickly trace and verify the origin of newly emerging samples. Therefore, the use of selected discriminant element fingerprint features to trace the origin of new samples has been proven to be feasible. By further discriminating and verifying the muscle element fingerprints of new samples, the discrimination rate is high. Therefore, Multivariate analysis for muscle element fingerprint can be used for origin traceability of samples of unknown origin in market supervision.

Enhancing soil fertility and maize productivity is crucial for sustainable agriculture. This study aimed to evaluate the effects of tillage practices, nitrogen management strategies, and acidified hydrochar on soil fertility and maize productivity. The experiment used a randomized complete block design with split-split plot arrangement and four replications. Main plots received shallow tillage and deep tillage. Sub-plots were treated with nitrogen (120 kg ha⁻¹) from farmyard manure and urea, including control, 33% FYM + 67% urea (MU), and 80% FYM + 20% urea (MF). Acidified hydrochar treatments H0 (no hydrochar) and H1 (with hydrochar, 2 t ha⁻¹) were applied to sub-sub plots. Deep tillage significantly increased plant height, biological yield, grain yield, ear length, grains ear-1, thousand grain weight, and nitrogen content compared to shallow tillage. MU and MF improved growth parameters and yield over the control. Hydrochar effects varied; H1 enhanced certain yield components but reduced plant height and soil properties compared to H0. Canonical discriminant analysis linked deep tillage and MU/MF nitrogen management with improved yield and soil characteristics. In conclusion, deep tillage combined with integrated nitrogen manage-ment enhances maize productivity and soil health. However, hydrochar application requires op-timization. These findings highlight the importance of selecting appropriate tillage and nitrogen strategies for sustainable maize production. These insights guide policymakers, agronomists, and agricultural extension services in adopting evidence-based strategies for sustainable agriculture, enhancing food production, and mitigating environmental impacts. Future research should in-vestigate long-term impacts across diverse environments to validate these results.

Urbanization is a dynamic process. At different stages of urbanization, polices of managing car-bon emissions should be adjusted. In this paper, two types of nighttime light data and statistical data of 68 cities in China from 2002 to 2021 are used to establish an urban development evalua-tion system comprising population, economy, construction, and society subsystems. Then, cities were categorized into expanding, contracting, and stable types to understand the uncertainty of urbanization's impact on carbon emissions. The main conclusions are as follows: (1) Integrated nighttime light data is used to extract built-up area boundaries, achieving an overall accuracy ranging from 68.75% to 98.4%, with kappa coefficients ranging between 0.60 and 0.94. (2) Ex-panding cities were concentrated in the eastern coastal regions, while contracting cities were mainly located in inland cities with traditional industries. Urban agglomerations in the Beijing-Tianjin-Hebei region, Yangtze River Delta region, and Chengdu-Chongqing metropolitan demonstrate synchronized characteristic of urbanization development. (3)Economy and con-struction have a greater impact on carbon emissions than population and society. For expanding cities, the focus of carbon emission governance should prioritize rational land use planning. Conversely, for contracting cities, emphasis should be placed on adjusting economic structures.

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.

The presented work offers a novel process scheme for valorizing historical Pb-Zn slag, which involves crushing, grinding, and then using separation techniques to concentrate valuable components (nonferrous metals). This methodology could have a significant impact on the global beneficiation of metallurgical slags since, in theory, it is significantly more simple, environmentally friendly, and cost-effective than standard pyrometallurgical or hydrometallurgical procedures. According to previous physicochemical and mineralogical studies, Pb-Zn slag from "Topilnica" Veles is a valuable secondary raw material. This inhomogeneous technogenic resource contains substantial amounts of nonferrous metals (Pb, Zn, Cu, and Ag). However, laboratory tests have indicated that the Pb-Zn slag contains a very uneven valuable metals amounts, ranging from several g/ton to several tens of g/ton. The main issue is that traditional metallurgical procedures for releasing beneficial elements are not commercially viable since the valuable elements are "trapped" within amorphous aluminosilicates or intergrowths of alloy grains and glassy phases. Gravity concentration (Wilfley 13 shaking table) and magnetic separation (Davis separator and disk separator) were used to obtain the final concentrate following comminution and grindability testing. The gravity concentration proved more effective. This streamlined approach of valorizing Pb-Zn slag can reduce the need for hazardous chemicals used in hydrometallurgical refinement operations, as well as the extremely high temperatures required for pyrometallurgical processing. This is the first investigation into the feasibility of this novel approach, which includes direct investigations of the Pb-Zn slag feed using these alternative technologies. After extracting the valuable metals, the amorphous aluminosilicate part of the Pb-Zn slag can be reapplied as an alternative raw material in the building sector, adding to the circularity of the suggested approach..

From the various perspectives of Machine Learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the Early Detection (ED) of anoma-lies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision-making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLM) operate with a focus on ED in a multidisci-plinary manner and specifically how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVM), De-cision Trees (DT), Random Forests (RF), Naive Bayesian Classifier (NB), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBM) and Stacking Ensemble Models (SEM). It was identified that MLMs for ED in multiple areas under SBM and SEM implementation achieved accuracies greater than 80% and 90%, respectively. n fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

With the aim to produce new heterocycle molecules, the previously reported 2-(aminomethyl)-2-(4-chlorophenyl)-2,3-dihydroquinazolin-4(1H)-one was converted efficiently by reacting with N,N’-dithiocarbonyldiimidazole (DTCI), to produce the substituted imidazolidine-2-thione moiety inserted in a three fused ring scaffold of the title compound. The molecular composition was confirmed by a high-resolution MS experiment, and its structure was elucidated by 1H, 13CNMR, and IR analyses. The thioacetamide form of the product was supported by density functional theory (DFT)-NMR analysis where 13C chemical shifts of thioacetamide form and of its iminothiol tautomer were calculated in chloroform at the BP86 /Jgauss-TZP2 level of theory. The very good linear correlation between 13C chemical shifts by experiment and by calculation for the NHC=S form confirmed the structure.

This paper presents the five-parameter Exponentiated Weibull Weibull (EWW) distribution with Weibull base failure rate parameterization, developed to model the dropout times of students in an online class module. Parameters of the model were estimated using both Maximum Likelihood and Bayesian estimation procedures to determine the most effective estimation method. Simulation results indicate that, across various hazard types and censoring levels, Bayesian estimates are more accurate and precise than Maximum Likelihood estimates. Real-life data analysis of online students module presentation supports these findings, showing significantly lower standard deviations for Bayesian estimates compared to Maximum Likelihood estimates, highlighting the efficiency of Bayesian estimation for the EWW distribution. Additionally, a comparative goodness-of-fit analysis demonstrated that the EWW distribution better fits the student dropout times dataset than Weibull Weibull (WW), Weibull Exponential (WE), and Exponential Weibull (EW) distributions. The median dropout time estimated using the EWW distribution is 24 days, suggesting that instructors should closely monitor student attendance around this period.

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.

Abstract: Background: MAb114, REGN-EB3, Remdesivir, and ZMapp, which are monoclonal antibody-based treatments, have been compared and shown to be promising therapies against the Ebola Virus Disease (EVD). There has been no comparison between these med-ications and standard treatment (without antiviral). Our study aimed to examine the con-tribution of each regimen compared to standard treatment on the survival of EVD patients and assess whether this association was modified by EVD vaccination (rVSV-ZEBOV Ebola vaccine) status. Methodology: We performed a secondary analysis of data obtained from four EVD treatment centers located in Katwa, Mangina, Butembo, and Beni in the North Kivu region. The main outcome measure was mortality within a 28-day period. A Cox model was used to identify predictors of survival in hospitalized EVD patients. Results: Vaccinated EVD patients were 1.7 times less likely to die compared to unvac-cinated patients (3.70 days vs. 5.00 days; p=0.0002). Delaying care and treatment at EVD treatment centres increased mortality risk by 5% for each day following symptom onset. Compared to the standard treatment group, adjusted mortality rates were significantly re-duced in the groups receiving MAb114 (0.29, p

This paper presents a comprehensive cost-benefit analysis of producing alternative phosphorus (P) fertilizers from recycled water treatment residuals (Fe-WTR) and cowshed effluents, under the umbrella of circular economy approach. Fe-WTR is compared to a synthetic adsorbent, layered double hydroxide (LDH). Dairy wastewater is used as a P source for both adsorbents. The cost of 1 ton P-Fe-WTR sums up to 329 Euro. In contrast, the use of synthetic adsorbents (P-LDH) incurs significantly higher costs, amounting to 3329 Euros per ton, underscoring the economic advantage of utilizing waste-derived materials. Adopting Fe-WTR fertilizer not only offers substantial cost savings by avoiding landfill fees (40-150 Euros per ton) and wastewater discharge fines (500 Euros per ton) but also reduces environmental impacts. This sustainable method mitigates eutrophication and pollution, presenting a dual benefit of cost-effectiveness and environmental protection. Although our results are based on a specific case study, they reflect the potential value of extending the circular economic approach to P production worldwide: Recycling P from waste streams can be economically viable and environmentally preferable at large, aligning with global sustainability goals and demonstrating the practicality of circular economy principles in environmental management.

Abstract: Thermophilic fungi constitute an interesting study model for understanding ecologi-cal mechanisms of adaptation in diverse environments and have attractive toolboxes with a wide range of biotechnological applications. This work describes for the first time an endophytic and thermophilic strain of Aspergillus brasiliensis that was isolated in the crater of the active volcano "El Chichonal" in Mexico. The average temperature of the collection site was around 60 °C and the soil pH was moderately acidic. The complete genome of A. brasiliensis E_15.1 was sequenced and assembled in about 37 Mb of genomic DNA. We performed a comprehensive phylogenomic anal-ysis for the precise taxonomic identification of this species as a novel strain of Aspergillus brasil-iensis. Likewise, the predicted coding sequences were classified according to various functions including Carbohydrate-Active Enzymes (CAZymes), biosynthetic gene clusters of secondary metabolites (BGCs), and metabolic pathways associated with plant growth promotion. Among the functional traits identified was the degradation of chitin, chitooligosaccharides, xylan, and cellulose, as well as the biosynthesis of clavaric acid, a triterpene with antitumor activity due to its ability to inhibit Farnesyl-protein transferase. In addition to the genomic analysis, a set of en-zymatic assays confirmed the thermostability of extracellular xylanases, cellulases of A. brasili-ensis E_15.1. Our findings suggest that the enzymatic repertoire of A. brasiliensis E_15.1 has a high potential for industrial application due to its thermostability and can promote plant growth at high temperatures. Finally, this strain constitutes an interesting source of terpenoids with pharmacological activity.

Keywords: Antibacterial activity, Antioxidant activity, Bioactive compounds, Elementary analysis, Nutritional and phytochemical composition, Seaweed.

The demonstration of compliance of rolling stock against disturbance limits for railway signaling, and in particular track circuits, is subject to a large deal of variability, caused by the diverse values of the electrical parameters of the railway line and resulting transfer functions, as well as operating conditions of rolling stock during tests. Instrumental uncertainty is evaluated with a Type B approach and shown to be much less than the experimental variability. Repeated test runs in acceleration, coasting, cruising, braking conditions are considered, deriving both max-hold (spread) and sample dispersion curves compared to the respective mean values (Type A approach to the evaluation of uncertainty). The major source of variability affecting a significant portion of the spectrum is caused by the superposed oscillations of the onboard LC filter, for which different choices of the transformation window duration are discussed. The test runs and the acquired data covered overall 1 day of tests along about 300 km of the Italian 3 kV DC railway network.

Jump rope is a widely-applied basic training in various sports, yet it is understudied biomechanically. This study investigates the impact of cycle-tempo-induced motor control changes in elite jump rope athletes, addressing the biomechanical gap of cyclic skill control. The hypothesis posited two accelerations per jump cycle—one in front of and one behind the body—and anticipated that increased cycle frequency would alter the distribution of acceleration time within a cycle. Using 3D motion analysis, kinematic parameters were obtained and analyzed. The results confirmed the presence of two distinct accelerations per cycle. As tempo increased, the percentage of rear acceleration time increased while front acceleration time decreased, along with peak velocities increasing significantly (p<0.01). Rope trajectory analysis indicated a consistent movement pattern across tempos, primarily in the sagittal plane. Variations in skill control showed shorter contact phases and reduced vertical range of motion for the center of gravity and feet at higher tempos (p<0.05), along with significant reductions in joint range of motion for the lower limbs (p<0.01). These findings enhance the understanding of motor control adaptations to different tempos and have practical implications for developing coaching programs aimed at optimizing performance, stability, and efficiency in jump rope training.

Using data from the Swedish National Diabetes Register, this study examines the gender disparity among patients with type I diabetes who have experienced a specific cardiovascular complication, while exploring the association between weight variability, age group, and gender. Fourteen cardiovascular complications have been considered. This analysis is conducted by using three-way correspondence analysis (CA) which allows for the partitioning and decomposition of Pearson’s three-way chi-squared statistic. The dataset comprises of information organized in a data cube, detailing how weight variability among these patients correlates with a cardiovascular complication, age group and gender. The three-way CA method presented in this paper allows for one to assess the statistical significance of the association between these variables and to visualize this association, highlighting the gender gap among these patients. It is worth noting that the association between weight variability, age group, and gender varies among different types of cardiovascular complications.

The United Nations’ Agenda 2030 has established a series of Sustainable Development Goals to address global challenges, including poverty, food insecurity, access to education, and social inequality. In this context, charitable organizations such as Caritas play a crucial role in mitigating the negative effects of these challenges and promoting fair and sustainable development. This study aims to analyze prevalent needs among individuals seeking assistance from Caritas in Campania and examine how the organization contributes to achieving the Agenda 2030 Goals in the region. The statistical investigation techniques considered include tandem analysis [], which considers a dimension-reduction technique, such as multiple factor analysis, and, then cluster analysis to identify similar groups of individuals. These exploratory data analysis methods have allowed for the identification of common needs, including food assistance, support for education, employment, and housing assistance. Subsequently, Caritas programs and initiatives aimed at meeting these needs and promoting sustainable development are explored. The results indicate that Caritas plays a significant role in addressing the urgent needs of the vulnerable population in Campania and contributes to the goals of Agenda 2030, particularly those related to poverty alleviation, immigration, health promotion, education, employment, and reduction of social inequalities. This study provides an important perspective on the relevance and effectiveness of Caritas’ work in the context of Agenda 2030.

Natural risks encompass a range of disasters and dangers, requiring comprehensive management through assessments, anticipations, and advanced alert systems. Our specific focus is on landslides in challenging terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum approach, integrating qualitative parameters. Despite the challenges of the rugged terrain in Northern Algeria, it paradoxically hosts a dense population attracted by valuable hydro-agricultural resources. Our research is dedicated to studying landslide risks in these areas, particularly in the Mila region, with the aim of constructing a statistical model integrating considerations of both danger and vulnerability. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. The result is a valuable tool for effective risk management, strengthening safety and resilience against natural disasters. By understanding the intricate interplay of geological, environmental, and social elements, our research can contribute to the development of targeted interventions, fostering a more resilient community in the face of natural adversities.

Simple summary This reassessment of the findings from our Phase I/II study seeks to evaluate the effectiveness of SBRT for treating local intermediate-high risk prostate cancer in the absence of androgen-deprivation therapy due to patient’s refusal, with a median follow-up of 6.5 years. Our primary focus is on analysing the treated areas compared to recurrence patterns in the seven patients who experienced local relapse. Additionally, we provide updated data on late toxicities of grade 2 or higher, both genitourinary and gastrointestinal. Abstract Objectives: We investigated spatial patterns between primary and recurrent tumor sites and assessed long-term toxicity after dose escalation stereotactic body radiation therapy (SBRT) to the dominant intraprostatic nodule (DIN). Materials and methods: In 33 patients with intermediate-high risk prostate cancer (PCa), doses up to 50 Gy were administered to the DIN. Recurrence sites were determined and compared to the original tumor development sites through multiparametric MRI and 68Ga-labelled prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (68Ga-PSMA-PET/CT) images. Overlap rates, categorized as 75% or higher for full overlap, and 25-74% for partial overlap, were assessed. Long-term toxicity is reported. Results: All patients completed treatment, with only one receiving concomitant androgen deprivation therapy (ADT). Recurrences were diagnosed after a median of 33 months (range: 17-76 months), affecting 13 out of 33 patients (39.4%). Intraprostatic recurrences occurred in 7 patients (21%), with ≥ 75% overlap in two, a partial overlap in another two, and no overlap in the remaining three patient. Notably, 5 patients with intra-prostatic recurrences had synchronous bone and/or lymph node metastases, while six patients had isolated bone or lymph node metastasis without intraprostatic recurrences. Extended follow-up revealed grade 2 or higher late GU and GI toxicity in 18% (n=6) and 6% (n=2) of the patients. Conclusions: Among patients with intermediate-high risk PCa undergoing focal dose-escalated SBRT without ADT, DIN recurrences were infrequent. When present, these recurrences were typically located at the original site or adjacent to the initial tumor. Conversely, relapses beyond the DIN and in extra-prostatic (metastatic) sites were prevalent, underscoring the significance of systemic ADT in managing this patient population. Advances in knowledge: focal dose escalated prostate SBRT prevented recurrences in the dominant nodule, however extra-prostatic recurrence sites were frequent.

In this study, we utilized the UniProt database to extract breast cancer genes Total 2658 genes in Homo sapiens are obtain. Then 166 genes are taken and after delating duplicates 158 genes were remains. These genes were then analyzed for their biological pathways using Gene Ontology enrichment analysis through the Genecodis platform, resulting in the identification of 12 unique genes involved in disease pathways. Protein-protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software, identifying ANLN,TREM1,LZTFL1,OXSM,PLA2G3,TAS2R13,ABCB10,ECT2,TRPC,BCAS3,PLA2G3,ZDHHC7 and ERBB2 as potential gene targets for breast cancer.

In Korea, laver (Gim) is the leading traded marine product and an important food source, accounting for 82.0% of the world’s exports. Laver exports reached $100 million in 2010 and steadily increased to $600 million in 2020. An increase in laver exports improves the income of laver farmers and promotes the growth of fisheries. However, marine algae are vulnerable to climate change. In this study, we aimed to predict changes in laver production due to climate change using environmental variables, including water temperature, rainfall, and sunlight duration. The results showed that water temperature considerably affected laver production; when water temperature increased by 1 °C, production decreased by 13.78% (t) in the same month. By contrast, when sunlight duration and rainfall increased by 1 h and 1 mm, respectively, 1 month before harvesting laver, production increased by 0.32% (t-1) and 0.13% (t-1), respectively. Using four scenarios based on climate change published by the Intergovernmental Panel on Climate Change in a modeling exercise, we found that laver production in 2100 in Wando in Korea may decline by 73.3% owing to water temperature changes (based on 2020 production), threatening food security. Accordingly, short-, mid-, and long-term countermeasures are needed for sustainable laver aquaculture. Appropriate water temperature (21–22 °C) during seeding is necessary to control environmental variables, based on scientific data and activation of land-based seeding methods. Moreover, mid- and long-term countermeasures should include development of high-temperature resistant laver seeds, identification of suitable new laver aquaculture grounds, and research on seeds that can adapt to climate change.

In this study, a new diphosphate compound, Ag0.2Na0.6FeP2O7, was synthesized using a solid-state reaction method and extensively studied using various techniques. The compound's structure was confirmed through X-ray diffraction analysis, revealing a single-phase monoclinic structure with the P21/C space group. The electrical behavior of the compound was investigated, and its conductivity followed Jonscher's universal law. The correlated barrier hopping (CBH) model explained the temperature-dependent ac conductivity and parameter "s" well. The study also explored the contribution of single-polaron and bipolaron hopping to the a.c. conductivity and discussed the ionic conductivity in relation to the structural characteristics of the sample. Overall, this investigation significantly enhances our comprehension of the distinctive electrical properties of Ag0.2Na0.6FeP2O7 and its potential practical applications.

The understanding of spatiotemporal variations in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman-Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (EMA) in the state of Mato Grosso, spanning the period 2007-2020. The statistical metrics used for evaluation include Bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the RCP 2.6, 4.5, and RCP 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007-2020) and projected (2020-2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas.

E-waste management (EWM) refers to the operation-management of discarded or unproductive electronic devices and components, a challenge exacerbated due to overindulgent urbanization. This article presents a multidimensional cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties - which contemplate the 3 pillars of sustainability in an e-waste recycling plant, including the production-delivery-utilization process. The framework incorporates material recovery from a single e-waste facility provisioning for chemical and mechanical recycling. Each module is ranked using independent Machine Learning (ML) protocols: a) Analytical Hierarchical Process (AHP) and b) combined AHP and Principal Component Analysis (PCA). From a long list of possible contributors, the model identifies and ranks two key sustainability contributors to the EWM supply chain: overall energy consumption and volume of carbon dioxide generated. Another key finding is a precise time window for policy resurrection, which for the data considered, happens to be 400-600 days from the start of operation. Another interesting outcome is the quality of prediction using a combination of AHP and PCA, which consistently produced better results than any of these ML methods individually implemented. Model outcomes have been verified using a case study to outline a future E-waste sustained roadmap.

The integration of digital technologies into construction practices has emerged as a pivotal area of interest, reflecting its substantial influence on the future trajectory of the construction industry. This study undertakes a comprehensive bibliometric analysis of literature on construction digitalization spanning from 2013 to 2023, sourced from the Elsevier Scopus database. Employing key terms such as "construction digitalization," "BIM," "construction 4.0," and "digital construction methodologies," the research methodically extracts and scrutinizes relevant articles. Advanced tools like the VOS viewer were utilized to delve into the bibliometric networks, offering insights into prevailing research trends within the built environment sector. The findings reveal a pronounced emphasis on digital modeling, automation, blockchain technologies, and emerging paradigms such as smart contracts and modular construction. This investigation enriches the academic discourse by elucidating the varied subtleties, ongoing shifts, and prospective directions in the ever-evolving landscape of construction digitalization research.

Teaching, learning, research, and development are all supported in a favorable atmosphere at the University of Lagos (UNILAG). The wetland systems made up of freshwater that are extremely biodiverse makeup about 10% of the total land area. Flooding, deforestation, excessive sedimentation, blockages to streams, abuse, overuse, and invasion of wetlands are some of the recent environmental challenges that the university faced. Urban heat island impacts, contaminants in the air, wetland degradation, social issues including stress, and a detachment from nature are all major concerns. With the green corridor behind the faculties of law and management science serving as a model supported by sustainability as a true foundation for UNILAG's campus landscapes, this article investigates the sustainable restoration of wetlands within the boundaries of the university. The use of qualitative techniques was employed, utilizing the Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Additionally, a geospatial mapping of the wetlands and tributaries that connect the Lagos Lagoon was conducted to evaluate the intrinsic ecological goods and services through a series of analytical steps, beginning with an analysis of the land area, current biodiversity, hydrology, and its potential through landscape mapping and analysis. The biophysical setup, landscape features, climatic data analysis, the ecological goods and services derived from the wetlands, and the records of flooding and rainfall are all included in the geospatial evaluation and analysis. The educational institution's site planning for the remediation of its wetlands takes into account the actual evolution of the area's drainage network. Throughout the wetlands, low carbon emissions are generated by off-road bike lanes, trails for walking, and raised hardwood platforms. Combining native plant species and park furniture, the academic landscape atmosphere will be improved in each of the wetlands and aesthetic values. the country's "University of First Choice" and its biophysically restored wetlands will help illuminate the landscape while the green corridors have an innate ability to attract human affinity within the academic community. All of these elements are implemented following an environmentally friendly landscape planning, management, design model, and enforcement process.

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. Firstly, we consider the conventional GARCH involved volatility models. We next extend the corresponding GARCH based forecasting and calculate a specific probability associated with the predicted volatility levels. Since the probability evaluation is based on a stochastic model, we next develop an additional data-driven estimation of this probability. The proposed advanced statistical estimation uses real (historical) market data. The obtained theoretic results for the statistical probability of levels are also discussed in the framework of the integrated volatility concept. A possible application of the established probability estimation to the volatility clustering problem is also mentioned. Our paper includes a concrete practical implementation of the resulting (combined) volatility prediction tool and considers a novel module for trading and volatility estimation in crypto markets recently developed by 1ex Trading Board group in collaboration with the GoldenGate Venture. Moreover, we discuss shortly a possible using of the proposed model based and data-driven volatility prediction methodology in the financial risk management.

In order to tackle the issue of severe rocking phenomenon under seismic conditions in structures equipped with steel spring isolation bearings, this paper investigates a novel type of anti-rocking bearing. Firstly, the structural configuration and working principle of the novel anti-rocking bearing are introduced, and a design method for bearing parameters is proposed. Secondly, a finite element analysis model is established using SAP2000 software to conduct nonlinear dynamic time history analysis under seismic loading. The analysis results show that the structural arrangement of the novel anti-rocking bearing reduces both the vertical displacement difference and the rocking angle of the isolation layer. The bearing exhibits a certain level of anti-rocking effect, but it may cause significant tensile forces in some bearings, necessitating the installation of anti-tensile reinforcement. The effectiveness of the anti-rocking effect improves as the stiffness of the steel tension rod in the bearing increases; however, it also leads to higher maximum tension in individual bearings as well as the total tension in all bearings. For structures equipped with the novel anti-rocking bearing, the acceleration amplifies under most cases, with amplification coefficients ranging from 0.82 to 1.55. The performance of the novel anti-rocking bearing varies significantly for different seismic waves. The stiffness of the steel tension rod should be determined considering the reference pressure of the bearing and the maximum designed tension of the bearing. Through the finite element simulation of the bearing, the mechanical properties of the bearing are basically the same as the theoretical analysis results. For the cylinder and the joint bearing, certain structural measures need to be taken to ensure its strength and stiffness.

This paper investigates the impact of debt financing on dividend payments when they face the agency costs of free cash flow. Focusing on a sample of 120 firms listed on the Saudi Stock Exchange during the period 2011-2021. The study found a negative association between agency cost of free cash flow and dividend payment. More importantly, our research highlights the significant role of long-term debt in making more prudent use of free cash flow. The obligation to meet interest and principal payments acts as an incentive for them to steer clear of unprofitable expenses and risky investments. Concurrently, long-term debt imposes restrictive clauses in debt contracts, such as minimum dividend distribution requirements, which further encourage higher dividend payments. Since interest and debt repayments are fixed obligations, using free cash flow for dividend disbursement is considered a more profitable and beneficial approach for shareholders in the context of Saudi Arabia.

To get more data concerning the influence of fertilizers (organic and mineral) on different forage plants existing in North-eastern Romanian grassland, the mass fractions of 14 essential, enzymatic, or considerd toxic elements was determined by Instrumental Neutron Activation Analysis together with the amount of crude proteins, ash, fibres as well as ether fact extract. Final results showed a significant variance of the content of analysed elements on organic as well as on mineral fertilized experimental plots, i.e. the content increased in 24% of plots, decreased in 23%, and remained unchanged in 45% while in 8% of plots their content was below the detection limits, as the case of Se. At the same time, an increase content of crude protein and ether extract was evidenced in fertilized grasses for all applied fertilizers while other global indicators such as neutral and acid fibre of sufuric lignin content decreased, suggesting significant higher nutritional values of fertilized forage plant.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

The licensing of new reactors implies the use of verified and validated neutronics codes. Numerical validation can rely on sensitivity and uncertainty studies but they require repeated execution of time-consuming neutron flux and depletion calculations. The computational costs can be shortened by using perturbation theories. However, Depletion Perturbation Theory is restricted to single integral values such as a nuclide density. Relying on reduced basis approaches, which reconstruct the whole nuclide densities at once, is one way to get around this restriction. Furthermore, the adjoint-based reduced-order model uses the direct and adjoint equations for the projection. For diffusion or transport calculations, Exact-to-Precision Generalized Perturbation Theory has been developed. Still, no models for depletion calculations are readily available. Therefore, this paper describes a novel adjoint-based reduced order model for the Bateman equation. It uses a range-finding algorithm to create the basis and the Depletion Perturbation Theory for the reconstruction of the nuclide densities at first order. Our paper shows that for several perturbed cases, the depletion reduced-order model successfully reconstructs the nuclide densities. As a result, this serves as a proof of concept for our adjoint-based reduced order model which can perform sensitivity and uncertainty burn-up analysis in a shorter time.

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.

Adopting Building Information Modeling (BIM) within the Architecture, Engineering, and Construction (AEC) industry presents an opportunity to address enduring challenges, including chronic productivity deficits and emerging imperatives such as cleaner production and sustainability. However, the pace of BIM implementation (BIMI) across European Union (EU) countries is uneven due to varying contexts and the inherent complexity of BIM adoption. Notably, Portugal lags in BIMI among these countries. Using the Portuguese context, the manuscript endeavours to identify the main barriers to BIMI by the late-adopting countries in the EU and, based on the dynamics of their relationships, design effective mitigation measures to lessen their impact and eventually leverage BIMI. A comprehensive list of barriers was compiled from the literature and ranked by experts through a Delphi survey, resulting in 15 critical barriers. Subsequently, an Interpretive Structural Modelling (ISM) model was constructed to elucidate the hierarchical relationships among these barriers, while a fuzzy Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) analysis was employed to determine their driving and dependence powers. The resulting main barriers were the lack of evaluation and feedback mechanisms on BIMI, ignorance of BIM capabilities/benefits, scarcity of BIM-capable professionals, lack of experience and cooperation in BIMI, resistance to change, and lack of support from top management. Finally, experts formulated mitigation measures aimed at tackling these main barriers. These measures were designed to not only address each barrier individually but also to have a comprehensive and practical impact on the entire barrier system as a whole. These findings will assist researchers, policymakers, and practitioners in comprehending the barriers and hierarchical structures affecting BIMI in EU late-adopting countries.

This report demonstrates the first-time asymmetric synthesis of enantiopure structured triacylglycerols (TAGs) of the ABC type presenting three non-identical fatty acids, two of which unsaturated. The unsaturated fatty acids included the monounsaturated oleic acid (C18:1 n-9) and the polyunsaturated linoleic acid (C18:2 n-6). This was accomplished by a six-step chemoenzymatic approach starting from (R)- and (S)-solketals. The highly regioselective immobilized Candida antarctica lipase (CAL-B) played a crucial role in the regiocontrol of the synthesis. The synthesis also benefited from the use of the p-methoxybenzyl (PMB) ether protective group that enabled incorporation of two different unsaturated fatty acids to the glycerol skeleton. The total of six such TAGs were prepared, four constituting the unsaturated fatty acids in the sn-1 and sn-2 positions with a saturated fatty acid in the remaining sn-3 position of the glycerol backbone. In the two remaining TAGs the different unsaturated fatty acids accommodated the sn-1 and sn-3 end positions with the saturated fatty acid present in the sn-2 position. Enantiopure TAGs are urgently demanded as standards for enantiospecific analysis of intact TAGs in fats and oils.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

Wastewater-based epidemiology (WBE) has emerged as a key method for continuous monitoring of COVID-19 prevalence including circulating SARS-CoV-2 lineages. WBE addresses limitations of traditional clinical COVID-19 surveillance such as clinical test availability, fluctuating testing rates and increased reliance on rapid antigen tests. Our study in Perth, Western Australia, found a significant positive correlation between SARS-CoV-2 concentrations in wastewater and clinical PCR positivity rates (rs = 0.772; p < 0.001) over an 18-month period that included four successive COVID-19 waves. A strong positive correlation was apparent between the proportions of SARS-CoV-2 lineages in wastewater and clinical cases within the same region (rs = 0.728; p < 0.001), including earlier detection of Omicron and recombinant lineages in wastewater before clinical case confirmation. The successful integration of WBE with healthcare data underscores its critical role in enhancing public health decision-making and pandemic management. This approach not only demonstrates the value of WBE in current global health surveillance efforts but also highlights the potential of WBE to address future public health challenges, as a comprehensive disease monitoring and response approach.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcel-lular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

In volleyball, the jump serve is a fundamental and crucial skill that is one of the most commonly used serving techniques. This study aimed to examine the kinematic differences between straight line (SL) and diagonal line (DL) in volleyball jump serves by focusing on torso and attack arm joint motions. Three-dimensional coordinate data were captured using a motion capture system (200 Hz) in 14 right-handed male professional volleyball players who performed SL and DL jump serves. This result suggests that the pelvis and torso backward rotation angles during the arm-clocking phase were smaller when performing DL serves. During ball contact, the servers exhibited greater forward rotation angles of the pelvis and torso to control the upper body facing the diagonal direction of the target zone. In addition, lowering the forward tilt angle of the trunk helped the servers maintain body balance in the air and increased their wrist flexion angle, preventing the ball from flying out of bounds when performing DL. These findings highlight the importance of controlling pelvic and torso rotations in the transverse plane when adjusting for different serving lines. These insights can guide volleyball athletes and coaches in refining their serving techniques.

XPS and ARXPS characterization of surface layers resulting from the functionalization of polymers such as PVC modified with MPTMS and APTES is challenging due to the signals overlap, deriving both from the substrate and the functionalized layers. In this work, a freshly cleaved, ideally flat gold surface was used as carbon-free model substrate functionalized with MPTMS and subsequently grafted with APTES. Avoiding the overlap of signals from carbon atoms present in the substrate, the signals in the C1s, O1s, Si2p, S2p and N1s high-resolution spectra could be assigned to the MPTMS / APTES functionalized layer only and the curve fitting parameters could be determined. Quantitative analysis was in very good agreement with the expected stoichiometry of the functionalized layer, confirming the adopted curve fitting procedure. In addition, it was found that one molecule of APTES grafted two MPTMS via silane groups. ARXPS allowed determining the thickness of the functionalized layers: MPTMS thickness was found to be 0.5 (0.2) nm, MPTMS + APTES thickness 1.0 (0.2) nm in good agreement with Avogadro model calculations. This approach can be considered a powerful tool for characterising functionalized surfaces of more complex systems by XPS.

Bioinformatics is a rapidly developing field enabling scientific experiments through computer models and simulations. Considering the vast databases of biological data available, it is ex-tremely important to develop efficient methods and algorithms for their processing. Sequence comparison is the best method for studying the evolutionary interaction between genes. It is based on alignment – the process of arranging two or more sequences to achieve the maximum level of identity and degree of similarity. The paper presents a new version of the algorithm for pairwise DNA sequences alignment, based on a new method called CAT, where a dependency with a previous match and the closes neighbor are taken in consideration to increase uniqueness of the CAT profile and to reduce possible collisions, i.e. two or more sequence having same CAT profiles. This makes proposed algorithm suitable for finding exact match of a concrete DNA se-quence in a big set of DNA data faster. The generation of CAT profiles is made once before data has been uploaded to the database, allowing the profiles to be used as metadata for the sequenc-es. It consists of an algorithm to calculate a CAT profile against the selected reference sequences and an algorithm to compare two sequences based on the calculated CAT profiles. Improve-ments in generation of the CAT profiles, are detailed described in the paper. Block scheme, pseudo code tables and figures were updated according to the proposed new version and ex-perimental results. Experiments have been carried out with the new version of the method and different datasets to align DNA sequences based on the CAT method. New experimental results in terms of collisions, speed, and efficiency of the proposed solutions are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. An analysis of the per-formance of the proposed CAT based algorithm against Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is one of the most widely used for searching biological data, was performed. The impact of prior matching dependencies on uniqueness for generated CAT pro-files is investigated. The analysis of the experimental results obtained by sequence alignment shows a small deviation of the proposed algorithm based on the CAT method, which can be ig-nored if this deviation is acceptable at the expense of performance. The time efficiency of the CAT algorithm remains constant, regardless of the length of the sequences. Therefore, the ad-vantage of the proposed method is its fast processing in the alignment of large sequences, for which the execution of the exact algorithms takes a long time. The example code realization of the CAT Method, under the protection of the GNU General Public License v3.0, can be accessed on GitHub at: https://github.com/HristoS/CATSequenceAnalysis.

Abstract: This study investigates the transition to sustainable shipping industry within the framework of the IMO's 2050 carbon neutrality objective, focusing on socio-technical systems analysis. The research delves into the sustainable paradigm shift towards alternative marine fuels such as LNG, methanol, ammonia, and hydrogen from a socio-technical transition perspective, analyzing the multi-level per-spective challenges and opportunities. Specifically, it scrutinizes the implications of adopting these al-ternative fuels for global shipping, identifying technical uncertainties, financial constraints, and infra-structure needs. Through a comprehensive review of existing literature on ship emissions and alterna-tive fuels, coupled with socio-technical transition theory, the study addresses the multifaceted chal-lenges and opportunities in meeting the IMO's environmental objectives. It emphasizes the necessity of collaborative governance, innovative niche strategies, and a holistic approach to leverage the current window of opportunity in facilitating the maritime industry's transition to sustainable fuel options. Fi-nally, this research enhances our understanding of the intricate interplay between technological, social, industrial, and cultural dynamics in the greening of shipping and offers insights into the strategic adop-tion of environmentally friendly marine fuels for a sustainable future.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

In recent years, there has been a surge of proposals for innovative approaches to public planning. It has become apparent that the traditional emphasis on "economic development" as the predominant objective of public policy is no longer adequate to promote a thriving society. Common Agricultural Policy in Europe faces challenges to adopt and implement the dynamic of culture. However, culture as a pillar of sustainable development is not a new topic. This concept has been incorporated into economic studies with increasing tension. The present study aims to measure scientific interest through published scientific studies and papers related to the contribution of culture in the context of European development programs and strategies. A total of (n=902) published documents during the period 1990-2023 from the Scopus database were explored to identify (n=27) appropriate publications for research analysis based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach and flow diagram. Bibliometric analyses using Vos Viewer (Center for Science and Technology Studies, Leiden University, Leiden, The Netherlands) and constructed term co-occurrence network plots based on bibliometric evidence were performed. The results show that there was no scientific interest. The systematic review and bibliometric analysis of the present paper could provide a valuable and seminal reference for the researchers of rural development and the relevant policymakers.

This study rigorously investigates the impact of COVID-19 on the Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility. Interestingly, certain indices related to government interventions exhibit no substantial impact on volatility, indicating a weak resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more nuanced outcomes in terms of correlations of volatility to the same metrics. Notably, the study recognizes the potential influence of uncertainties linked to the duration of lockdowns on financial market volatility. The positive and long-term impact on volatility of the US Equity Market uncertainty and VIX is evident through the Autoregressive Distributed Lag Model (ARDL), whereas economic policy uncertainty shows no significant impact. This indicates a potential vulnerability of the Tunisian stock market to future shocks, emphasizing the need for government efforts to instill investor confidence. This comprehensive exploration contributes to our understanding of the dynamics between the pandemic’ metrics, government interventions, and financial market stability. Our findings provide valuable insights for implementing risk management strategies amid the COVID‐19 crisis.

The goal of this article is to provide a review of the experimental techniques and procedures using vibration methods for the Structural Health Monitoring (SHM) of Polymer-Matrix Composites (PMC). It aims to be a guide for any researchers to carry out vibration experiments. The linear methods are first introduced. But as PMC is a complex material, these classic methods showed some limits, such as low accuracy for small damages, and a high environmental dependency. This is why the nonlinear methods are secondly studied, considering that the complexity of PMC induces a nonlinear behavior of the structure after damage occurrence. The different damage mechanisms are well explained in order to evaluate the potential of each vibration method to detect them.

The variety of risks due to climate change with a potential impact on Cultural Heritage CH requires a multidisciplinary approach and a preliminary overview of the existing methods for risk analysis and assessment in different fields. Thus, the starting point of the research conducted by the Sapienza PNRR working group consists in a systematic literature review of risk assessment approaches, methods and indicators. As a first outcome, the research has designed a multidisciplinary comprehensive framework consisting of the definition of risk categorization, the framework represented by a measurable index and the main target, according to the classification of Cultural Heritage based on the harmonization of European vocabularies definitions and protocols. The risk assessment framework sets the methodological core of a future multi criteria matrix and digital platform for decision focused on the historic built environment and it is functional to the definition of specific operational guidelines connected to each risk for elaborating descriptive/predictive Architectural Heritage’s transformation scenarios to support the development of mitigation and adaptation strategies.

Recently, volatile organic compounds (VOCs) have been shown to act as precursors of secondary organic particles that react with ultraviolet rays in the atmosphere and contribute to photochemical smog, global warming, odor, and human health risks, highlighting the importance of VOC management. In this study, we measured VOC concentrations in various contexts including industrial and residential areas of Bucheon, Korea, through mobile laboratory and proton-transfer-reaction time-of-flight mass spectrometry methods to determine winter VOC concentrations, visualized the data based on spatial information, and used the results to develop an air quality management plan. For this purpose, global information system (GIS)-based air quality data and various environmental variables were comprehensively reviewed to assess spatial and temporal concentrations in three dimensions rather than in tables and graphs. Among VOCs, the levels of toluene, methanol, and n+i-butene were relatively high, with average concentrations of 48.3 ± 67.2, 34.4 ± 102.7, and 32.6 ± 57.7 ppb, respectively, at the end of the working day. The highest concentrations occurred near the Ojeong Industrial Complex. Mobile pollution sources are also a major driver of VOCs, highlighting the necessity of comprehensively reviewing traffic variables such as road level, estimated traffic volume, and average speed when identifying hotspots of air pollution. GIS-based visualization analysis techniques will improve the efficiency of air quality management.

The global focus on geopolymer binder production has increased due to the adoption of waste materials and industrial byproducts. Given the gradual decline of the availability of fly ash and ground granular blast furnace slag (GGBFS) resulting from the decarbonization process in electricity and steel productions, waste clay brick powder (WCBP), could be a viable substitute of these pozzolanic by-products. This research is dedicated to thoroughly investigate the techno-eco-efficiency performance of WCBP when utilized as a geopolymer precursor. The favorable mechanical characteristics exhibited by fly ash-GGBFS-WCBP-based geopolymer binder emphasize the importance of assessing its sustainability alongside its technical viability. The study employed life cycle analysis (LCA), following ISO 14040-44 standards, and using the Simapro software, to evaluate the environmental implications of the use of WCBP-based geopolymer mixtures. Human toxicity emerged as the primary impact. Moreover, the analysis of life cycle costs highlighted key financial factors, with around 65-70% attributed to alkaline activators of the total cost. The production of alkaline activators was identified as a critical point for both environmental impact and economic considerations due to energy consumption. While WCBP-rich samples exhibit a 1.7-0.7% higher environmental impact compared to the control mix (CM), their high mechanical strength and cost-effectiveness make them technologically and economically efficient geopolymer mixes. In conclusion, the portfolio analysis for techno-eco-efficiency affirms that mixes containing 40%, 30%, and 20% WCBP are more efficient than those using 10% and 0% WCBP, respectively.

This study employs molecular docking techniques to investigate a spectrum of targets within Escherichia coli (E. coli), with a specific focus on four key proteins: Beta-lactamase, MccE protein, Penicillin G acylase, and DNA polymerase II. The research explores the binding interactions of various natural compounds, emphasizing Hypericin. Through computational analyses, the study aims to unveil the potential interactions between these compounds and essential proteins in E. coli. Notably, Hypericin demonstrates remarkable binding energy scores of approximately -10.5 kcal/mol within the Ligand Binding Site of the specified proteins. This observation suggests Hypericin's potential as a potent modulator of E. coli targets, prompting further investigation into its antimicrobial properties and therapeutic applications.

High rates of resistance to antibiotics are associated with healthcare-related infections, thus appealing for the urgent need for effective antimicrobials against these maladies. The present study aims to determine the chemical composition of essential oil (EO) from Clausena anisata leaves, and evaluate their antibacterial activity against selected nosocomial bacteria. Fresh leaves of Clausena anisata were subjected to a steam distillation using a Clevenger apparatus to afford the essential oil. The phytochemical composition of this oil was analyzed using gas chromatography-mass spectrometry (GC-MS). The essential oil was further tested against selected nosocomial bacteria, including Staphylococcus and Klebsiella species, among others, using a microdilution method. As a result, 0.77% of essential oil was extracted from fresh leaves of Clausena anisata. The GC-MS analysis revealed that the as-prepared essential oil contain E-anethole (70.77%), methyl isoeugenol (13.85%), estragole (4.10%), γ-terpinene (3.33%), myrcene (2.82%) and sabinene (0.77%), with E-anethole being the major constituent. Meanwhile, twenty two compounds were identified in the EO of Clausena anisata leaves through gas chromatography. Upon antibacterial test against selected nosocomial pathogens, the E-anethole-rich essential oil exhibited minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values ranging from 3.91 to 125 µg/mL and 7.81 to 125 µg/mL, respectively, indicative of a bactericidal orientation of the plant’s essential oil (MIC/MBC ratio: 4). This novel contribution highlights the scientific validation of the use of Clausena anisata leaves in the traditional treatment of various infectious diseases. However, toxicity and pharmacokinetic studies, mechanistic basis of the antibacterial action, and in vivo antibacterial experiments of the E-anethole-rich essential oil of Clausena anisata should be investigated to successfully use this plant in the treatment of infectious diseases.

The increasing complexity of the space flight program and the increase in the duration of missions require improvement of psychological monitoring tools for astronauts in orbit. The article summarizes the experience of using quantitative content analysis of communication between the crews and the Mission control center (MCC). This method allows to assess dynamics of astronaut’s psycho-emotional state, to identify the communicative style, and to detect the communication phenomena of board-MCC communication. The method is based on a combination of coping strategies approach by Lazarus and Folkman as well as B.F. Lomov’s concept about the three functions of communication. We found influence of workload on the structure and volume of communication, defined the main stable communication styles of crewmembers, and confirmed presence of the emotional transfer phenomenon. We detected that astronauts successfully solve problems that arise in orbit using the capabilities of their communication style. An ineffective MCC communication style the leads to the psycho-emotional ill-being manifesting in emotional transfer phenomenon. The presence of “third quarter” phenomenon was not confirmed by materials from six-month space flights.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

Observations collected during cold season precipitation periods at Utquagvik, Alaska and at the mul-tidisciplinary drifting observatory for study of Arctic climate (MOSAiC) are used to statistically ana-lyze relations among the atmospheric water cycle parameters including the columnar supercooled liq-uid and ice amounts (expressed as liquid water and ice water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While correlation between snowfall rate and LWP is rather weak, correlations coefficients between radar-derived snowfall rate and IWP are high (~ 0.8), which is explained, in part, by generally low LWP/IWP ratios during significant precipitation. Corre-lation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (r<0.3). Results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15-minute averaging versus daily averages). Observationally-based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations.

Along time, many minted coins were withdrawn from circulation, being replaced with new ones. The returned obsolete metal coins were melted in order to sustainable reuse of the alloy for other purpose. Between the withdrawing and melting, some of the metal coins were cancelled by destruction of the original shape and dimension, using some adequate tools. In the first part, the paper is focused to present some aspects insight of the cancelling method used on the Romanian old nickel coins; also, there are presented some examples. The introductive part also includes the literature review in the field of coin manufacturing, covering the subjects as metal behavior under the striking load, aspects about the 3D modeling and FEM analysis or explaining some striking errors. Result the paper main purpose, to study the particularities of one or another cancelling method applied on subjected coins, which conducted to more or less valuable collection metal pieces. In the paper second part, is introduced the adequate 3D model, computed for the cancelling dies, respectively the coin. Then, there are introduced the assembled models, corresponding to each cancelling case, which consist by the obverse and reverse cancelling dies, having the coin inside them. For the each model, the finite element analysis is realized and is achieved for different initial conditions. The final part of the paper presents the analysis results and also the discussions and conclusions.

This study aimed to use spatiotemporal tensor data to measure the level of employment quality in China's provinces and analyzed the magnitude and direction of its influencing factors in the spatiotemporal dimension. Taked 30 provinces, autonomous regions and municipalities directly under the central government in China from 2011 to 2020 as the research object, the employment quality evaluation system was constructed from six dimensions of employment environment, employment status, employability, labor remuneration, social security, and labor relations. The employment quality index data was expressed as a three-order high-dimensional tensor spatio-temporal data form, and the employment quality of China's provinces was measured from the spatio-temporal perspective by using principal tensor analysis, and the visual analysis of the development and change process of employment quality was carried out. The spatial autocorrelation analysis of employment quality was carried out, and the time-space dual fixed effect model of the spatial Dubin model was selected to analyze the direction and magnitude of the influence factors of employment quality on itself and its neighboring provinces. The research showed that: (1) The overall level of employment quality in China was not high and the employment quality varies greatly among provinces, and the employment quality development gap among provinces showed a trend of widening. (2) The development of employment quality in western China was relatively fast, while the development of employment quality in central China showed insufficient stamina. (3) Sichuan Province had a strong radiation effect on the development of employment quality in neighboring provinces, and Beijing and Tianjin had a strong siphon effect on the development of employment quality in neighboring provinces. (4) The level of industrialization and informatization promoted the development of employment quality in China's provinces, while the industrial structure had a significant negative effect on the development of employment quality.

Home-dwelling older people without health services might develop health issues and challenges. To live longer at home and manage well, it is important to promote health-promotion towards this target group. One approach is through health-promotion dialogues conducted by healthcare professionals in the municipality. The aim of this study is to explore healthcare professionals’ experiences who are conducting health-promotion dialogues to older home-dwellers over 75 years without services. Focus-groups were used to collect data, and a total of nine participants formed three focus groups. Thematic analysis was applied to analyse the material, which resulted in one major theme “The challenging dialogues” comprising three sub-themes, “Mobilise coping skills and health awareness”, “Uncover vulnerability”, and “The ambiguity of the dialogues”. The findings were also discussed by using empowerment as a theoretical lens. The health-promotion conversation uses a resource perspective for elderly to remain independent in old age and can reveal vulnerability and underlying needs. The conversation purpose appears ambiguous for the target group, which leads to unclear service expectations and rejections of the offer. Nevertheless, it seems to be important that this health-promotion service to a larger degree addresses a clear purpose in order to meet the needs of the target group in a broader sense in the aging process.

The advent of AI in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. With the growing number of AI applications and commercially available tools, there is an increase in their usage in orthodontic practices worldwide. This review aims to explore the principles of artificial intelligence (AI), its applications in the diagnostic process of modern orthodontic practices, and concerns associated with the implementation of AI algorithms in clinical practice. A comprehensive review of the literature was conducted, focusing on five categories where AI has been applied in orthodontics: dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, and extraction decision making. AI has demonstrated high efficacy in all those fields. However, variations in performance and the necessity of manual supervision indicate that AI should be used cautiously in clinical settings. Nevertheless, the high complexity and potential unpredictability of AI algorithms call for cautious implementation and regular manual validation of results. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for the successful integration of AI into orthodontic practice.

Plant cells are omnipotent and breeding of new varieties can be achieved by protoplast fusion. Such fusions can be achieved by treatment with poly(ethylene glycol) or by applying an electric field. Microfluidic devices allow for controlled conditions and targeted manipulation of small batches of cells down to single cell analysis. To provide controlled conditions for protoplast fusions and achieve high reproducibility, we developed and characterized a microfluidic device to reliably trap few of Arabidopsis thaliana protoplasts and induced a cell fusion by controlled addition of poly(ethylene glycol) (PEG, with a molecular weight of 6000). Experiments were conducted to determine the survival rate of isolated protoplasts in our microfluidic system. Afterwards, PEG induced fusion was studied. Our results indicate that the following fusion parameters had significant impact on the fusion efficiency and duration: PEG concentration, osmolarity of solution, and flow velocity. PEG concentration below 10% led to only partial fusion. Osmolarity of the PEG fusion solution was found to strongly impact the fusion process; complete fusion of two source cells sufficiently took part in slightly hyper osmotic solutions, whereas iso-osmotic solutions led to only partial fusion at 20% PEG concentration. We observed accelerated fusion for higher fluid velocities. Up to this study, it was common sense that fusion is one directional, i.e. once two cells were fused into one cell they stay fused. Here, we present for the first time reversible fusion of protoplasts. Our microfluidic device paves the way to a deeper understanding on the kinetics and processes of cell fusion.

There are two major proposed forms of dark energy: the cosmological constant and the quintessence scalar field. However, the former has the cosmological constant problem and the latter does not have observational evidence to support the theory. In this paper, we propose a dark energy paradigm to solve the cosmological constant problem and to predict lepton masses as evidence. The dark energy paradigm is a unified dark sector that is based on Planck's dimensional analysis, Lambda CDM, energy conservation, and force equilibrium. We find that dark energy can vary from the cosmological constant to the Planck scale. We find that the predicted values of lepton masses based on the dark energy paradigm agree with the observed values to 1% (or within error ranges).

High-methoxyl apple pectin (AP) derived from apple was employed as the main ingredient facilitating rheological modification features for developing dysphagia-friendly fluidized alimentary matrices. Xanthan gum (XG) was also included as a composite counterpart to modify the viscoelastic properties of the thickened system under different thermal processes. The results indicate that AP is extremely sensitive to thermal processing, and the viscosity is greatly depleted under a neutral pH level. Moreover, the inclusion of calcium ions echoed a modification effect on the rheological properties of AP, and both the elastic property and viscosity value are promoted after thermal processing. The modification effect of viscoelastic properties (Gʹ and Gʹʹ) is observed while XG is incorporated into the composite formula. By promoting the XG ratio from 7: 3 to 6: 4 (AP: XG), it triggers the rheological transformation from a liquid-like form to a solid-like state, and the viscosity value shows that the AP-XG composite system exhibits better thermal stability after thermal processing. The ambient modifiers of pH (pH &lt; 4) and calcium chloride concentration (7.5%) with an optimal AP-XG ratio of 7:3 lead to weak-gel-like behavior (Gʹʹ &lt; Gʹ), helping to maintain the texture properties of dysphagia-friendly features similar to those prior to the thermal processing.

In the case of solid slabs made of reinforced concrete that are usually subjected to bending, large areas of the structure are stressed well below their load-bearing capacity. Contrary to this are shell structures, which can bridge large spans with little material if designed according to the force flow. To improve the efficiency of ceiling slabs, we want to utilize the shell load-bearing behavior on a smaller scale by resolving the solid interior accordingly. In order to be able to study a wide range of such constructions virtually, a parametric multiobjective simulation environment is being developed in an ongoing research project. The basic analysis approaches which were implemented are presented in this paper. The basic workflow, the used programs and material models and their calibration on the tests on textile-reinforced concrete (TRC) samples are described.

Molecular docking, a computational method predicting how molecules bind to target proteins, is employed in this study to explore Polydatin's interactions with Ras-related protein Rab (Rab) proteins using the Mcule Database. This database validates the docking results and offers insights into the pharmacological properties of Polydatin. Notably, specific Rab proteins like Rab-5A, HRas, and Rab-18 exhibit robust interactions with Polydatin, indicated by favorable binding energies.These findings illuminate the potential roles of Polydatin in therapeutic and biological contexts, emphasizing its interaction with Rab proteins. The Mcule Database enhances the credibility of the study, providing additional perspectives on Polydatin's applications. The observed interactions, particularly with key proteins like HRas, present a promising avenue for modulating cellular signaling pathways. This modulation holds therapeutic potential, potentially addressing conditions associated with pathway overactivity, including specific cancer types.

The recent rise in ocean temperatures, accompanied by other environmental changes, has notably increased the occurrence and spread of diseases in Octocorallia, of which many species are integral to shallow tropical and subtropical coral reef ecosystems. This study focuses on the understanding of these diseases, which has been largely limited to symptomatic descriptions, with clear etiological factors identified in only a fraction of cases. A key example is the multifocal purple spots syndrome (MPSS) affecting the common Caribbean octocoral sea fan Gorgonia ventalina, linked to the gall-forming copepods of the genus Sphaerippe, a member of the widespread family Lamippidae. The specialized nature of these copepods as endoparasites in octocorals suggests the potential for the discovery of similar diseases across this host spectrum. Our investigation employed four molecular markers to study disease hotspots in Saint Eustatius, Curaçao, northwest and southwest Cuba, and Bonaire. This led to the discovery of a group of copepod species in these varied Caribbean locations. Importantly, these species are morphologically indistinguishable through traditional methods, challenging established taxonomic approaches. The observed diversity of symbionts, despite the host species' genetic uniformity, is likely due to variations in larval dispersal mechanisms. Our phylogenetic analyses confirmed that the Lamippidae copepods belong to the order Poecilostomatoida (Copepoda) and revealed their sister group relationship with the Anchimolgidae, Rhynchomolgidae, and Xarifiidae clades, known for their symbiotic relationships with scleractinian corals. These results add to our understanding of the evolutionary and ecological interactions of copepods and their hosts, and the diseases they cause, important data in a changing climate.

Increasing groundwater levels (GWL) may become one of the most serious issues for Odessa City, Ukraine. This study investigated the spatial distribution characteristics and multifractal scaling behaviour of groundwater level/depth fluctuation for a Quaternary aquifer in Odessa City using a geostatistical approach and a Multifractal Detrended Fluctuation Analysis (MF-DFA). These two methods were applied to monthly GWL fluctuation time series from 1970 to 2020 to monitor 72 hydrogeological wells situated in different parts of Odessa City. The spatial distribution of GWL revealed an overall trend of decline and recovery from 1970 to 2020 in the study area, except for most of the southern region, where a persistent recovery of groundwater depth was observed. The MF-DFA results suggest that the dynamics of GWL fluctuations have multifractal characteristics in the Odessa City area. In addition, both long-range correlation and fat-tail probability distribution contribute to multifractality. However, long-range correlations among fluctuations made a major contribution to the observed multifractality of the GWL fluctuations time series. The generalized Hurst exponent shows a wide range of change (0.20 &lt; h(q) &lt; 2.85), indicating the sensitivity of GWL fluctuations to changes in small scale factors and large-scale factors. Regarding the long range correlations of GWL depth, the Hurst exponents (q = 2) demonstrated the positive persistence of groundwater depth recovery in the southern region and the persistence of groundwater depth variation in the other regions of the study area. The dynamic changes in GWL depth in the Odessa City area may be affected by both natural structural and anthropogenic factors.

Tuberculosis (TB) caused by Mycobacterium tuberculosis remains a predominant cause of mortality, especially in low- and middle-income nations. Recently antimicrobial peptides were discovered that at low concentrations could stimulate the growth of M. tuberculosis (hormetic response). In this study, such a peptide was used to investigate the effects on the time to positivity (TTP). A systematic substitution analysis of peptide 14D was synthesized using Spot synthesis technology, resulting in 171 novel peptides. Our findings revealed a spectrum of interactions, with some peptides accelerating M. tuberculosis growth, potentially aiding in faster diagnostics, while others exhibited inhibitory effects. Notably, peptide NH2-wkivfiwrr-CONH2 significantly reduced the TTP by 25 hours compared to the wild-type peptide 14D, highlighting its potential in improving TB diagnostics by culture. Several peptides demonstrated potent antimycobacterial activity, with a minimum inhibitory concentration (MIC) of 20 µg/mL against H37Rv and a multidrug-resistant M. tuberculosis strain. Additionally, a reduction in the formation of cord-like structures was observed, which is indicative of reduced virulence and transmission potential. This study underscores the multifaceted roles of antimicrobial peptides in TB management, from enhancing diagnostic efficiency to offering therapeutic avenues against M. tuberculosis.

The meaning of a good social outcome in land use transport policy/planning has been relatively underdeveloped, compared to economic and environmental sustainability goals, but this is now changing. Cities are increasingly prioritizing reducing social exclusion, with the allied intent of providing all residents with equitable access to the benefits of their city. However, in jurisdictions that use cost-benefit analysis to guide government decision-making, this poses a challenge: monetization of the benefits of reducing social exclusion is poorly developed. An evaluation gap thus confronts benefit-cost based assessment of initiatives intended to reduce social exclusion, compared to initiatives directed towards societal economic and environmental goals. This is a particular problem for public transport services that primarily enable people at risk of mobility-related social exclusion to access more opportunities in their society (social transit). The benefits of reducing social exclusion associated with these services have not been monetized, so social transit initiatives are poorly placed to argue their merits against competing initiatives where benefit monetization is more advanced (e.g., mass public transport and roads). The paper summarizes Australian research that has developed monetized measures for the value of increased trip making, bridging/bonding social capital, sense of community and several conceptions of wellbeing, and of reducing neighbourhood disadvantage, as these contribute to reducing social exclusion. New Singaporean research on the value of trip making to support reduced exclusion is also presented, affirming the Australian trip values. Trip values are then used in case study examples to show they can make a material difference to the societal worth of initiatives intended to reduce exclusion. Using these trip values, and other pathway values linked to reducing social exclusion, closes or at least substantially reduces the evaluation gap confronting social goal achievement in land use transport policy and planning, strengthening the case for reducing exclusion. These values will assist jurisdictions that are seeking to provide their residents with more equitable access to the benefits of their city.

The growing adoption of distributed energy production technologies, and the potential of energy underutilization when it is produced by non-connected groups, has raised interest in ‘sharing economy’ concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to ‘democratically’ control their own energy supplies for local development, and improved energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms is slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields of P2P trading in renewable energy, and the paper identifies a list of such barriers, and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design.

This study investigates the effect of Re addition on the microstructure evolution and oxidation resistance of Co-Cr-Ta-Ti-C alloys. The alloys tested in this study consist of a Co-rich matrix with mixed HCP and FCC phases, together with TaTiC2 and Ti-based carbides. The addition of Re promotes the precipitation and growth of a CoCr-rich σ phase. Differential thermal analysis (DTA) showed that the melting temperatures did not change drastically up to 10 at% Re addition, whereas 15 at% Re addition significantly increased the melting temperature of the alloys. Oxidation tests in air at 1200°C showed that excessive Re content could lead to deterioration in oxidation resistance, mainly due to the accelerated formation of volatile Re oxides.

A rigorous perturbation analysis of the singular value decomposition of a real matrix of full column rank is presented. It is shown that the SVD perturbation problem is well posed only in case of distinct singular values. The analysis involves the solution of coupled systems of linear equations and produces asymptotic (local) componentwise perturbation bounds of the entries of the orthogonal matrices participating in the decomposition of the given matrix and of its singular values. Local bounds are derived for the sensitivity of the singular subspaces measured by the angles between the unperturbed and perturbed subspaces. An iterative scheme is described to find global bounds on the respective perturbations. The analysis implements the same methodology used previously to determine componentwise perturbation bounds of the Schur form and the QR decomposition of a matrix.

Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

Accurate pathogenic detection in wastewater is critical for safeguarding public health and the environment. However, the presence of free nucleic acids in wastewater samples poses significant challenges to molecular detection accuracy. This comprehensive review explores the current status and future potential of pretreatment methods to remove free nucleic acids from wastewater samples. The study contributes a comprehensive analysis of the mechanisms, strengths, and limitations of various pretreatment approaches, including physical, chemical, and enzymatic processes. The effect of various factors on the removal efficiency of these pretreatment methods is also discussed. This review enhances our comprehension of pretreatment techniques and their vital role in achieving precise pathogenic detection in complex wastewater matrices. Furthermore, it outlines future perspectives and developments for improving the speed and effectiveness of pathogenic detection, contributing significantly to disease surveillance, early warning systems, and environmental protection.

A scientific investigation into visual preferences of rural recreation path landscapes is crucial for enhancing visual qualities and visitors' experience. Taking Baofeng, a representative traditional village in Yunlong, as an example, this study analyzed the visual preference of three typical linear recreational paths with different landscape attributes. We used eye-tracking technology to collect eye movement data and conducted a subjective semantic differential evaluation. Our findings manifest a general inclination amongst individuals to favor closer views and paths with clear trajectories and rich elevation changes while walking, despite preference differences on different types of paths. Regarding group diversity, more inclusive design should be made to meet the sensory needs of different groups. Moreover, despite the limited level of correlation between subjective and objective evaluation methods, combining these two approaches remains indispensable. This synthesis advances prior quantitative landscape evaluation with a specific focus on rural path landscapes and upholds the integrity of results by combining objectivity with subjectivity. It also provides relevant design suggestions for further rural renewal and path planning of Baofeng village.

(1) Background: Ulcerative colitis (UC) is characterized by immune system dysregulation with frequent extraintestinal manifestations, including airways involvement. Reduction in CO diffusing capacity and functional alterations in small airways have been described. The extended analysis of Fractional Exhaled Nitric Oxide (FeNO), may distinguish the sites of production and the presence of small airways inflammation being a useful, non-invasive marker for these patient’s follow up. The aim of our study was to compare PFTs, FeNO and CANO values of UC patients with different clinical disease activity and healthy subjects to reveal lung function abnormalities and the presence of subclinical airway inflammation. (2) Methods: We enrolled 42 adult outpatients with different clinical activity stages of UC (mean age 39±13 years) and a healthy control group of 41 subjects (mean age 29±3 years). C-reactive protein (CRP) and FeNO values at different flows (50,100 and 200 ml/sec) were collected. All patients performed pulmonary function tests (PFT’s) with static volumes and diffusing capacity (DLCO). (3) Results: FENO, CANO and CRP values were significantly increased in UC patients when compared with controls (p=0.02 and p&lt;0.0001, respectively) and proportionally to the disease activity (FeNO class 3: 27.1 ppb vs class 1-2: 6.2 ppb; CANO values class 3: 8.1 ppb vs class 1-2: 2.0 ppb (p &lt; 0.0001). TLC and DLCO were significantly reduced in UC patients compared to controls (p=0.011 and p&lt;0.0001, respectively). (4) The results of this study show significant lung functional abnormalities in UC patients and suggest the presence of airway inflammation directly correlated with the disease activity suggesting an integrated approach in routine assessment.

To systematically assess the effect size of different methods for patients with low back pain. PubMed, Cochrane Library, Web of Science, and Google Scholar. databases were searched in January 2023. This study has been developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement. The key search terms were: “Low back pain”, “Back pain”, “SF-36”, “VAS”, “VASP”, “Roland-Morris”, “Oswestry Disability Index”, and “conservative treatment”. The risk of bias was determined for each randomized trial using the Cochrane Risk of Bias Tool, and the methodological index for non-randomized studies (MINORS). The outcomes included SF-36 Mental, SF-36 Physical, VAS, Roland-Morris, and Oswestry Disability Index. R 4.0.5 software was used, and standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated for continuous outcomes, random model. Twenty-five studies were included. Depending on the outcome being measured, the effect size of different methods in treating low back pain varies from small to large as follows: SF-36 Mental (SMD = 0.39, p &lt; 0.0001), SF-36 Physical (SMD = 0.55, p &lt; 0.0001), VAS (SMD = -0.84, p &lt; 0.0001), Roland-Morris (SMD = -0.45, p &lt; 0.0001), and Oswestry Disability Index (SMD = -0.61, p &lt; 0.0001). Our meta-analysis indicates the positive effects of applying different methods in the treatment of low back pain. PROSPERO registration number: CRD42022371282

Wellness tourism is a growing segment that destinations can rely on to increase tourism flows and revive underdeveloped rural areas. This study elicits tourists' preferences for wellness tourism products and the redevelopment of a wellness destination in a depopulated rural area. The re-search included 595 participants in Central Greece using on-site, face-to-face surveys. The survey data was analyzed using a two-step process. First, conjoint analysis was applied to determine the combination of wellness tourism product attributes that maximize visitors’ utility. In the second step, we elicited visitors' willingness to pay (WTP) for improvements in tourism product attributes using the contingent valuation method. Findings indicate that the combination of attributes that maximizes the preference structure of respondents includes time of visit from September to Oc-tober, duration of visit from 1 to 7 days, wellness tourism as the primary purpose of the visit, and wellness activities that contain both physical activities and training as well as spiritual and emo-tional enhancement. Using binary logistic regression, the analysis indicates that respondents are willing to pay 47.33€ per visit on average further to develop wellness tourism products in the study area. Empirical results confirmed that sustainability issues, quality, and entrepreneurship factors positively impact visitors' WTP for revitalizing wellness products. In contrast, a longer duration of stay in the area and respondents' age negatively impact their WTP. These findings suggest that structuring a customer-driven tourism product will enhance the rural destination's efforts to attract high-yield visitors and advance the rural economy. Furthermore, practical im-plications reveal that Destination Management Organizations will benefit from research results to foster targeted management plans.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

Sierra Madre Mountain Range (SMMR) is the backbone of Luzon Islands that contains high concentration of highly important ecological resources distributed among the 68 protected areas therewith. The present study aimed to assess the tree species composition and diversity in a secondary forest within the SMMR. A 2.25-km transect with 10 900-m2 was established to record tree species with a diameter at breast height of at least 10 cm. The findings revealed 148 individuals of trees from 38 morphospecies, 28 genera, and 20 families. Importance values unveiled the Aurora endemic Macaranga stonei Whitmore as the most important species in terms of the relative values of its abundance, frequency, and dominance. The area was also found to be home for 33 natives, 12 endemics, five IUCN threatened species, and nine Philippine threatened trees. Furthermore, the study site was also found to have a considerably high diversity with Shannon Weiner Index value of 3.269 and a relatively even distribution of individuals among species supported by the Simpson’s Evenness index value of 0.9453. Significant correlational relationships were also found among species richness, Shannon Weinder index, and Simpson’s Evenness index with correlation coefficients ranging from 0.881 to .934 all significant at p < .001. Lastly, the study was able to produce a distribution map necessary in implementing targeted conservation strategies. These findings provided valuable implications for future research and implementation of targeted and participatory biodiversity conservation and protection strategies.

Due to the highly nonlinear, multi-stage, and strongly time-varying marine lysozyme fermentation process, it is difficult to assure the stability and prediction accuracy of the traditional single global soft sensor model on a global scale. This study innovatively proposed a soft sensor model based on an improved seagull optimization algorithm (ISOA) combined with Gaussian process regression (GPR) weighted ensemble learning. First, the sample data set is divided into multiple local sample subsets by the improved density peak clustering algorithm (ADPC). Second, the Gaussian process regression model is optimally altered with an improved seagull optimization algorithm for the purpose of establishing the corresponding sub-prediction model. Finally, the prediction model's fusion strategy is ultimately determined depending on the degree of connection between the test samples and a subset of local pieces. Simulation results show that the proposed soft sensor model, which searches GPR based on ISOA and combines various sub-models, can predict the key biochemical parameters of the marine lysozyme fermentation process better with less prediction error under the condition of fewer training data, and it can be expanded to the soft sensor models of general nonlinear systems, according to simulation results.

In this work, we propose a generalized partially functional linear model, which not only models the relationship between multiple scalar and functional predictors and response, but also automatically estimates the link function. Specifically, we use the functional principal component analysis method to reduce the dimensionality of functional predictors, estimate the regression coefficients using the maximum likelihood estimation method, estimate the link function using the method of local linear regression, iteratively obtain the final estimator, and prove the asymptotic normality of the estimator. The asymptotic normality is illustrated through simulation experiments. Finally, the proposed model is applied to study the influence of environmental, economic, and medical levels on life expectancy in China. In the study functional predictors are the daily air quality index, temperature, and humidity of 58 cities in 2020 and scalar predictors are GDP and number of beds in hospitals.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

In recent years, frequent occurrences of low temperature disasters due to global climate change have significantly impacted the normal growth of crops. Research has indicated that inorganic selenium (which is more toxic) can not only enhance the effects of low temperature stress but also increase selenium content on plants; however, there is a scarcity of studies investigating the impacts of organic selenium and nano-selenium on crops. In order to investigate whether organic selenium and nano-selenium can enhance low-temperature tolerance and increase selenium content in pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’), different concentrations (0, 5, 10, 20 mg L-1) of exogenous selenium were applied in this experiment to assess their effects on plant growth, nutritional quality and antioxidant properties. RNA-Seq technology was used to sequence the transcriptome of the leaves. Based on the transcriptome data, a Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a network that associates with physiological traits related to stress resistance. Two highly correlated gene co-expression modules were identified, and within them, nine hub genes associated with endocytosis, antioxidant stress, absorption, transport, and metabolism of selenium were discovered. The results indicated that the beneficial effects on yield and total selenium content under low temperature were attributed to (1) protection of photosynthetic pigments for enhancing photosynthetic capacity by the up-regulation of LHca2, LHcb1, LHca1, LHcb4 in KEGG pathway: photosynthesis-antenna proteins; (2) activation of antioxidant system for efficient ROS homeostasis such as SOD, POD and CAT by the genes such as Superoxide dismutase, Monodehydroascorbate and (3) selenium absorption by endocytosis, seleninum transportation by ABC transporter gene family and selenium metabolism related genes such as Cysteine synthase, Glutaredoxin. These findings provide a foundation for further investigation into the molecular mechanisms underlying the effects of organic selenium and nano-selenium on cyanochloride production at low temperatures.

Vancomisin resistant enterococcus(VRE), is a resistant microorganism which colonizates and causes infections in hospitalized patients. It was aimed to show the spread of vancomycin-resistant Enterococcus (fVREfm) step-by-step in all intensive care units, which started with the growth of vancomycin-resistant Enterococcus faecium (VREfm) on 02.12.2021 in the blood culture of a patient hospitalized in the anesthesia intensive care unit of our hospital and was found to have reached epidemic size in the surveys. Rectal swab samples were taken from all patients hospitalized in intensive care units, VRE colonization was determined and the VanA and VanB resistance genes associated with vancomycin resistance of VREfm isolates were determined by PCR method, and clonal association analysis was performed by Arbitrarily Primed-PCR (AP-PCR) and Pulsed-Field Gel Electrophoresis. In our study, VRE was detected in 61 of 2601 rectal swab samples. 54 (85.52%) of the VRE isolates were Enterococcus faecium, 3 (4.91%) was Enterococcus faecalis, 3 (4.91%) was Enterococcus gallinorum, 1 (1.63%) was Enterococcus casseliflavus. It was determined that all of the 54 VREfm isolates, which were the most detected among all VRE isolates, carried the vanA gene. In the clonal association analysis of the isolates by AP-PCR and PFGE methods, it was found that they had 12 different genotypes, 48 of them were included in any cluster, the clustering rate was 88.8%, and the largest cluster was genotype 1 cluster with 36 isolates. Of the 54 patients with VREfm isolated recently, 18.51 percent of the clinical samples were isolated before the survey, and 9.24% were isolated after the survey. It was determined that 100% of VREfm isolates were resistant to ampicillin, levofloxacin, ciprofloxacin, high-level gentamicin, trimethoprim-sulfamethoxazole, teicoplanin, 7.4% to tigecycline and 1.85% to linezolid. In our study, in the clonal association analysis performed by isolating VREfm in rectal swab samples, it was found that 88.8% of the samples were indistinguishably similar and the increase in the number of VREfm infections after the index case in our hospital was associated with the epidemic. VREfm infections cause long term hospitalization, costs and also deaths, which shows the seriousness of the event, and the importance of the combination of epidemiological and molecular analysis in epidemic research.

The transferability of structure-property relationships for laser-pretreated metal adhesive joints to laser-pretreated metal – carbon fiber reinforced plastic (CFRP) bonds is investigated. Single-lap shear tests were performed on hybrid AW 6082-T6 – CFRP specimens pretreated with the same pulsed laser surface parameter sets on the metal surface as previously tested AW 6082-T6 – E320 metal adhesive joints. The fracture surfaces were characterized to determine the type of failure and elucidate differences and commonalities in the link between surface structures and single-lap shear strengths. Digital image analyses of the hybrid specimens fractured surfaces were used to quantify remaining CFRP fragments on the metallic joint side. The results indicate that high surface enlargements and the presence of undercut structures leads to single-lap shear strengths exceeding 40 MPa and 35 MPa for unaged and aged hybrid specimens respectively. While for the metal-polymer joints the trend from high strength to weakly bonded specimens was largely continuous with the degree of surface structuring, hybrid metal – CFRP joints exhibit a drastic drop in joint performance after aging if the laser-generated surface structures are less pronounced with low surface enlargements and crater depths. Surface features and hydrothermal aging determine whether the specimens fail cohesively or adhesively.