In Åhlberg (2021, 2022b), I presented evidence for 57 health-promoting substances that all green vascular WEPs contain. I continued searching for health-promoting substances in the plant cells' basic structures and processes. While reading research reports of five invasive species, I found eight more health-promoting substances all green vascular wild edible plants have. This article adds eight new substances to the earlier 57, bringing the total to 65. This is a novel finding for which I provide research evidence. I present the latest research on five invasive wild edible plants (WEPs) species. Each contains at least the same 65 health-promoting substances and a varying number of species-specific health-promoting substances. Each WEP has 1) shared health-promoting substances that all green vascular plants have and 2) species-specific health-promoting substances. I also present how many of these substances prevent Alzheimer’s disease, according to experimental research.

Fiber-optic Mach-Zehnder interferometers are widely used in research areas such as telecommunications, spectroscopy, and quantum information. These optical structures are known to be affected by phase fluctuations that are usually modeled as multiparametric noise.This multidimensional noise must be stabilized or compensated for to enable fiber-optic Mach-Zehnder architectures for practical applications. In this work, we study the effectiveness of a modified Perturb-and-Observe (P&O) algorithm to control multidimensional phase noise in fiber-based multi-arm Mach-Zehnder interferometers. We demonstrate the feasibility of stabilizing multidimensional phase noise by numerical simulations using a simple feedback control scheme and analyze the algorithm’s performance for systems up to dimension 8x8. We achieved minimal steady-state errors that guarantee high optical visibility in complex optical systems with N×N matrices (with N=[2,3,4,5,6,7,8]).

This review highlights the therapeutic potential of epigallocatechin gallate (EGCG) and forskolin in managing retinal diseases, with a focus on glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy. EGCG, a potent polyphenol from green tea, exhibits significant antioxidant, anti-inflammatory, and neuroprotective effects, making it a promising candidate for reducing oxidative stress and inflammation in ocular tissues. Forskolin, a diterpene from Coleus forskohlii, increases cyclic AMP (cAMP) levels, which helps lower intraocular pressure (IOP) and provides neuroprotection. Both compounds target critical pathways involved in retinal disease progression, including oxidative stress, mitochondrial dysfunction, and inflammation, offering complementary therapeutic benefits. The review consolidates preclinical and clinical studies, highlighting the potential of EGCG and forskolin as adjunctive or alternative treatments for retinal diseases. Future research should explore the synergistic effects of these compounds, particularly in combination therapies aimed at addressing multiple pathogenic mechanisms in retinal health.

Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater Based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time and resource-efficient manner, as wastewater samples are representative of all cases within the catchment area, whether they are clinically reported or not. However, analysis and interpretation of WBS datasets for decision-making during public health emergencies, such as the COVID-19 pandemic, remains an area of opportunity. In this article, a database obtained from wastewater sampling at wastewater treatment plants (WWTPs) and university campuses in Monterrey and Mexico City between 2021 and 2022 was used to train simple clustering and regression-based risk assessment models to allow for informed prevention and control measures in high-affluence facilities, even if working with low-dimensionality datasets and a limited number of observations. When dividing weekly data points based on whether the seven-day average daily new COVID-19 cases were above a certain threshold, the resulting clustering model could differentiate between weeks with surges in clinical reports and periods between them with an 83.3% accuracy rate. Moreover, the clustering model provided satisfactory forecasts one week (79.2% accuracy) and two weeks (72.9%) into the future. However, the prediction of the weekly average of new daily cases was limited (R2 = 0.452, MAPE = 180.2%), likely because of insufficient dimensionality in the database. Overall, while simple, WBS-supported models can provide relevant insights for decision-makers during epidemiological outbreaks, regression algorithms for prediction using low-dimensionality datasets can still be improved.

The expanding Arabic user base presents a unique opportunity for researchers to tap into vast online Arabic resources. However, the lack of reliable Arabic word embedding models and limited availability of Arabic corpora pose significant challenges. This paper aims to address these gaps by conducting an experiment to train word embedding models with Arabic corpora and examine the influence of varying hyperparameter values on different NLP tasks. To gather data for training the models, we collected information from three distinct sources: Wikipedia, newspapers, and 31 Arabic books. Each corpus’s specific impact on the outcomes was analyzed. The experiment involved assessing the models across diverse NLP tasks, including sentiment analysis, similarity tests, and analogy tests. The findings revealed that both the corpus size and hyperparameter values had distinct effects on each test. In the Analogy test, employing a larger vocabulary size positively influenced the outcomes, with Fasttext models using skip-gram approaches performing well in solving Analogy Test questions. For sentiment analysis, we discovered that vocabulary size played a crucial role. In the similarity score test, Fasttext models achieved the highest scores, with lower window sizes and smaller vector sizes leading to better results. Overall, our models performed well, achieving a score of 99%, 90% accuracies for sentiment analysis and Analogy Test, respectively. Moreover, they achieved a similarity score of 8 out of 10. Our findings indicate that our models can serve as a valuable resource for Arabic NLP researchers, equipping them with a robust tool for handling Arabic text.

Bone fractures are common injuries that require quick and accurate diagnosis to provide the right medical care. The “convolutional neural network” technique relies mostly on manual inspection, by radiologists and can be laborious and prone to error. The objective of this work was to increase the efficiency and accuracy of fracture identification by automating the examination of X-ray images using convolutional neural networks. A “convolutional neural network” model created to detect bone fractures in X-ray images was used in the present study. The model was trained and validated using an extensive dataset of X-ray images, which included both fractured and nonfractured bones. Multiple convolutional layers were used in the “convolutional neural network” architecture for feature extraction, and pooling and fully connected layers were added for classification. The main measures used to assess the model's performance were sensitivity, specificity, and accuracy. In regard to identifying bone fractures, the “convolutional neural network”-based model outperformed the conventional technique. With significant gains in sensitivity and specificity, this approach achieved a high accuracy rate and decreased the frequency of false positives and false negatives. We used “convolutional neural network” tool in PyTorch for bone fracture detection and we outlined the important considerations that must be considered when attempting to achieve this goal Additionally, we contrasted every study with our baseline. Here our maximum accuracies were 99.05%, 98.60%, 99.45% and 99.74% for epochs 8, 9, 10 and 11, respectively. These findings highlight the model's ability to improve the diagnostic efficiency and accuracy in clinical settings. Using “convolutional neural network” to identify bone fractures from X-ray images is a promising development in medical imaging. In the end, our method improves patient outcomes by ensuring faster and more reliable fracture diagnosis while also lessening the diagnostic burden on radiologists. Subsequent investigations will focus on incorporating this system into clinical procedures and investigating its utilization in real-time emergency situations. Since there was no medical intervention on human subjects in this investigation, trial registration regulations were not applied.

Spontaneous preterm birth (sPTB), defined as live birth before 37 weeks of gestational age, is associated with immune dysregulation and pro-inflammatory conditions that profoundly impact newborn health. The question of immune integrity at the maternal-fetal interface is a focus of recent studies centring not only sPTB but the conditions often affiliated with this outcome. Regulatory T cells (Tregs) play a critical anti-inflammatory role in pregnancy, promoting fetal tolerance and placentation. Due to this gestational role, it is hypothesised that decreased or dysfunctional Tregs may be implicated in cases of sPTB. This review examines studies comparing Treg presence in healthy term pregnancies and those with sPTB-associated conditions, revealing conflicting findings across different conditions and within sPTB itself. Treg deficiencies may contribute to health issues in preterm newborns. Current sPTB treatments are limited, underscoring the potential of in utero therapies targeting inflammation, including T cell interventions. Future research aims to establish consensus on the role of Tregs in sPTB and associated conditions and advancing understanding of mechanisms leading to Treg deficiencies in adverse pregnancy outcomes.

Natural wood is generally perceived as expensive, while wood-based boards are cheap, but re-search has yet to explain definitively why these two materials receive such disparate evaluations. This study aimed to uncover the reasons behind this phenomenon by proposing an emo-tion-driven approach to material research. We collected 10 types of natural wood and artificial boards as experimental samples and tested 20 subjects' subjective evaluation of the samples and physiological indicators (electroencephalography and electrodermal activity). Through data analysis, we identified the relationships between the subjective and physiological data and the impact of material composition on these data. Specifically, we observed significant correlations between subjective and physiological data; however, no correlation was found between subjective and physiological valence. We also discovered that the components of materials significantly influence participants' subjective and physiological emotions. For example, materials with a rough texture elicited higher positive affectivity than those with a smooth texture, materials with coarse and mixed textures elicited higher positive affectivity than those with fine textures, and the brighter materials evoke more positive emotions in participants. This study revealed how material components influence participants' emotions and, more importantly, analyzed the processing techniques behind these components, providing a theoretical basis for the future development of high-value-added products made from wood-based boards.

Large genomic rearrangements of PALB2 gene, particularly deletions and duplications, have been linked to hereditary breast-ovarian cancer. Our research specifically focuses on delineating the intronic breakpoints associated with rearrangements of PALB2 exon 11, which is crucial for understanding the mechanisms underlying these genomic changes in patients with hereditary breast and ovarian syndrome. By next-generation sequencing, we identified one duplication and three deletions of PALB2 exon 11, confirmed by Multiplex Ligation-Dependent Probe Amplification analysis. To assess the impact on transcription and potential splicing issues, Reverse-Transcription PCR was performed on patients' RNA. For detailed characterization of intronic breakpoints, primer walking approach and long-range PCR were implemented, followed by Sanger sequencing. Our analysis revealed a tandem duplication of 5134 base pairs (bp) mediated by AluY repeats located in introns 10 and 11, respectively. Moreover, identical deletions were identified in three unrelated patients, encompassing an approximate 8050 bp region mediated by AluSx elements. Both genomic alterations resulted in a truncated PALB2 protein due to introduction of a premature stop codon. This study underscores the remarkable instability of intronic regions flanking exon 11 of PALB2 and identifies a previously unreported hotspot involving Alu repeats with very high sequence homology in introns 10 and 11 of the gene. Our findings suggest avenues for further research, such as investigating the prevalence of similar genomic rearrangements in larger cohorts and exploring functional studies to understand how these alterations contribute to hereditary breast cancer pathogenesis.

Background and Objective: Diabetes Mellitus is a long-term, multifaceted metabolic condition that necessitates ongoing medical management. Hypogonadism is a syndrome that is a clinical and/or biochemical indicator of testosterone deficiency. Cross-sectional studies have reported that 20-80.4 % of all men with Type 2 diabetes have hypogonadism, and Type 2 diabetes is related with low testosterone. This study presents an analysis of the use of ML and EL classifiers in predicting testosterone deficiency. In our study, we compared optimized traditional ML classifiers and three EL classifiers using grid search and stratified k-fold cross-validation. We used the SMOTE method for the class imbalance problem. Methods: This database contains 3397 patients for the assessment of testosterone deficiency. Among these patients, 1886 patients with type 2 diabetes were included in the study. In the data pre-processing stage, firstly outlier/excessive observation analyses were performed LOF and missing value analyses were performed with random forest. The SMOTE is a method for generating synthetic samples of the minority class. Four basic classifiers, namely MLP, RF, ELM and LR, were used as first level classifiers. Tree ensemble classifiers, namely ADA, AGBoost, and SGB, were used as second level classifiers. Results: After SMOTE, while the diagnostic accuracy decreased in all base classifiers except ELM, sensitivity values increased in all classifiers. Similarly, while the specificity values decreased in all classifiers, F1 score increased. The RF classifier gave more successful results on the base-training dataset. The most successful ensemble classifier in the training dataset was the ADA classifier in the original data and in the after SMOTE data. The testing data, XGBoost is the most suitable model for your intended use in evaluating model performance. XGBoost, which exhibits a balanced performance especially when SMOTE is used, can be preferred to correct class imbalance. Conclusions: SMOTE is used to correct the class imbalance in the original data. However, as seen in this study, when SMOTE was applied, the diagnostic accuracy decreased in some models, but the sensitivity increased significantly. This shows the positive effects of SMOTE to better predict the minority class.

Objective: Colorectal cancer (CRC) is the third most prevalent malignant tumor type and the second leading cause of cancer-related death. Sequence similarity family 50 member A (FAM50A) plays a vital role in numerous disease processes, including tumor progression. This study aimed to evaluate the prognostic significance of FAM50A in CRC and to explore its role in CRC cell proliferation. Methods: The Cancer Genome Atlas (TCGA) database was used to assess the mRNA expression levels of FAM50A in CRC. Immunohistochemical staining was used to study the expression of FAM50A in CRC tissues. Patient survival data were used to assess the prognostic significance of FAM50A in CRC using Kaplan–Meier analysis and Cox regression analysis. The Cell Counting Kit-8, 5-ethynyl-2′-deoxyuridine, and colony-formation assays were employed to assess the impact of FAM50A knockdown on tumor cell proliferation. Co-expression and correlation heatmaps were used to identify genes whose expression levels were highly correlated with FAM50A, and to further investigate the possible mechanism of action of FAM50A in CRC cell proliferation. Results: Analysis of data from TCGA database revealed a notable upregulation of FAM50A expression levels in CRC tissue compared with adjacent normal tissue. Moreover, FAM50A expression was positively correlated with N, M, and TNM stages in 145 patients with CRC. Cox regression analysis and construction of a nomogram revealed that high FAM50A expression was a prognostic indicator for poor overall survival in patients with CRC. Knockdown of FAM50A decreased cell proliferation ability, the proportion of EdU positive cells, and the number of CRC cell colonies, whereas overexpressing FAM50A promoted proliferative phenotypes. The expression levels of three genes in CRC were significantly negatively correlated, while the expression levels of 18 genes were significantly positively correlated, with the expression level of FAM50A. Conclusion: FAM50A may be a novel prognostic marker for CRC, and may participate in regulating tumor progression by interacting with some highly correlated molecules that drive CRC cell proliferation.

This paper presents in detail the foundation of security theory for ``Quantum stream cipher based on Holevo-Yuen theory". This type of cipher is a technology that provides information-theoretic security (ITS) to optical data transmission by randomizing ultrafast optical communication signals with quantum noise. In general, a quantitative security of ITS is discussed in terms of unicity distance in Shannon theory. However, the quantum version requires modeling beyond the Shannon model of random cipher to utilize the characteristics of the physical layer. Therefore, as first step, one has to develop a generalized unicity distance theory and apply it to evaluation of security. Although a complete theoretical formulation has not yet been established, this paper attempts to give a primitive structure of a generalization of Shannon random cipher and shows that the realization of this is generalized quantum stream cipher. In addition, we introduce several implementation methods of the generalized quantum stream ciphers and their security performances.

The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM based treatment recommendations can rival those of multidisciplinary tumor boards (MTBs); however, such data are currently lacking for urological cancers. This study provides the methodological foundation for the prospective CONCORDIA trial, which will generate this data for the first time. In this preliminary work, we evaluated the proposed measurement tool for the CONCORDIA study -the System-Causability-Scale (SCS) and its modified version (mSCS) - based on recommendations from ChatGPT-4 and an MTB for 40 urological cancer-scenarios. Both scales demonstrated strong validity, reliability (all aggregated Cohen’s K > 0.74), and internal consistency (all Cronbach’s Alpha > 0.9), with the mSCS showing superior reliability, internal consistency, and clinical applicability (p<0.01). Two Delphi processes were used to define the LLMs to be tested in the CONCORDIA study (ChatGPT-4 and Claude 3.5 Sonnet) and to establish the acceptable non-inferiority margin for LLM recommendations compared to MTB recommendations. The forthcoming ethics-approved and registered CONCORDIA non-inferiority trial will require 110 urological cancer scenarios, with an mSCS difference threshold of 0.15, a Bonferroni corrected alpha of 0.025, and a beta of 0.1. Blinded mSCS assessments of MTB recommendations will then be compared to those of the LLMs. In summary, this work establishes the necessary prerequisites prior to initiating the CONCORDIA study and validates a modified score with high applicability and reliability for this and future trials.

Objectives: The Implicit Prosody Hypothesis (IPH) posits that individuals generate internal prosodic representations during silent reading, mirroring those produced in spoken language. While converging behavioral evidence supports the IPH, the underlying neurocognitive mechanisms remain largely unknown. Therefore, this study investigated the neurophysiological markers of sensitivity to speech rhythm cues during silent word reading. Methods: EEG was recorded while participants silently read four-word sequences, each composed of either trochaic words (stressed on the first syllable) or iambic words (stressed on the second syllable). Each sequence was followed by a target word that was either metrically congruent or incongruent with the preceding rhythmic pattern. Results: Single-trial event-related potentials (ERPs) and time-frequency representations (TFRs) triggered by the target words were examined using a hierarchical mass univariate general linear modeling approach to investigate the effects of metrical expectancy and stress type. ERP findings revealed significant differences based on expectancy, with words carrying unexpected stress eliciting larger negativities between 240-628 ms after word onset. TFRs further revealed that different frequency bands were sensitive to distinct aspects of rhythmic structure in language. Alpha activity, linked to attention, reflected expectations about the rhythmic pattern. Theta and beta activity, associated with processing rhythm and timing, were sensitive to both the expected rhythm and the specific location of stressed syllables within words. Conclusions: These findings highlight comparable neurophysiological responses to both implicit and explicit speech rhythm, further supporting the IPH and emphasizing the role of prosody in silent reading.

Pursuing sustainable energy solutions has prompted researchers to focus on optimizing energy extraction from renewable sources. Control laws that optimize energy extraction require accurate modeling, often resulting in time-varying, nonlinear differential equations. An energy-maximizing optimal control law is derived for time-varying, nonlinear, second-order, energy harvesting systems. We demonstrate that sustaining periodic motion under this control law when subjected to periodic disturbances necessitates identifying appropriate initial conditions, inducing the system to follow a limit cycle. The general optimal solution is applied to two point absorber wave energy converter models: a linear model where the analytical derivation of initial conditions suffices and a nonlinear model demanding a numerical approach. A stable limit cycle is obtained for the latter when the initial conditions lie within an ellipse centered at the origin of the phase plane. This work advances energy-maximizing optimal control solutions for nonautonomous nonlinear systems with application to point absorbers. The results also shed light on the significance of initial conditions in achieving physically realizable periodic motion for periodic energy harvesting systems.

This paper explores the application of AI-guided instruction combined with Alternate Reality Games (ARG) to enhance learning performance, specifically focusing on students' learning emotions, engagement, and control beliefs. Based on the Retrieval-Augmented Generation (RAG) Large Language Model (LLM) framework, this study aims to address challenges caused by traditional AI-led instruction, such as emotional detachment and learning disengagement. By integrating immersive ARG experiences, the study evaluates the learning outcomes of students with different cognitive styles in an introductory digital learning course. A qualitative grounded theory approach is used, analyzing data from interviews with 65 students to understand their learning experiences. The findings indicate significant improvement in students' engagement, emotional connections, and sense of control over their learning process when ARG elements were incorporated. Implications for the future of digital learning, as well as recommendations for addressing challenges in AI-enhanced education, are discussed.

Hyperbaric oxygen therapy (HBOT) involves inhaling nearly 100% oxygen in a pressurized environment and is commonly used to treat various diseases and injuries. Despite its well-known safety, HBOT is associated with complications, with frequent middle ear barotrauma (MEB) and oxygen toxicity. Understanding the characteristics and risk factors associated with these complications is critical for improving patient’s compliance and treatment outcomes. This retrospective multicenter study aimed to analyze the characteristics and factors associated with complications during HBOT using a computer-controlled pressurized monoplace hyperbaric chamber. We conducted a retrospective observational study across two tertiary hospitals in Korea, involving patients who received HBOT from October 2017 to June 2020. Data were extracted from electronic medical records and hyperbaric chamber logs, including patient demographics, medical history, HBOT indications, and details of complications. Statistical analyses, including chi-square and t-tests, were used to compare variables. A total of 247 patients (mean age: 59.35 ± 15.05 years, 63.56% male) were included. The most common indications for HBOT were sudden sensorineural hearing loss (27.94%) and post-graft/flap (24.29%). Hypertension (46.15%) and diabetes mellitus (39.27%) were the most frequent comorbidities. Otalgia was the most prevalent side effect (33.20%), followed by chest discomfort (2.02%) and headache (1.62%). A significant proportion of patients (11.74%) terminated HBOT due to complications, with most pauses occurring at pressures between 1.2 to 1.4 ATA (26.67%). Complications, particularly otalgia, significantly impact patient compliance with HBOT. The incidence of complications varies by pressure level during treatment, suggesting the need for tailored strategies to minimize complications. This study highlights the importance of patient monitoring and education to improve the safety and efficacy of HBOT in monoplace chambers.

The increasing environmental concerns related to no-biodegradable plastics have driven the development of biodegradable alternatives derived from renewable resources. Polylactic acid (PLA), a biodegradable polymer known for its favorable mechanical strength and biocompatibility, suffers from brittleness and poor ductility. This study aims to improve the flexibility of PLA by blending it with polycaprolactone (PCL) and polyethylene oxide (PEO). Specifically, thin films were fabricated from neat PLA, PCL, PLA/PCL, and PLA/PEO blends using both electrospinning and solvent casting to investigate the effects of processing methods on their mechanical behavior. The films were characterized using SEM, FTIR, XRD, DSC, uniaxial tensile testing, and water contact angle measurements. The surface morphology of single electrospun fibers was further examined using AFM. We found that electrospun PLA films exhibited higher elongation at break and ultimate strength with a slightly lower Young’s modulus in comparison to its solvent-cast films. FTIR, XRD, and DSC analyses consistently showed that the electrospun PLA films had significantly lower crystallinity compared to their solvent-cast counterparts. Notably, AFM phase imaging revealed that the electrospun PLA fibers exhibited an aligned crystalline structure. Blending PLA with PCL significantly improved elongation at break in electrospun films, which, unlike solvent-cast blends, retained a Young's modulus comparable to electrospun PLA. This improvement is likely due to reduced phase separation and enhanced crystalline alignment. Furthermore, electrospun films of neat PLA, PCL and PLA/PCL blends were more hydrophobic than their solvent-cast counterparts. Although PLA/PEO blends demonstrated enhanced surface hydrophilicity, they exhibited a marked reduction in mechanical properties. Overall, this study demonstrates that electrospinning effectively enhances the flexibility and mechanical performance of PLA-based films by mitigating the effects of poor miscibility in polymer blends and promoting molecular alignment. These findings suggest that electrospinning is a promising processing method for producing PLA-based films with enhanced flexibility and mechanical integrity, without the need for plasticizers or compatibilizers.

Interspecific competition between mosquito larvae may affects adult vectorial capacity, potentially reducing disease transmission. It also influences population dynamics, cannibalistic and predatory behaviors. However, knowledge of interspecific competition between Ae. aegypti and Anopheles species is limited. The study examined interspecific competition between Ae. aegypti larvae and either An. arabiensis, An. gambiae, or An. funestus on individual fitness in semi-field settings. The experiments involved density combinations of 100:100, 200:0, and 0:200 (Ae. aegypti: Anopheles), reared with and without food, in small habitat (8.5 cm height × 15 cm diameter) with 0.5 litre and medium habitats (15 cm height × 35 cm diameter) with 1 litre of water. The first group received Tetramin® fish food (0.02 g), while the second group was unfed to assess cannibalism and predation. While, interspecific competition affected both genera, Anopheles species experienced greater effect, with reduced survival and delayed development, compared to Ae. aegypti. The mean wing lengths of all species were significantly small in small habitats in mixed population (p < 0.001). The presence of food reduced cannibalism and predation compared to its absence. These interactions have implications for diseases transmission dynamics and can serve as biological indicators to signal the impacts of vector control interventions.

Polyamines (PA), such as putrescine, spermine, and spermidine, are bioactive molecules that play a vital role in plant responses to stresses. Although they are frequently applied to achieve higher levels of stress tolerance in plants, their function in seed biology is still not fully under-stood. Only in a few papers PA were described as seed priming agents, but most of the data re-port only the physiological and biochemical PA effects, and only a few of them concern the molecular mechanisms. In this review, we summarized PA seed priming effects on germination, seedling establishment, and young plant response to abiotic stresses, and tried to draw a general scheme of PA action during early developmental plant stages.

Ice cream is popular but contains high amounts of saturated fats and few health-promoting ingredients. In the presence of xanthan gum (0.25%), the blueberry peel particles prepared through ball-milling treatment (BMP) were used to prepare ice cream containing camellia oil as a fat replacer. BMP possessed smaller particle sizes, larger contact angles, and higher contents of anthocyanin aglycone, as compared with commonly-milled blueberry peel particles. BMP with the smallest D_[4,3] value (about 10 μm) and the largest contact angle (66.30°) were obtained by ball-milling the blueberry peel at 15 Hz for 6 h (BMP_{15 Hz 6 h}). The ice cream mixes were depicted as linear viscoelastic gel-like solids, and their apparent viscosity, G′ and G′ increased with the increase in BMP_{15 Hz 6 h} concentrations. Ice cream with strong antioxidant activity and good freeze-thaw stability was acceptable and desirable in the presence of less than 0.5% BMP_{15 Hz 6 h}.

A contribution of Epstein-Barr Virus (EBV), Human Cytomegalovirus (HCMV), and certain types of Human Papilloma Virus (HPV) to the overall pathogenesis of upper airways cancers seems a priori reasonably conceivable. The existing evidence mostly relates them to nasopharyngeal squamous and undifferentiated carcinomas; indications emerge about their involvement in other upper airways cancers too. Markers of EBV, HCMV, HPV16, HPV18, (and Mycoplasma pneumoniae as a negative control) were searched in a series of 68 samples of neoplastic tissues from 57 patients diagnosed with different upper airways cancers. The results provide clues in favor of an actual role of EBV, HCMV, and HPV18, in the web of causation of upper airways squamous and undifferentiated carcinomas, possibly of upper airways adenocarcinomas too.

We study the crossed Andreev reflection and the nonlocal transport in the staggered graphene/superconductor/periodic line defect superlattice (LDGSL) junctions. The staggered pseudospin potential in the left graphene electrode suppress the local Andreev reflection, while the elastic cotunneling of K′ valley electrons is inhibited due to the exclusive rightward motion of K valley electrons in the right LDGSL electrode, thereby enabling the realization of perfect intravalley crossed Andreev reflection for incident electrons from the K′ valley. Meanwhile, the intravalley elastic cotunneling occurs while both local Andreev reflection and crossed Andreev reflection are completely eliminated for incident electrons in the K valley. Furthermore, the probability of intervalley crossed Andreev reflection scattering is significantly lower than that of intravalley CAR scattering across a broad range of incident angles and electron energies. Our results are helpful for designing the flexible and high-efficiency Cooper pair splitter based on the valley degree of freedom.

Physical properties such as shape, volume, and size influence the dynamics of biological systems. In this context, we focus on the geometric properties of limb movements and their physiological and biomechanical effects. Using hand grasping as a paradigmatic example, we describe how dynamic changes in geometric configuration can affect the pathophysiology of grasping. We focus on precision pinch, the simplest and most basic form of grasping, where one finger remains stable while another moves against it (Brand and Hollister, 1999). The thumb and index finger come together to grasp small objects with great precision, such as a pen. During the precision pinch, the geometric configuration of the hand changes, modifying the distribution of forces and the functional connectivity between the fingers. These changes may offer a new approach to the biomechanics of artificial arms. While current approaches consider grasps as homogeneous structures, we argue that local variations in hand geometry can lead to new biomedical effects with intriguing operational implications.

Harmful cyanobacterial blooms produce cyanotoxins which can adversely affect humans and animals. Without proper monitoring and detection programs, tragedies such as the loss of pets or worse are possible. Multiple factors including rising temperatures and human influence contribute to the increased likelihood of harmful cyanobacteria blooms. Current approaches to cyanobacteria and their toxins monitoring included microscopic methods, immunoassays, liquid chromatography coupled with mass spectrometry (LCMS), and molecular methods such as qPCR. This review highlights current research into early detection methods for harmful cyanobacterial blooms and the pros and cons of these methods.

Purpose: The study aims to investigate racial disparities in fall-related injuries among Black and White breast cancer survivors who received chemotherapy, focusing on the risks associated with different chemotherapy regimens. Methods: This retrospective cohort study involved 11,400 breast cancer survivors (Stage 1-3) treated with chemotherapy between January 1, 2019, and December 31, 2023. Chemotherapy types studied include Doxorubicin, Cyclophosphamide, Paclitaxel, Docetaxel, and Carboplatin. Logistic regression and descriptive analyses were used to assess fall risk associated with each chemotherapy type, stratified by race. Results: The overall fall-related injury rate was higher among Black survivors (14.7%) compared to White survivors (10.0%), with a significant difference in fall risk between the two groups (p < 0.001). Fall risk was notably elevated for Black patients treated with Cyclophosphamide, Docetaxel, and Carboplatin. Conclusions: The findings highlight significant racial disparities in fall-related injuries among breast cancer survivors receiving chemotherapy. These disparities may be influenced by socioeconomic factors and healthcare access. Implications for Cancer Survivors: Interventions targeting fall prevention and equitable healthcare access are critical to reducing these disparities among Black breast cancer survivors.

Thoracic ultrasonography (TUS) has emerged as a critical tool in the diagnosis and management of respiratory diseases in cattle, particularly bovine respiratory disease (BRD), which is one of the most economically significant health issues in feedyard operations. The objective of this review is to explore TUS in veterinary medicine including the historical development, methodologies, and clinical applications for diagnosing and prognosing respiratory diseases. The review also emphasizes the importance of operator training, noting that even novice operators can achieve diagnostic consistency with proper instruction. Ultrasound was introduced in the mid-20th century for back-fat thickness measurements, TUS has evolved to offer a non-invasive, real-time imaging modality that allows for the detection of lung and pleural abnormalities such as consolidations, pleural effusions, and B-lines. These features are vital indicators of respiratory disease, and their early identification through TUS can significantly improve clinical outcomes. Compared to traditional diagnostic methods like auscultation or radiography, TUS provides superior accuracy in detecting both subclinical and advanced respiratory conditions, particularly in high-risk populations. Furthermore, TUS has demonstrated strong prognostic value, with studies showing that the extent of lung consolidation correlates with higher relapse risk, reduced growth performance, and increased mortality.

Oxidosqualene cyclases (OSCs), enzymes are responsible for converting linear triterpenes into teracyclic ones, which known as precursors of other important and bioactive metabolites. Two OSCs genes encoding parkeol synthase and lanostadienol synthase have been found in representatives of the genera Apostichopus and Stichopus (family Stichopodidae, order Synallactida). Since the limited number of sea cucumber OSCs were so far studied, OSCs encoding gene(s) of the sea cucumber Eupentacta fraudatrix (family Sclerodactylidae, order Dendrochirotida) were investigated to fill this gap. Here we employed RACEs, molecular cloning and Oxford Nanopore Technologies to identify candidate OSC mRNAs and genes. The assembled cDNAs were 2409 bp (OSC1) and 3263 bp (OSC2) which were shared the same CDS size of 2163 bp encoding a 721-amino-acid protein. The E. fraudatrix OSC1 and OSC2 had higher sequence identity to each other (77.5%) than to other holothurian OSCs (64.7–71.0%). According to the sequence and molecular docking analyses, OSC1 with L436 is predicted as parkeol synthase, while OSC2 with Q439 is predicted as lanostadienol synthase. Based on the phylogenetic analysis, E. fraudatrix OSCs cDNAs clustered with other holothurian OSCs forming the isolated branch. As a result of gene analysis, high polymorphism and larger size of the OSC1 gene suggest that this gene may be an ancestor of the OSC2 gene. These results implied that E. fraudatrix genome contains two OSC genes with different from Stichopodidae OSCs genes evolutionary pathway.

Detection of volatile organic compounds (VOCs) is a rapidly growing research area due to importance of VOCs in environmental pollution, human health assessment, food quality control, and homeland security. Optical sensing materials based on porphyrins are particularly appealing for VOCs detection, owing to availability of porphyrins, their exceptional optical and binding properties, as well as their photo- and chemical stability. As research and technology continue to advance, optical sensors involving these materials are expected to play an increasing role in various applications. This article presents a comprehensive overview of porphyrin-based sensing materials developed for use as optical sensors for VOCs. First, sensing films composed exclusively of porphyrin molecules are discussed followed by materials obtained by grafting or incorporation of porphyrins into organic and inorganic polymer matrices. Considering growing interest to multianalyte analysis with porphyrin-based sensor arrays, special section is devoted to this area.

This paper analyzes ServiceNow’s overall strategy, business model, mission, vision, and key strategic risks. ServiceNow, a platform-as-a-service provider, has experienced significant growth and is poised for further expansion. The paper identifies strategic risks such as legislative and regulatory challenges, strategy development and execution risks, and competition in the cloud computing industry. Using Porter’s Five Forces framework, the assignment explores these risks and proposes mitigation strategies. Recommendations are made to protect intellectual property, maintain customer loyalty through subscription models, and ensure innovation in response to evolving market demands.

The use of DL algorithms in the domain of DM for AVs has garnered significant attention in the literature in the last years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid DM module in an AD stack, integrating the learning capabilities from the experience of DRL algorithms and the reliability of classical methodologies. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of AD modules. Specifically, the authors address the DM problem by employing a POMDP formulation and offer a solution through the use of DRL algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort.

This research was conducted to study the incidence of seed-borne fungi in stored sunflower seeds under ambient conditions as well as their influence on seed viability and vigour. Certified seeds, Quality Declared Seeds (QDS) and Farmer saved seeds (FSS) were stored for six months. Seed samples were taken every month testing for fungal infection percent (FIP), germination percentage (G%) and seedling vigour index (SVI). Eight fungal species namely as Aspergillus flavus, A. niger, Alternaria padwickii, A. zinniae, Botrytis cinerea, Curvularia lunata, Fusarium vericillioides and Rhizopus arrihizus were identified. At the end of experiment, the maximum species infection percentage were recorded on F. vericillioides (14.39), B. cinerea (14.28) in FSS and the minimum by A. zinniae (1.83) with QDS. Maximum total fungal infection incidence was recorded with FSS (73.11%) and minimum with QDS (66.94%) and certified seeds (67.06%). The QDS recorded the highest G% (81.56) and certified seeds (81.33%), the maximum SVI (96.76) was in certified seeds and 95.69 (in QDS). The minimum G% (60.67) and SVI (65.98) were found in FSS. The highest G% and SVI were recorded in seeds with minimum FIP. These results suggest that, fungal infections may negatively affect the seed viability and vigour. For the first time, the incidence of seed-borne fungi in stored sunflower seeds have been determined in Tanzania.

The presence of various pesticides in natural streams and wastewater is a significant environmental issue due to their high toxicity, which causes harmful consequences even at low quantities. One cost-effective method to remove these pollutants from water could be through adsorption using inexpensive, easily obtained adsorbent - biochar. This study demonstrates the effectiveness of using biochar produced from wheat grains to eliminate alachlor from water. The sorption properties of the biochar and the likely removal mechanisms have been defined. The study found that the biochar removed 76-94% of alachlor, depending on the initial concentration of the pesticide in water. The pseudo-second-order kinetic model (R² = 0.999) and the Langmuir isotherm model (R² = 0.996) effectively characterized the elimination of alachlor by wheat grains biochar. The analyzed biochar, with its micropores and various surface functional groups, can effectively adsorb alachlor and trap it within its structure.

Background: Front-of-package labelling (FoPL) is shown to be effective in assisting consumers make healthier food choices. Several countries are implementing various types of FoPL systems. Objective: This study explored consumers’ perceptions and understanding of three different FoPLs and their insights into features that could influence the effectiveness of the symbols in Kenya Methods: A qualitative study was conducted in four Counties in Kenya; Nairobi, Mombasa, Garissa, and Kisumu. Data was collected through twelve focus group discussions with 137 adults drawn from diverse socio-demographic and socio-economic backgrounds. Three FoPLs were assessed: Red and Green (RG) Octagon, Red and Green Octagon with icons and text (RGI), and the Black Octagon Warning label (WL). The FGDs explored participants’ perceptions of the symbols’ visibility and memorability, their understanding, potential effectiveness, and cultural appropriateness. Printed images displaying four different commonly consumed products bearing specific FoPLs were used during data collection. Data was analyzed thematically and organized using NVivo version 14.0. Results: Results showed that Kenyan consumers had varied perceptions about the proposed FoPL symbols. Majority of the participants found the black Octagon WL to be most visible and memorable due to its good blend of black and white colours. Although the RG octagon symbol and RGI octagon symbols were also found to be eye-catching due to their bright colours, it emerged that the different colours often confused some participants on their meaning. Most participants understood the WL compared to the RG and RGI because they found the additional text more educative. Generally, the WL was preferred as the symbol most likely to influence consumer purchase behaviours and food consumption. All the three FoPLs tested were found to be culturally appropriate. Conclusions: The Black Octagon Warning Label is the most easily visible and understood among the three FoPLs tested and has the potential to influence purchase and consumption behaviours among Kenyan consumers. Although the RG and RGI symbols are visually appealing to many, confusion related to different colours could affect their effectiveness. Consumers need to be educated on the use of FoPLs to ensure they discourage the purchase and consumption of unhealthy foods.

Heavy infestations of weedy rice can reduce rice yields and affect farmers' incomes. So far, the control of weedy rice is still unsolved and depends more on the use of herbicides, which can harm the environment and human health. The phenomenon of allelopathy can be used to develop a new strategy to control weedy rice that does not depend on the use of herbicides. The objective of this study was to evaluate the allelopathic ability of T. procumbens against the growth of weedy rice with different morphological characteristics. Although morphologically different, seedling growth of all weedy rice was found to be inhibited by the presence of leaf leaching and root exudates of T. procumbens, as determined by sandwich and plant box methods. GC-MS analysis identified 21 allelochemicals present in the leaves of T. procumbens, mainly consisting of various fatty acids such as hexadecanoic acid. Further investigation revealed that the aqueous extract of T. procumbens at higher concentration (100%) reduced the germination rate of weed variants WR3 and WR7 by 24% and 19%, respectively. Seedling growth of the weedy variants was inhibited with increasing concentration of leaf extract, with radicle growth of both WR3 and WR7 reduced by 100% and 98%, respectively, at the highest concentration. The leaf debris incorporated into the soil similarly inhibited the growth of the seedlings and the chlorophyll content of the weedy rice. However, the cultivar MR220 CL2 showed lower sensitivity to T. procumbens, and its total chlorophyll content increased with increasing concentration of leaf debris. Our study revealed that T. procumbens is suitable as a bio-herbicide for future sustainable control of weedy rice, especially in the granaries of MR220 CL2

An update was made on the flora of the Serra dos Órgãos National Park, located in the Atlantic Forest domain, Rio de Janeiro state, Brazil. Collections of plant samples were carried out from 2007 to 2010. These data were complemented by consultations with national and international herbaria, and scientific publications related to the flora of the Park. The total number of known plants is 3096, divided into 2412 angiosperms, 1 gymnosperm, 434 ferns and lycophytes and 249 bryophytes. Of these, 33 are endemic to the Park, 190 are endemic to the state of Rio de Janeiro, and 97 are classified in one of the IUCN threat categories. With these results, Serra dos Órgãos National Park becomes the Brazilian protected area with the richest known flora, harboring 38% of the species known to Rio de Janeiro state in an area of about 0,5% of its size. Despite the park’s great plant diversity, about 38% of the species had only one record and may be considered locally rare. Encouraging research and botanical collection in the park may increase the representation of locally rare species, rediscover species which are considered extinct and record new species for science.

Disabled people have been all but invisible in refugee research. The same can be said of refugees in disability studies. We propose an analytical framework that integrates critical and trauma approaches to inform studies of the experiences of disabled refugees as they express their agency and engage in their communities. Critical disability theory questions the assumptions undergirding the social model that have led to “fixed” conceptions of disabled people and ignored their innate agency. By equating disability with oppression, social model proponents have argued that the emancipation of disabled people may only be achieved through shifts in oppressive structures. On the other hand, understanding how trauma affects disabled refugees helps avoid defining them simply as dependent victims and omitting or overlooking their resilience. We contend that a sociocultural understanding of trauma allows for a dynamic and contextual perspective on agency and resilience; one that challenges the “traumatized refugee” narrative. The principal claim underpinning the framework is that disabled refugees should be regarded as capable agents of change confronting contexts of continuing trauma, displacement and vulnerability. This article offers an integrative and interpretive understanding of the lived experiences of disabled refugees that highlights the possibilities inherent in their human agency.

This paper addresses the author’s current understanding of the physics of interactions in polymers under a voltage field excitation. The effect of a voltage field coupled with temperature to induce space charges and dipolar activity in dielectric materials can be measured by very sensitive electrometers. The resulting characterization methods, Thermally Stimulated Depolarization (TSD) and Thermal-Windowing Deconvolution (TWD), provide a powerful way to study local and cooperative relaxations in the amorphous state of matter that are, arguably, essential to understand the glass transition, molecular motions in the rubbery and molten states and even the processes leading to crystallization. Specifically, the paper describes and tries to explain ‘interactive coupling’ between molecular motions in polymers by their dielectric relaxation characteristics when polymeric samples have been submitted to thermally induced polarization by a voltage field followed by its depolarization at constant heating rate. Interactive coupling results from the modulation of the local interactions by the collective aspect of those interactions, a recursive process pursuant to the dynamics of the interplay between the free volume and the conformation of dual-conformers, two fundamental basic units of the macromolecules introduced by this author in the “Dual-Phase” model of interactions. This model reconsiders the fundamentals of the TSD and TWD results in a different way: the origin of the dipoles formation, induced or permanent dipoles; the origin of the Wagner space charges and of the Tg, transition; the origin of the TLL manifestation; the origin of the Debye elementary relaxations’ compensation or parallelism in a relaxation map; finally, the Dual-Phase origin of their super-compensations. In other words, this paper is an attempt to link the fundamentals of TSD and TWD activation and deactivation of dipoles that produce a current signal with the statistical parameters of the “Dual-Phase”model of interactions underlying the Grain-Field-Statistics.

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D, and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in recreating 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used including a building and a university campus. As the result, 3D Gaussian data has proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twin.

The canine distemper virus (CDV) is a morbillivirus implicated in several signs in the Canidae family, in dogs, conjunctivitis, digital hyperkeratosis and neuropathologies are common signs of its infection. Even with the existing vaccination, the canine distemper disease persists worldwide so the molecular pathways implicated in the infection processes has been an interesting and promisor area in the new therapeutic drugs research over recent years. It is known that in the process of virus infection the endoplasmic reticulum organelle lost its homeostasis inducing stress and the subsequence unfolded protein response or UPR in which three ER-trans-membrane proteins are implicated: PERK, IRE1 and ATF6. Moreover, in a prolonged ER stress the apoptosis dead are induced through CHOP protein, as a final step of virus infections. Molecular and cell culture techniques as RT-qPCR and RT-PCR were used in the present study. We demonstrate the activation in vitro of the three UPR pathways after infection with an attenuated strain of CDV. Also, the implication of a MAPK pathway through p38 protein and the apoptotic protein CHOP was funded to participate in all times of infection. Even more, our study suggested that CDV replication occur in a PERK-dependent manner.

This study explores the application of the BPM lifecycle to optimize the Market Analysis process within the Market Intelligence department of a major energy company. The semi-structured, virtual nature of the process necessitated careful adaptation of BPM methodology, starting with process discovery through data collection, modeling, and validation. Qualitative analysis, including value-added and root-cause analysis, revealed inefficiencies. The redesign strategy focused on selective automation using Python scripts and Power BI dashboards, incorporating techniques such as linear programming and forecasting to improve process efficiency and quality while maintaining flexibility. Post-implementation, monitoring through a questionnaire showed positive results, though ongoing interviews were recommended for sustained performance evaluation. This study highlights the value of BPM methodology in enhancing decision-critical processes and offers a model for adaptable, value-driven process improvements in complex organizational environments.

The global burden of respiratory syncytial virus (RSV) and severe associated disease is prodigious. RSV specific vaccines have been launched recently but there is no antiviral medicine commercially available. RSV polymerase (L) protein is one of the promising antiviral targets along with fusion and nucleocapsid proteins. During medicinal chemis-try campaign, two potent L-protein inhibitors (PC786 and PC751) were identified. Both compounds inhibited RSV A/B-induced cytopathic effect in HEp-2 cells equally, but PC786 was more potent than PC751 in bronchial epithelial cells. Repeated treatment with escalating concentrations on RSV A2 infected HEp-2 cells revealed both inhibitors led to a Y1631H mutation in the L protein, but only PC786 induced a mutation in the M protein (V153A). By L-protein fragment and M protein binding analysis, we showed that the M protein interacts with the 1392-1735 amino acid region of the L protein, where potentially PC786 binds. In addition, PC786 treatment or PC786 induced mutant RSV was found to increase M protein nuclear localisation later in infection concomitant with delayed fusion protein localisation at the budding viral filaments. As M protein is known to play a key role in virus assembly and budding late in infection, our data suggests that disrupting the interaction between M and L could provide a novel target for antiviral development.

I want to meaningfully average a pathalogical function (i.e., an everywhere surjective function whose graph has zero Hausdorff measure in its dimension). In case this impossible, we wish to average a nowhere continuous function defined on the rationals. We do this by taking the most generalized, satisfying extension of the expected value, w.r.t the Hausdorff measure in its dimension, on bounded functions with domains of finite measure which takes finite values only. As of now, I’m unable to solve this due to limited knowledge of advanced math and most people are too busy to help. Therefore, I’m wondering if anyone knows a research paper which solves my doubts. Unlike the previous paper, “Finding a Research Paper Which Meaningfully Averages Pathalogical Functions”) we add motivations, examples, and change parts of the original paper.

During the Covid-19 pandemic, although the experience of workers and managers was examined, the role, experiences and functions of supervisors was relatively underexplored, with no investigation into their changing health and safety responsibilities. This project attempted to fill this gap. Twenty supervisors across Canada were interviewed for an hour. A Framework Method guided the study. We used a conceptual framework of 10 supervisor functions to help direct the data collection, identify codes, manage and organize the data analysis, and identify major themes which were highlighted in the findings. What was found was that since supervisors did not have access to workers’ homes, they could not execute most of their OHS functions. They were obliged to give workers more control over how and when they worked. They Increased their communications with their workers in response to workers’ psychological health concerns. Notably, supervisors reported that they were under extreme stress. A hybrid work environment, with workers sometimes working at home, has become the new norm. Supervisor stress will continue to escalate unless upper management provides more support, supervisors get training on how to deal with the psychological health and safety of workers, and supervisors’ responsibilities are re-defined for at-home workers.

Generative Adversarial Networks (GANs) are powerful models for generative tasks but face significant challenges such as instability, mode collapse, and inefficient convergence. This paper introduces a theoretical framework leveraging Fermi-Dirac statistics, Ruppeiner geometry, phase transition dynamics, and graph theory to address these issues. The proposed theorems provide a robust mathematical foundation for enhancing GAN stability, diversity, and convergence. Experimental validation on the MNIST dataset demonstrates that the application of these theorems results in improved Inception Scores, reduced Fréchet Inception Distances, and stable training dynamics, as indicated by Ruppeiner curvature analysis. The findings suggest that these theoretical insights offer a comprehensive solution to the persistent challenges in GAN training, paving the way for more reliable and effective generative models.

In this paper, a "local" algorithm is determined for the construction of two recurrent sequences of positive primes ( ) and ( ), (( ) dependent of ( ) ), such that for each integer n their sum is equal to 2n . To form this, a third sequence of primes ( ) is defined for any integer n by : = Sup( p ∈ : p ≤ 2n - 3 ) , where is the infinite set of primes. The Goldbach conjecture has been proved for all even integers 2n between 4 and 4. In the table of terms of Goldbach sequences given in appendix 10 , values of the order of 2n = are reached. This " finite ascent and descent " method proves the binary Goldbach conjecture ; an analogous proof by recurrence is established and an increase in by 0.7( is justified. Moreover, the Lagrange-Lemoine-Levy conjecture and its generalization, the Bezout-Goldbach conjecture, are proven by the same type of procedure.

Climate change has significant implications for agriculture, especially in viticulture, where temperature plays a crucial role in grapevine growth. Mendoza's climate is ideal for producing high-quality wines, but 21st-century climate change is expected to have negative impacts. This study aimed to evaluate the effects of increased temperature on the phenology, physiology, and yield of Malbec, Bonarda, and Syrah. A field trial was conducted over two seasons (2019-2020 and 2020-2021) in an experimental vineyard with an active canopy heating system (+2-4ºC). Phenological stages (budburst, flowering, fruit set, veraison, harvest), shoot growth (SG), number of shoots (NS), stomatal conductance (gs), chlorophyll content (CC), chlorophyll fluorescence (CF), and water potential (ψa) were measured. Additionally, temperature, relative humidity, light intensity, and canopy temperature were recorded. Heat treatment advanced all phenological stages by approximately two weeks, increased SG and NS, and reduced gs and ψa during the hottest months. CC and CF remained unaffected. The treatment also resulted in lower yields, reduced acidity, and increased °Brix in both seasons. Overall, rising temperatures due to climate change advance the phenological phases of Malbec, Syrah, and Bonarda, leading to lower yields, higher °Brix, and lower acidity, although physiological variables remained largely unchanged.

To address the instability of wind power caused by the randomness and intermittency of wind generation, as well as the challenges in power compensation by Hybrid Energy Storage Systems (HESS), this paper proposes a State of Charge (SOC) balancing control strategy based on Successive Variational Mode Decomposition and multi-fuzzy control. First, a consensus algorithm is used to enable communication between energy storage units to obtain the global average SOC. Then, the Secretary Bird Optimization Algorithm (SBOA) is applied to optimize the Successive Variational Mode Decomposition (SVMD) and Variational Mode Decomposition (VMD) for the initial allocation of wind power, resulting in the smoothing power for hybrid energy storage and the grid integration power. Finally, considering the deviation between the current SOC of the storage units and the global average SOC, dynamic partitioning is used for multi-fuzzy control to adjust the initial power allocation, achieving SOC balancing control. Simulations of the control strategy were conducted using Matlab/Simulink, and the results indicate that the proposed approach effectively smooths wind power fluctuations, achieving stable grid integration power. It enables the SOC of the HESS to quickly align with the global average SOC, preventing the HESS from entering unhealthy SOC regions.

The RECONNECT project addresses the fragmentation of Ireland's public healthcare systems, aiming to enhance service planning and delivery for chronic disease management. By integrating complex systems within the Health Service Executive (HSE), it prioritizes data privacy while supporting future digital resource integration. The methodology encompasses structural integration through a Federated Database design to maintain system autonomy and privacy, semantic integration using a Record Linkage module to facilitate integration without individual identifiers, and the adoption of the HL7-FHIR framework for high interoperability with the national electronic health record (EHR) and the Integrated Information Service (IIS). This innovative approach features a unique architecture for loosely coupled systems and a robust privacy layer. A demonstration system has been implemented to utilize synthetic data from the Hospital Inpatient Enquiry (HIPE), Chronic Disease Management (CDM), Primary Care Reimbursement Service (PCRS) and Retina Screen systems for healthcare queries. Overall, RECONNECT aims to provide timely and effective care, enhance clinical decision-making, and empower policymakers with comprehensive population health insights.

Background and Objectives: Acute aortic dissection (AD) is a life-threatening condition and early detection can significantly improve patient outcomes and survival. This study evaluates the clinical benefits of integrating a deep-learning (DL)-based application for automated detection and prioritisation of AD on chest CT angiographies (CTAs), focusing on the reduction of scan-to-assessment (STAT) and interpretation times (IT). Materials and Methods: This retrospective Multi-Reader, Multi-Case (MRMC) study compared AD detection with and without artificial intelligence-(AI) assistance. Ground truth was established by two U.S. board-certified radiologists, while three additional expert radiologists participated as readers. All participants assessed the same CTAs without AI-assistance (pre-AI arm) and, after a 1-month washout period, with the help of the device outputs (post-AI arm). STAT and IT were compared between the two phases. Results: The study included 285 CTAs (95 per reader, per arm), with a mean patient age of 58.5 years ±14.7(SD), 52% men, 37% prevalence. AI assistance significantly reduced STAT for detecting 33 true positive AD cases, from 15.84 minutes (95% CI: 13.37–18.31min) without AI to 5.07 minutes (95% CI: 4.23–5.91min) with AI, a 68% reduction (p<0.01). IT also decreased significantly, from 21.22 seconds (95% CI: 19.87–22.58s) without AI to 14.17 seconds (95% CI: 13.39–14.95s) with AI (p<0.05). Conclusions: Integrating a DL-based algorithm for AD detection on chest CTAs significantly reduces both STAT and IT. By prioritising the most urgent cases, AI ensures faster diagnosis and improves the workflow efficiency in clinical radiology practice, compared to standard First-In First-Out (FIFO) workflow.

According to statistics, the leading cause of death among young people is road car accidents. Moreover, one of the factors for road crashes is low-quality road infrastructure. Road signals are part of the road infrastructure, and they are required for safe driving. Furthermore, road signals are essential for an autonomous mobile machine vision to function properly. Hence, road signals should be clear and transparent in all transportation lanes. Unfortunately, road lane markings – one of the most important signals – fade and deteriorate over time because of the paint quality, weather, and exposure to any external object. Moreover, maintaining those markings manually is considered difficult and tedious by some. Literature review shows few autonomous solutions for such a problem. However, none of the solutions is robust for all kinds of markings. In particular, the literature review shows that the present autonomous solutions require human interference to guide the robot in a specified path to paint a new line where usually the robot will localize itself using GPS. This project targets road markings maintenance with a novel idea to use machine vision for inspecting the line state while moving along the line, and if needed, the robot will repaint the road line. The robot was designed following this idea, and the design can be divided into the following: Driving unit, Paint unit, Vision unit, Control unit, Power unit, UI unit. First, the driving unit is made using a three-wheeled differential drive system, where two wheels are motorized wheels, and the third is a freewheel. Moreover, the painting unit contains an air compressor, an HVLP paint sprayer, a pulley system, and a servo motor. At the same time, the robot vision unit consists of a camera, two IR sensors, and two ultrasonic sensors. Also, the control unit is made of a Raspberry Pi, Arduino, and Motor controllers. Furthermore, the power unit has a 12V lead-acid battery, 48V Li-po battery, four 1.5 batteries, and a power bank. Finally, the UI unit includes a cooling fan and a 10” screen. The software is made of a machine vision code using the OpenCV library in Python language. This code is implemented on the RPI to image process the feed from the camera and then communicate with Arduino using the Serial library. Moreover, Arduino code uses C++ language to decode the message from the RPI and control a relay and a servo for the painting unit and motor controllers for the driving unit. After the design and parts acquisition, the whole system was assembled at home using several manufacturing techniques such as drilling, threading, filing, soldering, 3D printing, adhesion, and zipping. Then, each subsystem was calibrated individually. Finally, the testing phase started for each system individually and ended by testing the system as a whole. In the end, the project progress was hindered by several failed tests. However, it was completed within the timeframe with achieving the main performance measures of the mechanical design/assembly, localization/path planning, road line inspection accuracy, and painting performance.

This study empirically investigates the relationship between realized higher-order moments and the Fear and Greed Index as a measure of sentiments. We estimate daily realized moments for five different sentiment levels using 5-minute return data of the S&P 500 index from January 3, 2011, to September 18, 2020. We found that the Fear and Greed Index significantly impacts realized volatility during periods of extreme fear. Additionally, various sentiment indicators influence realized skewness and realized kurtosis. The VIX index significantly reduces realized skewness across all sentiment levels. Bearish and bullish sentiments have a significant negative relationship with negative realized skewness during periods of extreme fear and extreme greed. Conversely, the Fear and Greed index, bearish and bullish sentiments have a significant positive relationship with positive realized skewness. During extreme fear, the Fear and Greed index, bearish, and bullish sentiments have a significant negative relationship with realized kurtosis. These results remain consistent when considering the non-linear characteristics of the Fear and Greed Index during periods of extreme fear and extreme greed. These findings highlight the relevance of understanding sentiment in financial risk management and its significant relationship with asymmetric and extremity characteristics of asset returns.

Background: Immune evasion in cancer is a multifaceted process that synchronizes pro-tumoral immune infiltration, immunosuppressive inflammation, and inhibitory immune checkpoint expression (IC). Recent developments have immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy effectively impede this process but benefit a limited patient cohort. This investigation introduces a systemic immunotherapeutic strategy by inhibition of master-regulators of immune evasion (MR-IE). Methods: We utilized the TCGA PanCancer Atlas transcriptomic data to subset and stratify cancer samples based on IC expression levels and CIBERSORT to estimate immune cell infiltration. Differential gene expression analysis was conducted to unravel pathways associated with the immune evasion. Transcription Factor Enrichment Analysis and Survival Analysis was conducted to identify and rank candidate MR-IE per cancer type. The top-ranking candidate MR-IE of cholangiocarcinoma (CCA) was validated for its regulation of ICs using quantitative PCR and western blots. Orbitrap Proteomics was employed to assess changes in the immune-related proteome of CCA cells after MR-IE is inhibited or knocked-down. Lastly, CCA cell viability and immune-mediated cell death was assessed after MR-IE inhibition in combination with fourth-generation anti-folate receptor alpha (FRɑ) CAR-T cell therapy. Results: This workflow ranked and identified candidate MR-IE for 33 tumor types. MYC was identified as the high-ranking candidate MR-IE for CCA. MYC modulated PD-L1 expression adn key markers of inflammation and IC expression in CCA cells. Moreover, MYC inhibition was able to potentiate CAR-T mediated cell death in CCA cells. Conclusions: Cumulatively, these results offer a candidate list of MR-IE immunotherapeutic targets per cancer type that can be further explored and validated. This study also highlights the promise of MR-IE inhibition as a novel potent immunotherapeutic strategy for the treatment of CCA.

Introduction: Despite the well-documented benefits of smoking cessation interventions, the implementation and success of these programs in primary care settings often encounter significant barriers. A primary care provider’s personal smoking status has been identified as a potential barrier to tobacco treatment delivery. The aim of this qualitative study is to explore the experiences and perspectives of primary care providers regarding their role in, delivering smoking cessation interventions to patients based on their personal smoking status. Materials and methods: Semi-structured interviews were conducted with 22 primary care providers from 6 public primary care units in Central Macedonia Region, Greece. Thematic analysis was used to analyze data. Results: Healthcare providers who are current smokers may face unique challenges in effectively counseling patients on smoking cessation. On the contrast, non-smoking and especially previous smoking healthcare providers were noted to exhibit greater confidence and efficacy in delivering cessation support, often serving as role models for patients aiming to quit smoking. Participation in structured cessation training programs often catalyzed personal reflection among healthcare professionals regarding their own smoking behaviour. Conclusions: Personal smoking status of primary care providers impacts the delivery of tobacco treatment, affecting their credibility and effectiveness in providing cessation support. Understanding the complex factors that influence the delivery of smoking cessation interventions in primary care is crucial, as these settings represent a vital point of contact for reaching individuals who smoke and providing them with the necessary support to quit.

Understanding creation as divine kenosis, which has become rather widespread since the last century, may emphasize the strong connection between the theology of creation and the key Christian doctrine of the Incarnation while contributing to the dialogue between theology and science. At the same time, this theological project requires a thorough rethinking of divine action in order to avoid representing divine agency and natural causality as competitive factors and to affirm divine kenosis as the definitive trait of God without compromising divine freedom. To this end, the concept of “divine kenotic creativity” is suggested and discussed in this paper.

Many interesting materials in a broad range of applications have in their composition the coexistence of zinc and iron. Their characterization by XPS should identify and quantify both elements. The most intense regions for these elements Zn 2p and Fe 2p are, therefore, used to accomplish that purpose. Using sphalerite as an example of a material where Zn and Fe may coexist and using XPS with two different X radiation sources to study them, it is demonstrated that Fe 2p region, when the iron is the minor component, may be seriously affected by the Auger Zn L3M1M23 structure. The sphalerite (Zn1-xFexS) here studied has x0 and is a good example to show how an XPS spectrum obtained with the X-ray Al Kα, the most used radiation in monochromatic equipments, leads to a wrong assignment of the structures existing in the binding energy region of Fe 2p. The simultaneous use of Mg Kα radiation showed that the Fe 2p is below the XPS detection in this specific sample of sphalerite. Its attested absence reveals that the structure detected, when using Al Kα in “Fe 2p” region, is assigned to a zinc Auger multiple peak and not to the expected 2p photoelectron doublet peak of iron.

This study presents the innovative design and fabrication of a 3D-printed prosthesis arm aimed at enhancing automated hand rehabilitation for targeting hemiplegic patients who cannot open and close their hands. By leveraging surface electromyography (EMG) signals, the prosthesis arm translates muscle contractions in the forearm into precise hand movements, facilitating more effective rehabilitation. A comprehensive analysis of existing prosthesis designs guided the creation of a new, improved prototype. Using a concept scoring evaluation matrix, the best concept design was selected, designed, and fabricated through additive manufacturing. The finished prosthesis improves grip stability by imitating natural finger movements with flexible materials and a tendon system made of elastic ropes and fishing lines. Experimental comparisons between the designed prosthesis arm and a human hand showed significant similarities in flexion angles, though further refinement is needed for measurement accuracy and tactile feedback. The result presents the designed prosthesis arm validity and can be used in automated control rehabilitation in the future. This research also highlights the potential of 3D printing and EMG technology in creating customizable, efficient prosthesis arms for automated rehabilitation, suggesting pathways for future improvements and broader applications.

Background: Central and branch retinal artery occlusion (CRAO and BRAO) are critical causes of acute vision loss, predominantly affecting older adults with systemic vascular pathology. These occlusions typically result from embolic events, leading to partial or complete retinal ischemia; Methods: This retrospective case series report details of our ten-year experience using the 1064 nm Nd:YAG laser for Transluminal Nd:YAG Embolysis (TYE) in order to lyse visible emboli whitin the retinal arteries; Results: We conducted a retrospective case series involving 18 patients treated by TYE for different retinal arteries occlusions between 2014-2024. TYE effectively restored blood flow in the occluded arteries, with variable but generally favorable visual outcomes. Our article highlights significant clinical and interventional aspects of three treated patients: a BRAO case with multiple transluminal emboli, a case of double BRAO and a CRAO case. We also report the successful use of TYE for intraluminal lysing of an embolus located outside the optic disc. The procedures were well-tolerated, with minor complications such as preretinal or vitreous hemorrhages resolving spontaneously; Conclusions: TYE is a minimally invasive therapeutic option for retinal artery occlusion with visible emboli, particularly when intervention occurs shortly after the onset of symptoms. Compared to observation, TYE can improve visual outcomes and reduce the risk of permanent vision loss, presenting a significant advancement in the management of this ophthalmic emergency. The worldwide experience with TYE is continuously increasing. Further research with larger cohorts is recommended to validate these results and refine the treatment protocol.

Tire wear particles (TWP) are among the most relevant sources of microplastic pollution of the environment. Nevertheless, common analytical methods like IR- and Raman spectroscopy are highly impaired by additives and filler materials, leaving only thermo-gravimetric methods for chemical analysis of TWP in most cases. We herein present quantitative NMR spectroscopy (qNMR) as an alternative tool for the quantification of the polymeric material used for the production of tires including natural rubber (NR), styrene-butadiene-copolymer (SBR), polyethylene-polypropylene-copolymer (EPR) and polybutadiene (BR). Limits of quantification between 3 µg and 43 µg per sample and recovery rates of 72-92% are achieved for all tested polymer types. The first results of combining these measurements with Soxhlet extraction as a sample preparation tool are presented alongside the qNMR experiments.

This study presents a comprehensive systematic review that addresses the critical transition from ROS 1 to ROS 2, spotlighting the significant enhancements and the pressing need for a detailed exploration of ROS 2 within the robotics community. Despite the extensive deployment and adaptations of ROS in varied robotics applications, literature lacks a cohesive synthesis that delineates the advancements, limitations, and broader impacts of ROS 2 compared to its predecessor, ROS 1. Our contribution bridges this gap by assembling the largest database of ROS-related research, encompassing 7,498 articles, with a focused analysis in this survey on 431 ROS2-specific publications. We categorize these into i.) articles that discuss and analyze core ROS 2 concepts, ii.) articles that propose frameworks or tools for ROS 2, and iii.) articles utilizing ROS 2. Furthermore, we summarize literature findings of ROS 2 challenges, advancements, and future direction in the fields of a.) security, b.) real-time, c.) middleware, d.) embedded and distributed systems, e.) communication reliability and QoS, and f.) multi-robot systems. The methodology involved meticulous data collection and categorization from multiple databases, facilitating an in-depth online accessible resource. Results underscore ROS2’s enhancements in modularity, real-time capabilities, and security, extending its applicability across various robotic platforms and industries. However, challenges in scalability and reliability persist, signaling avenues for future enhancements. This review not only deepens the understanding of ROS2’s contributions but also charts a path for ongoing improvements in robotic systems design. The original data presented in the study are openly available in https://www.ros.riotu-lab.org/.

The relationship between the global climate crisis associated with environmental risks and occupational hygiene has not been extensively studied. This study develops a framework for identifying how climate change and the climate crisis could impact the workplace environment, workers, and occupational morbidity, mortality, and injury. A framework based on a review of scientific literature published from 2012-2024 that addresses climate risks, their interaction with occupational hazards, and their effects on the workforce. Eight categories of climate-related hazards are identified: increased high temperatures, dust and air pollution, sun and cosmic ultraviolet exposure, pandemics, and infectious diseases, diseases transmitted by insects and changes in ecosystems, industrial occupational diseases, changes and crises in the built environment, and extreme weather events. It is important to consider the possibility of interactions between known hazards and new conditions and the productivity of workers, especially those who are most at risk of heat-related illnesses.

The main objective of the present study is to demonstrate the possibility of actively influencing the position of the bow shock wave and the main parameters of supersonic flow over a blunt body by organizing a gas discharge near the front surface, in the region between the body and the bow shock wave. The research is carried out using both experimental and numerical methods. The working gas was xenon. It is shown that the steady bow shock wave stand-off distance along with the current and power of the discharge, is associated with the change in the adiabatic index of the plasma created by the discharge, which, in turn, is determined by the plasma parameters, such as the degrees of nonequilibrium and the degree of ionization. It is shown that the adiabatic index with the power supplied to the impact zone in the range of 30-120 kW can both increase and de-crease in the range of 1.25-1.288. The study of the discharge created plasma zone was conducted and the correspondence between the discharge current and power, and the average parameters in the plasma zone created by the discharge is presented. A good agreement between the numerical and experimental data was shown. The results may be useful in developing control systems for high-speed civil aircrafts.

With a recent advance in photonic integrated circuits (PIC), a new field of frequency agile coherent radar systems is being thoroughly researched. In contrast to the traditional all-electronics radiofrequency (RF) radar techniques, a fully photonics-based system does not suffer from bandwidth limitation and noise degradation when switching from S to X or K-band and can achieve excellent phase noise even at frequencies exceeding 20 GHz. However, recent state-of-the-art PICs still exhibit high processing losses in the optical domain, which must be addressed in the electrical part of the RF domain. This study presents a comprehensive analysis of the challenges in designing RF front-end amplifier chains for microwave photonic radars, particularly in maintaining stability, minimizing noise, and reducing intermodulation distortion. Furthermore, a comprehensive demonstration of the RF front-end, encompassing both the optical generation of RF signals and experimental measurements of a rotor blade's Doppler fingerprint to validate the system's performance is presented.

Vector published map, as a result of pre-press printing of paper map digital publishing, is a page file in line with PostScript standards. With large amount of data and a large number of map page graphics objects, the browsing speed is slow and the hardware performance is very high, which seriously affects the breadth and depth of the application of vector published map. In response to this problem, this paper proposes a visualization method of large-size vector published map. This method adopts the idea of tiling. The vector published map is physically cropped into several PDF tiles to form a map logically. Data encoding and organization are based on the GeoSOT earth division grid to realize the rapid loading, scaling, display of map data and the correlation retrieval of map product "map and spatial data integration". The experiment shows that the response time of map data loading and browsing is much less than that of professional software, and the method is insensitive to data volume and has low requirement on hardware performance. This method can not only make up the disadvantages of image tiles in the existing network map service, but also make the vector published map can be used in embedded devices, mobile terminals, network modes and other cross-media applications.

Background: The diagnosis of Pulmonary Embolism (PE) is based on the application of a priori probability scales such as the Wells scale or PERC criteria. However, the clinical heterogeneity of this pathology results in the absence of a target population to apply these algorithms. Moreover, these scales do not consider the possible influence of the presence of an objective alternative diagnosis to PE, complicating its clinical assessment and possibly leading to an increase in unnecessary testing or missed diagnoses. Objective: The aim of this study is to evaluate the discrimination capacity of clinical objective factors with a high negative predictive value for PE, compared to PERC in terms of reducing unnecessary testing across different risk strata of the Wells scale. Materials and Methods: This was a single-center retrospective cohort study, including patients who underwent chest CT angiography to rule out PE at a University Hospital between 2008 and 2017, considering the presence of PE as the study outcome. The study collected demographic data, comorbidities, and clinical presentation data. The presence of objective criteria for pneumonia, heart failure, exacerbation of COPD, or the use of anticoagulation in non-oncological patients was considered a priori criteria with a high negative predictive value. Results: The analyses were performed on a cohort of 399 patients with an average age of 65 years and 53% females. A total of 139 patients were diagnosed with PE by CT angiography. The presence of factors with a high NPV showed a sensitivity and NPV of 100% in low-risk patients according to Wells, with sensitivity dropping below 50% in other populations. The association of these factors in the PERC plus criteria would allow a reduction of up to 34% in CT angiographies in patients with low risk according to the Wells scale. Conclusions: The combination of risk stratification of the Wells scale and PERC plus criteria allows an absolute reduction of 34.3% in the performance of CT angiographies in patients classified as low risk with a sensitivity and a negative predictive value of 100%. The preexistence of an alternative diagnosis does not allow ruling out PE in patients with intermediate or high risk according to the Wells scale.

Food processing can modify protein biochemical properties altering its immunoreactivity and allergenicity. Resistance to enzymatic digestion is one of the main characteristics of food allergens, specially to pepsin and trypsin. In this work, the aim is to compare the digestomes of raw and heat and /or pressured treated peanuts, hazelnut, pistachio and cashew resulting from the INFOGEST harmonized digestion protocol, as well as the different behaviour regarding IgE-binding capacity, by means of in vitro analysis. Protein patterns, resolved by SDS-PAGE, were assayed with sera from allergic patients and confirmed by competitive ELISA in hazelnut and peanut. The results of our study reveal that different processing methods affect differently gastrointestinal (GI) digestion of peanut, hazelnut, pistachio and cashew allergens. Simulated GI digestion led to extensive de-struction of the protein structure, reducing but not abolishing the IgE reactivity of peanut, ha-zelnut, pistachio and cashew allergenic proteins. Boiling for 60 minutes did not affect the protein profile of these nuts, although enzymatic activity was stimulated, decreasing the IgE binding capacity. When heat and pressure was applied to these nuts, simulated digestion promoted an almost complete inhibition of the allergenic potential.

Detection of adverse events, for example convulsive epileptic seizures, can be critical for the patients suffering from variety of pathological syndromes. Algorithms using remote sensing modalities, such as video camera input, can be effective for real-time alerting but the broad variability of environments and numerous non-stationary factors may limit their precision. In this work, we address the issue of adaptive reinforcement that can provide flexible application of the alerting devices. The generic concept of our approach is the topological reinforced adaptive algorithm (TOREADA. Three essential steps, embedding, assessment and envelope act iteratively during the operation of the system, providing thus a continuous, on the fly reinforced learning. We apply this concept on the case of detecting convulsive epileptic seizures where three parameters define the decision manifold. Monte-Carlo type simulations validate the effectiveness and robustness of the approach. We show that the adaptive procedure finds the correct detection parameters, providing optimal accuracy, from a large variety of initial states. With respect to the separation quality between simulated seizure and normal epochs, the detection reinforcement algorithm is robust within broad margins of signal generation scenarios. We conclude that our technique is applicable to a large variety of event-detection systems.

Fluctuations in kynurenic acid (KYNA) and brain-derived neurotrophic factor (BDNF) levels in the brain reflect its neurological status. The aim of the study was to investigate the effect of transiently elevated KYNA concentration in the cerebroventricular circulation on the expression of BDNF and its high-affinity tropomyosin-related kinase receptor B (TrkB) in specific structures of the sheep brain. Intracerebroventricularly cannulated anestrous sheep were subjected to a series of four 30-min infusions of KYNA: 4×5 μg/60 μL/30 min (KYNA20, n=6) and 4×25 μg/60 μL/30 min (KYNA100, n=6) or a control infusion (n=6), at 30-min intervals. Sections of the hippocampal CA3 field, amygdala (AMG), prefrontal cortex (PCx), and the hypothalamic medial-basal (MBH) and preoptic (POA) areas were dissected from the brain immediately after the experiment. The highest concentration of BDNF protein was found in the CA3 field (P&lt;0.01), which was 8-fold higher than in the AMG and 12-fold higher than that in the PCx (MBH and POA were not analyzed). The most pronounced BDNF mRNA expression was observed in the MBH followed by the PCx, POA, AMG and CA3, while the highest abundance of TrkB mRNA was recorded in the AMG followed by the MBH, PCx, CA3 and POA. KYNA increased (P&lt;0.05-P&lt;0.01) BDNF protein levels and the expression of its gene in the brain structures examined, with the effect varying by dose and brain region. KYNA, particularly at the KYNA100 dose, also increased (P&lt;0.01) TrkB gene expression, except for the AMG, where the lower KYNA20 dose was more effective (P&lt;0.01). These findings suggest a positive relationships between KYNA levels in the cerebroventricular circulation and BDNF-TrkB expression in specific brain regions in a sheep model. This indicates that a transient increase in CSF KYNA concentration can potentially restore BDNF production, whose deficiency underlies numerous neurological disorders.

A cross-sectional survey was distributed to 1322 members of a 1-on1 personalized strength training studio. 366 respondents (n=134 male and n=232 female), all aged over 20 years reported considerable training experience; 55% of males and 42% of females reporting 5+ years. Data were analyzed and reported descriptively with differences &gt;5% identified based on use of the 5-point likert scale, sample size and nature of the observations. Disparity between males and females were identified; males reported higher perceptions of managing effort, technique, and programming without supervision compared to females. Safety was noted as being more important to females compared to males. Qualitatively, additional themes were raised including analogy of the personal relationship between trainer and trainee being similar to that of medical professionals. This was validated where participants discussed their adaptations from supervised strength training including quality of life in aging, and recovery from medical conditions and injury. Data is discussed in context of a previous body of literature suggesting males falsely report higher confidence in tasks compared to females, particularly in relation to effort, role models, and verbal encouragement. This research proves beneficial for strength training practitioners in enhancing their understanding and expectations of clients, as well as hopefully proving insightful in engaging more people in strength training.

This study examines the relationship between financial investment, shareholder preference, corporate social responsibility (CSR) performance and life satisfaction for BP and Shell within the UK, together with their alignment with specific Sustainable Development Goals (SDGs). about, using regression analysis, in both projects Hypotheses were tested to assess the significance of these predictors in the performance of CSR developed. The results showed that financial investment, shareholder ownership, and social satisfaction did not significantly predict CSR performance for BP and Shell. Furthermore, there were no significant differences in the impact of these predictors between the two companies. Further research revealed Shell’s strong commitment to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), with both initiatives drawing on Shell’s significant investments in renewable energy and climate recognition in the management system established him as a leader in sustainable development efforts. BP’s goal of achieving net zero emissions by 2050, especially focused on SDG 7 and SDG 12 (Responsible Consumption and Production), this proposal was adopted. Comparing the two sectors, Shell showed significant advantages, which can be attributed to its diversified capabilities and its strong alignment with the sustainability objectives of BP, while growing largely, it faces significant financial challenges in its transition to clean energy. The study concludes that although both companies are committed to sustainability, Shell now leads in terms of effective CSR and financial performance

Objectives: Cardiogenic shock [CS] is associated with a high-mortality. Suitable patients maybe successfully bridged using newer intravascular micro-axial left-ventricular assist devices [M-LVAD] for recovery or determination of definitive therapy. Methods: Between January-2020 and July-2024, 107 patients underwent placement of M-LVAD for CS. The cohort was divided into 4 groups based on their destination; group-1: 34 patients [32%] receiving transplant; group-2: 25 patients [23%] receiving durable LVAD; group-3: 42 patients [39%] bridging from post-cardiotomy CS [PCCS]; and group-4: 6 patients [5.6%] bridging decision/recovery [these were excluded from analysis]. Multivariable logistic-regression [MLR] and Cox-regression [MCR] models identified predictors of early-hospital and late mortality, with data reported as odds ratios [ORs], and hazard ratios [HRs], respectively. P<0.05 was statistically significant. Results: Complications included device-malfunction [n=6, 6%], gastrointestinal bleed [n=9, 9%], stroke [n=11, 11%]; long-term hemodialysis [n=21, 21%]. Early-hospital mortality included 13 patients [13%]: 2 in group-1, 1 in group-2 and 10 in group-3 [p=0.02]. In the MLR model, the category of cardiogenic shock requiring M-LVAD placement was statistically significant [OR=4.7 (0.9-24), P=0.05]. Patients were followed for up to 4.5-years, and 23 deaths occurred; group-1: 3 patients, group-2: 5 patients, and group-3: 15 patients [p=0.019]. At 4.5-years, actuarial survival was 90.7±5.1% in group-1, 79.2±8.3% in group-2, and 62.8%±7.7% in group-3 [P=0.01]. In the MCR model, M-LVAD category [HR=3.63 (1.03-12.9) P=0.04], and long-term postoperative dialysis [HR=3.9 (1.6-9) P=0.002], emerged as statistically significant predictors of long-term mortality. Conclusions: In cardiogenic shock, our mid-term outcomes demonstrate good survival with M-LVADs as bridge to transplant and durable LVAD, and reasonable survival as bridge to recovery following cardiotomy, reduced ECMO usage and early ambulation/rehabilitation.

Fish is essential to feeding the world's population and provides hundreds of millions of people with a substantial amount of protein in their diets. Fish is one of the most affordable and readily available forms of animal protein, making up roughly 20% of total animal protein intake. It is simple to obtain these fish through cultivated or captured fisheries. However, the aquaculture industry is viewed as the main means of supplying the world's growing fish demand given the ongoing indiscriminate harvests in capture fisheries. Global aquaculture systems are under serious threats from climate change, which will have an impact on disease prevalence, species mix, water quality, and overall production. The possible impacts of climate change on freshwater aquaculture production and its consequences for the sustainability of the sector are reviewed in this paper. Rising temperatures, El Niño Southern Oscillation events, lowered dissolved oxygen, toxic algal blooms, altered rainfall patterns, water pH, altered salinity, and extreme weather events are a few of the aspects of a changing climate that have been discussed.

Suppose n ∈ N. We wish to meaningfully average ‘sophisticated’ unbounded sets (i.e., sets with positive n-d Hausdorff measure, in any n-d box of the n-d plane, where the measures don’t equal the area of the boxes). We do this by taking the most generalized, satisfying extension of the expected value, w.r.t the Hausdorff measure in its dimension, on bounded sets which takes finite values only. As of now, I’m unable to solve this due to limited knowledge of advanced math and most people are too busy to help. Therefore, I’m wondering if anyone knows a research paper which solves my doubts. (Unlike previous versions with similar names, we add examples, motivations, and explanations to this version.)

We assess simultaneous adoption and impacts of multiple improved technologies promoted as a bundle and recommended for legumes intensification systems for smallholder farmers in Ethiopia. We use DNA fingerprinting data to precisely identify our key treatment - “adoption to improved bean varieties” in the study. The results show significant positive impacts of adopting bundled interventions on, agricultural incomes and household food security but vulnerability to food insecurity persists. We find that growing improved varieties with fertilizers increased household agricultural revenue, allowing for more legume consumption and enhancing their likelihood of achieving adequate food consumption and food security outcomes. However, the vulnerability to food insecurity of the adopters remains high due to pre-existing resource degradation issues. Given similarity in production contexts in Sub Saharan Africa, our results provide perspective for similar development interventions. We use the results of our analysis to discuss potential policy implications and programmes to support technology intensification among small holder farmers.

Plants are rich source of specialized metabolites, such as alkaloids, terpenes, phenolic acids, flavonoids, coumarins, and volatile oils, which provide various health benefits including anticancer, anti-inflammatory, antiaging, skin-altering and anti-diabetic properties. However, challenges such as low and inconsistent yields, environment and geographic factors, and species-specific production of some specialized metabolites limit the supply of raw plant material for the food, cosmetic or pharmaceutical industries. Therefore, biotechnological approaches using plant in vitro systems offer an appealing alternative for the production of biologically active metabolites. Among these, hairy root cultures induced by the Rhizobium rhizogenes have firmed up their position as “green cell factories” due to their genotypic and biosynthetic stability. Hairy roots are valuable platforms for producing high-value phytomolecules at low cost, are amenable to pathway engineering and can be scaled up in bioreactors making them attractive for commercialization. This review explores the potential of hairy roots for specialized metabolites biosynthesis focusing on biotechnology tools to enhance their production. Aspects of morphological peculiarities of hairy roots, the diversity of bioreactors design, and process intensification technologies for maximizing biosynthetic capacity, as well as examples of patented plant-derived (green-labeled) products produced through hairy root cultivation at lab and industrial scales are addressed and discussed.

Acrostichum aureum is a halophytic pantropical invasive fern growing in mangroves and swamps. Its association with arbuscular mycorrhizal fungi (AMF) has been reported in Asia. AMF and their symbiosis (AM) commonly colonise the absorption organs of terrestrial plants worldwide. Fur-thermore, AMF/AM are well known for their capacity to bioaccumulate toxic elements and to alleviate biotic and abiotic stress (e.g. salinity stress) in their hosts. However, how AMF are in-volved in the halophytism of A. aureum, and the structures where NaCl accumulates remains unknown. This study shows that A. aureum forms AM in Neotropical thermal margins of natural ponds, NaCl accumulation occurs inside AMF vesicles, and CaSO4 precipitates in colonised roots. Therefore, AM would be contributing to the halophytic nature of this fern, allowing it to thrive in saline and thermal environments by capturing NaCl from fern tissues, compartmentalizing it inside its vesicles and precipitating CaSO4.

'Xinomavro' (V. vinifera L.) is an important native red wine grape variety in Northern Greece, particularly in PDO (Protected Designation of Origin) regions. Despite its significance, there is limited research on the effects of pruning type and severity on 'Xinomavro' vine physiology, yield, and berry quality across diverse environmental conditions. This study aimed to address this knowledge gap and provide growers with crucial information for optimizing vineyard management practices. The study was conducted over two consecutive years (2016 and 2017) in a vineyard in Thessaloniki, Northern Greece. Four treatments ( B12:12 buds on 6 spurs; B24:24 buds on 12 spurs; M12:12 buds on 2 canes; M24:24 buds on 4 canes) combining two bud load levels (12 or 24 count nodes) and two pruning types (short spurs or long canes) were applied to 'Xinomavro' vines in complete block randomized design. The vine water status, gas exchange, canopy characteristics, yield components, and berry composition were measured. Bud load and pruning type significantly influenced vine canopy development, microclimate, and yield components. Short pruning with high bud load (B24) resulted in denser canopies and higher yields, whereas cane pruning (M12 and M24) led to more open canopies and improved berry quality indicators. Treatment effects on berry composition were inconsistent across years, but showed a tendency for higher anthocyanin and total phenol content in cane-pruned vines. This study demonstrated that pruning type (short or long fruiting units) may have a greater impact on vine growth, yield, and berry composition than bud load alone in 'Xinomavro' vines. Cane pruning appears to be a more effective strategy for achieving vine balance and potentially improving grape quality under given experimental conditions.

The State of Zacatecas (Mexico) is one of the places most affected by pollution from the mining industry. 21 samples from two areas have been collected (6 samples from zone A and 15 samples from zone B) to evaluate the level of radioactivity and the degree of contamination by heavy metals. The activity concentration for 238U and 232Th is within typical values, but 226Ra exceeds the value of 60 Bq kg-1 in both areas. In some places, the concentration of 40K is higher than the world average, according to UNSCEAR. The radium equivalent activity Raeq and outdoor gamma exposure dose (D) indicate that some sites present a radiological risk for the population. Excess lifetime cancer risk (ELCR) presents a higher risk than the world average in both sampled areas. Other parameters, such as the radiation index Ri, the external risk index Hex, and the internal risk index Hin, also indicate that there is a greater risk due to radiation exposure in these areas. In the case of heavy metals, some parameters, such as the enrichment factor (EF), the geoaccumulation index (Igeo), and the contamination factor (CF), show that some places in zone A and zone B are contaminated with As and Pb. However, the concentration of selenium obtained by ICP-MS in the sampled soil is higher than the average in the Earth's crust, both in mine waste dumps and in unaltered soil, which indicates that the background concentration for selenium in these places is higher than the world average.

Introduction: Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence (AI)--powered mobile apps for automated ulcer segmentation and their application in clinical settings. Methods: A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane and Google Scholas databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. Results: Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. Discussion: AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. Conclusion: AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems.

Child malnutrition is a critical public health concern that significantly hampers children's physical and mental development and has serious economic repercussions. The World Health Organization (WHO) attributes half of all deaths among children under five to malnutrition, with long-term consequences such as lower educational achievement, decreased productivity, and deepened poverty. The economic impact of child malnutrition extends beyond public health, imposing substantial costs on health systems, households, and the broader economy. This study estimated the burden of child malnutrition in Colombia among children up to four years old using a Markov chain model from a societal perspective. The model simulated six health states, integrating direct and indirect costs, with epidemiological data and international literature guiding the transition probabilities. Surveys were conducted with caregivers to assess indirect costs. Results indicated that malnutrition accounted for 419.84 Disability-Adjusted Life Years (DALYs) per 1,000 inhabitants. The total cost of malnutrition over four years was approximately USD 243.58 million, with an annual average cost of USD 60.89 million, with 65% of the burden falling on households. The findings emphasize the significant economic and health burden of child malnutrition, underscoring the importance of early and comprehensive interventions. Investing in child nutrition can improve health outcomes, reduce economic strain, and enhance productivity, making it a crucial priority in health and economic development agendas.

Adventitious root (AR) formation in plants origins from non-root organs such as leaves and hy-pocotyls. Auxin signaling is essential for AR formation, but the role of other phytohormones is less clear. In Arabidopsis, at least two distinct mechanisms can produce ARs either from hypocotyls as part of the general root architecture, or from wounded organs during de novo root regeneration (DNRR). In previous reports, gibberellin acid (GA) seems to play reverse roles in both types of AR formation, since GA treatment blocks AR formation from hypocotyls, whereas depletion of GA signaling results in reduced DNRR from detached leaves. Here, we report the positive effects of GA biosynthesis inhibitor paclobutrazol (PBZ/PAC) treatment on both types of AR formation in Arabidopsis. Consistently, loss of GA synthesis and signaling promoted DNRR in our conditions. Furthermore, PBZ treatment can rescue declined AR formation from leaf explants in erecta recep-tor mutants. Transcriptional profiling revealed that PBZ treatment altered GA, brassinosteroids and auxin responses, which included up-regulation of LBD16 that plays a pivotal role in AR initi-ation. This study reveals that depletion of GA biosynthesis promotes DNRR in Arabidopsis, and thus will contribute to further clarification of the role of GA during AR formation in plants.

Facial appearance significantly influences several life outcomes, and a convex profile often prompts patients to seek treatment to enhance their facial esthetics. This study aimed to assess the facial appearance outcomes of combined orthodontic and orthognathic intervention compared to orthodontic camouflage treatment in convex facial profile individuals. The study sample comprised 36 consecutively selected Class II Division 1, convex facial profile patients. Two groups of 18 non-growing patients with similar characteristics were compared. Group A was treated with orthodontics and orthognathic surgery, whereas Group B with orthodontics exclusively. Pre- and post-treatment profile and frontal facial photographs were presented simultaneously to orthodontists, oral and maxillofacial surgeons, convex profile patients, and laypeople, asking them to assess changes in facial appearance. Considerable positive changes in facial appearance were perceived for Group A, compared to no changes for Group B, with a 17/100 VAS units difference. The rater groups demonstrated a high degree of consistency (ICC > 0.88). Multivariate analysis revealed significant differences in perceived changes between the two treatment groups (F = 14.63, P < 0.001, Pillai's Trace = 0.36, partial η2 = 0.36), with no significant effects from the rater group (P > 0.05). Similar results were evident when only profile photos were rated (P > 0.05). The combined orthodontic and surgical intervention is an efficient approach for the enhancement of facial appearance in convex profile cases. On the contrary, orthodontic treatment alone has no such impact. These findings should be properly communicated during patient consultation and considered for treatment planning.

Living systems are capable on the one hand, of eliciting a coordinated response to changing environment (aka adaptation) and on the other hand, they are capable of reproducing themselves. Notably, adaptation to environmental change requires the monitoring of the surroundings, while reproduction requires monitoring oneself. These two tasks appear separate and making use of different sources of information. Yet both the process of adaptation as well as that of reproduction are inextricably coupled to alterations of genomic DNA expression, while a cell behaves as an indivisible unity in which apparently independent processes and mechanisms are both integrated and coordinated. We argue that at the most basic level, this integration is enabled by the unique property of the DNA to act as a double coding device harboring two logically distinct types of information. We review biological systems of different complexity and infer that inter-conversion of these two distinct types of DNA information represents a fundamental self-referential device underlying both systemic integration and coordinated adaptive response.

This study analyzes how various audio features influence speaker change detection in different unsupervised approaches. Ten different audio features, including MFCC, mel-spectrogram, spectral centroid, and pitch, etc. were chosen for analysis. Two distinct unsupervised methods were selected for comparing the effectiveness of these feature: the Bayesian information criterion with Gaussian mixture model (BIC-GMM), a model-based approach, and the Kullback-Leibler divergence with Gaussian (KL-GMM), a metric-based approach. In order to gain a prior insight into which features have a significant impact on the SCD problem, we calculate statistic for two probabilities: the probability of speaker change given there is a feature change, and vice versa. To experimentally check the prior insights, a set of experiments carried out to analyze the influence of various audio feature for SCD. The results showed that in both methods, MFCC demonstrates a robust performance, and ranks at the top. Zero crossing rate, chroma and spectral contrast gives a higher performance in BIC-GMM. Mel-spectrogram consistently ranks at the bottom, suggesting it has the least impact on performance across both methods.

Low back pain (LBP) affects over 60% of individuals in their lifetime and is a leading cause of disability and increased healthcare expenditure. Facet joint pain (FJP) occurs in 27% to 40% of LBP patients but is often overlooked or misdiagnosed. Additionally, there is no clear correlation between clinical examination, radiological findings, and clinical presentation, com-plicating the diagnosis and treatment of FJP. This narrative review aims to provide an overview of the literature regarding facet joint pain and discusses the utility of medial branch blocks (MBBs) and intra-articular (IA) injections as diag-nostic and therapeutic tools prior to radiofrequency ablation (RFA). RFA is considered the gold standard for managing FJP, employing techniques that include precise needle placement and stimulation parameters to disrupt pain signals. Promising alternatives such as cooled RFA and cryodenervation require further research on their long-term efficacy and safety. Endoscopic denervation and multifidus stimulation are emerging therapies that may benefit chronic LBP patients, but additional research is needed to establish their effectiveness. When conservative management fails, RFA provides significant and lasting relief in well-selected patients and has a favourable safety profile. Current literature does not support surgical inter-ventions for LBP management.

Alphaviruses are known for being model viruses for studying cellular functions related to viral infection but also for causing epidemics in different parts of the world. More recently, alphavirus-based expression systems have demonstrated efficacy as vaccines against infectious diseases and as therapeutic applications for different cancers. The self-replicating feature of alphaviruses has provided the advantage of extremely high transgene expression of vaccine-related antigens and therapeutic anti-tumor and immunostimulatory genes, which has also permitted significantly reduced doses for prophylactic and therapeutic applications potentially reducing adverse events. Furthermore, alphaviruses have shown favorable flexibility as they can be delivered as recombinant viral particles, RNA replicons or DNA replicon-based plasmids. In the context of infectious diseases robust immune responses against the surface proteins of target agents have been observed and protection against challenges with lethal doses of infectious agents in rodents and primates. Similarly, expression of anti-tumor genes and immunostimulatory genes from alphavirus vectors has provided tumor growth inhibition, tumor regression and cure in animal cancer models. Moreover, protection against tumor challenges has been observed. In clinical settings, clinical benefits have been reported. Alphaviruses have also been considered for the treatment of neurological disorders due to their neurotrophic preference. Attention has been paid to alphavirus vector and expression system improvement. Point mutations in the non-structural alphaviral replicase genes have generated enhanced transgene expression and created temperature-sensitive expression vectors. The recently engineered trans-amplifying RNA system has facilitated vector production and enhanced the expression capacity.

The incorporation of electric vehicles (EVs) with vehicle-to-grid (V2G) offers the development of multi-energy microgrid (MMOs) models. However, MMOs have among their goals to decrease electricity costs by coordinating with vehicle-to-grid (V2G) scheduling. Controlling V2G models poses uncertainties about the voltages developed and creates the need for power generation disruptions. In this work, a Feed-Forward-Back-Propagation Network (FFBPN) is developed with Matlab software, based on the Levenberg-Marquardt algorithm by varying the number of hidden neurons to realize better performance thanks to the value of the Maximum Coefficient of De-termination (R2) and the Minimum Mean Squared Error (MSE). In this article, the dataset from NASA PCoE has been used to make proper consideration of the model that best calculates the life cycle of the battery, demonstrating relevant implications for future development of this type of system based on FFBPN for the integration of EV into V2G. The proposed FFBPN demonstrates better performance than other methods taken from the literature. In conclusion, the comparison between the phases of training, validation and test are used to completely represent the validity of the proposed model and precisely to identify the characteristics curves of FFBPN for future ap-plications highlighting its performance to create profitability, efficiency, production, energy saving and minimum environmental impact.

The tryptophan-kynurenine (KYN) pathway has long been recognized for its essential role in gen-erating metabolites that influence various physiological processes. Traditionally, these metabolites have been categorized into distinct, often opposing groups, such as pro-oxidant versus antioxidant, excitotoxic versus neuroprotective, or neurotoxic versus neuroprotective. This dichotomous framework has shaped much of the research on conditions like neurodegenerative and neuropsy-chiatric disorders, as well as cancer, where metabolic imbalances are a key feature. The effects are significantly influenced by various factors, including the concentration of metabolites and the par-ticular cellular milieu in which they are generated. A molecule that acts as neuroprotective at low concentrations may exhibit neurotoxic effects at elevated levels. The oxidative equilibrium of the surrounding environment can alter the function of KYN from an antioxidant to a pro-oxidant. This narrative review offers a comprehensive examination and analysis of the contemporary under-standing of KYN metabolites, emphasizing their multifaceted biological functions and their rele-vance in numerous physiological and pathological processes. This underscores the pressing ne-cessity for a paradigm shift in the comprehension of KYN metabolism. Understanding the intricate, context-dependent functions of these metabolites is crucial for establishing novel therapeutic tar-gets, especially in conditions such as neurodegeneration, immunological disorders, and cancer, where metabolic dysregulation serves as a primary catalyst of pathogenesis.

This paper considers the question of what kind of knowledge is produced by deep learning. Ryle’s concept of knowledge how is examined and is contrasted with knowledge with a rationale. It is then argued that deep neural networks do produce knowledge how, but, because of their opacity, they do not in general, though there may be some special cases to the contrary, produce knowledge with a rationale. It is concluded that the distinction between knowledge how and knowledge with a rationale is a useful one for judging whether a particular application of deep learning AI is appropriate.

This study investigates the application of fractal geometry to the analysis of vertebrate respiratory systems, focusing on the branching patterns of the bronchial tree across diverse species. Using a combination of mathematical modeling and computational visualization, we explore how fractal dimensions and branching characteristics reflect evolutionary adaptations to various habitats and physiological demands. The research encompasses a range of vertebrates, including humans, horses, dolphins, chickens, iguanas, and bullfrogs, representing diverse taxonomic groups and ecological niches. Our analysis reveals a clear gradient of complexity in respiratory structures, correlating strongly with metabolic rates and environmental adaptations. Mammals, particularly those adapted for high-performance activities like horses, demonstrate the most complex fractal patterns, indicating highly efficient gas exchange systems. In contrast, ectothermic species such as iguanas and bullfrogs exhibit simpler structures, reflecting their lower metabolic demands and alternative respiratory strategies. The study employs fractal dimension calculations, L-system modeling, and lacunarity analysis to quantify and visualize these differences. Our findings suggest that fractal analysis provides valuable insights into the relationship between form and function in vertebrate respiratory systems, offering a novel perspective on comparative physiology and evolutionary biology. This research not only enhances our understanding of respiratory system evolution but also has potential applications in biomimetic design, medical diagnostics, and ecological physiology. It demonstrates the power of interdisciplinary approaches, combining advanced mathematics with biological principles to uncover fundamental patterns in nature.

The development of pre-hierarchical follicle (PHF), especially small yellow follicles (SYFs), directly affects the recruitment of dominant follicles and thus affects the egg-laying performance of chickens. However, the development of PHF, especially the development of SYF, and its regulatory mechanism remain unclear. Therefore, we performed transcriptomic and metabolomic analyses of large white follicles (LWFs) and SYFs in chickens. Transcriptome sequencing revealed 258 differentially expressed genes (DEGs) between SYF and LWF, of which 172 were upregulated and 86 were downregulated. The DEGs were mapped to 17 KEGG pathways, mainly the gap junction, calcium signaling, and neuroactive ligand-receptor interaction pathways. The results of the metabolome analysis revealed 129 significant differential metabolites (DMs), including 36 upregulated DMs and 93 downregulated DMs. The DMs were mapped to 9 KEGG pathways for follicle tissue, which included mainly glutathione metabolism, alpha-linolenic acid metabolism, and tryptophan metabolism, etc. The combined transcriptional and metabolic analysis revealed significantly enriched pathways, and four metabolically related pathways, including glutathione metabolism, alpha-linolenic acid metabolism, linoleic acid metabolism, and pyrimidine metabolism, were identified. Glutathione metabolism is a critical signaling pathway that may affect ovarian follicle development to regulate the SYF reserve process in chickens. These results can act as a reference for improving the egg-laying performance of Tengchong snow chickens.

A new maritime order is emerging – the blue economy – characterised by a plethora of non-traditional security threats. Using the case of the Western Indian Ocean I demonstrate how the creation of spaces of risk in response to traditional security threats by world powers has led to a new, regionally-led governance regime in response to the new maritime order. International efforts to protect shipping lanes from the threat of piracy off the coast of Somalia have evolved into wider environmental and human security measures. I analyse these changes through the lens of governmentality and argue that this case represents an example of a global governmentality in which states are responsibilised to act collaboratively by the international community through discourses of risk. Regional States have turned this to their advantage, acting together to construct and secure a new development frontier - a shared blue economy space in an extensive and difficult-to-govern territory.

Background/Objectives: Leptospirosis is a globally distributed zoonosis with symptomatology similar to other febrile syndromes in tropical countries, making clinical diagnosis difficult. This study aimed to analyze the performance and agreement between serological diagnostic tests for the detection of acute and convalescent human leptospirosis compared to the micro agglutination test (MAT) in an endemic region of the Colombian Caribbean. Methods: A prospective descriptive study was conducted on 275 patients with suspected leptospirosis. Paired serum samples were collected, and an epidemiological survey completed. Positive and negative predictive values, sensitivity, specificity, and kappa index were calculated using MAT as the gold standard. A Bayesian latent class model was used to compare the diagnostic tests. Results: In 223 paired serum samples, the sensitivity values for different stages of the disease varied between 10.8% to 54.1% in the acute and 6.1% to 66.7% in the convalescent phase when compared to MAT; and when using the Bayesian model 9.5% to 75.3% in the acute and 5.7% to 85.3% in the convalescent phase. The Kappa value, an indicator of agreement, was moderate for the IgM ELISA (0.553) in the acute phase, and substantial (0.692) in the convalescent phase. Conclusions: MAT was the best confirmatory test in both acute and convalescent phases. Despite the high specificity of ELISA, 21.62% of patients diagnosed as negative by IgM-ELISA in the acute and convalescent phases were confirmed as positive by MAT. It is necessary to re-evaluate diagnostic guidelines that do not employ MAT for confirmation, and to strengthen diagnostic and clinical identification of leptospirosis in healthcare institutions and public health laboratories, while providing a rapid and reliable test for its implementation.

Multi-agent pathfinding has been extensively studied in the robotics and artificial intelligence communities. The classical algorithm, Conflict-Based Search(CBS), is widely used in various real-world applications due to its ability to solve large-scale conflict-free paths. However, classical CBS assumes discrete time-space planning and overlooks physical constraints in actual scenarios, making it unsuitable for direct application in Unmanned Aerial Vehicle(UAV) swarm. Inspired by the decentralized planning and centralized conflict resolution ideas of CBS, we propose an optimal and efficient UAV swarm motion planner that integrates State Lattice with CBS, named SL-CBS. SL-CBS is a two-layer search algorithm: (1) The low-level search utilizes an improved State Lattice. We design emergency stop motion primitives to ensure complete UAV dynamics and handle spatio-temporal constraints from high-level conflicts. (2) The high-level algorithm defines comprehensive conflict types and proposes a motion primitive conflict detection method with linear-time complexity based on Sturm’s theory. Additionally, our modified Independence Detection(ID) technique is applied to enable parallel conflict processing. We validate the planning capabilities of SL-CBS in classical scenarios and compare it with State-Of-The-Art(SOTA) algorithms, showing great improvements in success rate, computation time, and flight time. Finally, we conduct large-scale tests to analyze the performance boundaries of SL-CBS+ID.

The increasing demand for sustainable construction practices has prompted the exploration of innovative materials, such as waste plastics, to enhance both environmental and mechanical performance in concrete, particularly for rigid pavements. This review investigates the mechan-ical properties of concrete incorporating four types of waste plastics—High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), Polyvinyl Chloride (PVC), and Polypropylene (PP). The primary focus is on how these materials affect key mechanical properties, including com-pressive strength, tensile strength, and flexural strength. The analysis reveals that HDPE and PP, at optimal levels (5-10%), can enhance flexural and crack resistance, making them suitable for non-structural applications. Conversely, LDPE and PVC tend to reduce both compressive and tensile strengths at higher substitution levels due to poor bonding with cementitious materials. Despite these challenges, incorporating waste plastics into concrete presents significant envi-ronmental and economic benefits, including plastic waste reduction and lower reliance on natural aggregates. The review also highlights the need for further research on improving plastic-cement bonding through surface treatments and hybrid mix designs. This paper contributes to the growing body of knowledge aimed at promoting the use of waste plastics in concrete, offering insights for the development of sustainable, high-performance construction materials.

The article reviews selected solar systems that utilize solar energy for cooling production. Particular attention is given to research on individual components of these systems, aimed at improving their efficiency and performance. The article focuses on the analysis of literature concerning the design of thermal storage units, with an emphasis on the use of computational fluid dynamics (CFD) as a research tool. Conclusions from scientists’ research regarding the impact of tank shape, thermal insulation, flow parameters, and the use of stratification partitions on heat storage efficiency have been presented. The literature review indicates that thermal storage units play a key role in the efficiency of solar systems, and thermal stratification within them can significantly improve their performance.

The distribution of impervious surface is one of the important assessment indicators for evaluating the quality of urban ecological environment. Taking Xuzhou city as an example, we study the influence of the spatial distribution of impervious surfaces on urban surface temperature, using Landsat 8 remote sensing imagery and nighttime lighting data to extract the impervious surface information from 2013 to 2022 and carry out surface temperature inversion. The study area was divided into built-up and non-built-up areas using nighttime lighting data and the relevant indices were used to establish interest zones and finally the extraction of impervious surfaces was completed using supervised classification methods. The experimental results show that the kappa coefficient is not less than 0.8562, which makes the result more reliable. By extracting the impervious surface of Xuzhou City, it was found that the impervious surface of Xuzhou City continued to increase from 2013 to 2022, in which the growth rate was faster in 2014-2016 and 2019-2021, and slowed down in 2017-2018 and 2021-2022. The expansion of impervious surface was found to be one of the reasons for the increase of urban surface temperature through stacked analysis and linear regression analysis.

Acute pancreatitis is a complex inflammatory disorder with significant morbidity and mortality. This review aims to integrate current knowledge of pancreatic morphology and immunology with the pathogenesis of acute pancreatitis, providing a comprehensive understanding of this critical condition. We conducted an extensive literature review, synthesizing data from recent studies and authoritative sources on pancreatic anatomy, histology, immunology, and the pathophysiology of acute pancreatitis. We also incorporated epidemiological data, clinical features, diagnostic criteria, and prognostic factors. The pancreas exhibits a complex morphology with intricate interactions between its exocrine and endocrine components. Its unique immunological landscape plays a crucial role in maintaining homeostasis and orchestrating responses to pathological conditions. In acute pancreatitis, disruption of intracellular calcium signaling leads to premature enzyme activation, triggering a cascade of events including mitochondrial dysfunction, ATP depletion, and release of pro-inflammatory mediators. This process can escalate from localized inflammation to systemic complications. The interplay between pancreatic morphology, immune responses, and pathophysiological mechanisms contributes to the varied clinical presentations and outcomes observed in acute pancreatitis. Understanding the intricate relationships between pancreatic morphology, immunology, and the pathogenesis of acute pancreatitis is crucial for developing more effective diagnostic and therapeutic strategies. This integrated approach provides new insights into the complex nature of acute pancreatitis and may guide future research directions in pancreatic disorders.

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational cost. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system.

Additive Manufacturing (AM), also know as 3D Printing (3DP), is a widely used layer-by-layer manufacturing process, it is evolving rapidly both in research and industry. Among all AM methods, Material Extrusion (ME) is one of the most popular techniques. Based on ME, another new AM method is developed, which is Low-cost Metal Material Extrusion (LCMME). In this newly developed process, pure metallic parts could be fabricated after sintering the metal infused additively manufactured parts. Both AM and sintering process parameters will have influence on the quality of the final pure metallic parts. In this research, several statistics methods were used to analyze the data gotten from the experiment. Then two Machine Learning (ML) algorithms were used to predict the Surface Roughness (SR) of the final specimens. Additionally, the results show that the neural network (NN) is more accurate than the support vector regression (SVR) on prediction.