This study presents the Bayesian-Optimized Attentive Neural Network (BOANN), a novel approach enhancing image classification performance by integrating Bayesian optimization with channel and spatial attention mechanisms. Traditional image classification struggles with the extensive data in today's big data era. Bayesian optimization has been integrated into neural networks in recent years to enhance model generalization, while channel and spatial attention mechanisms improve feature extraction capabilities. This paper introduces a model combining Bayesian optimization with these attention mechanisms to boost image classification performance. Bayesian optimization optimizes hyperparameter selection, accelerating model convergence and accuracy; the attention mechanisms augment feature extraction. Compared to traditional deep learning models, our model utilizes attention mechanisms for initial feature extraction, followed by a Bayesian-optimized neural network. On the CIFAR-100 dataset, our model outperforms classical models in metrics such as accuracy, loss, precision, recall, and F1 score, achieving an accuracy of 77.6%. These technologies have potential for broader application in image classification and other computer vision domains.

(1) Background: Eosinophils importance as T2 inflammation markers is known to be fundamental in asthma, chronic sinusitis, vasculitis. In cancer, despite their potential antiproliferative effect, their role remains unclear. Our purpose was to describe the relation between baseline blood eosinophil count (EOS) and overall survival in pancreatic ductal adenocarcinoma (PDAC) patients. (2) Methods: We have analyzed retrospectively the data of 137 adult patients, who underwent surgical treatment of PDAC between years 2012-2019 with no history of systemic steroid use directly before surgical treatment and no metastases found intraoperatively. (3) Results: EOS<0,1 G/l (vs. ≥0,1 G/l) was an independent prognostic factor for OS both the uni- and multivariate Cox regression (respectively: HR=1,48, p=0,035 and HR=1,57, p=0,021). According to EOS and PDAC stage, median OS were: in stage I-III, EOS ≥0,1 G/l group: 14,5 months, in stage I-III, EOS <0,1 G/l group: 8,0 months, in stage IV, EOS ≥0,1 G/l group: 7,0 months, in stage IV, EOS <0,1 G/l group: 5,0 months. (4) Conclusions: Peripheral eosinophilia seems to be a potential independent prognostic factor. Further studies are necessary to confirm this hypothesis, since our findings suggest that T2 inflammation may be the factor directly or indirectly lengthening survival of PDAC patients.

Probability is initially defined on a Boolean algebra or a lattice of projectors. We propose here to start from a Heyting algebra.

This study, which investigates the impact of foreign bank entry on the efficiency and sustainability of domestic banks in developing countries using a Meta-Frontier analysis to estimate efficiency scores, presents findings of significant importance to banking and finance. By incorporating financial, social, and environmental sustainability proxies—such as efficiency, loan portfolio composition, and macroeconomic conditions—this study assesses whether foreign competition enhances or undermines the long-term stability of domestic banking sectors. The results show that while foreign banks can improve financial efficiency, they may destabilize domestic banks, notably smaller or less capitalized institutions. Additionally, the findings suggest that banks with higher investments in SME lending and green projects demonstrate better social and environmental sustainability. Policymakers and financial institutions must consider these dual effects when promoting foreign bank entry.

Obviously, the discussion of different factors that could have contributed to the origin of life and evolution represents a clear speculation, since there is no way to check the validity of the most of the related hypotheses on practice, as the corresponding events are not only already happened, but took place in a very distant past. However, there are a few undisputable facts obviously present at the moment, such as the existence of a broad variety of living forms and the abundant presence of intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains and intrinsically disordered regions (IDRs) in all those living forms. Since it seems that the currently existing living forms have originated from a common ancestor, their variety represents a result of evolution. Therefore, one could ask a logical question of what role(s) those structure-less and highly dynamic but vastly abundant and multifunctional IDPs/IDRs might have in evolution. This study represents an attempt to consider various ideas pertaining to the potential roles of protein intrinsic disorder in origin of life and evolution.

Inconel 718 is a widely popular aerospace superalloy known for its high-temperature performance and resistance to oxidation, creep, and corrosion. Traditional manufacturing methods, like casting and powder metallurgy, face challenges with intricate shapes that can result in porosity and uniformity issues. On the other hand, Additive Manufacturing (AM) techniques such as Powder Bed Fusion (PBF) and Direct Energy Deposition (DED) can allow the creation of intricate single-part components to reduce weight and maintain structural integrity. However, AM- parts often exhibit directional solidification, leading to anisotropic properties and potential crack propagation sites. To address this, post-processing treatments like HIP and heat treatment are necessary. This study explores the effects of the raster and stochastic spot melt scanning strategies on the microstructural and mechanical properties of IN718 parts fabricated using Electron Beam Powder Bed Fusion (EB-PBF). The research demonstrates that raster scanning produces columnar grains with higher aspect ratios. In contrast, stochastic spot melt scanning promotes the formation of equiaxed grains, enhancing microstructural refinement and reducing anisotropy. Microhardness values also show variation in strength in build direction, denoting changes in mechanical performance due to scanning strategies. These findings emphasize the critical role of scanning strategies in optimizing the EB-PBF process for improved material properties.

Mortality rates are fundamental to understanding the overall health of a population. However, statistics are usually reported using the primary underlying cause of death, overlooking potentially relevant contributing causes listed on death certificates. This paper presents indicators for multiple cause-related mortality in Canada from 2000 – 2022. Deaths from the Canadian Vital Statistics Database (2000 – 2022) were merged with multiple cause files and classified into 136 cause of death groupings. Summary statistics for multiple causes were calculated, including the Standardized Ratio of Multiple to Underlying (SRMU), which is also calculated by neighbourhood income quintile. Age-Standardized Mortality Rates (ASMR) were calculated for the underlying cause of death (ASMRUC) and compared to rates including any mention of each respective cause (ASMRAM). These were then compared to ASMRs based on a contributing-cause weighting scheme (ASMRW). The average number of causes reported on death certificates has increased from 2.79 in 2002 to 3.19 in 2021. Those in the lowest income quintiles have a higher average number of causes (3.31 in 2021) compared to those in the highest income quintile (3.09 in 2021). When employing multiple cause weighting strategies, the rank order of age-standardized mortality rates is significantly elevated for conditions including renal failure, hypertension, pneumonia, septicemia, arterial fibrillation, and artery diseases. Multiple cause-of-death approaches provide further insight into the patterns of mortality and highlight conditions that become leading causes using weighted approaches. This provides evidence to support efforts to address these conditions. There are also differences in multiple causes of death reporting by income quintile which warrants further investigation.

This work aimed to determine the effect of PBZ concentrations and application protocols on the ornamental quality and global preference of customers. PBZ was applied in five concentrations by three protocols (drench at transplanting, drench at 30 days after transplanting and immersion). At commercial maturity, the plants were evaluated for plant growth parameters related to height and canopy characteristics, fruits and leaves, in addition to sensory analysis. PBZ produced plants with darker green leaves and the drench method was more effective in modulating plant growth pa-rameters compared to immersion. Conversely, the sensory analysis showed a greater preference among evaluators for plants treated with PBZ at 2.5 and 5 mg L-1 via drench at 30 DAT or through immersion (IM), even though IM was not associated with any of the ornamental quality parameters evaluated. Likewise, the application of PBZ by drench during transplanting is not recommended as the plants showed a reduced number and size of fruits, which affected the ornamental value of the plants and global preference. Therefore, PBZ applied by drench 30DAT at concentrations of 2.5 and 5 mg L-1 produced ‘Biquinho Vermelha’ pepper plants with attractive ornamental characteristics being an alternative method for producers of potted ornamental plants.

This article provides a comprehensive historical analysis of Brazil's industrialization, tracing its evolution from the 19th century to the present day. It examines key periods and policies that have shaped the nation's economic landscape, beginning with early industrial efforts during the Imperial era and the Old Republic. The analysis delves into the Vargas regime's Import Substitution Industrialization policies, post-war industrial expansion under developmentalism, the military regime's economic strategies during the "Economic Miracle," and the challenges posed by the 1980s debt crisis. The discussion highlights contemporary issues faced in the 21st century, including premature deindustrialization and the complexities during the administrations of Presidents Lula da Silva and Dilma Rousseff. It explores the impact of Operation Lava Jato on industrial development and assesses the ramifications of President Jair Bolsonaro's policies during the COVID-19 pandemic. Central to this exploration is the critical examination of regional disparities, social inclusion efforts, and environmental sustainability initiatives. The article emphasizes that Brazil's most significant challenges lie in persistent inequality and structural racism, which serve as fundamental barriers to economic progress and industrial advancement. It argues that addressing these issues through comprehensive policies focused on education reform, economic inclusion, healthcare access, legal and institutional changes, community development, and cultural recognition is essential. By integrating social justice into economic strategies, Brazil can achieve sustainable and inclusive industrial development, fostering a more equitable and prosperous society.

BACKGROUND: The composition of the gut microbiota varies significantly among healthy individuals and insomniacs, hypersomniacs and healthy people. On the other hand, there is a substantial bacterial strain shared across these populations with distinct intra-population, mother-to-infant and intra-household transmission patterns. Based on these premises, we propose a hypothesis stating that person-to-person bacterial transmission can change the sleep pattern in couples. SUBJECTS AND METHODS: In this pilot study, 157 couples who had been married and living together for an average of 5.91 months were enrolled. A wide range of questionnaires were administered to participants to measure sleep patterns. Gut microbiota composition was analyzed at two time points: baseline and 3 months. RESULTS: Three months after the marriage, spouses with healthy sleep pattern were significantly more likely to resemble their insomniac or hypersomniac couples. CONCLUSIONS: The research will have important implications in terms of diagnosis and treatment of sleep disturbances through microbiota modulation.

This paper proposes a methodology for multi-criteria calibration of a thermo-mechanical model for numerical simulation of welding in vacuum. A finite element model of a steel plate is created. Experimental and computational data are obtained. An inverse problem is formulated for the vector identification of five calibration parameters from the heat flow model. They are evaluated for adequacy with controlled accuracy according to four criteria. An optimization problem is solved using a two-step interactive procedure. The parameter space studying method (PSI) has been applied to the study of multidimensional regions by means of quasi-uniform sounding. A Pareto-optimal set is defined. It is used to determine reduced ranked Pareto subsets by -selection. Salukvadze optimum is also determined.

Background: Narcolepsy is characterized by excessive daytime sleepiness (EDS) and episodes of cataplexy, frequently triggered by psychological stress, significant disturbances in sleep-wake cycles, pregnancy, or trauma. Modafinil is widely used as the preferred treatment due to its effectiveness in alleviating EDS, improving disease outcomes, and enhancing overall quality of life. Nonetheless, long-term safety and efficacy data are still lacking, underscoring the importance of future research to advance treatment options for narcolepsy. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of Modafinil in the treatment of Narcolepsy. Methods: We searched in electronic databases (PubMed, Embase, Google Scholar, Scopus, Science Direct, Web of Science) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 8th July 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Maintenance of Wakefulness Test (MWT) and Epworth Sleepiness Scale (ESS) . We performed the meta-analysis utilizing the Review Manager software, version 5.4. To assess the outcomes, we compared the Modafinil-treated group with the placebo group, employing the mean difference (MD) and a 95% confidence interval (CI) for statistical analysis. Result: In total, 3833 articles were identified for screening, with nine studies included in the systematic review and five in the meta-analysis. These studies involved 997 adult patients with Narcolepsy treated with Modafinil. MWT revealed a significant increase in total scores (MD= 3.56, 95% CI [2.25 to 4.86], p < 0.00001) and ESS revealed a significant decrease in scores (MD= -3.34, 95% CI [-4.13 to -2.56], p < 0.00001). Conclusion: The research emphasizes the potential of Modafinil in alleviating EDS associated with Narcolepsy. While the results are promising, they are based on short term scale studies. It is crucial to undertake larger-scale, extended-duration, placebo-controlled studies that include a broad range of demographic populations. These trials are essential to confirm the efficacy, verify the safety profile, and fine-tune dosing strategies to maximize the long-term therapeutic benefits.

Background: Nonsurgical treatment in uncomplicated appendicitis is in many cases a reasonable option, despite the sparsity of robust, easy access, externally validated and multimodally informed clinical decision support systems (CDSS). Developed by OpenAI, the Generative Pre trained Transformer 3.5 model (GPT 3), may provide enhanced decision support for surgeons in less certain appendicitis cases or those posing a higher risk for (relative) operative contra indications. Our objective was to determine whether GPT 3.5, when provided high throughput clinical, laboratory and radiological text based information will come to similar clinical decisions as a machine learning model and a board certified surgeon (reference standard) in decision making for appendectomy versus conservative treatment. Methods: In this cohort study we randomly collected patients presenting at the Emergency Department (ED) of two German hospitals (GFO, Troisdorf and University Hospital Cologne) with right abdominal pain between October 2022 and October 2023. Statistical analysis was performed using R, version 3.6.2, on RStudio, version 2023.03.0+386. Overall agreement between GPT 3.5 output and the reference standard was assessed by means of inter observer kappa values as well as accuracy, sensitivity, specificity, positive and negative predictive value with the “Caret” and “irr” package. Statistical significance was defined as p < 0.05. Results: There was agreement between surgeon decision and GPT 3.5 in 102 of 113 cases and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT 3.5. The estimated model training accuracy was 83.3 % (95% CI: 74.0, 90.4), while the validation accuracy for the model was 87.0 % (95% CI: 66.4, 97.2). This in comparison to the GPT 3.5 accuracy of 90.3% (95% CI: 83.2, 95.0), which did not perform significantly better in comparison to the machine learning model (P = 0.21). Conclusions: This to our knowledge first “intended use” for surgical treatment GPT 3.5 study comparing surgical decision making versus algorithm found a high degree of agreement between board certified surgeons and GPT 3.5 for surgical decision making in patients presenting to the emergency department with lower abdominal pain.

This paper explores the use of GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE, in modeling blockchain data. A specific focus is given to their applications in cryptocurrency transaction networks, where GNNs can detect fraudulent activities and track illicit fund flows by learning from transaction patterns and network structure. Additionally, future directions for GNN research in blockchain are discussed, including scalability challenges, privacy-preserving models, and multi-chain graph analysis. Through these advancements, GNNs offer significant potential for enhancing the security, transparency, and efficiency of blockchain and cryptocurrency ecosystems. This paper also shows an example of fraud detection for Ethereum cryptocurrencies.

The 14-3-3 protein in dimorphic fungi from the Ajellomycetaceae family, including Blastomyces spp., Histoplasma spp., Paracoccidioides spp., Emergomyces spp., and Emmonsia spp., play a crucial role in host-pathogen interactions. Thus, we performed a comparative genomic analysis across 21 fungal species which revealed the presence of two 14-3-3 gene copies in most fungi, with exceptions in Histoplasma ohiense and Emergomyces africanus which revealed one and three gene copies, respectively. Moreover, in this study it was found a high degree of similarity (over 70%) between fungal 14-3-3 proteins and the human 14-3-3ɛ isoform, particularly in conserved Ser/Thr phosphorylation sites. However, distinct differences were noted in the C- and N-terminal regions, where fungal 14-3-3 proteins have additional amino acids. Though not directly linked to pathogenicity, these structural differences may offer insights into new therapeutic strategies. The conserved regions and 3D similarities identified could serve as potential targets for broad-spectrum antifungal drug development and vaccine production. Further research is necessary to validate these in silico findings and explore the functional roles of these phosphorylation sites, which could contribute to understanding fungal pathogenicity and developing specific antifungal interventions.

Background: Post-operative pancreatic fistula (POPF) is a common complication associated with pancreatic resections. Early identification of this adverse event could avoid otherwise prolonged hospital stay due to clinical management and treatment of symptoms created by POPF. This retrospective study investigated the prevalence of pancreatic fistula among patients who underwent oncological pancreatic resection between August 2018 and December 2023 at Bnai-Zion Medical Center. Elevated C-reactive protein levels have been utilized as an early postoperative marker indicating the presence of inflammation due to tissue injury complication. Objective: The aim of the current study was to evaluate the prediction potential of elevated C-reactive protein levels in detecting the risk for infection created by pancreatic fistula. Methods: Demographic and clinical data was collected from medical records files of ninety-four pancreatic cancer patients’ cohort who were subjected to resection. Results: A significant increase in C-reactive protein levels was found on day three following surgery at the group of forty-six patients having developed pancreatic fistula compared to patients without pancreatic fistula (p=0.002). There was no statistical difference among the groups in any demographic characteristics (such as age, gender, BMI, smoking status, etc.) and any other clinical features (e.g. serum albumin levels, chemotherapy protocol, surgical technique, hospitalization time, lymph node involvement). Conclusions: The results of the study suggest that C-reactive protein could serve as an early biomarker for pancreatic fistula formation in patients undergoing pancreatic resection for cancer.

Additive Manufacturing (AM) methods are increasingly beign abopted in the industry as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and specially to impact strength. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in term of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientation. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed in this study, and each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, a factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m2, followed by PA6 GF30 at 8.1 kJ/m2 and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m2. When compared to the literature and manufacturer datasheets, it was concluded that the impact strength values have significantly increased, and the chosen experimental factors and their levels, particularly nozzle temperature, were effective.

Issues related to the duration of university studies, have attracted the interest of many researchers from different scientific fields, as far back as the middle of the 20th century. In this study, a Survival Analysis methodology, and more specifically a Cox Proportional hazards model, has been pro-posed to evaluate a theoretical framework/model that relates the risk a student either graduating on time or having a late graduation, with a number of observed and latent factors that have been proposed in the literature as the main determinants of degree completion. The major findings of the analysis suggest that the factors contributing to the shortening of the duration of studies in-clude high academic achievements at early stages, positive motivation, expectations, attitudes, and beliefs about studies. On the contrary, external situations, negative academic experiences and some individual characteristics of the students contribute to an extended duration of studies.

Lignin is a complex organic polymer. It has a crucial role in providing structural support, rigidity, and defense against disintegration. Following cellulose, it ranks as the second most abundant natural polymer discovered on Earth. Lignin is composed of several phenolic compounds that form a complex, three-dimensional framework. Lignin, unlike cellulose, is a non-carbohydrate polymer rather than a polysaccharide. Lignin's characteristics pose both difficulties and prospects in several sectors, spanning from agriculture to renewable energy. The investigation of the composition of Lignin and its manipulation through engineering are closely intertwined. Disruptions of genes implicated in the lignin biosynthesis pathway can result in substantial alterations in the composition and architecture of Lignin. These modifications can offer novel approaches for altering Lignin and deliberately engineering the polymer to enhance its worth or simplify its extraction from cell wall polysaccharides. As a result, the definition of Lignin is constantly expanding and being adjusted to accommodate its diversity and adaptability. This review examines explicitly the versatility of the lignin molecule.

Achieving complete remission (CR) is crucial for acute myeloid leukemia (AML) patients undergoing curative therapy, but relapse often occurs within months, highlighting the need for strategies to prolong leukemia-free survival (DFS). Our phase III study compared the efficacy and safety of azacitidine (AZA) versus best supportive care (BSC) in elderly AML patients who achieved CR after intensive induction and consolidation therapy. This ancillary study (QOL-ONE Trans-2) evaluated biological changes in bone marrow using Next Generation Sequencing (NGS). We analyzed baseline, randomization, and 6-month post-remission samples from 24 patients (median age 71, 12 males). High-throughput NGS targeted 350 myeloid malignancy-related genes, considering variants with a Variant Allele Frequency (VAF) ≥ 4%. At diagnosis, all patients had 5 to 17 (median 10) mutations, with DNMT3A (42%), NPM1 (33%), and TET2 (33%) being most frequent. FANCA mutations in 4 patients were linked to a higher relapse risk (HR 4.96, p=0.02) for DFS at both 2 and 5 years. Further HLA-specific NGS analyses are ongoing to confirm these results and their therapeutic implications.

The basic helix loop helix (bHLH) transcription factors play crucial roles in plant growth, development, and stress responses. However, their identification and insight understanding in rubber trees remain largely uncovered. In this study, the bHLH gene family was explored and characterized in rubber trees using systematic bioinformatics approaches. In total, 180 bHLH genes were identified in the rubber tree genome, distributed unevenly across 18 chromosomes, and phylogenetic analysis classified these genes into 23 distinct subfamilies. Promoter regions revealed a high density of cis-elements responsive to light and hormones. Enrichment analysis indicated involvement in numerous biological processes, including growth, development, hormone responses, abiotic stress resistance, and secondary metabolite biosynthesis. Protein interaction network analysis identified extensive interactions between HbbHLH genes and other functional genes, forming key clusters related to iron homeostasis, plant growth, and stomatal development. Expression profiling of HbbHLH genes have demonstrated varied responses to endogenous and environmental changes. RT-qPCR of eleven HbbHLH genes in different tissues and under ethylene, jasmonic acid, and cold treatments revealed tissue-specific expression patterns and significant responses to these stimuli, highlighting the roles of these genes in hormone and cold stress responses. These findings establish a framework for exploring the molecular functions of bHLH transcription factors in rubber trees.

Images are widely used in educational materials due to their ability to effectively convey complex concepts. However, unauthorized image use often leads to legal issues related to copyright infringement. To address this issue, we introduce a dual-module system designed specifically for educators. The first module, a copyright infringement detection system, leverages deep learning techniques to verify the copyright status of images. It employs a Convolutional Variational Autoencoder (CVAE) model to extract meaningful features from copyrighted images and compares them against user-provided images. If infringement is suspected, the second module, an image retrieval system, recommends alternative copyright-free images using a Vision Transformer (ViT)-based hashing model. Evaluation on benchmark datasets demonstrates the system's effectiveness, achieving a mean Average Precision (mAP) of 0.812 on the Flickr25k dataset. Furthermore, a user study with 65 teachers indicates high satisfaction levels, particularly in addressing copyright concerns and ease of use. Our system significantly aids educators in creating educational materials compliant with copyright regulations.

Background and Objectives: Total rehabilitation of atrophic maxillae with zygomatic implants (ZI) has become a more common practice nowadays. However, the need for training and expertise of surgeons to correctly perform the technique in order to minimize possible complications has been described. The objective of this article is to describe an atypical case of tearing for 24 hours after the installation of a zygomatic implant. Materials and Methods: A 58-year-old male patient, with dark skin, without systemic alterations, who during the installation of one of the ZIs suffered a greenstick fracture of the zygomatic body, thus, a second, deeper milling was performed in the zygomatic body. Results: After the uneventful surgical procedure, the molding was performed, and the patient complained of excessive tearing in his right eye with regression 24 hours after surgery. The implant-supported prosthesis was installed one week after surgery and the patient continues to rehabilitate without functional or aesthetic complaints.Conclusions:Therefore, since the literature on the subject of postoperative complications of ZIs is still scarce, we believe that since the milling and installation of IZs are always close to the foramen of the zygomaticofacial nerve, this type of complication may become more frequent due to the increase in indications for rehabilitation using ZIs.

When assessing potential mates is costly, there will be selection for copying others. Mate-choice copying, which is the increased chance of mating with another individual after observing them mating with someone else (i.e., individual-based copying), has been documented in several species, including humans. It is touted as an example of the influence of culture on animal and human mating, but alone it is unlikely to lead the cultural evolution of mating. Rather, it requires mate-choice copying with generalisation (i.e., trait-based copying), which is the effect of immediate observations on future mating. This sort of mate-choice copying has been documented in six species, including humans. Here, I extend an existing game-theory model of mate-choice copying to include generalization (Extension 1), a cost to immediate copying (Extension 2), and both previous extensions (Extension 3). The results show that Extensions 1 and 2 decrease the adaptive value of mate-choice copying when compared to the original model. Extension 3 suggests that adding generalisation to mate-choice copying with a cost to immediate copying is a more likely evolutionary trajectory than adding it to mate-choice copying without this cost These results have implications for illuminating the emergence of the cultural evolution of mating preferences.

Background/Objectives: The approval of direct acting antiviral (DAA) therapy for hepatitis C (HCV) resulted in a highly effective oral treatment for patients. The primary objective of this study was to compare patients who were not treated to patients who were treated. Identifying potential reasons for the failure to treat can provide a pathway to interventions using evidence-based data. Methods: The electronic medical records in an urban predominately African American (AA) population were searched for all patients with HCV seen at least once in a GI or ID clinic 2019. Data collected included demographics, treatment visits, laboratory, insurance, and ZIP codes for median income. Results: Of the 441 patients who were not yet treated at the first 2019 visit, only 43% were treated by July 2020. Insurance and median income were not factors in failure to treat. Patients with an average of 4 visits were more likely to be treated than those with 2 or less thus confirming that failure to follow up was a significant factor for patient treatment (42% vs 8% p

In this paper, we explore the theoretical foundation of Graph Neural Network (GNN) model and apply it to various traditional supply chain logistics problem. In particular, we study the Route Optimization Problem, Demand Forecasting Problem, Risk Assessment and Anomaly Detection, Supplier Section and Procurement Optimization, Inventory Optimization Problem, and Green Supply Chain Problem. The study helps us the expand the use the GNN and gives daily supply chain management a boost.

Thurstonian scaling, i.e., Thurstonian discriminal distance δ or d' can be used as a sensory measurement index to measure and monitor food sensory difference/similarity between test and control samples due to potential food contamination. It can be obtained from any one of the sensory discrimination methods. Thurstonian scaling is theoretically independent of methods or scales used for its estimation. This paper discusses statistical inference including estimations and tests of hypothesis for d'. Ten basic sensory discrimination methods including 6 forced-choice methods and 4 methods with response bias are used in this paper to estimate d' values and their variances. Statistical tests are conducted based on the estimated d' values and their variances. The statistical tests include difference testing and equivalence/similarity testing for individual d' values for test and control samples and for two or multiple d' values for test samples. Application and significance of Thurstonian scaling for sensory discrimination methods is discussed generally. R codes for estimations and tests for d' values are provided in the paper.

This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. For this purpose, an experimental database comprising 1592 samples of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated by an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of the mortar with good prediction accuracy and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF =0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, bulk density and the open porosity of the substrate are significant factors. Furthermore, the study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.

Grilling has become a widespread method of thermal food processing. However, food prepared this way may be a source of carcinogenic organic compounds, such as polycyclic aromatic hydrocarbons (PAHs). The present study aimed to evaluate the impact of different marinades and grilling tools on PAH contamination of chicken breast tenderloins. Together with the determination of PAHs carried out using the QuEChERS–HPLC–FLD/DAD method, the amount of meat weight loss after the thermal process and the color of raw and grilled samples were analyzed. Statistically, the highest levels of PAH contamination were stated in samples prepared on a charcoal grill without a tray, whereas the lowest using the ceramic contact grill. Meat marination showed that universal and chicken marinades can be barriers against PAHs. Following requirements set in Commission Regulation (EU) No. 915/2023, none of the analyzed samples exceeded the maximum allowable level for B[a]P (5.0 µg/kg), and the sum of 4 marker–heavy PAHs (30.0 µg/kg). Thus, preparing meat before the thermal process, including marinades rich in phenolic compounds, and selecting a grilling method with appropriate grilling tools can ensure food safety and effectively reduce PAH contamination in grilled poultry meat.

Previous studies in our lab show that that administration of oxytocin (OT) into the fourth ventricle (4V; hindbrain) elicits weight loss and stimulates interscapular brown adipose tissue temperature (T_IBAT) in male diet-induced obese (DIO) rats. What remains unclear is whether chronic hindbrain (4V) OT can impact body weight in female high fat diet-fed (HFD) rodents and whether this involves activation of brown adipose tissue (BAT). We hypothesized that OT-elicited stimulation of sympathetic nervous system (SNS) activation of interscapular brown adipose tissue (IBAT) contributes to its ability to activate BAT and reduce body weight in female high HFD-fed rats. To test this hypothesis, we determined the effect of disrupting SNS activation of IBAT on OT-elicited stimulation of T_IBAT and reduction of body weight in DIO rats. We first measured the impact of bilateral surgical SNS denervation to IBAT on the ability of acute 4V OT (0.5, 1, and 5 µg ≈ 0.5, 0.99, and 4.96 nmol) to stimulate T_IBAT in female HFD-fed rats. We found that the high dose of 4V OT (5 µg ≈ 4.96 nmol) stimulated T_IBAT similarly between sham rats and denervated rats (P=NS). We subsequently measured the effect of bilateral surgical denervation of IBAT on the effect of chronic 4V OT (16 nmol/day ≈ 16.1 mg/day) or vehicle infusion to reduce body weight, adiposity and energy intake in female HFD-fed rats (N=7-8/group). Chronic 4V OT reduced body weight gain (sham: -18.0±4.9 g; denervation: -15.9±3.7 g) and adiposity (sham: -13.9±3.7 g; denervation: -13.6±2.4 g) relative to vehicle treatment (P<0.05) and these effects were similar between groups (P=NS). These effects were attributed, in part, to reduced energy intake evident during weeks 2 (P<0.05) and 3 (P<0.05). To test whether these results translate to other female rodent species, we also examined the effect of chronic 4V infusion of OT on body weight and adiposity in two strains of female HFD-fed mice. Similar to what we found in the HFD-fed rat model, we also found that chronic 4V OT (16 nmol/day) infusion resulted in reduced body weight gain, adiposity and energy intake in female DIO C57BL/6J and DBA/2J mice (P<0.05 vs vehicle). Together, these findings suggest that 1) sympathetic innervation of IBAT is not necessary for OT-elicited increases in BAT thermogenesis and weight loss in female HFD-fed rats and 2) the effects of OT to elicit weight loss translate to other mouse models of diet-induced obesity (DIO).

Machine learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel sub-stantial portions of the data in each iteration, complicating classifier development without suffi-cient data. In critical fields like healthcare, there is a growing need to identify and analyze small yet significant subgroups within data. To address these challenges, we introduce a novel classifier based on the Patient Rule Induction Method (PRIM), a subgroup discovery algorithm. PRIM finds rules by peeling minimal data at each iteration, enabling the discovery of highly relevant regions. Unlike traditional classifiers, PRIM requires experts to select input spaces manually. Our inno-vation transforms PRIM into an interpretable classifier by starting with random input space se-lections for each class, then pruning rules using Metarules, and finally selecting definitive rules for the classifier. Tested against popular algorithms such as Random Forest, Logistic Regression, and XGBoost, our Random PRIM-based Classifier (R-PRIM-Cl) demonstrates comparable robustness, superior interpretability, and the ability to handle categorical and numeric variables. It discovers more rules in certain datasets, making it valuable especially in fields where understanding the model's decision-making process is as important as its predictive accuracy.

The advent of Web3 technologies promises a paradigm shift toward decentralized and autonomous economic interactions enabled by blockchain and smart contracts. However, the lack of robust trust and reputation mechanisms hinders its evolution into a fully functional economic system. This paper introduces the Dmany Nexus Protocol, a decentralized reputation system that quantifies user trustworthiness through verified on-chain and off-chain actions. By integrating principles from information economics, game theory, and mechanism design, the protocol addresses issues of information asymmetry, moral hazard, and adverse selection inherent in decentralized networks. Leveraging the Dmany Quest Engine for decentralized task management and employing zero-knowledge proofs for privacy preservation, Dmany Nexus establishes a foundation for trust and cooperation in the Web3 ecosystem. The protocol enhances economic efficiency, mitigates security risks like Sybil attacks, and fosters mass adoption by enabling secure, privacy-preserving interactions among pseudonymous actors.

The quality of education in Bangladeshi universities has long been a topic of concern, yet comprehensive studies on its statistical impact remain limited. This research aims to assess the current state of education quality in Bangladeshi higher education institutions using secondary data from national surveys, academic reports, and global rankings. A variety of statistical methods are employed to identify key factors influencing educational outcomes, including faculty-to-student ratios, infrastructure, research output, and employability of graduates. The findings reveal significant disparities in academic standards across public and private universities, highlighting challenges such as inadequate funding, lack of modern teaching practices, and low research productivity. This study provides data-driven insights and recommendations for policymakers and educators to enhance the quality of university education in Bangladesh, with the ultimate goal of aligning it with global standards.

This study examines the rationale and perceptions surrounding AI in military technologies, focusing on its application in autonomous weapons systems (AWS), intelligence, surveillance, and reconnaissance (ISR), and decision-support systems (DSS). AI models, such as unmanned aerial and ground platforms, showed high mission success rates, with 92% mission accomplishment, 95% true positive threat identification, and low false alarms. AI's effectiveness surpassed human operators, particularly in response time and threat detection. A global survey revealed mixed opinions: two-thirds believed AI enhances national security, yet 70% opposed autonomous weapons due to ethical concerns, particularly civilian casualties. The study highlights AI's advantages in improving decision-making speed and accuracy but acknowledges challenges such as ethical considerations and public trust in autonomous systems. Future research should address ethical AI models, international humanitarian law (IHL) standards in military operations, human-AI interaction in hazardous environments, and policies related to military robotics and accountability. Understanding and enhancing public awareness of AI in military contexts is also essential.

Background: The healthcare professional who performs sedation for digestive endoscopy procedures is usually the nurse. Therefore, knowledge and training on the part of the nurse is an important factor for the correct sedation of the patient and may affect, among other factors, the quality of health care and the recovery and well-being of the patient. Objective: To determine the training opportunities and resources available to the specialists involved indigestive endoscopy services in hospitals in Spain in which sedation is performed by the nursing staff.Methods: This was a descriptive cross-sectional study performed in the Digestive Endoscopy Service at Toledo University Hospital and nearby hospitals in central Spain. The sample consisted of 75 nurses who administer sedation in digestive endoscopic procedures. Results: Regarding the training of nurses, the vast majority were generalist nurses, and only a small percentage obtained specialized training through ongoing training. Most had been working in the service for more than two years, and very few had previous experience in sedation, although the vast majority currently applied sedation. Life support training was also critical. There were significant differences in the availability of resources between different hospitals. Conclusions: The training of nurses who perform sedation in digestive endoscopy services could be improved since there is a high proportion of personnel without specific training in sedation before starting to work in the service. It is crucial to implement targeted and ongoing training programs to improve competencies in this area, as they are essential to ensure the safety and effectiveness of the procedure. Given the variability in resources and personnel training that exists between different hospitals, it seems important to establish standards at the institutional level.

Rural areas are not only faced with the problems of rural depopulation but also with the problem of water supply and water quality which is a basic necessity of life. In light of this, this paper assesses the rural water supply situation and the bacteriological quality of different water sources in the Obokun Local Government Area of Osun State, Nigeria. The safety of the water sources for human consumption and possible occurrence of the water borne diseases in the study area were determined. The study employed descriptive and analytical methods of data analysis to achieve the objectives of the inquiry. Findings showed that the study area suffered from inadequate water supply and poor locations of the sources of water: The study also revealed that the microbiological quality of the water sources used was unsafe, poor and not acceptable for human consumption. In addition, many man- days-hours were wasted in search of water. It is therefore recommended that the government should make the rural water supply and the maintenance of water quality a priority in its policy formulation.

The consistent recognition of both explicit short term and implicit long term elements of post-retirement contracts between employees and the firm can have a significant implication for estimating a firms long term sustainable cost of capital. In this paper, we show that the significant levels of investment in company own stock by pension and post-retirement plans can effectively dilute the interest of pre-existing shareholders in the enterprise value of corporations in ways which are not revealed by a classic entity theory perspective on equity. We test the empirical implications of adopting a pre-existing shareholder model of the reporting entity to re-examine the relationship between a firm’s sustainable cost of capital and various sources of idiosyncratic risk. We find that the estimated cost of capital is sensitive to: (a) alternative explicit versus implicit definitions of pension liability; (b) the nature and scope of long-term deferred compensation arrangements; and (c) the scope and nature of investment-related risks through investment in sponsoring company stock that are associated with these pension arrangements.

A sublingual vaccine comprising Poly(I:C) adjuvant and influenza HA antigen was evaluated for safety in both mice and macaque monkeys relative to its intranasal counterpart. Safety was assessed with harmful effects corresponding to upregulation of the inflammation-associated genes: Saa3, Tnf, IL6, IL1b, Ccl2, Timp1, C2, Ifi4, Aif1, Omp, Nos2, and/or Gzmb in mice and SAA2, TNF, IL6, IL1B, CCL2, TIMP, C2, AIF1, and GZMB in macaques. Quantitative gene expression analyses were performed using RT-qPCR with RNA samples from four tissues: olfactory bulb, pons, lung, tongue, and (submandibular) lymph node from both mice and macaques. In mice, the intranasally delivered vaccine markedly upregulated the inflammation-related genes in the olfactory bulb 1 day and 7 days after vaccination. The adverse effects of intranasal vaccination were also observed in macaques, however to a lesser extent than in mice. The intranasal vaccination also upregulated these genes in the pons of both mice and macaques. In contrast, the sublingual vaccine did not adversely affect the olfactory bulb or pons in either mice or macaques. The intranasally administered vaccine significantly upregulated these genes in the lungs only 1 day after vaccination, but not 7 days later, in both mice and macaques. We conclude that intranasal vaccination results in unfavorable side effects corresponding to upregulated inflammatory genes in the brain (olfactory bulb and pons). Sublingual vaccination, however, did not induce these side effects in either mice or macaque and was hence evaluated as safe.

Situational awareness detection and characterization of mental states have a vital role in medicine and many other fields. An electroencephalogram (EEG) is one of the most effective tools for identifying and analyzing cognitive stress. Yet, the measurement, interpretation, and classification of EEG sensors is a challenging task. This study introduces a novel machine learning-based approach to assist in evaluating situational awareness detection using EEG signals and spiking neural networks (SNNs) based on a unique spike-continuous-time-neuron (SCTN). The implemented biologically inspired SNN architecture is used for effective EEG feature extraction by applying time-frequency analysis techniques and allows adept detection and analysis of the various frequency components embedded in the different EEG sub-bands. The EEG signal undergoes encoding into spikes and is then fed into an SNN model which is well-suited to the serial sequence order of the EEG data. We utilize the SCTN-based resonator for EEG feature extraction in the frequency domain which demonstrates a high correlation with the classical FFT features. A new SCTN-based 2-D neural network is introduced for efficient EEG feature mapping aiming to achieve a spatial representation of each EEG sub-band. To validate and evaluate the performance of the proposed approach a common publicly available EEG dataset is used. Experimental results show that by using the extracted EEG frequencies features and the SCTN-based SNN classifier the mental state can be accurately classified with an average accuracy of 96.8% for the common EEG dataset. Our proposed method outperforms existing machine learning-based methods and demonstrates the advantages of using SNN for situational awareness detection and mental state classifications.

During recent archeological excavations in the Roman city of Carnuntum three spots with residues of streets, houses and side buildings from the time span between 1st and 4th century have been investigated. 40 soil samples taken from different layers gave the possibility to investigate the local soil bacterial communities by Next Generation Sequencing (NGS) of 16S rRNA. The background of this study was to check similarities and differences in the composition of microbial communities and to check of special types of proved bacteria or groups of them could be related to the archaeological findings. In result, the 16S rRNA data shows similarities between some samples, but also important differences in the compositions of soil bacterial communities of single samples. On the one hand, some samples are distinguished by the dominance of single major components from other samples. On the other hand, specific differences between soil samples are reflected by the patterns of mediate and less abundant bacterial groups. This supports the hypothesis, that differences in soil bacteria composition are strongly influenced by differences of the deposed soil material and, therefore, dependent on ancient human activities which created the deposits and local ancient environmental conditions many hundred years ago. This “echo” of ancient human impact on local soil bacteria composition is reflected, for example, by manure- and oil-related OTUs originating from two different street surfaces as well as by contaminated and mixed soil fills and ancient hearths and fire places. The findings support the concept of “ecological memory of soil” meaning that early impacts impress traces in the composition of soil bacterial communities which are detectable up to the present.

Digital transformation, powered by technologies like AI, IoT, and big data, is reshaping industries and societies at an unprecedented pace. While these innovations promise smarter energy management, precision agriculture, and efficient resource utilization, they also introduce serious environmental challenges. This paper examines the dual impact of digital technologies, highlighting key threats such as rising energy consumption, growing e-waste, and increased extraction of raw materials. For instance, global e-waste reached 62 million metric tons in 2022, and data centers alone accounted for nearly 1% of the world's electricity demand in 2019. The review synthesizes findings from studies on topics like the energy use of blockchain technologies and the environmental costs of raw material extraction in the smartphone industry. Moreover, it identifies critical research gaps, particularly in understanding the environmental impact of digital usage at individual and household levels. Practical strategies such as integrating circular economy principles, promoting renewable energy, and green computing are proposed to balance technological advancement with sustainability goals. This study highlights the need for a holistic approach, suggesting future research directions to minimize digital transformation’s environmental footprint while maximizing its sustainability benefits.

Recent advancements highlight the utilization of vegetable oils as additives in polymeric materials, particularly for replacing conventional plasticizers. Buriti oil (BO), extracted from the Amazon's Mauritia flexuosa palm tree fruit, boasts an impressive profile of vitamins, minerals, proteins, carotenoids, and tocopherol. This study investigates the impact of incorporating buriti oil as a plasticizer in linear low-density polyethylene (LLDPE) matrices. The aim of this research was to evaluate how buriti oil, a bioactive compound, influences the thermal and rheological properties of LLDPE. Compositions of buriti oil\LLDPE were prepared via melt intercalation techniques, and the resulting materials were characterized through Thermogravimetric Analysis (TGA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), mechanical property testing, and contact angle measurement. The addition of buriti oil was found to act as a processing aid and plasticizer, enhancing the fluidity of LLDPE polymer chains. TGA revealed distinct thermal stabilities for buriti oil/LLDPE under different degradation conditions Notably, buriti oil exhibited an initial weight loss temperature at 402°C, whereas LLDPE at 466.4°C. This indicated a minor reduction in the thermal stability of buriti oil/LLDPE compositions. The thermal stability, as observed through DSC displayed a nuanced response to the oil's incorporation, suggesting a complex interaction between the oil and polymer matrix. Detailed mechanical testing indicated a marked increase in tensile strength and elongation at break, especially at optimal concentrations of buriti oil. SEM analysis showcased a more uniform and less brittle microstructure, correlating with the enhanced mechanical properties. Contact angle measurements revealed a notable shift in surface hydrophobicity, indicating a change in the surface chemistry. This study demonstrates that buriti oil can positively influence the processability and thermal properties of LLDPE contributing to expand its potential applications as an effective plasticizer.

Azolla is a substitute compost that has the potential to enhance nutrient cycling in agricultural systems for sustainable development. In this study, four experiments were conducted to compare the Department of Agriculture (DOA, Thailand)'s methodology for determining the suitable type and rate of animal manure and the optimal light intensity for the growth and yield of Azolla (Azolla microphylla). The results revealed that applying 100% pig manure gave the highest yield of Azolla compared to the other manure. However, there was no discernible difference in yield across the various doses (20.16, 30.16, and 40.16 gN m-2) of pig manure treatments, for which the minimal pig manure dosage of 20.16 gN m-2 was chosen. For further experimentation in the optimal light in-tensity, the 40% shading gave the highest yield of Azolla compared to no shading or 20 and 60% shading. When compared with the DOA Thailand methodology (1.27 kg m-2 of cow manure and covered with a size 32 mesh net), the findings indicated that the modified method (20.16 gN m-2 of pig manure + 40% shading) gave a 16% greater Azolla yield than that under the DOA Thailand methodology.

Federated unlearning represents a sophisticated evolution in the domain of machine learning, particularly within federated learning frameworks. In financial applications, where data privacy and security are paramount, federated unlearning allows institutions to selectively remove or “unlearn” specific data from trained models without compromising their overall performance and accuracy. This capability is essential for ensuring that models remain adaptable, secure, and compliant with regulatory requirements, while minimizing the need for expensive retraining. In this paper, we explore various financial applications where federated unlearning can have a significant impact, including fraud detection, portfolio management, and credit risk modeling. By allowing targeted removal of outdated, erroneous, or sensitive data, federated unlearning enhances the agility of financial models, enabling institutions to keep pace with the dynamic financial landscape. Using practical numerical examples, we demonstrate how unlearning improves model accuracy and decision-making while maintaining data privacy across distributed systems. This paper underscores the critical role of federated unlearning in addressing the challenges of modern financial institutions, offering insights into its practical applications and future potential.

A novel approach for designing acceptance sampling plans is presented. It is based on the Bayesian conformity assessment framework from JCGM 106. In particular, the concept of conformance probability plays a central role in the new approach. The new approach takes into account both the producer's and the consumer's perspective by allowing each to have their own prior. In parallel, the JCGM 106 framework allows the definition of Bayesian consumer's and producer's risks which complement the "classical" risks defined in the ISO 2859 and ISO 3951 series. The new approach also casts a new light on the plans from the ISO standards.

Objectives: The aim of this study was to determinate the validity and the reliability of the self-administered Timed Up and Go (TUG) test, a gold standard test for fall risk screening, when compared to the traditional face-to-face assessment carried out by a physiotherapist. Methods: A total of 37 community-dwelling adults, mean age 61.78±6.88, 73% female, who took part in fall risk screening actions in the central region of Portugal, were assessed. The protocol included sociodemographic and history of falls questions, the Self-Efficacy for Exercise questionnaire, the Activities and Participation Profile Related to Mobility (PAPM) and three functional tests, namely the 10-Meter Walking Speed (10-MWS), TUG and 30 Seconds Sit to Stand (30s STS) tests. Within an interval of 18-24 hours after the face-to-face moment, the participants were instructed to self-administer the TUG test at home. The validity and reliability of self-administered TUG test were examined using the limits of agreement, clinically acceptable limit, intra-class correlation coefficients (ICC), paired t-tests and Pearson´s coefficient correlation (r). Results: The limits of agreement for self-administered assessment were within the clinically acceptable limits. The average result of the face-to-face TUG test and self-administered TUG test was 7.47 ± 2.45 and 7.57 ± 3.10 seconds, respectively. When comparing the two evaluations, they were strongly associated (r=0.716, p<0.001), with an excellent ICC of 0.82 (0.65-0.91), for a 95% confidence interval. Conclusion: The use of the self-administered TUG test for the screening of risk of fall, using low-cost technology, appears to be valid and reliable in community-dwelling adults aged 50 and over.

The paper presents two novel mathematical approaches to prove Goldbach's conjecture. The conjecture states that every even number greater than or equal to 6 can be expressed as the sum of two odd prime numbers. We propose that the conjecture has only four genetic codes, and as long as one of them remains valid, the conjecture continues to hold. Through these new mathematical ideas, we aim to prove the validity of Goldbach's conjecture comprehensively.

In a time of ecological and social crises, designing new institutions is crucial for meeting future challenges. Institutions emerge from the imaginations of individuals, moving into social movements and crystallizing in legal structures. As present movements seek to develop tomorrow’s governances, they turn to decentralized structures: distributed networks with little hierarchy, characterized by a diversity of actors and dynamics. While movements and technologies may seek to utilize decentralized ideas, they lack design principles: only recently has it been possible to decentralize at the national stage. Designs may come from biology and ecological networks; fungi, plants, and slime molds in soil to the neurons, glia, and blood vessels within our own bodies. This article blends these biological analogies with present technologies like blockchain and Web3 to posit future decentralized institutions, movements and legalities that give greater consideration to the more-than-human world.

Obesity is a significant metabolic disorder associated with excessive fat accumu-lation and insulin resistance. In this study, we explored a gene therapy approach to treat obesity in agouti mice using adeno-associated viruses (AAVs) carrying PRDM16, FoxP4, or Follistatin (FST) genes, which are known to promote the browning of white adipose tissue. Mice treated with AAVs encoding PRDM16, FoxP4, or FST genes showed a reduction in body weight (9-12%) within the first three weeks after admin-istration, compared to control groups. Lipidomic analysis of adipose tissue revealed a dramatic reduction in triacylglycerol (TAG) species with low carbon number (40-54 acyl carbones) in treated mice.

The California Genetic Disease Screening Program (GDSP) employs a fixed immunoreactive trypsinogen (IRT) cutoff followed by molecular testing to screen newborns for Cystic Fibrosis (CF). The cutoffs approximate a 1.6% yearly IRT screen positive rate, however seasonal variation in IRT population means led us to develop a model to establish fixed IRT cutoffs that anticipate seasonal variation and minimize missed cases below cutoff. We utilized an ARIMA model to fit monthly IRT screen-positive percentiles and estimated regular seasonal expectations. We established a retrospective cohort followed for at least 1.5 years to capture missed false negative CF cases. We compared missed CF cases identified by seasonal cutoffs vs floating cutoffs. GDSP screened 7,410,016 newborns, from July 2007 to Dec. 2022, and missed 36 CF cases below the fixed cutoff; five of the 36 were within 3 ng/mL below the cutoff. There was a regular, seasonal cycle that varied from 1.4% in summer to 1.8% in winter. We would have missed 59 CF cases using a 1.6% daily floating cutoff. California would need to use a 4% daily floating cutoff to improve our current detection rate which would double the number of specimens sent for costly molecular analysis.

Non-compliance with iron supplementation consumption contributes to the high rate of anemia among adolescent girls in Indonesia. This study explores the barriers and enablers perceived by adolescent girls regarding regular iron supplement consumption. Using a qualitative design, eight focus group discussions (FGD) were conducted in Makassar and Padang involving 32 girls grade 8 and 9 from four junior high schools. Data on perceived barriers and enablers were collected using a social cognitive theory framework and analyzed thematically. The average age of the informants was 13,65±0,60 years. Although all participants received supplement at school, most did not consume them regularly. Key barriers include dislike of the taste and smell, parental prohibition negative experience consuming iron supplement, and the belief that iron supplements increase menstrual blood flow and volume. Enablers include self-awareness of the supplement’s benefits, trust in school-provided supplements, and positive support from parents and peers. Behavior change strategies are needed to address these barriers and strengthen supporting factors to improve iron supplementation consumption.

Understanding visual engagement in marketing is crucial for optimizing user experience and enhancing campaign effectiveness. This study introduces the Visual Interaction Analysis (VIA) methodology, utilizing advanced eye-tracking technology within immersive virtual reality (VR) environments to examine generational and gender differences in visual engagement after exposure to advertising materials. Using the Cognitive 3D platform for precise data capture, this research analyzed fixation patterns—total fixations, duration of fixations, and average duration per fixation—across Millennial and Generation Z cohorts. The analysis involved 44 participants, who were exposed to three posters in controlled VR settings. Results revealed significant generational differences: Generation Z demonstrated more focused attention, with higher fixation counts and longer total fixation durations compared to Millennials, suggesting that differences in media consumption and technological adaptation influence engagement strategies. However, both generations showed a similar depth of engagement, as indicated by the average duration per fixation. Gender analysis within Generation Z further revealed that female participants engaged for longer periods per fixation, indicating deeper processing of visual information. This methodology offers a robust framework for quantifying visual engagement, providing actionable insights that can help marketers tailor strategies and optimize campaign designs before launch.

Aims: The aim of our work was to demonstrate the possibility, in carefully selected cases, to perform operations under local anesthesia with a benefit to the patient and savings for national health care. Method: 123 patients with breast cancer underwent quadrantectomy or wide tumorectomy and sentinel lymph node removal under local anesthesia in out-patient surgery. Results: Of the 123 patients, 9 had micro-metastases and 4 had macro-metastases and only three of this had metastasis in other lymph nodes. Conclusions: Our results demonstrate that it is possible in selected cases to operate on breast cancer in out-patient surgery, with an advantage for the patient and savings for national health care.

Central composite design (CCD) is a methodology that utilizes experimental conditions based on the combination of variable points to be studied. This study aimed to select a hydrogel from the combinations provided by CCD for 3D bioprinting applications. Three biopolymers were chosen: sodium alginate (SA), gelatin (GEL), and carboxymethylcellulose (CMC), and their maximum and minimum concentrations were defined. After CCD provided the suggested combinations, the hydrogels were mixed and analyzed for their swelling degree (SD) in phosphate buffer saline (PBS) and Dulbecco's modified Eagle medium (DMEM), and printability was assessed in 2D and 3D assays. The hydrogel with the concentrations of 7.5%, 7.5%, and 2.5% for SA, GEL, and CMC, respectively, was chosen for presenting the highest SD in both fluids and good printability parameters in manual and 3D printer-based extrusion. This study demonstrated the potential utilization of Design of Experiments (DoE) methodologies to assist the development of hydrogels for 3D bioprinting purposes.

Outbreaks of avian pathogens such as Newcastle disease virus, avian influenza virus and Salmonella have a major impact on economies and food security worldwide. Some pathogens also pose a significant zoonotic potential, especially avian influenza viruses. Vaccination plays a key role in controlling many poultry diseases and there are many vaccines licenced in the United Kingdom for diseases of poultry caused by viruses, bacteria, and parasites. However, these vaccines often do not provide complete protection and can cause unwanted side effects. Several factors affect the potency of poultry vaccines including the type of vaccination used, the mechanism of delivery and the use of adjuvants. Advancements in technology have led to the study and development of novel vaccines and vaccine adjuvants for use in poultry. These induce stronger immune responses compared to current vaccine technology and have the potential to protect against multiple poultry diseases. This review aims to discuss existing poultry vaccine technology, the effect of delivery mechanisms of vaccine efficacy, the use of current and novel adjuvants, the ability to target antigen to antigen presenting cells, the use of probiotics, multivalent vaccines, and nanotechnology to enhance the potency of poultry vaccines.

Telomerase and telomeres are crucial in cancer cell immortalization, making them key targets for anticancer therapies. Currently, 6-thio-dG combined with the anti-PD1 inhibitor Cemiplimab is under Phase 2 clinical investigation (NCT05208944) in NSCLC patients resistant to prior immunotherapies. This study presents the design, synthesis, and evaluation of novel bimodular conjugate molecules combining telomere-targeting nucleoside analogs and phosphatidyl diglyceride groups. Among them, dihexanoyl-phosphatidyl-THIO (diC6-THIO) showed high anticancer activity with sub-µM EC50 values in vitro across various cancer cell lines. In mouse colorectal cancer models, diC6-THIO demonstrated strong anticancer effects alone and in combination with PD1/PD-L1 inhibitors. Administration of this compound resulted in the efficient formation of Telomere dysfunction Induced Foci (TIFs) in vitro, indicating an on-target, telomerase-mediated telomere-modifying mechanism of action for the molecule. Systemic treatment also activated CD4+ and CD8+ T cells while reducing regulatory T cells, indicating immune system enhancement. These findings highlight diC6-THIO as a promising telomere-targeting prodrug with dual effects on telomere modification and immune activation.

Cellulose-based materials are now commonly used, including in the field of acoustic comfort. Often presented as a less environmentally impactful alternative to traditional acoustic absorbents (such as melamine, glass wool, etc.), these cellulose-based materials are more frequently derived from recycling, thus undergoing in most cases the technical process allowing these cellulose fibers to be obtained, thus inheriting the acoustic properties of the latter, with limited or even non-existent control. This paper proposes a manufacturing process that allows for the production of cellulose foam with precise control over its porosity, pore size, and interconnections. In addition to exhibiting remarkable sound absorption properties, this process also enables the fabrication of gradient porous structures and other hybrid materials that can result in remarkable sound absorption properties.

Statement of problem: The stereo-optical method can be used for assessment of strains and deformations of different fixed or removable prosthodontic constructions. Material and methods: The study used two master casts of a lower jaw with bilateral terminal edentulous seats. Their topography was identical, with missing lower molars, the left lower second premolar, and the right canine and incisors, indicating a partially edentulous lower jaw according to Eichner's B3 (and C2) classification (i.e. Kennedy class 1). The control cast had conventional oval copings for overdenture. The experimental cast was almost the same as the control, with the exception of the surfaces simulating three specific milled copings covering the remaining tooth substance. Controlled loading was measured using a gnathodynamometer. Loading stages were at 400, 800, and 1000 N. Measurements of strain and displacement were provided by the Digital Image Correlation Method and the associated software. ‘Wilcoxon Lambda’ two sample tests based on the simple linear rank statistics were used in the analysis of differences in the displacement and deformation between the control rounded and experimental milled copings under loading numerical values. Results: It was found that smaller values of deformations are measured on milled copings when compared to the controls. There were statistically significant differences in displacement between the control rounded and experimental milled copings (Pr > |Z| = 0.001) as well as in deformations (Pr > |Z| = 0.0298). Conclusions: Experimental milled copings are less susceptible to deformation compared to the control conventional oval copings. These findings may have clinical significance in improvements of primary and secondary retention of overdentures on milled copings.

The properties of carbon powder filled nitrile rubber (NBR) were investigated. The effect of carbon powder content on the mechanical strength, vulcanization properties, mechanical properties, electrical conductivity, and thermal conductivity of NBR was explored. The purpose of the research is to pursue the optimal filling ratio and conditions of carbon powder to optimize the performance of rubber products.The reasonable filling range of carbon powder has been compared with N660 carbon black filled NBR and determined to evaluate the advantages and disadvantages of carbon powder. The suitable application scenarios of carbon powder is clarified .The research results indicate that carbon powder can delay the sulfurization of NBR in the sulfurization system. The research results indicate that carbon powder can delay the sulfurization of NBR. In terms of filling system, the tensile strength, tear strength, and elongation at break of NBR gradually enhance as the amount of carbon powder increases. Carbon powder can replace carbon black in the application of NBR to improve the comprehensive performance of NBR, which provides a new kind of reinforcing material for NBR.

This paper reexamines climate justice through the framework of environmental sociology, offering fresh perspectives on the intersection of social and ecological systems in the face of escalating global climate crises. It emphasizes that inequality lies at the heart of global climate politics, often obstructing pathways toward achieving true climate justice. Drawing from established traditions within environmental sociology—such as the new ecological paradigm, the post-growth society, and the environmental justice paradigm—the paper advocates for profound systemic and structural reforms in political and economic systems to tackle entrenched inequalities. By integrating these frameworks, the paper proposes a comprehensive model of climate justice, encompassing material, procedural, compensatory, and transformative justice. This holistic approach not only addresses environmental sustainability but also prioritizes social equity, ensuring that marginalized communities are included in the global response to climate change. The paper thus positions climate justice as a critical component of broader environmental and social transformation.

This study outlines the development and optimization of a Long Short-Term Memory (LSTM) network designed to analyze and classify time-series data from tribological experiments, with a particular emphasis on identifying stationary phases. The process of fine-tuning key hyperparameters was systematically optimized through Bayesian optimization, coupled with K-fold cross-validation, to minimize the inherent randomness associated with training neural networks. The refined LSTM network achieved a weighted average accuracy of 84%, demonstrating a high level of agreement between the network's identified stationary phases and those manually determined by researchers. This result suggests that LSTM networks can reliably mimic manual identification processes in tribological data, providing a promising avenue for automating data analysis. The study underscores the potential of neural networks to transcend their traditional role in predictive modeling within tribology, opening up new possibilities for their application across a broader spectrum of tasks within the field.

Parametric decay instabilities frequently occur in wave interactions. These instabilities typically have thresholds for wave amplitudes. They arise when nonlinearity, such as a spatial pressure inhomogenety, couples different waves. For these instabilities to arise, the waves must satisfy frequency and wavenumber matching conditions, which follow from the laws of energy and momentum conservation. However, parametric instabilities are not limited to wave interactions; the simples form of parametric instability is parametrically coupled oscillators. Magnetized plasma represents a unique wave medium in which numerous waves of various polarizations and spatial-temporal scales are easily excited and weakly damped. The greater the number of wave types, the easier it is to satisfy resonance conditions like the frequency and wavenumber matching conditions, and thus parametrically excite, or "drive" new waves from the initial wave. Therefore, in magnetoactive plasma, all known types of parametric instabilities can arise.

Magnetic nanoparticles coated with gold nanoparticles are hybrid nanostructures combining the magnetic properties of iron oxide with the optical and chemical versatility of gold. The iron oxide core gives the nanoparticles magnetic functionality, which is useful in magnetic resonance imaging (MRI), targeted drug delivery, and hyperthermia treatment. The gold coating enhances biocompatibility, provides resistance to oxidation, and allows for surface functionalisation with biomolecules for targeted applications. This combination makes these nanoparticles ideal for a range of biomedical applications, including diagnostics, therapeutics, and imaging, due to their magnetic, optical, and chemical properties. Transmission electron microscopy, dynamic light scattering, zeta potential and magnetisation of nanoparticles are key techniques that provide a comprehensive understanding of the nanoparticles’ size, structure, optical properties and magnetic behaviour, which are important factors for determining their potential use in various applications.

Abstract: In the present study, we investigated to what extent the soybean has been used in in-dustrial and homemade sausage produced in Kosovo, as well as its potential impact on the fatty acid profile. In total, 63 samples, 42 industrial and 21 traditional sausages, were collected either from the market or the production site. All samples were tested by means of real-time PCR for the detection and quantification of soy content, as well as the GC-FID to determine the potential re-flection in the fatty acid profile. The presence of soy DNA was detected in 54 out of 63 samples. 41 out of 42 industrial sausage samples were positive for soy DNA, with an average ct value of 22.60 and a standard deviation (SD) of 4.28, whereas 13 out of 21 traditional sausage samples were positive for soy DNA, with a mean ct value of 23.36 and a standard deviation (SD) of 4.56. There was a statistically significant difference in means between industrial sausage and traditional sausage, p≤0.001 based on CT values (ANOVA test). We investigated the correlation between ct values in real-time PCR for soy DNA with each fatty acid content. There is a moderate correlation of soy DNA ct values with C16:0 palmitin (decrease), C18:0 stearic acid (decrease), C18:1 oleic acid (in-crease), and overall saturated fatty acids (decrease). With the exception of C14:0 Myristin, C18:1 Oleic, and C20:0 Arachin acids and monosaturated fats, the ANOVA test reveals a significant dif-ference in means between groups for the majority of the fatty acids between industrial sausages and traditional sausages. The current study demonstrates that the fatty acid composition of sau-sages is influenced by the amount of soy present in them. The extent to which other components affect the fatty acid profile is unknown. However, an increase in oleic acid and a decrease in stearic and overall saturated fatty acids are expected.

This study explores the use of Generative Artificial Intelligence (GAI) in assessing pilot fatigue risk by integrating facial recognition and physiological signals with Inertial Measurement Units (IMUs). By leveraging IMU technology's precise, real-time data on movement and combining it with GAI's advanced data analysis capabilities, the study aims to enhance the accuracy of fatigue prediction models. The analysis reveals that while traditional classifiers like Extreme Random Trees and Random Forests offer modest performance, advanced models such as Support Vector Machines and Naive Bayes demonstrate superior recall rates, highlighting their potential to identify true positives. This integration of AI and IMUs offers a promising approach to developing comprehensive, real-time fatigue monitoring systems, improving safety and efficiency in aviation by providing actionable insights and facilitating more effective fatigue management.

As is known, the Maxwell stress-energy tensor typically used in electromagnetism is not a canonical tensor in the sense of Noether, since its four-divergence is not zero in the presence of sources, in other words, outside of free space. Indeed, the result provided by the calculation of the four-divergence of this tensor is the opposite of the four-vector generalizing the Lorentz force density. The idea is to associate with it a tensor constructed from potentials and sources, such that its four-divergence is the opposite of that of the Maxwell tensor. The distribution of sources will also be analyzed in light of fluid mechanics, allowing us to account for its influence in terms of generated pressure.

Terpenoids are crucial secondary metabolites found in plants, essential for numerous physiological functions such as defense strategies, signal communication, and adaptation to environmental stresses. The enzymes referred to as Terpene Synthases (TPS) play crucial roles in the production of these compounds, fulfilling an essential role throughout the entire biosynthetic pathway.Despite their importance, systematic studies on the TPS genome in Physcomitrium patens (P. patens) remain inadequate. This research offers an extensive bioinformatics evaluation of the TPS genes in P. patens, aiming to clarify their possible functions in plant development, growth, and responses to stress. Preliminary examinations were conducted on the physicochemical characteristics, secondary protein structures, subcellular distribution, and genetic configurations of the TPS genes in P. patens. Initial analyses were performed on the physicochemical properties, protein secondary structures, subcellular localization, and gene structures of the TPS genes in P. patens. The findings demonstrate that TPS family members are dispersed across six chromosomes, with ten TPS family members identified. Analysis of conserved motifs revealed that the PpTPS members exhibit high sequence conservation, suggesting that these enzymes maintain stable structural characteristics during terpene synthesis. Further examination of evolutionary pressures indicated that TPS family members are predominantly subjected to purifying selection, which helps preserve the structural stability of TPS enzymes. Furthermore, an analysis of cis-acting elements indicated that TPS genes might respond to various factors, including light, plant hormones, tissue development, and environmental stressors. This suggests that TPS genes could be vital in managing how plants adapt to changes in their environment. Tissue expression profiling revealed that TPS family members have significant functions across various developmental stages and tissues. This is demonstrated by qRT-PCR analyses, indicating that the expression of the PpTPS gene is influenced by abscisic acid (ABA), methyl jasmonate (MeJA), sodium chloride (NaCl), and polyethylene glycol (PEG). The findings suggest that the PpTPS genes could play an essential role in the functional development of plant leaves and in their responses to abiotic stress.In summary, this research presents a comprehensive examination of the TPS family members in P. patens, yielding important perspectives on the functions of the PpTPS7 and PpTPS8 genes in the development of leaves and responses to abiotic stress.The findings contribute to a deeper understanding of the functions and regulatory mechanisms of TPS genes and present new perspectives and strategies for the enhancement and application of P. patens.

The dawn of quasispecies theory revolutionized our understanding of viral evolution and pathogenesis. This theory conceptualises viruses as dynamic populations of closely related but genetically diverse variants that constantly mutate and adapt to environmental pressures. Quasispecies dynamics govern key aspects of virus-host interactions, such as adaptive evolution, immune evasion, drug resistance, and host tropism. In this article, we discuss the fundamental role of quasispecies theory in elucidating viral fitness landscapes, shaping antiviral strategies, and predicting viral emergence and evolution. We provide a concise overview of the original quasispecies model and its latest advancements, which enable the study of the connection between viral dynamics and the significant genetic diversity exhibited by viruses. We then point out some key features of virus dynamics that need to be incorporated into quasispecies theory. We continue with examples of convergence between theory and real viruses by discussing theoretical results supported by RNA virus data and vice versa. Next, we discuss the need to extend the concept of sequence space beyond the classical hypercube towards more complex, multidimensional connected sequence spaces that we have called ultracubes. Finally, we highlight the necessity of developing multi-scale models to understand how viral evolutionary dynamics within a host can affect epidemiological patterns. We also examine the limitations of quasispecies theory in predicting virus evolution and emergence.

The increasing demands on global resources due to technological development driven by consumer expectations and demands have resulted in significant problems with ecological sustainability and material availability. The creation of biocomposites has resulted in notable advancements in the green industry within the materials science area this century, owing to concerns regarding sustainability and the environment. Globally, there is a surge in the creation of highly efficient materials derived from natural resources. In aviation applications, plant-fiber-supported polymer composite materials are becoming increasingly popular. Aerospace materials are typically used in aircraft construction as structural materials to support loads throughout different flight phases. There are many diverse mechanical qualities of natural fibres; therefore, selecting one for the interior parts of an aircraft cabin based only on its attributes leads to a multiple-attribute decision-support issue. In this paper, the effective natural fibre and polymer choice for use as reinforcing materials in composite materials is represented for the composite materials’ improvement to aircraft cabin luggage for aerospace implementations. This study can guide material designers in investigating different hybrid materials with the most effective natural fiber and polymer obtained by hierarchical strategy by elucidating the effective material choice to meet the criteria determined for the aircraft cabin luggage. For this purpose, the definitive rankings of the twelve polymers and sixteen natural fibers in terms of performance score were assessed using hierarchical strategy methodology.

This study investigates the impact of stakeholder participation on the efficiency of irrigation projects by the implementation of Farmers Water User Committees (FWUCs). Utilising a difference-in-difference methodology, the research analyses data from 79 irrigation projects over a 13-year period. The findings reveal a statistically significant positive correlation between FWUC establishment and improved project effectiveness, even when controlling for variables such as project size, age, and socio-economic factors. Smaller, newer projects exhibited higher effectiveness, whilst remote locations faced greater challenges. The temporal analysis indicates that the benefits of participation accrue gradually, underscoring the importance of sustained implementation. These results carry significant implications for public enterprise reform, particularly in irrigation management. The study contributes to the growing body of evidence supporting the efficacy of stakeholder participation in public enterprise management, offering valuable insights for policymakers seeking to enhance the efficiency and sustainability of irrigation systems.

In the fields of partnership research and social innovation, there have been an increasing number of calls for evidence regarding the kind of collaborative multi-level governance structures that might support large scale transformation processes, specially towards climate-neutral cities. This paper conceptualizes a new type of intermediary actor; the Systemic Collaborative Platform (SCP). Using the example of a multi-stakeholder arrangement set up to design and implement Madrid’s Climate Neutrality Roadmap, the key features of an SCP include a focus on multi-stakeholder and cross-sectoral collaboration for public policy development, in this case operating at both a systemic level and a city scale, and a strong link to a key actor within this regime (a city council). Our study reveals how a SCP can contribute to articulation of a shared mandate among city actors and enhance the efficiency and effectiveness of a targeted transition by attracting, aligning and managing a wide and diverse organizational ecosystem through distributed facilitation, collaborative leadership and continuous learning.

Novel photocatalysts were synthesized through the association of carbon quantum dots (CQDs) with commercial (P25) titanium dioxide (TiO2) by sonication. The resulting TiO2/CQDs compo-site was then incorporated into the polyamide 66 (PA66) biopolymer nanofibers using the elec-trospinning technique, considering a composite nanoparticles-to-polymer ratio of 1:2 in the elec-trospinning precursor solution. The produced nanofibers presented suitable morphology and were tested for the photocatalytic degradation under simulated solar radiation of 10 mg/L of amoxicillin (AMX) and sulfadiazine (SDZ), in phosphate buffer solution (pH 8.06) and river wa-ter, using 1.5 g/L of photocatalyst. The presence of the photocatalyst increased the removal of AMX in phosphate buffer solution by 30 times, reducing the AMX degradation half-life time from 62 ± 1 h (without catalyst) to 1.98 ± 0.06 h. Moreover, SDZ degradation half-life time in phosphate buffer solution was reduced from 5.4 ± 0.1 h (without catalyst) to 1.87 ± 0.05 h in the presence of the photocatalyst. Furthermore, the PA66/TiO2/CQDs were also efficient in river wa-ter samples, and maintained their performance in at least three cycles of SDZ photodegradation in river water. The presented results evidence that the produced photocatalyst can be a promis-ing and sustainable solution for antibiotics’ efficient removal from water.

The increasing adoption of distributed recycling via additive manufacturing (DRAM) has facilitated the revalorization of materials derived from waste streams for additive manufacturing. Recycled materials frequently contain impurities and mixed polymers, which can degrade their properties over multiple cycles. This degradation, particularly in rheological properties, limits their applicability in 3D printing. Consequently, there is a critical need for a tool that enables rapid assessment of the flowability of these recycled materials. This study presents the design, development, and manufacturing of an open-source melt flow index (MFI) apparatus. The proposed MFI tool offers a user-friendly and cost-effective solution for evaluating the flow properties of materials from waste streams, thereby enhancing their viability for additive manufacturing applications.

Historically, botanical preparations have been used to improve human health. Their active ingredients are influenced by multiple factors as intraspecies variations, environmental conditions, collection time and methods and the part of the plant used. To ensure the efficiency and safety of these herbal drugs, qualitative and quantitative analyses are required. Tessaria absinthioides decoction (DETa) was reported as hypocholesterolemic, anti-inflammatory, cytotoxic, antitumor, and antioxidant. This work aimed to analyze DETa, by correlating its chemical composition with cytotoxic and antioxidant properties, to promote its research as an anticancer agent. DETa collections (2017, 2018, 2019 and 2022) were analyzed by UHPLC-UV-Vis, UHPLC-DAD-FLD and UPLC-MS/MS; cytotoxicity was assessed on the MCF-7 breast cancer cell line, and antioxidant capacity was evaluated by DPPH and FRAP methods; correlation analysis was used to determine biological and chemical markers. The results evidence that biological activities were consistent across collections. Among the quantified compounds, apigenin, naringin, gallocatechin gallate, ginnalin A, myricetin, epicatechin, OH-tyrosol, quercetin, and chlorogenic, tessaric, p-coumaric, vanillic, caffeic, caftaric, ellagic and rosmarinic acids correlated as bioactive and chemical markers. Moreover, tessaric acid could be established as a species marker. Altogether, these findings add relevant information to DETa properties encouraging further exploration of its potential application as anticancer botanical.

BackgroundThe clean energy transition required for decarbonisation of our societies make policymakers targets for broad business and non-business advocacy to ensure one’s often conflicting interests are protected or taken into consideration in public policies. The concept of policy entrepreneurs foregrounds the role of agency in understanding such acts of advocacy. This paper compares strategies used by policy entrepreneurs from different social spheres, advocating policy change or status quo, in four longitudinal cases related to European Union energy and climate policy 2011–2023. ResultsPolicy entrepreneurship was mainly of a cultural-institutional nature, aiming at altering or diffusing people’s perceptions, beliefs, norms and cognitive frameworks, worldviews, or institutional logics. But the actions of the European Commission did also include structural entrepreneurship, aiming at overcoming structural barriers to enhance governance influence by altering the distribution of formal authority and factual and scientific information. Motifs of policy entrepreneurs in the four cases differ, but strategies do not differ significantly between the public, private and civic spheres of society. Results indicate that civil society policy entrepreneurs focus on building broader coalitions than public and private sector entrepreneurs. There is no indication that policy entrepreneurs from a certain sector would be more successful than others in setting the agenda, changing perceptions of policy actors, or influencing actual policy change. However, policy entrepreneurs advocating policy change are more active and use more elaborated strategies than policy entrepreneurs advocating status quo. ConclusionsIt is concluded that policy entrepreneurs advocating policy change are more active and use more elaborated strategies than policy entrepreneurs advocating status quo. Thus, they are more successful in influencing policy outcomes. The European Commission was the only policy entrepreneurs to use structural entrepreneurship. the Commission acts to expand its reach into areas where the European Union does not hold much legal competence according to the constitution of the European Union. This comes with democratic deficits related to accountability and legitimacy and should be combated to reinstate and reinforce the position and powers of both the national and European legislators in taking the important decisions that impact the lives of European citizens.

This study investigates the effect of different variables in the optimization of a hybrid air charging systems for marine diesel engines, exploring the potential for energy recovery and assistance using electric machines. The optimization parameters include turbine speed, compressor power, start of injection, turbomachine size, and valve timing. At low engine speeds, the larger improvements come from increasing compressor power, which increases air-to-fuel ratio compared with the baseline turbocharging. As free-floating turbocharged engines are known to have low air-to-fuel ratio at low engine speeds, the relative improvement of the hybrid architecture is large at low engine speeds, being near to 10% less BSFC. At high engine speeds, the largest influence is marked by the SOI, an early injection obtains a faster combustion, which improves gross efficiency. However, the relative improvement compared with baseline turbocharger is less than 1% at high engine speeds. Due to the distribution of relative improvement of BSFC, this architecture has a deeper impact on fuel consumption in vessel operating cycles that focus on low engine speeds and loads, such as those of tugboats.

Valles Marineris (VM), the most prominent example of a Martian Valley Network, has been extensively studied for over 50 years, yet no detailed examination of the principal azimuths of the system exists. To address this, two methods are presented to precisely calculate the principal azimuths, a Bezier Spline analysis, and a GIS technique. The medial axis of the main canyon of VM was determined analytically from cubic polynomial splines fitted to 93 coordinate points along both north and south edges of the canyon. These splines were optimized, and medial axis points were calculated through numerical techniques that ensured orthogonality between the tangents of each spline and their connecting normal lines. 1,000 medial axis points were extracted, and various regression models constructed, including fitting to sinusoidal and cubic polynomial curves, achieving accuracies with R² values of 0.98 and 0.99, respectively. Principal azimuths were obtained using the sinusoidal equation with the slope of the tangent at any point x simply determined by the derivative of the curve’s equation. This analytic approach was cross-validated by a GIS method (using QGIS software), where a vector medial axis was obtained which produced principal azimuths that agreed with values from the analytic study with a correlation coefficient of 1.00, and a p value of 6.43e-65. The findings demonstrate that an azimuthal framework can be rigorously constructed as a potential standard reference in VM geoscience, replacing less precise and ambiguous compass bearings with the accurate azimuths necessary for high-resolution spatial analysis for future investigations.

Crystallization of soluble salts represents a significant deterioration mechanism for porous building materials in cultural heritage, with substantial economic consequences. These salts can originate from geogenic sources (such as soil leachate, sea salt aerosols, and material incompatibilities in historical constructions) or anthropogenic sources (including environmental pollution and organic waste). This study investigates the capillary rise of retained and sustained water within soil, which is subsequently absorbed by calcareous rocks in monuments. This process leads to a range of damages, from superficial staining and efflorescence to more severe material degradation and mass loss. The paper examines the capillary rise phenomenon and analyzes the typology and origin of salts that predominantly impact the durability and mechanical resistance of calcareous rocks used in the construction of historic buildings in Spain.

Gastrointestinal cancers are still the leading cause of death worldwide. This is related, among other things, to non-specific symptoms, especially in the initial stages, and also to limited possibilities of treatment. Therefore, research is still being conducted to improve the detection of this type of cancer and increase the effectiveness of therapy. The potential application of natural compounds in cancer management deserves special attention. In the group of such products there are polyphenolic compounds which reveal e.g. anti-oxidative, anti-carcinogenic, anti-inflammatory, anti-diabetic and neuroprotective properties. One of such polyphenols is rosmarinic acid, commonly found in plants such as Boraginaceae and Nepetoideae subfamilies of the Lamiaceae (mint) family, including Origanum vulgare, Rosmarinus officinalis Thymus masticmasti chinaythia koreana, Ocimum sanctum, and Hyptis pectinate. A number of studies considers the positive effects of rosmarinic acid in the treatment of many cancers, including gastrointestinal ones such as oral, stomach, pancreas, colon, and liver. The main aim of this paper was to focus on the summarized mechanisms of the action of rosmarinic acid in gastrointestinal cancers.

In the present paper and inspired with a similar construction on Hermite functions, we construct two series of Gelfand triplets each one spanned by Laguerre-Gauss functions with a fixed positive value of one of their parameters, considered as the fundamental one. We prove the continuity of different types of ladder operators on these triplets. Laguerre-Gauss functions with negative value of the fundamental parameter are proven to be continuous functionals on one of these triplets. Different sorts of coherent states are considered and proven to be in some spaces of test functions corresponding to Gelfand triplets.

Latent tuberculosis infection (LTBI) remains a significant global health concern, particularly in regions with high tuberculosis (TB) prevalence, such as South Africa. This pilot study aimed to evaluate the prevalence of LTBI and assess patient knowledge about the condition in a primary healthcare clinic in rural Eastern Cape, South Africa. A cross-sectional design was used, and convenience sampling recruited outpatients aged 18 years and older with no prior history of TB. Blood samples were analyzed using the QuantiFERON-TB Gold assay to determine LTBI status, and a survey assessed patient knowledge of LTBI. Strong positive correlations were observed between what patients understand by the term LTBI and how is LTBI different from TB (0.70), patients understand by the term LTBI and the risk factors for developing LTBI (0.70), how is LTBI different from TB and the risk factors for developing LTBI (0.78), and how is LTBI different from TB and the recommended treatments for LTBI (0.79), indicating overlap in understanding. In contrast, negative correlations between if patients ever heard of latent LTBI before and patients understanding the term LTBI (-0.25), the risk factors for developing LTBI (-0.22), LTBI progressing to active TB (-0.27), and the recommended treatments for LTBI (-0.27). This divergence points to different dimensions of patient knowledge and awareness. Age, gender, occupation, comorbidities, and HIV status showed varying LTBI positivity trends. Among younger patients aged 20-29, 15.4% tested positive, while the 30-39 group showed a nearly equal split between positive (48.1%) and negative cases. A higher positivity rate was seen in females (39.1%) compared to males (31.6%). Unemployed individuals had higher positivity rates, suggesting socioeconomic factors' influence. Comorbidities, especially hypertension, diabetes, and asthma, correlated with higher LTBI positivity among females, but this was less evident in males. HIV-positive patients had a higher LTBI-negative rate compared to HIV-negative patients. These findings suggest that demographic and health factors, including age, gender, occupation, comorbidities, and HIV status, may predict LTBI positivity.

Background: The COVID infection became the source of serious disagreements between scientists, doctors, politicians and private citizens, directly after it’s detection in the population in January 2020. The pandemic had some very unusual properties as well as the people’s reaction to it. It was feared, that the virus might be used, intentionally or accidentally, to interfere with the usual, democratic way of the upcoming election. Objective: Re-search the original COVID statistic and metadata to obtain better understanding of the pandemic including the biological (viral) as well as the social (human) aspects of it. Provide a professional, independent, non-biased view of the pandemic, that is not polluted by political and media suggestions. Methods: Publicly available data – covering the first 5 months of the COVID pandemic, 2020 Jan-May - were collected and analyzed with the usual, common statistical methods, like student’s t-test and regression analyses.The political attitude of the states were characterized by a ratio (D/R) of the number of left, democrat (D) and right, republican (R) oriented citizens in the respective states, as determined by opinion research. Results: De novo statistical analyzes of publicly available data (covering the initial five months of the pandemic) suggests that the negative medical consequences of COVID pandemic were significantly more expressed in “democrat-dominated” (D) states than in “republican-dominated”, (R) states. The proportion of COVID test positive persons was higher in D-states (6.831 +/- 1605, n=14 and 1,165 +/- 323, n=13 respectively, p<0.001). The difference in mortality was even more striking, 8-times more person/million population died in COVID related conditions in solid D states than in solid R states (438 +/- 128, and 59 +/- 13; p<0.001, respectively). The COVID associated mortality increased two fold from right to left on the political scale of the states (2.7% +/- 0.3 vs. 5.3% +/- 0.5, p<0.001). The correlation between COVID deaths (mortality) and the age/pre-existing medical condition(s) of the diseased is very strong (R2= 0.98) and the overwhelming majority of COVID “victims” are persons who were close to death even independently of the epidemic. Conclusions: Alarming signals regarding the dangerousness of COVID infection seems to have originated in a few states with predominantly left oriented citizens (high D/R ratio). The COVID mortality statistic seems to be seen and analyzed with little or no respect for the context of mortality caused by other well-known diseases or even by the natural, age related deaths. We conclude that it is absolutely necessary to pay serious attention to the correct analyses and interpretation of the epidemic data to avoid being misled or manipulated by false circumstances.

No data on wild mammals run over along the roads in Osijek-Baranja County, in the north-eastern part of Croatia, have been available so far. Mammals run over from 2016 to 2024 on the Zmajevac-Osijek road, 33.4-km long, were recorded using the road sampling method while travelling back and forth from Zmajevac to Osijek. A total of 86 run-over specimens of wild mammals, classified into eight species and one subspecies, were recorded. The northern white-breasted hedgehog, Erinaceus roumanicus, topped the list with 38 individuals, followed by the European badger, Meles meles, with 23 individuals. Together, these two species amounted to 70.93% of the run-over mammal fauna. Other species and subspecies were recorded in lower numbers. The number of wild mammals run over per kilometer of the observed road section was on the rise from 0.09 to 0.20 in the period from 2016 to 2022, while in 2023 it increased to 0.77 and in 2024 to 0.86. The high proportion of run over wildlife per kilometer is likely due to the high volume of traffic, which disrupts wildlife activities during both day and night.

In the current environmental scenario, the proposal of alternatives for petroleum-based products has considerably increased, looking for bioproducts with interesting properties like biodegradability, sustainability and efficiency, among others. In this sense, the role of biolubricants is promising, offering a wide range of possibilities through different methods and operating conditions. Specifically, double transesterification could be a suitable process in a biorefinery context. The aim of this work was to produce a biolubricant through double transesterification with methanol and neopentyl glycol (NPG) under different reaction conditions, by using homogeneous catalysis (sodium methoxide). As a result, different catalyst concentrations, among other changes in reaction conditions, were used, getting high conversion values (96%) and a final product with viscosity values (20.7 cSt) that allow its use as engine oil (SAE 5W). In conclusion, biodiesel and biolubricant production was feasible through homogeneous catalysis, proving the feasibility of this process at laboratory scale. Further studies, including the use of different heterogeneous catalysts, as well as the implementation of this process at semi-industrial scale, is recommended.

Federated Learning (FL), as a distributed machine learning method, is particularly suitable for training models that require large amounts of data while meeting increasingly strict data privacy and security requirements. Although FL effectively protects the privacy of participants by avoiding the sharing of raw data, balancing the risks of privacy leakage with model performance remains a significant challenge. To address this, this paper proposes a new algorithm—FL-APB (Federated Learning with Adversarial Privacy-Performance Balancing). This algorithm combines adversarial training with privacy protection mechanisms to dynamically adjust privacy and performance budgets, optimizing the balance between the two while enhancing and ensuring performance. Experimental results demonstrate that the FL-APB algorithm significantly improves model performance across various adversarial training scenarios, while effectively protecting the privacy of participants through adversarial training of privacy data.

The retrofitting of existing reinforced concrete (RC) buildings with Cross-Laminated Timber (CLT) panels presents a promising approach for enhancing seismic performance and overall structural resilience. However, effective integration of CLT with existing RC structures poses significant challenges, particularly concerning the design of connections between CLT panels and the RC structure. This paper introduces a novel connection that addresses these challenges by focusing on both structural and architectural considerations. Structurally, the connection is engineered to provide optimal stiffness, strength, and deformation capacity, ensuring robust performance under seismic and dynamic loads. Architecturally, the design incorporates a pre-defined weak component that facilitates easy access and rapid replacement of damaged parts, thereby reducing downtime and maintenance efforts. The proposed connection was evaluated through a series of monotonic and cyclic loading tests, demonstrating its structural efficiency and reliability. The results indicate that the new connection system not only meets the necessary structural requirements but also offers practical benefits for maintenance and repair, contributing to the overall sustainability and resilience of retrofitted RC buildings. This innovative approach represents a significant advancement in the field of structural retrofitting, providing a viable solution for integrating CLT panels into existing RC frameworks.

This paper presents the development and testing of the passive Almaty Ankle Exoskeleton V.2. The device features a new design with three electric linear actuators, and the shin and foot platforms are connected by links with spherical and universal joints. Functional testing demonstrates the prototype's ability to operate effectively within the natural range of ankle joint movements. Preliminary results indicate that the exoskeleton can reduce calf muscle activation in the work limb where the device is applied. In this the passive exoskeleton is designed for dorsiflexion and plantarflexion, whose operation with numerical results is highlighted by focusing on parameters such as angular velocity, linear acceleration, and angles. These measurements provide valuable information about the exoskeleton's performance and efficiency.

Polymers synthesized with end-of-life consideration allow for recovery and reprocessing. ”Living-anionic polymerization (LAP)” and hydrosilylation reaction were utilized to synthesize hair-end furan functionalized hairy nanoparticles (HNPs) with hard polystyrene (PS) core and soft polydimethylsiloxane (PDMS) via a one-pot approach. The synthesis was carried out by first preparing the living core through cross-linking styrene with divinylbenzene using sec-butyl lithium, followed by the addition of the hexamethylcyclotrisiloxane (D3) monomer to the living core. The living polymer was terminated by dimethylchlorosilane to obtain the HNPs with Si-H functional end groups. The furan functionalization was carried out by the hydrosilylation reaction between Si-H of the functionalized HNP and 2-vinyl furan. Additionally, furan functionalized polystyrene (PS) and polydimethylsiloxane (PDMS) were also synthesized by LAP. 1H NMR and ATR-IR spectra confirm the successful synthesis of the target polymers. Differential scanning calorimetry shows two glass transition temperatures indicative of a poly(dimethylsiloxane) soft phase and a poly(styrene) hard phase, suggesting that the HNPs are microphase separated. The furan functionalized HNPs form thermo-reversible networks upon cross-linking with bismaleimide (BMI) via a Diels−Alder coupling reaction. The kinetics of the forward Diels–Alder reaction between the functionalized polymer and BMI were studied at three different temperatures: 50 °, 60 °, and 70 °C by UV-Vis spectroscopy. The activation energy for the furan functionalized HNPs reaction with the bismaleimide was lower compared to the furan functionalized polystyrene and poly(dimethylsiloxane) linear polymers. The crosslinked polymer network formed from the Diels−Alder forward reaction dissociates at around 140 °–154 °C, and the HNPs are recovered. The recovered HNPs can be re-crosslinked at 50 °C. The results suggest that furan functionalized HNPs are promising building blocks for preparing thermo-reversible elastomeric networks.

Complexity of issues in cancer research has led to the introduction of powerful computational tools for helping experimental in vivo and in vitro methods. These tools, typically focused on studying cell behavior and dynamic cell populations, range from systems of differential equations solved numerically, to lattice models and agent-based simulations. In particular, agent-based models (ABMs) are increasingly used for they capability to incorporate multi-scale features ranging from the individual up to the population level, thus combining statistically aggregated assumptions with individual heterogeneity. In this work, we present an ABM that simulates tumor progression in a colonic crypt, with the aim of providing an experimental in silico environment for testing results achieved in traditional laboratory research and developing alternative scenarios of tumor development. The model also allows some speculations about causal relationships in biologically inspired systems.

Our main result is that chaos in dimension $n+1$ is a one-dimensional geometrical object embedded in a geometrical object of dimension $n$ which corresponds to a $n$ dimensional object which is either singular or non-singular. Our main result is then that this chaos occurs in the first case as either on an isolated or non-isolated singularity. In the first case this chaos is either boundary chaos or spherical chaos which is what happens also in the non-singular case. In the case of an isolated singular geometry one has chaos which can either be boundary, spherical or tubular chaos. We furthermore prove that the prime numbers display quantum behaviour.

Heart rate and blood pressure, along with body temperature and respiratory rate, are two of the four parameters used to assess the state of bodily functions, as both measures are indicators of heart activity. Therefore, these measurements become a basic procedure in patient diagnosis, necessitating techniques that can obtain them accurately and reliably, while being as non-intrusive as possible for the patient. Using video data, these two vital signs can be determined at any time and place. To achieve this, artificial intelligence techniques such as machine learning and deep learning 6 can be applied to overcome the main limitations of conventional remote photoplethysmography (rPPG). This document explains the technical and methodological aspects involved, including the acquisition and preprocessing of video data, the extraction of the rPPG signal, and its subsequent processing, as well as the artificial intelligence model for heart rate estimation and the mathematical modeling for blood pressure estimation. We compared the results for heart rate and blood pressure extracted from videos recorded by a basic smartphone front-camera to a blood pressure monitor and achieved results with reasonable accuracy and correlation. These results are part of the AIVA project, grant agreement 2021/C005/00145071. This project has been funded by Red.es through the Spanish 2021 call for grants aimed at research and development projects in artificial intelligence and other digital technologies.

The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of the electrical equipment. In particular, modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of Deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. Particularly in the paper, different Neural networks used in scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modelling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices.

This paper presents the redesign process of FLIC, a web application developed for creating personal learning pathways in higher education, now adapted for primary and secondary education as FLIC-IPAFLEX. This redesign addresses the need to personalise learning through flexible pathways and supports teachers in designing learning situations (LS) in accordance with the criteria established by the new Education Law (LOMLOE). The SCRUM model, integrated into Design-Based Research (DBR), was employed, implementing several iterative cycles involving teachers to validate the tool's utility. The platform facilitates the creation, publication, and reuse of LS by enabling the filtering of competencies, evaluation criteria, and basic knowledge, as well as integrating with Google Classroom, an application widely used by teachers. The results show a positive user evaluation, highlighting its ease of use and capacity to foster collaboration among teachers, thereby promoting the co-design of flexible and adaptive LS. FLIC-IPAFLEX aims to contribute to the field of learning design technologies, aligning with the demands of compulsory education and supporting collaborative processes within the educational environment.

The gut microbiota plays a pivotal role in host metabolism, influencing the production of small metabolites from non-digestible compounds, which can accumulate in tissues and play important role in fatty acids synthesis for improving the meat quality. We investigate the breed-specific differences in longissimus dorsi (LD) muscle fatty acid profile, gut microbiota, and metabolites across the cecum, ileum, and rectum in Chinese Songliao Black pigs (SBP) and commercial Large White × Landrace crossbred pigs (LWLDP) and interactions. Significant correlations highlighting potential microbiota-metabolite interactions that contribute to muscle fatty acid composition. LWLDP pigs exhibited a higher number of significant fatty acids and maintained higher operational taxonomic units (OTUs) in the cecum and rectum, with no differences observed in the ileum between breeds. Firmicutes, Bacteroidetes, Proteobacteria, and Spirochaetes were the predominant phyla across both breeds. Differentially expressed metabolites (DEMs) were more abundant in the cecum and ileum of LWLDP, while rectum showed higher expression in SBP. Integrative analysis identified significant correlations between fatty acids, metabolites, and microbial taxa. DHA was linked to cecal metabolites and rectal microbial families, while saturated and unsaturated fatty acids were associated with different microbes and metabolites. This study demonstrates distinct breed-specific interactions between gut microbiota, metabolites, and fatty acid composition, offering insights into potential microbial interventions to enhance pork quality through fatty acid modulation.

Anaerobic digestion is a feasible solution for the treatment of organic wastes. The process can reduce the amount of biowaste by stabilizing the organic material and producing biogas susceptible to energetic valorization. However, the digestate needs further valorization when land application is considered unfeasible. Thermal treatments, such as gasification, pyrolysis, and hydrothermal carbonization, are alternatives capable of transforming this material into valuable syngas, obtaining, in many cases, a carbonized stream known as biochar. The feasibility of the process depends on the energy demand for the drying stage and the treatments available for removing contaminants from the syngas, attaining high-quality products, and treating the process-derived water. In the present manuscript, these critical aspects were reviewed considering the characteristics of digestates based on their origin, the modifications of this material during anaerobic digestion, and the way digestate structure affects the final thermal valorization outcome. Emphasis was placed on the energy demand of the global approach and by-product treatments.

Mercury, a toxic heavy metal produced through both natural and anthropogenic processes, is found in all major Earth’s systems. Mercury's bioaccumulation characteristics in the human body have a significant impact on the liver, kidneys, brain, and muscles. In order to detect Hg2+ ions, a highly sensitive and specific fluorescent biosensor has been developed using a novel, modified seven amino acid peptide, FY7. The tyrosine ring in the FY7 peptide sequence forms a 2:1 complex with Hg2+ ions that are present in the water-based sample. As a result, the peptide's fluorescence emission decreases with higher concentrations of Hg2+. The FY7 peptide's performance was tested in the presence of Hg2+ ions and other metal ions, revealing its sensitivity and stability despite high concentrations. Conformational changes to the FY7 structure were confirmed by FTIR studies. Simultaneously, we designed a miniaturized setup to support an in-house-developed micro-volume capillary container for volume fluorometry measurements. We compared and verified the results from the micro-volume system with those from the commercial setup. The micro-volume capillary system accommodated only 2.9 µL of sample volume, allowing for rapid, sensitive, and selective detection of toxic mercury (II) ions ranging from 0.02 µM.

Menstruation is a significant periodical factor in women's lives, affecting them from menarche to menopause. This study investigates the menstrual status and its impact on the performance of Korea Ladies Professional Golf Association (KLPGA) 1st Tour players using Latent Profile Analysis (LPA). The study involved 119 female KLPGA tour license professionals who participated in over 30 tournaments annually, requiring them to manage their menstrual cycles during competitions. A questionnaire collected data on demographics, menstrual cycle characteristics, and training hours. Four distinct player profiles were identified based on self-esteem, anxiety, and perceived performance. Profile 4 exhibited the highest self-esteem and perceived performance with the lowest anxiety levels, while Profile 1 showed the highest anxiety levels and lowest self-esteem. Cross-tabulation and ANOVA analyses highlighted significant differences in practice hours and athletic experience across profiles. The findings suggest that regular menstrual cycles and athletic experience are crucial in defining player profiles. These insights are valuable for coaches and professionals to develop tailored training and management strategies to enhance performance. Future research should expand the sample size and employ longitudinal methods to validate these results.

The aim of this study was to evaluate the psychometric properties of the Communication Skills Scale (CSS) among Peruvian nurses and its association with job insecurity during the COVID-19 pandemic. We explored five models of confirmatory factor analysis for the CSS and its four subscales and assessed the convergent validity and criterion validity of the scale by analyzing its connection with job insecurity through stepwise multiple regression. We used insights from a focus group for the cultural adaptation of the scale. In the psychometric phase, 225 nurses participated through a virtual survey. The psychometric analysis revealed that the CSS and its subscales have a robust internal structure—similar to the original questionnaire—and are optimally reliable in the Peruvian population. Furthermore, the results show that job insecurity was associated with contract type, communication skills, empathy, and job satisfaction. The CSS and its subscales are valid and reliable to be applied to Peruvian nurses. Initiatives should be undertaken to strengthen communication skills and increase job satisfaction among nursing professionals by improving their working conditions, especially in times of crisis, to reduce job insecurity and promote well-being.

A large amount of sea ice exists in the polar regions, which is prone to ship-ice collisions leading to ship breakage and sinking events. Complex environmental factors pose challenges to navigation safety, and it is particularly important to perceive the polar environment and make decisions in advance. In this paper, You only look once (YOLOv5) is targeted to be optimized according to the characteristics of ice remote sensing images, which includes adding Squeeze-and-Excitation (SE) attention mechanism, improving spatial pyramid pooling, and replacing Flexible Rectified Linear Unit (FReLU) activation function. In order to verify the effect of the optimized algorithm, ablation experiments are set up, and the accuracy of the optimized algorithm is improved by 3.5% com-pared with the original YOLOv5. Comparison experiments with other target identification algo-rithms are conducted, and the results show that the optimized YOLOv5 has higher accuracy and better robustness, and can identify small target sea ice that is difficult to identify in large-size re-mote sensing images. Polar ship path planning based on the identification results. The identifica-tion output of remote sensing ice images is used as the input for the construction of path planning maps, and raster maps corresponding to the actual polar scenes are built. Taking the path length, the number of ship turns, and the sea ice risk value as the objective function, the Any-Angle Path Planning on Grids (Theta*) algorithm is used to simulate the path planning under different sea conditions with different sea ice concentration, and a path with a shorter length, a smoother path, and a higher safety coefficient is found.

Background/Objectives: COVID-19 led to a pandemic that has brought misery to millions of people but more so to those with pre-existing conditions. For this infection, several antiviral drugs were employed, including remdesivir (R) and paxlovid [nirmatrelvir/ritonavir (NR)]; Methods: The current study compared the effectiveness of remdesivir and paxlovid treatment for COVID-19 patients with comorbid conditions. Data from a cohort of 151 adult patients with COVID-19 who also had associated comorbidities were used in this study. These patients were treated with antivirals according to local guidelines. The subjects included 78 case-patients assigned to group R and 73 to group NR; Results: In group NR, a considerable improvement in oxygen saturation was seen in the first 24 hours of treatment (p=0.010), but the levels were significantly higher from the second day of treatment (p &lt;0.001) in group R of patients. At the end of the 5 days of treatment, the oxygen saturation improved statistically significantly compared to the admission day, but only in the R group (95.11±1.80; 91.76±1.80; p&lt;0.001); Conclusions: Both drugs can be considered a breakthrough in the current treatment approach to the COVID-19 disease since they provide readily available options that can alleviate the severity of the disease and, hence, the prognosis of patients. That is why their effectiveness relies on the correct administration time and choosing the patient with suitable characteristics regarding the presence of comorbidities and the likelihood of the critical further development of the process.