: Information on the detection of the presence and potential for degradation of synthetic polymers (SPs) under various environmental conditions is of increasing interest and concern to a wide range of specialists. At this stage, there is a need to understand the relationship between the main par-ticipants in the processes of (bio)degradation of SPs in various ecosystems (reservoirs with fresh and sea water, soils, etc.), namely the polymers themselves, the cells of microorganisms (MOs) partici-pating in their degradation, and humic substances (HSs), which constitute a macrocomponent of natural non-living organic matter of aquatic and soil ecosystems, formed and transformed in the processes of mineralization of bioorganic substances in environmental conditions. Analysis of the main mechanisms of their influence on each other and the effects produced that accelerate or inhibit polymer degradation can create the basis for scientifically based approaches to the most effective solution to the problem of degradation of SPs, including in the form of microplastics. This review is aimed at comparing various aspects of interactions of SPs, MOs and HSs in laboratory experiments (in vitro) and environmental investigations (in situ) aimed at the biodegradation of polymers, as well as pollutants (antibiotics and pesticides) that they absorb. Comparative calculations of deg-radation velocity of different SPs in different environments are presented. A special place in the analysis is given to the elemental chemical composition of HSs, which are most successfully in-volved in the biodegradation of SPs. In addition, the role of photooxidation and photoaging of polymers under the influence of the ultraviolet spectrum of solar radiation under environmental conditions on the (bio)degradation of SPs in the presence of HSs is discussed.

The growing emphasis on sustainability and environmental stewardship in the pharmaceutical industry necessitates innovative solutions to manage pharmaceutical waste, particularly medication bottles. This research paper explores the potential of integrating Radio Frequency Identification (RFID) and blockchain technology to enhance the traceability, accountability, and efficiency of recycling empty medication bottles. By utilizing RFID for real-time tracking and blockchain for secure, immutable data storage, this proposed system improves transparency across the supply chain and incentivizes consumer participation in recycling programs. The solution provides benefits to pharmaceutical manufacturers, regulators, and consumers, contributing to sustainability, regulatory compliance, and environmental protection.

This research centres on advancing the utilisation of a novel Deep Ensemble model called RicEns-Net for predicting crop yields through sophisticated multi-modal data engineering. Initially, data for the study were acquired from Ernst \& Young (EY), a prominent global technology and management consulting firm, as part of their annual data science challenge (EY Open Science Challenge 2023). The predictive models leverage multi-modal data by integrating radar and optical-type remote sensing data alongside meteorological data to create supplementary features essential for predicting crop yield. The primary data features are derived from spectral indices extracted from Sentinel 2 satellites, while more advanced features are generated through combinations and interactions of these fundamental features. Furthermore, data from Sentinel 1 and 3, along with meteorological measurements such as surface temperature and rainfall, account for the intricate natural factors influencing crop yield, creating a comprehensive set of data features based on existing research. To tackle the complexity introduced by numerous data features, extensive data engineering was conducted to identify the most predictive features among 100+ and mitigate the "curse of dimensionality," resulting in 15 features from 5 different modalities. The proposed RicEns-Net comprises multiple machine learning algorithms in a deep ensemble approach to leverage the best features of each of these techniques, achieving optimal performance. The proposed model achieves an MAE value of 341 kg/Ha for prediction, surpassing the performance of the EY Open Science Challenge 2023 winner models and state-of-the-art deep/machine learning approaches.

Background/Objectives: Obstructive Sleep Apnoea Syndrome (OSA) in children disrupts standard breathing patterns and sleep architecture, potentially leading to severe consequences. Early identification and intervention for OSA are crucial to prevent these issues. This study explored the relationships between waiting times for polysomnography (PSG), clinical history, age at the time of PSG, and PSG outcomes in children; Methods: Two hundred and fourteen children were prospectively enrolled. Data were extracted from medical records on the age at referral for PSG and at the time of its execution, the waiting time for PSG, diagnosis (primary snoring, mild, moderate, and severe OSA), history of snoring and apnoea, medications taken, surgical interventions, passive smoke exposure, and allergies. The records also included the Pediatric Sleep Questionnaire (PSQ); Results: The mean age at referral for PSG was 5.2 (2.8) years, the waiting time for PSG was 5.2 (5.0) months, and the age at the time of PSG was 5.6 (2.8) years. Children with a shorter history of OSA (≤12 months) underwent PSG at an earlier age (4.9 (2.7) years) compared to those with a more extended history (6.0 (2.7) years; p=0.006). A younger age at referral for PSG was associated with a lower PSQ score (r=0.167; p=0.015), a shorter history of apnoea (r=0.216; p=0.003) and snoring (r=0.187; p=0.023), but also with greater OSA severity, expressed as oxygen desaturation index (r=-0.152; p=0.026). In the correlation analysis, the waiting time for PSG, adjusted for age at referral, was longer in children with a prolonged history of apnoea (r=0.195; p=0.023) and snoring (r=0.259; p=0.002) but with a lower PSQ score (r=-0.172; p=0.045); Conclusions: Some children receive delayed treatment, increasing the risk of long-term complications. In evaluating children with suspected OSA, the duration of symptoms and the PSQ score at referral emerge as crucial factors that should be considered to optimise prioritisation for PSG access.

There is a growing awareness of the detrimental environmental impact of the construction industry, primarily due to the utilization of building materials that necessitate energy-intensive processes for their production. The objective of this paper is to explore whether mixing wood and cement could serve as a viable solution to mitigate this adverse environmental effect. The data was obtained by extensively investigating the Clarivate Web of Science database, adhering to the PRISMA technique guidelines, and subsequently processed using Bibliometrix and VOSviewer software. An in-depth critical analysis of the literature revealed a heightened interest in this subject matter in recent years, with the focal points evolving from initial experimental endeavors aimed at understanding the behavior of the innovative wood cement composites, primarily in terms of their mechanical properties, to assessing their environmental impact and the advantages associated with their use. The researchers suggest that optimal results can be achieved by treating the wood with tetraethyl orthosilicate, incorporating cellulose nanocrystal particles or wollastonite into the mixture, and utilizing wood from the Pinus species. Furthermore, it was discovered that a noticeable improvement in strength is only attainable if a low percentage of wood, up to 30%, when wood ash is employed. Ongoing research concerning wood and cement composites continues to progress, with regular advancements and breakthroughs, thus offering promising prospects for the development of superior and eco-friendly building materials.

The study focused on assessing passenger risks specifically at the platform-train interface (PTI). The primary aim of this investigation is to analyze how various factors, including station design, passenger flow, and weather conditions, impact the frequency and types of incidents occurring at this critical interaction point. To achieve this, the study utilized a comprehensive data analysis approach, examining records from the Valparaiso Metro for the period of 2022-2023. During this timeframe, a total of 361 incidents were documented, providing a substantial dataset for evaluating incident patterns and their correlations with different influencing factors. The analysis revealed that incidents are notably influenced by peak hour conditions and weekdays. Specifically, the study found that crowded conditions inside the train during peak hours, which typically occur in the morning and evening rush periods, contribute significantly to the occurrence of incidents. When examining incidents occurring on the platform, the study identified that these are closely related to the type of station and the presence of stairs for station access. Conversely, stations designed with more accessible features might experience fewer such incidents. Future studies will focus on incorporating additional factors and analysing new data to provide a more comprehensive understanding of incident dynamics.

The study put forward a data fusion approach for urban remote sensing that combines SAR (Synthetic Aperture Radar) and optical satellite data. By integrating datasets from different sensors and spatial-temporal scales, the technique aims to extract more accurate information. The fusion approach utilizes two methods: feature-based fusion, where relevant features are extracted and fused, and simple layer stacking (SLS), where the original datasets are directly stacked as multiple layers. This study extracted features using SAR textures (using Sentinel-1) and modified indices (using Landsat-8), and then classified these features using an XGBoost algorithm implemented in Python and Google Earth Engine. Researchers examined five cities, each representing a distinct climatic zone and urban dynamic: Cape Town, Guangzhou, Los Angeles, Mumbai, and Osaka. An accuracy assessment was conducted using random validation points, achieving an overall accuracy of 89.5% using the proposed MSFI method. A comparison was also performed with three well-known global products. The proposed approach, outperformed all three global products achived 89% accuracy while ESA (84%), ESRI (81%) and Dynamic World (82%). Additionally, Land surface temperature analysis was accomplished to investigate the relationship between extracted UIS and Land Surface Temperature (LST) across selected cities to show the practical use of proposed MSFI method. Los Angeles, a warm temperate city, showed the highest LST among all five cities. The datasets, along with the GEE and Python codes, are available at https://github.com/mnasarahmad/sls.

Background: Several GLP-1 receptor agonists (GLP-1 RAs) are used to treat type 2 diabetes (T2DM). Their cardio- and renal protective effects and substantial weight loss have been evident and progressively expanded their role in the American Diabetes Association (ADA) guidelines, which are endorsed by the European Association for the Study of Diabetes (EASD). The increased demand leads to a global shortage. Methods: We utilized a repeated cross-sectional design, drawing data from national prescribing databases, to analyze six GLP-1 RAs: Dulaglutide, Enatide, Liraglutide, Lisenatide, Semaglutide, and Tirzepatide. AutoRegressive Integrated Moving Average (ARIMA) models with exogenous variables were applied to assess trends over time and regions. Results: The prescription rates significantly differ between regions. Wales shows the highest prescribing for most GLP-1 RAs. The ARIMA models indicated a significant increase in their prescribing after ADA guidelines 2022 (e.g., Dulaglutide: Post-ADA effect of 15.22, 95% CI: [12.97, 17.47]). Following GLP-1 RAs shortages in July 2023, the prescribing rates, particularly for Semaglutide, increased (Shortage effect: 74.36, 95% CI: [71.92, 76.80]). Model diagnostics, including the Akaike Information Criterion (AIC) and Durbin-Watson statistics, confirmed the robustness of these trends. Conclusion: Informed decisions should be made by considering prescribing trends before and after important events such as the issuing of new guidelines or safety alerts.

The purpose of the study is to analyse and to identify specific structural characteristics of melanocytic nevi, to youth pacients. Using the optical microscope, could be possible a proper describtion related melanocytic nevi. In this study play a significant role, different factors, such as genetic, epigenetic, microbiomic and proteomic factors. Future directions refers to preventive and prophylactic methods. Disease diagnostic and management directions are also important key points.

Millet is a staple food in Northern Nigeria, widely used for the preparation of various millet-based dishes. However, traditional processing methods, such as de-husking, can significantly affect the grain's nutritional and antioxidant properties. This study evaluates the antioxidant composition and nutritional value of four millet varieties cultivated in Northern Nigeria, with a particular focus on the impact of de-husking on these parameters. Our analysis encompasses the proximate composition, phytochemical content, antioxidant vitamins, mineral elements, amino acid profile, and antioxidant activities of the millet samples. The findings reveal that all millet varieties contain significant levels of antioxidant compounds, including phenolics, flavonoids, and vitamins, although their concentrations varied due to genetic differences and environmental factors. Notably, in the mineral composition, higher (p

The knowledge in chemical composition of propolis is crucial for understanding the characteristics of products of different origin, but also for quality control and regulatory purposes. To date, offical monographs or official analyses that allow to evaluate propolis in a proper way have not yet been released. This study focuses on the characterization of twenty-seven Italian propolis and the identification of chemical markers that define its geographical provenance. Total polyphenol (TP) and total flavonoid (TF) content, alongside the quantification of pinocembrin, chrysin, galangin, and caffeic acid phenethyl ester (CAPE), were identified as as potential markers. Additionally, DPPH assays was conducted to evaluate antiradicalic activity of propolis samples. Our findings demonstrated that TP, TF and pinocembrin differed in propolis of different origin, especially in samples from the islands. However, the quantification of the sum of chrysin and galangin and CAP provided a clear distinction of the geographical origin of the propolis samples. In contrast, the DPPH assay did not prove useful for this purpose, as most results were similar and, therefore, not significant. This study lays the groundwork for future research on propolis. These findings could contribute to the development of more refined methods for distinguishing propolis origins, enhancing the understanding, valuation and quality control of this natural product in various applications.

A biobased and conductive ink composed of elastomeric vinyl acetate-vinyl laurate copolymer (PVAc-VL) and carbon nanofibers (CNF) has been developed to create stretchable and flexible tactile devices (taxels) for robotics and prosthetics applications. The ink can be sprayed onto elastomeric surfaces (Ecoflex) to produce an electronic skin for applications such as the iCub robot and the Hannes prosthetic hand. Electron microscopy analysis confirms good dispersion of the CNF within the ink, and the ink remains conductive even after 150 mechanical cycles of stretching up to 25% and unloading. Compression tests up to 50% deformation show a small influence on the coating, and stretching cycles allow for rearrangement of the CNF, improving the electrical behaviour due to mechanical energy dissipation. The measured electrical performance exhibits a twofold increase in the R/R0 ratio, succeeded by a rapid decline (recovery), and eventually stabilizes near the initial value, indicating enhanced electronic performance.

The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main scores used by different approaches like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis to study the nutrient interactions and balance in plant leaves. However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis's early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars' potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops. In the present study, a collected database of more than 924 scores including the grain yield (kg ha-1) and the nutrient composition (mg kg-1) of different corn varieties is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A 'nutrient signature' model is proposed based on the impact of the environ-mental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains. The yield threshold used to differentiate between low and high-yielding subpopulations is established at 12.000 kg ha-1and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and vali-dated by the Cate-Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model's predictive accuracy for GNII is signif-icantly affected by soil pH, organic matter, and rainfall. These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions.

Inertinite, a product of wildfire, holds important information on global temperature change. The relationship between its reflectance and temperature has been widely used to identify wildfire events in paleo-sedimentary environments, but the currently used equations relating inertinite reflectance and combustion temperature are subject to large errors. Therefore, to clarify the relationship between inertinite reflectance and combustion temperature further, we systematically analyzed the changes in inertinite reflectance under different combustion durations based on literature data. The results confirmed that inertinite reflectance is related to combustion duration. Disregarding combustion duration, the combustion equation is T=267.52+110.19*Ro(R²=0.91), where T is the combustion temperature, Ro% is the measured inertinite reflectance, and R² is the correlation coefficient. Under a combustion duration of 1 hour, the equation is T=273.57+113.89*Ro(R²=0.91), and under a combustion duration longer than 4 hours, the equation is T=232.91+110.6*Ro(R²=0.94). These three equations not only account for the temporal factor but are also more precise than the commonly used formula. This study provides a scientific basis for research on paleo-wildfire.

This research introduces a novel approach to evaluate energy utilization in agribusiness within a cost-based framework, addressing the complexities associated with uncertain input energy content. The study focuses on the agricultural sector in Latin America, with particular emphasis on banana plantations, examining the sector's growing dependence on energy-intensive methods and inputs. We differentiate between technical and allocative efficiency, employing a frontier-based methodology. The analysis encompasses data from 21 state-owned banana plantations in Latin America, revealing significant room for improvement in energy efficiency. Our findings indicate an average Technical Efficiency of 0.69, Allocative Efficiency of 0.91, and overall Energy Efficiency of 0.59, suggesting potential reductions in input and energy consumption of up to 38%. The study underscores the importance of addressing both input mismanagement and misallocation to enhance energy efficiency. While the proposed methodology offers valuable insights for public sector enterprises, particularly in agriculture, further research is necessary to extend this approach to scenarios with uncertain input energy content, potentially incorporating stochastic frontier analysis or fuzzy logic methodologies.

One-third of approximately 10 million deaths due to sepsis in a year are due to acute lung injury, and there is no effective treatment yet. Due to its high phenol content, Punica granatum L. bark extract exhibits high antioxidant activity. Amifostine is a free radical scavenger that converts into active thiol in tissues. This study aimed to investigate the antioxidative and anti-inflammatory effects of 2 different doses of PG peel extract and amifostine in sepsis-related acute lung injury. Fifty rats were randomly allocated into 5 groups. Group 1 served as the control group with no sepsis. The remaining groups 2-5 served as the cecal-ligation-puncture (CLP) groups. Group 3 was administered 200 mg/kg amifostine intraperitoneally (ip) 15 minutes before sepsis induction. Groups 4 and 5 received, respectively, 250 mg/kg and 500 mg/kg PG peel extract by oral gavage 9 days before and 30 minutes after the CLP procedure, with a total of 10 doses. MMP-2 and MMP-9 were investigated by immunohistochemical methods. MMP-2 and MMP-9 concentrations significantly increased in the sepsis group compared to the other 4 groups. This study provides evidence that Amifostine and Punica granatum pericarp benefit lung tissue in a rat model of sepsis induced by cecal-ligation puncture.

.This paper explores the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and traditional Mean-Variance optimization in financial portfolio management. Using a dataset comprising global financial assets, we applied both methodologies to optimize portfolios based on multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III outperforms the Mean-Variance method in achieving a more diverse set of Pareto-optimal portfolios. NSGA-III portfolios exhibited superior performance in balancing risk and return, demonstrated by higher Sharpe ratios, more favorable skewness, and lower kurtosis. Additionally, NSGA-III's ability to simultaneously optimize across multiple conflicting objectives highlights its robustness in navigating complex financial landscapes, offering enhanced portfolio resilience. In contrast, the Mean-Variance approach, while effective in achieving balanced risk and return, was limited in addressing higher-order moments of the return distribution. These results underscore NSGA-III's potential as a powerful tool for portfolio optimization, providing a comprehensive alternative to traditional methods in modern financial markets where multiple objectives must be considered.

Background/Objectives: The COVID-19 pandemic reduced in-person pediatric visits in the United States by over 50%, while telehealth visits increased significantly. The national use of telehealth for children and the factors influencing its use have been rarely studied. This study aimed to investigate the prevalence of telehealth use during the COVID-19 pandemic and explore the potential factors linked to its use at the state level. Methods: A cross-sectional study of the National Survey of Children’s Health (2021-22) sponsored by the federal Maternal and Child Health Bureau was performed. We used the least absolute shrinkage and selection operator (LASSO) regression to predict telehealth use during the pandemic. A bar map showing the significant factors from the multivariable regression was created. Results: Of the 101,136 children, 15.25% reported using telehealth visits due to COVID-19, and 3.67% reported using telehealth visits due to other health conditions. The Northeast states showed the highest telehealth use due to COVID-19. In the Midwest and Southern states, children had a lower prevalence of telehealth usage unrelated to COVID-19. The LASSO regressions demonstrated that telehealth usage (due to and not due to) was associated with age, insurance type, household income, usual source of pediatric preventive care, perceived child health, blood disorders, allergy, brain injury, seizure, ADHD, anxiety, depression, and special needs. Conclusions: This study demonstrated significant variability in the use of telehealth among states during the COVID-19 pandemic. Understanding who uses telehealth and why, as well as identifying access barriers, helps maximize telehealth potential and improve healthcare outcomes for all.

While the surface of the Earth plays a key role in weather forecasting through its interaction with the atmosphere, in ensemble numerical weather predictions the uncertainty on the surface is only represented with perturbations in the parameterisations representing the surface processes. Data representing the surface, such as the land cover, are not perturbed. As fully data-driven forecasts without parameterisations are growing in importance, sampling the uncertainty on the land cover data brings a new way of making ensemble forecasts. Our work describes a method of generating ensemble land cover maps for numerical weather prediction. The target land cover map has the ECOCLIMAP-SG labels, used in the SURFEX surface model, and therefore is expected to have all relevant labels for surface-atmosphere interactions. The method translates the ESA WorldCover map to ECOCLIMAP-SG labels and resolution using auto-encoders. The land cover ensemble members are obtained by sampling the land cover probabilities in the output of the neural network. This paper builds upon the work done in a companion paper describing the high-resolution version of ECOCLIMAP-SG, called ECOCLIMAP-SG+, used for the training and evaluation of the neural network. The output map presented here, called ECOCLIMAP-SG-ML, improves upon the ECOCLIMAP-SG map in terms of resolution (from 300~m to 60~m), overall accuracy (from 0.41 to 0.63) and the ability to produce ensemble members.

In this work, we investigate how to obtain the optimal active elements in Hybrid Relay-Reflecting Intelligent Surface (HR-RIS) systems while considering enhancing the sum rate of the communication system in the presence of malevolent eavesdropper. Therefore, when dealing with numerous elements in the HR-RIS, the complicated optimization task of activating and deactivating the active elements of RIS renders typical exhaustive search methods impracticable. Standard techniques, such as alternating optimization, often frequently converge and unfortunately become trapped in local optima, leading to less-than-ideal results. To address these issues, we suggest performing the Jaya algorithm, which modifies its search strategy in response to historical data to find a compromise between investigating novel avenues and taking advantage of well-established answers. Our simulation findings demonstrate that this strategy outperforms the most recent techniques, yielding higher sum rates with the best possible user location and robust security against eavesdropping. It’s interesting to observe that even at lower total power levels, the Jaya algorithm performs incredibly well and efficiently identifies the optimal amount of active elements. This demonstrates how it can decrease interference, increase signal quality, improve Signal-to-Interference-plus-Noise Ratio (SINR), and increase secrecy rates, opening the door for more efficient and secure wireless communication systems.

Invertebrate striated muscle myosin filaments are highly variable in structure. The best characterized myosin filaments are those of insect indirect flight muscle (IFM) in which the flight powering muscles are not attached directly to the wings. Four insect orders, Hemiptera, Diptera, Hymenoptera and Coleoptera, have evolved IFM. IFM thick filaments from the first three orders have highly similar myosin arrangements but differ significantly among the non-myosin proteins. Cryo-electron microscopy of isolated IFM myosin filaments from the Dipteran Drosophila melanogaster described here reveal the coexistence of two distinct filament types. One presenting a tubular backbone like previous work, the other a solid backbone. Inside an annulus of myosin tails, tubular filaments show no noticeable densities; solid filaments show four paired paramyosin densities. Both myosin heads of tubular filaments are disordered; solid filaments have one completely and one partially immobilized head. Tubular filaments have the protein stretchin-klp on their surface; solid filaments do not. Two proteins, flightin and myofilin, are identifiable in all IFM filaments previously determined. In Drosophila, flightin assumes two conformations, compact in solid filaments, and extended in tubular filaments. Nearly identical solid filaments occur in the large water bug Lethocerus indicus, which spends most of its time under water, suggesting solid filaments conserve ATP utilizing the super relaxed state. The solid filaments in Drosophila may function similarly.

The study used the data from emerging markets to examine the impact of restrictive macroprudential policies on income inequality from 2000–2019 using Bayesian panel vector autoregression and Bayesian panel dynamics generalized method of moments models. The chosen models are suitable for addressing multiple entity dynamics, accommodating a wide range of variables, handling dense parameterization, and optimizing formativeness and heterogeneous individualspecific factors. The empirical analysis utilized various macroprudential policy proxies and income inequality measures. The results show that when the central banks tighten systems using macroprudential policy instruments to sticker debt-to-income and financial instruments for lower-income borrowers (the bottom 40% of the income distribution) promote income inequality in these countries while reducing income inequality for high-income borrowers (the high 1 per-cent of the income distribution). The impact loan-to-value ratios were found to be insignificant in these countries. Fiscal policy through government expenditure and economic development reduces income inequality, while money supply and oil price shocks exacerbate it. The study suggests implementing a progressive debt-to-income (DTI) ratio system in emerging markets to ad-dress income inequality among lower-income borrowers. This would adjust DTI thresholds based on income brackets, allowing lenient credit access for lower-income borrowers while stricter limits for higher-income borrowers. This would improve financial stability and reduce income disparities. Additionally, targeted financial literacy programs and a petroleum-linked basic income program could be implemented to distribute oil revenue to lower-income households. Monetary Supply Stabilization Fund could also be established to maintain financial stability and prevent excessive inflation.

Prime numbers are the building blocks of number theory, and their properties have intrigued mathematicians for centuries. In this paper, we explore a specific categorization of primes, which we refer to as "twin primes", “cousin primes” and "sexy primes". We begin by examining the form of prime numbers greater than 7 and analyze their potential to be expressed in terms of $6n + 5$ or $6n + 7$. This investigation leads to the identification of conditions under which prime numbers can form twin, cousin and sexy pairs. We further develop a general framework to characterize the product of all twin (except 15), cousin (except 21) and sexy primes, providing insights into their algebraic structure.

The objective of this study was to compare single-needle arthrocentesis with the conventional two-needle arthrocentesis, as well as, additional intracapsular injection of hyaluronic acid or platelet rich fibrin. 96 patients with established osteoarthritis (OA) (n= 48) or with internal de-arrangement (DD) (n= 48) were assigned for single-needle arthrocentesis with distension of the joint or conventional two-needle arthrocentesis with or without intracapsular injection of the medication (hyaluronic acid- HA or platelet-rich plasma- PRP) performed every month in the period of 6 months. The maximum mouth opening and pain as measured by the visual analog scale (VAS) were compared. Each group exhibited significant improvement: decrease of pain and in-creased mouth opening. Single- puncture technique provided similar pain reduction as two needle approach but provided significantly better results in maximum mouth opening. Reduction of pain was similar when comparing between the OA and DD cohorts, however patients with disc dis-placement reached significantly better mouth opening than OA. Intracapsular application of medication contributed to significant decrease of pain in both HA and PRP groups, with plate-let-rich fibrin being significantly superior in terms of mouth opening increase over HA.

Background: We explored the potential value of serum MUC5AC (sMUC5AC) as a biomarker to guide perioperative management of early-stage pancreatic ductal adenocarcinoma (PDA) patients receiving neoadjuvant therapy (NAT). Methods: We performed enzyme-linked immunoassays using a human MUC5AC kit (NBP2-76703) in serum samples obtained from The Ohio State Uni-versity biorepository (between January 2010 and June 2021). Univariate (UV) and multivariate (MV) Cox regression models were used to quantify progression-free survival (PFS); clinical and pathological variables were adjusted in the MV models. UV logistic regression analysis was utilized to examine the association of sMUC5AC with pathological features and survival. Results: Overall, 23 samples (19 FOLFIRINOX, 3 gemcitabine/nab-paclitaxel, 1 FOLFOX) were available for analysis. The median age was 66 years, and 52% were females. sMUC5AC was associated with a) treatment response, margin status, and residual disease (R0 vs. R1/R2) (all P

Abstract: Emerging risk factors for childhood (

ECOCLIMAP-SG+ is a new 60~m land use land cover dataset, which covers a continental domain, and represents the 33 labels of the original ECOCLIMAP-SG dataset. ECOCLIMAP-SG is used in HARMONIE-AROME, the numerical weather prediction model used operationally by Met Éireann and other national meteorological services. ECOCLIMAP-SG+ was created using an agreement-based method to combine information from many maps to overcome variations in semantic and geographical coverage, resolutions, formats, accuracies, and representative periods. In addition to ECOCLIMAP-SG+, the process generates an agreement score map, which estimates the uncertainty of the land cover labels in ECOCLIMAP-SG+ at each location in the domain. This work presents the first evaluation of ECOCLIMAP-SG and ECOCLIMAP-SG+ against the following trusted land cover maps: LUCAS 2022, the Irish National Land Cover 2018 dataset, and an Icelandic version of ECOCLIMAP-SG. Using a set of primary labels, ECOCLIMAP-SG+ outperforms ECOCLIMAP-SG regarding the F1-score against LUCAS 2022 over Europe and the Irish national land cover 2018 dataset. Similarly, it outperforms ECOCLIMAP-SG against the Icelandic version of ECOCLIMAP-SG for most of the represented secondary labels. The score map shows that the quality ECOCLIMAP-SG+ is hetereogeneous. It could be improved once new maps once they become available but we do not control when they will be available. Therefore, the second-part of this publication series aims at improving the map using machine learning.

Existing scales assessing family relations are usually quite long. Our aim was to develop and validate the Family Functioning and Cohesion Scale (FFCS), a self-reported short instrument consisting of 14 items. The validation of the FFCS was based on its administration through an online platform to 481 subjects living in 46 different countries around the world. Cronbach's alpha was 0.85 (ranging from 0.83 to 0.86 if any one item was deleted), signifying high internal consistency. The scale can be considered as a sole factor based on its high consistency, while factor analysis produced three factors corresponding to “communication”, “anger/resentment/aggression” and “values and beliefs”. Test-retest reliability correlation coefficient was found to be 0.88 at a 2-week interval. As far as external validity is concerned, the correlation coefficient of the FFCS with the General Functioning subscale of the McMaster Family Assessment Device (FAD) was 0.83. The high measures of consistency, reliability, and validity of the FFCS, combined with its short length, make it an invaluable tool for use in research as well as in any professional dealing with families in the clinical fields of psychiatry, psychology, social work or any other relevant specialty.

The dynamic development of robotics is affecting people's lives. Flying robots replace pilots and crews in performing dangerous tasks. There are various designs of the above-mentioned robots such as multi-rotors, fixed-wing aircraft, helicopters, among which wing-in-ground (WIG) units can also be distinguished. WIG vehicles, which utilize ground effect to enhance efficiency and stability, share similarities with UAVs in their control and aerodynamic challenges. The article presented the results of the autopilot test for the NeoX UAV, which have the potential to be utilized in WIG UAVs. The NeoX was developed at the Air Force Institute of Technology as a reconnaissance flying robot and is a tailless mini UAV. It has a characteristic feature which is the flying wing with a small fuselage. The electric engine with the pusher propeller ensures thrust. The tailless airplane has a greater drag-to-lift ratio than a conventional airplane, but this airplane configuration needs a control system that ensures a stable flight. The structure of auto-pilot for the horizontal flight phase (a cruise phase), the climbing, and the descending phase were presented. The authors concentrated on the longitudinal channel of autopilot and showed the recorded results from the flight tests. The research focused on developing autopilot system that could be adapted for use in WIG craft, which operate with similar aerodynamic principles but require unique control adaptations due to their proximity to the water or ground. The findings from the NeoX UAV tests are instru-mental in enhancing the stability and performance of these specialized crafts, ensuring safer and more efficient operation in their respective environments.

This study presents a novel Fe-CNs-P/S carbon composite material, synthesized by doping elements P and S into NH2-MIL-101 (Fe) using carbonization method. The material's application in sustainable water treatment was evaluated, focusing on its effectiveness in activating persulfate for pollutant degradation. The research thoroughly investigates the synthesis process, structural characteristics, and performance in degrading pollutants. Results indicate that Fe-CNs-P/S-5 with 50% P and S co-doping is higher than that of other samples, where the degradation rate of TC in 30min is as high as 98.11% under the optimum conditions, that is temperature at 25 ℃, 0.05 g/L of catalyst concentration, and 0.2 g/L of PMS concentration. The composite material demonstrates robust versatility and stability, maintaining high degradation efficiency across multiple organic pollutants, with no significant reduction in catalytic performance after four cycles. Furthermore, the free radical quenching experiments displays that the singlet oxygen 1O2 is the main active species. It is demonstrated that the doping of P and S play a role in the enhancement of PMS activation over the Fe-CNs-P/S catalyst. This material demonstrates remarkable efficacy in treating a range of organic contaminants and exhibits excellent reusability, presenting a promising approach for enhancing sustainability in water treatment applications.

Small and medium-sized enterprises (SMEs) face unique challenges that can be effectively addressed through the adoption of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and efficacy of BI and data mining technologies across SME sectors, assessing their impact on operational efficiency, strategic decision-making, and market competitiveness. Therefore, drawing from a methodologically rigorous analysis of 93 scholarly articles published between 2014 and 2024, the review elucidates the evolving landscape of BI tools and techniques that have shaped SME practices. It reveals that advanced analytics such as predictive modeling and machine learning are increasingly being adopted, though significant gaps remain, particularly shaped by economic factors. The utilization of BI and data mining enhances decision-making processes and enables SMEs to adapt effectively to market dynamics. Despite these advancements, SMEs encounter barriers such as technological complexity, high implementation costs, and substantial skills gaps, impeding effective utilization. Our review, grounded in the analysis of business intelligence tools used indicates that dashboards (31.18%) and clustering techniques (10.75%) are predominantly utilized, highlighting their strategic importance in operational settings. However, a considerable number of studies (66.67%) do not specify the BI tools or data mining techniques employed, pointing to a need for more detailed methodological transparency in future research. The predominant focus on the ICT and manufacturing sectors underscores the industrial context sector specific applicability of these technologies, with ICT accounting for 45.16% and manufacturing 22.58% of the studies. We advocate for targeted educational programs, development of user-friendly and cost-effective BI solutions, and strategic partnerships to facilitate knowledge transfer and technological empowerment in SMEs. Empirical research validating the impacts of BI and data mining on SME performance is crucial, providing a directional pathway for future academic inquiries and policy formulation.

There are currently no effective screening measures to detect early or occult tubo-ovarian cancers resulting in late-stage detection and high mortality. This work explores whether an optical imaging catheter can detect early-stage tubo-ovarian cancers or precursor lesions where they originate in the fallopian tubes. This device collects co-registered optical coherence tomography (OCT) and autofluorescence imaging (AFI). OCT provides three-dimensional assessment of underlying tissue structures; autofluorescence imaging provides functional contrast of endogenous fluorophores. Ex vivo fallopian tubes (n=28; n=7 cancer) are imaged; we present methods for the calculation of and analyze eleven imaging biomarkers related to fluorescence, optical attenuation, and OCT texture for their potential to detect tubo-ovarian cancers and other lesions of interest. We visualize folded plicae, vessel-like structures, tissue layering, hemosiderin deposits, and regions of fibrotic change. High grade serous ovarian carcinoma appears as reduced autofluorescence paired with homogenous OCT and reduced mean optical attenuation. Specimens containing a cancerous lesion demonstrate significant increase in median autofluorescence intensity and decrease in entropy compared specimens with no lesion. Non-cancerous specimens demonstrate an increase in optical attenuation in the fimbriae when compared to the isthmus or ampulla. We conclude that this approach shows promise and merits further investigation of diagnostic potential.

This paper explores the application of the generalized double reduction method to the (1+1)-dimensional Kaup-Boussinesq (K-B) system, which models nonlinear wave propagation. Double reduction method is a structured and systematic approach in the analysis of partial differential equations (PDEs). We first identify the Lie point symmetries of the K-B system and construct four non-trivial conservation laws using the multiplier method. The association between the Lie point symmetries and the conservation laws is established, and the generalized double reduction method is then applied to transform the B-K system into second-order differential equations or algebraic equations. The reduction process allowed us to derive two exact solutions for the K-B system, illustrating the method’s effectiveness in handling nonlinear systems. This work highlights the effectiveness of the generalized double reduction method in simplifying and solving nonlinear nonlinear systems of PDEs, contributing to a deeper understanding of the systems.

Various measures have been attempted to prevent infectious diseases in calves, such as envi-ronmental improvement and vaccine administration. Probiotics are commonly used to im-prove the body condition of newborn calves and prevent disease. In our previous research, Lactiplantibacillus plantarum RGU-LP1 (LP1) inhibited the expression of inflammatory cyto-kines in PBMC from cows fed LP1. In this study, we evaluated the effect of LP1 on calf growth and disease control. Methods: Twenty-six one-week-old Holstein bull calves were divided into two groups (13 each), LP1 group (LP1 treated) and CN group (no LP1 fed), and tested as follows. The LP1 group was fed lyophilised LP1 (10⁹ CFU/head/day) in milk replacer for 40 days. The CN group was fed the same diet only. Calves were followed for 63 days. During this period, the weight and treatment times of the calves were recorded. Faeces and blood were collected from each calf during this period. Faeces were examined for gut microbiota, and blood for immune assay and cytokine gene expression. The LP1-treated group showed a de-crease in disease incidence and an increase in body mass compared to controls. Results: The average treatment cost during the observation period was significantly reduced compared to the CN group. The expression of TGFβ and IL10, inhibitory cytokines of inflammation, was sig-nificantly increased. The simultaneous expression of this set of inhibitory molecules resulted in low serum IL1β levels during the growth period. Conclusions: The Th1-type cytokine IFNγ was also significantly increased in LP1-treated calves. By reducing the number of disease treatment times and increasing body weight, LP1 is effective in preventing infectious diseases in calves. In addition, the increase in IFNγ by LP1 indicates improved Th1-type immunity in calves. These results show that LP1 has effects on the regulated inflammatory response and growth of calves.

The study addresses the fabrication of indium aluminum nitride (InAlN) layers by DC co-sputtering at room temperature, highlighting their potential in optoelectronic and solar cell applications. Experiments were carried out with different powers for the aluminum target, observing changes in the stoichiometry and material properties. XPS and AES analysis confirmed the variation in indium and aluminum concentration, directly affecting bandgap and electrical properties such as mobility and free carrier concentration. The layers showed smooth surfaces with low roughness, suitable for high efficiency interfaces. This method promises to reduce costs and improve the accessibility of efficient solar technologies based on InAlN semiconductor alloy.

The knee joint plays a vital role in human movement, bearing mechanical loads during daily and athletic activities. This review explores the knee joint's response to mechanical loading, particularly focusing on mechanotransduction—the process by which cells convert mechanical stimuli into biochemical signals. These signals trigger cellular responses crucial for maintaining joint health, promoting repair, and adapting to mechanical changes. Understanding mechanotransduction is key to developing effective rehabilitation strategies for knee injuries and managing conditions like osteoarthritis. This review synthesizes current research on the cellular and molecular pathways involved in knee joint mechanotransduction, examining how various loading conditions—such as compression, tension, and shear—affect tissues like cartilage, synovium, ligaments, and tendons. The review discusses the impact of mechanotransduction on cellular components such as chondrocytes, synoviocytes, and fibroblasts, which are essential for maintaining tissue integrity and function. It also highlights the role of signaling pathways like MAPK, NF-κB, and Wnt in mediating cellular responses to mechanical stimuli, influencing gene expression, protein synthesis, and metabolism. By understanding these mechanisms, clinicians can develop targeted therapeutic interventions, including controlled loading protocols, exercise regimens, and manual therapies. This review underscores the importance of integrating mechanobiology insights into rehabilitation practices to enhance tissue repair, restore function, and prevent further joint degeneration, ultimately improving knee joint health and patient outcomes.

The study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. The models were evaluated using multiple error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), and R-squared. RNN and LSTM were tested with both 30 and 60-day windows, with performance compared to SVR. LSTM delivered better R-squared values, with a 60-day LSTM achieving the best accuracy (R-squared = 0.993) when using a combination of endogenous market data and technical indicators. SVR showed reliable results in certain scenarios but struggled in fold 2 with sudden spike that shows high probability of not capturing the entire underlying NGX pattern in the dataset correctly as witnessed with the high validation loss during the period. Additionally, RNN faced the vanishing gradient problem that limits its long-term performance. Despite challenges, LSTM's ability to handle temporal dependencies, especially with the inclusion of On-Balance Volume, led to significant improvements in prediction accuracy. The use of the Optuna optimization framework further enhanced model training and hyperparameter tuning, contributing to the performance of the LSTM model.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

Objective: Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine learning (ML) to replicate outcomes obtained from FEA when simulating mechanical responses and predicting cartilage degeneration within knee joint. Design: Two ML models based on the Gaussian Process Regression (GPR) algorithms were developed. The first model (GPR1) utilized age, weight, and anatomical joint dimensions as predictor variables to predict tissue mechanical responses and cartilage degeneration based on FEA data. The second model (GPR2) utilized age, weight, height, and gender to predict anatomical joint dimensions, which were then used as inputs in the GPR1 model. Finally, the GPR1 and combined GPR1+GPR2 models were used to investigate the importance of clinical imaging when making personalized predictions for knees from healthy subjects with no history of knee injuries. Results: In the GPR1 model, R2 of 0.9 was exceeded for most of the predicted mechanical parameters. The GPR2 model was able to predict knee shape with R2 of 0.67 - 0.9. Both GPR1 and combined GPR1+GPR2 models offered equally good performance (AUC = 0.73 - 0.74) at classifying patients at high risk for the onset and development of knee osteoarthritis. Conclusions: In the future, real-time and easy-to-use GPR models may provide a rapid technology to evaluate mechanical responses within knee for researchers or clinicians who have no former knowledge from FEA.

Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities can remain undisclosed. Data informing an ongoing incident is often sparse, with a large proportion of relevant data only coming to light after the incident culminates or after police intervene - by which point it is too late to make use of the data to aid real-time decision making for the incident in question. Much of the data that is available to police to support real-time decision making is highly confidential so cannot be shared with academics, and is therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a model designed by an academic team to a model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness and tardiness seen in such a secure environment. The parallel development described by this protocol ensures that any sensitive information collected by police, and missing to academics, remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry - a suspected vehicle attack - can be embedded into such a police library of criminal plots.

Background: In response to the COVID-19 pandemic, the European Association of Urology (EAU) Guidelines defined priority groups to guide the prioritization of surgery for urological malignancies. The objective of this study was to evaluate the impact of the COVID-19 prioritization recommendations on tumor pathological characteristics in a real-world setting at our academic referral Institution. Methods: We compared baseline and pathological tumor features of all patients with urological malignancies treated during the pandemic period (2020-2021) versus in the post-pandemic period (2022-2023). Our institution adhered to the International recommendations and prioritized those cases defined as “high-risk”. Results: Data of 9196 patients treated for urological malignancies were reviewed and grouped according to period of surgery (4401 in the pandemic vs 4785 in non-pandemic period). The overall number of surgical procedures was comparable for all diseases except for the number of radical prostatectomy (1117 vs 1405; p=0.03) and partial nephrectomy (609 vs 759; p=0.02) which were significantly lower in the pandemic period. Regarding tumors’ pathological features, none of the recorded variable was found to differ according to period of surgery, including disease stage, tumor grading, presence of necrosis, lymphovascular invasion, histological variants. Conclusion: A correct policy of prioritization of oncological pathologies during emergency periods and a centralization of oncological cases in reference centers reduces the possible risk of worsening cancer disease features related to the reorganization of healthcare resources.

Cervical cancer is 4th the most common cancer in women worldwide. Due to the prevalence of HPV in the population (80-90%), the scientists are likely to discover even more associations of this pathogen with other diseases in the future. In recent years, ISH assays that use automated signal-detecting methods in ormalin-fixed, paraffin-embedded (FFPE) cervical tissue, such as the enzyme-categorized signal-detecting system, have shown a higher sensitivity. To evaluate automatic signal detection of in situ hybridization (ISH) assay for detecting human papillomavirus (HPV) DNA, we compared the ability of an ISH probe, Inform HPV II and III (Ventana Medical Systems, Tucson, AZ), to that of PCR assays to detect HPV DNA in cervical tissue specimens with cervical intraepithelial neoplasia (CIN; CIN 1, 28 cases; CIN 2, 22 cases; and CIN 3, 20 cases) and normal cervix (2 cases). Our findings showed significant relation was confirmed between ISH III level and HPV outcome (positive/negative). Patients with positive HPV outcomes had significantly lower ISH III levels, MD = -7961.82 CI95 [-17230.00; -199.21], p = 0.005. Automatic signal detection of ISH assay is not particularly applicable to cervical tissue material. A more useful method of confirming the presence of HPV in the cervix is ​​the HPV test with genotyping, as it allows for collecting a larger amount of material from the cervical disc and canal. The interpretation of a positive or negative ISH test must be guided in the context of clinical history and morphology.

Human papillomavirus (HPV) is the most sexually transmitted infection associated with the development of cervical cancer. This study investigated cervical HPV prevalence, characteristics and distribution according to age and human immunodeficiency virus (HIV) among women attending public community health facility in the Eastern Cape Province of South Africa. A total of 325 participants (aged 18 to 60) visiting a community health facility for any reason were recruited. Cervical HPV infection was detected using the Seegene Anyplex™ II HPV28 assay. Overall HPV prevalence was 65.2% (95% CI: 59.9–70.2%), with the highest prevalence of 80.9% (95% CI: 67.2–89.8%) observed in the 18–25 years age group and lowest prevalence of 46.3% (95% CI: 35.8–57.1%) in 46–60 years of age. HR-HPV infection was found to decrease with increasing age among women who were HPV infected by any type (p=0.02), but not when stratified according to HIV status. A proportion of 12.9% (42/325) were infected with one or more HPV types covered by the Cervarix® HPV vaccine (HPV-16 and/or -18), 18.8% (61/325) by those covered by Gardasil®4 (HPV-6, -11, -16 and/or -18) and 42.2% (137/325) by those covered by Gardasil®9 (HPV-6, -11, -16, -18, -31, -33, -45, -52 and/or -58). Alpha-9 HPV species were the most dominant species (40.6%, 132/325), followed by alpha-7 species (29.8%, 97/325). In the overall population, alpha-9 HPV species were always part of the most dominant HPV alpha species co-infection, which were alpha-9 and -7 species (17.5%, 57/325), alpha-9 and -5 species (9.8%, 32/325), and alpha-9 and -6 species (9.5%, 31/325). High overall HPV, HR-HPV, and alpha-9 species prevalence were observed among women attending the public health facility. These findings contribute to the limited HPV distribution data among Eastern Cape women, which could be used to improve HPV-related policy and assess the effectiveness of the HPV vaccination.

This article aims to explore the existence and stability of solutions to differential equations involving a regularized ψ−Hilfer fractional derivative, which, compared to standard ψ−Hilfer fractional derivatives, provide a clear physical interpretation when dealing with initial conditions. We discovered that the regularized ψ−Hilfer fractional derivative can be represented as the inverse operation of the ψ−Riemann–Liouville fractional integral, and used this property to prove the existence of solutions for linear differential equations with a regularized ψ−Hilfer fractional derivative. Additionally, we applied the Mönch’s fixed-point theorem and knowledge of non-compactness measures to demonstrate the existence of solutions for nonlinear differential equations with a regularized ψ−Hilfer fractional derivative, and further discussed Ulam-Hyers-Rassias stability and semi-Ulam-Hyers-Rassias stability of these solutions. Finally, we illustrated our results through case studies.

Modular inversion on large operands is a time-consuming calculation used in elliptic curve cryptography. Its hardware implementation requires extensive hardware resources such as lookup tables and registers. We investigate state-of-the-art modular inversion algorithms and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at short execution time and low hardware cost. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance with other implementations. Our implementation on the Altera Cyclone V FPGA chip uses 1227 ALMs (Adaptive logic modules) and 1037 registers. The modular inversion calculation takes 3.67 microseconds. The AT (Area time) factor is 8.30, outperforming other implementations. We also present an implementation of elliptic curve cryptography using the proposed radix-8 modular inversion algorithm. The implementation results also show that our modular inversion algorithm is more efficient in area time than other algorithms.

A tritium resistant coating on the surface of materials is one of the main methods to reduce tritium permeability. In actual operating processes, mechanical damage such as spalling and scratches will occur on the surface of tritium resistant coatings. These mechanical damages will affect the hydrogen permeability resistance of the coatings. It is an important engineering problem that evaluate the effect of these damage on the tritium resistance of the coating. In this experiment, the mechanical damage of FeAl/Al2O3 tritium resistant coating on 316L stainless steel was simulated by scratching, debonding and thermal shock, and the gas hydrogen isotope permeation (GDP) test was carried out. The influence of mechanical damage degree on the tritium permeation behaviour of the coating was evaluated and discussed.

BACKGROUND: Endoscopic ultrasound-guided tissue acquisition (EUS-TA) is essential technique for sampling solid pancreatic lesions. However, the optimal needle type for achieving high histological diagnostic capability remains unclear. This study compares the histological diagnostic capabilities of the 19G FNA needle and the 22G FNB needle for solid pancreatic lesions. METHODS: This single-center, retrospective study was conducted in patients who underwent EUS-TA for solid pancreatic lesions. Patients were allocated to either Group A, utilizing the 19G FNA needle, or Group B, utilizing the 22G FNB needle. The primary outcome was the diagnostic accuracy based on histology alone. RESULTS: 143 patients were analyzed, with 61 in Group A and 82 in Group B. Diagnostic accuracy was significantly higher in Group B for both histology alone (85.4% vs. 68.9%, p=0.02) and overall diagnostic accuracy (85.4% vs. 73.8%, p=0.02). Logistic regression analysis revealed that lesion size ≥25 mm was an independent factor contributing to higher diagnostic accuracy (OR 3.32, 95% CI 1.28-8.64, p=0.01). Subgroup analysis showed that diagnostic accuracy for 15–25 mm lesions was significantly higher in Group B (90.0% vs. 63.2%, p=0.02). Additionally, trans-gastric punctures had higher accuracy in Group B (94.7% vs. 73.5%, p=0.01). CONCLUSIONS: Our study showed the overall superiority of the 22G FNB needle for EUS-TA of solid pancreatic lesions. However, 19G FNA needle remains a valuable option under specific conditions.

Breast cancer is the most prevalent type of disease among women. It has become one of the foremost causes of death among women globally. Early detection plays a significant role in administering personalized treatment and improving patient outcomes. Mammography procedures are often used to detect early-stage cancer cells. This traditional method of mammography while valuable has limitations of potential for false positives and negatives, patient discomfort, and radiation exposure. Therefore, there is a probe for more accurate techniques required in detecting breast cancer, leading to exploring the potential of machine learning in the classification of diagnostic images due to its efficiency and accuracy. This study conducted a comparative analysis of pre-trained CNNs (ResNet50 and VGG16) and Vision Transformers (ViT-Base and SWIN Transformer) with the inclusion of ViT-Base trained from scratch model architectures to effectively classify breast cancer mammographic images into benign and malignant cases. The Swin transformer exhibits superior performance with 99.8% accuracy and a precision of 99.8%. These findings demonstrate the efficiency of deep learning to accurately classify breast cancer mammographic images for the diagnosis of breast cancer, leading to improvement in patient outcomes.

In this paper, we define the consumer preference relation from scratch. We study algebraic and topological properties for the preference magma that reflects the consumer. We also consider the influence of society on an individual's preference.

In both capitalist and socialist economies, agricultural planning has been un-derstood as the allocation of planting areas to certain products, and the exist-ence of farmers, who are the main productive actors, as humans, their pro-duction potential and their ability to act rationally have been neglected. In ad-dition, in both economic structures, the desired results have only been achieved partially by determining prices. These experiences also reveal two important results: Agriculture is not fully planned, but it cannot be continued without planning either. Secondly, in a structure where production is social, it is nec-essary for agricultural products to have an exchange value, but price is a mul-tifunctional phenomenon and how it is determined is important. The current equivalent of the claim that prices are determined through the market mecha-nism is to advocate that prices are determined by capitalist companies that have monopoly power in the markets. The increasingly severe problems and in-creasing fragility of agricultural-food markets, which are already largely under the control of these institutions, already prove that this structure is not a solu-tion. On the other hand, it is no longer possible to provide food security by planning primary agricultural products alone, agri-food production and dis-tribution have acquired different structural characteristics, and it has become necessary to address food security within the framework of agri-food systems. Therefore, effective agricultural planning actually requires the planning of the agricultural food system as a whole. The e-nam system, which is being popu-larized in India and has achieved a certain success, points out that it is possible to organize and manage agricultural food markets after primary production. Moreover, it also shows that technological opportunities offer more effective planning opportunities. In this study, first of all, based on historical and current experiences, the inadequacies of the market mechanism, the fact that agricul-tural production is carried out by using living things to produce living things and depending on uncontrollable factors have been taken into consideration. Then, it has been concluded that a democratic, mix of centralized and decen-tralized planning, aiming to determine both production quantities and prices by utilizing the capacity and conditions of farmers and technological opportunities will meet today's needs. And in accordance with this result, a basic planning proposal has been presented.

Mortality due to suicide is amongst the largest public health concerns across the world today and in Bharat (India). There have been concerns that the COVID-19 pandemic has contributed to worsening mental health outcomes across the world, and in Bharat. Furthermore, long COVID has been proposed to be a major consequence of COVID-19, which can also worsen mental health outcomes. Therefore, our objective in this study was to analyse trends in suicide mortality across Bharat, and to compare these trends to changes prior to the COVID-19 pandemic; as well, we aimed to analyse if long COVID had any role in these changes. It was found that, at a national level, the average annual increase of the suicide rate between 2019 and 2022 was 0.7 per 100,000 people (6.41% increase)., There was also an average annual rise in suicide rates across 27 states/union territories (out of the 33 that were analysed). States/UTs with the highest annual increases since the start of the COVID-19, despite a decrease from 2018 to 2019, were Tamil Nadu (increase by 2.7; 15.17% increase), and Telangana (increase by 1.9; 9.22% increase), Multi-linear regression had shown that the annual suicide rate changes were not associated with COVID-19 deaths per 10,000 people (standardized beta coefficient = 0.077; p = 0.605) but were associated with COVID cases per 100 people (standardized beta coefficient = 0.578; p

A comprehensive 3D Multiphysics model was developed to simulate a plate fin heat exchanger designed for hydrogen liquefaction, incorporating ortho-para- hydrogen conversion catalyst in the hot fin channel. The model encompassed the 3D serrate fin structure, turbulent flow within the cold fin channel, and porous flow through the catalytic hot fin channel. Species transportation within the hot fin channel is coupled with ortho-para hydrogen conversion kinetics, while heat transfer mechanisms between the hot and cold fin channels are rigorously accounted for. Addi-tionally, the state- of- art equation of state is employed to accurately describe the thermodynamic properties of ortho- and para- hydrogen within the model. Numerous operational parameters, including the gas hourly space velocity, cold gas velocity, ortho-para- hydrogen conversion ki-netics, and operating pressure, were systematically varied to identify the kinetic and heat transfer constraints during the heat exchanger operation. The findings revealed that the ortho-para- hy-drogen conversion kinetic parameter predominantly governs operations requiring high gas hourly space velocity, particularly in large- scale hydrogen liquefaction processes. Furthermore, significant pressure drop within the catalytic filled channel was observed, however, operating at higher pressure mitigates this issue while mildly enhancing ortho-para-hydrogen conversion kinetics.

Objective: African Americans (AA) carry the largest burden for almost every type of cancer in the US and are also more likely to die from cancer. Approximately 10% of cancers can be explained by a hereditary factor and detected earlier. Many AA however have inequitable access to hereditary cancer risk assessment (HCRA) tools and information, further exacerbating cancer disparities. Innovative communication strategies to promote community-based HCRA information have promise to bridge AA to optimal primary cancer screening. The current pilot study followed a participatory process where researchers engaged with a Community Advisory Board (CAB) to explore how Communication Asset Mapping could assist Lay Health Advisors with dissemination of evidence-based HCRA information within AA faith communities. Methods: The research team and CAB conducted exploratory community group discussions with residents (N=21) guided by Communication Infrastructure Theory, and a community-engaged mapping process to inform the development of a Communication Asset Mapping (CAM) dissemination strategy. Results: Themes included: (1) Optimal locations (e.g. community centers) within identified networks (specified neighborhood networks) should have representatives who are trusted ambassadors to assist with HCRA information dissemination; (2) Trusted community member voices should fully represent the trusted network; and (3) Well-known and frequented geographic locations should be a true representation of participants’ neighborhoods for creating a robust health information network concerning HCRA. Conclusion: CAB and community residents appreciated the engagement process however, felt the impact was limited due to missing community voices within their neighborhoods to identify communication assets within the network for optimal HCRA information dissemination. CAM therefore is an important public health strategy in identification of trusted networks. The strategy was also helpful in pinpointing people who are critical communicators of emerging health information akin to HCRA.

Gut dysbiosis and an inflamed bowel are growing concerns in mammals, including dogs. Probiotic supplements have been used to restore the natural microbial community and improve gastrointestinal health. Biofilm formation, antimicrobial activities, and immunological responses of probiotics are crucial to improving gut health. Thus, we tested a commercial probiotic blend (LabMAX-3), a canine kibble additive comprising Lactobacillus acidophilus, Lacticasibacillus casei, and Enterococcus faecium for their ability to inactivate common enteric pathogens; their ability to form biofilms, epithelial cell adhesion and anti-inflammatory response on Madin-Darby Canine Kidney (MDCK) cell line. Probiotic LabMAX-3 blend, or individual isolates, showed a strong inhibitory effect against Salmonella enterica, Listeria monocytogenes, enterotoxigenic Escherichia coli, and Campylobacter jejuni. LabMAX-3 formed biofilms comparable to Staphylococcus aureus. LabMAX-3 adhesion to the MDCK cell line (with or without lipopolysaccharide, LPS) pretreatment showed comparable adhesion and biofilm formation (p < 0.05) to L. casei ATCC 334 used as a control. LabMAX-3 had no cytotoxic effects on the MDCK cell line during 1 h exposure. The IL-10 and TNFα ratio of LabMAX-3, compared to the L. casei control, showed a significant increase (p < 0.05), indicating a more pronounced anti-inflammatory response. The data show that LabMAX-3, a canine kibble supplement, can improve gastrointestinal health.

Introduction: Pharmaceutical tablets are solid forms administrated orally and they constitute 80% of the market because of its best stability and ease of large-scale production, adherence and treatment. The Brazilian Legislation establishes that for a drug to be registered as a similar or generic drug is necessary to verify the pharmaceutical equivalence in relation to the reference drug, indicated by ANVISA, through quality control tests, ensuring that the drug is approved with satisfactory quality. Objective: to evaluate the reference, generic and similar glibenclamide tablets through quality control tests, comparing the results with each other and relate clinical impacts on the patient in the face of changes in the quality control of medicines. Methodology: tests of average weight, friability and disintegration were done, according to the Brazilian Pharmacopoeia VI Edition, in uncoated tablets of Glibenclamide 5mg Reference (Daonil), Generic (Glibenclamide) and Similar (Glionil), and the results obtained were compared with the that advocates the scientific literature. Results: Data referring to the average weight: Reference 0.1591g, Generic 0.1002g and Similar 0.0996g; For friability: Reference value 0.54%, Generic value 0.49% and Similar value 0.28%. The disintegration tests showed the following times: Reference 35 seconds, Generic 29 seconds and Similar 33 seconds. Conclusion: the tests to which the glibenclamide tablets were submitted showed results within the parameters established by the Brazilian Pharmacopoeia.

Background: Old age and illnesses can limit physical activity (PA). Objectives: To assess the level of PA and its limitations. Design: Cross-sectional study, January and June 2024. Setting: Diabetes Center. Subjects: Patients (N=207) >64 years with diabetes (DM). Methods: Surveying using: IPAQ, Accompanying Survey (AS). The significance was assessed by: Student’s t-test, Mann-Whitney U test, Pearson’s test. The relationships between the IPAQ and the AS results were analyzed based on a meta-analysis model for variable effects, OR with a 95% CI. ROC curve was used to determine the threshold values for age, BMI. Correlations between selected key variables were evaluated using the PCA. Results: The median age: 72 years (65–87), BMI: 28.70 kg/m2 (18.61–49.69). The median PA level: 1,837 MET-min/week (0–9,891). The individuals who obtained insufficient (n=28), sufficient (n=75), and high levels of PA (n=53) were 17.95%, 48.08%, and 33.97%, respectively. Diseases were the main factor limiting PA (53,54%) in patient’s opinion, followed by the lack of a companion or motivation. The meta-analysis found no diseases linked to insufficient PA, but those with BMI > 33.3 kg/m² and over 75 years old more often had insufficient PA. PCA revealed several characteristics that predispose individuals to insufficient PA. Conclusions: People over 75 years of age tend to avoid PA more than younger seniors, leading to its insufficient level, similarly like higher BMI. The individual with DM who has an insufficient level of PA is typically a single, woman, over 75, BMI>33.

Background/Objectives: Congenital Radioulnar Synostosis (CRS) is a rare elbow joint disorder resulting from abnormal fusion of the radius and ulna during fetal development. Methods: Although predominantly sporadic, familial cases exist with autosomal dominant inheritance. Diagnosis relies on clinical evaluation and radiological findings, showcasing synostosis. Surgical decisions hinge on factors like age and symptom severity. Results: Multiple surgical treatments have been described but derotation osteotomy offers predictable results. Challenges include potential complications and cultural considerations influencing final forearm positioning. Conclusions: A nuanced approach is crucial for optimal management, highlighting the need for ongoing research to refine diagnostic and therapeutic strategies for improved outcomes in CRS patients.

Dual generalized commutative quaternions have broad application prospects in many fields. Additionally, the matrix equation AXB = C has important applications in mathematics and engineering, especially in control systems, economics, computer science, and other disciplines. However, research on the matrix equation AXB = C over the dual generalized commutative quaternions remains relatively insufficient. In this paper, we derive the necessary and sufficient conditions for the solvability of the dual generalized commutative quaternion matrix equation AXB = C. Furthermore, we provide the general solution expression for this matrix equation, when it is solvable. Finally, a numerical algorithm and an example are provided to confirm the reliability of the main conclusions.

The field of smart materials is attaining global industrial importance rapidly. The ionic polymer metal metaIl composites withheld there importance in this field. They are not only used as biocompatible devices but also in the field of thin and conformal bio coatings. The nafion 117 is unstable at elevated temperatures and its water intake resistance is low, so the nafion 117 is coated with the noble material to increase its thermal stability and water resistant property. The high cost of noble metal make its too costly to use in day to day applications so parylene has been used as substitute for noble metal in order to reduce the cost. The high thermal stability and water intake property if parylene make its suitable for the same. Multiple chemical treatments like plasma ionization and others have been done on the parylene coated nafion to increase its desirable properties considerably. The low adhesion rate between parylene and nafion 117 has been increased upto the certain extend by the use of ionized plasma. This paper reviewed the plasma chemical treatment of paryelene coatings for the biocompatible devices and provides an insight knowledge on classification, structure and thermal stability of the parylene coatings

Objective: A 1964 study challenged the consensus that saturated fat consumption increased the risk for myocardial infarction (MI). Roseto Pennsylvania, an Italian-American enclave of 1600 people, had similar rates of cigarette smoking, obesity and higher saturated fat consumption as neighboring towns, but far lower MI death rates. More than 50 years later, it remains uncertain if Roseto’s residents had better heart health than the average American and if so, what factors were responsible. Methods: We compared MI deaths in Roseto and neighboring towns to the Framingham Heart Study cohort matched for age and sex. Results: We found no evidence that MI deaths were lower in Roseto, PA than in Framingham, MA when controlling for age and sex. While the role of social support in health is uncontested, methodological issues, confounding factors and biases challenge the validity of the Roseto study. Conclusions: The dramatically lower MI and MI mortality rates among males in Roseto reflects biases in sampling and comparison populations, which also impacted the contrasting Diet-Heart hypothesis that saturated fats cause heart disease. Although social support enhances health outcomes, the Roseto study neither supported nor refuted this connection.

Background: Chronic diseases are major causes of morbidity, disability, and death worldwide. In many cases, they are potentially preventable and treatable. Glaucoma is one of the leading causes of morbidity and blindness, the early recognition of which can help reduce the adverse outcomes of the disease. Health illiteracy among these patients and the absence of self-management of these diseases are barriers to effective management worldwide. Therefore, the present study aimed to calculate the level of health literacy and activity level of patients’ activation in self-management and to explore the link between them among patients with glaucoma in outpatient clinics. Material and methods: Convenience sampling of patients with glaucoma attending the outpatient ophthalmology clinics of a university hospital in Heraklion-Greece was performed. Convenience sampling was used to recruit 312 participants. Data were collected between November 2023 and May 2024. The Patient Activation Measure-13 (PAM-13) questionnaire, in its Greek version, was used to evaluate patients’ activation level in self-management, whereas the Greek version of the European Health Literacy Survey Questionnaire 16 (HLS-EU-16) was used to assess health literacy. Results: Participants were classified as level 1(disengaged & overwhelmed) level in self-management (= 40.7, SD: ±29.9) and a sufficient health literacy level (= 10.7, SD ±3.7). However, there was no statistically significant relationship between health literacy levels and patient activation in self-management (p=0.602). Conclusions: Glaucoma patients experience challenges with health literacy and self-management activation, though there is no significant relationship between these two parameters. Multifaceted interventions seem essential for enhancing patients’ activation. Further research involving larger sample sizes, multi-center studies, and interventional design is needed.

Following the course of critical researchers in management and organisation studies, striving to move away from traditional norms of academic writing that has created an empty, homogenous time with a focus on counting rather than content towards a more vertical writing, I dig deep down in this paper along the slope of what touches me deeply – a democratic decline in climate politics. Resonating with my own experiences of hate and threat campaigns, not locking my own vulnerabilities in, I analyse qualitatively the usage, reasons, contents, senders and targets of ‘nasty rhetoric’ in Swedish climate politics, and the implications thereof for democracy. With the 2022 elections to the Swedish parliament, a far-right populist nativist party gained tangible powers dictating the content and process of Swedish climate politics. In less than two years, Swedish climate politics have turned into an antidemocratic divisive politics portraying climate science as “a point of view”, female journalists writing about climate change and climate policy as “crypto environmentalists”, “motherfuckers” and “moron hags” that should be “fired” and “raped”, and the climate justice movement as “terrorists” and “a threat to Swedish democracy” that should be “sent to prison” and “executed”. Such ‘nasty rhetoric’ is used not only by anonymous trolls in social media, but by the prime minister, cabinet ministers and parliamentarians, even in debates in the Swedish parliament. It has inspired the far-right extremist movement to use physical violence. Use of nasty politics aims to silence the opponents to the government’s climate policy, the political opposition, climate scientists, the climate justice movement and journalists, breaching liberal and deliberative democratic norms such as legitimacy, accountability and justice. It is a powerful tactic that leaves its targets with an anxious fear of losing control and often a need for disappearance. Many targets resign or stay silent. I chose to break the silence and write differently about the issue.

The emerging working mode of coordinated trucks and drones has demonstrated significant practical potential in various fields, including logistics and delivery, intelligence surveillance reconnaissance, area monitoring, and patrol. The seamless collaboration between trucks and drones is garnering widespread attention in academia and has emerged as a key technology for achieving efficient and secure transportation. This paper provides a comprehensive and in-depth review of the research status on the routing problem for coordinated trucks and drones, covering aspects such as application background, cooperative modes, configurations, issues have been taken into consideration, and solution methodologies.

Background: The advantage of a robotic assisted (RARP) over a laparoscopic (LRP) approach in radical prostatectomy (RP) remains to be demonstrated. Aim of the study is to use a homogeneous population in real life and single surgeon surgery, to analyze the oncological and functional results based on the type of the surgical approach. Methods: This is a prospective trial on non-metastatic prostate cancers (PC) patients considered after multidisciplinary decision to RP, using RARP or LRP. A real-life setting was analyzed at our Urological Departments, using homogeneous criteria for the management of PC cases and a one surgeon experience on 444 cases (284 LRP and 160 RARP) .Results: Mean operative time was significantly lower in RARP (173.33±44.3 min) than in LRP (153.21±25.1 min) (p<0.001). In cases submitted to an extended lymph node dissection (eLND), the mean number of lymph nodes removed was 15.16±7.83 and 19.83±4.78 respectively in LRP and RARP procedures (p<0.001), but positive lymph nodes (pN1) were similarly found in 15.8% of LRP patients and 13.6% of RARP patients (p=0.430). Surgical margins (SM) positivity was not significantly higher in RARP group (20.0%) when compared to LRP group (15.9%) (p=0.145). During the postoperative follow-up, a biochemical recurrence (BCR) was detected in 14.4% and 7.5% of cases in the LRP and RARP group respectively (p=0.014). Better results of PAD test at 3-month interval using RARP approach (mean pad weight 75.57±122 grams and 14±42 grams respectively in LRP and RARP (p<0.01)) were described. Conclusions: In the comparison between RARP and LRP approach, a clear advantage of the robotic approach is a significant reduction in operating times, days of hospitalization and postoperative catheterization compared to laparoscopic surgery. It is not possible to describe any certain oncological advantage both in terms of surgical margins and pathological lymph nodes removed. In RARP cases a reduction to the limit of significance is described in terms of biochemical recurrence. RARP produces a more rapid recovery of urinary continence at 3 months postoperatively without significant advantages in terms of erective potency recovery.

Background/Objectives: Malnutrition is a common issue among cancer patients, leading to adverse outcomes and reduced survival rates; therefore, nutritional screening and assessment are essential. Our study aims to evaluate the diagnostic efficacy of the GLIM criteria to assess malnutrition and to compare the performance of various nutritional screening tests in evaluating nutritional risk. Methods: A cross-sectional observational study was conducted on outpatient oncologic patients. Malnutrition was assessed using PG-SGA and GLIM criteria. Nutritional screening was performed using NRS-2002, MUST, MST, and NUTRISCORE. Anthropometric measurements, handgrip strength, cancer medical records and descriptive data were collected and analyzed. Results: A total of 396 patients were involved. Malnutrition was diagnosed in 37.4% according to GLIM criteria and in 25.8% according to PG-SGA (p<0.001). The agreement between GLIM criteria and PG-SGA was substantial (ƙ= 0.63). The diagnostic performance of PG-SGA showed a sensitivity of 93% and a specificity of 96.3%, whereas the GLIM criteria demonstrated a sensitivity of 81.3% and a specificity of 91.2%. Both tools had a significant association with low dynamometry and low BMI (p<0.001). Nutritional risk was identified in 22.7% of the patients using NRS-2002, 25.8% with MUST, 26.5% with MST, and 12.9% with NUTRISCORE. The highest agreement among the nutritional screening tests was observed between MUST and PG-SGA (ƙ =0.64). Conclusions: GLIM criteria detected a higher prevalence of malnutrition compared to PG-SGA; however, PG-SGA demonstrated higher sensitivity and specificity. Both significantly agree with low dynamometry and low BMI. Among the nutritional screening tests, MUST and PG-SGA showed the highest agreement.

Various cultivars of citrus fruits have unique construction, such as thick outer skin. These constructions generate gas diffusion resistance between atmosphere and fruit, which can limit a gas exchange of O2 and CO2. However, it has not been sufficiently investigated. In this study on 7 cultivars of citrus fruit, it was aimed firstly to investigate a gas diffusion resistance utilizing ethane efflux method and secondly to investigate internal gas concentration of O2 and CO2. As a result, a cultivar of citrus fruit with slimer outer skin thickness had lower resistance. For the internal gas, a high CO2 concentration in comparison with the atmosphere was observed even in the fruits with the minimum resistance, which no considerable difference among all cultivars was observed in regardless of gas diffusion resistance value. However, when the fruits stored at 25 °C for 2 weeks, it was tended to increase CO2 gas concentration and decrease O2 gas concentration with increase in the resistance value. Therefore, when respiration of citrus fruits is activated at ambient temperature, self-control system of internal gas concentration can be driven to suppress their respiration, which was induced by gas diffusion resistance generated from their construction.

A prime gap is the difference between consecutive prime numbers. The $n^{\text{th}}$ prime gap, denoted $g_{n}$, is calculated by subtracting the $n^{\text{th}}$ prime from the $(n+1)^{\text{th}}$ prime: $g_{n}=p_{n+1}-p_{n}$. Oppermann's conjecture is a prominent unsolved problem in pure mathematics concerning prime gaps. Despite verification for numerous primes, a general proof remains elusive. If true, the conjecture implies that prime gaps grow at a rate bounded by $g_{n}<{\sqrt {p_{n}}}$. This note presents a proof of Oppermann's conjecture using the square root function on inequalities involving prime numbers. This proof simultaneously establishes Andrica's, Legendre's, and Brocard's conjectures.

The key towards a sustainable future is the reduction of mankind’s impact on natural systems via the development of new technologies and the improvement of source apportionment. Though days, years and seasons are arbitrarily set, their mechanisms are based on natural cycles driven by Earth’s orbital periods. This is not the case for weeks, which are a pure anthropic category and are known from literature to influence emission cycles. For the first time since it started data gathering operations, CO (carbon monoxide), CO₂ (carbon dioxide), CH₄ (methane) and eBC (equivalent black carbon) values detected by the Lamezia Terme WMO/GAW station in Calabria, Southern Italy have been evaluated via a two-pronged approach accounting for weekly variations in absolute concentrations, as well as the number of hourly averages exceeding select thresholds. The analyses were performed on seven continuous years of measurements, from 2016 to 2022. Moreso, the two results have been combined into a new parameter: the hereby defined WDWO (Weighed Distribution of Weekly Outbreaks) normalizes weekly trends of CO, CO₂, CH₄ and eBC on an absolute scale as percentages, with the scope of providing regulators and researchers alike with a new tool meant to better evaluate anthropogenic pollution and mitigate its effects.

This research paper investigates the enhancement of strength and performance of self-compacting concrete (SCC) for integration in Department of Public Works and Highways (DPWH) projects using a value engineering approach. The study explores key elements of SCC's strength and performance, including mix design optimization, construction practices, and durability considerations. Strategies to optimize SCC's strength, durability, and workability are discussed, emphasizing the benefits of SCC in improving construction efficiency and quality control within DPW infrastructure. The study also identifies challenges and gaps in SCC utilization, such as standardized performance verification and cost considerations, and proposes future directions for advancing SCC technology in civil infrastructure development.

The bioremediation induced by bacteria offers a promising alternative for the contamination of aromatic hydrocarbons, due to their metabolic processes suitable for the removal of these pollutants, as many of them are carcinogenic molecules and dangerous to human health. Our research focused on isolating a bacterium from the rhizosphere of the tara tree with the ability to degrade polycyclic aromatic hydrocarbons, using draft genomic sequencing and computational analysis. Enterobacter sp. strain UNJFSC 003 possesses 4,460 protein‐coding genes, 2 rRNA genes, 77 tRNA genes, and a GC content of 54.38%. A taxonomic analysis of our strain revealed that it has an average nucleotide identity of 87.8% <95%, indicating that it is a new endemic enterobacterium. Additionally, pangenomic analysis with 15 strains demonstrated that our strain has a phylogenetic relationship with strain FDAARGOS 1428 (E. cancerogenus), with a total of 381 core genes and 4,778 accessory genes. Orthologous methods predicted that strain UNJFSC 003 possesses genes with potential for use in hydrocarbon bioremediation. Genes were predicted in the subpathways for the degradation of homoprotocatechuate and phenylacetate, primarily located in the cytoplasm. Studies conducted through molecular modeling and docking revealed the affinity of the predicted proteins in the degradation of benzo[a]pyrene in the homoprotocatechuate subpathway, specifically hpcB, which has enzymatic activity as a dioxygenase, and hpcC, which functions as an aldehyde dehydrogenase. This study provides information on native strains from the Lomas de Lachay with capabilities for the bioremediation of aromatic hydrocarbons and other compounds.

The paper presents two procedures for creating 3D discrete models of apple using the finite element method (FEM). The study focused on a new Polish apple variety, Chopin at development, ripening, and senescence maturity stages. The analysis of the results allowed for the determination that, depending on the desired outcomes, the parenchymal tissue can be considered as a material with either elastic or elastoplastic properties. Model validation was performed using displacement-force relationships and changes in contact area as a function of loading force obtained from compression tests. Elastoplastic models reflect empirical studies much better. The fit of the elastic models, based on force vs. displacement curves, ranged from 79% to 83%, while the fit of the elastoplastic models to empirical data ranged from 91% to 96%. Elastic models, on the other hand, are well-suited to the modified force vs. displacement curves, which reflect the elastic behavior of apples under compression test. Contour plots of pressures for elastoplastic models align much better with the test results than those for elastic models. 4. Similarly, in the case of contact area values, the elastoplastic models outperformed the elastic models, achieving a fit between 92% and 96%, while the elastic models reached only 82% to 83% compliance.

This article develops duality principles applicable to originally non-convex primal variational formulations. More specifically, as a first application, we establish a convex dual approximate variational formulation for a non-linear Kirchhoff-Love plate model. The results are obtained through basic tools of functional analysis, calculus of variations, duality and optimization theory in infinite dimensional spaces. We emphasize such a convex dual approximate formulation obtained may be applied to a large class of similar models in the calculus of variations. Finally, in the last section, we present a duality principle and respective convex dual formulation for a Ginzburg-Landau type equation.

In recent years, Big Data (BD) has become a crucial tool for Small and Medium-sized Enterprises (SMEs), impacting their performance and growth in significant ways. This systematic literature review aims to analyze the effects of BD on SMEs by examining 93 research papers published from 2014 to 2024. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key drivers and barriers to BD adoption, including business improvement, economic performance, and revenue growth. The methodology involved a comprehensive analysis of research methodologies used in the studies, addressing biases, gaps, and the need for diverse approaches. The findings reveal that while BD has led to notable enhancements in operational efficiency and revenue for many SMEs, challenges such as limited resources and technical expertise remain. A significant reporting bias was observed, with 47% of the literature comprising quantitative studies, followed by 28% case studies, while mixed-methods and qualitative studies were underrepresented (22% and 17%, respectively). This imbalance suggests a potential overemphasis on quantitative approaches, limiting the diversity of insights available. Addressing these biases is essential to fully harness the potential of BD for SMEs to drive innovation, enhance competitiveness, and achieve better performance in the increasingly data-driven business environment.

Transactive response DNA-binding protein of 43 kDa (TDP-43) is a major component of pathological inclusions in various neurodegenerative disorders, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). The detection of TDP-43 in biofluids is crucial for the development of diagnostic and prognostic indicators of disease and therapeutic development for TDP-43 related proteinopathies. Despite its potential as a biomarker for numerous neurological disorders, the lack of a sensitive and reproducible TDP-43 assay hinders progress in TDP-43-based therapy development, underscoring the need for an effective and standardized method for accurate quantification. In this study, we developed and validated a sensitive immunoassay for the detection of TDP-43 in human biofluids using the Meso Scale Discovery (MSD) platform. We used this immunoassay to quantify TDP-43 levels in the plasma and serum of healthy controls and ALS patients. Our results indicate a reduction of total TDP-43 in the blood of ALS patients compared to healthy controls.

The moiré effect is typically observed in regular periodic structures and sometimes in random (aperiodic) structures. However, only regular structures were used in measurements. In this study, we propose structures whose images are not regular but not completely random, and resemble a series of lines, such as a grid of dotted lines or a matrix of dots. A relatively simple image processing technique can make the moiré effect in such structures very similar to regular structures. We demonstrated using five different structures (including text) in measurements, with similar results.

Mesenchymal stem cells (MSCs) hold immense therapeutic potential due to their regenerative and immunomodulatory properties. However, to utilize this potential, it is crucial to optimize their in vitro cultivation conditions. Three-dimensional (3D) culture methods using cell-laden hydrogels aim to mimic the physiological microenvironment in vitro, thus preserving MSC biological functionalities. Cellulosic hydrogels are particularly promising due to their biocompatibility, sustainability, and tunability in terms of chemical, morphological, and mechanical properties. This study investigated the impact of (1) two physical crosslinking scenarios for hydrogels derived from anionic cellulose nanofibers (CNF) used to encapsulate adipose-derived MSCs (adMSCs) and (2) physiological culture conditions on the in vitro proliferation, differentiation, and extracellular vesicle (EV) production of these adMSCs. Our results revealed that additional Ca2+-mediated crosslinking, intended to complement self-assembly and gelation of aqueous to-CNF in the adMSC cultivation medium, adversely affected both the mechanical properties of the hydrogel spheres and the growth of the encapsulated cells. However, cultivation under dynamic and hypoxic conditions significantly improved the proliferation and differentiation of the encapsulated adMSCs. Furthermore, we demonstrated that adMSCs in CNF hydrogel spheres exhibited potential for scalable EV production with potent immunosuppressive capacities in a bioreactor system. These findings underscore the importance of physiological culture conditions and the suitability of cellulosic materials for enhancing the therapeutic potential of MSCs. Overall, our study provides valuable insights for optimizing the in vitro cultivation of MSCs for various applications, including tissue engineering, drug testing, and EV-based therapies.

In 1974, Stephen Hawking made the groundbreaking discovery that black holes emit thermal radiation, characterized by a specific temperature now known as the Hawking temperature. While his original derivation is intricate, retrieving the exact expressions for black hole temperature and entropy in a simpler, more intuitive way without losing the core physical principles behind Hawking's assumptions is possible. This is obtained by employing the Heisenberg uncertainty principle which is known to be connected to the vacuum fluctuation. This exercise allows us to easily perform more complex calculations involving the effects of quantum gravity. This work aims to answer the following question: \textit{Is it possible to reconcile Prigogine's second law of thermodynamics for open systems and the second law of black hole dynamics with Hawking radiation}? Due to quantum gravity effects, the Heisenberg uncertainty principle has been extended to the Generalized Uncertainty Principle (GUP) and successively to the Extended Uncertainty Principle (EUP). The expression for the EUP parameter is obtained by conjecturing that Prigogine's second law of thermodynamics and the second law of black holes are not violated by Hawking thermal radiation mechanism. The modified expression for the entropy of a Schwarzschild black hole is also derived.

The cement industry is a significant contributor (around 8 %) to CO2 global emissions. About 60 % of emissions come from limestone calcination, essential for clinker production, while 40 % result from fuel combustion. Reducing these emissions is challenging due to limestone’s role as the primary raw material for cement. Cement plants are required to achieve carbon neutrality by 2050, as outlined in the 13th United Nations Sustainable Goals. One strategy to achieve this goal, involves Carbon Capture and Utilisation (CCU). Among the options for CO2 utilisation, the Power-to-Liquid (PtL) strategy offers a means to mitigate CO2 emissions. In PtL, CO2 captured from cement industrial flue gas is combined with hydrogen generated by renewable electrolysis (green hydrogen) and catalytically converted into renewable methanol (e-MeOH). In this sense, this review provides a comprehensive overview of the worldwide existing pilot and demonstration units, and projects funded by EU across several industries. It specifically focuses on PtL technology worldwide within cement plants. This work covers 18 locations worldwide, detailing technology existent at plants of different capacities, location, and project partners. Finally, the review analyses techno-economic assessments related to e-MeOH production processes, highlighting the potential impact on achieving carbon neutrality in the cement industry.

The cement industry has significant environmental impacts, stemming from natural resources extraction and fossil fuels combustion. Notably, carbon dioxide (CO2) emissions are a major concern associated with cement production. The cement industry emits 0.6 tons of CO2 per ton of cement production, which is around 8 % of the total CO2 emissions in the world. Meeting the 13th United Nations Sustainable Goals, cement plants aim to achieve carbon neutrality by 2050, resulting from reduction in CO2 emissions (change in the composition of cementitious materials) and the adoption of carbon capture and utilisation (CCU) technologies. A promising approach involves converting CO₂ into valuable chemicals and fuels, such as methanol (MeOH) through the power-to-liquid (PtL) technologies. In this process, CO2 captured from cement industry flue gas with hydrogen generated from renewable sources through electrolysis of water, catalytically transformed into renewable methanol (e-MeOH), offering a sustainable solution. To achieve this, it is crucial to advance the development of novel, highly efficient catalysts specifically designed for direct CO2 hydrogenation. In this sense, this review discusses recent developments and improvements in CO2 catalytic conversion, emphasizing catalyst performance, selectivity, and stability.

There is a bifurcation in the academic voice regarding the adoption or rejection of generative AI in the classroom. This division in pedagogy remains ever present as educators struggle to catch up with advancing AI technology and its staunch integration into student’s everyday lives. A resounding concern from educators is for the authenticity of student work and the building of critical thinking skills, both of which become threatened by generative AI tools used without proper guidelines. Educators have a right to these concerns, as studies continue to show how generative AI tools successfully pass assessments and how the AI-coauthored work is challenging to detect or differentiate from student-authored work. In the field of Computer Science, the challenges compound as generative AI technology integrates seamlessly into already approved code-generation tools and development environments. Therefore, an informed and thoughtful decision must be made so that student learning can continue to flourish and evolve in a safe and inclusive setting. This research attempts to go beyond introductory course assessments, where a gap in research currently hangs. As curriculum becomes more challenging and abstract, can generative AI continue to pass assessments and be useful educational assistants? Or will these tools create more confusion for students as prompts become more advanced and prove difficult to interpret? During this empirical study, GitHub Copilot is assessed against the assessments of an advanced CS programming course. The findings present a resounding optimism for generative AI tools, with all assessments passed successfully. The study concludes with recommending an integrated approach of introducing and encouraging the use of generative AI. Collaboration with a partnership mindset will produce the best results.

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies.

Objectives: the aim of this study was the assessment and monitoring of the safety and quality of the existing water supplies in Ibb and Taiz governorates from the microbial side and testing the quality of drinking water in the Ibb and Taiz governments and comparing it with the World Health Organization. Methods: the water samples (99 samples) taken from some water sources, distribution reservoirs, and different regions were studied for bacteriological analysis, in which they were inoculated into culture media using two methods: the presumptive test for the most probable number and the confirmatory test for bacterial identification. Results: the results indicated the non-existence of indicator bacteria that were used to indicate the possible presence of fecal bacteria among 24 (24.24%) water samples taken from Taiz governorate, while the presence of an unacceptable number of Escherichia coli in house tanks (30%) was indicated among 75 (74.25%) water samples taken from Ibb governorate. Conclusions: the results of the study in Taiz governorate show that the water is fit for human consumption, as well as the samples taken from water sources and distribution networks in Ibb governorate, but the results of the samples taken from house tanks showed that the water is not fit for human consumption and needs to be treated.

Over-indebtedness has become an increasingly serious problem among young people in China. Utilizing atlas.ti, a qualitative analysis software, this paper delves into the textual data of the online community named “Debtors Avengers” on on the Chinese website Douban.com. Employing a combined analytical framework of life cycle and credit liberalization hypotheses, the paper examines the underlying causes of youth indebtedness. This study unveils three key findings: First, the escalating debt among young people is not merely a consequence of irrational consumer behaviors, fueled by rampant consumerism at an individual level. Rather, it is also intricately linked to their economic activities within specific life cycle phases. Second, from a structural risk perspective, youth indebtedness is not tied only to broad socio-economic risks, but is also a byproduct of the shift towards socio-financial digitization and the normalization of credit usage. Finally, the burgeoning issue of youth indebtedness stems from a confluence of factors. These include individual aspects such as distinct life situations in youth life stages, the probability of unexpected events, and a lack of financial literacy. Concurrently, youth indebtedness is influenced by the tenuous position of the youth in the labor market, risks associated with significant public safety occurrences, and a ‘spiral of indebtedness’ instigated by the digitalization of finance and the normalization of credit. In light of the deepening integration of financial inclusion, it becomes imperative to refine financial policies, enhance financial education, and establish a framework for intervening against financial aggression. This approach is crucial in addressing and mitigating the challenges posed by youth indebtedness in this evolving economic landscape.

This study examines the factors influencing consumer adoption of on-demand social media platforms characterized by ephemeral content. As these platforms rapidly gain popularity, understanding adoption drivers is crucial for researchers and practitioners. The study develops and tests a comprehensive model integrating traditional technology acceptance factors with platform-specific constructs. Using partial least squares structural equation modelling on data from 180 social media users in Kerala, India, the research reveals that convenience is the strongest predictor of adoption intention, followed by perceived usefulness, interpersonal influence, and enjoyment. Surprisingly, factors like perceived ease of use, social influence, and platform-specific features such as ephemerality and visual focus did not significantly impact adoption directly. Age showed a marginal negative effect, while gender had no significant impact. The findings challenge some prevailing assumptions about on-demand social media adoption and highlight the need for refined models specific to this context. The study contributes to technology adoption literature by demonstrating the primacy of convenience in driving the adoption of these platforms. It also offers practical insights for developers and marketers, suggesting that prioritizing convenience and perceived usefulness in design and marketing may be more effective than emphasizing specific features. Future research directions are proposed to address limitations and further explore this evolving digital landscape.

Pluripotent stem cells (PSCs) hold many potential research and clinical benefits due to their ability to differentiate into nearly every cell type in the body. They are often used as model systems for early stages of ontogenesis to better understand key developmental pathways, and for drug screening. However, in order to fully realise the potential of PSCs and their translational applications, a deeper understanding of developmental pathways, especially in humans, is required. Several signalling molecules play important roles during development and are required for proper differentiation of PSCs. The concentration and timing of signal activation is important, with perturbations resulting in improper development and/or pathology. Bone morphogenetic protein (BMP) is one such key signalling molecule involved in the specification and differentiation of various cell types and tissues in the human body including tooth and otic development. In this review, we describe the role of BMP signalling and its regulation, the consequences of BMP dysregulation in disease and differentiation, and how PSCs can be used to investigate the effects of BMP modulation during development, mainly focusing on otic development. Finally, we emphasise the unique role of BMP4 in otic specification and how refined knowledge in controlling its regulation could lead to the generation of more robust and reproducible human PSC-derived otic organoids for research and translational applications.

Drag reduction by laminarization, an innovative technology in aerodynamics design, provides the possibility to improve significantly the aerodynamic performance of aircraft and remains to be one of the most promising and effective technologies. In this paper, the design method for transonic natural laminar flow (NLF) wing at high Reynolds number is investigated in details and a new optimization model is established to solve conflicts between the increase of a laminar flow region and the shock wave strength at the trailing edge. In order to numerically handle this competitive situation efficiently, a two-objective hierarchical variable fidelity Pareto evolutionary optimization model based on search space contraction is proposed and implemented. Numerical experiments show that it can capture simultaneously a Pareto front of the two-objectives: a wave drag minimization and a laminar flow region maximization. The results also show that both wave drag and skin friction drag performances of non-dominated Pareto members can be significantly improved via the optimal laminar wing shape and shock wave control device. Nearly 40% of the laminar flow area on the wing surface is achieved at the Reynolds number of 107. It is concluded that the present hierarchical variable-fidelity multi-objective evolutionary optimization method is computationally efficient, and illustrate its potential for solving complex engineering design problems.

China is a large agricultural country, and the crop economy has an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve rapid non-destructive detection of crop diseases and pests, as well as non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging tech-nology in crops both domestically and internationally. It then summarizes the methods of hyper-spectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant pre-processing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of ag-ricultural products. In conclusion, hyperspectral imaging technology can effectively serve the ag-ricultural economy, making agricultural production more intelligent and holding significant im-portance for the development of agriculture in China.

This paper examines the impact of renewable energy heating on air quality in northern China, using the Qinling Mountains-Huaihe River line as a quasi-natural experiment to distinguish between regions with central heating and those without. Employing a difference-in-differences approach and analyzing panel data from 299 cities between 2014 and 2022, our findings indicate that the renewable energy heating policy has significantly improved air quality. Specifically, the policy led to substantial reductions in the Air Quality Index (AQI) and concentrations of key pollutants: SO2 by 28.31%, CO by 7.57%, NO2 by 5.72%, and PM2.5 by 7.15%. The policy's effects are most pronounced in regions with lower temperatures and in the eastern parts of the country. Further analysis emphasizes the critical role of energy transition, environmental regulations, and government investment in technology as key drivers of these air quality improvements.

In 2020, the Covid-19 outbreak led many countries across the globe to introduce lockdowns (LDs) that effectively caused most anthropic activities to either stop completely or be significantly reduced. In Europe, Italy played a pioneeristic role via the early introduction of a strict nationwide LD on March 9th. This study is aimed at evaluating, using both chemical and meteorological data, the environmental response to that occurrence as observed by the Lamezia Terme (LMT) GAW/WMO station in Calabria, Southern Italy. The first 2020 lockdown has therefore been used as a “proving ground” to assess CO, CO2, CH4, eBC, and NOx concentrations in a rather unique context by exploiting the location of LMT in the context of the Mediterranean Basin. In fact, its location on the Tyrrhenian coast of Calabria and local wind circulation both lead to daily cycles where western-seaside winds depleted in anthropogenic pollutants can be easily differentiated from northeastern-continental winds, enriched in anthropogenic outputs. Furthermore, the first Italian LD occurred during the seasonal transition from Winter to Spring and, consequently, Summer, thus providing new insights on emission outputs which are normally linked to domestic heating and other forms of season-specific activities.

Empirical findings support the important role played by prosocial behavior and scholastic per-formance in significantly affecting adolescents’ long term psychosocial adjustment. Furthermore, several studies showed the positive impact of academic self-efficacy in affecting adolescents’ social and academic functioning; however, no studies have investigated the effect of other important personality structures, such as students’ feedback sensitivity. The present study aims at investi-gating the effects of adolescents’ feedback sensitivity (reward sensitivity/punishment sensitivity) on their prosocial behavior and scholastic performance also examining the potential indirect effect of academic self-efficacy on the association between reward/punishment sensitivity and prosocial behavior/scholastic performance. Participants were 132 adolescents (53.1% males; Mage = 16.42, SD) from Rome (Italy). Students’ reward/punishment sensitivity, academic self-efficacy, prosocial behavior and scholastic performance were self-reported. A path analyses model showed a positive and significant effect of academic self-efficacy on scholastic performance, a positive significant ef-fect of punishment sensitivity on prosocial behavior, a positive significant effect of reward sensi-tivity on academic self-efficacy and a significant indirect effect of academic self-efficacy on the association between reward sensitivity and scholastic performance. Results of this study are useful in term of practical implications to promote students’ adjustment.

This study aimed to identify whether the combined use of functional electrical stimulation (FES) reduces the motor torque of a gait exercise rehabilitation robot in spinal cord injury (SCI) and to verify the effectiveness of the developed automatic assist level adjustment in people with paraplegia. Acute and chronic SCI patients (1 case each) performed 10 min of gait exercises with and without FES using a rehabilitation robot. Reinforcement learning was used to adjust the assist level automatically. The maximum torque values and assist levels for each of the ten walking cycles when walking became steady were averaged and compared with and without FES. The motor's output torque and the assist level were measured as outcomes. The assist level adjustment allowed both the motor torque and assist level to decrease gradually to a steady state. The motor torque and the assist levels were significantly lower with the FES than without the FES under steady conditions in both cases. No adverse events were reported. The combined use of FES attenuated the motor torque of a gait exercise rehabilitation robot for SCI. Automatic assistive level adjustment is also useful for spinal cord injuries.

This study examines the relationship between chondroitin sulphate, proteinoids, and computational neuron models, with a specific emphasis on the Izhikevich neuron model. We investigate the effect of chondroitin sulphate-proteinoid complexes on the behaviour and dynamics of simulated neurons. Through the use of computational simulations, we provide evidence that these biomolecular components have the power to regulate the responsiveness of neurons, the patterns of their firing, and the ability of their synapses to change within the Izhikevich architecture. The findings suggest that the interactions between chondroitin sulphate and proteinoid cause notable alterations in the dynamics of membrane potential and the timing of spikes. We detect adjustments in the features of neuronal responses, such as shifts in the thresholds for firing, alterations in spike frequency adaptation, and changes to bursting patterns. The findings indicate that chondroitin sulphate and proteinoids may have a role in precisely adjusting neuronal information processing and network behaviour. This study offers new and valuable information about the complex connection between the many components of the extracellular matrix, protein-based structures, and the functioning of neurons. In addition, our analysis of the proteinoid-chondroitine system using game theory uncovers a significant Prisoner’s Dilemma scenario. The system’s inclination towards defection, due to the appeal of cheating and the significant penalty for cooperation, with a mean voltage of −9.19 mV, indicates that defective behaviors may prevail in the long-term dynamics of these neuronal interactions.

Background/Objectives: Asthma is a chronic inflammatory disorder of the airways affecting over 10% of the global population. It is characterized by airway inflammation, mucus hypersecretion, and bronchial hyperresponsiveness, driven predominantly by type 2 helper T cells (Th2) and type 2 innate lymphoid cells (ILC2s) in a subset of patients. However, a significant portion of asthmatic individuals present with "type 2-low" asthma, often refractory to standard inhaled corticosteroid (ICS) therapy. Therefore, developing innovative therapeutic strategies has become essential. Recent studies have highlighted cannabidiol (CBD) as a promising anti-inflammatory agent capable of modulating immune responses. This study investigates the therapeutic potential of a high-CBD extract (CBD-X) in asthma; Methods: We evaluated the effects of CBD-X on cells involved in asthma pathogenesis using primary human Th2 cells, neutrophils and asthma mouse model; Results: Our findings indicate that CBD-X extract inhibits Th2 differentiation and reduces the secretion of IL-5 and IL-13, which are crucial cytokines in asthma. Additionally, CBD-X significantly reduces pro-inflammatory cytokines IL-8 and IL-6 in neutrophils and impairs their migration, a critical step in airway inflammation. In a murine asthma model, CBD-X administration led to marked downregulation of IgE and pro-asthmatic cytokines, along with reduced leukocyte, eosinophil, and neutrophil infiltration in lung tissues; Conclusions: These results suggest that CBD-X extract could offer a novel, complementary approach to managing both type 2-high and type 2-low asthma by targeting key inflammatory pathways and modulating immune cell behavior.

This study investigates the correlation between gut microbiota diversity and various hematological and meat quality parameters in different cattle breeds, including Angus, Simmental, Braunvieh, Brahman and F1(SMxBR). Using 16S rRNA and 18S rRNA gene sequencing, microbial communities were profiled to discern breed-specific impacts on gut microbiota. Significant correlations were identified between gut microbiota diversity and health parameters, emphasizing the role of microbial diversity in influencing skin condition, fat deposition, and muscle quality. Key bacterial genera such as Blautia, Christensenellaceae_R-7_group, and Coprococcus exhibited significant correlations with meat quality traits. In fungi, genera like Rhodotorula exhibited positive correlations with muscle quality, while protist genera such as Cryptosporidium negatively correlated with meat quality parameters. These findings suggest that host genetics significantly shape the gut microbiota and highlight the potential for targeted microbiota management to optimize gut health, enhance feed efficiency, and improve meat quality in livestock.

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. The integration of AI-based technologies for fault diagnosis and decision-making, coupled with proper personnel training, has become essential for optimal ship operation, with AI-assisted predictive maintenance emerging as the key strategy to ensure onboard systems function within desired parameters and enhance equipment availability. This study proposes a machine-learning algorithm for evaluating onboard systems performance by analyzing sensor data and mapping it to fault patterns to estimate functional states, validated through tests on a seawater cooling system from an oil tanker, demonstrating operational efficiency and reliability. The immediate advantage is reduced time for fault diagnosis by rapidly assessing the system using operational data, the algorithm enabling real-time monitoring and fault data management to minimize efforts as part of predictive maintenance assisted by machine learning tools.

Nitrogen loss in water from farmland has become a highly concerned environment issue due to the different nitrogen-fertilized input, which is the main cause of agricultural non-point source pollution in the Lake Basin. However, characteristics of nitrogen forms loss from farmland and how the root system responds is lacking. The effectiveness of 100–0% (T1), 75–25% (T2), 50–50% (T3), 25–75% (T4), and 0–100% (T5) nitrate-ammonium nitrogen fertilizer treatment was investigated to determine nitrogen loss in water and root morphological parameters of tobacco in Mile and Chengjiang Counties. Two sites result suggested that: Compared with the T1, T4, and T5 treatments, the TN loss of the T2 and T3 treatments in runoff was reduced by 4.67–27.32%, 11.85–23.20%, and 9.56–31.43%, respectively. Similar results in the nitrogen loss of infiltration were observed with that from runoff. Correlation analysis suggested that the root biomass was negatively correlated with the nitrogen loss. T2 and T3 treatments were larger than those of the T1, T4, and T5 treatments in terms of root biomass, root surface area, and root spatial distribution. This study suggested that 50–50% nitrate-ammonium nitrogen fertilizer can facilitate the root growth of tobacco and reduce nitrogen loss, which provides a reference for agricultural sustainable development.

Africa's health landscape is rapidly changing, requiring new solutions such as a focus on health literacy. However, there is currently a limited overview of the development and application of health literacy in African countries and societies. To inform progress, a scoping review was conducted, including a systematic literature search of research approaches, historical developments, geographic origins, target populations and settings, and topical interests. 336 articles were retrieved from six databases following PRISMA guidelines. Sources identified included reports, policy briefs, book chapters, and research publications. The analysis provided the most comprehensive overview of the current scope and scale of health literacy in Africa to date. The publications represented 38 of the 54 African Union countries and dated back to 2001, although most were published in the last decade. The content analysis identified 13 broad themes, including mental health, communicable diseases, non-communicable diseases, maternal health, digital health, information and communication, health care, prevention and health promotion, conceptual perspectives, cultural perspectives, outcomes and measurement. The stakeholder analysis revealed a wide range of actors involved in different settings, mostly in health care or community settings. These comprehensive and novel findings can be used to prioritize future actions for public and professional capacity building, policy development and improved practice to improve health literacy for all in Africa.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of DNA repair gene-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning between the Wnt and X. In this re- search work, we cover combinations of RAD with X-ray repair cross complementing (XRCC) family, 5’-3’ exoribonuclease 2 (XRN2), NFKB repressing factor (NKRF), B cell CLL/lymphoma (BCL) family, exosome component (EXOSC) family, FA comple- mentation group (FANC) family and XRCC with EXOSC.

In the context of energy conversion from renewable sources to distribution grids (insulated or not), a converter is often required to transfer energy from a low voltage source, towards three-phase grids. This paper presents the HW design, the simulation results and the conversion performance of a CSI converter intended to interface low voltages renevable sources to three-phase grids. The main focus of this paper is to obtain the best performance in terms of voltage increase towards the output stage while maximizing the conversion efficiency. In comparison with the currently used energy conversion systems for small photovoltaic systems, hereafter some solutions were adopted to level and maximize the energy flow from the source to the DC-link and improve the quality of current supplied in terms of harmonic distortion. The proposed system is composed of two conversion stages: the first, voltage-to-current, the second current-to-current via a three-phase CSI bridge modulated with SVM technique. The stages are not completely decoupled from an electrical point of view, therefore, in order to mitigate the effects of these interactions, synchronization strategies have been adopted.

This study examines the relationship between Gross Domestic Product (GDP) and income inequality in Greece across three key periods: the pre-crisis period (2003-2008), the crisis and memoranda period (2009-2014), and the post-crisis period (2015-2020). Using data from the World Bank Open Data database, the study employs correlation analysis, and linear regression models to analyze how economic changes influence income distribution. The findings reveal that during the pre-crisis period, economic growth was moderately correlated with increasing inequality, although this relationship was not statistically significant, indicating uneven distribution of growth benefits. In the crisis period, a strong and statistically significant negative relationship between GDP and the Gini coefficient was identified, highlighting the sharp rise in inequality as the economy contracted due to austerity measures. In the post-crisis period, the relationship between GDP and inequality weakened, showing a statistically insignificant correlation, suggesting that economic recovery alone did not significantly reduce inequality. The study concludes that economic growth is insufficient to address income inequality without targeted policy interventions, particularly during periods of crisis. These findings underscore the importance of comprehensive social policies to mitigate inequality and suggest areas for future research on the impact of specific interventions on income distribution.