The study offers an in-depth investigation of the impact of climate conditions on various terrain features in the Kaffiøyra area, southwest Svalbard, focusing on the changes of shorelines, glacier termini, and outwash areas. We utilized cost-effective, consistent remote sensing data and GIS techniques to analyze historical long-term changes in these terrain features, evaluated the effects of temperature variations on glacier retreat, and developed a predictive model for future glacier area changes based on temperature data from climate change scenarios and the ground observation station near the site. The results demonstrated a range of responses in shoreline stability and changes across different zones in the study site. Based on the predefined baseline, the glacier-shorelines (tidewater glacier) exhibit significant erosion, with the averaged rates varying from -64.7 to + 9.2 m/yr. However, the land-shorelines show relatively stable, with changes ranging from -2.2 to +3.7 m/yr, and the outwash areas exhibited minor increases of less than 10% as compared to the data obtained in 1985. The analysis of seven glacier termini indicated a general reduction in the glacier area; notably, Aavatsmarkbreen, a tidewater glacier, retained only 32% of its original size by 2023. The land glaciers such as Waldemarbreen, Irenebreen, Elisebreen, Eivindbreen, Andreasbreen, and Oliverbreen, preserve 54.8%, 61.3%, 65.0%, 74.0%, 55.5%, and 43.7% of their areas, respectively. A marked negative linear correlation between temperature and remaining glacier areas was observed, enabling the prediction of future glacier variations under various climate conditions. The study outlines the evolution of terrain features in Kaffiøyra in nearly 40 years. Results show the relative stability of land-shorelines, significant glacier retreats in response to warming temperatures, and slight growth in outwash areas. The study highlights the critical need for robust land monitoring systems that enhance our understanding and enable effective decision-making through predictive modeling of climate impacts.

Background/Objectives: Recent advancements in artificial intelligence (AI) have spurred interest in developing computer-assisted analysis for imaging examinations. However, the lack of high-quality datasets remains a significant bottleneck. Labeling instructions are critical to improving dataset quality but are often lacking. This study aimed to establish a liver MRI segmentation protocol and assess its impact on annotation quality and inter-reader agreement; Methods: This retrospective study included 20 patients with chronic liver disease. Manual liver segmentations were performed by a radiologist in training and a radiology technician on T2 weighted imaging (wi) and T1wi at the portal venous phase. Based on the inter-reader discrepancies identified after the first segmentation round, a segmentation protocol was established, guiding the second round of segmentation, resulting in a total of 160 segmentations. Dice Similarity Coefficient (DSC) assessed inter-reader agreement pre- and post-protocol with a Wilcoxon signed-rank test. Slice selection at extreme cranial or caudal liver positions was evaluated using the McNemar test; Results: The per-volume DSC significantly increased after protocol implementation for both T2wi (p<0.001) and T1wi (p=0.03). Per-slice DSC also improved significantly for both T2wi and T1wi (p<0.001). The protocol reduced the number of liver segmentations with a non-annotated slice on T1wi (p=0.04), but the change was not significant on T2wi (p=0.16); Conclusions: Establishing a liver MRI segmentation protocol improves annotation robustness and reproducibility, paving the way for advanced computer-assisted analysis. Moreover, segmentation protocols could be extended to other organs and lesions and incorporated into guidelines, thereby expanding the potential applications of AI in daily clinical practice.

Global climate has undergone significant changes in the past 50-60 years. The transition from stable climate conditions to current warming has significantly influenced permafrost landscapes and infrastructure in cold regions. Arctic and Subarctic landscapes had distinct reactions to these changes. The objective of this study is to analyze the regional aspects of climate and permafrost changes in Yakutia's Arctic and subarctic regions from the past 50-60 years. Our analysis focused on climate change patterns during different periods, including cold period, pre-warming, and modern warming. We analyzed data on mean annual air temperature, thawing and freezing indices, and precipitation. During these periods, we determined the reaction's characteristics in various landscape regions. The most significant impact on permafrost began in the mid-2000s, as our observations confirmed. Global warming directly causes permafrost temperature to rise, the active layer to thicken, and cryogenic processes to develop. The conducted research provides the information needed to study the effects of global warming on permafrost and adapt socio-economic conditions in northern Yakutia.

This paper presents a novel privacy-preserving approach, Fed-CNN1D (Federated Convolutional Neural Network 1D), based on federated learning for monitoring air quality and classifying different activities of daily living in indoor spaces. The system employs six different types of sensors to collect measurement parameters, which are then used to train a 1D CNN model locally for activity recognition. The proposed model is lightweight and edge deployable, making it suitable for real-time applications. Experiments were conducted using an air quality dataset specifically curated for Activity of Daily Living (ADL) classification. The results demonstrate that our approach Fed-CNN1D achieves 96.50% accuracy, 96.27% F1-Score, 96.54% precision, 96.21% recall, 0.11% loss, with low communication cost of 0.099Mb, and a swift detection time of 15 ms.

Essential Tremor (ET) is among the most common neurological movement disorders, with incidence rates increasing with age. The disorder is characterized by a bilateral action tremor of the upper limbs, enduring for a minimum of three years in duration, and shows early signs of onset with progression over time. Recent terminology has categorized a subcategory of ET, known as Essential-Tremor Plus (ET-Plus), which consists of a combination of symptoms that include tremors at rest and additional neurological signs. This includes memory impairment, gait disturbances, or various other neurological symptoms that do not have full clinical significance for diagnosis and suggest a more complex underlying neurological condition. Abnormalities located primarily in the cerebellum, including the loss and damage to Purkinje Cells (PC), were core pathological features of cerebellar damage in postmortem ET. Given the complexity and uncertainty of the pathophysiological aspects of tremor generation, treatment options are still limited, and current therapeutic measures lack the capacity to fully alleviate symptoms. In consideration, there are current pharmaceutical, stimulation, and genetic studies being conducted to characterize the etiology and pathophysiology of ET and aid in the reduction of progression and symptoms. In this review, we will discuss various aspects of ET including neurophysiology, pathophysiology, genetic factors and etiology, treatment options and future directions of advancements for ET management.

This study addresses the challenge of predicting the dynamic behavior of the structures under seismic excitation. Accurate prediction of such systems' responses is critical for the design and evaluation of buildings and infrastructure. Traditional methods, including numerical models and differential equation solvers, often face significant computational burdens, especially with nonlinear hysteretic behaviors and large-scale problems. To overcome these limitations, a novel Fuzzy-based Convolutional Neural Network (FuzzyCNN) model is developed. This model integrates fuzzy logic principles with convolutional neural networks to effectively manage the uncertainties and complexities inherent in soil-structure interaction under seismic loads. The model's performance is validated through both numerical simulations and experimental data from a mid-rise concrete building subjected to seismic events. Comparative analysis with a traditional Physics-informed CNN (PhyCNN) model demonstrates the superior accuracy and robustness of the FuzzyCNN in predicting seismic responses. Key results show that the FuzzyCNN model not only enhances prediction accuracy but also handles uncertainties more effectively than the PhyCNN model. The findings suggest that the FuzzyCNN model can significantly improve the efficiency and accuracy of dynamic response predictions. This advancement offers valuable implications for engineering design, seismic risk assessment, and the development of more resilient infrastructure.

In this paper, we used meteorological data to simulate vertical profiles of optical turbulence at the Special Astrophysical Observatory (Russia, 43°40′19 ′′ N 41°26′23′′E, 2100 m a.s.l.), site of the 6-m Big Telescope Alt-azimuthal. The vertical profiles of optical turbulence are calculated using ERA-5 reanalysis data. These profiles are corrected using DIMM measurements as well as estimations of atmospheric boundary layer heights. We may note that the method basically reconstructs the most important features of the shape of the measured profile under clear sky. Atmospheric turbulent layers were identified, and the strength of optical turbulence in these layers was estimated. The model hourly values of seeing corresponding to the obtained vertical profiles range from 0.85 to 2.10 arcsec, values of the isoplanatic angle vary in the range from 1 to 3.0 arcsec (at λ = 500 nm). These estimations are close to the measured median of seeing (1.21 arcsec).

Abstract: Health coaching and telemonitoring are both considered a growing healthcare strategy that focuses on a patient-centered motivational approach to support behavior change and enhance adherence to health goals including dietary interventions. This review aimed to define and de-scribe both health coaching and telemonitoring in weight management interventions, including a literature review on the effectiveness of incorporating health coaching and telemonitoring into di-etary weight loss interventions, addressing the ongoing global challenge of obesity and its asso-ciated health risks, including diabetes and cardiovascular disease. Traditional dietary interven-tions often yield initial weight loss, however sustaining these results remains difficult due to fac-tors associated with lifestyles and motivation. Health coaching offers a personalized support and guidance to help clients establish and maintain healthier habits by setting achievable foals and building self-efficacy and fostering accountability, whereas telemonitoring via digital tool such as mobile apps and wearable devices provided tracking dietary and health measures allowing continuous monitoring and self-awareness. Our review of randomized controlled trials (RCTs) from the past decade confirms that combining health coaching with telemonitoring improves weight loss outcomes, enhance adherence to lifestyle changes and positively impact chronic con-dition markers such as blood pressure and HbA1C levels. These integrated interventions also show promise in boosting mental well-being and enhance the quality of life. Findings highlights the potential of such integrative approaches in creating a sustainable, accessible, and scalable weight management in particularly for individuals at risk of obesity related health issues. Future research should focus on optimizing health coaching and telemonitoring tools to accommodate diverse populations for a long-term adherence.

Atmospheric pollution caused by aerosols deteriorate air quality, increasing public health risk. Anthropogenic aerosols are mostly concentrated within the atmospheric boundary layer (ABL), that presents a daytime evolution that determine the vertical mixing of the air pollutants generated near the surface and therefore, their ground-level concentration from local sources. Proper characterization of this layer is of crucial importance in numerical weather forecasting and climate models; however, their estimation nowadays presents some spatial and temporal limitations. Lidars have demonstrated their capabilities to study the aerosol vertical distribution. A particular type of lidars, ceilometers, are capable of providing continuous aerosol vertical profiles with good spatial resolution and a large vertical range. Advanced methods, such as the recently developed STRATfinder algorithm, are required to estimate the ABL height due to difficulties with residual layers. The algorithm tracks ABL heights to provide values every hour during a 24-hour period. More complex situations occur due to advection of aerosols (e.g. due to long-range transport of desert dust, volcanic eruptions, or pyrocloud convection), producing lofted layer in the free troposphere that may remain decoupled from the local ABL but can also be mixed. Aerosol-based methods for determination of the ABL height are challenging in those situations. The main objective of this research is the assessment of the impact of Saharan dust intrusions on the ABL using ceilometer signals, along four years period 2020-2023. The database of ABL heights of continuous measurements have been classified regarding the most frequent patterns of synoptic circulation. Six synoptic meteorological patterns were identified through cluster analysis of sea level pressure fields as the main responsible of the weather conditions in the Iberian Peninsula. Counter-intuitive behavior is obtained in particular synoptic situations due to the presence of dust-rich aloft layers. These results are relevant for health advice during Saharan dust intrusion days.

Characteristics of rainfall events and its spell durations are the most important information for natural disasters such as floods, coastal erosion, landslides, water hazards etc. In the present study, we investigated the probability distributions of spell durations for rain exceeding various thresholds (0.5, 1, 2mm/10min) and discussed the peak intensity events (2, 5, 10, 20mm/10min) associated with the rain spell durations from very high timescale (10min) observations at 1296 AMeDAS stations across Japan. Our results infer that the heavy rainfall events (>2mm/10min) over Japan last for shorter duration (up to 1.5hr) and the light rainfall events (<1mm/10min) last for longer duration (up to 2hr). The low intensity events with 2mm/10min peak are found to be short-lived and last up to 3 hours, while the high peak intensity events of more than 5mm/10min are long-lived and last up to 10 hours. The analysis of longer spell wet events (last for at least 12 hours) indicates that the ~15hrs spell duration with 2-10mm/10min peak intensity events occurs more frequently than longer duration event. The very high peak intensity events (up to 10-20mm/10min) are noticed to last up to 24-33hours. These results suggest that the intensity and duration of rainfall events in Japan may play an important role in natural hazards.

This paper proposes a novel approach to represent the geometry of the corrugated board profile during compression by using graphs. Graphs are lighter than images, and the computational time of compression analysis is then significantly reduced compared to using the original image data for the same analysis. The main goal of using such graphs is to gain more knowledge about the mechanical behavior of corrugated boards under compression compared to the current load-deformation curve approach. A node-tracking algorithm is applied to characterize the different phases happening during the compression test in order to predict physical phenomena, including buckling and contact. The main results show that analyzing the nodes provides significant insights into the compression phases, which was not achieved in the current state-of-the-art. The authors believe the object of this research is crucial to better understand the physics of corrugated boards under compression, and it can also be extended to other engineering structures.

Infectious diseases impose a significant burden on global health systems due to high morbidity and mortality rates. According to the World Health Organization, millions die from infectious diseases annually, often due to delays in accurate diagnosis. Traditional diagnostic methods in clinical microbiology, primarily culture-based techniques, are time-consuming and may fail with hard-to-culture pathogens. Molecular biology advancements, notably the Polymerase Chain Reaction (PCR), have revolutionized infectious disease diagnostics by allowing rapid and sensitive detection of pathogens' genetic material. PCR has become the gold standard for many infections, particularly highlighted during the COVID-19 pandemic. Following PCR, Next-Generation Sequencing (NGS) has emerged, enabling comprehensive genomic analysis of pathogens, thus facilitating the detection of new strains and antibiotic resistance tracking. Innovative approaches like CRISPR technology are also enhancing diagnostic precision by identifying specific DNA/RNA sequences. However, the implementation of these methods faces challenges, particularly in low- and middle-income countries due to infrastructural and financial constraints. This review will explore the role of molecular diagnostic methods in infectious disease diagnosis, comparing their advantages and limitations, with a focus on PCR and NGS technologies and their future potential.

In this paper, we present a method to optimize the attainable moment set (AMS) to increase control authority for electrical vertical-take-off-landing vehicles (eVTOLs). As opposed to 3D AMS for conventional airplanes, hover control of eVTOLs requires vertical thrust produced by the powered lift system in addition to three moments. The limits of the moments and vertical thrust are coupled due to input saturation, and as a result, the concept of traditional AMS is extended to 4D Generalized Moment Set to account for this coupling effect. Given a required moment set (RMS) derived from system requirements, the optimization is formulated as a 4D convex polytope coverage problem, i.e., the AMS coverage over the RMS, such that the system's available control authority is maximized to fulfill the prescribed requirements. The optimization accounts for not only nominal flight, but also for one critical engine inoperative situation. To test the method, it is applied to an eVTOL with 8 rotors to optimize for the rotors’ orientation w.r.t the body axis. Results indicate highly improved coverage of the RMS for both failure-free and one engine inoperative situations. Closed-loop simulation tests are performed for both optimal and non-optimal configurations to further validate the results.

This study was conducted to identify the key elements of transitional care for families with pre-term infants in South Korea. We used an integrative review methodology proposed by Whittemore and Knafle in 2005. During the problem identification step, the review question was constructed via the population, intervention, outcome, and timeframe (PIOT) format. During the literature search step, integrative reviews of published literature from nine electronic databases were undertaken and a total of 14 studies were reviewed that met our inclusion criteria. During the data evaluation step, the quality of the literature was assessed using the Mixed Methods Appraisal Tool (MMAT) developed by Hong et al. in 2019. We identified three domains and 10 key attributes of transitional care for families of pre-term infants. The parenting empowerment domain included growth and development, developmental promotion, nutrition, safe environment, general parenting, and parent-infant interaction. The emotional support domain included counseling, advocacy, and community network. The social support domain included professional collaboration. Efforts should continue to further develop programs and policies to enhance transitional care for families of pre-term infants that reflect South Korean nursing practices.

A nuclear export signal (NES) is a cluster of hydrophobic amino acids that can maintain the dynamic shuttling of target proteins between the nucleus and cytoplasm. Bioinformatics analysis showed that the hydrophobic region of 92PLLLHKFLLA in Bm65 is very likely to be an NES and may be involved in the production of infectious virions. In this study, we generated several mutations in 92PLLLHKFLLA of Bm65, which were further used to generate recombinant viruses to study their roles in viral propagation. Subcellular analysis revealed that the 92PLLLHKFLLA sequence is an NES involved in the dynamic transport of Bm65. Mutations in the hydrophobic region could block the formation and accumulation of Bm65 aggregates, resulting in a uniform distribution of Bm65 in BmN cells. The ribosomal protein L13 (RPL13) of silkworm was previously reported to interact with Bm65. Here, intracellular co-localization analysis showed that the interaction between Bm65 and RPL13 was regulated by the 92PLLLHKFLLA of Bm65. In summary, the interaction between Bm65 and RPL13 is essential for the production and accumulation of Bm65 aggregates and may play an important role in the regulation of viral propagation.

Consisting of more than 11,000 members distributed over five families, the tautomerase superfamily (TSF) is a large collection of proteins with diverse biological functions. While a lot of attention has been given to individual TSF enzymes, a majority remain structurally and functionally uncharacterized. Given its large size, studying a representative member of each family offers a viable approach for extracting mechanistic insights applicable to the entire superfamily. In this study, cis-3-chloroacrylic acid dehalogenase (cis-CaaD), 5-carboxymethyl-2-hydroxymuconate isomerase (CHMI), malonate semialdehyde decarboxylase (MSAD), and 4-oxalocrotonate tautomerase (4-OT) were referenced against the well-studied macrophage migration inhibitory factor (MIF) and D-dopachrome tautomerase (D-DT) using triplicate 1 μs molecular dynamics (MD) simulations for a total of 18 μs. Through root mean square fluctuation (RMSF) measurements and correlation analyses comparisons to previous crystallographic structures, we reveal key mechanistic insights that promote understanding of the catalytic activities in TSF. Collectively, our findings from these functionally diverse TSF proteins provide key information on allosteric coupling, long-range intra- and intersubunit communications as well as structure-activity relationships that enable new studies in the superfamily.

Interest among consumers is increasing for the availability of organically produced fruits and vegetables. Seafood demand is increasingly being met by fish raised using aquaculture methods that provide fish excretory products that can meet organic standards for nutrient sources for organic vegetables. We conducted an experiment in a glass greenhouse to evaluate fish biosolids as a growth media component for organic tomato transplant production. We compared the fishbiosolids treatment to several different organic fertilizers along with a commonly used inorganic slow-release fertilizer (Osmocote). All treatments used a target N concentration of 400 mg/L incorporated into the substrate and we also included fish biosolids treatments of 200 and 800 mg/L. Plant performance was montiored for 5 weeks starting with commercially available 2-week old seedlings. Results showed that the 800 mg/L fish biosolids treatment compared very favorably with the inorganic Osmocote treatment at the conclusion of the trial. The 800 mg/L fish biosolids treatment exceeded the Osmocote treatment for chlorophyll content and for leaf number for the first 4 weeks of the 5 week trial. Grower management protocols could further improve seedling performance by providing additional top dressings of fish biolsolids or fish effluent waters as the plants age.

The COVID-19 pandemic had a global impact on mental health. Identification of individuals at higher or lower risk of mental health issues may assist with targeting prevention, support and treatment efforts during future pandemics. Using a Canadian national mental health survey that collected data (March 2022 – December 2022) during the pandemic period, this study examined vulnerability of participants reporting exposure to child abuse or neglect by examining the risk of mood, anxiety and substance use disorders over a one year period. Psychiatric disorders were identified using the Composite International Diagnostic Interview (CIDI). Because childhood adversities are well-known risk factors for mental disorders, the analysis focused on interactions between childhood adversities and pandemic stressors by estimating the relative excess risk due to interaction (RERI). RERIs provide evidence of synergy based on the occurrence of greater than additive interactions. Evidence of synergy was consistently found between physical and sexual abuse and mood and anxiety disorders. There was no such evidence for substance use disorders. Childhood adversities increase vulnerability to stressors and may be useful for identification of individuals with greater mental health needs during public health emergencies.

Two novel lipids, acylglycerol and glycerophospholipid containing succinate-linked provitamin D3 at sn-2 position of glycerol backbone, have been synthesized from dihydroxyacetone (DHA) and sn-glycerophosphocholine (GPC), respectively. Three-step synthesis of 1,3-dipalmitoyl-2-(7-dehydrocholesterylsuccinoyl)glycerol involved esterification of DHA with palmitic acid, reduction of carbonyl group and conjugation of resulting 1,3-dipalmitoylglycerol with 7-dehydrocholesterol hemisuccinate (7-DHC HS). Using NaBH3CN as a reducing agent was crucial to avoid any acyl migration and obtain the final product with 100% regioisomeric purity. 1-Palmitoyl-2-(7-dehydrocholesterylsuccinoyl)-sn-glycero-3-phosphocholine was produced in two-step process by the esterification of GPC at sn-1 position with palmitic acid and subsequent conjugation of 1-palmitoyl-sn-glycero-3-phosphocholine with 7-DHC HS. Apart from the main product, some amount of its regioisomer containing provitamin D3 linked at sn-1 position and palmitic acid at sn-2 position was detected which indicated the acyl migration of palmitic acid from sn-1 to sn-2 position in the intermediate 1-palmitoyl-sn-glycerophosphocholine. Synthesized novel lipids were fully characterized by spectroscopic data. They can find application as novel lipid-based prodrugs as the additives to sunscreen creams, providing controlled release of provitamin D3 in the epidermis.

Δ9-Tetrahydrocannabinol (THC) is the primary psychoactive component of cannabis which is in-creasingly consumed by pregnant people. In humans, THC is sequentially metabolized in the liver to its circulating metabolites 11-hydroxy-THC (11-OH-THC, psychoactive) and 11-nor-9-carboxy-THC (THC-COOH, non-psychoactive). Human and macaque data show that fetal exposure to THC is considerably lower than its corresponding maternal exposure. Through perfused human placenta studies, we have shown that this is due to active efflux of THC (fe-tal-to-maternal) by a placental transporter(s) other than P-glycoprotein or breast cancer resistance protein. The identity of this placental transporter(s) as well as whether THC or its metabolites are substrates or inhibitors of hepatic solute carrier transporters is unknown. Therefore, we investigated whether 5 μM THC, 0.3 μM 11-OH-THC, and 2.5 μM THC-COOH are substrates and/or inhibitors of placental or hepatic solute carrier transporters at their pharmacological-relevant concentrations. Using HEK cells overexpressing human OATP1B1, OATP1B3, OATP2B1, OCT1, OCT3, OAT2, OAT4, or NTCP, and prototypic substrates/inhibitors of these transporters, we found that THC and THC-COOH were substrates but not inhibitors of OCT1. THC-COOH was a weak substrate of OCT3 and a weak inhibitor of OAT4. THC, 11-OH-THC, and THC-COOH were found not to be substrates/inhibitors of the remaining transporters investigated.

In modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution are pillars of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utility, play a critical role in the wider economic development. Electricity load and price forecasting has been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into three main sections. First, the first section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. The second section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in load demand forecasting. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The third section focuses on electricity price forecasting methods, summarizing important research papers investigating various modeling approaches to price forecasting. By examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges, and future directions in the field. Overall, this paper provides an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting, being an important resource for researchers and professionals in the energy industry.

Purpose: This study investigates radiomics efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization for prognosis and therapy guidance. Background: Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows qualitative injury evaluation, standard imaging alone has limitations for precise SCI diagnosis, severity stratification, and pathology characterization needed to guide prognosis and therapy. Radiomics enables quantitative tissue phenotyping by extracting a high-dimensional set of descriptive texture features from medical images. However, the efficacy of postoperative radiomic quantification in the presence of metal-induced MRI artifacts from spinal instrumentation has yet to be fully explored. Methods: 50 healthy controls and 12 SCI patients post-stabilization surgery underwent 3D multi-spectral MRI. Automated spinal cord segmentation was followed by radiomic feature extraction. Supervised machine learning categorized SCI versus controls, injury severity, and lesion location relative to instrumentation. Results: Radiomics differentiated SCI patients (Matthews correlation coefficient (MCC) 0.97; accuracy 1.0), categorized injury severity (MCC: 0.95; ACC: 0.98) and localized lesions (MCC: 0.85; ACC: 0.90). Combined T1 and T2 features outperformed individual modalities across tasks with gradient boosting models showing highest efficacy. Conclusion: The radiomic framework achieved excellent performance, differentiating SCI from controls and accurately categorizing injury severity. The ability to reliably quantify SCI severity and localization could potentially inform diagnosis, prognosis, and guide therapy. Further research is warranted to validate radiomic SCI biomarkers and explore clinical integration.

The energy crisis and environmental degradation are pressing challenges, intensified by popula-tion growth and the excessive generation of solid waste. Converting waste into energy, especially through pyrolysis, is a viable and sustainable alternative. This thermal process transforms waste such as banana peels and used tires into high-value products such as gas, coal and oil. This study aims to evaluate the production of pyrolytic oil from the pyrolysis and co-pyrolysis of these ma-terials, considering different proportions and temperatures, as well as using a decision tree to predict the composition of the bio-oils. The pyrolysis tests with 100% banana peel and 75% banana peel mixed with 25% tire showed a decrease in oil yield with increasing temperature, with a drop of around 30% when comparing 500 ºC to 400 ºC. In contrast, co-pyrolysis with 50% of each ma-terial and 100% of the tire resulted in increases in oil yield as the temperature rose. FTIR analysis of the oils showed the presence of relevant functional groups, while elemental analysis and de-cision tree modeling provided accurate predictions of carbon, hydrogen and nitrogen content. The results suggest that the co-pyrolysis of waste tires and banana peels is a viable alternative for the production of bio-oil.

Colposcopy image classification is vital for early cervical cancer detection, yet it remains challenging due to the significant variation in lesion appearances. Although deep learning models have advanced medical image classification, few studies have explored combining different model architectures to enhance diagnostic accuracy in colposcopy. This study addresses this gap by proposing a lesion-specific, multi-branch architecture that integrates attention mechanisms, deep feature extraction, and ensemble learning. Multi-task learning is employed to manage multiple lesion-specific classification tasks, while an ensemble of classifiers—Logistic Regression, XGBoost, and CatBoost—enhances decision-making accuracy. The architecture includes deep learning branches using EfficientNetB0 and MobileNetV2 for rich feature extraction from colposcopy images, with their outputs combined through a soft voting ensemble. Hyperparameter tuning, k-fold cross-validation, PCA visualization, and AUC plots for multiclass performance were used to optimize and assess model effectiveness. Training and validation accuracy were tracked in two phases: after the training phase, training accuracy reached 97.85\% and validation accuracy was 97.33\%; after the final ensemble classification, training accuracy improved to 99.95\% and validation accuracy to 99.85\%, surpassing individual model performance and demonstrating enhanced generalization. This model shows substantial promise for improving colposcopy classification accuracy, providing a valuable tool for clinical decision support in cervical cancer diagnosis.

This study investigates the potential of reusing punched steel waste, a significant component of solid inorganic waste, in composite materials for construction applications. Driven by the growing global demand for raw materials (projected to quadruple by 2050) and the need for sustainable waste management practices, this research explores the creation of a composite material (PPLK) incorporating punched steel tape (LPM-4 grade) embedded in a polypropylene matrix. Experimental testing of PPLK specimens (310 x 90 x 6.30 mm) and finite element analysis (FEA) were employed to evaluate the mechanical properties and stress concentration coefficient. Results show that the PPLK composite exhibits a load-carrying capacity of 21.64 kN, exceeding the sum of its individual components by 11.37%, demonstrating a synergistic effect between the steel (average tensile strength 220.65 MPa) and polypropylene. FEA further revealed that increasing the matrix's modulus of elasticity to 42 MPa significantly reduces the stress concentration coefficient in the steel component, resulting in a 24% enhancement of the elastic force. The findings suggest a viable path toward sustainable waste management and improved material utilisation in the construction industry.

AI on spaceborne platforms optimizes operations and increases automation, crucial for satellites with limited downlink capacity. It can ensure that only valuable information is transmitted, minimizing resources spent on unnecessary data, which is especially important in hyperspectral Earth Observation missions, producing large data volumes. Our previous work showed that the 1D-CNN, 1D-Justo-LiuNet, outperformed 2D-CNNs and Vision Transformers for hyperspectral segmentation, making our model the best choice for in-orbit deployment. In this work, we mark the first deployment and testing of a 1D-CNN in a satellite. We implement a C version of the 1D-Justo-LiuNet and, after ground validation, we deploy it on the on board the HYPSO-1 satellite. We demonstrate in-flight segmentation of hyperspectral images via the 1D-CNN to classify pixels into sea, land, and cloud categories. We show how in-orbit segmentation improves satellite operations, increases automation, and optimizes downlink. We give examples of how in-orbit segmentation addresses mission challenges in HYPSO-1, such as incomplete data reception, incorrect satellite pointing, and cloud cover, helping decide whether to transmit or discard data on board. An additional CNN autonomously interprets segmented images, enabling on-board decisions on data downlink.

The vaccine industry has undoubtedly contributed massively in the medical world, with hundreds of millions of lives saved as a result of the successful development of prophylactic and therapeutic vaccines against various infectious and oncological diseases. Simultaneously, there has been an ongoing evolutionary battle between polymorphic microbes and the human immune system, and various microbes have developed counter-responses against the human host immune system, by creating diverse “holes” of vulnerability in the innate immune system. Such mechanisms may have resulted due to the lower prophylactic and therapeutic focus upon sharpening the innate immune lines as directly as necessary. The overall objective of vaccine-development should be the sharpening of all immune departments that are targeted by various microbial agents for the purpose of transient innate immune suppression and the distribution of the microbial count/load at a systemic level. Likewise, it could be viable for the clinical research community to start focusing upon filling in the “gaps” of vulnerability that are now present in the pathways responsible for the proper natural immune activation and proportionate signalling toward the adaptive immune system. Perhaps, through such a process, the “road” toward the adaptive immune system, which is “old” and filled with “holes”, could be gradually transformed into a perfectly uniform “highway”, with all the required signals transmitted robustly from the innate to the adaptive immune system, before microbes manage to translate the various pathogenic proteins that transiently prevent such transmission altogether, until it becomes too late for the immune response to possibly develop in a proportionate and non-harmful manner to the organism. The potential “missing piece of puzzle” in vaccine-based clinical research could involve training of the human organism to develop immune memory without the host organism reaching a threshold of clinical disease onset even if viruses translate proteins that silence the host interferon system and make it unable to trace produced toxins whilst not directly causing clinical pathophysiology. Whilst it is realistically almost impossible to train the human immune system to develop responses and memory against viral proteins that do not directly cause immunopathology, there could be a set of clinical approaches involving a stimulation for more robust activation of the host interferon system, with an example of such stimuli being the same principal elements of the interferon system. Another example of pharmaceutical approaches would involve the development of drug-like components to target such viral proteins, though they would be more costly and consequently more restricted in usage; due to the fact that viruses are obligate intracellular, particle-based parasites and can only synthesise their proteins inside host cells. Such a scenario would be possible, given that it is almost impossible for human evolutionary growth to eventually stall and not risk experiencing a decrease as a result. Simultaneously, it is important for human society to bind by the principles of maintaining a healthy and nature-friendly lifestyle, given the philosophical quote of “We are what we eat.” The overall objective should be the facilitation of human and animal immune evolution over viral and bacterial molecular self-camouflaging from all possible directions, perhaps by transferring a concept called “a United Immune System” into clinical practice, as a sharper human and animal evolutionary growth resulting from such actions could “cost” the sharper evolutionary growth of microbes as such, given that evolutionary patterns are based upon the models observed in Physics, in Newton’s Third Law of Motion, as well as in the First Law of Thermodynamics, which concerns the Conservation of Energy.

Prenatal hypoxia plays a crucial role in programming long-term cardiovascular dysfunction through mechanisms like endothelial dysfunction and nitric oxide system impairment, highlighting the potential of therapeutic agents, such as thiotriazoline, angiolin, mildronate, and L-arginine, for mitigating these adverse effects. Methods. Levels of sEPCR, Tie2 tyrosine kinase, VEGF-B, SOD1/Cu-Zn SOD, GPX4, and GPX1 were measured in the heart's cytosolic homogenate using ELISA. Results. Modeling prenatal hypoxia (PH) resulted in significant alterations in the concentrations of proteins linked to endothelial function and oxidative stress in the heart cytosol of experimental animals, including an increase in sEPCR and reductions in Tie-2, VEGF-B, Cu/ZnSOD, GPX4, and GPX1 levels. Postnatal administration of nitric oxide modulators (L-arginine, thiotriazoline, angiolin, and mildronate) demonstrated differential efficacy in normalizing these proteins. Notably, angiolin produced the most substantial therapeutic effect, restoring Tie2, VEGF-B, and antioxidant enzyme levels to near-normal levels. Our results highlight the efficacy of Angiolin and Thiotriazoline in restoring endothelial function and antioxidant enzyme levels in the car-diovascular system following prenatal hypoxia, supporting their potential as early postnatal in-terventions to prevent long-term cardiovascular dysfunction.

In this study, we aim to enhance the accuracy of product quality inspection and counting in the manufacturing process by integrating image processing and human body detection algorithms. We employed the SIFT algorithm combined with traditional image comparison metrics such as SSIM, PSNR, and MSE to develop a defect detection system that is robust against variations in rotation and scale. Additionally, the YOLO v8 Pose algorithm was used to detect and correct errors in product counting caused by human interference on the load cell in real-time. By applying the image differencing technique, we accurately calculated the unit weight of products and determined their total count. In our experiments conducted on products weighing over 1kg, we achieved a high accuracy of 99.268%. The integration of our algorithms with the load cell-based counting system demonstrates reliable real-time quality inspection and automated counting in manufacturing environments.

The potential benefits of bioconversion of commercial broiler poultry litter (PL) to vermicompost (with Eudrilus eugeniae) were studied by optimizing the carbon-nitrogen ratio (C/N) using coconut (Cocos nucifera) coir pith (CP) and farm yard manure (FM) as co-substrate. In this experiment, after optimizing the C/N at the levels of 25, 30, and 35, the pre-composting of the PL followed by vermicomposting was done, and the PL and FM alone were used as the control group. After pre-composting, the earthworms (Eudrilus eugeniae) were introduced, and the vermicomposting process was continued for 90 days and the vermicompost samples were analyzed on the 45th and 90th days. The study revealed a significantly (P

In this study, 2 chalcone analogues were synthesized and assessed through in silico and experimental methods for their potential to inhibit the lipoxygenase enzyme, which plays a role in the inflammation pathway. The structure elucidation of each chalcone was conducted through a combination of Nuclear Magnetic Resonance (NMR) and Density Functional Theory (DFT). Α “LOX-chalcone” complex, predicted by docking studies, was further examined using Molecular Dynamics (MD) simulations to evaluate the stability of the complex. After fully characterizing the “LOX-chalcone” complexes in silico, the atomic details of each chalcone’s interaction with LOX-1 and 5-LOX were revealed through Saturation Transfer Difference (STD) NMR (Nuclear Magnetic Resonance). Finally, their selectivity profile was investigated for human 15-LOX-1 and general Lipoxidase activity. The in silico methods suggest that chalcones could be promising lead compounds for drug design targeting the LOX enzyme.

Instrumental production is a crucial component of industrial and innovative development in the metalworking industry, where the drilling process plays a key role. Traditional twist drills with conventional cutting-edge designs, including a transverse edge, face several issues during drilling, such as difficult chip evacuation and high friction between the tool and the workpiece. These phenomena, in turn, lead to a deterioration in the quality of machined holes and a reduction in tool life. Existing methods of drill geometry modification only partially address these problems by reducing axial forces and increasing tool durability, but they do not provide a comprehensive improvement of the drilling process overall. The aim of this study is to develop and examine a new drill design—a double-point twist drill without a transverse edge, which ensures favorable cutting conditions, reduces tool wear, and increases tool durability during the drilling process. In the course of the study, experimental samples of the double-point drill were manufactured at the machine-building company LLP «Format Mach Company» (Pavlodar). For the experimental tests, a setup was developed that included the ZET 058 strain gauge system and Kistler piezoelectric force sensors. This combination allowed for a detailed investigation of cutting forces in a dynamic mode and recorded the loads acting on the tool in various directions, contributing to an objective evaluation of its performance. The experimental results showed that the double-point drill without a transverse edge significantly improves the drilling process by reducing axial and radial cutting forces, which helps to reduce tool wear and increase drill durability. This confirms the feasibility of applying this drill design to enhance the quality of hole machining and the efficiency of production processes in the metalworking industry. An increase in the durability of the new tool - a double-point twist drill without a transverse edge - was established at 2.2 times.

Background/Objectives: Chronic liver disease (CLD) is highly relevant in clinical practice due to its increasing incidence and associated mortality. We aimed to exhaustively characterize the multimorbidity in patients with CLD. Methods: Retrospective observational study of patients aged 18 years and older with a diagnosis of CLD in 2015 in the EpiChron Cohort (Spain). We analysed the prevalence of comorbidities according to sex and age, conducted a logistic regression analysis with CLD as the independent variable and each of the comorbidities as dependent variables to obtain odds ratios (OR) adjusted for age and sex, and performed an exploratory factor analysis to identify the presence of multimorbidity patterns. Results: 6836 adults had a diagnosis of CLD (mean age 61.6 years; 62.5% women), with an average of 13 co-existing chronic conditions. Arterial hypertension, lipid metabolism disorders, diabetes, obesity, and musculoskeletal diseases were the most frequent diseases. From the list of 110 chronic conditions analysed, 75 were systematically associated with CLD, including (OR, 95% confidence interval) chronic pancreatitis (41.2, 33.5-50.6), and inherited metabolic disorders (14.9, 11.8-18.8). Three multimorbidity patterns were identified in both men and women, and called cardiovascular, metabolic-geriatric, and mental-substance use, with some differences by sex. Conclusions: Multimorbidity is the norm in patients with CLD. Our results reveal that a comprehensive and integral approach based on person-centred care, which should take into account their entire spectrum of multimorbidity, is necessary during the care of this type of patients in clinical practice.

The effect of the Mn3+ ion on the local distortions of the FeO6 octahedra in orthoferrite samples was investigated. Mössbauer spectroscopy measurements for a series of the HoFe1−xMnxO3 (x=0.0−0.7) orthoferrite samples with space group Pnma was carried out at temperatures above the Néel point (700 K). The electric field gradient (EFG) tensor on Fe ions for these compounds was found using the first-principle calculations. The concentration dependence of the quadrupole splitting was resulted from the experimental and theoretical data. The absence of an electron contribution to the quadrupole splitting at all the manganese concentrations was demonstrated. It was shown by the theoretical calculations that the values of all the electric field gradient components increase significantly with the manganese concentration in the system, and the eigenvectors Vxx and Vyy of the electric field gradient tensor sharply change their direction at concentrations of x > 0.1.

The basic idea is to expand the completed zeta function $\xi(s)$ in MacLaurin series (infinite polynomial), which can be further expressed as infinite product (Hadamard product) by conjugate complex roots. Finally, the functional equation $\xi(s)=\xi(1-s)$ leads to $(s-\alpha_i)^2 = (1-s-\alpha_i)^2, i \in \mathbb{N}$ with solution $\alpha_i= \frac{1}{2}, i \in \mathbb{N}$, where $\alpha_i$ are the real parts of the zeros of $\xi(s)$, i.e., $s_i =\alpha_i\pm j\beta_i, i\in \mathbb{N}$. Therefore, a proof of the Riemann Hypothesis is achieved.

A telemetry system of water depth monitoring is required to have bidirectional communication to obtain an effective and efficient measurement system. It should be run at any meaningful time requested by user so that the collected data become more crucial and eventually generate an energy and memory saving modem. This work proposed the scheme to be applied in a campus lake with flooding and drainage warning systems to avoid the common natural disaster in raining and dry seasons, respectively. The system based on Arduino board and Ultrasonic sensor equipped by Wi-Fi modem to communicate with user’s phone who able to start a measurement and monitored the water depth through a website. The result showed that in the laboratory test, the alert was successfully sent to user’s phone whenever the depth rose past the threshold value. In the field test, the measurement of the lake’s water depth had been effectively monitored via website throughout 12 hours using 6000 mAh battery lasted for 72 times of data transmission.

The present work analyses the behaviour of an activated carbon fabricated from almond shell for the removal of cationic dyes (methylene blue and malachite green) by adsorption from aqueous solutions. The carbonized precursor was activated with KOH at a 1:1 (w/w) ratio with the objective of increasing both surface area and pore volume. Both non-activated and activated carbon were characterized in different aspects of interest in dye adsorption studies (surface structure, point of zero charge, surface area, pore size distribution). The effect of dye initial concentration and adsorbent dosage upon dye removal efficiency and carbon adsorption capacity has been studied. Adsorption kinetics was analysed under different experimental conditions and different models were essayed to determine the adsorption mechanism. Dye adsorption in the adsorbent surface could be considered the rate-limiting step. Adsorption equilibrium isotherms have also been obtained, and Langmuir and Freundlich equations showed the best fitting to the experimental data. Moreover, Adsorption kinetic and equilibrium data have been used to simulate breakthrough curves in a packed-bed column using different conditions (bed length, liquid flowrate, dye initial concentration). Simulation results showed that almond shell activated carbon is a suitable adsorbent for methylene blue and malachite green removal from wastewater.

Duchenne muscular dystrophy (DMD) is a severe genetic muscle disease due to mutations of dystrophin gene. There is no cure for DMD. Using a dystrophin-/-utrophin-/- (DKO-Hom) mouse model, we investigated the PGE2/EP2 pathway in the pathogenesis of dystrophic muscle and its potential as a therapeutic target. We found that Ep2, Ep4, Cox-2, 15-Pgdh mRNA, and PGE2 are significantly increased in DKO-Hom mice compared to wild-type (WT) mice. The EP2 and EP4 receptors are mainly expressed in CD68+ macrophages and are significantly increased in the muscle tissues of both dystrophin-/- (mdx) and DKO-Hom mice compared to WT mice. Osteogenic and osteoclastogenic gene expression in skeletal muscle also increased in DKO-Hom mice, which correlates with severe muscle heterotopic ossification (HO). Treatment of DKO-Hom mice with the EP2 antagonist PF04418948 for 2 weeks, increased body weight, reduced HO and muscle pathology by decreasing both total macrophages (CD68+) and senescent macrophages (CD68+P21+), while increasing endothelial cells (CD31+). PF04418948 also increased bone volume/total volume (BV/TV), trabecular thickness (Tb.Th) of the tibia trabecular bone and the cortical bone thickness of both the femur and tibia without affecting spine trabecular bone microarchitecture. In summary, our results indicate that targeting EP2 improves muscle pathology and maintains bone mass.

This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data from medical-grade sensors. Gradient Boosting Models (XGBoost, LightGBM, CatBoost) and sequential models (LSTM, GRU) were trained on physiological and demographic data from the MIMIC-III and IV datasets. A robust preprocessing pipeline addressed missing data, class imbalances, and incorporated synthetic data flagged with masks. Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability, making them well-suited for real-time decision-making. While their performance was comparable to that of sequential models, the GBMs used score features from six physiological variables derived from the enhanced National Early Warning Score (NEWS) 2, which we termed NEWS2+. This approach significantly improved prediction accuracy. While sequential models handled temporal data well, their performance gains didn’t justify the higher computational cost. A 5-minute prediction window was chosen for timely intervention, with minute-level interpolations standardizing the data. Feature importance analysis highlighted the significant role of mask and score features in enhancing both transparency and performance. Temporal dependencies proved to be less critical, as Gradient Boosting Models were able to capture key patterns effectively without relying on them. This study highlights ML's potential to improve triage and reduce alarm fatigue. Future work will integrate data from multiple hospitals to enhance model generalizability across clinical settings.

Streptococcus agalactiae is an important pathogen responsible for cases of high mortality in farmed and wild fish worldwide. In Brazil, this bacterium has been commonly associated with outbreaks in Nile tilapia farms, but other native fish species are also susceptible. Since floating cages are one of the most common culture systems used in the country, the close contact between farmed tilapia and native fish species presents a risk concerning the transmission of this pathogen. In this study, we characterized a mortality outbreak in free-living trahira and in farmed arapaima, as well as the genetic and antimicrobial susceptibility patterns of the isolates obtained. During the outbreaks, moribund fish were sampled and subjected to bacterial examination, after which the isolates were identified via MALDI-ToF analysis. Genotyping was evaluated using repetitive sequence-based PCR (REP-PCR) and multilocus sequence typing (MLST). Antimicrobial susceptibility was evaluated using disc diffusion assays. In addition, whole genome analysis also was performed. S. agalactiae was identified in all diseased fish, all of which belonged to serotype Ib, however, trahira strains were classified as non-typeable lineages in the MLST assay, while arapaima strains were classified as ST260. These isolates were shown to be similar to the main genotype found in Nile tilapia in Brazil, using REP-PCR, MLST and phylogenomic analysis. The pathogenicity of bacterium was confirmed by Kock’s postulates for both fish species. The antimicrobial susceptibility assay showed variable results to the same antibiotics among the isolates, prompting four of the isolates to be classified as multidrug-resistant. This study represents the first report of a natural outbreak of Streptococcus agalactiae infection in wild trahira and farmed arapaima inhabiting the same aquatic environment with Nile tilapia.

Abstract: Background/Objectives: For years, research has been conducted on the relationship between anti-inflammatory diets and the incidence and risk of various diseases. In recent years, much attention has been paid to the beneficial effects of fish oil (FO) or shark liver oil (SLO) consumption, which is increasingly being used clinically. Health benefits of FO have been asso-ciated mainly with omega-3 polyunsaturated fatty acids (n-3 PUFAs), but also alkylglycerols (AKGs) and squalene. n-3 PUFAs, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), the main components of FO, have been shown interfere with host immunity, but also various stages of viral infection, particularly viral entry and replication. Methods: In this narrative review we tried to answer the question on the role of n-3 PUFAs, AKGs and squalene in modulation of antiviral response based on the available literature from 2001 to 2023, starting from in vitro and ex vivo studies through animal studies coming up to the human studies. Conclusions: In general, DHA and EPA have an inhibitory effect on the activation of both innate and adaptive immune cells, while squalene and AKGs were shown to enhance antimicrobial and antitumor immune response.

introduction In the professional practice of nursing, differences have been described between the habitus shared in the academic training process and the professional practice developed in the field of health systems; in this scenario, nurses tend to be affected by how their professional habitus has been established, which, in some way, alters the cultural capital established in their academic training process. The research objective was to identify aspects that facilitate and limit the development of the theoretical component in nursing practice.Method: The research was based on a qualitative hermeneutic study addressed from a critical approach that linked Bourdieu's field theory. The study included 17 students, 11 nurses active in health services, and six university professors. Data were collected from a sociodemographic questionnaire, episodic interviews, and focus groups. The information collected was analyzed using the hermeneutic understanding process from the grounded theory.Results: The research revealed a central thematic core called clarification of the role of the nurse (professional habitus) and three secondary thematic cores related to the need to strengthen the nursing essence (identity habitus), the adjustment of training processes (optimization habitus) and, finally, the strengthening of the nurse's credibility (validation habitus).Conclusion: The professional habitus of nursing professionals is shaped by different elements of the social field of the health system. This field is repressed by hierarchical structures that succeed in disconnecting theory from praxis. Aspects such as subordination to the hegemonic biological discourse and the medical profession, the limited appreciation of humanized care, restrictions on the recognition of the nursing process by health institutions, and limited solidarity and leadership end up conditioning the accumulation of symbolic capital and social capital of nursing, which leads to the loss of autonomy and the advancement of professional development.

The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on paired-samples t-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance.

Large Language Models (LLMs) are emerging as promising tools in hardware design and verification, with recent advancements suggesting they could fundamentally reshape conventional practices. In this survey, we analyze over 54 research papers to assess the current role of LLMs in enhancing automation, optimization, and innovation within hardware design and verification workflows. Our review highlights LLM applications across synthesis, simulation, and formal verification, emphasizing their potential to streamline development processes while upholding high standards of accuracy and performance. We identify critical challenges, such as scalability, model interpretability, and the alignment of LLMs with domain-specific languages and methodologies. Furthermore, we discuss open issues, including the necessity for tailored model fine-tuning, integration with existing Electronic Design Automation (EDA) tools, and effective handling of complex data structures typical of hardware projects. This survey not only consolidates existing knowledge but also outlines prospective research directions, underscoring the transformative role LLMs could play in the future of hardware design and verification.

Time irreversibility, i.e. the lack of invariance of a system under the operation of time reversal, has long attracted the attention of the statistical physics community; and has been shown to be a relevant marker of altered dynamics in many real-world problems. We here introduce and analyse the complementary problem of its manipulation. In other words, we ask whether, given a time series, this can be manipulated to achieve a desired irreversibility while maintaining the original dynamics. We show how this problem can be tackled using Continuous Ordinal Patterns, a non-linear transformation of a time series based on the local structure created by neighbouring values. We further illustrate the relevance of this problem in the context of brain dynamics, obtaining that schizophrenic patients and control subjects are characterised by different "distances to irreversibility". We finally discuss some open questions, including the meaning of such manipulation from both theoretical and applied viewpoints.

Objectives: The goal of the study was to assess if body mass index (BMI) affects the pace of cardiac muscle recovery in children after PIMS-TS. Methods: A prospective single-center study enrolled consecutive children hospitalized with PIMS-TS between October 2020 and February 2022 and followed-up after 6 weeks and 6 months. In all children three-dimensional echocardiography and global longitudinal strain were used to assess ventricular function and the results were analyzed according to patients’ BMI status. Results: The study enrolled 170 patients aged 1-17 years, median 8.5+4.43 years. Children with normal BMI (>5th and 85th percentile; n=40) made 23.53% and underweight (85th percentile LVEF 61.15+3.21%, BMI 85th per-centile may indicate poorer left ventricular performance. Children with normal BMI may present more advantageous cardiac recovery pace after PIMS-TS.

Abstract: Objective: Chronic low back pain (CLBP) is a significant global concern. Its prevalence is increasing, and current management strategies demonstrate at best, moderate effectiveness. The purpose of this study was to explore the concepts of hope, uncertainty and how they both influence emotion regulation because these concepts are extremely prevalent for patients and clinicians during CLBP consultations. Methods: A soccial constructivist meta-ethnographic is a highly interpretative type of qualitative review that generates new theory, enabling a valuable insight to this unique area. A framework was followed; its iterative analytical process involves multiple search strategies, exploring how studies relate, generating ideas and developing a substantive theory. Results: This review represents the first worked example of a theory generating review process. One model was created (see figure 5) that focuses on how the individual with CLBP regulate their emotions and considers factors that feed into the process and result from the process. The outcome of the model produced either adaptative or maladaptive emotional regulation strategies (ERS). Conclusions: Hope and uncertainty are key concepts that are interlinked and play a vital role on health outcomes of people with CLBP. Research is required to explore how the model can be operationalised in clinical practice.

Indigenous plants from Southeast Asia, specifically those from the Philippines, remain underex-plored in terms of their essential oils (EOs) potential. The chemical composition of hydrodistilled leaf EOs from two indigenous EOs-bearing species, namely Litsea leytensis (Lauraceae) and Piper philippinum (Piperaceae), was described, using dual-column/dual-detector GC-MS analysis. When analysed using two columns differing in their polarity (HP-5MS/DB-WAX), L. leytensis EO had high amounts of caryophyllene oxide (15.70/16.76%) as the primary component, followed by β-caryophyllene (11.04/11.12%) and α-copaene (8.85/8.87%). Ishwarane (26.98/24.9%), nerolidol (10.14/10.42%) and 3-ishwarone (6.84/2.46%) were the most abundant constituents of P. philippinum EO. Additionally, the in vitro growth-inhibitory activity of the EOs in the liquid and vapor phases against Staphylococcus aureus was evaluated using the broth microdilution volatilization assay; however, both EOs had no anti-staphylococcal effect. This is the first study evaluating antibacterial activity and chemical composition of EOs from L. leytensis and P. philippinum. However, the iden-tification of potentially bioactive compounds in both EOs suggests further investigation of these species.

This research offers an extensive analysis of a family of prime-generating polynomials \( P_k(n) = n^2 - (2k - 79)n + [41 + (k - 39)(k - 40)] \), designed to generate prime numbers for integer values of \( n \) up to \( k \) where \( k \in \{1, 2, \ldots, 80\} \). We elaborate on mathematical clarity, consistency in proofs, error bounds, prime repetition analysis, and cryptographic implications. This study provides in-depth inductive proofs, graphical error bounds analysis, prime repetition statistics, and cryptographic security assessments, offering novel insights and future directions for research in number theory and secure prime generation.

One-step methods for the formation of efficient thin film catalysts for wastewater treatment under sunlight spectrum is a topic of interest of many research groups. This article reports on the facile preparation of photocatalytic coatings by plasma electrolytic oxidation processing from 0.01 M sodium tungstate electrolyte solution containing both 13 X zeolite and CeO2. Obtained coatings are characterized with respect to their surface morphology, chemical and phase composition, and for their possible application as photocatalysts in photodegradation of organic pollutants. All prepared coatings contain elements originating from both substrate and electrolyte solution. Addition of 1 g/L of 13X zeolite and CeO2 in various concentrations to electrolyte solution results in increased photodecomposition of model organic pollutant. The highest photodegradation under simulated sunlight is observed for coatings formed in 0.01 M sodium tungstate with addition of 1 g/L of 13 X zeolite and 1 g/L of CeO2, reaching 50 % after 6 h of irradiation.

The research presented in this review highlights the critical importance of integrating Artificial Intelligence (AI) in aquaculture to enhance efficiency, sustainability, and fish health management. Current aquaculture practices face significant challenges, such as data quality issues, incomplete and noisy data, and the complexity of managing diverse environmental and biological factors affecting fish health. To address these challenges, the review explores advanced AI methodologies, including deep learning, the Internet of Things (IoT), and data fusion techniques, to develop robust predictive and health management (PHM) systems in aquaculture. The study summarizes various AI-based approaches for real-time monitoring, disease detection, and optimized feeding strategies, which improve the overall management of aquaculture operations. These methods involve the use of high-precision sensors, automated image and acoustic data acquisition, and sophisticated data preprocessing and integration techniques. Collectively, these techniques aim to ensure accurate, real-time monitoring and predictive analysis of fish health and environmental conditions. The research findings demonstrate the successful application of AI techniques in achieving significant improvements in fish growth rates, reducing environmental impact, and enhancing operational efficiency. For example, AI-driven PHM systems have shown high detection accuracy and efficient management of feed and water quality parameters, leading to better fish health outcomes and lower mortality rates. In conclusion, the integration of AI in aquaculture presents a transformative approach, offering valuable insights and practical solutions for sustainable and intelligent fish farming. The study underscores the value of AI in enhancing predictive capabilities, optimizing resource use, and ensuring the ecological sustainability of aquaculture practices, thereby contributing to the growing global demand for efficient and sustainable fish production systems.

Background/Objectives: Continuous Monitoring Blood Culture Systems (CMBCS) are revolutionary automated instruments that facilitate the rapid identification of pathogens in blood samples from patients with sepsis. However, with only a few CMBCS being widely used as references, user dependency on these limited options has grown. In response, a new CMBCS was developed and compared with existing systems to evaluate microbial growth. Methods: HubCentra84 was compared to BacT/Alert® 3D and BACTEC™ FX. Staphylococcus aureus (SAU), Streptococcus pneumoniae (SPN), Escherichia coli (ECO), Pseudomonas aeruginosa (PAE), Bacteroides fragilis (BFR), and Candida albicans (CAL) were selected as representative clinically infectious microorganisms. Colonies from pure cultures were diluted with normal saline to create simulated sepsis specimens (SSS). The SSS was injected into dedicated culture bottles for each instrument. Thirty paired tests were performed for each strain. Results: Colony-forming units of the added SSS were consistent according to bacteria, and all strains demonstrated robust growth in CMBCS. The growth alarm times were uniformly observed according to the instruments used. CMBCS detected the growth of the clinically significant bacteria SAU, SPN, ECO, and PAE approximately 2 h faster than the other two systems. However, it was approximately 200 min slower for CAL and 3,000 min for BFR. Conclusions: The novel CMBCS demonstrates advantages in detecting the growth of common clinical bacteria. Although slow growth was detected for certain microorganisms, it successfully captured the growth of all tested microorganisms.

It is established that BCG vaccination results in the development of both a specific immune response to mycobacterial infections and a nonspecific (heterologous) immune response, designated as trained immunity (TRIM), to other pathogens. We hypothesized that local BCG immunization may induce early immune response of bone marrow and spleen innate immunity cells. The early transcriptomic response of various populations of innate immune cells, including monocytes, neutrophils, and natural killer (NK) cells, to BCG vaccination was examined. To this end, C57Bl/6J mice were subcutaneously immunized with 10⁶ CFU of BCG. Three days following BCG administration, the three cell populations were collected from the control and BCG-vaccinated groups by FACS. All cell populations obtained were utilized for the preparation and sequencing of RNA-seq libraries. The analysis of FACS data revealed an increase in the proportion of splenic NK cells and monocytes 3 days post-vaccination. Transcriptomic analysis revealed deregulation of genes associated with regulation of immune response (according to Gene Ontology terms) in NK cells, monocytes, and unsorted bone marrow. Two NK cell-specific immune ligands (Tnfsf14 and S100a8) and two bone marrow-specific immune receptors (C5ar1 and Csf2rb) were identified among differentially expressed genes. No alterations were identified in neutrophils in either the percentage or at the transcriptomic level. Thus, in this study we demonstrated that BCG vaccination provides the early increase in the proportion of murine bone marrow and spleen immune cell populations, as well as the transcriptomic alterations in monocytes, NK cells, and non-sorted bone marrow. This early innate immune response may be beneficial for enhancing TRIM.

Mevcut çalışmada, (p, q)−tam sayıya dayalı iki boyutlu Chlodovsky tipi Bernstein operatörlerini tanıtıyoruz. Yeni operatörümüzün yaklaşıklık özelliklerini Korovkin tipi teorem yardımıyla inceliyoruz. Ayrıca, yerel yaklaşıklık özelliklerini sunuyoruz ve süreklilik modülü ve Lipschitz tipi maksimal fonksiyon vasıtasıyla yakınsama oranlarını belirliyoruz. Ayrıca, bu operatörler için bir Voronovskaja tipi teorem veriyoruz. Ve, bu operatörlerin ağırlıklı yaklaşıklık özelliklerini inceliyoruz ve aynı uzayda yakınsama oranını tahmin ediyoruz. Son olarak, Maple'ın yardımıyla, açıklayıcı grafikler bu operatörlerin belirli fonksiyonlara yakınsama oranlarını göstermektedir. Sistem kontrolü sırasında operatörler tarafından yaklaşıklık hızlarının optimizasyonu, kararlılık ve performansta önemli iyileştirmeler sağlar. Sonuç olarak, dinamik sistemlerin kontrolü ve modellenmesi yenilikçi yöntemlerle daha verimli ve etkili hale gelir. Kesirli diferansiyel denklemlerin modellenmesi ve kontrol teorisi alanındaki bu gelişmeler, hem modelleme hem de optimizasyon süreçlerine önemli faydalar sunarak bu alanlardaki uygulama yelpazesini genişletmektedir.

Glioblastoma Multiforme (GBM) remains one of the most aggressive and challenging brain tumors to treat. Even with maximal treatment, the prognosis remains poor with low median survival. Extensive surgical resection is evidenced to increase glioblastoma life expectancy; however, complete resections are not usually achieved due to lack of sensitivity and the accuracy of margin neurosurgical guidance technologies. Recent advancements in fluorescence-guided surgery (FGS) and imaging techniques have helped enhance the precision and extent of glioblastoma resections. This study explores the impact of NIR fluorescence imaging on tumor visualization and surgical outcomes, including the extent of resection, cost-effectiveness, and patient survival.  A systematic search of PubMed, Scopus, Google Scholar, and Embase identified studies published on the effect of NIR on glioblastoma surgery. A total of 135 studies were included: 10 reviews, 3 clinical studies, 10 randomized controlled trials (RCTs), 10 preclinical studies, and 4 case reports, all focusing on the application of NIR fluorescence imaging in glioblastoma resection. Our findings show that NIR fluorescence imaging significantly improved tumor visualization, leading to an 18-22% increase in gross total resection rates across clinical studies. Prolonged fluorescence provided continuous real-time feedback, reducing the need for repeat imaging, and lowering operational costs. Patient outcomes improved, including extended progression-free survival and overall survival, and fewer postoperative neurological deficits. This Review highlights the potential for the use of NIR imaging to become the new standard for intraoperative glioblastoma management.

Vaccine adjuvants are crucial for reinforcing the immunogenicity of vaccines. Therefore, they are widely used in the aquaculture sector as vaccine components facilitating the efficient prevention of infectious diseases and promoting sustainable teleost fish growth. Despite their benefits, there has been a growing concern about the potential adverse effects of vaccine adjuvants in teleost fish, connoting a valid impact on their overall health and welfare. Among adjuvants used in aquaculture vaccinology, nanoparticle-based formulations have given rise to a promising new alternative to traditional options, such as oil-based emulsions and aluminum compounds, offering the benefit of minimizing relevant side-effects. The aim of this paper was to review the current status of the adjuvants used in aquaculture, provide a description and an evaluation of their mode of action and side-effects, and explore the potential use of nanoparticle formulations as adjuvants to improve the efficacy of aquaculture vaccines. By demonstrating and assessing the equilibrium between teleost fish welfare and immunological efficacy, this review presents a collective perspective that will assist in establishing a framework for the utilization of effective, species-specific practices around adjuvant use in aquaculture, while also addressing the challenges of welfare-friendly immunization.

Business Finland 6G Visible project’s objective is the development of 6G-era service and architecture solutions utilizing autonomous and semi-autonomous driving, with both physical and logical computational elements and with the use cases for real-life verification and validation. Finnish Meteorological Institute is focusing especially on weather and safety related services for autonomous vehicles. We are tailoring our road weather services for the special needs of autonomous driving, keeping in mind that autonomous vehicle is more sensitive to the harsh winter weather conditions and benefit from more accurate weather information considering the sensor systems of each vehicle. Employing weather radar-based Nowcasting of more accurate short-term precipitation forecasting benefits autonomous traffic especially in cases of heavy local precipitation by re-routing/route-planning and avoiding heaviest precipitation. Evaluation of autonomous vehicles’ sensor systems sensitivity to harsh weather conditions allows the weather forecasting based on real vulnerability of each vehicle.

The present study aimed to evaluate the multivariate relationships between variables related to burnout and job stress in healthcare workers, evaluating if the relationship between these dimensions, the variables related to personal factors (age, seniority of service), and work–family balance factors (overwork related to unused vacation days, accumulated overtime hours) change when the worker is engaged in double-caregiving activities. Indeed, the twofold activities of home caregiving and caring at work might expose workers to challenging situations. To accomplish our aim, we carried out network analyses on data from 466 workers (77.90% females). Participants completed the Link Burnout Questionnaire (LBQ) and the Job Satisfaction Scale (OSI). Contrary to expectations, the variables related to work–life balance played a marginal role with respect to job satisfaction and burnout risk for the whole sample. In addition, no significant differences emerged between workers who reported dual- caregiving tasks, compared with those who did not. However, some peculiar aspects of the relationship between burnout and job satisfaction emerged in the two subsamples. The findings are discussed, highlighting a framework for understanding the interactions among the assessed variables.

The lunar calendar is often overlooked in time series data modelling, despite its importance in understanding seasonal patterns as well as economic, natural phenomena, and consumer behavior. This study aims to investigate the effectiveness of the lunar calendar in modelling and forecasting rainfall levels using various machine learning methods. The methods employed included Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally pro-vided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems, while also opening new opportunities for further research into lunar calendar applications across various domains.

Background/Hypothesis: Motion sensitivity symptoms, like dizziness or unsteadiness, are frequently reported as non-headache symptoms of migraine. Postural imbalance has been observed in subjects with vestibular migraine, chronic migraine, and aura. We aimed to assess the ability of largest Lyapunov’s exponent for short time – series (sLLE), which reflects the ability to cope with internal perturbations during gait, to detect differences in local dynamic stability between individuals with migraine without aura (MO) with an episodic pattern and healthy subjects (HS). Methods: Trunk accelerations of 47 MO and 38 HS were recorded during gait using an inertial measurement unit. The discriminative ability of sLLE was assessed through receiver operating characteristics curves and cutoff analysis. Partial correlation analysis was conducted between the clinical and gait variables, excluding the effects of gait speed. Results: MO showed higher sLLE values, and reduced pelvic rotation, pelvic tilt, and stride length values. sLLEML and pelvic rotation showed good ability to discriminate between MO and HS and correlated with the perceived pain, MIgraine Disability Assessment Score, and each other. Conclusions: These findings may provide new insights into the postural balance control mechanism in subjects with MO and introduce the sLLEML as a potential measure of dynamic instability in MO.

Prostate cancer (PCa) is the most prevalent malignancy and the second leading cause of can-cer-related death in men. Although current therapies can effectively manage the primary tumor, most patients with late-stage disease manifest with metastasis in different organs. From surgery to treatment intensification (TI), several combinations of therapies are administered to improve prognosis of patients with metastatic PCa. Due to the high frequency of mutation during the metastatic phase, the Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 genetic engineering tool can accelerate the effects of TI by enhancing targeted gene therapy or immunotherapy. This review describes the genetic backgrounds of metastatic PCa and how CRISPR/Cas9 technology can contribute to the field of PCa treatment development. It also dis-cusses current limitations of conventional PCa therapy and the potential of CRISPR-based-PCa therapy.

In a high-brightness photocathode gun, the non-uniformity in both the transverse distribution of the photocathode drive laser and the quantum efficiency (QE) map can lead to a degradation in electron beam quality. This study explores an adaptive transverse laser shaping technique and its application to improve the uniformity of the electrons emitted from the photocathode. By utilizing a spatial light modulator (SLM) with a feedback algorithm, we effectively control the drive laser distribution and achieve single-shot QE map measurement and QE compensation. The experiments conducted on the DC-SRF-II gun demonstrate a rapid and efficient QE measurement process, as well as a notable improvement in electron beam uniformity. Our study offers a promising approach for improving beam quality in accelerator applications that demand high-brightness electron sources.

This research was carried out as part of a program for the conservation and improvement of the strawberry tree. Accessions’ prospecting was conducted in different Portuguese provenances. Accessions (204) were identified, and mature fruits were collected in autumn. The sugar contents in the fruit pulp (glucose, fructose, maltose, sucrose) were analyzed by HPLC and the correlation between the average sugar content, climatic classification (Thornthwaite Method and Xerothermic Index), and edaphic characteristics was investigated. The predominant sugar was fructose, which ranged from 7.89±0.55% to 17.01±1.46% (f.w.), respectively, under an Attenuated Thermomediterranean climate at limestone-derived soil and an Accentuated Mesomediterranean climate at schist-derived soil. The lowest sugar contents, linked to a reduction in photosynthesis, were found: 1) in the north, despite soil water availability, due to the low temperature, high precipitation and short photoperiod, particularly during the fruit maturation (autumn); 2) in the south, due to the excessive number of dry days, linked to low soil water availability during the active growth period, particularly at limestone zones prone to water retention. The relationship found between the total sugar content and climate classification by the Xerothermic Index allows to enhance fruit production, advise on the establishment of new orchards and restore natural areas.

The increasing complexity of modern power systems, driven by the integration of renewable energy sources and growing electricity demand, has led to a need for optimizing smart grids. Traditional grid management approaches are often insufficient in addressing the challenges of efficiency, reliability, and minimizing losses. In this paper, we propose a novel AI-based optimization framework for smart grids, designed to reduce transmission and distribution losses, enhance system efficiency, and improve grid reliability. We conduct a comparative analysis of traditional optimization techniques and state-of-the-art AI algorithms and demonstrate the superiority of the proposed method using the IEEE 33-bus benchmark system. Results show a significant reduction in power losses and an improvement in voltage profile, leading to a more efficient and reliable grid.

Importance. Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. These investigations will identify individual circumstances where appropriate therapy will extend survival while minimizing adverse events.Objective. Our goal is to develop high confidence deep learning (DL) models for predicting survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment and adverse events variables. Our plan is to improve the predictive accuracy of deep learning (DL)-based predictive models by considering time-fixed covariates, i.e., patient and cancer data at the time of diagnosis, together with time-varying events that occur after initial diagnosis. Design, Setting, and Participants. We used the SEER-Medicare linked dataset from 1991-2016 to investigate women diagnosed with stage IV BC who enrolled at 65 years or older for age eligibility. We outlined time-fixed variables, including date of diagnosis, age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status and comorbidity index, prior therapy, adverse events, and changes in comorbidity. We delineated the time-varying covariates at each visit, including administered treatments, adverse events, comorbidity index, and age. We extended four DL-based predictive survival models (DeepSurv and DeepHit. Nnet-survival and Cox-Time) that deal with right-censored time-to-event data to consider both a patient's time-fixed covariates and a patient's time-varying covariates. We predicted the survival of five hypothetical patients to demonstrate the model's utility. We found high concordance between the performance metric, time-dependent concordance, and each of the model's hyperparameters to demonstrated prediction validity.Results. By incorporating time-varying variables with the time-fixed variables, the models reduced the error rates of the concordance index, the most commonly applied evaluation metric in survival analysis, from 28-38% to 2-12%, and significantly improved the integrated Brier score, a metric of the model's discrimination and calibration. Conclusions and Relevance. By combining the consideration of time-fixed with those time-varying variables in our predictive models, we decreased the predictive error rate to under 10% in the predicted survival of stage IV BC patients. Our established models can predict survival in individual stage IV patients with high confidence based on their circumstance-specific situations generated by considering their unique patient, cancer, treatment and adverse events variabels. These models will serve as an important adjunct to treatment decisions in patients with stage IV BC to optimize therapy for extending patient lives and minimizing adverse events.

This paper experimented with a methodology of machine learning modelling using virtual samples generated by a fast CFD (Computational Fluid Duy) simulations, in order to predict the greenhouse natural ventilation. However, the output natural ventilation rates using fast two-dimensional (2D) CFD models are not always consistent with the three-dimensional (3D) one for all the scenarios. The first contribution of this paper is a proposed comparative modelling methodology between two-dimensional and three-dimensional CFD (Computational Fluid Dynamics) studies, regarding its validity, especially when buildings are in rows. The results show that the error on the ventilation rate prediction could exceed 50%, if 2D models are not properly used. Subsequently, in those scenarios where the 2D and the 3D model had equal accuracy, nearly one thousand samples were generated using fast 2D CFD simulations to train natural ventilation rate regression tree-model. This model is efficient to deal with the combined effect of wind pressure and thermal gradients under various vent configurations, with only four necessary inputs. In addition, by analyzing the wind speed distribution contour of the outdoor wind field around the greenhouse rows, the optimal wind speed measuring locations were determined to eliminate interference for predicting the natural ventilation rate.

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, surface quality, and overall process efficiency. While ML models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. This study bridges the gap by validating a ML-based chatter detection system using both simulated and real-world data. We apply a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1,600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. Our results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

The depletion of conventional oil reserves has intensified the search for enhanced oil recovery (EOR) techniques. This research investigates the synthesis and application of magnetic graphene oxide nanoparticles (MGONs) for EOR in heavy crude oil reservoirs. MGONs were synthesized and stabilized in high-total dissolved solids (TDS) formation brine with xanthan gum polymer. Two coreflooding experiments were conducted on sandstone cores. The first experiment in high-permeability sandstone (843 mD) showed no formation damage; instead, permeability increased to 935 mD after MGON injection. Irreducible water saturation (Swirr) and residual oil saturation (Sor) were 25.1% and 31.5%, respectively. The second experiment in lower-permeability rock (231.3 mD) evaluated nanoparticle retention. Results showed 0.09511 mg of MGON adsorbed per gram of rock under dynamic conditions. Iron concentration in effluents stabilized after 3 pore volumes, indicating steady-state adsorption. The successful synthesis, stability in high-TDS brine, favorable interfacial properties, and positive effects observed in coreflooding experiments collectively highlight MGONs' potential as a viable solution for enhancing oil recovery in challenging reservoirs, without causing formation damage.

Currently, there are no U.S. Food and Drug Administration (FDA)-approved medical countermeasures (MCMs) for radiation combined injury (RCI), partially due to limited understanding of its mechanisms. Our previous research suggests that endothelial dysfunction may contribute to a poor prognosis of RCI. In this study, we demonstrated an increased risk of mortality, body weight loss and delated skin wound healing in RCI mice compared to mice with skin wound alone or radiation injury (RI) 30 days post-insult. Furthermore, we evaluated biomarkers of endothelial dysfunction, inflammation, and impaired wound healing in mouse at early time points after RCI. Mice were exposed to 9.0 Gy total body irradiation (TBI) followed by skin wound. Samples were collected on days 3, 7, and 14 post-TBI. Endothelial dysfunction markers were measured by ELISA, and skin wound healing was assessed histologically. Our results show that endothelial damage and inflammation are more severe and persistent in RCI compared to wound-alone. Additionally, RCI impairs granulation tissue formation, reduces myofibroblast presence, and delays collagen deposition, correlating with more severe endothelial damage. TGF signaling may plays a key role in this impaired healing. These findings suggest that targeting endothelial dysfunction and TGF-β pathways may provide potential therapeutic strategies for improving delayed wound healing in RCI, which could subsequently influence outcomes such as survival after RCI.

Heterocyclic pharmaceuticals are emerging contaminants due to their toxic, carcinogenic nature and detrimental impact on the natural ecosystem. These compounds pose a significant environmental concern given their widespread use in medical therapy, constituting over 90% of new medications. Their unique chemical structure contributes to their persistence in various environmental matrices, necessitating urgent measures to mitigate their risks. This review comprehensively examines the sources, environmental fate, toxicity, and long-term risks associated with heterocyclic pharmaceuticals, proposing potential remediation strategies. The article commences with an overview of the diverse types of heterocyclic pharmaceuticals and their applications, focusing on compounds containing heteroatoms such as nitrogen, oxygen, and sulfur. Subsequently, it explores the sources and pathways through which these pollutants enter the environment, including wastewater discharge, agricultural runoff, improper disposal, resistance to biodegradation, and bioaccumulation. The toxic effects and long-term consequences of exposure to heterocyclic pharmaceuticals are then discussed, encompassing neurotoxicity, genotoxicity, mutagenesis, cardiovascular and metabolic toxicity, carcinogenicity, and teratogenesis. Additionally, the review summarizes various remediation strategies and treatment solutions aimed at reducing the environmental impact of these compounds, drawing insights from the literature. The research concludes by identifying critical areas for future research, emphasizing the urgent need for more effective remediation strategies to address the growing concern posed by these emerging contaminants.

The use of neutron reflectors is an effective method for improving the quality of neutron sources and neutron delivery systems. In this work, we further develop the method based on Bragg scattering of neutrons in crystals with large interplanar distances. We compare samples of differently prepared fluorine intercalated graphites by measuring the total cross section for the interaction of neutrons with the samples, depending on the neutron wavelength. The Brag scattering cross section is the dominant part of the total cross section in all the cases. The results show that all samples provide high reflection efficiency over the entire range of the so-called “neutron reflectivity gap” and beyond it, and also allow choosing the optimal intercalation methods.

Human often cross their legs unconsciously while sitting, which can lead to issues like shifting the center of gravity, lower back pain, decreased blood flow and pelvic distortion. Detecting unconscious leg-crossing is important for maintaining correct posture. In this study, we explored the detection of leg-crossing postures using machine learning on data from body pressure distribution sensors. Collected 180 seconds of pressure data from 4 male subjects (25.8 ± 6.29 y.o.) in three conditions: no leg crossing, right leg crossed, and left leg crossed. Seven classifiers, including SVM and RF, were evaluated using Accuracy, Recall, Precision, and Specificity. As a result, SVM was difficult to classify.

Batik SMEs, as key contributors to Indonesia’s heritage and economy, their inability to fully harness innovative and sustainable practices threatens not only their survival but also their contribution to economic. However, their path to sustainability is hindered by many limitations in adopting circular economy (CE) principles, a framework widely acknowledged to enhance organizational perfor-mance. This study examines and evaluate the application of innovation within Indonesia's batik SME sector, focusing on the simultaneous impact of open innovation and circular economy princi-ples—an area that has been largely unexplored within this specific sector. Our study analyzed the influence of open innovation and circular economy on organizational performance measured sim-ulant among 70 Batik SMEs in Banyuwangi, Indonesia, using Structural Equation Modeling-Partial Least Square (SEM-PLS). The results reveal that while both open innovation and CE enhance performance, some dimensions of CE—such as waste reduction—proved less relevant to the business models of Batik SMEs based on model iteration. Moreover, this includes a discussion about several elimination items within the iteration. These findings suggest that Batik SMEs benefit more from resource optimization and innovation networks than from rigid waste management practices. In conclusion, integrating open innovation with tailored circular economy strategies can improve the operational efficiency and sustainability of Batik SMEs, enabling them to better compete and grow. This research highlights the need for context-specific adaptations of CE principles to ensure their practical impact on different sectors, emphasizing the role of innovation in overcoming resource limitations.

Soil microbial communities are essential drivers of ecosystem functions, yet the factors shaping their structure and function, particularly at different altitudes and between invasive and native plants, remain insufficiently understood. Our study investigated the influence of plant species and altitude on soil bacterial communities in the Qinling Mountains, a biodiversity hotspot in central China. We analyzed the microbial communities associated with Galinsoga quadriradiata (an invasive species) and Artemisia lavandulifolia (a native species) across an altitudinal gradient ranging from 896 m to 1889 m. Using high-throughput Illumina sequencing, we assessed the composition, diversity, impact factors and functional potential of these communities. The results revealed that both plant species and altitude significantly influence soil bacterial diversity and community structure. G. quadriradiata and A. lavandulifolia were found to host distinct microbial communities, with variations driven by species-specific traits and environmental factors such as soil pH, nutrient content, and altitude. Functional analysis indicated that A. lavandulifolia soils were more associated with nitrogen cycling processes, while G. quadriradiata soils contributed more to organic matter decomposition. These findings advanced our understanding of plant-microbe interactions along altitudinal gradients and highlighted that invasive and native plants harbored microbial flora with different nutritional preferences and metabolic characteris-tics.

Magnesium (Mg) is not prominent among the list of well known anti-aging agents. Yet the signs and symptoms of aging mimic those of Mg deficiency. Mg is a required cofactor for over 800 enzymatic reactions (as of 2022). This review does not correlate Mg status with clinical data on agents linked to longevity. The approach is physiologic and highlights specific Mg dependent reactions required by these longevity linked biomarkers. Many of these share common pathways to extend healthspan. Mg is a required cofactor in the synthesis of vitamin D and melatonin and activation of five of the eight B vitamins. It is a required cofactor for all CYP450 enzymes. It is directly responsible for the appropriate methylation of proteins and DNA, which control the epigenome. The MTHFR (methylenetetrahydrofolate reductase) 677T allele that compromises methylation is present in a majority of Americans. Aberrant methylation predicts the severity of Covid-19 and its persistence into long Covid. Mg is a silent benefactor that may indirectly link these longevity agents, but only if viewed in context with calcium (Ca), i.e., Ca:Mg. Both compete for the same receptor. To fully exploit these longevity agents sufficient Mg is required. The pertinent physiology is presented.

This review explores the intersection of developmental biology and differential topology in understanding vertebrate ontogenetic constraints. We examine how the topological organization of gene regulatory networks (GRNs) influences developmental trajectories and evolutionary possibilities. By analyzing the mathematical frameworks of differential topology and their application to chromatin architecture, we demonstrate how spatial gene arrangements create constraints that both channel and limit morphological evolution. Special attention is paid to the role of topologically associating domains (TADs) and their conservation across vertebrate lineages, suggesting their fundamental importance in maintaining developmental stability. We propose that the hierarchical nature of GRN topology serves as a primary mechanism for establishing evolutionary constraints while simultaneously facilitating phenotypic innovation within defined parameters. This synthesis provides new insights into how physical genome organization shapes the possibilities and limitations of vertebrate body plan evolution.

Antimicrobial resistance (AMR) is a growing global threat, affecting the effectiveness of antimicrobials and posing significant challenges in the treatment of bacterial and viral infections. The emergence of the coronavirus in 2019 and the ongoing menace of infectious diseases such as HIV/AIDS, tuberculosis, and hepatitis remind us of the impact infections have on economic stability and global health, and therefore, the need for a more holistic and long-term solution as opposed to antibiotics - measures that were previously relied on. This review explores recent advances in immunotherapeutic strategies, particularly cytokine-based therapies, adoptive cell therapy, monoclonal antibodies, and immune checkpoint inhibitors, in the control of AMR. It focuses on pathogens with high clinical relevance, including Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Mycobacterium tuberculosis, and viral threats such as HIV and SARS-CoV-2. New inventive approaches such as CAR T cell therapy and MAIT cells were discussed in the context of bacterial and viral infections, highlighting promising results from clinical trials and addressing the challenges of toxicity, immune evasion, and therapy resistance inherent in these diseases. Future priorities include optimizing combination therapies and exploring new immunomodulatory targets to improve these interventions' effectiveness in treating AMR and other infectious diseases.

Global warming and CO₂ emissions have become increasingly pressing concerns, with the aviation industry contributing significantly to these issues. In response, efforts have been made to develop environmentally sustainable aviation solutions. This paper examines the Direct Operating Costs (DOC) of the Wright Spirit, one of the options for electric flight, compared to the conventional BAe 146 on which the Wright Spirit is based. Utilizing methodologies adapted from previous studies (largely the AEA method), the analysis investigates various factors influencing DOC, including battery prices, flight duration, and charging time. Results indicate a 73% increase in overall DOC from the BAe 146 to the Wright Spirit, largely influenced by battery costs and lifespan. However, an 83% reduction in fuel/energy costs suggests the potential viability of the Wright Spirit, particularly with anticipated reductions in battery prices. For instance, a quartering of battery prices could result in a £5 increase in costs for 1-hour flights, compared to the BAe 146. Moreover, the analysis finds the battery lifespan and charging time to be the most important factors to control, in order to increase commercial feasibility. Ticket price comparisons suggest that the Wright Spirit's costs align closely with current market prices, with a flight from Paris to Heathrow predicted to cost the airline £136.10 per passenger. Future research could explore alternative electric aircraft designs, to further assess their impact on DOC and ticket prices.

Cationic polymers were previously shown to assemble with negatively charged proteins yielding nanoparticles (NPs). Poly-diallyl-dimethyl-ammonium chloride (PDDA) is a cationic polymer able to combine with ovalbumin (OVA) yielding a stable colloidal dispersion of OVA/PDDA NPs eliciting significant anti-OVA immune response. Dendritic Cells (DC), as sentinels of foreign antigens, exert a crucial role in the induction of antigen-specific T cell activation and consequent adaptive immune response. Objective: The present study aimed at evaluating the involvement of DCs in the adaptive immune response induced by OVA/PDDA. Methods/Results: Confirming the potent induction of adaptive immune response against OVA/PDDA, the data showed increased CD19+CD138+ plasma cells and CD19+CD38+CD27+ memory cells in spleens of mice immunized with OVA/PDDA-NPs 28 days before. OVA/PDDA-NPs also induced the migration and maturation of DCs to draining lymph nodes on days 3 and 4 of mice immunization. The in vitro results with bone-marrow differentiated DCs (iBM-DCs) showed an increase of the binding and uptake of OVA/PDDA NPs by these cells compared with soluble OVA. In addition, OVA/PDDA NPs were able to induce DC maturation and upregulation of costimulatory and MHC-II molecules, TNF- and IL-12 production. iBM-DCs incubated with OVA/PDDA NPs promoted high OVA-specific T cell proliferative response. Conclusion: Altogether, the data demonstrated the central role of DCs in the induction of antigen-specific immune response by OVA-PDDA-NPs, thus proving these NPs as potent adjuvants for subunit vaccine design.

We perform cosmological parameters estimation on Planck Cosmic Microwave Background (CMB) maps masking the recently discovered foreground related to nearby spiral galaxies. In addition, we also analyse the association between these foreground regions and recent claims of cosmological causal horizons in localized CMB parameter estimates. Our analysis shows consistent cosmological parameter values regardless of the masking approach, though reduced sky areas introduce larger uncertainties. By modelling the new extragalactic foreground, we identify a resemblance with local parameter variation maps with a statistical significance at the 3 sigma level, suggesting that a simplified foreground model partially accounts, (40-50)% correlation with 15% uncertainty, for the observed causal horizons. These findings add new evidence to the existence of the new foreground associated with large spiral galaxies and show that estimates of cosmological parameters on smaller patches on the sky can be largely affected by these foregrounds, but that the parameters taken over the full sky are unaltered.

The impact of pluvial flooding will hamper the economic process in South Jakarta. The best solution for pluvial flooding is to use mobile pumps and its equipment. There has been no previous research regarding the use of mobile pumps in pluvial floods, either in South Jakarta or elsewhere. The aim of this research is to optimize mobile pumps and their equipment for pluvial floods in South Jakarta through hydrological calculations, hydraulic engineering and risk analysis. The method used is to carry out hydrological analysis compared with the hydraulic capabilities of existing channels, correlation of rainfall intensity and flood depth and analysis of hazard risks through the impact of flood depth and duration of flooding. A depth of flood 75 cm has a 20-50% chance of occurring. The hourly rain distribution pattern in South Jakarta was in the first 2 hours with a percentage weight of 56.6% in the 1st hour and 43.4% in the 2nd hour. BMKG Radar Image as the main choice for rain detection. Optimization of mobile pumps and their equipment can be done through a hierarchy of sufficient and insufficient channel capacity, flood frequency, high impact and time of flooding, mobile pump entry access, mobile pump SOP and equipment. There are 8 locations that require mobile pumps in South Jakarta and it is possible to install these mobile pumps. Seven of them require weather prediction tools and SCADA and 3 of them require mobile pump SOP. From the simulation results using the outlet condition that is out of the system, it is found that the configuration heading to the upstream of the flooded channel cross-section shows the most optimal results. This is because the mobile pump is able to trim the flood hydrograph from the beginning, namely in the flooded upstream. In addition, the criteria for using the type of mobile pump used with weather prediction is a mobile pump with a minimum capacity of 0.5 m3/s. The use of mobile pumps is a solution to reduce the risk of pluvial flooding in South Jakarta and other urban areas.

The objective of this study was to define the association between abnormal pronuclear numeration and molecular ploidy status. At the time of fertilization check on day 1 of embryo development, when there was no visible pronuclei (0PN; n=291), 56% were confirmed to be diploid and normally fertilized by SNP analysis. The remaining 41.9% were aneuploid and 2.1% contained only 23 haploid chromosomes. Upon analysis of the 1PN blastocysts (n=217), just over a third (36.4%) contained only 23 haploid chromosomes (23XO), with another third (31.8%) identified as aneuploid and the remaining third (31.8%) confirmed to be diploid and normally fertilized. In contrast, 50% of the 3PN blastocysts (n=172), showed the presence of a third set of 23 parental chromosomes, confirming triploidy with 41.9% identified also as aneuploid and interestingly, a small percentage (8.1%) confirmed to be diploid with normal fertilization. To date, there have been 74 euploid embryo transfers from zygotes originally identified with an alternate pronuclear numeration resulting in 16 ongoing pregnancies and 32 healthy live births. Accurate identification of haploid and tripoid zygotes is critical to prevent implantation failure and pregnancy loss and allows for the identification of all euploid embryos in a cohort which has the potential to increase cumulative live birth rates for infertility patients.

In traditional models of molecular and cellular biology, RNA has been limited to primarily intracellular function in mammalian cells, and has only been subject to chemically simple post-transcriptional modifications. Recent advances have identified an isoform of RNA that challenges this model in two major ways: by expanding its domain to the mammalian cell surface, and identifying it as a target for glycosylation. This isoform, termed glycoRNA, has elevated the nucleic acid from an enzymatic biopolymer to a potential cell signaling molecule with implications for human disease phenotypes. In this review, we summarize the key features of glycoRNA and explore early studies as to its cellular role. We also collate information on glycoRNA-expressing cell lines and high-confidence glycosylated transcripts, providing several resources to assist future studies. Finally, we highlight the important challenges and next steps to characterize this novel glycoconjugate.

Transpiration, being the main water transport mechanism in plants, can be significantly impacted by the concentration of salts in the soil. This study aimed to investigate the influence of soil salinity and sun exposure on the transpiration of African mahogany seedlings in a semi-arid region. The research was conducted using drainage lysimeters, in which African mahogany (Khaya senegalensis) seedlings were grown, and different levels of salinity were applied to the irrigation water, ranging from 0.5 dS.m-1 to 5 dS.m-1 in seven different treatments. Each treatment was replicated three times, totaling 21 experimental units (plants), with the aim of optimizing the management of water resources in semi-arid environments. Relevant environmental data, such as photosynthetically active radiation (Qleaf), water potential and electrical conductivity, were collected directly in the field over four months after planting the seedlings in the lysimeters, located in an experimental field in Bahia. Perspiration assessment was performed using a steady-state diffusion porometer model LCpro-SD. The electrical conductivity of the irrigation water was monitored with a portable conductivity meter, being adjusted with the addition of sodium chloride (NaCl) to achieve different salinity levels. Regression analyzes were applied to understand how environmental variables influence the transpiration of mahogany plants. The results indicate that leaf transpiration decreases as the electrical conductivity of the water increases, especially in periods of water restriction. These findings provide valuable insights into the response of African mahogany seedlings to soil salinity and sun exposure, contributing to a better understanding and management of water resources in semi-arid regions.

The application of photopolymerization 3D printing technology in the field of piezoelectric ceramics can achieve high precision, customization, and personalized design of piezoelectric ceramics, providing a better technical means for the fine processing of ceramic materials. We have studied the preparation of PZT piezoelectric ceramic 3D printing slurry, as well as the analysis of forming errors of printed green bodies, debinding process, and sintering. We successfully printed and sintered various porous honeycomb structures of D-type and G-type using a PZT slurry with a solid content of 55 wt.%.

Background/Objectives: The interest in performing Total Ankle Arthroplasty (TAA) to address end-stage ankle osteoarthritis (OA) is continuously growing. Sports activity plays an important role in our world. There is sparse literature regarding return sports activity following TAA. Levels and types of sports in TAA are rarely reported. The purpose of this prospective case series study is to investigate sports activity in ankle osteoarthritis (OA) and TAA in terms of rate, frequency and type as well as clinical outcomes with a minimum 2 years of follow-up after surgery. Methods: 103 patients (105 implants, 52 female, and 51 male), mean age 60.5 years (range, 23-84 years) with end-stage ankle OA were treated using a three-component, uncemented, mobile-bearing VANTAGE Total Ankle System. The mean follow-up was 2.9 years (range, 2-5 years). Visual Analogic Scale Pain Score (VAS, 0-10 points), Ankle Dorsiflexion/Plantarflexion (DF/PF) Range of Motion (ROM; degrees), functional American Orthopedic Foot and Ankle Society (AOFAS) Ankle/Hindfoot Score (0-100 points), Subjective Patients' Satisfaction Score (0-10 points), Sports Activity Rate, Sports Frequency Score, and Sports Type were assessed. Results: Mean preoperative VAS Pain Score was 6.7 points (range, 3-10 points) and postoperative 0.2 points (range, 0-3 points) (p < 0.001). The mean DF/PF ROM was 24.9° (range, 0°-60°) pre-operative and was 52.9° post-operative (range, 15°-85°) (p < 0.001). The mean preoperative functional AOFAS Ankle/Hindfoot Score was 39.5 points (range, 4-57 points) and postoperative 97.8 points (range, 75-100 points) (p < 0.001). Mean postoperative Subjective Patients’ Satisfaction Score was 9.7 points (range, 7-10 points). The preoperative Sports Activity Rate was 31.1%, postoperative 85.4% (p < 0.001). All the groups exhibited substantial Sports Frequency Score increases (p < 0.001). The most practiced sports were hiking, biking, fitness, and swimming. Conclusions: Total Ankle Arthroplasty (TAA) is an effective treatment for end-stage ankle OA. TAA allows a noteworthy increase in sports activity. This research offers important sports insights to patients with ankle OA and TAA.

The emergence of mRNA vaccines represents a significant advancement in public health by conferring protection against a variety of infectious diseases. However, concerns regarding the potential risk of inducing or exacerbating autoimmune disorders, particularly connective tissue diseases (CTDs), have arisen. This paper explores the mechanisms by which mRNA vaccines may lead to the onset or worsening of CTDs, focusing on the immune responses induced by Pathogen-Associated Molecular Patterns (PAMPs), the interactions between Toll-like receptors 7/8 and 9 (TLR7/8 and TLR9), and the activation of self-reactive B cells. Furthermore, guidelines are suggested for administering vaccines to individuals at elevated risk, such as those with a positive antinuclear antibody (ANA) test or specific rheumatic disease genes. Our findings underscore the necessity for targeted vaccination strategies that mitigate risks while maintaining public health efficacy.

Amyotrophic lateral sclerosis (ALS) is a progressive disease of both upper motor neurons (UMNs) and lower motor neurons (LMNs) leading invariably to a decline in motor function. The clinical exam is foundational to the diagnosis of the disease and ordinal severity scales are used to track its progression. However, the lack of objective biomarkers of disease classification and progression delay clinical trial enrollment and muddle inclusion criteria. Ultimately, biomarker evidence of therapeutic target engagement will support and perhaps supplant more traditional clinical trial outcome measures. Electrophysiology tools of nerve conduction study and electromyography have already been established as diagnostic biomarkers of LMN degeneration in ALS, with the more recent introduction of motor unit number estimation (including MScanFit), motor unit number index, and electrical impedance myography. Dysfunction of UMN and non-motor brain areas is being increasingly assessed with transcranial magnetic stimulation, high-density electroencephalography, and magnetoencephalography. Although most of these techniques are used to explore the underlying disease mechanisms of ALS in research settings primarily, they have the potential on a broader scale to noninvasively identify disease subtypes, predict progression rates, and assess physiologic engagement of experimental therapies.

Our recent study demonstrated that fulvic and humic acids are the major contributors to the adsorption of phenoxyalkanoic acid herbicides in soils. At very low pH, the neutral forms of these herbicides are bound directly to fulvic and humic acids, whereas at higher pH, their anionic forms are adsorbed mainly via bridges created by Al3+ species. The number of active sorption sites associated with Al3+ species complexed with fulvic acids is pH-dependent, whereas the number of corresponding sites in humic acids is pH-independent. Based on the results of the FTIR analysis, research into adsorption thermodynamics, and molecular modeling, an attempt was made in the present study to explain the adsorption mechanisms of six phenoxyalkanoic herbicides used currently in the European Union on the surfaces of the above fractions of humic substances. The obtained values of standard enthalpy (ΔH0) for the adsorption of the anionic forms of phenoxyalkanoic herbicides on fulvic or humic acids complexed with Al3+ were in the range of physical adsorption, i.e. from -8.4 kJ/mol to -2.9 kJ/mol for the former, and from -5.3 kJ/mol to -2.4 kJ/mol for the latter. The study demonstrated that the neutral forms of phenoxyalkanoic herbicides were bound to humic substances mainly via H-bonds, π-π stacking interactions, and hydrophobic interactions. Al3+ species were complexed with fulvic and humic acids to form outer-sphere complexes. Ternary outer-sphere complexes were also created between the anionic forms of phenoxyalkanoic acid herbicides and positively charged Al3+ species complexed with fulvic acids. The mechanisms of adsorption on humic acids involved a ligand exchange between a loosely bound hydroxyl group of hydrolyzed Al3+ complexed with this adsorbent and the anionic form of the herbicide. However, in this case, adsorption took place only in the presence of sufficiently strong hydrophobic and π-π stacking interactions supported by H-bonds. These findings elucidate why phenoxyalkanoic herbicides are mobile in the soil profile and are often rapidly degraded in soils.

A machine learning (ML)-based methodology for predicting frosts is applied to the southern and southeastern regions of Brazil, as well as to other countries including Uruguay, Paraguay, northern Argentina, and southeastern Bolivia. The machine learning model (using TensorFlow (TF)) was compared to the frost index IG (from the Portuguese: Índice de Geada) developed by the National Institute for Space Research (INPE, Brazil). The IG index is estimated using meteorological variables from a regional weather numerical model (RWNM). After calculating the two indices using the ML model and the RWNM, a voting committee (VC) was trained to select between the computed outputs. The AdaBoostClassifier algorithm was employed to implement the voting committee. The study area was subdivided into three distinct subregions: R1 (outside Brazil), R2 (the southern of Brazil), and R3 (the southeastern of Brazil). Two forecasting time scales were evaluated: 24 hours and 72 hours. The 24-hour forecasts from both approaches (TF and RWNM) exhibited similar performance in terms of the number of accurate predictions. However, in the region covering Uruguay and northern Argentina, the TensorFlow model demonstrated superior frost prediction accuracy. Additionally, the TensorFlow model outperformed the RWNM for the 72-hour forecast horizon.

The observation of moving bio-object s is currently a topic of great interest in both fundamental and practical research. To illustrate, when developing and testing pharmaceuticals and vaccines, it is imperative to study their impact on the physiological state and behavior of an animal or human subject. The advent of deep learning algorithms has enabled the automation of qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. It is essential that video data undergoes the appropriate preprocessing prior to training deep learning models. It is essential to consider additional factors, such as the background noise in the frame, the rapidity of bio-object movement, and the necessity to reflect information about the previous (past) and subsequent (future) pose of the bio-object in a single video frame. Moreover, the preprocessed dataset should be suitable for verification by its experts. This paper proposes a method of data preprocessing for the identification of bio-object behavior, with the laboratory animal experiments involving video data collection serving as an illustrative example. The method is based on the combination of information about a behavior event presented in a sequence of frames, with the addition of the native image and further boundary extraction by Sobel filter. Consequently, the behavior event representation obtained is readily comprehensible for both human experts and neural networks of varying architectures. The paper presents the outcomes of training multiple neural networks on the acquired data set and proposes an effective neural network architecture (accuracy = 0.95) for the identification of discrete behavior events of bio-objects.

Self-organization in complex systems is a process in which internal entropy is reduced and emergent structures appear that allow the system to function in a more competitive way with other states of the system or with other systems. It occurs only in the presence of energy gradients, facilitating energy transmission through the system and entropy production. Being a dynamic process, self-organization requires a dynamic measure and dynamic principles. The principles of decreasing unit action and increasing total action and the principle of decreasing unit entropy and increasing total entropy are dynamic variational principles that are viable to utilize in a self-organizing system. Based on this, average action efficiency can serve as a quantitative measure of the degree of self-organization. Positive feedback loops connect this measure with all other characteristics of a complex system, providing all of them with a mechanism for exponential growth, and indicating power law relationships between each of them as confirmed by data and simulations. In this study, we apply those principles and the model to agent-based simulations of ants traveling between two locations on a 2D grid. We find that those principles explain self-organization well and that the results confirm the model. We derive a compact model of ant-behavior based on the action of their trajectories, and then estimate a variety of metrics from the simulated behavior. By measuring action efficiency we can have a new answer to the question: "What is complexity and how complex is a system?". This work shows the explanatory and predictive power of those models, which can help understand and design better complex systems.

Water networks are critical infrastructure components, ensuring the continuous supply of high-quality drinking water to consumers. To secure such water supply, regular maintenance, including the replacement of deteriorating pipelines, is essential. In this study, a methodology has been developed for determining optimal pipeline replacement solutions in water supply systems at water utilities with limited data availability. Hydraulic analysis has been conducted on the segment of 25 km of the water supply network using the free software EPANET. Applying water network optimization, eight pipeline replacement projects according to 13 pre-defined criteria have been identified and evaluated. The paper outlines the methods for evaluating the criteria, including defining specific quantitative limits. The Analytical Hierarchical Process (AHP) method was used in the paper to determine the weights of the criteria. The reason for applying this method refers to problems that involve a set of criteria with a mixed structure, including both quantitative and qualitative aspects. Also, the paper describes the steps of the multi-criteria optimization method VIKOR, used to select the optimal project. This paper, considered as case studie, describes a method, i.e. application of a new principles and an innovative way to solve a problem for developing countries.

Endolichenic fungi represent an important ecological group of microorganisms that form associ-ations with photobionts in the lichen thallus. These endofungi that live in and coevolve with li-chens are known for synthesizing secondary metabolites with novel structures and diverse chemical skeletons making them an unexplored microbial community of great interest. As part of our search for new phytoprotectans, in this work we studied the endolichenic fungus Xylaria sp. isolated from the lichen Hypogymnia tubulosa, which grow as epiphyte on the bark of the endemic tree Pinus canariensis. From the extract of liquid fermentation, we isolated two new piliformic de-rivatives, (+)-9-hydroxypiliformic acid (1) and (+)-8-hydroxypiliformic acid (2), along four known compounds, (+)-piliformic acid (3), hexylaconitic acid A anhydride (4) and two hydroxy-phenylacetic derivatives (5-6). Their structures were elucidated based on NMR and HRESIMS data. The extract and compounds were tested for their antifeedant (Myzus persicae, Rhopalosiphum padi and Spodoptera littoralis), antifungal (Alternaria alternata, Botrytis cinerea and Fusarium ox-ysporum), nematicidal against Meloidogyne javanica and phytotoxic effects on mono and dicotyle-donous plant models (Lolium perenne and Lactuca sativa). Among these compounds 4, 5 and 6 were effective antifeedants against M. persicae and 4 was also active against R. padi. Moreover, com-pound 4 and 3 showed antifungal activity against B. cinerea and 4 was the only nematicidal. Phytotoxic test showed that the extract has strong effect against L. sativa and L. perenne growth. The phytotoxic compounds were 3, 4 and 5. (+)-Piliformic acid (3) and hexylaconitic A anhydride (4) displayed root growth stimulation on low doses and inhibitory effect at higher doses of di-cotyledon specie L. sativa.

In many developing countries human health problems are solved using local plants. Knowledge of their chemical composition and biological activities can contribute to the creation of natural-based products usefully employed in human health. In this work, we analyzed Paederia grandidieri leaves extracted with diverse eco-compatible procedures and subjected to chemical, microbiological, and cellular compatibility assays. Fresh leaves of P. grandidieri were harvested in southern Madagascar, dried and powdered. Distilled water and ethanol at different temperatures and times were used for extraction. Polyphenol composition, antioxidant, antibacterial, prebiotic and cytotoxic properties of the extracts were analyzed. The aqueous extracts contained higher levels of flavan-3-ols and flavanones, while the hydro-alcoholic extracts were richer in flavonols and flavones. The aqueous extracts showed the highest total phenolic and total flavonoid contents, and antioxidant activity, but lower cytotoxicity with respect to the hydro-alcoholic extracts. None of the extracts had significant antibacterial effects against selected oral pathogens, but prebiotic effects against Streptococcus salivarius were detected. This study indicates that P. grandidieri possesses significant biological properties which make it a suitable candidate for the production of pro-health products for the oral cavity.

Climate change is significantly altering the dynamics of airborne allergens, affecting their seasonality, allergenicity, and geographic distribution, which correlates with increasing rates of allergic diseases. This study investigates aeroallergen sensitization among populations from Tenerife, Spain, and Lima, Peru—two regions with similar climates but distinct socio-economic conditions. Our findings reveal that Spanish individuals, particularly those with asthma, demonstrate higher sensitization levels to a broader range of allergens, especially mites, with 85% of participants reacting to at least one mite allergen. In contrast, Peruvian patients exhibit a narrower spectrum of sensitization. These results highlight the influence of environmental factors, such as pollution and socioeconomic disparities, on allergen exposure and immune responses. Moreover, the study underscores the necessity for region-specific diagnostic and therapeutic strategies to effectively address these variations. By elucidating the intricate relationship between climate change, environmental factors, and allergen sensitization, this research offers insights into respiratory allergic conditions, advocating for tailored interventions to mitigate their impact across diverse populations.

Implants are increasingly used in dentistry. Nevertheless, several factors can cause treatment failure. To assess initial wound healing, various indices have been developed. The purpose of this study was to assess the relationship between early wound healing and implant success. Fifty single implants (25 in women and 25 in men) were placed in patients meeting the inclusion. The implants (Ticare®) were placed via the two-stage technique. Patients were assessed at 24 hours, one week, one month, two months and one week postimplantation. At the final evaluation, implantation failure or success was recorded. Postoperative pain in these patients at one week after placement was assessed with a visual analogue scale (VAS), and the Wachtel soft tissue early healing index was used to measure healing. Two months after implant placement, the success rate was 92%. No statistically significant relationship was found between the Wachtel index and short-term implant success or failure. Additionally, there was no relationship between implant success or failure and variables such as smoking, diabetes status, age, sex or guided bone regeneration (GBR). Neither the Wachtel index nor any of the other variables studied is a predictor of early implantation success.

4E-BP2 deamidation significantly alters protein production in the brain and nervous system. It maintains the balance of protein production required for a healthy nervous system. Any imbalance in protein production in the brain and nervous system causes all neurodegenerative diseases. Discovering what causes 4E-BP2 deamidation only in neurons will make it possible to control this balance of protein production and cure neurodegenerative diseases such as memory loss, Alzheimer's, and Parkinson's. Alzheimer’s disease is caused by the absence of 4E-BP2 protein in the brain's neurons. This protein undergoes deamidation spontaneously only in the neurons and, in this way, controls the production of proteins in the neurons. This, in turn, determines whether or not Alzheimer’s and Parkinson’s disease start or progress. The purpose of this work is to discover the neurobiological mechanism that causes the deamidation reaction in the 4E-BP2 protein by performing immunoblotting in the retinal ganglia, the optic nerve, the dorsal root ganglia, the sciatic nerve, and the whole brain, extracted via dissection from 2-month-old, Wild-type male mice. The results showed that deamidation mainly occurs in the axons or axon-enriched regions of the optic nerve, the whole brain, and the sciatic nerve, confirming conclusively that axons are the critical factors behind the neurobiological mechanism of 4E-BP2 protein deamidation. The results confirm that they are also the reason why 4E-BP2 deamidation occurs only in neural tissue.

At the time of accession, Ukraine will be covered by the full CAP instrumentarium, the system of direct payments included. Due to the size of the country, including agricultural land, the total amount of payments to Ukraine may reach the amount of EUR 96.5 billion (over 7 years) according to some estimates. The purpose of the article is to try to verify this amount. This will be done by reviewing the latest analyses, made in EU countries, on the estimates of future direct payments in Ukraine. At the same time, author’s own account of the amount of payments to agricultural producers in this country shall be presented. Unlike other studies, the calculation takes into account two elements that significantly reduce the amount of subsidies. Firstly, after accession, Ukrainian farmers will probably receive only part of the payments, which will be gradually increased (phasing-in mechanism), as was the case with Poland and other countries joining the European Union in 2004. Secondly, the calculation attempts to count the effects of the so-called capping, i.e. a ceiling for the amount of direct payments, which will probably also be applied. The result shows a smaller amount of future payments than the other studies.

Leaf senescence in plants is the last stage of leaf development that is characterized by a decline in photosynthetic activity, an active degeneration of cellular structures and the recycling of accumulated nutrients to areas of active growth such as buds, young leaves, flowers, fruits and seeds. This process holds economic significance as it can impact yield, influencing the plant´s ability to maintain an active photosynthetic system during prolonged periods, especially during the grain filling stage, which affects plant weight and oil content. It can be associated with different stresses or environmental conditions, manifesting itself widely in the context of climate change and limiting yield, especially in crops of agronomic relevance. In this work we study the stability of two widely described sunflower genotypes belonging to the INTA Breeding Program against differential N conditions, to verify their yield stability in control conditions and under N supply. Two inbred lines were utilized, namely R453 (early senescence) and B481–6 (late senescence), with contrasting nitrogen availability in the soil but sharing the same ontogeny cycle length. It was observed that, starting from R5.5, the B481-6 genotype not only delayed senescence but also exhibited a positive response to increased nitrogen availability in the soil. This response included an increase in intercepted radiation, resulting in a statistically significant enhancement in grain yield. Conversely, the R453 genotype did not show significant differences under varying nitrogen availability and exhibited a tendency to decrease grain yield when nitrogen availability was increased. The response to nitrogen can vary depending on the specific genotype.