Brain Tumor is one of the severe diseases and occurrence of this disease threats human life. Detection of brain tumor in advance can secure patient’s life from unwanted loss. Well-timed and swift disease detection and treatment strategy can lead to improved quality of life in these patients. This paper attempts to use Machine Learning based ensemble approaches for recognising patients with brain tumor. Ensemble technique based AdaBoost classifier and 10-fold stratified cross-validation method are assembled in single platform is proposed in this paper for prediction of brain tumor. This prediction is compared against three baseline classifiers such as Gradient Boost, Random Forest and Extra Trees classifier. Experimental result implies the superiority of this model with an accuracy of 98.97%, f1-score of 0.99, kappa statistics score of 0.95 and MSE of 0.0103.

We first explore negative-ion formation in fullerenes C44, C60, C70, C98, C112, C120, C132 and C136 through low-energy electron elastic scattering total cross sections calculations using our Regge-pole methodology. Water oxidation to peroxide and water synthesis from H₂ and O₂ are then investigated using the anionic catalysts C44ˉ to C136ˉ. The fundamental mechanism underlying negative-ion catalysis involves hydrogen bond strength-weakening in the transition state. DFT transition state calculations found C60ˉ numerically stable for both water and peroxide synthesis, C100ˉ increases the energy barrier the most and C136ˉ the most effective catalyst in both water synthesis and oxidation to H2O2.

Water stress (WS) and heat stress (HS) have a negative effect on soybean plant growth and crop productivity. During WS, soybean plants opt for survival through ion homeostasis and the conformations of proteins are disconcerted as plant cells lose water while HS leads to difficulties in flowering and fruiting. Some of these changes include oxidative stress leading to the destruction of photosynthetic apparatus, macromolecules within cells and the onset of complex signaling cascades. Changes in the physiological characteristics, proteome, and certain metabolites investigated on molecular and cellular functions were studied in two soybean cultivars exposed to different heat and water stress conditions independently and in combination. Leaf protein composition was studied using 2-DE and complemented with MALDI TOF mass spectrometry. While two cultivars displayed genetic variation in response to water and heat stress, thirty-nine proteins were significantly altered in their relative abundance in response to WS, HS and combined WS+HS in both cultivars; a majority of them involved in metabolism, response to heat and photosynthesis showing significant cross-tolerance mechanisms. Functional analysis revealing a majority of heat responsive-proteins were more abundant during HS and combined stress (WS+HS) whereas these proteins were low to WS in cultivar PI 471938 and heat shock proteins were in low abundance to water, heat and combined stresses in cultivar R95-1705. Most protein abundances were not correlated with their expression at mRNA levels in PI cultivar, however, in cultivar R 95, the expression levels of transcript follow their relative abundance in proteins. Our systems bioinformatics analyses revealed that MED37C, a probable mediator of RNA polymerase transcription II protein showed potential interacting partners in Arabidopsis and our studies signifies the marked impact of this protein in PI cultivar. Elevated activities in antioxidant enzymes indicate that the PI-371938 cultivar has the ability to restore the oxidation levels and sustain the plant during the stress. Our study hypothesizes the plant’s development of cross-stress tolerance which will help foster the ongoing ventures in genetic modifications in stress tolerance.

Mapping forest composition using multiseasonal optical time series is still challenging. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on ten Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the SVM algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

This research was conducted to manufacture thermally expandable microspheres (TEMs) for vehicles’ underbody coating and to apply them on an industrial scale. TEMs heat resistance was studied depending on the ratios of a cross-linking agent and an initiator. This research focused on the content of a cross-linking agent and how it affected the results. The TEMs’ outer shell was thickened to solve the problem of the foam expansion ratio’s reduction that occurred due to the shrinkage after the maximum expansion (Tmax) was reached. After foaming, the cross-sectional thickness and surface of the sample with thickened outer shell were observed. The TEMs with the thickened shell showed the least shrinkage, which indicated excellent shrinkage stability, even after prolonged exposure to heat.

Hardy Cross originally proposed a method for analysis of flow in networks of conduits or conductors in 1936. His method was the first really useful engineering method in the field of pipe network calculation. Only electrical analogs of hydraulic networks were used before the Hardy Cross method. A problem with the flow resistance versus the electrical resistance makes these electrical analog methods obsolete. The method by Hardy Cross is taught extensively at faculties and it still remains an important tool for analysis of looped pipe systems. Engineers today mostly use a modified Hardy Cross method which threats the whole looped network of pipes simultaneously (use of these methods without computers is practically impossible). A method from the Russian practice published during 1930s, which is similar to the Hardy Cross method, is described, too. Some notes from the life of Hardy Cross are also shown. Finally, an improved version of the Hardy Cross method, which significantly reduces number of iterations, is presented and discussed. Also we tested multi-point iterative methods which can be used as substitution for the Newton-Raphson approach used by Hardy Cross, but this approach didn’t reduce number of required iterations to reach the final balanced solution. Although, many new models have been developed since the time of Hardy Cross, main purpose of this paper is to illustrate the very beginning of modeling of gas and water pipe networks or ventilation systems.

Studies in the area of cross-linguistic influence (CLI) have attracted the focus of multi-linguistic learners. However, little research on CLI deals with Asian learners, particularly Chinese-speaker with knowledge of two or more foreign languages. The present study explores CLI in L1 Chinese learners with both English (L2) and Spanish (L3) as foreign languages who are studying in Madrid, a Spanish-speaking community; their studies coincided with data collection. English learners were instructed to speak for analysis purposes, with the following aims: (i) to observe the most frequent category (functional transfer, code-switching, borrowing and coinage) in the CLI instances; (ii) to determine the source language of CLI; (iii) to investigate whether CLI factors, including language distance, L2 status, proficiency and recency of use, intervene in the appearance of CLI instances in the participants. Data was gathered from 16 female Chinese students at Complutense University of Madrid (UCM). These were master students aged 22 to 26, who visited Spain for more than 5 months when they participated in the present study. The instrument used was an English semi-structured interview. Results primarily reveal that (a) borrowing is the most prevalent category, accounting for 70% of the CLI instances; (b) Spanish is the main source language of CLI while Chinese plays a functional role in the transfer process; (c) language distance proves to be the strongest predictor of CLI.

Lignin has interesting functionalities to be exploited in adhesives for medicine, foods and textiles. Nanoparticles (NPs) <100 nm coated with poly(L-lysine), PL and poly(L-glutamic acid) PGA were prepared from the laccase treated lignin to coat nanocellulose fibrils (CNF) with heat. NPs ca. 300 nm were prepared, β-casein coated and cross-linked with transglutaminase (Tgase) to agglutinate chamois specimens. Size exclusion chromatography (SEC) and Fourier-transform infrared (FTIR) spectroscopy were used to characterize polymerized lignin, zetapotential and dynamic light scattering (DLS) to ensure coating of colloidal lignin particles (CLPs). Protein adsorption on lignin was studied by quartz crystal microbalance (QCM). Atomic force microscopy (AFM) was exploited to examine interactions between different polymers and to image NPs with transmission electron microscopy (TEM). Tensile testing showed, when using CLPs for the adhesion, the stress improved ca. 10 and strain ca. 6 times compared to polymeric lignin. For the β-casein NPs the values were 20 and 8, respectively, and for the β-casein coated CLPs between these two cases. When NPs were dispersed in adhesive formulation, the Young's moduli confirmed significant improvement in the elasticity of the joints over the adhesive alone. Exploitation lignin in nanoparticulate morphology is a potential method to prepare bionanomaterials for advanced applications.

As an emerging building solution, cross-laminated timber (CLT) floors have been increasingly used in mass timber construction. The current vibration design of CLT floors is conservative due to the assumption of simple support conditions in the floor-to-wall connections. It is noted that end fixity occurs as a result of clamping action at the ends, arising from the gravity load applied by the structure above the floor and by the mechanical fasteners. In this paper, the semi-rigid floor-to-wall connections are treated as elastically restrained edges against rotations to account for the effect of partial constraint. A rotational end-fixity factor was first defined to reflect the relative bending stiffness between CLT floors and elastic restraints at the edges. Then, for the design of vibration serviceability of CLT floors as per the Canadian Standard for Engineering Design in Wood (CSA O86), restraint coefficients were defined and their analytical expressions were derived for natural frequencies and the mid-span deflection under a concentrated load, respectively. In particular, a simplified formula of the restraint coefficient for the fundamental frequency was developed to assist engineers in practical design. At last, by comparing with reported experimental data, the proposed design formula showed excellent agreement with test results. In the end, the proposed end fixity factor with their corresponding restraint coefficients is recommended as an effective mechanics-based approach to account for the effect of end support conditions of CLT floors.

Cross-laminated timber (CLT) possesses both good shape stability and the possible two-way force transfer ability due to its crosswise lamination. However, the transverse layers in CLT are prone to rolling shear failure under an out-of-plane load. An innovative multi-layer composite laminated panel (CLP) was developed by combining structural composite lumber (SCL) and dimension lumber to overcome the rolling shear failure while maintaining high mechanical performance and aesthetic appearance of natural wood. The mechanical properties of 5-layer CLP consisted of laminated strand lumber (LSL) and dimension lumber with different layups were evaluated by both static and modal tests. The results showed that the shear resistance, bending stiffness and moment resistance of CLP were up to 143%, 43% and 87% higher than their counterparts of regular CLT, respectively. The failure modes observed in both shear and bending tests indicated that the use of LSL in transverse layers could eliminate the potential rolling shear failure in CLT. With the lamination properties from components tests as inputs, the validity of shear analogy method was assessed by test results. The mechanical properties can be well predicted by shear analogy method except for the bending moment resistance of CLP and CLT with either rolling failure in the cross layer or tension failure in the bottom layer.

Background: Disparity in adult mortality (AM) with reference to social dynamics and health care has not been sufficiently examined. This study aimed to identify the gap in the understanding of AM in relation to religion, political stability, economic level, and universal health coverage (UHC). Methods: A cross-national study was performed with different sources of data, using the administrative record linkage theory. We created a new data set using data from the 2013 World Bank data catalogue by region, The Economist (Political instability index 2013), Stuckler David et al. (Universal health coverage, 2010), and religious categories of all UN country members. Descriptive statistics, a t-test, an ANOVA followed by a post hoc test, and a linear regression were used where applicable. Result: The average AM rate for males and females was 0.20 ± 0.10 and 0.14 ± 0.10, respectively. AM was significantly higher in economically weak countries, countries with political instability, countries with traditional religion, without achievement of UHC, and Sub-Saharan Africa (p<0.01). There was high disparity of AM between countries with and without UHC (F = male: 61.89, female: 51.85, p<0.001) and between groups with low and high income (F = male: 36.33, female: 42.39, p<0.001). UHC and political stability would significantly reduce AMR by > 0.41 in both sexes and high economic status would reduce male AMR by 0.44, and female AMR by 0.70, in relation to countries without UHC, with political instability, and low economic status. Conclusions: Disparities in AM can be reduced after the achievement of UHC and economically productive activities for those adults affected by conflict and political unrest.

Wear phenomena on natural stone of the cultural heritage are induced from various sources. Consequently, the development of multifunctional protective materials which combine two or more useful properties is an effective strategy to address the synergistic effects of various wear mechanisms. A multifunctional coating, consisting of a silane-based precursor and zinc oxide (ZnO) nanoparticles (NPs), is produced and tested for the protection of limestone. The hybrid coating combines the following three properties: superhydrophobicity, including water-repellency, photocatalytic self-cleaning and biocidal activity. The relative concentration of the NPs (0.8% w/w), used for the suggested composite coating, is carefully selected according to wetting studies, colourimetric measurements and durability (tape peeling) tests. The non-wetting state is evidenced on the surface of the composite coating by the large contact angle of water drops (≈153°) and the small contact angle hysteresis (≈5°) which gives rise to the physical self-cleaning scenario (lotus effect). The photocatalytic chemical self-cleaning is shown with the removal of methylene blue, induced by the UV-A radiation. Moreover, it is shown that the suggested coating hinders the incubation of E. coli and S. aureus as the inhibition were 94.8 and 99.9 %, respectively. Finally, preliminary studies revealed the chemical stability of the suggested coating.

Soil, geochemical, microbiological and archaeological studies were conducted at eight settlements dating from the Paleolithic to Late Medieval and New Times near the Southern Trans-Urals mountains, Russia. The forest-steppe landscapes, rivers and abundance of mineral resources have attracted people to region since ancient times. Properties of cultural layer (CLs): close to neutral pH, well structured, absence of salinity, enrichment with exchangeable calcium and anthropogenic phosphorus (0.2–0.4%). The majority of CLs start from 3–25 cm and last up to 40–60 cm and contain 6–10% organic carbon (Corg), in a layer of 0–20 cm because modern processes are developing here, at Ishkulovo site (around 14th century) Corg decreases to 1.3%, because CL is below 80 cm and in the absence of fresh organic material it is mineralized. The content of microbial biomass of the 0–10 cm layer is 520–680 μg С/g of soil, microbes cause the emission of 0.2–1.0 μg C–CO2/g soil per h. The proximity of the studied sites to deposits of copper, chromium, zinc, manganese and Magnitogorsk Metallurgical and Uchaly processing plants creates geochemical anomalies of soils. Pollutants are also released into the CLs from bronze products, ore, ceramic fragments and raw materials from their manufacture. It leads to the concentration of pollutants of the 1st (arsenic, lead, zinc) and 2nd (cobalt, copper, nickel, chromium) hazard classes in the studied CLs, often in quantities higher than the clarke and less often – more than the allowable content for human health.

This article presents the graphical user interface of the ACUX-R mobile recommendation system, tailored for the cultural tourism domain. ACUX-R offers personalized recommendations based on visiting preferences, augmenting the overall user experience. Building upon a comprehensive methodology and recommendation algorithms from previous work, this contribution focuses on the user interface aspects of the ACUX-R by highlighting key design considerations, user interface elements and functionalities that contribute to an effective and engaging user experience. In an effort to evaluate the proposed interface, an initial case study with a dataset consisting of points of interest from the City of Athens, Greece has been performed. In the framework of the aforementioned study, the proposed user interface attained high ratings with respect to inspiring, exciting, interesting, and enthusiastic user experiences.

To expand the application area of the reference group and enrich exercise theoretical research, based on Stimulus-Organism-Response (S–O-R) framework, this study examines the factors that motivate for adherence to exercise from the external. Taking reference group and strategy and cultural fit as the main stimulus, and personal investment and life satisfaction as mediating variables, this study try to explore the influence of external stimulus on residents’ exercise behavior. In order to enrich the sample size, two surveys of 734 Chinese residents in two cities (Xiamen vs. Fuzhou) were conducted using factor analyses, regression analysis, and T-test analysis. The results indicated that the reference group and strategic and cultural fit as external stimulus have impact on residents’personal investment, life satisfaction and exercise adherence, personal investment and life satisfaction as the organism has impact on residents’ exercise adherence. Personal investment and life satisfaction play a chain mediating role between the reference group and exercise adherence, strategy and cultural fit, and exercise adherence. Moreover, the T-test determined the differences between Xiamen and Fuzhou residents’ exercise adherence and life satisfaction. Residents’ surroundings affect their exercise behavior and life satisfaction. These findings have implications for policymaking aimed at promoting national exercise, which could gradually improve residents’ physical fitness, particularly in light of the current coronavirus emergency.

The international debate on the adaptive re-use of cultural heritage sites following the Sustainable Development Goals becomes more central than ever in the implementation of circular economy models for urban policies. The new values that characterise the cultural assets, considered as the result of a collaborative process, can enhance both the manufactured capital and the human capital, and to carry out the system of relationships that bind them. At the same time, the values of historical-artistic assets and produced by community-based regeneration processes are particularly relevant when they characterise abandoned commons and cult buildings, to which communities attribute an identity and symbolic value. Starting from the definition of the concept of Complex Social Value, we propose a methodological process that combines approaches and techniques typical of deliberative evaluations and collaborative decision-making processes. The aim is to identify the complex value chains generated by adaptive re-use, in which intrinsic values can play a driving role in the regeneration strategies of discarded cultural heritage. The experimentation, tested with the project “San Sebastiano del Monte dei Morti Living Lab” (SSMOLL), activates a creative and cultural Living Lab in the former church of “Morticelli”, in the historic centre of Salerno, in southern Italy. The re-use project is part of a more comprehensive process of social innovation and culture-led urban regeneration triggered in Salerno starting from SSMOLL.

Primocane-fruiting (PF) blackberries are adaptable to different production systems. To increase yields in PF blackberries, their primocanes are typically tipped or topped in summer to encourage branch formation from axillary buds below the cut. In this study, we determined in PF ‘Prime-Ark Traveler’ whether early emerging primocanes were more productive than those that emerged later in the season and the effect of primocane bending and defoliation on flowering. The primocanes that emerged in April produced 64% more flower shoots than those that emerged after May. Also, these findings indicate the alternative primocane management practices of selecting the early emerging primocanes and bending to orient primocanes horizontally and leaf removal increase budbreak and flower shoot emergence. The present work contributes toward a better understanding of primocane emergence time and orientation-flowering relations and how they mediate crop performance in PF blackberry.

In the second decade of the 21st century, social media changed the nature of communication and cooperation between participants of the culture services market. They became, among other things, an important marketing instrument in the area of contact with the customers of the cultural offer. However, despite their growing importance in various areas of activity of organisations in the cultural sector, the issue of building the cultural institution's brand equity by social media users is relatively seldom raised. Research on the impact of online consumer activity on brand equity is at an early stage of development. Therefore, this article is an attempt to fill the research gap in this area. The article presents the results of a survey conducted in 2018 on a group of 1021 consumers of cultural services, who at the same time regularly used social media. The statistical analysis carried out and the research results obtained prove that the 3C sustainable system developed by the authors, concerning the activity of consumers of cultural services in social media, stimulates the consumer-based brand equity (CBBE). Statistically significant relations have been observed in particular for CBBE components related to the awareness of a cultural institution's brand and for the relationship related to the perception of its quality. The study opens a review of literature on social media and consumer-based brand equity (CBBE). Next, based on the COBRA model (consumer's online brand-related activities), a proposal of the 3C sustainable system, concerning the activity of consumers of cultural services in social media, has been presented. The further part of the article presents research hypotheses, a conceptual model, research methodology as well as results and conclusions. The last part of the article discusses the results obtained and indicates the existing management implications.

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for emotion recognition that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

Juvenile Polyposis Syndrome (JPS) is a rare autosomal dominant disorder characterized by multiple juvenile polyps in the gastrointestinal tract, often associated with mutations in genes such as Smad4 and BMPR1A. This study explores the impact of Smad4 knockout on the development of intestinal polyps using Collaborative Cross (CC) mice, a genetically diverse model. Our results reveal a significant increase in intestinal polyps in Smad4 knockout mice across the entire population, emphasizing the broad influence of Smad4 on polyposis. Sex-specific analyses demonstrate higher polyp counts in knockout males and females compared to their WT counterparts, with distinct correlation patterns. Line-specific effects highlight the nuanced response to Smad4 knockout, underscoring the importance of genetic variability. Multimorbidity heat maps offer insights into complex relationships between polyp counts, locations, and sizes. Heritability analysis reveals a significant genetic basis for polyp counts and sizes, while machine learning models, including k-Nearest Neighbours and Linear Regression, identify key predictors, enhancing our understanding of juvenile polyposis genetics. Overall, this study provides a comprehensive understanding of the intricate genetic interplay in the context of Smad4 knockout, offering valuable insights into potential therapeutic targets for juvenile polyposis and related diseases.

Sharding shows great potential for extending the blockchain. The current challenge facing sharded blockchain technology lies in addressing the extended transaction confirmation times caused by isolated states between shards and unbalanced transaction allocation strategies. These factors contribute to an increase in cross-shard transactions and disproportionate shard workload, ultimately resulting in indefinite confirmation delays for cross-shard transactions. A critical priority for sharded blockchain systems is to conduct a comprehensive qualitative analysis to better understand and mitigate the prolonged transaction confirmation times. We introduce a Product-Form Queue Network (PFQN) model to address the transaction confirmation time problem in sharded blockchains and incorporate a new confirmation queue to more accurately simulate the actual transaction confirmation process in the blockchain. In addition, we provide a detailed quantitative analysis of the relationship between network load, consensus efficiency, and security in sharded blockchains, laying a theoretical foundation for achieving robustness and efficiency in sharded blockchains. This research not only contributes to addressing the scalability issues in sharded blockchains but also offers a new perspective for future research directions.

Understanding the dynamics of teamwork and communication among healthcare professionals is crucial in the face of evolving healthcare challenges. This study assessed relational coordination among healthcare professionals in the South Tyrolean healthcare system in Italy, focusing on communication and teamwork dynamics in a cross-sectional survey. Using the validated Relational Coordination Survey (RCS) instrument and 525 completed online responses, the questionnaire aimed to understand the implications of different levels of relational coordination ratings by general practitioners, hospital physicians, nurses, and administrative personnel (response rate 26%). The demographics of the participants revealed a predominance of female professionals (64%), with an average age of 50 and 18 years of service. The resulting RCS scores varied significantly across professional groups, with nurses reporting the highest within-group scores, indicating moderate coordination, and administrators reporting the lowest scores, reflecting areas of weak coordination. Between-group relational coordination was generally perceived as weak across professional groups, with the least weakness observed between general practitioners and nurses. German or Italian language and health district affiliation emerged as significant factors influencing relational coordination ratings, highlighting the need for differentiated understanding and strategies in multilingual and diverse regional settings. Assessments of interdisciplinary feedback and referral practices highlight the variation in teamwork and communication weaknesses and underscore the need for targeted interventions to improve relational coordination. This study provides insights into the complexity of relational dynamics in health care settings. It suggests that improving relational coordination through tailored strategies could significantly improve team effectiveness, quality of patient care, and overall system efficiency.

Current VAT rules for cross-border e-commerce companies in the EU are not sustainable, because they are extremely complex and often opaque, due to the lack of tax harmonization. The aim of the research is to explore, using a qualitative method, what kind of harmonization would make the current legislation more uniform, simpler, more transparent, and easier to understand, without conflicting with the rules of any Member State. To this end, the aspects of the new platform regulation introduced on 1 July 2021 and its potential for expansion are assessed through information from possible sales models. The research suggests that the rules on platforms do not cover all possible sales models, which in our view imposes an unnecessary extra administrative burden on some businesses and makes their tax activities less transparent. In the light of the results of the research, we propose that the current EU platform regulation should be extended to cover B2B, B2C transactions and services. As a result of this suggested harmonization, a simpler, more transparent, uniform and sustainable tax system would contribute to the whitening of the economy, generating additional tax revenue for Member States.

Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research will also assess the reliability of cross-project defect prediction. Well-known machine learning techniques such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks were applied to publicly available PROMISE datasets. The interpretability of this approach has been demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.

Static and functional occlusal relationships are the well-defined objectives of orthodontic therapy. To accomplish it, the jaws must be positioned in centric relation (CR)/ Central occlusion (CO) and appropriately proportioned in three space planes. Orthodontists have well-established techniques when analyzing the skeletal connection between the maxilla and mandible in both sagittal and vertical planes. Although there exist multiple analyses for the skeletal transverse dimension (STD), their inclusion in conventional orthodontic diagnosis is not widely acknowledged. Transverse tooth compensations have been visually represented in prosthodontic literature using a crosshatch arch that passes through the buccal and palatal cusps of maxillary molars, known as the Wilson curve. An inflated Wilson curve has long been seen as a prosthetic maxim, increasing the possibility of working and non-working side interferences. Research has indicated a connection between higher masticatory muscle activity and posterior occlusal contacts or interferences. When skeletal transverse issues are concealed by dental expansion, there is an elevated susceptibility to gingival recession in orthodontic patients who have a small Maxilla or constricted upper jaw. The temporomandibular joints, muscles, periodontal tissue, and airway might all suffer in a susceptible patient with a transverse insufficiency brought on by a narrow maxilla. As for orthodontists, the objective is to create skeletal connections and functional occlusion that are as near ideal as feasible. This will reduce the likelihood of occlusion-related inconsistencies exacerbating the negative consequences on the dentition, periodontium, or joints. An accurate skeletal and dental diagnostic in all three space planes is required. Our goal is to explain all approaches to enable readers to use a transverse skeletal diagnostic in their practice regardless of the technological level at their disposal. The initial step is to ascertain the kind and presence of deficiencies in the maxilla. Since fewer soft tissue alterations are caused by maxilla hypoplasia in the transverse direction, skeletal transverse dimension (STD) assessment is a more significant challenge than vertical or sagittal discrepancy assessment. Model analysis, clinical assessments, radiographic measurements, and occlusograms have all proven successful in producing accurate assessments. Most of the time, surgically assisted maxillary expansion (SARME) is reported to have higher stability than orthopedic maxillary expansion (OME). We examine and discourse on possible research opportunities using the Collaborative Cross (CC) mouse model as a substitute research model.

Despite well-documented health benefits of probiotics Saccharomyces, its application in oral health has not been comprehensively assessed. Dental caries is a transmissible disease initiated by acid production of cariogenic bacteria and yeast, such as Streptococcus mutans and Candida albicans, on tooth enamel and followed by subsequent enamel demineralization. Here, we investigated the effect of two Saccharomyces strains (Saccharomyces boulardii, Saccharomyces cerevisiae) on S. mutans-C. albicans cross-kingdom interactions using a cariogenic planktonic model. Viable cells, pH changes and gene expression were measured. S. cerevisiae and S. boulardii inhibited the growth of C. albicans either in dual-, or multi-species conditions. Saccharomyces also inhibited C. albicans hyphal formation. Furthermore, Saccharomyces reduced the acidity of the culture medium which usually plummeted below pH 5 when S. mutans and C. albicans were present in the model. The presence of Saccharomyces maintained the culture medium above 6 even after overnight incubation, demonstrating a protective potential against dental enamel demineralization. S. boulardii significantly downregulated S. mutans atpD and eno genes expression. Overall, our results shed light on a new promising candidate, Saccharomyces, for dental caries prevention due to its potential to create a less cariogenic environment marked by a neutral pH and reduced growth of C. albicans.

Hypertension stands as the foremost modifiable risk factor for cardiovascular disorders in South Asia. This research demonstrates the superiority of statistical models over traditional approaches in identifying clinical risks related to hypertension. Leveraging individual-level data from the Bangladesh Demographic and Health Survey (2017-18), a unified dataset was created. Adhering to JNC7 and ACC/AHA guidelines for hypertension classification, two prominent regression models were employed to estimate the prevalence of undiagnosed hypertension. A substantial 16% of hypertension cases in Bangladesh remain undetected. The study identifies undiagnosed hypertension as a heightened risk, particularly among the youth and residents of Sylhet and Rangpur divisions. Statistical models exhibit efficacy in discerning undiagnosed hypertension and associated factors in South Asia. Subsequent investigations should incorporate biological markers to enhance statistical models and ascertain their practicality.

Image segmentation is the process of partitioning an image into multiple segments where the goal is to simplify the representation of the image and make the image more meaningful and easier to analyze. In particular, semantic segmentation is an approach of detecting the classes of objects, based on each pixel. In the past, most semantic segmentation models were for only one single style, such as urban street views, medical images, or even Manga. In this paper, we proposed a semantic segmentation model called Dual-Pyramid Wide Residual Network (DPWRN) to solve the segmentation for cross-style datasets, which is suitable for diverse segmentation applications. The DPWRN integrated the Pyramid of Kernel paralleled with Dilation (PKD) and Multi-Feature Fusion (MFF) to improve the accuracy of segmentation. To evaluate the generalization of the DPWRN and its superiority over most state-of-the-art models, three datasets with completely different styles are tested in the experiments. Finally, the DPWRN verified its generalization and also showed its superiority over most state-of-the-art models.

This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in a freely accessible repository in the BE2SHARE-EUDAT platform so that they can be easily reproduced.

Background and Objectives: The prevalence of osteoporosis is high, reportedly affecting 200 million people worldwide. A major problem associated with osteoporosis is that femoral fractures cause a decline in general function and loss of independence, greatly reducing patients’ quality of life. Notably, osteoporosis is an asymptomatic chronic metabolic disease, and its detection is thus often delayed. Interestingly, mandibular cortical index (MCI) classification using dental panoramic radiography is reportedly useful for early detection of osteoporosis. However, this visual classification method can lead to differences in diagnosis among surgeons. Therefore, this preliminary study was performed to analyze cone-beam computed tomography (CBCT) data and examine an objective MCI classification using the findings obtained. Materials and Methods: MCI classification (classified as C1, C2, or C3) was performed by three examiners on 70 women (91 sites) aged ≥20 years. The mandibular cortical width of all sites was measured using CBCT images. Results: The results showed that the mandibular cortical width was not particularly correlated with age or number of present teeth, and no significant quantitative differences were found between C1 and C2. However, coronal CBCT images of C2 revealed multiple characteristic bone trabecular structures. Conclusions: These structures may be an important finding affecting the classification of two-dimensional dental panoramic radiography images.

As an established and widely available infrastructure, wireless local area networks (WLANs) have emerged as a viable option for indoor localization of both mobile and stationary users. In planning a mobile communications network and radio system design, the critical role of coverage prediction becomes evident, empowering network operators to optimize cellular networks and elevate the overall customer experience. Moreover, WLANs present several challenges that must be fulfilled when it comes to localization based on Wi-Fi signals to get a proper coverage prediction map. This paper presents a study based on application of the extra trees regression (ETR) for indoor localization by using coverage prediction maps. The aim of the proposed method is to accurately estimate a user’s position within a radio environment map (REM) area using collected received signal strength indicator (RSSI) values. The proposed scheme investigates several machine learning (ML) regression algorithms for localization, where the training dataset is obtained from the REM by using the nearest neighbors method. Parameter tuning is conducted to optimize the performance of the ETR scheme by using 10-fold cross-validation. In the numerical results, we first demonstrate the effectiveness of utilizing ML regression techniques for generating coverage maps, which enables accurate estimation of the Wi-Fi signal strength in indoor environments. Then, we showcase the superior performance of the proposed ETR-based method compared to several other ML schemes for indoor localization using the REM. ML algorithms, including decision tree regression and the ETR, are compared to evaluate the system model. Based on error metrics, the proposed ETR-based approach exhibits the best performance among the evaluated techniques. The combination of coverage map generation and localization using regression techniques offers a powerful approach for analyzing the radio frequency (RF) environment in indoor spaces.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

Statistical based reconstruction methods and signal processing tooling techniques are implemented and used to detect delaminations or debondings within composite items. From literature it appears that, although a single procedure for the estimation of the structural health is a fast solution, a multiple analysis based on different reconstruction methods or different damage parameters could provide more detailed information about the location and the severity of possible failures. This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on ultrasonic guided waves (GW) tomographic technique and piezoelectrics (PWAS) in pitch-catch configuration. In this sense, this work evidences an improvement in damage detection when the cross-correlation is included as part of the GW-based system for damage assessment approach. The specimens used as test structures to demonstrate the validity of the methodology derive from an aircraft wing test article where damages are specified as skin delamination produced by low energy impact.

The environment is seldom optimal for plant growth and changes in abiotic and biotic signals, including temperature, water availability, radiation and pests, induce plant responses to optimise survival. The New Zealand native plant species and close relative to Arabidopsis thaliana, Pachycladon cheesemanii grows under environmental conditions that are unsustainable for many plant species. Here we compare the responses of both plant species to different stressors (low temperature, salt and UV-B radiation) to help understand how P. cheesemanii can grow in such harsh environments. The stress transcriptomes were then determined and comparative transcriptome and network analyses discovered similar and unique responses within species between different stresses, and between the two plant species. A number of widely studied plant stress processes were highly conserved in A. thaliana and P. cheesemanii. However, in response to cold stress, Gene Ontology terms related to glycosinolate metabolism were only enriched in P. cheesemanii. Salt stress was associated with alteration of the cuticle and proline biosynthesis in A. thaliana and P. cheesemanii, respectively. Anthocyanin production may be a strategy to cope with UV-B radiation stress in P. cheesemanii only. These results allowed us to construct broad stress response pathways in A. thaliana and P. cheesemanii and identify possible novel plant strategies that help mitigate environmental stress.

Irrigation and fertilizer are two essential factors affecting rice root traits and yield. In this respect, a pot experiment was performed at the boro (dry season irrigated) season of 2021-22 in the Department of Agronomy of Bangladesh Agricultural University, Mymensingh, Bangladesh. The variety Binadhan-10 was planted at two irrigation conditions i.e. saturation (S) and continuous flooding (CF); two potassium (K) dose e.g. 65 kg ha-1 (K65) and 98 kg ha-1 (K98) and two nitrogen (N) dose i.e. 140 kg ha-1 (N140) and 210 kg ha-1 (N210). The experiment was laid in split plot design and replicated thrice. The findings confirmed the effectiveness of irrigation, K and N on root number (RN), root length (RL), root volume (RV), leaf area index (LAI), total dry matter (TDM), yield attributes and yield. Considering interaction among irrigation, K and N, S condition with K65 and N140 showed best performance. At 80 DAT, the highest RN (373.00), RL (1700.00 cm), RV (8.90 cm3 hill-1), LAI (4.94) and TDM (25.83 g plant-1) was obtained from this combination. Grain yield (GY) and root traits except root porosity, showed a significant positive association. Grain yield (GY) was highest (27.12 g pot-1) in S condition with K65 and N140.

The calculation of moments of area is one of the most fundamental aspects of engineering mechanics for calculating the properties of beams or for the determination of invariants in different kind of geometries. While a variety of shapes such as circles, rectangles, ellipses or their combinations can be described symbolically, such symbolic expressions are missing for freeform cross-sections. In particular, periodic B-spline cross-sections are suitable for an alternative beam cross-section, e.g. for the representation of topology optimization results. In this work, therefore, a symbolic description of the moments of area of various parametric representations of such B-splines is computed. The expressions found are then compared with alternative computational methods and checked for validity. The resulting equations show a simple way for fast conceptual computation of such moments of area of periodic B-splines.

In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying latest methodology of quantile cross-spectral dependence. The findings indicate a positive return interdependence between BTC and EPU is high in short-term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on above interdependence is also extracted by applying wavelet coherence analysis and the estimation results suggest that correlation between BTC and EPU is positive during short-term investment horizon. Furthermore, more diversification benefits of BTC can be obtained during USA-EPU as compared to China-EPU.

The crystal nucleation and overall crystallization kinetics of cross-linked poly(ε-caprolactone) was studied experimentally by fast scanning calorimetry in a wide temperature range. With an increasing degree of cross-linking, both the nucleation and crystallization half-times increase. Concurrently, the glass transition range shifts to higher temperatures. In contrast, the temperatures of the maximum nucleation and the overall crystallization rates remain the same independent of the degree of cross-linking. The cold crystallization peak temperature generally increases as a function of heating rate, reaching an asymptotic value near the temperature of the maximum growth rate. A theoretical interpretation of these results is given in terms of classical nucleation theory. In addition, it is shown that the average distance between the nearest cross-links is smaller than the estimated lamellae thickness, which indicates the inclusion of cross-links in the crystalline phase of the polymer.

Background Due to the Covid-19 pandemic and the strict national policies regarding social distancing behavior in Europe, America and Australia, people became reliant on social media as a means for gathering information and a tool for staying connected to family, friends and work. This is the first trans-national study exploring the qualitative experiences and challenges of using social media while in lockdown or shelter in place during the current pandemic. Methods This study was part of a wider cross-sectional online survey conducted in Norway, UK, USA and Australia during April/May 2020. The manuscript reports on the qualitative free text component of the study asking about the challenges of social media users during the Covid-19 pandemic in UK, USA and Australia. 1991 responses were included in the analysis. Thematic analysis was conducted independently by two researchers. Results Three overarching themes identified were: Emotional/Mental Health, Information and Being Connected. Participants experienced that using social media during the pandemic amplified anxiety, depression, fear, panic, anger, frustration and loneliness. They felt that there was information overload and social media was full of misleading or polarized opinions from which was difficult to switch off. Nonetheless, participants also thought that there was an urge for connection and learning which was positive and stressful and the same time. Conclusion Using social media while in shelter in place or lockdown could have a negative impact on the emotional and mental health of some of the population. To support policy and practice in strengthening mental health care in the community, social media could be used to deliver practical advice on coping and stress management. Communication with the public should be strengthened by unambiguous and clear messages and clear communication pathways. We should be looking at alternative ways of staying connected.

Nowadays, the human population is facing the third and may be the worst pandemic caused by human coronaviruses (CoVs). The virus was first reported in Wuhan, China on 31 December 2019 and spread within short time to almost all countries of the world. Genome analysis of the early virus isolates has revealed high similarity with SARS-CoV and hence the new virus was officially named SARS-CoV-2. Since CoVs have the largest genome among all RNA viruses, they can adapt many point mutation and recombination events; particularly in spike gene, that enable these viruses to rapidly change and evolve in nature. CoVs are known to cross the species boundaries by using different cellular receptors. SARS-CoV-2 is believed to originate in bats and transmitted to human being through an ill-defined intermediate host. In the current review, different aspects of SARS-CoV-2 biology and pathogenicity are discussed including virus genetics and evolution, spike protein and its role in evolution and adaptation to novel hosts, and virus transmission and persistence in nature. In addition, the immune response developed during SARS-CoV-2 infection is demonstrated with special reference to the interplay between immune cells and their role in disease progression. We believe that SARS-CoV-2 outbreak will not be the last and spillover of CoVs from bats will continue. Therefore, establishing intervention approaches to reduce the likelihood of future CoVs spillover from the natural reservoirs is a priority.

There is a growing public health need for effective preventive interventions against dengue, and a safe, effective and affordable dengue vaccine against the four serotypes would be a significant achievement for disease prevention and control. Two tetravalent dengue vaccines, Dengvaxia (Sanofi Pasteur) and DENVax (Takeda Pharmaceutical Company), have now completed phase 3 clinical trials. While Dengvaxia resulted in serious adverse events and is restricted to individuals with prior dengue infections, DENVax has shown, at first glance, some encouraging results. Using the available data for the TAK 003 trial, we estimate, via the Bayesian approach, vaccine efficacy (VE) of the post-vaccination surveillance periods. Although better measurement over long time was expected for the second part of the post-vaccination surveillance, variation in serotype-specific efficacy needs careful consideration. Besides observing that individual serostatus prior to vaccination is determinant of DENVax vaccine efficacy, we also compare the VE estimations for 12 and 18 months and we observe that the efficacy is decreasing over time. The comparison of efficacies over time is informative and very important, bring up the discussion of the role of temporary cross-immunity in dengue vaccine trials and the impact of serostatus prior to vaccination in the context of dengue fever epidemiology.

Cyclodextrin polymers have high applicability in pharmaceutical formulations due to better biocompatibility, solubility enhancement, loading capacity and controlled drug release than parent the cyclodextrins. The cytotoxicity and cell uptake of new cationic beta-cyclodextrin monomers and polymers were evaluated as suitable material for nasal formulations and their protective effects on cells exposed to hydrogen peroxide were studied. PC12 and CACO-2 cells were selected as the neuronal and epithelial type cells, respectively, to mimic the structure of respiratory and olfactory epithelia of the nasal cavity. All cationic beta-cyclodextrin polymers tested showed dose- and time-dependent toxicity; nevertheless, at 5 µM concentration and 60 min of exposure, the quaternary-ammonium-beta-cyclodextrin soluble polymer could be recognized as non-toxic. Based on these results, fluorescently labelled quaternary-ammonium-beta-cyclodextrin monomer and polymer were selected for uptake studies in CACO-2 cells. The monomeric and polymeric beta-cyclodextrins were internalized in the cytoplasm of CACO-2 cells; the cationic monomer showed higher permeability than the hydroxypropyl-beta-cyclodextrin, employed as comparison. Therefore, these cationic beta-cyclodextrins showed potential as excipients able to improve the nasal absorption of drugs. Furthermore, amino-beta-cyclodextrin and beta-cyclodextrin soluble polymers were able to reduce oxidative damage in PC12 and CACO-2 cells and thus could be studied as bioactive carriers or potential drugs for cells protection against oxidative stress.

Lung cancer is known as lung carcinoma. It is a disease which is malignant tumor leading to the uncontrolled cell growth in the lung tissue. Lung Cancer disease is one of the most prominent cause of death in all over world. Early detection of this disease can assist medical care unit as well as physicians to provide counter measures to the patients. The objective of this paper is to approach an automated tool that takes influential causes of lung cancer as input and detect patients with higher probabilities of being affected by this disease. A neural network classifier accompanied by cross-validation technique is proposed in this paper as a predictive tool. Later, this proposed method is compared with another baseline classifier Gradient Boosting Classifier in order to justify the prediction performance.

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

Since 2015, outbreaks of foot-and-mouth disease (FMD) in the Middle East have been caused by a new emerging viral lineage, A/ASIA/G-VII. In-vitro vaccine matching data indicated that this virus poorly matched (low r1-value) with vaccines that were being used in the region as well as most other commercially available vaccines. The aim of this study was to assess the performance of two candidate vaccines against challenge with a representative field virus from the A/ASIA/G-VII lineage. The results from an initial full dose protection study provided encouraging data for the A/MAY/97 vaccine, while the A22/IRQ/64 vaccine only protected 2/7 vaccinated animals. In view of these promising results, this vaccine was tested in a potency test (PD50) experiment in which 5 cattle were vaccinated with a full dose, 5 cattle with a 1/3 dose and 5 cattle with a 1/9 dose of vaccine. Vaccines were prepared as would be done during an emergency vaccination campaign using a double oil emulsion adjuvant. At 21 days post vaccination these vaccinated cattle and 3 control cattle were subsequently challenged intradermolingually with a field isolate from the A/ASIA/G-VII lineage. All cattle from the full vaccine dose, 4 cattle from the 1/3 vaccine dose and 2 cattle from the 1/9 vaccine dose were clinically protected against challenge with FMDV A/ASIA/G-VII, resulting in a heterologous potency of 6.5 PD50/dose. These data support previous studies showing that a high potency emergency vaccine can protect against clinical disease when challenged with a heterologous strain of the same serotype. Not only the r1-value of the vaccine, but also the homologous potency of a vaccine should be taken into account when advising vaccines to control an outbreak.

This article analyzes the emergence of partnerships between corporations and nongovernmental organizations (NGOs) through voluntary product labeling schemes. The economics, management, and business literatures are reviewed to highlight cross-checking, consistencies, and complementarities among these disciplines. The objective is to identify and analyze the motives, risks, and joint benefits of partnering via voluntary product labeling, using examples from the agri-food sector. This study is an attempt to offer a framework of corporate-NGO partnerships by showing that the drivers and risks of each partner merge because each takes a step into the sphere of the other. The main risks – namely, a loss of profitability, credibility and legitimacy – are related to the financial and existential dependency and the asymmetric information between the partners in favor of corporations, inducing an ``NGO-capture'' risk.

The article discusses the issue of the sustainable development of the cross-border market for cultural services in a city divided by the state border. The article uses the example of Cieszyn and Český Těšín, a city divided following the decision of the Council of Ambassadors in 1920. The research carried out so far indicates the main constraints in the harmonious functioning of the cross-border market for cultural services in this city, such as: different cultural policies implemented on both sides of the city, language barriers as well as legal and administrative differences. Therefore, the authors undertook research aimed at recognising the role of Euroregional structures in stimulating the sustainable development of this region. On the basis of the analysis of the Cieszyn Silesia Euroregion's documentation and the results of qualitative and quantitative research, the article describes the role of the Euroregion in building a cross-border market for cultural services. Recommendations were also prepared that could constitute the principles of a common cultural policy not only for Cieszyn and Český Těšín, but also for other European cities in the Schengen Area, which, like Cieszyn and Český Těšín, have been divided by a state border.

The geometry of Poincare disk itself is interpreted without any mapping to dierent spaces. Our approach might be one of the shortest and is intended for educational contribution.

As a universal pathogen leading to neonatal defects and transplant failure, Human cytomegalovirus (HCMV) has strict species specificity that the inability to using this virus in animals has hampered its pathogenesis study. However, the mechanism of cross-species barrier remains elusive that no non-human cell model has been established to fill this knowledge gap. We observed that primary dermis fibroblasts (TSDF) isolated from the Chinese tree shrew (Tupaia belangeri chinensis), a small laboratory animal with close affinity to primates, were permissive to HCMV replication. In TSDF infected with GFP-expressing HCMV, the green fluorescence and cytopathic effect were observed and the expression of 3 kinetic genes and replication of viral genome were detected. The cell-free viruses produced in TSDF reached 103 pfu/mL at 96 hpi, which were 10-fold lower than in primary human foreskin fibroblasts. Our results demonstrated that TSDF supported low level of lytic replication of HCMV. The TSDF model provides a useful platform for the mechanism study of species barrier of HCMV.

In this paper, we analyze the measurements of the normalized radar cross-section(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, we verify the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. The Amazon rainforest experiment results show that the accuracy of obtained calibration constant meets the requirements. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it has an approximate cosine modulation with the wind direction when the wind speed is greater than 8m/s. The cross-polarized system noise floor is low enough to ignore it in wind retrieval. Furthermore, we also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ_VV^0 retrieved from new PR models as well as σ_HH^0 is in good agreement with σ_VV^0 extracted from SAR images directly. And it is also shown that considering the azimuth angle can improve polarization conversion accuracy.

In thermal power plants equipped with air-cooled condensers, axial cooling fans operate under the influence of ambient flow fields. Under inlet cross-flow conditions, the resultant asymmetric flow field is known to introduce additional harmonic forces to the fan blades. This effect has previously been studied only numerically or using blade mounted strain gauges. For this study, Laser Scanning Vibrometry was used to assess fan blade vibration under inlet cross-flow conditions in an adapted fan test rig inside a wind tunnel test section. Two co-rotating laser beams scanned a low pressure axial fan, resulting in spectral, phase resolved surface vibration patterns of the fan blades. Two distinct operating points were examined, with and without inlet cross-flow influence. While almost identical fan vibration patterns were found for both reference operating points, overall blade vibration increased by 100% at low fan flow rate due to cross-flow, and by 20% at high fan flow rate. While numerically predicted natural frequency modes could be confirmed from experimental data as minor peaks in the vibration amplitude spectrum, they were not excited significantly by cross-flow. Instead, primarily higher rotation rate harmonics were amplified, i.a. a synchronous blade tip flapping was strongly excited at the blade pass frequency.

Education and heritage are fundamental to achieving sustainable development goals. Integrating heritage into education is crucial for cultivating children's connection to heritage and promoting sustainable development. However, the lack of a scientific framework to guide educational goals across age groups results in unclear integration. This study aims to develop a framework for cultural identity enlightenment within heritage education by conducting literature research and validating it through field observations in kindergartens. It identifies cognitive, emotional and behavioral dimensions, outlining the developmental trajectory of these goals. This framework illustrates how children's understanding progresses from direct experiences to deeper appreciation and creative involvement. It provides teachers with a tool to set goals that empower children as agents, fostering the emergence of cultural awareness and identity. This approach not only enhances cultural identity but also supports sustainable development by advancing the transmission and innovation of cultural heritage.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative investigation delves into the intricate interplay of cultural factors within supply chain integration, aiming to elucidate how diverse cultural dimensions shape communication dynamics, decision-making processes, and collaborative efforts among global supply chain practitioners. Employing a phenomenological approach, the study explores the lived experiences and subjective perceptions of supply chain managers and professionals, providing nuanced insights into the profound impact of national culture, organizational culture, digital technologies, and sustainability considerations on supply chain operations. Key findings highlight the significant influence of national culture, as conceptualized by Hofstede's cultural dimensions, on supply chain interactions. These dimensions—such as power distance, individualism versus collectivism, uncertainty avoidance, and masculinity versus femininity—play crucial roles in shaping how individuals perceive authority, manage risks, negotiate agreements, and engage in collaborative practices across diverse cultural contexts. Cultural norms and values deeply influence both intra-organizational dynamics and inter-organizational relationships within global supply chains. Organizational culture emerges as a pivotal determinant of supply chain integration success. Cultures characterized by transparency, trust, inclusivity, and a commitment to continuous improvement create environments conducive to effective cross-border collaboration. Conversely, organizations with conflicting or ambiguous cultural norms face challenges in aligning goals, fostering mutual understanding, and maintaining open communication channels with international partners. The study underscores the transformative role of digital technologies in enhancing supply chain management practices. Technologies such as blockchain, artificial intelligence, and the Internet of Things improve supply chain visibility, optimize operations, and enhance decision-making processes, strengthening resilience against disruptions. The adoption and integration of these technologies depend on organizational readiness, technological capabilities, and cultural acceptance within supply chain networks. Cultural factors influence the adoption pace, implementation strategies, and utilization patterns of digital technologies, emphasizing the need for strategic alignment between technological investments and cultural norms to maximize their potential benefits. Furthermore, sustainability considerations are critical in shaping cultural values and priorities within supply chain networks. Organizations increasingly recognize the importance of environmental sustainability, ethical sourcing practices, and corporate social responsibility in shaping their reputations, stakeholder relationships, and long-term business goals. Cultural factors significantly influence organizations' approaches to sustainability, guiding their strategic decisions and operational practices.

A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores how web and immersive technologies, such as the VRRO platform, can improve tourism by offering interactive experiences to augment traditional travel. The study underscores the role of VRRO's design in enhancing user interaction and engagement, thereby encouraging the generation of user content and the development of a virtual tour network. An empirical analysis investigates user engagement, revealing that the platform is accessible to users regardless of their technological familiarity. It suggests a high likelihood of sustained use and recommendations. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism modernize travel experiences and significantly contribute to the evolution of the broader tourism ecosystem.

Depression and anxiety are the most common mental health disorders worldwide. Internet-Delivered Cogni-tive Behaviour Therapy (ICBT) can reduce barriers to care for broad cross sections of the population. How-ever, People of Diverse Ethnocultural Backgrounds (PDEGs) other than White/Caucasian underutilize mental health services and are underrepresented in clinical trials of psychological interventions. To address this research gap, we adapted an evidence-based ICBT program for PDEGs. The current pilot study explores the effectiveness, satisfaction, and engagement in the adapted ICBT by PDEGs (N=41) when benchmarked against a sample of PDEGs (N=134) drawn from a previous non-adapted version of the ICBT program. Inntent-to-treat analyses showed that the adapted ICBT program is effective in reducing anxiety and de-pression symptoms among PDEGs. Large within-group pre-to-post-treatment Cohen’s effect sizes of d = 1.23, 95% CI [0.68, 1.77] and d = 1.24, 95% CI [0.69, 1.79] were found for depression and anxiety, respectively. Further, 81.8% of the PDEGs who received the adapted ICBT program reported high overall satisfaction, 90.9 % reported increased confidence in managing symptoms, and 70.7% of participants completed the ma-jority of the psychoeducational lessons in the ICBT program. No statistically significant differences in clinical outcomes, engagement, and satisfaction were found between the pilot study and benchmark sample. Future directions for ICBT research with PDEGs are described.

The symmetry properties of an ornament contain information about its civilisation and its interactions with other cultural sources. Two-dimensional periodic ornaments can be strictly classified into a limited set of 17 mathematical symmetry groups, also known as wallpaper groups. The collection of ornaments thus classified for a civilisation is characteristic of the cultural group and serves as a fingerprint to identify that group. If the distribution of wallpaper groups is available for several societies, mathematical methods can be applied to determine similarities and differences between the art practices of these communities. This method allows a systematic approach to the general ornamental practices within a culture and their interactions in the form of similarity of fingerprints. We test the feasibility of the method on examples of medieval Armenians, Byzantium, Seljuks first in Persia and then in Anatolia and among Arabs in the Middle East. For this purpose we present the distribution of the planar ornaments and calculate the Euclidean distances in pairs. We tested to what extend geographical and religious factors could account for the observed similarity of ornamental groups between cultures. The results suggest an intensive interaction between the Seljuk Turks and Arab craftsmen who produced the ornaments. Therefore the cultural interactions are religiously motivated.

One of the robust explanation related to sociological educational inequality is Bourdieu’s cultural reproduction theory. Admittedly, prior work investigated that children’s cultural capital (CC) have significant effects on educational success. Although, it remain unclear in the context of developing countries specifically Vietnam. Also, how children’s conversion mechanism of CC works and drive educational success. Meanwhile, Bourdieu theory argue that CC effects educations outcomes largely via teachers biasness by using support of school grades, in contrast some of the scholars argue that absorption of children’s CC may directly transmit into educational abilities and skill development. This study employed partial least square structural equation modelling to analyze 5th grades students’ data collected from educational policy and data center. This study argue that conversion mechanism grounded on CC dimensions. This study provide implications for the policy makers and future researchers.

In this paper, we thoroughly explore the complex realm of cryptography, examining its historical, cultural, and metaphorical aspects. We delve into the roots of cryptography, tracing back to its Ancient Greek origins where it meant the study of secure and hidden writing. We also reveal the historical significance of cryptography in times of war and conflict, showcasing how it has been a vital tool for encoding messages and maintaining confidentiality. Furthermore, we uncover the surprising connection between cryptography and love, highlighting moments from history, such as the encrypted letters between Marie Antoinette and Axel von Fersen during the French Revolution, which showcase the human desire to communicate and connect even during times of turmoil. This paper delves into the cultural and metaphorical representations of cryptography, with a specific focus on the iconic characters Alice and Bob and how they are reinterpreted in different cultural contexts. The paper draws parallels between the complexities of the intertwining of logical, emotional, technical, and personal narratives in cryptographic discourse, offering insights into the lasting legacy of cryptographic metaphors in explaining complex concepts and emphasising the universal human desire for secure and intimate connections.

The introduction provides an overview of beginning the development of a plastic design of metal thin-walled cross-sections. This large study investigates in detail together 14 steel and four extruded aluminium cross-sections. Six groups of various shaped cross-sections that consist of: four I-shaped doubly symmetric sections (two I-sections, one I-section with lips, one H-section); three monosymmetric sections with axis of symmetry z (monosymmetric I-section, T-section, diamond section); four monosymmetric sections with axis of symmetry y (two channels, channel with inside lips, channel with outside lips); two point symmetric sections (Z-section with and without lips); four asymmetric sections (L-profile, sigma section, two closed sections: oblique and irregular sections; non-warping sections along midline, but with negligible warping along the element's thicknesses: Tsection and L-profile are included in the above groups. Four extruded aluminium cross-sections are: I 200a section, diamond section and closed oblique and irregular sections. For all 18 cross-sections, the plastic section moduli of three kinds were calculated: Wpl,y,nB, Wpl,z,nB for bimoment not considered to be constraint; Wpl,y, Wpl,z, Wpl,wfor bimoment considered to be restraint; maximum values Wpl,y,max, Wpl,z,max, Wpl,w,max. The values of cross-section plastic resistances Npl, Mpl,y,Rd, Mpl,z,Rd and Bpl are also calculated in numerical examples. The values of cross-section properties are calculated in different ways to verify the correctness of the results. The following ways of calculations are used: the rules in Eurocode EN 1993-1-1: 2022; own MathCad programmes; own software. Recommendations for educational institutes and designers in practice are provided, including simple formulae for all the cross-sectional properties for the doubly and monosymmetric I-shaped sections, channels and Z-sections. Formulae are presented in six tables containing formulae in the dimensionless form, which are useful for parametrical studies and formulae for direct designs. The background of the Eurocode rules in EN 1993-1-1: 2022 is explained together with recommendations about how to avoid problems with using them.

This study explores the influence of cultural differences on Supplier Relationship Management (SRM) within global e-commerce, examining communication styles, trust-building, negotiation strategies, and strategic alignment across diverse cultural contexts. Through qualitative research including interviews with e-commerce professionals, the study identifies how cultural factors shape SRM practices and outcomes, highlighting the importance of cultural sensitivity and adaptive strategies in fostering effective supplier relationships. Findings underscore the significance of communication preferences, trust norms, and strategic orientations in navigating cultural complexities to enhance operational efficiency and mitigate risks in global markets. Practical implications suggest integrating cultural dimensions into SRM frameworks, prioritizing cultural competence development, and leveraging cultural diversity for innovation and collaboration. Future research directions include exploring digitalization's impact on cross-cultural SRM and longitudinal studies on evolving cultural dynamics in global business. By advancing understanding in this area, e-commerce firms can enhance resilience, maintain competitive advantage, and achieve sustainable growth in the global marketplace.

This qualitative study explores consumer perceptions of supply chain sustainability and its impact on buying decisions. Through in-depth interviews and focus groups, the research investigates the multifaceted factors influencing consumer behaviors towards sustainable products. Findings reveal that environmental impact, social responsibility, transparency, economic considerations, and cultural influences are pivotal in shaping consumer attitudes. While consumers express strong motivations to support sustainability, practical barriers such as higher costs and limited availability often hinder consistent adoption of sustainable practices. Effective communication strategies, including transparency reports and sustainability certifications, emerge as crucial in bridging the gap between consumer intentions and actions. The study underscores the importance of aligning corporate sustainability initiatives with consumer expectations to build trust and foster sustainable consumption patterns. Insights suggest a need for targeted interventions and policy measures to address barriers and promote broader adoption of sustainable behaviors. By understanding and responding to consumer concerns, businesses can enhance their sustainability strategies and contribute to a more environmentally and socially responsible marketplace.

In recent years, the rapid development of computer-aided planning and design technology has provided a new perspective for the study of complex problems such as the generation of architectural complex forms. This study focuses on how to apply parameterization technology to analyze and articulate traditional spatial form composition rules, aiming to minimize reliance on subjective human judgment in the protection and renewal design of the historical style of traditional villages. It aims to establish digital generative design tools to address the challenges of accurately inheriting and innovatively utilizing historical and cultural information in traditional settlements. It introduces how to rely on parameterization technology to analyze the spatial form composition rules, parameter extraction rules, and spatial reconstruction rules of traditional villages, facilitating the complete process from spatial features to parameterization rules, and then to the application of computational methods to deduce spatial features. It also includes case studies demonstrating the application of parameterization technology tools for village protection and explores the role of generative design tools in preserving the spatial style of these settlements.

This research explores the influence of nationality and financial literacy on satisfaction with neobank services among university students. Through a comparative analysis between German and international students, the study unveils that international students exhibit significantly higher satisfaction levels, suggesting that cultural differences and the requirement for international financial services play a crucial role Oishi et al., 1999. Although financial literacy was anticipated to have a strong impact, its effect on satisfaction did not reach statistical significance, indicating a nuanced relationship. This perhaps because the study sample of students partaking in the survey was mostly Graduate students in the field of Economics, Finance and Management. Demographic factors such as gender and age were found to be less influential compared to nationality and financial acumen Gelfand et al., 2011. These findings highlight the importance of tailored financial services and the potential of financial education in enhancing neobank satisfaction Gerrans et al., 2014. The study contributes to the understanding of consumer behavior in digital banking, offering insights for neobanks aiming to improve service adoption and customer loyalty within the university student demographic.

The understanding of structural alteration mechanisms due to relative humidity impact on organic supports of paintings is of crucial significance for the preventive conservation strategies in order to ensure the long-term preservation of important cultural identity artworks. The effort in this paper is to simulate Museum realistic relative humidity condition in a climate chamber while monitoring the structural responses through a quantitative interferometric method allowing the quantitative correlation between the physical values of deformation and mass alteration relevant to relative humidity fluctuations.

This article proposes the use of a multi-scale and multi-sensor approach to collect and modelling 3D data concerning wide and complex areas in order to obtain a variety of metric information in the same 3D archive, based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors data, scales and resolutions is promising and could be useful for the generation of a model that could be defined as hybrid. The correct geometry, accuracy, radiometry and weight of the data models are hereby evaluated comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), terrestrial photogrammetry, using Total Station (TS) and Global Navigation Satellite System (GNSS) as topographic survey. The entire analysis underlines the potentiality of the integration and fusion of different solutions and is a crucial part of the “Torino 1911” project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

Imbalanced classes can cause machine learning models to classify positive class instances poorly, and the models require hyperparameter sets to optimal values. This study aims to develop random forest (RF) and support vector machine (SVM) models that use the optimal hyperparameters obtained through 5-fold cross-validation data. Both models were trained using optimal hyperparameter pairs that fit two data scenarios, the original and oversampling training sets, to produce the benchmark and best models. The model's performance was evaluated using six metrics, including training and testing data. The acquired optimal RF hyperparameter pair was (500, 10) for the minimum number of instances and tree depth level. In contrast, the acquired optimal SVM hyperparameter pair was (0.001, 500) for the gamma and constant values. The benchmark model performed approximately 98% on Accuracy, Precision, Recall, and F1 score metrics but failed to deliver any performance on Mathew's Correlation Coefficient (MC) and Area under Curve (AUC) metrics. The best RF and SVM perform less than both benchmark models in four famous metrics. Both best models have performance improvements of approximately 6% and 11% for the MCC and AUC metrics, respectively. The best RF performance was slightly better than the best SVM performance.

Sodium hypochlorite (NaOCl) is widely used in public healthcare facilities; this exposure can result in the development of bacterial tolerance to disinfectant, which has known links to antibiotic cross-resistance. However, the mechanism through which cross-resistance to antibiotics and disinfectants develops remains ambiguous. Therefore, this study aimed to examine the phenotypic, and transcriptomic changes caused by disinfectant exposure in gram-negative bacteria and determine the cause of cross-resistance to antibiotics. The results demonstrated that the misuse of disinfectants plays an important role in the emergence of disinfectant resistance and in the increase in antibiotic resistance. Antibiotic resistance may occur from exposure of gram-negative bacteria to subminimal inhibitory concentrations (MICs) of NaOCl. Ten passages of gram-negative bacteria in increasingly higher subMICs of NaOCl disinfectant was sufficient to increase the MIC to &gt;2,500 µg/ml NaOCl, particularly in K. pneumoniae and P. aeruginosa. To determine the development of cross-resistance to antibiotics due to NaOCl exposure, the MICs for each antibiotic before and after exposure of each strain to sublethal concentrations of NaOCl were compared. After overnight incubation with a sublethal concentration of NaOCl, a statistically significant increase in MIC was only observed for imipenem (P&lt;0.01). Investigation of the mechanism of cross-resistance by means of transcriptome analysis revealed that 1,250 µg/ml NaOCl-adapted K. pneumoniae and P. aeruginosa strains increased resistance to imipenem due to increased expression of the resistance-nodulation-cell division (RND) efflux pumps, such as AcrAB-TolC and MexAB/XY-OprM. Therefore, we suggest that exposure to NaOCl can influence the expression of RND efflux pump genes, contributing to imipenem cross-resistance.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an energy optimization approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAV). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared to EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

This paper presents an approach to the analysis of beams with rectangular cross-sections subjected to Saint-Venant torsion using the Fourier analysis, body forces being not considered. The Saint-Venant torsion of beams, also called free torsion or unrestrained torsion, is characterized by the absence of axial stresses due to torsion; only shear stresses are developed. The solution to this torsion problem consists of finding the Prandtl stress function that satisfies the governing equation and the boundary conditions, the governing equation being such that the Laplacian of the stress function has a constant value within the cross-section. In this paper, Fourier sine series was utilized to describe the constant value of the above mentioned Laplacian, while double sine series or simple sine series was utilized to describe the Prandtl stress function. Closed-form solutions in form of infinite series were derived for shear stresses, torsional constant and warping function. Those solutions were identical to the exact solutions.

Modern fighter aircraft increasingly need to conjugate aerodynamic performance and low observability. In this paper we showcase a methodology for a gradient-based bidisciplinary aero-stealth optimization. The shape of the aircraft is parametrized with the help of a CAD modeler, and we optimize with the SLSQP algorithm. The drag, computed with the help of a RANS method, is used as the aerodynamic criterion. The stealth criterion, a function is derived from the radar cross section in a given cone of directions, weighed with a function whose goal is to cancel the electromagnetic intensity in a given direction. Stealth is achieved passively by scattering back the electromagnetic energy away from the radar antenna and no energy is absorbed by the aircraft, which is considered as a perfect conductor. A Pareto front is identified by varying the weights of the aerodynamic and stealth criteria. The Pareto front allows for an easy identification of the CAD model corresponding to a chosen aero-stealth trade-off.

In recent years there has been a clear increase in bottled water consumption, globally which translates to significant health and environmental concerns. This cross-sectional study aims to understand the attitude and preferences of university students in Qatar towards use of bottled and tap water for drinking purposes using an online survey questionnaire. The number of students who responded to the online survey was 14% of the entire student population within the university (n = 688). Overall, majority of the students reported plastic bottled water as the main source of drinking water they preferred to use on campus (n=468; 68.02%), with a higher proportion of females (72.08%) preferring the use of plastic bottled water. Out of the 468 students who preferred plastic bottled water, safety was the most important factor (43.80%) followed by convenience (16.88%) and taste (15.60%). Cost (15.17%), personal/family habits (5.13%), Environmental concerns (2.14%) and minerals (1.28%) were found to be the least important factors. Among the 45 students who preferred tap water over plastic bottled water, cost (46.67%) was the dominant factor followed by convenience (20.00%) and environmental concerns and safety (13.33% each), taste (4.44%), and personal/family habits (2.22%). This study recommends campaigns to focus on attitude and behavior change and not solely emphasize knowledge; 71.7% believed that plastic water bottles are more harmful to the environment, yet the greater majority still resorted to this source. There is an immediate need to further educate students through environmental and health literacy programs on water consumption and quality. Enabling the population to understand the positive and negative aspects of their choices, may be an effective remedy to ensuring a healthy population and healthy environment.

Abstract Introduction: Poor structural quality of health facilities for tuberculosis (TB) diagnosis and treatment contributes to unfavorable health outcomes among TB patients. Improving the quality of healthcare services helps to avert TB-related morbidity. Despite these facts, the level of structural quality of health facilitieSs to deliver TB services is not in line with what the national TB and leprosy program (NTLP) recommends. Hence, the present study was conducted to assess the structural quality of health facilities to deliver quality TB services to TB patients among public health facilities in the Sidama region.Objectives: To determine the quality of the structural domain of TB services at public health facilities in the Sidama region of southern Ethiopia.Methods: An institution-based cross-sectional study was conducted from June to July 2023 among randomly selected public health facilities in the Sidama region. We included 40 randomly selected public health facilities that delivered TB services in the study area. Data was collected by experienced data collectors using facility audits, clinical observation checklists, structured questionnaires, and in-depth interviews. An Open Kit Mobile (ODK) application was used to collect data and exported to SPSS version 26. All statistical analysis was done using SPSS version 26.Results: The overall mean quality score for the structural dimension was 50.25 among the 40 health facilities audited in the study area.Conclusion: The quality of TB services regarding structural dimensions was poor in the study area. Therefore, any strategies must give careful attention to increasing the comprehensive structural dimension of quality TB services.

Rubber compounds based on styrene-butadiene rubber, ethylene-propylene-diene-monomer rubber and the combinations of both rubbers were cured with different sulfur and peroxide curing systems. In sulfur curing systems, two type of accelerators, namely N-cyclohexyl-2-benzothiazole sulfenamide, tetramethylthiuram disulfide and combinations of both accelerators were used. In peroxide curing systems, dicumyl peroxide, and combination of dicumyl peroxide with zinc diacrylate or zinc dimethacrylate, respectively were applied. The work was focused on investigation of the curing system composition as well as type of the rubber or rubbers combinations on curing process, cross-link density and physical-mechanical properties of vulcanizates. The dynamic-mechanical properties on the selected vulcanizates were examined, too. The result revealed correlation between the cross-link density and physical-mechanical properties. Similarly, there was recorded certain correlation between cross-linking degree and glass transition temperature. The tensile strength of vulcanizates based on rubbers combinations was higher when compared to that based on pure rubbers, which points out the fact that by rubbers combination, not only the features of both elastomers can be combined, but also improved tensile characteristics can be achieved. When compared to vulcanizates cured with dicumyl peroxide, materials cured with sulfur system exhibited higher tensile strength. By application of co-agents in peroxide vulcanization, the tensile strength overcame the tensile behavior of sulfur cured vulcanizates.

An optical frequency comb (OFC) generator based on an electro-optic Talbot laser and cross-phase modulation (XPM) in a high nonlinear fiber (HNLF) is designed and demonstrated. The Talbot laser is an electro-optic frequency shifting loop to produce repetition rate multiplied pulses, and these pulses working as a pump signal induce XPM process in the HNLF to modulate the phase of a probe signal. At the output of the HNLF, OFCs with a multiplied line-spacing can be generated. The effects of the pump power and the HNLF length on the performance of the generated OFCs are theoretically analyzed. In the experiments, the line-spacing of the generated OFCs is respectively multiplied to be 10 GHz, 15 GHz and 20 GHz with a factor of 2, 3 and 4. The center of the OFCs is tuned in a 4-nm range by adjusting the wavelength of the probe signal.

The field of program classification, a critical aspect of software engineering, facilitates understanding and categorization of code across various applications, including anomaly detection, and code quality assessment. The advancement of cross-language program classification opens up avenues for efficient code translation among different programming languages, significantly aiding developers in rapid coding and reducing development cycles. Existing research primarily focuses on semantic code analysis, with limited emphasis on cross-linguistic challenges. To address this, we introduce an innovative neural network model, CodeSemanticsNN, which leverages a refined Syntax Structure (SS) approach. This model consists of two integral mechanisms: first, it adopts a novel SS representation that combines sequential and graph-based SS structures, enhancing semantic feature capture. Second, it employs a 'unified vocabulary' strategy to bridge the gap between diverse programming languages, facilitating efficient cross-language classification. Additionally, we have compiled a comprehensive dataset of 20,000 files spanning five programming languages, serving as a benchmark for cross-language classification. Our experiments on this dataset demonstrate that CodeSemanticsNN surpasses existing models in four key metrics: Precision, Recall, F1-score, and Accuracy.

Increasing evidence of sexual dimorphism in pathophysiology of metabolic complications caused by sex steroids is under investigation. The gut microbiota represents a complex microbial ecosystem involved in energy metabolism, immune response, nutrition acquisition, and health of host organisms. Gender-specific differences in composition are present between females and males. The purpose of this study was to use cross-sex fecal microbiota transplantation (FMT) for the detection of sex-dependent metabolic, hormonal, and gut microbiota changes in female and male recipients. Healthy non-obese female and male Wistar rats were divided into donor, same-sex, and cross-sex recipient groups. After a 30-day period of FMT administration, biochemical markers (glucose and lipid metabolism) and sex hormones were measured and gut microbiota was analyzed. Cross-sex male recipients displayed significantly lower testosterone concentration compared to males that received same-sex FMT. Sex-dependent changes caused by cross-sex FMT were detected, while several bacterial taxa correlated with plasma testosterone levels. This study represents the first to study the effect of cross-sex changes in the gut microbiome concerning metabolic and hormonal changes/status in adult non-obese Wistar rats. Herein, we present the cross-sex FMT as a potential tool to modify sex-specific pathologies.

Background: Obesity and its attendant conditions have become major health problems worldwide and is currently ranked as the fifth most common leading cause of death globally. Complex environmental and genetic factors are the cause of the current obesity epidemic. Diet, lifestyle, chemical exposures, and other confounding factors are difficult to manage in humans. The mice model is helpful in researching genetics BW gain because genetic and environmental risk factors can be controlled in mice. Studies in mouse strains with various genetic backgrounds and established genetic structures provide unparalleled opportunities to find and analyze trait-related genomic loci. In this study, we used the Collaborative Cross (CC), a large panel of recombinant inbred mouse strains, to present a predictive study using CC mice SMAD4 knockout profiles to understand and effectively identify predisposition to body weight gain. Materials and Methods: Male C57Bl / 6J Smad4+/- mice were mated with female mice from 10 different CC lines to create F1 mice (Smad4+/- x CC). Body weight (BW) was measured weekly until week 16, then monthly until the end (week 48). The heritability H2 of the assessed traits was estimated and presented. Comparative analysis of various machine learning algorithms for predicting the BW changes and genotype of mice was conducted. Results and conclusions: Our data showed that the body weight records of F1 mice with different CC lines differed between wildtype and mutant Smad4 mice during the experiment. Genetic background affects weight gain and some lines gained more weight in the presence of Smad4 knockout, while others gained less, but in general, the mutation caused overweight mice, except for a few lines. In both control and mutant groups, female %BW had a higher heritability H2 value than males. Additionally, both sexes with wildtype genotypes showed higher heritability values than the mutant group. Logistic Regression provides the most accurate mouse genotype predictions using machine learning. We plan to validate the proposed method on more CC lines and mice per line to expand machine learning for BW prediction.

Sulfur, peroxide and combined sulfur and peroxide curing systems were applied for cross-linking of unfilled and carbon black filled rubber compounds based on ethylene-propylene-diene-monomer rubber. The results revealed that the type of curing system influenced the course and shape of curing isotherms. This resulted in the change of curing kinetics of rubber compounds. The cross-link density of vulcanizates cured with combined vulcanization systems was lower than that for vulcanizates cured only in the presence of sulfur or peroxide system. The results revealed good correlation between the cross-link density as well as the structure of the formed cross-links and physical-mechanical characteristics of the materials. It can be stated that by proper combination of vulcanization systems it is possible to modify tensile behavior of vulcanizates in targeted manner. On the other side, dynamical-mechanical properties were found not be significantly influenced by the composition of curing system.

In this paper, we introduce as a pedagogical strategy an internal division-free, straightforward, and symmetrically progressing algorithm in manually computing matrix inverse and solving systems of linear equations by revisiting the application of elementary row operations in the Gauss-Jordan reduction method and connecting it to the determinant method. The proposed cross-multiplication-flip (CMF) algorithm employs cross-multiplication similar to the butterfly movement in computing determinants as a strategic application of elementary row operations to efficiently reduce the rows and then applies flipping of rows and entries to put an upper triangular matrix into lower triangular form to continue the reduction process.

In recent years, there has been a growing interest in remote sensing image-text cross-modal retrieval due to the rapid development of space information technology and the significant increase in remote sensing image data volume. One approach that has shown promising results in cross-modal retrieval of natural images is the multimodal fusion encoding method. However, remote sensing images have unique characteristics that make the retrieval task challenging. Firstly, the semantic features of remote sensing images are fine-grained, meaning they can be divided into multiple basic units of semantic expression. Additionally, these images exhibit variations in resolution, color, and perspective. Different combinations of basic units of semantic expression can generate diverse text descriptions. These characteristics pose considerable challenges for cross-modal retrieval. To address these challenges, this paper proposes a multi-task guided fusion encoder (MTGFE) based on the multimodal fusion encoding method. The model incorporates three tasks: image-text matching (ITM), masked language modeling (MLM), and the newly introduced multi-view joint representations contrast (MVJRC) task. By jointly training the model with these tasks, we aim to enhance its capability to capture fine-grained correlations between remote sensing images and texts. Specifically, the MVJRC task is designed to improve the model’s consistency in feature expression and fine-grained correlation, particularly for remote sensing images with significant differences in resolution, color, and angle. Furthermore, to address the computational complexity associated with large-scale fusion models and improve retrieval efficiency, this paper proposes a retrieval filtering method. This method achieves higher retrieval efficiency while minimizing accuracy loss. Extensive experiments were conducted on four public datasets to evaluate the proposed method, and the results validate its effectiveness. Overall, this study focuses on remote sensing image-text cross-modal retrieval and introduces the MTGFE model, which combines multimodal fusion encoding with multiple tasks to enhance the model’s ability to capture fine-grained correlations. Additionally, a retrieval filtering method is proposed to improve retrieval efficiency. Experimental results demonstrate the effectiveness of the proposed method.

The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various Machine Learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduces two datasets from different markets, one with over 300,000 entries from Germany to serve as a training base for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we include extensive cross-market analyses by comparing the emerging Romanian market versus one of the world’s largest and most developed car markets, Germany. Our study uses several neural network architectures that capture complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R2 score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset is limited. Our experiments highlight the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision-making and provide valuable guidance in the used car market.

In winter sports, the equipment often comes into contact with snow or ice, and this contact generates a force that resists motion. In some sports, such as cross-country skiing, this resistive force can significantly affect the outcome of a race, as a small reduction in this force can give an athlete an advantage. Researchers have examined the contact between skis and snow in detail, and to fully understand this friction, the entire ski must be studied at various scales. At the macro scale, the entire geometry of the ski is considered and the apparent contact between the ski and the snow is considered and at the micro scale the contact between the snow and the ski base ski-base textures. In the present work, a method for characterising contact between the ski-base texture and virtual snow will be presented. Six different ski-base textures will be considered. Five of them are stone-ground ski bases, and three of them have linear longitudinal textures with a varying number of lines and peak-to-valley height, and the other two are factory-ground “universal” ski bases. The sixth ski base has been fabricated by a steel-scraping procedure. In general, the results show that a ski base texture with a higher Spk-value has less real contact area, and that the mutual differences can be large for surfaces with similar Sa-values. The average interfacial separation is, in general, correlated with the Sa-value, where a “rougher” surface exhibits a larger average interfacial separation. The results for the reciprocal average interfacial separation, which is related to the Couette type of viscous friction, were in line with the general consensus that a “rougher” texture performs better at high speed than a “smoother” one, and it was found that a texture with high Sa and Spk values resulted in a low reciprocal average interfacial separation and consequently low viscous friction. The reciprocal average interfacial separation was found to increase with increasing real contact area, indicating a correlation between the real area of contact and the Couette part of viscous friction.

The transition from undergraduate dental student to the actual practicing dentist is a crucial phase and ensuring the preparedness of graduates for the complexity and demands of contempo-rary dental practice is a challenging task. This study aimed to evaluate the self-perceived prepar-edness of undergraduate dental students and house officers in the dental colleges of Pakistan. Cross-sectional national study was planned to collect the information from dental students and new graduates in Pakistan. The pre-validated Dental Undergraduates Preparedness Assessment Scale (DU-PAS) was used. Purposive sampling technique was utilized to recruit house officers and undergraduate dental students from 27 dental schools in Pakistan. The data analysis was car-ried out using the R statistical environment for Windows (R Core Team, 2015). The total of 862 responses with 642 females and 219 males were analyzed in the study. Overall, clinical skills score was 30.56+9.08 and score for soft skills was 30.54+10.6. The mean age of the participants was 23.42+1.28. Deficiencies were reported in various soft skills and clinical skills attributes.The results highlighted the strengths and weaknesses of dental students and new graduates in Pakistani den-tal institutions. The findings may be used to further develop and strengthen teaching and training of dental students in Pakistan.

Abstract: The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.

A major task in Functional Time Series Analysis is measuring the dependence within and between processes, for which lagged covariance and cross-covariance operators have proven to be a practical tool in well-established spaces. This article deduces estimators and asymptotic upper bounds of the estimation errors for lagged covariance and cross-covariance operators of processes in Cartesian products of abstract Hilbert spaces for fixed and increasing lag and Cartesian powers. We allow the processes to be non-centered, and to have values in different spaces when investigating the dependence between processes. Also, we discuss features of estimators for the principle components of our covariance operators.

Finding the key factor and possible "Newton's laws" in financial markets has remained the central issue in this area. However, with the development of information and communication technologies, financial models are becoming more and more realistic but complex which is contradictory to the objective law “Greatest truths are the simplest”. Therefore, this paper attempts to discover the most critical parameter and establishes an evolutionary model which is independent of micro features. In the model, information is the only key factor and stock price is the emergence of the collective behavior. The statistical properties of the model are significantly similar to the real market. It also explains the correlations of stocks within an industry, which provide a new idea for the study of key factors and core structures in the financial market.

Spatial planning holds a key role in preventing or mitigating the impacts of climate change on both cities and rural areas, taking a forward-thinking and holistic approach to urban and regional development. As such, spatial planning deals with challenges occurring at different scales and across sectors. The international literature points out the need for horizontal and vertical cooperation to tackle climate change impacts. While there is abundant knowledge regarding the challenges related to climate change at different spatial levels, procedural integration into planning frameworks and practice is currently under-researched. This paper presents a novel theoretical framework that integrates various steps towards a holistic, integrative and adaptive climate proofing process. An iterative process was used for conceptual development, based on literature review followed by external feedback meetings and two workshops with the core team of planning experts responsible for exchange across federal states. By specifically addressing the challenges relating to cross-regional and cross-sectoral planning, this novel framework attempts to (i) facilitate a hierarchy of measures, (ii) maximise co-benefits for various adaptation purposes and climate change mitigation and (iii) foster the long-term institutionalisation of integrative processes across sectors, planning areas and policy levels.

Finding new ways for the preparation of cross-linked structures is a significant problem in terms of materials for biomedical application, lithium batteries electrolytes, and etc. Within this work we have studied the possibility to utilize hydrosilylation and Piers-Rubinsztajn reactions to obtain cross-linked model phosphazene compounds, containing eugenoxy and guaiacoxy groups. It was shown that Piers-Rubinsztajn reaction cannot be efficiently used to prepare tailored polymer-matrix, due to the catalyst deactivation by nitrogen atoms of phosphazene units. A number of cross-linked phosphazene-based materials was obtained with the use of hydrosilylation reaction and their properties were studied by NMR spectroscopy, FTIR, DSC, and TGA. This work showed a perspective for the use of eugenoxy functional groups for the preparation of three-dimensional hybrid phosphazene/siloxane-based materials for various applications.

Early education is critical for improving energy efficiency. The purpose of this study is to explore the feasibility of Interactive Cross-Border Classes to increase awareness of energy efficiency among middle school students. We designed and tested an Interactive Cross-Border class between Chilean and Peruvian 8th-grade classes. The classes were synchronously connected and all students answered open-ended questions on an online platform. Some of the questions were designed to check conceptual understanding while others asked for suggestions of how to develop their economies while keeping CO₂ air concentration at acceptable levels. In real-time, the teacher reviewed the students’ written answers and the concept maps that were automatically generated based on their responses. Students peer-reviewed their classmates’ suggestions. This is part of an Asia-Pacific Economic Cooperation (APEC) STEM Education project on Energy Efficiency using APEC databases. We found high levels of student engagement, where students discussed not only the cross-cutting nature of energy, but also its relation to socioeconomic development and CO₂ emissions, and the need to work together to improve energy efficiency. In conclusion, Interactive Cross-Border classes are a feasible educational alternative, with potential as a scalable public policy strategy for improving awareness of energy efficiency among the population.

Predator-prey interactions present heightened opportunities for pathogen spillover, as predators are exposed to novel parasites through consumption of prey harboring potentially infectious agents. Epizootics with high morbidity and mortality have been recorded following prey-to-predator spillover events with significant conservation implications, particularly for sensitive species. However, relatively few virulent infections following prey consumption are reported, given the very large number of exposures that presumably occur. Further, many transmitted agents are infectious but clinically silent and thus go unrecognized. Mechanisms that determine outcome of predator exposure to prey-based pathogens therefore represent an important, understudied component of disease dynamics that should be considered in modeling approaches and empirical research to better understand disease risk and emergence, particularly in vulnerable or threatened species.

COVID-19 is an infectious disease spreading through human touch. This study explored the risk perception and knowledge towards COVID-19 infection among Bangladeshi adult participants. Two self-administered online surveys were administered at two different time points from 26-31 March 2020 (Early lockdown) and 11-16 May 2020 (Late lockdown) through social media on 1005 respondents (322 and 683 participants, respectively) during COVID-19 lockdown period in Bangladesh. Univariate and multiple linear regression models were used to examine factors associated with risk perception and knowledge towards COVID-19. The mean knowledge (8.4 vs. 8.1, P=0.022) and risk perception (11.2 vs. 10.6, P < 0.001) scores differ significantly between early and late lockdown. Compared to the early lockdown period, the scores for perceived risk of contracting COVID-19 decreased significantly while public knowledge about COVID-19 was lower but not statistically significant. Female participants who practiced high quarantine particularly those who did so at the public health order during the lockdown reported increased knowledge towards the spread of COVID-19 and perceived high risk of contracting COVID-19. Education intervention using awareness to increase public knowledge and perception towards COVID-19 in Bangladesh should target male participants who practiced low quarantine and are less worried about the spread of such novel coronavirus even as the physical distancing persists.

COVID-19 has become a severe global public health concern. The critical illness has a mortality rate of 61.5%, and thus, reducing the severity and mortality is top priority. Currently, inflammatory storms are considered as the cause of critical illness and death due to COVID-19. However, After systematical review of the literature, we proposed that cross-reactive antibodies-associated antibody-dependent enhancement (ADE) may actually be the cause of cytokine storms. If the activation of memory B cells can be selectively inhibited in high-risk patients at an early stage of COVID-19 to reduce the production of cross-reactive antibodies of the virus, we speculate that the ADE can be avoided and severe symptoms can be prevented. The mammalian target of rapamycin (mTOR) inhibitors satisfy such conditions. We recommend that pharmaceutical companies conduct clinical trials urgently.

Diffusion bonding is often used on pre-machined parts to generate internal cavities, e.g. for cooling injection molding tools close to the mold cavity. Only then, the workpieces are finished to their final dimensions. In the case of micro-process devices, however, it is essential to precisely control the deformation, as otherwise uncontrollable pressure losses will occur with channel cross-sections in the sub-millimeter range. Post-processing is not possible. The most important process parameters for diffusion bonding are temperature, dwell time and contact pressure, with the bonding temperature and contact pressure acting in opposite directions and showing a strong non-linear dependence on deformation. In addition, the deformation is influenced by a number of other factors such as the absolute size of the cross-section and the aspect ratio of the parts, the dimensions and distribution of the internal cross sections and the overall percentage of the cross-section to be bonded. In micro process engineering, small material cross-sections in the range of the materials microstructure can facilitate additional deformation mechanisms such as grain boundary sliding, which are not relevant at all for larger structures. For parts consisting of multiple layers, tolerances in thickness and roughness of multiple surfaces must be levelled, contributing to the percentaged deformation. This makes it difficult, especially in micro process engineering and in single or small series production, to determine suitable joining parameters in advance, which on the one hand do not cause unforeseen large deformations, but on the other hand reliably produce highly vacuum-tight components. Hence, a definition of a fixed percentaged deformation does not work for all kinds of components. This makes it difficult to specify parameters for surely obtain high-vacuum tight parts. For successful diffusion bonding, atoms must diffuse over the bonding planes, forming a monolithic part in which the original layers are no longer visible. Only then, mechanical properties identical to those of the base material, which has been subjected to identical heat treatment, can be achieved. In this paper, the impacts of different material cross section widths as well as of the aspect ratio on deformation were investigated. By accident, it was found that also accuracy of the temperature measurement may have a serious impact in terms of deformation.

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Science (ESS) are no exception. However, within ESS specialized visualization requirements and data models --- particularly for those data arising from numerical models --- often make general-purpose visualization packages difficult, if not impossible, to effectively use. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR's utility in the study of a numerically simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

This study investigates the intricate relationships between happiness, sustainability, and cultural values across 74 countries. Utilizing a multidimensional scaling (MDS) and K-means clustering approach, we analyze six happiness indices alongside the Sustainable Development Goals Index (SDGI). Our findings indicate a positive correlation between economic development, social progress, and elevated levels of both happiness and sustainability. Nevertheless, instances like the United States highlight the necessity of alternative methodologies, highlining classification of nonlinear models. The MDS analysis identifies four distinct clusters of countries, each demonstrating similar profiles in terms of happiness, sustainability, and cultural values, as per Inglehart's framework. This clustering underscores the necessity for a comprehensive perspective that integrates economic, social, and cultural dimensions to produce robust results and mitigate disparities or contradictions in subjective measures like the World Happiness Score. Future research should explore the impact of institutional frameworks, governance models, and social capital. Additionally, longitudinal studies are needed to examine how these relationships evolve in response to global challenges. We acknowledge the limitations related to data heterogeneity, the subjectivity of cluster analysis, and the exclusion of outliers, emphasizing the need for further research to enhance the generalizability and robustness of these findings.

This qualitative study explores the intricate impact of cultural differences on global procurement practices within multinational supply chains. Cultural dimensions profoundly influence communication dynamics, negotiation strategies, decision-making processes, and supplier relationship management in diverse global contexts. Effective cross-cultural communication is crucial for fostering trust and clarity across different cultural norms, while negotiation strategies vary based on cultural preferences for collaboration or competition. Decision-making processes are shaped by cultural attitudes towards risk tolerance, time orientation, and decision authority, influencing procurement agility and resilience. Strategic implications of cultural diversity include opportunities for innovation and localized adaptation in procurement practices, tempered by challenges such as adaptation costs and regulatory compliance. Emotional intelligence (EI) emerges as pivotal in navigating cultural complexities, enhancing stakeholder engagement, conflict resolution, and collaborative partnerships critical for sustainable procurement practices in multicultural settings. Economic factors, including stability, currency fluctuations, and market volatility, impact procurement strategies and supply chain resilience, necessitating proactive risk management and adaptive approaches. Barriers to growth in global procurement, such as regulatory complexities and geopolitical risks, require strategic planning, stakeholder collaboration, and infrastructural investments for operational continuity. By understanding and addressing cultural influences, multinational corporations can enhance cross-cultural competence, strategic decision-making, and capitalize on global market opportunities. This study contributes insights and recommendations to enhance procurement effectiveness and competitiveness in an interconnected global economy, supporting ongoing efforts to optimize global procurement practices.

Determining precise boundaries and protective zones for historical and cultural objects enables their effective preservation. This is particularly crucial in the face of increasing tourism, urbanization, and natural disasters. Accurately defining boundaries and protective zones can aid in the planning of scientific research, archaeological excavations, and other studies aimed at exploring the history and cultural significance of these objects. The article presents the framework for establishing protective zones around historical and cultural heritage sites existing in Ukraine, using the example of the Citadel defensive complex located in Lviv (Ukraine). It proposes general and detailed conceptual models for the combined application of remote and non-invasive methods for investigating historical and cultural heritage sites. This conceptual article introduces the theory of integrating radar satellite imaging with ground-based georadar imaging. Additionally, it presents a software module that has been developed to analyze collected data on two-dimensional historical and cultural heritage objects, refine their boundaries, and establish protective zones around them.