Recent climate change and water pollution has affected algal diversity resulting in emergence of more tolerant species. They can be the indicators of water quality, pollution and seasonal changes. Present study revealed the green algal diversity and distribution pattern according to water parameters and season in the Lucknow City, India. The water sample were collected from seven sites within Lucknow and outskirts. The occurrence of a total of 36 taxa of green algae belonging to 11 genera are recorded. It has been observed that less polluted water is dominated by Spirogyra; where conductivity, TDS and salinity of water were low. Whereas the more polluted water was dominated by desmids. Dominance index was found highest in site 4 (0.61), where only three taxa were identified; whereas the lowest (0.13) was measured in site 1, where nine taxa were identified. This study also recorded several algal taxa for the first time from Lucknow.

Natural killer (NK) cells play a crucial role in innate immunity, particularly in combating infections and tumors. However, in hematological cancers, NK cells often exhibit impaired functions. Therefore, it is very important to activate its endosomal toll-like receptors (TLRs) as a potential strategy to restore its antitumor activity. We stimulate NK cells from the peripheral blood mononuclear cells from children with acute lymphoblastic leukemia and NK cells isolated, the NK cells were stimulated with specific TLR ligands (Poly I:C, Imiquimod, R848, and ODN2006) and evaluated changes in IFN-γ, CD107a, NKG2D, NKp44 expression, Granzyme B secretion, cytokine/chemokine release, and cytotoxic activity. Results revealed that Poly I:C and Imiquimod enhanced activation of both immunoregulatory and cytotoxic NK cells, increasing IFN-γ, CD107a, NKG2D, and NKp44 expression. R848 activated immunoregulatory NK cells, while ODN2006 boosted CD107a, NKp44, NKG2D, and IFN-γ secretion in cytotoxic NK cells. R848 also increased secretion of various cytokines/chemokines. Importantly, R848 and ODN 2006 significantly improved cytotoxicity against leukemic cells. Overall, TLR stimulation enhances NK cell activation, suggesting TLR8 (R848) and TLR9 (ODN 2006) ligands as promising candidates for antitumor immunotherapy.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

Background: The effectiveness of the anti-COVID-19 vaccine remains a matter to be clarified. Methods: The humoral and cellular immunity after the administration of three doses of the Pfizer–BioNTech and Oxford AstraZeneca vaccines against SARS-CoV-2 over one year and the appearance of post-vaccination COVID-19 have been studied. Results: Anti-SARS-CoV-2 IgG and IgA antibodies showed a progressive increase throughout the vaccination period. This increase was greatest after the third dose. The highest levels were observed in subjects who had anti-SARS-CoV-2 antibodies prior to vaccination. There was an increase in CD4+ αβ, CD8+ γδ and TEM CD8+ γδ T cells, and a decrease in apoptosis in CD4+CD8+ and CD56+ αβ and γδ T cells. Post-vaccination SARS-CoV-2 infection was greater than 60%. The symptoms of COVID-19 were very mild and were related to γδ T cell deficit, specifically CD8+ TEMRA and CD56+ γδ TEM, as well as lower pre-vaccine apoptosis levels. Conclusions: Those results demonstrate the important role that γδ T cells play in SARS-CoV-2 vaccine immunization.

In this paper, we derive the distribution of eigenvalues for a 2×2 random confusion matrix stemming from a machine learning (ML) evaluation problem. Additionally, we present the distributions of both the matrix’s trace and the difference between the two traces of random confusion matrices. We also illustrate the application of these derived distributions in calculating the superiority probability of four baseline ML models.

Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak and accumulated rainfall-based damage are likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index (SWRI24−hr−S), derived from normalising the existing 24-hourly water resources index (WRI24−hr−S) in literature, to monitor flash flood risks on an hourly scale. The proposed SWRI24−hr−S is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V) and peak (Q). The comprehensive result analysis establishes the practical utility of SWRI24−hr−S in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data is available, offering crucial information for flood risk monitoring, assessment, and the development of effective mitigation strategies.

There are rich proper noun derivative semantic relationships in geographic entity vector data. The identification of traditional proper noun derivative semantic relationships usually requires toponym researchers to examine the source of derived toponyms by consulting a large number of toponym documents. This method cannot adapt to the mining of proper noun derivative semantic relationships for large-scale geographical entity. In this regard, this paper proposes a method of identifying the proper noun derivative semantic relationships for geographical entity by combining prompt learning and XGBoost model. This method transforms the extraction problem of proper noun derivative semantic relationships into a binary classification problem by analyzing the characteristics of the derivation relationship of toponyms in the form of toponyms and the spatial and temporal distribution of geography. Firstly, prompt learning and gaussian mixture model are used to extract and cluster the derivation words of toponyms, so as to realize the recognition of derived toponyms collections. Then, enumerate the possible proper noun derivations in the toponym set, and then the XGBoost model is used to identify the semantic relations derived from the proper noun of geographical entities. In the experiment, the recognition method of proper noun derivative semantic relationships based on prompt learning and XGBoost proposed in this paper has achieved good relationship recognition effect in the experiment. This method has certain practical value in the fields of toponym management and toponym translation.

Some current systematized reviews of macroalgae are concise and descriptive, but it is important to conduct these of a larger number of taxa to facilitate visualizations of the types of primordial research, relevant methods, and areas of opportunity. The aim was to elaborate the state of the art in the taxonomic knowledge of Dictyota based on a systematized review of the generated information since its original description (1809 to 2023). We conducted a search for names and literature in different databases, followed by a temporal analysis (by century and bidecadal). To date, a total of 313 names have been registered for Dictyota; 108 are accepted names. The largest number of registered taxonomic names (207) was found for the 19th century, derived from large expeditions. Our analysis of literature involved 318 publications, 89 of which corresponded to alpha taxonomy, 187 to beta, and 42 to gamma. We determined that the trend in taxonomic works is integrative in nature because they include other disciplines to explain aspects that go beyond taxonomic description. Our analysis suggests that extensive reviews and monographic approaches are still required to achieve a better understanding of the genus. The effort in integrative taxonomy must be intensified, which allow improve the systematic of Dictyota.

The Maastricht VI/Florence consensus recommends, as one of the measures to enhance the efficacy of Helicobacter pylori (H. pylori) infection eradication, a personalized treatment approach involving the selection of an antimicrobial agent based on the pre-determined resistance of H. pylori. To meet the need to develop test systems for personalized drug selection, we conducted a study of H. pylori molecular resistance in Moscow using a self-developed Sanger sequencing test platform. We validated our test system on 25 pure culture samples of H. pylori with known resistance. Sensitivity and specificity for detecting resistance to clarithromycin was 100%, and to levofloxacin - 93% and 92%, respectively. Then we tested our platform on 112 H. pylori -positive patients who had not previously received proton pump inhibitors (PPI) or antibacterial drugs. Mutations indicating resistance to clarithromycin were found in 27 (24%) samples, and to levofloxacin in 26 (23%) samples. Double resistance was observed in 16 (14%) samples. The most common mutations leading to clarithromycin resistance were 2143G and 2142G, to levofloxacin resistance – 261A and 271A in the gyrA gene, which account for 69% of all identified genetic determinants in levofloxacin-resistant bacteria. Thus, a personalized approach to the selection of H. pylori eradication therapy based on the detection of bacterial resistance before prescribing first-line therapy could help avoid the prescription of ineffective H. pylori eradication therapies and, overall, contribute to the control of antibiotic resistance of H. pylori.

Quantum correlations, especially time correlations, are crucial in ghost imaging in order to significantly reduce the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy-state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon-number regimes for producing high-quality, noise-reduced images. The versatility of decoy-state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum communication.

The Amazon region has the most incredible diversity of freshwater stingrays; however, there are still places where information about this group is scarce. The present work aims to record the occurrence of freshwater stingrays in the Uatumã river basin, Amazonas, Brazil. For this, collections were carried out in three municipalities bathed by the Uatumã River, Presidente Figueiredo, Itapiranga and São Sebastião do Uatumã. The rays were collected through fishing using a hand net and, after capture, were identified through the pattern of the dorsal and ventral color of the disc, the absence or presence of the labial groove and the organization of the row of spines on the tail, too were also measured, weighed and sexed. In total, 69 specimens of Potamotrygonines were captured, 59 of which were from the genus Potamotrygon and ten individuals from the genus Paratrygon. The present study provides preliminary information on the characteristics of freshwater stingray species that may occur in the Uatumã River. Furthermore, new research is necessary to improve the identification of specimens to determine the diversity and elucidate aspects related to the biology of elasmobranchs in the Uatumã River Basin, Brazil.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into missing data problem formulation, assumption specification, and method development. In this paper, we highlight the gap particularly for observational data from healthcare facilities. We address this gap by identifying ten fundamental missingness scenarios arising during data measurement, recording, and pre-processing in observational health data, influenced by physicians, patients, healthcare facilities, and data scientists. We analyze the effect of scenarios on estimand formulation, missing data identification, estimation, and sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis (complete-case analysis, Missforest imputation, and inverse probability weighting estimation) for two estimands (variable mean estimation and classification accuracy). We advocate for our analysis approach as a reference for the analysis of observational health data. Furthermore, we posit that the proposed analysis framework is applicable to other medical domains, including medical wearable data analysis.

Revealing the spatial distribution mechanism of the coupling effect between soil water and salt is essential for managing saline-alkali farmland. This study utilized the multifractal method to analyze soil water and salt under varying water content levels in arid saline-alkali farmland. Soil samples were collected on the second (S1), fifth (S2), eighth (S3), eleventh (S4), and fourteenth (S5) days after a rainfall event. The findings showed that a significant decrease in soil water content, contrasting with an increase in soil salt content throughout the soil layers post-rainfall. As water content decreased, the spatial variability of soil water initially increased from S1-3 and then decreased, while the spatial variability of soil salt decreased. The spatial distribution between soil water and salt exhibited a high correlation at S3-4 (with relative water content of soil ranging from 0.52 to 0.75) due to their coupling effect. However, soil salt was unevenly leached by rainfall at high water content levels (S1-2) and precipitated at low water content levels (S5), resulting in low spatial variability correlations between soil water and salt. This study elucidated the coupling process of soil water and salt, identifying their spatiotemporal distribution mechanism in dryland agricultural areas.

In our study, we investigated the prognostic significance of hematological markers—NLR (Neutrophil-to-Lymphocyte Ratio), PLR (Platelet-to-Lymphocyte Ratio), and RDW-CV (Red Blood Cell Distribution Width - Coefficient of Variation)—in 117 glioblastoma patients. Data collected from January 2016 to December 2018 included demographics, clinical scores, and treatment regimens. Unlike previous research, which often examined these markers solely before surgery, our unique approach analyzed them at multiple stages: preoperative, postoperative, and before adjuvant therapies. We correlated these markers with overall survival (OS) and progression-free survival (PFS) using statistical tools, including ANOVA, Cox regression, and Kaplan-Meier survival analyses, employing SPSS version 29.0. Our findings revealed notable variations in NLR, PLR, and RDW-CV across different treatment stages. NLR and PLR decreased after surgery, with some stabilization post-STUPP phase (NLR: p=0.007, η2p=0.06; PLR: p=0.001, η2p=0.23), while RDW-CV increased post-surgery and during subsequent treatments (RDW-CV: p < 0.001, η2p=0.67). Importantly, we observed significant differences between the preoperative phase and other treatment phases. Additionally, higher NLR and RDW-CV at the second-line treatment and disease progression were associated with an increased risk of death (NLR at 2nd line: HR=1.03, p=0.029; RDW-CV at progression: HR=1.14, p=0.004). We proposed specific marker cutoffs that demonstrated significant associations with survival outcomes when applied to Kaplan-Meier survival curves (NLR at 2nd line < 5: p<0.017; RDW-CV at progression < 15: p=0.007). Elevated NLR and RDW-CV at later treatment stages correlated with poorer OS and PFS. No significant preoperative differences were detected. These biomarkers may serve as non-invasive tools for glioblastoma management.

Recent interest in preventing the development of osteoporosis has focused on the regulation of redox homeostasis. However, the action of lycopene (LYC), a strong natural antioxidant com-pound, on osteoporotic bone loss remains largely unknown. Here, we show that the administra-tion of LYC to OVX rats reduces body weight gain, improves lipid metabolism and preserves bone quality. In addition, LYC treatment inhibits ROS overgeneration in serum and bone marrow in OVX rats, and in BMSCs upon H2O2 stimulation, leading to inhibiting adipogenesis and pro-moting osteogenesis during bone remodeling. Mechanically, LYC may improve bone quality via an increase in the expressions of FoxO1 and Runx2, and a decrease in the expressions of PPARγ and C/EBPα in OVX rats and BMSCs. Collectively, these findings suggest that LYC attenuates osteoporotic bone loss through promoting osteogenesis and inhibiting adipogenesis via regula-tion of the FoxO1/PPARγ pathway driven by oxidative stress, presenting a novel strategy for os-teoporosis management.

A review of literature, supporting the view on the psychophysical origins of some acceptance effects of cyber-physical systems (CPSs), is presented and discussed in the paper. Psychological effects like the reactance to a robot or the uncanny valley phenomenon suggest that CPSs are perceived as a special type of ‘being’, a phylogenetically emergent category, or a novel ontology in a philosophical sense, culturally developing as a function of the social brain. Justification of this view is provided by the psychophysically-relevant human responses to technologically and socially ambiguous stimuli on the one hand, and the probabilistic independence of the proposed ontology to similar ontologies, subjectively experienced by the human, on the other. The results of the presented in the paper analysis demonstrate the feasibility of the proposed hypothesis of ontological ‘near independence’ of the distribution of the probabilistic psychophysical processes in response to the cyber-physical stimulus as a socially evolving phenomenon.

Este estudio empleó el algoritmo MaxEnt para evaluar la distribución potencial de la serpiente de cascabel de cola larga, Crotalus stejnegeri , una especie amenazada endémica de México. Los resultados demuestran una buena fiabilidad del modelo, alcanzando un AUC de 0,879. Destacan una distribución potencial concentrada en las estribaciones de la Sierra Madre Occidental, principalmente en el centro-sur de Sinaloa, suroeste de Durango y norte de Nayarit, caracterizada por bosque seco tropical. La temperatura durante el trimestre más seco y la estacionalidad de la precipitación se identifican como factores determinantes, mientras que la baja idoneidad en Chihuahua sugiere una afinidad tropical de la especie. La variabilidad en las precipitaciones durante el trimestre más frío enfatiza la importancia de ampliar el número de registros de ocurrencia de la especie mediante una mayor exploración de campo dentro de su área de distribución. Estos hallazgos proporcionan información valiosa para los esfuerzos de conservación y subrayan áreas clave para futuras investigaciones.

The medium-voltage DC (MVDC) technology has gained increasing interests in recent years. Power electronics devices dominate those grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system level control model of such a modern MVDC distribution grid. Aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/- 10 kV MVDC grid case study, including Solid State Transformer (SST) and Modular Multilevel Converter (MMC) based Active Front End (AFE). Additional aim of this paper is to supply an educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.

The PAX8/PPARγ rearrangement, producing the PAX8–PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble docking-based compound screening. Specifically, we investigated which pocket structure should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand binding pocket on the PPARγ side of the PPFP is common to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to PPFP for 97 drug or drug-like compounds with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ, registered in the DrugBank database. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics-Poisson–Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

The mechanism of immune activation and de-activation is very important. Host should trigger effective immunity against pathogen infection. And, host cannot trigger immunity against self-antigens that will lead to detrimental autoimmune disorders. Spleen and liver have opposite roles in mediating immune responses. Spleen is the organ involving in immune activation, and liver is the organ involving in immune de-activation. Sympathetic nerve system and parasympathetic nerve system have opposite roles in mediating immune responses. Sympathetic nerve system is involving in pro-inflammatory, and parasympathetic nerve system is involving in anti-inflammatory. ACTH and glucocorticosteroid have opposite roles in mediating immune responses. ACTH is involving in activating adaptive immunity with inhibiting innate immunity, and glucocorticosteroid is involving in activating innate immunity with inhibiting adaptive immunity. The molecular chaperons, heat shock proteins, which are induced by fever, have the vital roles in immune activation. On the contrary, IgD B cells and gamma-delta T cells are causing immune de-activation via the mechanism of clonal anergy. These mechanism can help us the know the work of immunological pathways better for better control of infectious diseases and autoimmune disorders.

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal-injection-based topology identification algorithm is favored for its speed and efficiency. This study proposes a new signal-injection-based topology identification algorithm aimed at solving the issues of reduced completeness and correctness of identification caused by missing feature signal records (FSRs). By analyzing the correlations between FSRs of different positions, the algorithm employs vertical and horizontal completion methods to effectively address missing data, combined with inclusion detection algorithms, to identify the topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the algorithm not only significantly improves the completeness and correctness of topology identification for LVDNs but can also increase the number of devices in operation by evaluating the status of topological equipment, further enhancing the topology identification performance.

The digital transformation of critical infrastructures, such as energy or water distribution systems, is essential for their smart management. Faster issue identification and smoother services enable better adaptation to consumers' evolving demands. However, these large-scale infrastructures are often outdated. Their digital transformation is crucial to enable them supporting societies. This process must be carefully planned, providing guidance that ensures that the data they rely on is dependable and that the system remains fully operational during the transition. This paper presents a formal model that supports reliable data acquisition in legacy critical infrastructures, facilitating their evolution towards a data-driven smart system. Our model provides the foundation for a flexible transformation process while generating dependable data for system management. We demonstrate the model's applicability in a use case within the water distribution domain and discuss its benefits.

This study aims to examine the non-volatile Particle Number (PN) emissions of a retrofitted Heavy-Duty Spark-Ignition (HD-SI) engine powered by liquefied petroleum gas (LPG) under both steady-state and transient conditions. The engine was tested under seven steady-state operating points to investigate the PN behavior and Particle Size Distribution (PSD) upstream and down-stream of the Three-Way Catalyst (TWC). This analysis intends to assess the impact of including particles with diameters ranging from 10 nm to 23 nm in the total particle count, a consideration for future regulations. The study employed the World Harmonized Transient Cycle (WHTC) for transient conditions to encompass the same engine working region used in the steady-state analysis. A Dekati FPS-4000 diluted the exhaust sample to measure the PSD and PN for particle diameters between 5.6 nm and 560 nm using the TSI-Engine Exhaust Particle Sizer (EEPS) 3090. Findings indicate that PN levels tend to increase downstream of the TWC under steady-state conditions in operating points with low exhaust gas temperatures and flows (equal to or less than 500 °C and 120 kg/h). Furthermore, the inclusion of particles with diameters between 10 nm and 23 nm leads to an increase in PN emissions by 17.70% to 40.84% under steady conditions and by an average of 40.06% under transient conditions, compared to measurements that only consider particles larger than 23 nm. Notably, in transient conditions, most PN emissions occur during the final 600 seconds of the cycle, linked to the most intense phase of the WHTC.

{This study investigates the coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence criteria based on real biological group experiments, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled force Cucker-Smale model with informed leaders. The inclusion of the informed leader aspect distinctively characterizes the dynamics of leaders and followers, enabling a more accurate representation of biological group behavior. The paper then proposes the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. By employing time slicing and rolling time windows, the study methodically analyses group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network's superior performance. The Kolmogorov-Smirnov test indicates that the FLH network follows a power-law distribution, highlighting the evolutionary intelligence of biological groups. This research offers new perspectives on the evolution of biological groups, contributing to our understanding of both natural and artificial systems, such as animal migration and autonomous drone operations.

This paper shows that the empirical distribution of cross-sectional analyst coverage in China's stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the unique distribution parameter (i.e., mean) is directly related to the amount of market-wide information. Average analyst coverage exhibits a significant negative predictive power for stock-market uncertainty, highlighting the role of security analysts in diminishing the total uncertainty. The exponential law can be derived from the maximum entropy principle (MEP). When analysts, who are constrained by average ability in generating information (i.e., the first-order moment), strive to maximize the amount of market-wide information, this objective yields the exponential distribution. Contrary to the conventional wisdom that security analysts specialize in the generation of firm-specific information, empirical findings suggest that analysts primarily produce market-wide information for 25 countries. Nevertheless, it remains unclear why cross-sectional analyst coverage reflects market-wide information, this paper provides an entropy-based explanation.

The two-parameter distribution introduced by Kumaraswamy (1980) is a very flexible alternative to the Beta distribution with the same (0,1) support. Originally proposed in the field of hydrology, it has subsequently received a good deal of positive attention in both the theoretical and applied statistics literatures. Interestingly, the problem of testing formally for the appropriateness of the Kumaraswamy distribution appears to have received little or no attention to date. To fill this gap, in this paper we apply the “biased transformation” methodology proposed by Raschke (2009) to several standard goodness-of-fit tests based on the empirical distribution function. A simulation study reveals that these (modified) tests perform well in the context of the Kumaraswamy distribution, in terms of both low size distortion, and respectable power. In particular, the “biased transformation” Anderson-Darling test dominates the other tests that are considered.

Contemporary financial systems, despite their inherent complexity and resilience, face significant challenges related to transparency, efficiency, and security. Notable deficiencies in transparency and emerging security vulnerabilities underscore the pressing need for innovative solutions in current financial practices. Hence, this paper introduces a financial distribution system based on a consortium blockchain. Given organizations’ preference for keeping sensitive data private, the partially decentralized nature of consortium blockchains plays a pivotal role in the proposed system’s architecture. Moreover, within this system, the proposed smart contract serves a dual role. It functions not only as a transactional tool but also as a specialized solution designed to improve transactional procedures. Its multifaceted capabilities include ensuring data accuracy, facilitating swift execution, and ensuring adherence to regulatory requirements within the dynamic financial distribution landscape. Through rigorous testing and empirical analysis utilizing the Hyperledger Besu platform, it has been observed that the performance of the proposed system surpasses traditional blockchain frameworks. The results unequivocally demonstrate the proposed system’s proficiency in handling high-frequency financial transactions with minimal computational burden.

The current paper provides a cutting-edge information data length- theoretic approach to human emotions. This is a revolutionary breakthrough that unifies information data length and the dynamics of emotions. The significant impact of both time and its mean value associate on both the upper and lower bounds of the obtained information data length is observed and noted. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

Quantum cryptography revolutionizes secure information transfer, providing defense against both quantum and classical computational attacks. The primary challenge in extending the reach of quantum communication comes from the exponential decay of signals over long distances. Addressing this challenge, we have developed Quantum-protected Control-based Key Distribution (QCKD), overcoming transmission range limitations by monitoring signal leakages and rendering leaked quantum states significantly non-orthogonal. This paper reports on the experiments with the QCKD, conducted over a 1,707 km fiber line. We demonstrate advancements in critical components of the protocol, showing its remarkable scalability and adaptability for extended distances and multi-user configurations.

In China's mountainous coastal terrain, the storm badly damaged low-rise buildings. At present, it is not clear how the relative position of buildings and mountains affects the surface of a low-rise building’s fluctuating coefficient. The study compared these results with the wind pressure distribution without the surrounding environment. The primary objective of this study is to examine the fluctuating coefficient variation rule based on the spacing between hillsides and buildings under 0° wind angle. Additionally, the study analyzes the fluctuating wind pressure coefficient across wind angles between 0°~90°. The distribution of wind pressure in different common hillside landforms was examined through a wind tunnel experiment, which also compared with the distribution in an open environment. The study examined the fluctuating coefficient as the distance between the building and the hillside changed, specifically for wind blowing at a 0° angle. Next, an analysis is conducted on the changing pattern of the fluctuating coefficient for low-rise buildings ranging from a wind angle of 0° to 90°. Simultaneously, the investigation examined the power spectrum and wind pressure’s probability distribution, while considering the proximity of the building to an adjacent hill. The findings indicated that the changing wind pressure factor on the surface of the structure differed based on the relative location of the hillside and the building. The roof's wind pressure coefficient fluctuated and gradually increased until it reached its peak, unaffected by the surroundings. The low-frequency energy decreased in windward eaves, while the high-frequency energy progressively rose as the distance between the hillside and the building increased. The wind pressure on the leeward side exhibited Gaussian characteristics in its probability distribution.

Our aim was to determine the secondary antibody deficiency profiles of patients in a mesoregion of São Paulo state, Brazil, focusing on infectious diseases. Demographic characteristics, clinical and laboratory data were obtained from electronic files; infections were classified as organ-specific and graded as mild, moderate, life-threatening, and fatal. Non-Hodgkin lymphoma (NHL) accounted for 30% of patients, nephrotic syndrome (NS) 25%; chronic lymphocyte leukemia 20%, and multiple myeloma 15%. Patients with NS were younger than those in other groups, and hypo-γ-globulinemia was detected in 94.1%, IgG <400 mg/dL in 60.0%, IgA <40 mg/dL in 55.0%, and CD19 <20 cells/mm³ in 30.0%. One hundred and one infections were found; 82.1% were classified as mild or moderate, 7.9% as life-threatening, and 1.9% as fatal. Respiratory tract infections were more prevalent (41.5%), and pneumonia accounted for 19.8%. Lower levels of infections were found in patients with NS compared with NHL (P=0.0001). Most patients progressed to hypo-γ-globulinemia and secondary antibody deficiency after treatment with immunosuppressants, and mild and moderate infections were predominant. These therapies are increasing in patients with different diseases, therefore monitoring hypo-γ-globulinemia and infections may help to identify patients at high risk for severe complications, antibiotic prophylaxis or treatment, and immunoglobulin replacement

Currently, there is no widely accepted consensus regarding a consistent thermodynamic framework within the special relativity paradigm. However, by postulating that the inverse temperature 4-vector, denoted as β, is future-directed and timelike, intriguing insights emerge. Specifically, it is demonstrated that the q-dependent Tsallis distribution can be conceptualized as a de-Sitterian deformation of the relativistic Maxwell-Jüttner distribution. In this context, the curvature of the de Sitter space-time is characterized by Λ/3, where Λ represents the cosmological constant within the ΛCDM standard model for Cosmology. For a simple gas composed of particles with proper mass m, and within the framework of quantum statistical de Sitterian considerations, the Tsallis parameter q exhibits a dependence on the cosmological constant given by q=1+ℓcΛ/n where ℓc=ℏ/mc is the Compton length of the particle and n is a positive numerical factor. This formulation establishes a novel connection between the Tsallis distribution, quantum statistics, and the cosmological constant, shedding light on the intricate interplay between relativistic thermodynamics and fundamental cosmological parameters.

Nature-Based Solutions (NBSs) can help to achieve multiple Water-Ecosystem-Food Nexus Do-main Objectives & Nexus Resilience Qualities, facilitating the transition to more resilient agrifood systems and providing an evidence base for a broader Nexus policy dialogue. This paper de-scribes the stepwise methodology developed in the EU-funded LENSES project to carry out a comprehensive analysis of NBSs in 6 pilot areas in 5 Mediterranean countries, and presents the results of NBSs implementation in 4 pilot areas, highlighting obstacles and opportunities. The methodology includes the development of an analytical evaluation framework and of a com-prehensive catalogue of Nexus-related NBSs, whose suitability needs to be assessed at the local level to achieve better use of protected/natural ecosystems, increase the sustainability and mul-tifunctionality of managed ecosystems and design and manage novel ecosystems. Subsequently, in a collective learning process which supported the operationalisation of the WEF Nexus, NBSs were identified that address specific contextual vulnerabilities, improve water distribution and enhance food security, while preserving ecosystems and supporting adaptation to climate change. The added value of the proposed methodology lies in the multi-stakeholder participatory approach to gain in-depth knowledge of local agri-food systems, including their main WEF-related challenges, and to facilitate overcoming barriers to NBS implementation.

This article describes a novel topical composition constituted by gamma-amino butyric acid (GABA) and microbial chondroitin sulfate. This composition, formulated by the author and manufactured in a certified facility in the United States of America, exhibits potential for non-invasive intervention in anxiety management, sleep quality enhancement, and promotion of brain-related functions like meditation and nonlocal consciousness. The core component comprises GABA, a key inhibitory neurotransmitter within the central nervous system, complexed with a suitable carrier molecule, microbial chondroitin sulfate. This complexation aims to enhance transdermal absorption and optimize delivery of GABA to target tissues. The proposed mechanism of action involves modulation of GABAergic signal transduction pathways. Application of the topical composition allows GABA penetration into the skin and potentially deeper tissues, where it interacts with GABA receptors. This interaction leads to mental calmness and relaxation as assessed by electroencephalography. The influence of GABAergic signaling on brain circuits demonstrated in this article, when associated with meditation and nonlocal consciousness, suggests potential for the composition to support these practices. The advantage of such an approach consists in avoiding the potential systemic side effects associated with oral or injectable administration of GABA, anxiolytics or hypnotics. Furthermore, the combination of GABA and microbial chondroitin sulfate may offer synergistic benefits, potentially enhancing the overall biological effects. In conclusion, this article presents a novel topical approach for managing anxiety, enhancing sleep quality, and potentially facilitating meditation and nonlocal consciousness through modulation of GABAergic signaling pathways. While further research is necessary, the potential for a non-invasive and potentially side-effect-free intervention in these areas is promising.

A study was carried out in a Waste Sorting Plant (WSP) located in France and treating dry recyclable household waste (DRHW) as well as dry recyclable commercial and industrial waste (DRCIW). The aim was to investigate the exposure to bioaerosol and dust. The aim of the study was to assess the extent to which the measurement of concentration, species composition and particle size distribution can contribute to a better assessment of the biological risks associated with exposure. Both stationary and personal bioaerosol and dust samples were collect in the (WSP). The results confirm that waste and waste sorting activities are sources of airborne fungi. Indeed, ambient concentrations ranged from 7.3×103 to 8.5×105 CFU/m3 for culturable moulds and up to up to 4 mg/m3 for dust. Personal exposure to inhalable dust was found up to 3 mg/m3 for dust and ranged from 8.6×103 to 1.5×106 CFU/m3 for fungi. Airborne fungal communities were found to be dominated by the Penicillium genera in both bioaerosols and settled dust samples, followed by the Aspergillus, Cladosporium, Wallemia, Mucor, and Rhizopus genera. Fungi were carried by particles of aerodynamic diameters mainly between around 2.0 and 10.0 µm. The findings dealing with size distribution and biodiversity of bioaerosols suggest that employees are exposed to complex bioaerosols during their work and helps to make a finer diagnosis of the risks involved, which is often difficult in the absence of any OEL for bioaerosols in general.

Mercury contamination in the Amazon is a significant public health concern arising from both natural sources and intensive mining activities in the region. This metal is used to separate Au from sediments, forming an amalgam that facilitates the extraction of precious metals. Accordingly, this study aimed to address the following question: What is the impact of mining on mercury contamination in the animal and human populations of the Amazon? The overall objective of this study was to conduct a systematic review of the existing literature to identify gaps in geographic coverage and assess the impact of Hg contamination on human and animal populations in the Amazon region. Our hypothesis was that higher mercury concentrations in tissue samples would be found closer to the mining areas. We employed the PECO and PRISMA-ScR protocols to collect articles published between 2017 and 2023, selected based on criteria representing projected points on a map within the biogeographic boundaries of the Amazon. Mercury concentrations increase with trophic levels, reaching high values of 3.7 µg/g in the muscles of predatory fish and 34.9 µg/g in human hair. The average level of mercury in humans exceeds 6 µg/g, surpassing tolerance levels. Although mining regions show high concentrations of Hg, the highest incidence was observed among populations with fish-based diets. Research on fish is crucial for assessing the risk to human health, given that fish are the main source of protein in the region.

The quiet-time solar wind electrons feature non-thermal characteristics when viewed from the perspective of their velocity distribution functions. They typically have an appearance of being composed of a denser thermal “core” population plus a tenuous energetic “halo” population. At first, such a feature was empirically fitted with the Kappa velocity space distribution function, but ever since the ground-breaking work by Tsallis, the space physics community has embraced the potential implication of the Kappa distribution as reflecting the non-extensive nature of the space plasma. From the viewpoint of microscopic plasma theory, the formation of non-thermal electron velocity distribution function can be interpreted in terms of the plasma being in a state of turbulent quasi equilibrium. Such a finding brings forth the possible existence of a profound inter-relationship between the non-extensive statistical state and the turbulent quasi-equilibrium state. The present paper further develops the idea of solar wind electrons being in the turbulent equilibrium, but unlike the previous model, which involves the electrostatic turbulence near the plasma oscillation frequency (i.e., Langmuir turbulence), the present paper considers the impact of transverse electromagnetic turbulence, particularly, the turbulence in the whistler-mode frequency range. It is found that the coupling of spontaneously-emitted thermal fluctuations and the background turbulence leads to the formation of non-thermal electron velocity distribution function of the type observed in the solar wind during quiet times. This demonstrates that the whistler-range turbulence represents an alternative mechanism for producing the Kappa-like non-thermal distribution, especially close to the Sun and in the near-Earth space environment.

We describe the development and validation of a HPLC-MS/MS method to assess the pharmacokinetics and tumour distribution of fenretinide, a synthetic retinoid chemically related to all-trans-retinoic acid, after administration of a novel oral nanoformulation of fenretinide, called bionanofenretinide (BNF). BNF was developed to overcome the major limitation of fenretinide: its poor aqueous solubility and bioavailability due to its hydrophobic nature. The method proved to be reproducible, precise, and highly accurate for the measurement of the drug and the mains metabolites. The lower limit of quantification resulted 1 ng/mL. The curve range of 1-500 ng/mL and 50-2000 ng/mL, for plasma and tumor homogenate respectively, was appropriate for the analysis, as demonstrated by the accuracy between 96.8% and 102.4% for plasma and 96.6-102.3% for tumor. The inter-days precision and accuracy determined on quality controls at 3 different levels were in ranges of 6.9%-7.5% and 99.3-101.0% and 0.96-1.91% and 102.3-105.8% for plasma and tumour, respectively.

Tourism is increasingly becoming one of the most important components of the economy. Coastal tourism is of particular importance in our country, this is because Albania has a very favorable geographical position. The tourist destination chosen for study in this paper is Durrës. It is analyzed as a complex dynamic system that evolves with a series of techniques and methods drawn from the area of network theory. The main objective of this paper is the comparison of technological and socio-economic networks, through different methods and techniques. The methodology of this research includes different data collection methods for both networks and their analysis. 504 nodes were taken into consideration. A significant part of the research is based on network theory, using algorithms and network analysis techniques to reveal their static and dynamic characteristics. Through this paper we show that the internet network and the socio-economic network of Durrës are similar in some aspects and have a great impact on the development of the tourist destination. The analysis of these two networks provides a deeper picture of the relations between the actors of the destination and helps to improve the tourism strategies and the economic development of the area. Thus, the research is an attempt to better understand the impact of technological and socio-economic networks in the development of the tourist destination of Durrës and uses different ways of network analysis to discover the similarities and differences between them.

Sustainable continuous cover forestry is defined and analyzed in several ways. The differential equation representing growth of the basal areas of individual trees, motivated by fundamental biological production theory by Lohmander (2017a), is analyzed and extended in different directions. From the solution of the differential equation, the basal area and the tree diameter are obtained as explicit functions of time. The diameter is a strictly increasing function of time. In the absence of competitors, the diameter increment is shown to be a strictly decreasing function of time. Hence, the diameter increment can also be interpreted as a strictly decreasing function of the diameter. Alternative forms of adjustment of the differential equation, with consideration of competition, are defined. If the competition is strong, with large trees in the vicinity of a particular tree, then the basal area increment, and the diameter increment, are reduced. The growth of a large tree is less sensitive than the growth of a small tree, to competition from other trees. Under strong competition, the basal area increment, and the diameter increment, are strictly concave functions of the size of the tree. The unique maximum of the diameter increment occurs at a higher diameter, if the competition increases. In dynamic equilibrium, the tree size frequency distribution is stationary. If natural tree mortality can be avoided via the harvest strategy, the tree size frequency distribution is a function of the size and competition dependent growth function, and the harvest strategy. Empirical tree size frequency data are used to simultaneously estimate parameters of a size and competition dependent growth function and the applied harvest strategy, via nonlinear optimization. The properties of the estimated growth function are consistent with the corresponding properties of the production theoretically motivated hypothetical function, and the properties of the estimated harvest strategy confirm the corresponding hypotheses. The R2 of the nonlinear regression exceeds 0.97. With access to an empirically estimated equilibrium tree size distribution, it is possible to: 1. Estimate size frequency relevant parameters of tree size and competition dependent growth functions for individual trees. 2. Estimate the applied harvest strategy. 3. Explain and reproduce the empirically estimated tree size equilibrium distribution.

Distribution System Operators (DSOs) are adapting to changing energy demands and the shift toward renewable and decentralized energy. Circularity is increasingly relevant for DSOs as their distribution systems age and need replacing. Taking circular action, however, is generally regarded as challenging as the current circular economy (CE) landscape is highly fragmented and rarely touches the implementation level. A longitudinal case study at Dutch DSO Liander on the circu-larity of distribution transformers was used to understand empirical success factors for taking circular action and to generate prescriptive knowledge on the operationalization of circularity. Six design propositions for circular action were formulated that suggest to 1) initiate small-scale cir-cularity experiments, 2) involve technical and strategic experts, 3) synergize circularity with more urgent, primary goals 4) translate circular initiatives bottom-up and top-down, 5) collaborate with other DSOs, and 6) create multidisciplinary teams. At DSO Liander, selecting the right interventions depending on the changing context contributed to operationalizing circularity for distribution transformers and achieving circularity targets. The generalizable design propositions could assist other DSOs in optimizing their strategy toward circular action.

Motivated by empirical observations, we propose a possible extension of Gibrat's law. By applying it into the random growth theory of income distribution (Gabaix, 2009), we find that the income distribution is described by a generalized Pareto-type distribution (GPD) with three parameters. We observe that there is a parameter /elta in the GPD that plays a key role in determining the shape of income distribution. By using the Kolmogorov-Smirnov test, we empirically show that, for typical market-economy countries, /elta is close to 0 significantly, such that the income distribution is characterized by a two-class pattern in which the bottom 90% of the population is approximated by an exponential distribution and the richest 1%~3% is approximated by an asymptotic power law. However, we empirically find that, for China both in planned economy period and in early stages of market reformation (from 1978 to 1990), /elta is significantly deviated from 0, such that the bottom of the population no longer conforms to an exponential distribution.

This study aims to reveal the revolutionary Gamma Distribution Manifold (GDM) info-geometric analysis combined with potential Gamma distribution (GD) applications to advance Satellite Earth Observations (SEOs). An exposition of the GDM Fisher Information Matrix (FIM) was undertaken, together with the analytic investigation of the existence of FIM’s inverse. The derivation of GDM’s Geodesic Equations (GEs) is undertaken. The solution of the obtained Geodesic Equations is optimally complicated, and it provides more extensions to numerous open problems. Notably, the current paper provides a first-time unification between three multi-interdisciplinary fields, namely, information geometry theory, probability theory, and Satellite earth observations. Looking at the other side of the coin, the provided revolutionary approach in this current work reports the significant impact of info-geometric analysis on the probabilistic dynamics as well as the influential role of information geometry on satellite and space industry. This poses numerous open issues that initiates a new form of contemporary unification between these inter-disciplinaries to discover another visionary thinking out of traditional statistical frames. In other words, refraining ourselves from using classical statistical frames of thinking and delving into a new info-geometric vision of statistical distributions as well as examining real life applications of information geometry to advance satellite earth observations. The curtain is drawn on this paper by providing several challenging open problems in combination with a concluding remark and the next phase of research.

In this work, we studied light-output properties, efficiency function as well as PSD capability of p-Terphenyl scintillator. The selected solid cylindrical scintillation detector has a size of (45 x 45) mm with PSD properties. Recently presented studies of light-output functions have been measured only for low neutron energies. Our motivation has been to determine more accurate the light output function for p-Terphenyl scintillator over a wider neutron energy range.

The ability of recombinant, SARS-CoV-2 Spike (S) protein to modulate the production of two COVID-19 relevant, pro-inflammatory cytokines (IL-6 and IFN-γ) in PBMC cultures of healthy, pre-Covid-19 subjects was investigated. We observed that cytokine production was largely and diversely modulated by the S protein depending on antigen or mitogen stimulation, as well as on the protein source, insect (S-in) or human (S-hu) cells. While both proteins co-stimulated cytokine production by polyclonally CD3-activated T cells, PBMC activation by the mitogenic lectin Concanavalin A (Con A) was up modulated by S-hu protein and down-modulated by S-in protein. These modulatory effects were likely mediated by the S glycans as demonstrated by direct Con A-S binding experiments and use of yeast mannan as Con A binder. While being ineffective in modulating memory antigenic T cell responses, the S proteins and mannan were able to induce IL-6 production in unstimulated PBMC cultures and upregulate the expression of Mannose Receptor (CD206), a marker of anti-inflammatory M2 macrophage. Our data point to a relevant role of N-glycans, particularly N-mannosidic chains, decorating the S protein in the immunomodulatory effects here reported. These novel biological activities of the S glycan ectodomain may add to the comprehension of Covid-19 pathology and immunity to SARS-CoV-2.

Energy and information are fundamental concepts that play crucial roles in the foundations of science, each contributing to different aspects of our understanding of the natural world. Here we encounter that Fisher' measure is able to link them. We also investigate some interesting relations between Fisher's information, Poisson's distribution, and thermal quantifiers in a grand canonical ensemble. These connections permit one to visualize interesting novel artifacts in statistical mechanics. We clearly see them emerge in discussing the ideal gas. For example, we encounter that the Poisson distribution is intrinsically involved in the physics of the ideal gas, and we are able to give a Fisher-estimation of the difference in informational content between the grand canonical and canonical ensembles.

This study aimed to evaluate the discrepancies between genetic and visual coat color assignment in the Sarcidano Horse and to elucidate potential reasons. Individual DNA from 90 Sarcidano Horses was used for genetic assignment of coat color to explore the correspondence with individual forms containing phenotypical traits. The MC1R exon 1 and ASIP exon 3 have been genotyped and sequenced to obtain a picture of the coat color distribution in this breed. Surprisingly, once we compared the genetic results with the individual forms reporting the phenotypic data for each subject, a certain degree of non-correspondence between the phenotypic and genetic data in relation to coat color emerged. From the genetic analysis, Chestnuts (n = 58) resulted the most common Sarcidano Horse (n = 58), followed by a quite large number of Blacks (n = 28) and a very small number of Bays (n = 4), whereas phenotypic distribution resulted in 38 Chestnuts, 40 Bays, only 2 Blacks, and 10 Grays (without the possibility of recognizing the true color they carried). Chestnut resulted a very representative coat color, while many horses that visually identified as Bays were genetically Blacks. This discrepancy, that could be due to a variety of individual and external factors, including age, time of year, living situation and dietary condition, suggesting the importance of accurate coat color identification to ensure adequate features registration and reliable prediction of offspring’s coat color.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

The article presents the issues related to load sharing in direct current grid and a novel control method which has advantages over known solutions. Unlikely of many similar sounding papers this article shows attempt of creation fully controllable non-isolated system which allows load sharing between permanent magnet alternator equipped with machine side converter (MSC) and dual active bridge (DAB) tied to batteries or supercapacitor. The power sharing is essential feature of parallel connected direct current generators and all types of voltage sources in this way are contributing power to the system. Such system should have possibility of the rapid voltage build up in the case of sudden electrical load changes. To keep the optimal efficiency of the alternator the rotational speed changes according to proper mapping of driving combustion engine. When rotational speed is too low then sudden electrical load can cause significant voltage drop and time needed for voltage build up will vary according to RPM increase. In such situations the undervoltage system can trip the breaker and finally there may be a blackout. To avoid such problem the DC systems which consist of batteries and supercapacitor were introduced. System components include a self-excited synchronous generator (SESG) operating at variable shaft speed as well as batteries and supercapacitors that provide electricity for sudden electrical load changes on the distribution grid. The core of presented system is in power distribution method which consists of programmed controllers structure allowing precise currents distribution. A novelty in the proposed method is the use of a cascaded system of current and DC voltage regulators which allows power distribution precise control. In contrary to previously presented solutions, the proposed system allows fast and accurate control of currents loading parallel connected DC voltage sources for wide range generator speed changes. In presented solution both converters have been equipped with Schottky diodes preventing flow of equalizing currents between closed transistors in the parallel mode of operation. An experimental test-stand of the described system is presented with its theoretical basis and experimental results.

The increasing expansion of the use of renewable distributed generation (RDG) technologies in electrical networks such as solar plants or wind farms and others has changed the nature of the distributed system and transformed it from the traditional system into an efficient system. This system has led to various technical effects in the host networks, with regard to voltage, electrical energy losses, fault levels, stability and electrical energy quality. These effects are not generic to any network; It can be positive, negative or neutral depending on the structure of the studied network, its operational condition and the various technology of the distributed generators. This research aims to quantitatively correct the effects of (RDG) in terms of voltage and electrical energy losses in the host networks. , since efficient distribution systems contain generators of a special nature and often contain bidirectional flux energy, it was necessary to construct a mathematical model of the distribution system hosting RDG units, which is fundamentally different from the traditional model of defining the reference electrical distribution bus bar. In order to achieve the systematic analysis, evaluation criteria and indicators were determined and mathematical formulas for them were completed. Based on the accomplished mathematical basis, an algorithm designed in such a way that it can be applied to any multi-voltage distribution system and to different distributed regenerative generation techniques is proposed. To demonstrate the algorithm's capabilities, it was tested on typical distribution networks using Factory Power DigSilent software, which is somewhat similar to MATLAB but more suitable for large capacity systems.

The performance analysis of the internal/external flows around a high speed vehicle in near-space is fundamental and critical to the configuration and structure design for high speed aircraft. In this paper, the two-dimensional coupled implicit Reynolds Average Navier-Stokes (RANS) solver and RNG k–ε turbulence model is employed to numerically simulate the flow field of an integrated high speed vehicle. The flow field are divided three parts to solve separately and compared with that solved by integrated simulation. The flow field are decomposed to save computer storage and simulation time. The first part is inlet/isolator, the second part is combustion chamber and the third is nozzle. In the computation process, the outlet condition of isolator is endowed to the inlet boundary of combustion chamber and the outlet boundary of combustion chamber is endowed to the inlet boundary of nozzle, then the whole flow field of integrated high speed vehicle is solved. Aerodynamic characteristic of fragment-computation and integration-computation is comparatively analyzed at different angles of attack, the results show that the maximum deviation of lift coefficient obtained by fragment-computation is less than 3% compared with that by integration, the maximum deviation of drag coefficient fragment-computation is less than 5% compared with that by integration, while pitching moment coefficient deviation less than 12%. However, for simulation time-consumption, the time used by fragment-computation reduces 45% compared with integration-computation. Meanwhile, by components force analysis, it can be concluded that the aerodynamic property of inlet/isolator can been obtained separately and hardly effected by other parts.

Everyone should therefore consistently get access to enough and safe water. The operation of the existing distribution network as well as the proper optimization and implementation of the new distribution systems are required for a sufficient and safe water supply. In Sekota town sufficient amount of water was not reached to the beneficiaries due to shortage of water at the source. The primary goal of this study was to evaluate the performance and optimize the existing Sekota water supply distribution system using Bentley Water GEMS.The system's hydraulic modeling is carried out by considering as a continuous supply system, and the evaluation procedure used the extended period simulation approach after calibration was done. Calibration is used to ensure the performance of the model using the observed pressure values. The output of the existing system shows pressure is very high means above the maximum pressure and velocity is very low at peak hour demand. The results of velocity and pressure before optimization have negative effect on the performance of the system. The number of pipes and junctions their velocities and pressures which were in admissible ranges are 15 and 42 pipes for velocities and zero and 59 junctions for pressure before and after optimization were evaluated respectively.

Recently, volatile organic compounds (VOCs) have been shown to act as precursors of secondary organic particles that react with ultraviolet rays in the atmosphere and contribute to photochemical smog, global warming, odor, and human health risks, highlighting the importance of VOC management. In this study, we measured VOC concentrations in various contexts including industrial and residential areas of Bucheon, Korea, through mobile laboratory and proton-transfer-reaction time-of-flight mass spectrometry methods to determine winter VOC concentrations, visualized the data based on spatial information, and used the results to develop an air quality management plan. For this purpose, global information system (GIS)-based air quality data and various environmental variables were comprehensively reviewed to assess spatial and temporal concentrations in three dimensions rather than in tables and graphs. Among VOCs, the levels of toluene, methanol, and n+i-butene were relatively high, with average concentrations of 48.3 ± 67.2, 34.4 ± 102.7, and 32.6 ± 57.7 ppb, respectively, at the end of the working day. The highest concentrations occurred near the Ojeong Industrial Complex. Mobile pollution sources are also a major driver of VOCs, highlighting the necessity of comprehensively reviewing traffic variables such as road level, estimated traffic volume, and average speed when identifying hotspots of air pollution. GIS-based visualization analysis techniques will improve the efficiency of air quality management.

This study investigated the impact of seven-strain probiotic supplementation on the frequency of T helper 17 (Th17) cells, regulatory T (T-reg) cells, Treg/Th17 ratio, and their gene expression in individuals with severe traumatic brain injury (STBI). Probiotics can influence the gut microbiota, modulate the immune response, and prevent or alleviate certain immune diseases. However, it is necessary to clarify whether probiotics can effectively impact Th cells in STBI patient. Despite the great attention to improving the immune system in most diseases, there is currently no accurate mechanism for the use of probiotics in diseases with damage to the head injury, which can be a basis for the use of probiotics. The primary objective of this study is to address this research gap. The present study was a randomized, double-blind, placebo-controlled trial that enrolled 48 STBI patients. They were treated with probiotics or placebo: 4 capsule/day for 14 days. The pre- and post-study evaluation included examining the frequency of Treg, Th17 cells, as well as the expression of associated immunological parameters (Foxp3 and RORγt) and Treg/Th17 ratio in both groups. No significant differences were found between the two groups in terms of subject characteristics (P>0.05). GCS scores at 14 days were significantly higher in the probiotic compared to the placebo group. The probiotic group showed a significant decrease in Th17 cell and RORγt gene expression (P = 0.022 and P< 0.001, respectively), a notable increase in T-reg and FoxP3 gene expression (P= 0.025 and P< 0.001, respectively), and a higher Treg/Th17 ratio compared to the placebo group. Our study provides promising evidence of the beneficial effects of seven-strain probiotic supplementation on Th cells in individuals with STBI.

The paper discusses the design and modeling of a simple and illustrative hardware kit for teaching the basics of quantum cryptography to engineering students. The novel solution differs from those already on the market in that it is focused on familiarising trainees with the physical principles of quantum key distribution, as well as with the basics of mathematical formalism in quantum mechanics. This is achieved by using a minimally sufficient set of optical elements with a simple mathematical description in the Jones formalism. This composition of the kit is targeted mostly at engineering students and does not require advanced training in physics as a prerequisite. The configurable architecture of the hardware educational kit contributes to the deeper involvement of students. By independently changing the modular configuration of the system, students can conduct experiments that were not directly provided by the developers. For instance, students can assess the impact of choosing the basic settings of phase retarders on the course of a man-in-the-middle attack. The proposed amount of mathematical and informational support, as well as ready-made formal models, is sufficient to reasonably put forward hypotheses for experimental verification and interpret the obtained empirical data. At this moment, the hardware kit is being replicated, distributed and successfully applied at universities and companies in the communications industry. The accumulated experience of educational use testifies to the high efficiency of the kit as a tool for basic QKD training for people without prior knowledge of quantum mechanics. A discrete event model simulating the operation of the hardware kit is implemented in the non-commercial modeling software CPN Tools and is openly distributed.

In order to achieve its carbon peak and neutrality targets, a high proportion of distributed power sources are connected to the distribution network in China, which greatly increases the risk of distribution network operation. Aiming at the above problems, this paper proposes an active distribution network risk planning model based on multi-source data from carbon capture and power internet of things, and conducts exceeding testing of distribution network based on the stochastic load flow algorithm with semi-invariant and level expansion, calculates the semi-invariant of each order of node state vectors and branch current vectors, and then utilizes the Gram-Charlier level expansion to obtain the exceeding probability density function and probability distribution function of the node voltages and line powers of the distribution network. The probability density function and probability distribution function of the exceeding are obtained using the Gram-Charlier series expansion. Combined with multi-source data, the active distribution network with integrated energy system considering carbon capture is modeled. According to the risk scenario of the distribution network, the nonconvex constraints in the model are simplified by the second-order cone relaxation, and the optimal planning scheme of the distribution network is solved by combining the gurobi solver with the risk index as the first-level objective and the economic benefit as the second-level objective. The simulation results of a coupled network consisting of a 39-node distribution network and an 11-node transportation network verify the effectiveness of the proposed model.

Abstract: Recent years demonstrate the increased attention to radon from two scientific directions. After neglecting radon as earthquake precursors in 1990-th it becomes again the subject of earthquake-forecast papers discussions due to growing networks of the radon monitoring in different countries, especially, the technologies of real-time radon measurements where the gamma-spectrometers become the leader of interest as the sources of 222Rn identification. The second fast developing direction is including the radon in the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) models as a source of the boundary layer ionization. And here we encounter with second direction which is not connected with the earthquake forecast problems. It is the role of air ionization by radon as a source of the Global Electric Circuit (GEC) modification. In this publication we try to unite all these problems to present more complex view on radon as important element of our environment.

Static voltage stability has become one of the significant risks faced by large-scale renewable energy integration. However, traditional methods for static voltage stability analysis are often overly complex. This paper constructs an equivalent impedance model for renewable energy integrated distribution power systems, proposing a static voltage analysis method for renewable energy integrated distribution power systems based on impedance model index. This method has been verified to be applicable not only to renewable energy single-infeed system but also to multi-infeed system. Furthermore, an analysis is conducted on the influence of the integration capacity, location of renewable energy, and the topology of networks on impedance model index, indicating that a higher impedance model index corresponds to greater static voltage stability margins in the system. Hence, during the planning of renewable energy integration, the plan with the highest impedance model index should be selected. Finally, the accuracy of the analysis methods and conclusions in this paper was validated based on the IEEE 14-node system.

Free-Living Amoebae (FLA) are widely distributed protozoa in both nature and artificial environments such as drinking water. Besides the ability of all FLA to transport various pathogenic microorganisms, certain species, Acanthamoeba spp. or Balamuthia mandrillaris, have intrinsic pathogenic abilities and cause severe cerebral infections. Previous work has shown an enrichment of FLA cysts in biofilm developed in upper levels of Drinking Water Storage Towers (DWST), suggesting that differences in densities of FLA cysts may play a role in their unequal distribution in the water column. To evaluate this hypothesis, a model of a water column was created for this study and used to analyze the vertical distribution of cysts of the FLA Acanthamoeba castellanii, Vermamoeba vermiformis and Balamuthia mandrillaris from 0 to 23 weeks. Interestingly, our data showed that the cysts of both A. castellanii and V. vermiformis were enriched in upper water levels during their aging. However, B. mandrillaris cysts were equally distributed in the water column during the entire study. These results showed that, besides the role of the water level variation in the DWST, some FLA cysts can become less dense during their aging, which contributes to their enrichment in upper water, and thus biofilm levels.

Heat-stressed lactating dairy cattle exhibit unique metabolic symptoms; many of which are undoubtedly involved in heat-induced subfertility. Because of its known systemic effects, we hypothesized that γ-aminobutyric acid (GABA) participates in the regulation of insulin and progesterone during heat stress. Multiparous lactating Holstein cows (n=6) were studied during four experimental periods: 1) thermoneutral (TN; d 1-5), 2) TN + hyperinsulinemic-hypoglycemic clamp (d 6-10), 3) heat stress (HS; d 16-20), and 4) HS + euglycemic clamp (d 21-25). Blood samples were collected once daily via coccygeal venipuncture into heparinized evacuated tubes. Analysis of GABA concentrations from all four treatment periods yielded no differences. In direct comparison to TN concentrations, plasma GABA tended to be decreased during HS (16.57±2.64 vs 13.87±2.28 ng/ml, respectively, P = 0.06). Both milk production and plasma insulin were moderately correlated with plasma GABA (r=0.35, P<0.01; r = -0.32, P<0.01). Plasma progesterone was correlated with plasma GABA concentrations during TN but not HS. These results are the first to indicate that peripheral GABA could be involved in regulating aspects of production and reproduction during heat stress. More research is needed to determine its precise role(s).

Proton exchange membrane (PEM) fuel cells have emerged as a viable alternative energy production source for stationary and transportation applications. Reliable and sustainable fuel cell operation requires effective water management. Membrane water content can vary along the stack during transients which can lead to losses in fuel cell performance. To control these variations a model that predicts the internal humidity dynamics of the stack is needed. In this study, a control-oriented model for predicting membrane water content variation was developed and implemented in MATLAB/Simulink. A lumped parameter model was initially developed and then further discretized into smaller control volumes to track humidity distribution along the stack. To validate the model’s predictions, the predicted results were compared to computer simulation results from GT-Suite. The root mean square error (RMSE) between the model’s prediction and GT-Suite’s simulation results was found to be within 1.5 membrane water content for all cases, demonstrating the model’s capability to capture the variation of membrane water content along the stack. The developed model will be useful for real-time control of membrane water content variation in PEM fuel cells.

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value at Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning from January 1st, 2017, to October 25th, 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.

Abstract: Earth’s Phanerozoic history is marked by about 100 major events (“golden spikes”) indicating the beginnings of new stages in the Geologic Time Scale (GTS). Stage boundaries signify major deterministic events that are relatively well-known and can be correlated worldwide, mostly on the basis of appearances or disappearances of fossil species. The latest stage (Anthropocene) is human-made and currently involves rapid (approximately linear) increase in average atmospheric temperature. The main cause of Earth’s past and current temperature increases is addition of CO2 to the atmosphere. There is continuous exchange of greenhouse gases between atmosphere and oceans, which (per unit of volume) contain 50 to 60 times as much CO2 as the atmosphere. Abundance of new very precise worldwide observations is allowing multifractal modeling of weather and climate during the latest Quaternary stages during which some variables like temperature of the atmosphere and frequency of forest fires can be described by using the Pareto-lognormal frequency distribution model with parameters that are subject to continuous (deterministic) changes, similar to those in the earlier Phanerozoic.

In this work a new method is proposed to tackle down the multiple objective optimization problem of the calculation of cation distribution in spinels structure, through the difference of intensity ratios between experimental and calculated intensities of cation sensitive Bragg planes, by introducing a new factor, Rabs, in Bertaut method. This factor is introduced as the sum of absolute R factors, and it is minimized by varying cation distribution. To test this method aluminate manganese spinel where synthesized though modified Pechini method and characterized though XRD. The results show to be in agreement with literature for the aluminum inversion in tetrahedral sites, which is near to the reported value of 10%. Also, it was noticed that the manganese atoms are present in octahedral sites with a mixed valence state of +2 and +3. Which might be an indicator that oxygen vacancies are present in the structure, so the vacancies value of oxygen is proposed considering the total charge of cations and thus proposing the value of 3.678 mol per formula unit of the spinel, thus having the next stoichiometry considering normalized Al and Mn, MnAl2O3.678. Finally, the last cation distribution proposed was [Mn0.867^(+2) Al0.129^(+3) ][Mn0.034^(+2) Mn0.098^(+3) Al1.871^(+3) ] O3.678 with a Rabs factor value of 0.6466, which considers the cation distribution and oxygen vacancies.

The study delves into the critical examination of the Size of Loss distribution patterns in the context of auto insurance during pre- and post-pandemics, emphasizing their profound influence on insurance pricing and regulatory frameworks. Through a comprehensive analysis of the historical Size of Loss data, insurers and regulators gain essential insights into the probabilities and magnitudes of insurance claims, informing the determination of precise insurance premiums and the management of case reserving. This approach aids in fostering fair competition, ensuring equitable premium rates, and preventing discriminatory pricing practices, thereby promoting a balanced insurance landscape. The research further investigates the transformative impact of the COVID-19 pandemic on these Size of Loss patterns, given the substantial shifts in driving behaviors and risk landscapes. Also, the research contributes to the literature by addressing the need for more studies focusing on the implications of the COVID-19 pandemic on pre- and post-pandemic auto insurance loss patterns, thus offering a holistic perspective encompassing both insurance pricing and regulatory dimensions.

To ensure the security of highly sensitive remote sensing images (RSI) during their transmission, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on encoder-decoder network architecture with Parallel Attention Mechanisms (PAM) by adversarial training was proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN) and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSI and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

With the increase of global human activities, a large number of technogenic magnetic particles (TMPs) generated by industrial activities such as coal combustion, traffic and so on enter sediments on continental shelves. However, there is currently a lack of understanding of the spatiotemporal distribution of TMPs on continental shelves, as well as the sedimentary effects they cause. This review work comprehensively analyzes the source, morphology, composition, and spatiotemporal distribution characteristics of TMPs. Then it elucidates their magnetic contribution to sedimentary environments in continental shelf seas, as well as the influence of heavy metals and active iron. At the same time, the existing problems and future development directions of TMPs on continental shelves are summarized.

Heat dissipation from Li-ion batteries is a potential safety issue for large-scale energy storage applications. Maintaining low and uniform temperature distribution, and low energy consumption of the battery storage is very important. We studied the fluid dynamics and heat transfer phenomena of a single cell, 16-cell modules, battery packs, and cabinet through computer simulations and experimental measurements. The results show that the temperature distribution of single cell is relatively uniform when the cell is facing the air flow. In a module, very high air flow rate and large gap between the cells can reduce the cell temperature. A pack with a good thermally conductive case is very important to ensure good heat dissipation of the battery module. Due to limited space in the cabinet, additional air inlets and outlets can be opened outside of the cabinet to slightly lower the pack temperature. Installing fins outside the cabinet can also slightly reduce the temperature inside the cabinet. Liquid cooling medium, such as water, is much better than the air-cooling medium.

Natural products contribute substantially to anticancer therapy; the plant kingdom provides an important source of molecules. Conofolidine is a novel Aspidosperma-Aspidosperma bisindole alkaloid isolated from the Malayan plant Tabernaemontana corymbosa. Herein, we report conofolidine’s anticancer activity together with that of three other bisindoles - conophylline, leucophyllidine and bipleiophylline against human-derived carcinoma cell lines. Remarkably, conofolidine was able to induce apoptosis (as observed in MDA-MB-468 breast cancer cells) or senescence (as detected in HT-29 colorectal carcinoma cells). Annexin V-FITC/PI, caspase activation and PARP cleavage confirmed the former while positive β-gal staining corroborated the latter. Evident cell cycle perturbations were observed comprising S-phase depletion, accompanied by downregulated CDK2, and cyclins (A2, D1) with p21 upregulation. Confocal imaging of HCT-116 cells revealed induction of aberrant mitotic phenotypes - multi-nucleation, membrane blebbing and DNA-fragmentation. The DNA integrity assessment of HCT-116 and MDA-MB-468 showed irreparable damage identified by increased fluorescent γ-H2AX during the G1 cell cycle phase. Furthermore, γ-H2AX foci were visually validated in HCT-116 and MDA-MB-468 cells using confocal microscopy. Conofolidine increased oxidative stress, preceding apoptosis- and senescence-induction in most carcinoma cell lines as seen by enhanced ROS levels accompanied by NQO1 expression. Collectively, we present conofolidine as a potential anticancer candidate capable of inducing heterogeneous modes of cancer cell death in vitro, encouraging further preclinical evaluation of this natural product.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials, and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 2 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.

We extend Ziv and Lempel's model of finite-state encoders to the realm of lossy compression of individual sequences. In particular, the model of the encoder includes a finite-state reconstruction codebook followed by an information lossless finite-state encoder that compresses the reconstruction codeword with no additional distortion. We first derive two different lower bounds to the compression ratio that depend on the number of states of the lossless encoder. Both bounds are asymptotically achievable by conceptually simple coding schemes. We then show that when the number of states of the lossless encoder is large enough in terms of the reconstruction block-length, the performance can be improved, sometimes significantly so. In particular, the improved performance is achievable using a random-coding ensemble that is universal, not only in terms of the source sequence, but also in terms of the distortion measure.

In ore regions impacted by mining and metal smelting emissions, distinguishing between geogenic anoma-lies and anthropogenic contamination poses a significant challenge. In a study from two areas with different mining histories in the Ore Mountains, Czech Republic, we demonstrate that targeted sampling of topsoils and subsoils respecting local geology and correct soil data treatment respecting soil textural variability effects are indispensable to correctly construct and interpret geochemical maps and identify anthropogenic contam-ination by As, Cu, Pb, and Zn. The list of analysed elements must include both potentially toxic and lithogenic elements, otherwise natural controls of soil composition cannot be deciphered. By using empirical cumulative distribution functions, we found that local backgrounds for As/Fe and Pb/Ti are significantly naturally ele-vated (5.7 to 9.8 times and 2.1 to 2.7 times higher, respectively) compared to the global averages. We con-structed geochemical maps with topsoil minus subsoil concentrations to show the main directions of spreading anthropogenic contamination. The anthropogenic diffuse contamination contribution was calcu-lated (1.12·10-4 and 1.42·10-4 for As/Fe and 16.5·10-4 and 16.2·10-4 for Pb/Ti, respectively), which corresponds to topsoil enrichment by ca. 15 and 14 mg kg-1 for As and ca. 35 and 42 mg kg-1 for Pb in the two study areas. Our study thus provided the first published quantitative estimates of geogenic and anthropogenic contribution to this well-known montane area. The obtained estimates were comparable to the results obtained previously from the local peat archives. The approach we used is efficient in deciphering natural and anthropogenic controls of PTEs in geochemically complicated areas.

In this study, the distribution patterns of southern horse mackerel are examined using commercial fine-scale resolution data. Using landings by size category and VMS data from the Portuguese commercial bottom trawl fishery, which consistently targets horse mackerel, this study provides a comprehensive analysis of horse mackerel age distributions spanning a decade (2010-2020). Importantly, the study addresses potential biases in commercial effort data and establishes the usefulness of commercial bottom-trawl gear as a suitable method for sampling and evaluate southern horse mackerel stock dynamics. Ordered regression models were applied to allow for the modelling of the distribution of multiple age categories and investigate spatio-temporal migrations off the Portuguese coast. Southern horse mackerel shows a widespread age distribution range and stable abundance with indications of seasonal and spatial patterns in the distribution of specific age groups. The insights derived from this research contribute valuable knowledge for understanding the dynamics and distribution patterns of fish populations.

Generalized distributions have been studied a lot recently because of their flexibility and relia-bility in modeling lifetime data. The two-parameter exponentially-modified logistic distribution is a new flexible modified distribution that was introduced recently. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, and log-logistic. In this study, the unknown parameters of the dis-tribution are estimated using the maximum likelihood method. The Grey Wolf Optimization al-gorithm, which is a new meta-heuristic algorithm, is applied in order to solve the nonlinear like-lihood equations of the study model. The performance of the Grey Wolf Optimization method is compared to that of the other meta-heuristic algorithms used in this study, which include the Whale Optimization Algorithm, the Sine Cosine Algorithm, and the Particle Swarm Optimiza-tion Algorithm. The efficiencies of maximum likelihood estimates for all algorithms are com-pared via an extensive Monte-Carlo simulation study. The likelihood estimates for the location α and scale β parameters of the exponentially-modified logistic distribution developed with the Grey Wolf Optimization algorithm are the most efficient among others, according to simulation findings. Four real datasets are analyzed to show the flexibility of this distribution.

Background and Aims: Late diagnosis of sepsis is associated with adverse consequences and high mortality rate. The aim of this study was to evaluate the diagnostic value of hematologic research parameters, that reflect the cell morphology of blood cells, available on the BC 6800 plus automated analyzer (Mindray) for the early detection of sepsis. Materials and Methods: A complete blood count (CBC) was performed by Mindray BC 6800 Plus Analyzer in 327 patients (223 with a confirmed diagnosis of sepsis following sepsis-3 criteria, 104 without sepsis), admitted at the Intensive Care Unit of the Novara’s Hospital (Italy) and in 56 patients with localized infection. Results: In univariate logistic regression, age, Hb, RDW, MO#, NMR, NeuX, NeuY, NeuZ, LymX, MonX, MonY, MonZ were associated with sepsis (p<0.005). In multivariate analysis, only RDW, NeuX, NeuY, NeuZ, MonX and MonZ were found to be independent predictors of sepsis (p<0.005). Morphological research parameters are confirmed to be predictors of sepsis even when analyzing the group with localized infection. Conclusions: In addition to already established biomarkers and basic CBC parameters, new morphological cell parameters can be a valuable aid in the early diagnosis of sepsis at no additional cost.

This article is devoted to overcoming the contradiction between the problem of maximizing the extraction of wind energy as the goal of optimizing wind turbines, and the use of traditional methods based on criteria for the aerodynamic quality of the blades. A modified technique is considered in which the optimization criterion is the directly extracted flow power. It is based on pulse theory methods that use specific power as an optimization criterion. A comparative analysis of the energy efficiency of different types of turbines is carried out, and the effects of blade inversion are considered. A method for calculating multi-rotor turbines based on the concept of uniform power distribution is presented. The compatibility of proprietary and traditional methods is considered by comparing the results of numerical experiments with calculated and experimental data from sources. Optimization computational algorithms generate data from numerical experiments and determine optimal parameter configurations for the turbines under consideration. It is shown that orthogonal Darrieus turbines in high-speed mode provide energy efficiency comparable to collinear turbines, and multi-rotor turbines with power uniformly distributed over the working section are not inferior in energy efficiency to basic turbines, with linear blade velocities halved.

We show succinctly a natural origin of the beautiful Gamma function in light of the WJ distribution, linking to quite distinct fields.

Abstract:Background: Foot ulcers, especially dorsal foot ulcers caused by straps in footwear, can lead to discomfort, pain, and severe complications, particularly in individuals with diabetes and leprosy. While plantar foot pressure measurements have been extensively studied, research on the impact of strap-induced pressure on the dorsal foot aspect remains limited.Research question: The main research question of this prospective study is: Does the implementation of a rolled inner seam strap design (RIS) in flip-flops result in reduced pressure at the toehold area compared to traditional strap designs (Control) in healthy volunteers?Methods: We studied healthy female volunteers, with 10 feet (5 left and 5 right) per arm of the study. Two types of flip-flops, RIS and Control, were compared in a standardized 5-step walk trial on a hardwood floor. Pressure at the toehold was measured using Pressure Sensitive Switches with portable, accurate, ultra-thin pressure sensors. Paired t-tests compared the maximum pressure at the toehold between RIS and Control flip-flops (p < 0.05).Results: The study revealed a statistically significant difference in pressure at the toehold between the RIS and Control flip-flops (p = 0.0014). The mean difference of -40.90 grams (95% CI: -61.22 to -20.58) indicated that the RIS exerted significantly lower pressure than the Control flip flops. The RIS group had a mean pressure of 29.30 grams. The Control group had a mean pressure of 70.20 grams at the toehold area.Significance: The findings of this study have significant clinical implications for individuals at risk of foot ulcers, particularly those with neuropathic conditions such as diabetes and leprosy. The implementation of the RIS design in flip-flops may offer a practical and cost-effective solution to reduce strap-induced pressure and mitigate the risk of strap-related foot ulcers, especially in resource-limited settings where comprehensive foot care is lacking.

Effective management of water distribution systems requires an integrated approach, but none of the available frameworks for one are in broad use in the water supply industry. Frameworks developed include a management standard of the American Water Works Association and Distribution System Optimization, a methodology for physical, hydraulic, and water quality performance assessment. The intelligent systems framework also offers a pathway to integration, but it lacks a definite structure. The voluntary aspect of adopting innovations within the fragmented and unregulated nature of the water utility industry poses a barrier to adoption of such innovations. Another barrier is the uncoordinated arrangements of water research stakeholders with different incentive structures. Intelligent water systems offer a way to incentive utilities to encourage implementation. They can provide a bottom-up approach where utilities see advantages, as opposed to a top-down approach where they are expected to adopt a method without seeing clear benefits. Research to develop new and improved tools is needed, but the research roadmap should prioritize implementation.

Ultra-low radioactive titanium alloy is a promising material for the manufacture of low-background detectors, which are being developed for experiments in astroparticle physics and neutrino astrophysics. Structural titanium is manufactured on an industrial scale from titanium-sponge. The ultra-low-background titanium-sponge can be produced on an industrial scale with a contamination level of less than 1 mBq/kg of uranium and thorium isotopes. The pathways of contaminants during the industrial production of structural titanium have been analyzed. The measurements have been carried out using two methods: inductively coupled plasma spectroscopy (ICP-MS) and gamma-spectroscopy using high-purity germanium detectors (HPGe). It has been shown that the level of contamination with radioactive impurities does not increase during remelting of titanium-sponge and mechanical processing. We examined titanium-alloys samples obtained at different stages of titanium production, namely, an electrode compaction, a vacuum arc remelting with a consumable electrode (VAR), and a cold rolling of titanium-sheets. It has been found out that all studied samples will be a source of uranium and thorium contamination of the final titanium-alloys. It has been established that the only product allowed obtaining ultra-low-background titanium was the VT1-00 which is manufactured without master alloys addition..

Recently, Shehata et al. [37] introduced the r+1Rs(B;C; z) matrix function and established some properties. The aim of this study established to devote and derive certain basic properties including analytic properties, recurrence matrix relations, dierential properties, new integral representations, k- Beta transform, Laplace transform, fractional k-Fourier transform, fractional integral properties, the k-Riemann{Liouville and k-Weyl fractional integral and derivative operators an extended version of r+1Rs;k matrix function. We establish its relationships with other well known special matrix functions which have some particular cases in the context of three parametric Mittag-Leer matrix function, k- Konhauser and k-Laguerre matrix polynomials. Finally, some special cases of the established formulas are also discussed.

The dermatological manifestations of Long Covid (LC) have languished in the shadows of chronic fatigue and brain fog. Yet they are all linked by gut dysbiosis and the cytokine triad of TNF-α, IL-1β, and IL-6. The gut microbiome common not only to LC, psoriasis, AA, and vitiligo but also to neurodegenerative disease has been recently described. This gut microbiome induces an altered tryptophan metabolism linked to autoimmune disease. SARS CoV2 invades enterochromaffin cells rich in ACE2 receptors and curtails absorption of the essential amino acid tryptophan and subsequent synthesis of serotonin and melatonin. This review suggests that an etiologic prebiotic (d-mannose)/probiotic (lactobacilli, bifidobacteria)/postbiotic (butyrate) approach to autoimmune skin disease that improves intestinal barrier integrity and that suppresses the triad of TNF-α, IL-6, and IL-1β may enhance or even eliminate the traditional immunotherapy of targeted monoclonal antibodies, Janus kinase inhibitors, and steroids. Health benefits of this approach extend well beyond suppression of autoimmune skin disease.

To study the oil and gas distribution law in the slope area of the hydrocarbon-bearing basin, the distribution of the effective dominant path of sand bodies transporting oil and gas is a fundamental subject. In this paper, we introduced an effective way to predict such transport paths and applied the method to study the sand bodies of the lower first member of the Shahejie Formation (E3s1L) in the southern area of the Qikou Depression in the Bohai Bay Basin (Qinan). We first define the dominant hydrocarbon transport paths of the sand body as the superimposed area of the contiguously distributed area of the sand body and the paleotectonic ridges at the top of the formation where the sand body is located. Then we define the oil and gas supply area of the oil source fault as the overlapped area of the favorable transport path of oil source faults and the width of the associated fracture zone. Stacking the two areas, we can delineate the distribution of the effective dominant transport paths of the sand bodies. The results from our case study of the Qinan area show that the effective dominant paths of sand bodies transporting oil and gas in the E3s1L Formation in the Qinan area are distributed throughout the region with better development in the west than in the east. The paths in the eastern part extend from north to south, while the paths in the western part extend from northeast to southwest, which is favorable for the convergence of oil and gas generated from the source rocks of the third member of Shahejie Formation (Es3) in the Northeastern main depression of Qikou and the formation of oil and gas accumulations in the l E3s1L Formation. The results are consistent with the fact that oil and gas discovered in the E3s1L Formation in the Qinan region are mainly distributed in the central and western regions, with a small amount in the southeast. Therefore, the suggested method is feasible to predict the effective dominant path of sand bodies transporting oil and gas.

Control over the composition of III-V ternary nanowires grown by the vapor-liquid-solid (VLS) method is essential for bandgap engineering in such nanomaterials and fabrication of functional nanowire heterostructures for a variety of applications. From the fundamental viewpoint, III-V ternary nanowires based on group V intermix (InSbxAs1-x, InPxAs1-x, GaPxAs1-x and many others) present the most difficult case, because the concentrations of highly volatile group V atoms in a catalyst droplet are beyond the detection limit of any characterization technique and therefore principally unknown. Here, we present a model for the vapor-solid distribution of such nanowires, which fully circumvents the uncertainties that remained in the theory so far, and link the nanowire composition to the well-controlled parameters of vapor. The unknown concentrations of group V atoms in the droplet do not enter the distribution, despite of the fact that a growing solid is surrounded by the liquid phase. The model fits quite well the available data on the vapor-solid distributions of VLS InSbxAs1-x, InPxAs1-x and GaPxAs1-x nanowires grown with different catalyst. Even more importantly, it provides a basis for the compositional control of III-V ternary nanowires based on group V intermix, and can be extended over other material systems where two highly volatile elements enter a ternary solid alloy through a liquid phase.

In this paper, we will use the maximum likelihood estimation (MLE) and the Bayes methods to perform estimation procedures for the reliability of stress-strength ℜ=P[X>Y] based on independent adaptive progressive censored samples that were taken from the Chen distributions. An approximate confidence interval of ℜ is constructed using a variety of classical techniques, such as the normal approximation of the MLE, the normal approximation of the log-transformed MLE, and the percentile bootstrap (Boot-p) procedure. The Bayesian estimation of ℜ is obtained based on the balanced loss function, which comes in two versions here, the symmetric balanced squared error (BSE) loss function and the asymmetric balanced linear exponential (BLINEX) loss function. When estimating ℜ using the Bayesian approach, all the unknown parameters of the Chen distributions are assumed to be independently distributed and to have informative gamma priors. Further, a mixture of Gibbs sampling algorithm and Metropolis-Hastings algorithm is used to compute the Bayes estimate of ℜ and the associated highest posterior density credible interval. In the end, simulation research are used to assess the general overall performance of the proposed estimators and a real data set is provided to exemplify the theoretical results.

Transforming Small-Scale LNG Business, Enhancing Efficiency and Resilience in an Evolving Supply Chain In various industries, innovative systems have proven their value due to their efficiency and adaptability. A prime example of this lies in the rapid growth of the liquefied natural gas (LNG) market. LNG, a fuel derived from natural gas, has gained significant traction in recent years. In light of this, we aim to explore optimal strategies for small-scale LNG businesses, with a focus on revolutionizing their supply chain to ensure adaptability and sustainability. Our objective is to enhance efficiency and sturdiness in this evolving sector. By devising innovative methods and implementing cutting-edge technologies, we aim to revolutionize the small-scale LNG market, paving the way for a more optimized and resilient supply chain. Through this transformation, we strive to create a sustainable and adaptable framework that will drive the growth and success of a small-scale LNG businesses.LNG' ability to be transported and stored more cost-effectively in its reduced volume compared to natural gas in its original state is a significant advantage. The distribution network for LNG starts at numerous supply terminals and extends to a diverse range of consumption centers, each with varying tank capacities. In the broader economic context, the establishment of efficient routes for LNG Truck tank plays a critical role.To tackle the challenge of selecting a pickup location and determining optimal destinations with specific unloading volumes for liquefied natural gas (LNG), this research presents an innovative solution utilizing state-of-the-art machine learning techniques. The proposed approach combines the power of reinforcement learning, recurrent neural networks, online learning, and graph theory. By leveraging these cutting-edge techniques, we aim to develop an intelligent system that can effectively determine the most efficient pickup location and sequence of destinations for LNG delivery. This system will take into account various factors such as transportation costs tank capacity, and demand fluctuations. Through the integration of reinforcement learning, the system will continuously learn and improve its decision-making process in real-time. The recurrent neural networks will allow for the analysis of historical aim to model and optimize the complex network of pickup and delivery routes, considering factors such as distance, capacity constraints, and operational efficiencies. With this innovative approach, we strive to revolutionize the selection and planning process for LNG pickup and delivery, ultimately enhancing efficiency and reducing cost within the LNG supply chain.Improving the distribution network offers several financial benefits, including cost reduction, faster delivery times, more affordable distribution, and enhanced efficiency in the utilization of tanker trucks for LNG transportation. By optimizing the distribution network, transit costs associated with LNG transportation can be significantly lowered. This can be achieved through intelligent route planning, taking into account factors such as distance, traffic conditions, and fuel consumption. Minimizing transit costs contributes to overall savings for businesses operating in the LNG market. Faster delivery times are another advantage of an enhanced distribution network. By designing efficient routes and eliminating unnecessary detours or delays, the time taken for LNG to reach its intended destinations can be reduced. This not only improves customer satisfaction but also allows for more frequent deliveries and better coordination of supply and demand. Cheaper distribution is a direct outcome of an optimized distribution network. By strategically planning pickup locations, destinations, and intermediate stops, the costs associated with transferring LNG between terminals and consumption centres can be minimized. This leads to savings in transportation expenses, ultimately benefiting both businesses and end-users. Efficient utilization of tanker trucks is crucial for maximizing profitability in the LNG industry. An improved distribution network enables better allocation of resources, ensuring that tanker trucks are utilized optimally. This includes reducing empty or underutilized trips, maximizing load capacities, and minimizing idle time. Such efficiency improvements translate to cost savings and increased revenue for businesses involved in LNG transportation. Overall, the financial gains from improving the distribution network are substantial. Lower transit costs, faster delivery times, cheaper distribution, and more efficient use of tanker trucks contribute to increased profitability, competitiveness, and sustainability in the LNG market.

The paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons. The probability distribution is derived both by tracing out the environment from a bosonic eigenstate of the union of environment and system of interest (the empirical approach) and by tracing out the environment from the mixed state of the union of environment and system of interest (the Bayesian approach). In the thermodynamic limit, the two coincide and are equal to the multinomial distribution. Furthermore, the paper proposes to identify the physical entropy of the bosonic system with the Shannon entropy of the occupancy numbers, fixing certain contradictions that arise in the classical analysis of thermodynamic entropy. Finally, by leveraging an information-theoretic inequality between the entropy of the multinomial distribution and the entropy of the multivariate hypergeometric distribution, Bayesianism of information theory and empiricism of statistical mechanics are integrated into a common ''infomechanical'' framework.

The Advanced Meteorological Image (AMI) onboard GEOKOMPSAT 2A (GK-2A) enables the retrieval of dust aerosol optical depth (DAOD) from geostationary satellites using infrared (IR) channels. IR observations allow the retrieval of DAOD and the dust layer altitude (24 hour) over surface properties, particularly over deserts. In this study, dust events in northeast Asia from 2020 to 2021 were investigated using five GK-2A thermal IR bands (8.7, 10.5, 11.4, 12.3, and 13.3 μm). For the dust cloud, the observed brightness temperature differences (BTDs) of 10.5 and 12.3 μm were found to be always negative, while the BTD of 8.7 and 10.5 μm varies relying on the dust intensity. Making the best use of optical properties, a physical approach for DAOD lookup tables (LUTs) was developed using IR channels to retrieve the DAOD. This study simulated the characteristics of thermal radiation transfer using the forward model and dust aerosols can explain by BTD (10.5, 12.3 μm), which is an intrinsic characteristic of dust aerosol. The DAOD and property of dust can be gained from the brightness temperature (BT) of 10.5 μm and the BTD of 10.5, 12.3 μm. Additionally, the cumulative distribution function (CDF) was employed to strengthen the continuity of 24-hour DAOD, which was applied to the algorithm by calculating the conversion value coefficient for the DAOD error correction of the IR with daytime visible aerosol optical depth (VAOD) as the true value. The results show that DAOD product can be successfully used during both daytime and nighttime to continuously monitor the flow of yellow dust from the GK-2A satellite in northeast Asia. In particular, the validation result of IR DAOD was similar to that of the active satellite product (CALIPSO/CALIOP), which showed a tendency similar to that for IR DAOD at night.

The integration of renewable energy and hybrid energy storage system (HESS) into electrified railways to build electric railway smart microgrid system (ERSMS) is beneficial for reducing fossil fuel consumption and minimizing energy waste. However, the fluctuations of renewable energy generation and traction load challenge the energy management effectiveness for such a complex system. In this work, an energy management strategy is proposed which firstly decomposes the renewable energy into low-frequency and high-frequency components by an integrated empirical mode decomposition (IEMD). Then, a two-stage energy distribution approach is utilized to appropriately distribute the energy flow among the ERSMS. Finally, the feasibility and effectiveness of the proposed solution are validated through case study.

The article presents a technology for producing nitrogen-containing nanoporous biocarbon with high degree of elemental composition homogeneity, confirmed by electron spectroscopy. The research explores the relationship among the electronic characteristics of supramolecular nanoporous carbon, its porous structure, and the capacitance at its interface with the electrolyte. The obtained biocarbon is characterized by micro and mesopores, as evidenced by the results of studies by the method of nitrogen adsorption/desorption. The specific area of the available porous surface was found to be 1247 m2/g. Comparing this result with the data of small-angle X-ray scattering, we conclude that micropores are open while mesopores remain closed. This material was found to have a saturation magnetization σs of 1.4 A∙m2∙kg-1 and a coercive force Hc of 10 kA/m. The impedance studies of electrochemical cells of supercapacitors made of the experimental biocarbon were carried out. Equivalent electrical circuits of the studied electrochemical processes were constructed and characterized.

The MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov) Florian Goebel telescopes are a system of two Cherenkov telescopes located on the Canary island of La Palma (Spain), at the Roque de Los Muchachos Observatory, and operating in stereo mode since 2009. Their low energy threshold (reaching 15 GeV) allows the study of Active Galactic Nuclei (AGNs) in the very-high-energy gamma ray range with an impressive sensitivity up to the redshift limit of the existing IACT systems. MAGIC has collected many discoveries and comprehensive studies of different kinds of galaxies and their AGNs, also in a multi-wavelength context, expanding the knowledge of our Universe. Here we report the highlights which have been achieved by the MAGIC collaboration since the beginning of the operations.

This study addresses the central question that how can a precise measurement of the gluon Parton Distribution Function (PDF) be attained, thereby enhancing our understanding of the intricate quark-gluon composition within nucleons? Through a dual approach involving Quantum Chromodynamics (QCD) and high-energy particle collisions, the research combines computational tools and experimental techniques, focusing on protons and neutrons. Scattering experiments and advanced detectors are utilized to probe nucleon structure. Theoretical frameworks like QCD equations and the Momentum Sum Rule investigate Parton Distribution Functions at specific momentum fractions and energy scales. The primary goal is to achieve an accurate measurement of the gluon PDF, crucial for unraveling strong force complexities and internal nucleon dynamics. The study's theoretical plots and equations illustrate hypothetical behaviors of gluon distribution, quark distribution, and splitting functions, contributing to a deeper understanding of fundamental particles.

The fungal order Entomophthorales in the Zoopagomycotina includes many fungal pathogens of arthropods. This review explores five genera in the Erynioideae subfamily, namely Erynia, Furia, Pandora, Strongwellsea, and Zoophthora. This is the largest subfamily in the Entomophthorales including 125 described species. The species diversity, global distribution, and host range of this subfamily are summarized, revealing that relatively few taxa are geographically widespread, and few have broad host ranges, contrasting with a large number of species with single reports, from one location and one host species. The insect orders infected by the greatest numbers of species are the Diptera and Hemiptera. Across the subfamily relatively few species have been cultivated in vitro and those that have require more specialized media than any fungi. Given their potential to attack arthropods and in the fungal evolutionary tree, which species might be adopted for biological control purposes or biotechnological innovations are highlighted. Current challenges in the implementation of these species in biotechnology include the limited ability or difficulty in culturing many in vitro, a correlated paucity in genomic resources, and considerations regarding the host ranges of different species.

(1) Background: Until December 2021, French Guiana (FG), located in South America, faced four consecutive COVID-19 epidemic waves. This study sought to analyze the mortality trend of severe COVID-19 patients admitted to the referral ICU of FG. (2) Methods: We conducted a prospective, observational, and non-interventional study in ICU at Cayenne Hospital. We included 383 patients older than 18 admitted with SARS-CoV-2-related pneumonia hospitalized from May 2020 to December 2021. The study covers three periods. Period 1 (Waves 1 and 2, original variant), period 2 (Wave 3, gamma variant), and period 3 (Wave 4, delta variant). (3) Results: The median age was 63 years (52-70). Frailty was diagnosed in 36 patients over 70 (32.4%). Only 4.8% of patients were vaccinated. The median ICU LOS was 10 days (6-19). Hospital mortality was 37.3%. It was 30.9% in period 1, 36.6% in period 2 (p=0.329 vs. period 1), and 47.1% in period 3 (0.015 vs. period 1). In multivariate analysis, independent factors associated with hospital mortality were age more than 40 years (]40-60 years] OR=5.2, 95%CI: 1.4-19.5; (]60-70 years] OR=8.5 95%CI: 2.2-32; (]70+ years] OR=17.9, 95%CI: 4.5-70.9), frailty (OR=5.6, 95%CI: 2.2-17.2), immunosuppression (OR=2.6, 95%CI: 1.05-6.7), and MV use (OR=11, 95%CI: 6.1-19.9). This model had an overall sensitivity of 72%, a specificity of 80.4%, a positive predictive value of 68.7%, and a negative predictive value of 82.8%. (4) Conclusions: The mortality of severe COVID-19 patients in French Amazonia was higher during the delta variant wave. This over-death is explained by the virulence of the responsible SARS-CoV-2 variant and the under-vaccination coverage of the studied population.

There are two basic methods for radial external load distribution calculation on rolling elements in a rolling element bearing: the discrete method and the integral method. Solving the discrete equilibrium quasi-static equation using the Newton-Raphson scheme, more accurate results are derived than those based on the integral method, with small theoretical and computational efforts. The Sjövall’s radial integral factors, as well as some approximations proposed in the literature, for line- and point-contacts, are revisited. Numerical approximations for the Sjövall's radial integrals are proposed. The approximations’ errors with respect to the Sjövall’s radial integral’s numerical integration are shown. The numerical results of the displacement between inner and outer rigid rings, maximum hertzian contact deflection and the most loaded element load are shown for some ball and roller bearings, for radial external load ranging from 0 to 10,000 N, and for positive, negative and zero radial clearance, solving the discrete equilibrium quasi-static equation using the Newton-Raphson method. A total of 16 figures is shown in this work, many of which are made up of sub-figures. A total of 58 figures and sub-figures is shown.

Monometallic (Ni, Co, Cu) and bimetallic (Ni-Co, Ni-Cu) 10-20 wt. % metal containing catalysts supported on fly ash zeolite were prepared by post-synthesis impregnation method. The catalysts were characterized by X-ray powder diffraction, N2 physisorption, XPS and H2-TPR methods. Finely dispersed metal oxides and mixed oxides were detected after the decomposition of impregnating salt on the relevant zeolite support. By reduction intermetallic, NiCo and NiCu phases were identified in the bimetallic catalysts. Hydrogeoxygenation of lignocellulosic biomass-derived levulinic acid to γ-valerolactone (GVL) was studied. Bimetallic, 10 wt. % Ni, 10 wt. % Cu or Co containing fly ash zeolite catalyst showed higher catalytic activity than monometallic ones. Their selectivity to GVL reached 70-85 % at 100 % conversion. The reaction proceeds through formation of 4-hydroxy pentanoic acid, as the only intermediate compound.

Increasing groundwater levels (GWL) may become one of the most serious issues for Odessa City, Ukraine. This study investigated the spatial distribution characteristics and multifractal scaling behaviour of groundwater level/depth fluctuation for a Quaternary aquifer in Odessa City using a geostatistical approach and a Multifractal Detrended Fluctuation Analysis (MF-DFA). These two methods were applied to monthly GWL fluctuation time series from 1970 to 2020 to monitor 72 hydrogeological wells situated in different parts of Odessa City. The spatial distribution of GWL revealed an overall trend of decline and recovery from 1970 to 2020 in the study area, except for most of the southern region, where a persistent recovery of groundwater depth was observed. The MF-DFA results suggest that the dynamics of GWL fluctuations have multifractal characteristics in the Odessa City area. In addition, both long-range correlation and fat-tail probability distribution contribute to multifractality. However, long-range correlations among fluctuations made a major contribution to the observed multifractality of the GWL fluctuations time series. The generalized Hurst exponent shows a wide range of change (0.20 &lt; h(q) &lt; 2.85), indicating the sensitivity of GWL fluctuations to changes in small scale factors and large-scale factors. Regarding the long range correlations of GWL depth, the Hurst exponents (q = 2) demonstrated the positive persistence of groundwater depth recovery in the southern region and the persistence of groundwater depth variation in the other regions of the study area. The dynamic changes in GWL depth in the Odessa City area may be affected by both natural structural and anthropogenic factors.

The migration paths and distribution driving factors of heavy metals in dry and windy area polluted by their production in the North China need a further research. To address this research gap, we collected 675 soil samples, 72 atmospheric deposition samples and 20 water samples around a production area and measured its heavy metal concentrations. Results showed that the Cu, Zn, As and Pb in 0-10 cm soil layer showed a fan-shaped distribution, which was consistent with their atmospheric deposition fluxes. It indicated the distribution patterns of these heavy metals were driven by strong winds in studied area. Although Cr concentrated to the production area in the 0-10 cm soil layer, principal component analysis showed that this migration was through wind as well. The concentration of Cd in the river increased from 0.257 mg/L to 0.460 mg/L along water flowing, and caused the same distribution trend in soil near the river from upstream to downstream. Unlike the above, surface runoff should drive the Cd migration. The concentration of Pb in the river was over threshold of pollution, and also led to an accumulation in the 5-10 cm soil layer. It suggested that the migration of Pb was through both wind and surface runoff. Six studied heavy metals showed different migration behaviors, and specific control strategies for individual heavy metal should be concerned.

We conducted a monocentric observational study aimed at evaluating the pharmaceutical service provided at a Community Pharmacy (C.PH.) during pandemia in our Puglia Region in the period from 29.12.2021 to 12.03.2022 on 550 patients of various ages, sexes, and with concomitant diseases. We collected anamnestic data and any post-vaccination adverse reactions. Interviews with the integrated EQ5 method on evaluating the quality of the service offered and any therapy preference were also performed. As expected, the vaccines were reactogenic after the first dose in the patients with mild-moderate reactions with younger age and female gender as risk factors. Immune-allergic reactions of moderate-severe degree were observed in adult females. In the elderly, the vaccination procedure was well tolerated. Comirnaty showed a favorable O.R.<;1 vs other vaccines. No myocarditis and thromboembolic events in our population were observed. All treatments in May 2023 were correlated with a viremia trend, Paxlovid was prescribed in 3% of the patients in our center and in 1.46% in the Puglia Region with no effects in the hospitalization. It was distributed/dispensed by a novel on behalf of third parties distribution/dispensation (D.P.C.) protocol by the C.PH. The C.PH. resulted in a safe vaccination center providing appropriate patient inclusion during vaccination.

Dental implant fracture is closely connected to the stress buildup surrounding the implant system during static loading. In areas where the cross section of the implant rapidly changes or where the geometry of the implant system has discontinuities, stress concentrations arise. Therefore, the implant's design is crucial in preventing early failure of the implant system, including fracture, screw loosening, and increased leakage, in addition to reducing stresses at the implant-abutment interface. In the current work, three-dimensional (3D) models of mechanically connected Ti6Al4V implant systems in various dimensions were constructed. Finite Element Analysis (FEA) was used to conduct a stress study of the created implants under actual acting forces static loading conditions in accordance with ISO 14801. In the created models, design elements including implant screw type, thickness, and taper angle of abutment were modified in order to increase the longevity of the implants. The results show that the equivalent stress level is dramatically reduced from 596.22 MPa to 212.72 MPa in the implant model, which exhibit more homogeneous stress pattern under static loading conditions. Furthermore, the relatively smoother stress transmission observed in this model holds promise for the development of new implants capable of withstanding the forces encountered in the oral environment.