The Oxford English Dictionary includes 17 definitions for the word “model” as a noun and other 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For in-stance, “model railways” refer to replicas of railways and trains at a smaller scale and a “model student” refers to an exemplar individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning of a model. Even if the context is narrowed, specifically to the research related to the tumour microenvironment, a “model” can be understood in a wide variety of ways, from an animal model, to a mathematical expression. This paper presents a review of the different “models” of the tumour microenvironment grouped by the different def-initions of the word into four categories: model organisms, in vitro models, mathematical models, and computational models. Then, the frequency of different meanings of the word “model” related to the tumour microenvironment is measured from the number of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequency of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, in particular xenografts and mouse models are the most common used “models”, the number of entries have been slowly de-creasing. Mathematical models, prognostic and risk models follow in frequency and these have been growing in use.

Radio-dynamic therapy (RDT), as an emerging cancer treatment method, has attracted attention due to its remarkable therapeutic efficacy using low-dose, high-energy radiation (such as X-rays), and has shown significant potential in cancer treatment. The RDT system typically consists of scintillators and photosensitizers. Scintillators absorb X-rays and convert them to visible light, activating nearby photosensitizers to generate cytotoxic reactive oxygen species (ROS). Challenges faced by the two-component strategy, including low loading capacity and inefficient energy transfer, hinder its final effectiveness. In addition, the tumor microenvironment (TME) with hypoxia and immunosuppression limits the efficacy of RDTs. Recent advances introduce one-component RDT systems based on nanomaterials with high-Z metal elements, which effectively inhibit deep-seated tumors. These novel RDT systems exhibit immune enhancement and immune memory, potentially eliminating both primary and metastatic tumors. This review comprehensively analyzes recent advances in the rational construction of RDTs, exploring their mechanisms and its application in the treatment of deep-seated tumors. Aimed at providing a practical resource for oncology researchers and practitioners, the review offers new perspectives for potential future directions in RDT research.

A model to rationalize the effects of test temperature and microstructural variables on the creep crack growth (CCG) and creep-fatigue crack growth (CFCG) rates in ferritic steels is described. The model predicts that as the average spacing between grain boundary particles that initiate creep cavities decrease, the CCG and CFCG rates increase. Further, CCG data at several temperatures collapse into a single trend when a temperature compensated CCG rate derived from the model is used. CCG and CFCG behavior measured at different temperatures is used to assess the effects of variables such as differences between basemetal (BM), weldmetal (WM) and the heat-affected zone (HAZ) regions. The model is demonstrated for Grade 22 and Grade 91 steels using data from literature. It is shown that differences between the CCG behavior of Grade 22 steel in new and ex-service conditions are negligible in the BM and WM regions but not in the HAZ region. The CCG behavior of Grade 91 steels can be separated into creep-ductile and creep-brittle regions. The creep-brittle tendency is linked to the presence of excess trace element concentrations in the material chemistry. Significant differences found in the CCG rates between BM and the WM and HAZ regions of Grade 91 steel were explained.

Uganda’s agriculture depends mainly on rainwater. As farmers are trying to increase on the food output to match the demands of a fast growing population, they are susceptible to make losses due to fluctuating weather patterns which are being caused by the global climate change. Therefore, it is necessary to explore ways of improving water use efficiency in rainfed agricultural systems to save farmers labour and input costs in situations where the grain harvest would be zero due to crop failure. The water driven FAO AquaCrop model is used as a support tool for making informed decisions during planning and situation analysis. In this study, AquaCrop model was evaluated for prediction of maize growth and yields at MUARIK in Uganda, for rainfed agriculture in three growing seasons. The model efficiency (E) and root mean square value (RMSE) for the maize canopy simulation during the September–December 2015 season was 0.945 and 7.24 respectively. The deviation of the simulated final biomass from measured data ranged from −15.4 to 11.6%, while the deviation of the final yield ranged from −2.8 to 2.0. The results suggest that the model can be used in the prediction of rainfed agricultural outputs, hence helping in guiding on management practices to increase food production.

In cancer patients, hyponatremia is detected in about 40% of cases at hospital admission and has been associated to a worse outcome. We have previously observed that cancer cells from differ-ent tissues show a significantly increased proliferation rate and invasion potential, when cul-tured in low extracellular [Na+]. We have recently developed an animal model of hyponatremia using Foxn1nu/nu mice. The aim of the present study was to compare tumor growth and inva-sivity of the neuroblastoma cell line SK-N-AS in hyponatremic vs. normonatremic mice. Ani-mals were subcutaneously implanted with luciferase-expressing SK-N-AS cells. When masses reached about 100 mm3, hyponatremia was induced in a subgroup of animals via desmopressin infusion. Tumor masses were significantly greater in hyponatremic mice, starting from day 14 and until the day of sacrifice (day 28). Immunohistochemical analysis showed a more intense vascularization and higher levels of expression of the proliferating cell nuclear antigen, chro-mogranin A and heme oxigenase-1 gene in hyponatremic mice. Finally, metalloproteases were also more abundantly expressed in hyponatremic animals compared to control ones. To our knowledge, this is the first in vivo demonstration that hyponatremia is associated to increased cancer growth by activating molecular mechanisms that promote proliferation, angiogenesis and invasivity.

Partial Differential Equations (PDEs) constitute a fundamental framework for modeling various physical phenomena across diverse fields such as physics, engineering, and finance. Solving PDEs accurately and efficiently remains a significant challenge, particularly when dealing with complex systems or high-dimensional data. In this paper, we present a novel approach based on deep learning techniques to investigate the effective factors in solving PDEs. Our methodology leverages the expressive power of deep neural networks to learn and represent the underlying relationships between input parameters and the solutions of PDEs. By incorporating domain knowledge into the network architecture and training process, our approach aims to provide insights into the key factors influencing the solution of PDEs. We demonstrate the effectiveness of our method through numerical experiments on various PDEs, showcasing its capability to identify important features and improve predictive accuracy. Our findings suggest that deep learning techniques offer a promising avenue for understanding and analyzing the complex dynamics inherent in PDEs, paving the way for enhanced computational methods in scientific and engineering applications.

In recent years, the rapid expansion of urban areas has led to changes in land use, a reduction in pervious surfaces, and a disturbance in the balance between surface waters and groundwater. These urban changes, combined with inadequate drainage systems and misdirection of runoff, have resulted in severe urban floods, causing numerous fatalities and damage to urban areas. As a result, investigating the effects of flood occurrences and determining proper mitigation measures to reduce flood damage is essential. This paper aims to establish a novel economic-hydrologic model to evaluate the efficacy of Low-impact Development (LID) measures in urban Bronx River catchment, NYC. The model examines reduced peak discharge of flood and imposed damage. Initially, the EPA’s SWMM is used to simulate urban flooding based on recent extreme events. LID scenarios are then implemented, including individual and combined scenarios, to mitigate the adverse impacts of flood occurrences of high to low frequency. By means of the HEC-GeoRAS tool, highly detailed flood inundation maps are generated and seamlessly incorporated into the flood damage estimation model, namely HAZUS, to obtain estimates of the potential damage caused by floods. Using the reduced monetary damages and the implementation cost of LID measures, the benefit-to-cost (BC) ratio is then calculated. Findings indicated that the combined LID scenario is the most effective approach in peak flow reduction. Moreover, Permeable pavement outperforms infiltration trench, bio retention cell, and rain barrel in terms of benefit-to-cost ratio, with rain barrel showing the lowest ratio. The proposed evaluation system for LID measures, with a focus on flood damage reduction as a key benefit, provides valuable insights for identifying optimal scenarios and facilitating informed decision-making.

The excessive intake of sodium (Na) and insufficient intake of potassium (K) are major concerns to prevent hypertension. Using low-Na/K seasonings (reducing 25% of the NaCl and adding K salt) may improve the dietary Na/K ratio and help prevent hypertension. To devise an intervention study using low-Na/K seasonings at a company cafeteria, we calculated the Na and K contents of the meals served at the cafeteria and estimated changes in the intakes when suitable low-Na/K seasonings were used. We also considered using milk as a good source of K. We used an ingredient list of a company cafeteria and calculated Na and K contents in each dish. The average amounts of NaCl and K per use were 5.04 g and 718 mg, respectively. Seasonings contributed 70.9% of the NaCl. With the use of low-Na/K seasonings, an estimated reduction in NaCl of 0.8 g/day and an estimated increase in K of 308 mg/day was achieved. An additional serving (200 mL) of milk, NaCl was reduced by 0.57 g/day and K was increased by 610 mg/day, with an overall decrease in the dietary Na/K ratio from 3.20 to 2.40. The use of low-Na/K seasonings and dairy may improve the dietary Na/K ratio among cafeteria users and help prevent hypertension.

Among others, UWB-based multi anchor localization systems have been established for industrial indoor localization systems. However, multi anchor systems have high costs and installation effort. By exploiting the multipath propagation of the UWB signal, the infrastructure and thus the costs of conventional systems can be reduced. Our UWB Single Anchor Localization System (SALOS) successfully pursues this approach. The idea behind SALOS is to create a localization system with a sophisticated signal modeling. Therefore, neither measured reference like fingerprinting nor training is required for position estimation. Although SALOS has been implemented and tested successfully already in an outdoor scenario with multipath propagation, it has not yet been evaluated in indoor environment with challenging and hardly predictable multipath propagation. For this purpose, we have developed new algorithms for the existing hardware mainly a three-dimensional statistical multipath propagation model for arbitrary spatial geometries. The signal propagation between the anchor and predefined candidate points for the tag position is modeled in path length and complex-valued receive amplitudes. For position estimation, these modeled signals are combined to multiple sets and compared to UWB measurements via a similarity metric. In the end, a majority decision of multiple position estimates is performed. For evaluation, we implement SALOS in a modular fashion and install the system in a building. For a fixed grid of 20 positions, SALOS is evaluated in terms of position accuracy. The system results in correct position estimations for more than 73% of the measurements.

It is important to realize the transformation behavior of wüstite because it greatly affects the final structure of the oxide layer and the surface quality of the steel products. In the present study, the transformation behaviors of the wüstite layer are examined under nearly-oxygen-free conditions, to simulate the cooling processes after the hot rolled strip is coiled. As the single phase wüstite was prepared at 950˚C, the 460˚C transformed oxide layer was composed of a mixture of iron and magnetite formed through eutectoid reactions. For the 750˚C-fabricated wüstite, only magnetite was observed after transformation, without iron precipitates and residual wüstite. It is speculated that the unusual transformation behavior of the low-temperature-made wüstite results from the pseudo-structural intermediate phase transformation between wüstite and magnetite. This pseudo-structure is a pre-transformed wüstite and of various concentration of ferrous ion, which is determined by the fabrication conditions. During the hot strip mill process, the so-called wet scale, wüstite, is produced continuously from finish mill to laminar flow sections and ended at 570˚C. Consequently, the final eutectoid transformation below 570˚C is dominantly controlled by the surface temperature ranged from 750˚C to 950˚C for low carbon steel.

To meet the dual-carbon objective, China's economy needs to move from a carbon-intensive to a green economy. Green technology innovation and renewable energy development are key to promoting green economic growth. The study uses 2010–2022 provincial panel data from 30 Chinese provinces. The super-efficient SBM model measures the province's green economic growth index. A panel fixed-effects model is used to experimentally evaluate how green technology innovation and renewable energy development affect green economic growth. Green financing moderates and a panel threshold model tests the threshold effect of renewable energy development on green economic growth. The results demonstrate that (1) China's green economic growth is rising and has a "U" shape. (2) all selected variables significantly impact green economic growth. However, population density, government intervention, and industrial structure are inversely associated with GEG. Green economic growth is significantly boosted by the moderate role of green finance in the development of renewable energy and green technological innovation. (3) Renewable energy development has a single threshold non-linear effect on green economic growth, depending on its development level and green technological innovation. Considering China's current condition and research findings, this study examines the green energy transition to promote green economic growth and high-quality economic development. Meanwhile, puts forward some important policy recommendations.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

The Societal Patterns Evolution Model (SPEM) (Gakh, 2023a) has been developed to apply to socio-technical systems. It contains patterns representing stages of societal development. Mapping of these patterns to the development of Information and Communication Technologies (ICTs) and Information Systems was presented at FedCSIS 2022 conference (Gakh, 2022c). This paper discusses the application of SPEM to model the development of three pillars of sustainability: economic, social, and environmental (Purvis, et al., 2019; United Nations, 2023).

Burnout is common in many countries and is associated with several other problems, such as depression, anxiety, insomnia and memory deficits, and prospectively it predicts long-term sick-leave, cardiovascular disease and death. Clinical burnout or its residual symptoms often last several years and a common assumption is that recovery takes a long time by nature despite full time sick-leave and absence of work stress. Literature suggests models that hypothetically explain the development, but not maintenance, of the syndrome. Based on cognitive and behavioral principles and stress theory this paper describes a theoretical model explaining how clinical burnout can develop and be maintained. While the development of clinical burnout is mainly explained by prolonged stress reactions and disturbed recovery processes due to work related stressors, maintenance of the syndrome is particularly explained by prolonged stress reactions and disturbed recovery processes due to the new context of experiencing burnout and being on sick-leave. Worry about acquired memory deficits, passivity and excessive sleep, shame, fear of stress reactions, and the perception of not being safe are examples of responses that can contribute to the maintenance. The model has important implications for research and how to intervene clinical burnout.

Quality of life (QoL) assessments are integral to cancer care, yet their effectiveness in providing essential information for supporting survivors varies. This study aimed to elucidate key indicators of QoL among colorectal cancer survivors from the perspective of healthcare professionals, and to evaluate existing QoL questionnaires in relation to these indicators. Two studies were conducted: a Delphi study to identify key QoL indicators and a scoping review of questionnaires suitable for colorectal cancer survivors. Fifty-four healthcare professionals participated in the Delphi study's first round, with 25 in the second. The study identified two primary QoL domains (Physical and Psychological) and 17 subdomains deemed most critical. Additionally, a review of 12 questionnaires revealed two instruments assessing the most important general domains. Findings underscored a misalignment between existing assessment tools and healthcare professionals' clinical priorities in working with colorectal cancer survivors. To enhance support for survivors' QoL, efforts are needed to develop instruments that better align with the demands of routine QoL assessment in clinical practice.

Despite numerous studies in statistical downscaling methodology, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

This paper investigates the impact of price and real exchange rate volatility on Foreign Direct Investment (FDI) inflows in a panel of 10 Latin American and Caribbean countries, observed between 1990 and 2012. Both price and exchange rate volatility series are estimated through the Generalized Autoregressive Conditional Heteroscedasticity model (GARCH). Our results, obtained employing the Fixed Effects estimator, confirm the theory of hysteresis and option value, in so far it is found a statistically significant negative effect of exchange rate volatility on FDI. Price volatility, instead, turns out to be positive but insignificant. Moreover, we show that human capital and trade openness are key for attracting foreign capital. From the policy perspective, our analysis suggests the importance of stabilization policies as well as the one of government credibility in promoting trade openness and human capital formation.

A techno-economic model was developed to investigate the influence of components on the system costs of redox flow batteries. Sensitivity analyses were carried out based on a example of a 10 kW/120 kWh vanadium redox flow battery system and the costs of the individual components were analyzed. Particular consideration was given to the influence of material costs and resistances of bipolar plates and energy storage media as well as voltages and electric currents. Based on the developed model it was possible to formulate statements about the targeted optimization of existing battery systems and general aspects for future developments of redox flow batteries.

The concept of economic self-reliance, widely known by Thai people as the philosophy of sufficiency economy, has been widely promoted in rural Thai societies. By practicing this philosophy, it is expected that the citizens’ quality of life and local environments could be sustainably improved. This study aims to explore the contribution of the community practices of the sufficiency economy philosophy to rural villagers’ quality of life improvement, and to investigate potential factors that determine the trust of villagers in the philosophy. With the purpose to propose strategies which could enhance trust and promote villagers’ practices of the philosophy, the study investigated influences of three relevant factors on trust towards the philosophy. Those factors included factors related to cognitive-based trust, factors related to emotional-based trust, and factors related to demographic characteristics. Questionnaire surveys and in-depth interviews with community leaders and local villagers were conducted in the Ban Jamrung community, in Thailand’s Rayong province. The results of the statistical analysis revealed that the residents who applied the sufficiency economy philosophy in their daily lives experienced a relatively better quality of life. Additionally, it was found that trust in the philosophy could be predicted more by rational factors than by emotional factors. These findings could be utilized to develop strategies to maintain and enhance the trust of the people in the philosophy of sufficiency economy.

Sustainable food consumption has attracted a widespread attention during last decades by scholars, policy makers and consumers. In line with this, farmers’ markets (FMs) have the potential to encourage sustainable agricultural production and consumption. By reducing the number of actors and distances along the food chain, these alternative food systems foster the reconnection between farmers and consumers and contribute to different social, economic and environmental sustainable goals. This paper provides insights on the role of consumers' sustainability concerns related to their motivation for shopping at FMs. By means of a choice experiment, we analyze the determinants of consumers’ WTP for buying apples at FMs. We are particularly interested in understanding how attitudes towards the three sustainability dimensions are related to consumer preferences in this context. We find that consumer attitudes towards direct contact with producers, contributing to farmers’ income, and environmental benefits can be directly related to product characteristics that are specific to FMs.

The pursuit of human-centered, sustainable and resilient production is shaping a future-oriented approach to manufacturing processes in the context of Industry 5.0. How can such production be implemented? For this purpose, this article analyses the effects of the developed Dresden Model of Adaptability on the integration of holistic sustainability. The focus is on investigating the promotion of economic, environmental and social sustainability goals in terms of the 17 Sustainable Development Goals as well as analysing strategies to increase resilience to changing environmental conditions. A human-centered perspective is considered. The model proves to be a holistic approach that drives sustainable development of the production system through the comprehensive integration of human, technology, and organisational structures.

Graph Neural Networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demands substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on Deep Neural Networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.

E-tourism is one of the most important modern services to achieve the development of the tourism sector while relying on sophisticated communication technologies. Certainly, these innovations in the field of tourism have disrupted this sector with new and different dimensions of tourism development. This has led various regions and countries to accelerate the search for the tools that are necessary to achieve excellence and sustainable development in tourism. This research aims to study the development factors of e-tourism to achieve excellence and sustainable development in the tourism of the Ha’il region. Therefore, we applied two analyses: in the first one, we used a statistical analysis based on the survey sent to employees from tourism companies. The objective of this analysis is to determine the main factors of e-tourism to achieve excellence in tourism in the Ha’il region. The results of the statistical analysis have given us the following elements: There are only four main factors that allow authorities to achieve excellence in tourism in the Ha’il region, namely ST, CP, HRS, and SC factors. Then we proceed with a second analysis, to determine the main factors of excellence in tourism that drive the achievement of sustainable development in tourism in the Ha’il region. For this reason, we applied an artificial intelligence method based on the unsupervised machine learning technique. The results obtained from the machine learning technique show that five factors determine the excellence in tourism and reveal the sustainable development of Ha’il’s tourism. These factors are ST, CP, FP, HRS, and GC. Consequently, we consider that all the factors found in both analyses are important to realise excellence in tourism and sustainable development in tourism. However, to achieve sustainable development in tourism in Ha’il, Saudi authorities must develop the three common factors found in the two analyses, namely ST, CP, and HRS.

Metabolites present in the hemolymph of honey bees play a crucial role in modulating the metamorphic process within the species. However, the precise alterations in metabolite composition, along with the associated variances and regulatory pathways implicated during the larvae-to-pupae metamorphosis of honey bees, remain incompletely elucidated. In this investigation, we gathered hemolymph samples from honey bee larvae at three distinct physiological stages-feeding, prepupal, and pupae and subjected them to metabolite analysis utilizing the liquid chromatography-mass spectrometry (LC-MS) technique. Employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), we identified significant differential metabolites and conducted a metabolic pathway analysis on those differentially up-regulated during the prepupal and pupae stages. Notably, metabolites up-regulated in the hemolymph of prepupal stage larvae primarily governed glucose metabolism and fat digestion and absorption, while those in pupae stage larvae were involved in regulating chitin and lipopolysaccharide precursor formation, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. These findings bear significant implications for advancing our comprehension of the metamorphic processes in honey bees.

In a previous study we could show a change of membrane morphology and gas separation performance by varying the recipe of a casting solution based on polysulfone in a determined solvent system. Although all results were very reproducible, all used solvents were harmful and not sustainable. In this study therefore, the solvents tetrahydrofuran (THF) and N,N-dimethylacetamide (DMAc) are replaced by the more sustainable solvents 2-methyl-tetrahydrofuran (2M-THF), N-butyl pyrrolidinone (NBP) and cyclopentyl methyl ether (CPME). The gas permeation performance and for the first time morphology of the membranes before and after solvent replacement were determined and compared by single gas permeation measurements and SEM microscopy. It is shown, that THF can be replaced by 2M-THF and NBP without decreasing the gas permeation performance. With CPME replacing THF no membranes were formed. Best gas permeation results showed systems with 2M-THF as THF alternative. Permeances for the tested gases oxygen (O2), nitrogen (N2), carbon dioxide (CO2) and methane (CH4) were 2.66·10-4, 3.89·10-5, 1.53·10-3 and 4.52·10-5 mL(STP)/cm²·min·bar, respectively. permselectivities of those membranes for the gas pairs O2/N2, CO2/N2 and CO2/CH4 were 6.7, 38.3 and 34.0, respectively. When additionally replacing DMAc in the solvent system no or only porous membranes were obtained, even if the precipitation procedure was adjusted. These findings indicate that a complete replacement of the solvent system without affecting the membrane morphology or gas permeation performance is not possible. By varying the temperature of the precipitation bath an easy adjustment to mechanically stable PSU membranes is possible, if just THF was replaced by 2M-THF.

The objective of this research is to propose archetypes of accessible design with a neuroarchitec-ture approach for the design of initial level educational centers, in Peru, especially in areas of ur-ban growth, many children do not manage to access initial education, there is even school drop-out. . At this educational level, with an increase of 4.4% in recent years, the process of developing mental abilities such as memory, perception, thinking, reactions, attention, language, among oth-ers, is of the utmost importance. in the first years of life. As a methodology, sensory design strate-gies were applied, likewise through instruments such as interviews, observation, photographic taking and measurement carried out during the field visit for the analysis of the space-child in-teraction and spatial stimuli, supported by digital tools (Autocad, Google Earth, Andrés Marsh). As results, accessible design archetypes were obtained based on neuroarchitecture based on 10 spatial indicators (Lighting; Green area; roofs; shape; size; distribution; accessibility; scale; colors; materiality). In conclusion, the provision of adequate spaces that provide exploratory facilities greatly contributes to cognitive development in early childhood. The environment where these cognitive activities take place needs to be conditioned to allow children to develop, bond and ap-propriate the space.

This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from Sentinel-1 SAR, Global Surface Water, HydroSHEDS, Global Human Layer Settlement, and MODIS Land Cover, our primary ob-jective is to delineate flood extents and compare them with centennial flood-prone areas, offering a comprehensive assessment of exposure during the period from July to October 2022 across Sen-egal's 14 regions. The findings underscore a total inundation area of 2 951 square kilometers, impacting 297 142 people, 175 square kilometers of urban and 16 square kilometers of crops. Notably, August wit-nessed the largest flooded areas, reaching 780 square kilometers, constituting 0.40% of the coun-try's surface. Subsequent regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experi-enced varying extents of flooding, with August data showing a 1.34% overall overlap compared to centennial flood-prone areas derived from hydrological and hydraulic modeling. This low per-centage reveals the distinct purposes and natures of the two approaches (remote sensing and modeling), as well as their complementarity. Turning to flood exposure, August emerges as the most critical month, affecting 76 595 people (0.43% of the total population). Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 100 707, 57 648, 31 579, and 26 581 people, respectively. These findings emphasize the imperative for comprehensive disaster preparedness and mitigation efforts. The study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies.

One consequence of the global technological advancement in conventional metallurgy is the fast depletion rate of valuable minerals, which are also becoming increasingly difficult to find in pure and economically viable forms. This has spurred more interest in technologies that investigate ability of different microorganisms to mobilize valuable metals from their ores via diverse metabolic processes. This study was carried out therefore to isolate, identify and characterize iron solubilizing bacteria from Iron stones of Agbaja iron ore mining site of Kogi State, Nigeria. Crushed samples in the range of 0.25µm and 0.75µm particle sizes were cultured in a modified 9k media to facilitate bacterial growth and pure cultures were then isolated and sub-cultured for further bioleaching studies. Morphological and biochemical analysis suggests that some of the bacteria identified are members of Acidithiobacillus spp, Pseudomonas spp, and Leptospirillum spp. Studies conducted on pure cultures and mixed consortium of the identified organisms shows that a mixture of the three organisms leached iron ore to about 96.16%. Also results of growth pattern due to bacteria countafter 24-72hours of incubation ranged between 0.1×10³ cfu/ml and 12.3 ×10³ cfu/ml for Acidithiobacillus spp. The need to explore the molecular characteristics of these organisms with a view to generating more information on the quality/quantity of their DNA for future cloning activities was also investigated in this work. DNA was extracted using zymo fungal/bacterial extraction mini prep kit ^TM (cat #6001) and subsequently subjected to 1% agarose gel electrophoresis. Visible bands were obtained with Alpha Innotech Gel Documentation Machine. DNA amplification was carried out using a pettier based thermo cycler PCR machine and electrophoresed on 1.5% agarose gel. Results of the PCR shows a visible band corresponding to 1.5kbp using this primer 27F (5¹-GAGTTTGATCCTGGCTCAG-3¹) and 1492R (5^1-GGTTACCTTGTTACGACT-3¹). DNA purity check shows two of the bacteria possess very good qualities for sequencing for further molecular analysis.

Count data are used in many fields of practice, especially Poisson distribution as a popular choice for the marginal process distribution. If these counts exhibit serial dependence, a popular approach is to use a Poisson INAR(1) model to describe the autocorrelation structure of process. In this paper, the explicit formulas are proposed to evaluate performance characteristics of Double Moving Average control chart (DMA) for Integer valued autoregressive of serial dependence Poisson process. The characteristics of the control chart are frequently measured as Average Run Length (ARL) which means that the average of observations are taken before a system is signaled to be out-of-control. These proposed explicit formulas of ARL are simple and easy to implement for practitioner. The numerical results show that the DMA chart performs better than others when the magnitudes of shift are moderate and large.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

Using a scaling symmetry, it is shown how to compute polynomial conservation laws, generalized symmetries, recursion operators, Lax pairs, and bilinear forms of polynomial nonlinear partial differential equations thereby establishing their complete integrability. The Gardner equation is chosen as the key example for it comprises both the Korteweg-de Vries and modified Korteweg de Vries (mKdV) equations. The Gardner and Miura transformations which connect these equations are also computed using the concept of scaling homogeneity. Exact solitary wave solutions and solitons of the Gardner equation are derived using Hirota’s method and other direct methods. The nature of these solutions depends on the sign of the cubic term in the Gardner equation and the underlying mKdV equation. It is shown that flat (table-top) waves of large amplitude only occur when the sign of the cubic nonlinearity is negative (defocusing case) whereas the focusing Gardner equation has the standard elastically colliding solitons. The paper’s aim is to provide a review of integrability properties and solutions of the Gardner equation and illustrate the applicability of the scaling symmetry approach. The methods and algorithms used in this paper have been implemented in Mathematica but can be adapted for major computer algebra systems.

: Background. Delivering bad news is a sensitive and challenging aspect of nursing healthcare, requiring a holistic approach that respects patients' preferences, cultural values, and religious beliefs to promote adaptation to the state's health. Aim: map the evidence of the dimensions present in the communication and management of bad news by nurses, with the person in a palliative situation, their caregivers, and family members. Methods: Based on Joanna Briggs Institute methodology, the search was conducted through MEDLINE Complete (EBSCOhost), CINAHL Complete (EBSCOhost), SciELO, and Open Access Scientific Repository in Portugal. From a total of 756 articles, 14 were included, published between 2018 and 2023. Results: Structure components in bad news are influenced by the characteristics of the palliative patients, their caregivers, family members; nurses; and the organizational environment. Promoting the quality of the communication process is desirable through continuous and advanced training in end-of-life; training in bad news; religiously and culturally sensitive nursing interventions centered on hope and maintaining faith; emotional management; and utilization of a checklist protocol. Conclusions: Honest communication allows people to actively participate in the decision-making process and in the trajectory of the care plan that is focused on themselves and their preferences, which has outcomes in functional capacity and readaptation.

Ever since the discovery of dendritic cells by Ralph Steinman and Zanvil Cohn in 1973, it is increasingly evident that dendritic cells are integral for adaptive immune responses, and there is an undeniable focus on them for vaccines development. Fungal infections, often thought in-nocuous, are becoming significant threats due to increased immunocompromised or im-mune-suppressed population and climate change. Further, the recent COVID-19 pandemic unraveled the wrath of fungal infections and devastating outcomes. Invasive fungal infections cause significant case fatality rates ranging from 20% to 90%. Regrettably, no licensed fungal vaccines exist, and there is an urgent need for preventive and therapeutic purposes. In this re-view, we discuss the ontogeny, subsets, tissue distribution, and functions of lung dendritic cells. In the latter part, we summarize and discuss the studies on the DC-based vaccines against pulmonary fungal infections. Finally, we highlight some emerging potential avenues that can be incorporated for DC-based vaccines against fungal infections.

Abstract Epithelial ovarian carcinoma (EOC) is the fifth-leading cause of cancer-related death in women, largely reflecting early, prediagnosis dissemination of malignant disease to the peritoneum. EOC is poorly responsive to immune check-point inhibitors due to immunologic features including limited tumor mutational burden (TMB), poor infiltration by immune cells and active immunosuppression. Thus, novel strategies are needed to overcome the frequent lack of preexisting immunity in patients with EOC. We developed and tested an autologous dendritic cell (DC)-based vaccine (DCVAC) which has recently been shown to be safe and to significantly improve progression-free survival (PFS) in two independent randomized phase II clinical trials enrolling patients with EOC (SOV01, NCT02107937; SOV02, NCT02107950). In addition, our exploratory data analyses suggest that the clinical benefits of DCVAC were more pronounced in patients with EOC with lower than-median TMB and scant CD8+ T cells infiltration. Thus, DC-based vaccine stands out as a promising clinical tool to jumpstart anticancer immunity in patients with immunologically „cold“ EOC. Our findings underscore the need for personalized immunotherapy and clinical relevance of potential tumor-related biomarkers within immunotherapy field. Additional clinical trials are needed to address these strategies as well as the potential value of TMB and immune infiltrate at baseline as biomarkers to guide the clinical management of EOC.

Most neuromorphic technologies use a point-neuron model, missing the spatiotemporal nature of neuronal computation performed in dendrites. Dendritic morphology and synaptic organization are structurally tailored for spatiotemporal information processing, enabling various computations like visual perception. Here, we report on a neuromorphic computational model termed ‘dendristor’, which integrates functional synaptic organization with dendritic tree-like morphology computation. The dendristor presents bioplausible nonlinear integration of excitatory and inhibitory synaptic inputs with silent synapses and diverse spatial distribution dependency. We show that the dendristor can emulate direction selectivity, which is the feature to react robustly to a preferred signal direction on the dendrite. We discover that silent synapses can remarkably enhance direction selectivity, turning out to be a crucial player in dendritic computation processing. Finally, we develop neuromorphic dendritic neural circuits that can emulate a cognitive function such as motion perception in the retina. Using dendritic morphology, we achieve visual perception of motion in 3D space by various mapping of spatial information on different dendritic branches. This neuromorphic dendritic computation innovates beyond current neuromorphic computation and provides solutions to explore new skylines in artificial intelligence, neurocomputation, and brain-inspired computing.

Background: Sulforaphane (SFN) is an isothiocyanate of vegetables origin with potent antioxidant and immunomodulatory properties. The pleiotropic activities characterization in human dendritic cells (DCs) is poorly summarized. The aim of this work was the study of the immunomodulatory power of SFN in response to an inflammatory microenvironment on human monocyte derived DCs (moDCs). Methods: The immunological response induced by SFN was studied, evaluating apoptosis and autophagy assays by flow cytometry in moDCs and cancer cell line (THP-1), including moDCs maturation, lymphocyte proliferation and cytokines production under different experimental conditions associated or not with an inflammatory microenvironment, which was induced by lipopolysaccharide (LPS). Results: Our results demonstrated that SFN can interact with moDCs, significantly reducing autophagy process and enhancing apoptosis, such as THP-1 cells, in chronic inflammatory microenvironment. Under this chronic inflammation, SFN modulated the phenotypical characteristics of moDCs, which reduction the expression of all markers (CD80, CD83, CD86, HLA-DR and PD-L1), and significantly reduced the Th2 proliferative response together with the reduction of the IL-9 and IL-13 levels. Although we did not find changes in the regulatory proliferative response, we observed an increase in IL-10 levels. Conclusion: These findings demonstrate that SFN exerts protective effects against LPS-induced inflammation through moDCs/T-cells modulation towards a regulatory profile. Therefore, SFN may be a potential candidate for use in the treatment of pathologies with an inflammatory profile.

In this paper thermo-mechanical behavior of 4D printed Polylactic Acid (PLA) was investigated, focusing on its response to varying strain rates and temperatures below the glass transition temperature. Dynamic mechanical analysis and uniaxial tensile tests confirmed PLA's dependency on strain rate, showcasing its sensitivity to external stimuli. Stress-strain curves exhibited typical thermoplastic behavior, with yield stresses varying with strain rates, underscoring PLA's responsiveness to different deformation speeds. Clear strain rate dependence was observed, particularly at quasi-static rates, with temperature and strain rate fluctuations significantly influencing PLA's mechanical properties, including yield stress and deformation behavior. Isothermal compression tests demonstrated predictable stress-strain curves with distinct yield points, while adiabatic tests reveal additional complexities such as heat accumulation leading to further softening. Thermal imaging revealed temperature increase during deformation, highlighting the material's thermo-sensitive nature. These findings provide the basis for future research with the focus on advanced modeling techniques and mitigation strategies for self-heating effects, aiming to enhance PLA-based product reliability and performance in applications with deformations at higher strain rates, while also developing models to simulate shape recovery in 4D printed PLA structures at both cold and hot programming temperatures.

Hyperspectral imaging, a rapidly evolving technique, furnishes exhaustive information on Earth's surface reflectance over an extensive spectrum of wavelengths. We have developed a more precise method for analyzing spectral data by meticulously selecting plant leaf pixels and employing a relative reference approach. This enhanced technique correlates camera pixel values with light intensity, bypassing the need for complex equations and thereby providing clearer insights into vegetation vitality. The findings of this study not only enhance the precision of hyperspectral imaging but also bolster our capability for ecological monitoring and understanding environmental health. A novel algorithm based on the red-edge slope was established to classify images, exploiting the spectral reflectance data gleaned from the hyperspectral Liquid Crystal Tunable Filter (LCTF) camera. Compared with conventional SVM and K-means algorithms, our novel algorithm demonstrated commendable performance, achieving an overall accuracy of 90% with a Kappa Coefficient of 0.86. This comparison highlights the novel algorithm's relative strength and potential for large-scale satellite and drone imagery analysis applications. The advancements facilitated by this research are instrumental in ecological assessments, conservation efforts, and resource management strategies, signaling a leap forward in the era of environmental monitoring and analysis.

Polyetheretherketone is a promising material for implants due to its good mechanical properties and excellent biocompatibility. Its accessibility to a wide range of applications is facilitated by the ability to process it with an easy-to-use manufacturing process such as Fused Filament Fabrication. The elimination of disadvantages associated with the manufacturing process, such as a poor surface quality, is a main challenge to deal with. As part of the mass finishing process, centrifugal disc finishing has demonstrated good results in surface optimization, making it a promising candidate for post-processing of additively manufactured parts. The objective of this study is to identify the key parameters of the centrifugal disc finishing process on the waviness of additively manufactured PEEK specimen, which has not been investigated yet. Waviness of the specimen was investigated by means of confocal laser scanning microscopy (CLSM), while weight loss was additionally tracked. Six parameters were investigated: type, amount and speed of media, use of compound, amount of water and time. Type of media, time and speed were found to significantly influence waviness reduction and weight loss. Surface electron microscopy images demonstrated the additional effects of deburring and corner rounding. Results on previous studies with specimens made of metal showed similar results. Further investigation is required to optimise waviness reduction and polish parts in a second post-processing step.

With a view to understand and resolve the complexity of the food matrix, ohmic technologies alone or in combination were extensively employed. In this sense, the newest developments and advances of proteomics, metabolomics, lipidomics whith their unique benefits could simplify and understand the link between physiological and pathological activities in biology, physiology, pathology, and food science and processing. This review aims to briefly introduce the basis of proteomics, metabolomics, lipidomics, then expansively review their impact on the assessment of meat quality and safety. Here, also, we discuss the application of proteomics, metabolomics, lipidomics for the authentication and adulteration of meat and meat derivatives from different sources, and provides some perspectives regarding the use of emerging techniques such as rapid mass spectrometry (MS) and non-invasive measurements for the analysis of meat quality and safety. This paper summarizes all significant investigation in these matters, and underlines the advances in analytical chemistry technologies and meat science areas. By emphasizing the requirement for additional examinations, this paper attempts a comprehensive knowledge of “foodomics” and their potential to improve their employment in meat science.

The paper examines the impact of inward remittances flows on per capita GDP growth in Bangladesh during 1976-2012. We find that the growth effect of remittances is negative at first but becomes positive at a later stage, an evidence of a non-linear. Unproductive use of remittances was rampant in the beginning when they were received by migrant families but better social and economic investments led to more productive utilisation of remittances receipts at later periods. This was the possible mechanism behind the U-shaped relationship. Unlike what is suggested in the literature that the effect of remittances is more pronounced in a less financially developed economy, our evidence do not show that the effect of remittances on per capita GDP growth in Bangladesh is conditional on the level of financial development.

The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

In recent times, using image-based deep learning techniques has been very useful for classifying fruits. However, mango is a particular challenge because its shelf life is directly related to the degree of maturity at the time of harvest, which is determined mainly through the visual assessment of the operators when sorting them, doing it incorrectly, coupled with the blows and compressions exposed during storage and transport, trigger the deterioration in their quality, hence the importance of this operation. In this sense, the purpose of this study focuses on designing a computational model based on Deep Learning that uses a convolutional neural network (CNN) parameterized to select mangoes according to their color tone based on their maturity stage. A dataset was established from 201 labeled images, then a CNN architecture was built, obtaining a model on which 3 criteria were evaluated: the number of convolution and grouping layers, the resolution of the input images and the number of training epochs required. As a result, an input image resolution of 32 x 32 pixels was achieved, the process included a convolution layer followed by pooling that fed another layer that processed the results and ended in an output layer with 2 neurons under the One Hot Encoding representation. The model required 20 epochs to perfect its training, achieving 96.04 % accuracy in mango image selection. This study supports the potential of CNNs in quality analysis through mango color selection, even in uncertainty scenarios.

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

This research provides a computationally efficient numerical tool for predicting 3-D turbulent flows over hydrofoils. It addresses a gap in existing numerical methods by extending the capabilities of the laminar VIScous Vorticity Equation (VISVE) solver to handle 3-D turbulent flow scenarios. The implementation integrates the k − ω SST model into the 3-D VISVE solver using the Finite Volume Method (FVM), extending its capability to handle turbulent flows. The turbulence model is also parallelized to improve computational efficiency. The application of the newly developed model to 3-D hydrofoil cases expands its scope and serves as preparatory work for future rotational propeller applications. Rigorous testing includes a convergence analysis concerning grid numbers and time step sizes, and validation against a Reynolds-Averaged Navier-Stokes (RANS) solver ensures the reliability and accuracy of the enhanced VISVE solver for turbulent flow simulations. The turbulent VISVE solver offers advantages such as reduced computational domain and costs through a vorticity-based approach. Turbulence concentration within boundary layers and free shear flows does not compromise the advantages of the small computational domain. The simplified meshing process, automatically generated by denoting the number of panels on the hydrofoil, makes the solver accessible for researchers and engineers.

Rice (Oryza spp.) is mostly grown directly from seed and sown on wet or dry seed beds or usually used as transplants on nursery bed. With all the economically important viral diseases in the world, Rice yellow mottle virus (RYMV), is only prevalent in rice-growing countries in Africa. RYMV had turned out to be the main rice production constraint in Africa, throughout the last 20–25 years, causing yield loss of 10 to 100%, depending on age of the plant at the time of infection, degree of varietal susceptibility and the exiting climatic conditions. Good agricultural practices and utilization of biotechnological tools in the development of improved resistant cultivars have been extensively active in controlling the disease. This review focused on RYMV, its epidemiology, serological and molecular typing, disease management and the way forward for sustainable rice production.

The organic fraction of municipal solid waste (OFMSW) is a significant environmental threat, and an economic and social challenge to manage. As such, the efficient treatment of OFMSW is a significant key factor in achieving sustainable waste management. Decentralized Composting (DC) develops a new framework of waste management. The DC Analysis Model (DCAM), proposed in our previous study, provides a powerful tool for decision makers, based on the quantification of the DC project characteristics. In this paper, we focus on the qualitative analysis as a complementary tool to support decision making, in cases where quantitative analysis is unequivocal. The qualitative analysis identifies the main players in the field, the critical stakeholders and the potential conflicts between them. It also reveals the root problems and the core competencies for the project's implementation. DCAM qualitative analysis in Shefa-Amr (case study) indicates that unresolved root problems, such as the “lack of national regulation”, “clear ownership of the project” and “lack of ongoing budget” can result in an unsustainable composting system. Countering that, “the commitment of the municipality”, together with “economic viability” and securing “suitable areas for placing composters” are among the most important core competencies for the effective implementation of DC projects.

No doubt pollution is a global problem which must be holistically tackled. In doing this, adequate knowledge of the sources of pollution is important, therefore the aim of this paper is to review source apportionment with reference to top-down and bottom-up methods. In this paper, dispersion modeling, emissions inventory, and sampling methods were discussed. Also, analytical methods involved in top-down source apportionment were mentioned. The two techniques are needed to evaluate pollutants and their sources. Based on these two approaches, pollution control strategy would be developed and decisions can be made in deciding the right approach to solve or reduce the pollution problems.

In this study, we explore the impact of quantum effects on synaptic activity and their implications for the deterministic model of neural processes. By simulating synaptic events in a large-scale neural network, we demonstrate how small probabilistic quantum events can accumulate and introduce variability in neural behavior. Our model incorporates Dirac delta functions to simulate sudden changes in synaptic probabilities, creating a non-linear, seesaw effect in the cumulative synaptic activity curve. The results reveal that even minimal quantum effects, when scaled to the vast number of synapses in the human brain, can lead to significant dissociations in neural activity. These findings challenge the traditional deterministic view, suggesting that neural processes—and by extension, decision-making and movements—may inherently incorporate elements of randomness. This probabilistic perspective aligns with theories of free will and underscores the need for further empirical research to validate the role of quantum mechanics in neuroscience. Our study provides a theoretical framework for understanding the complex interplay between quantum effects and neural dynamics, opening new avenues for exploring the nature of consciousness and the fundamental principles governing reality.

This study aimed to evaluate cell therapy with human neural precursor cells (hNPCs) in diabetic retinopathy (DR) Wistar rats, induced to diabetes by intraperitoneal injection with streptozotocin. Wharton's Jelly Mesenchymal stem cells (WJ-MSCs) were isolated, expanded, and seeded onto a biopolymer substrate without growth factors to develop neurospheres to obtain the hNPCs, characterized by immunocytochemistry. The animals were divided into three groups; non-diabetic (ND) n = four; diabetic without treatment (DM) n = nine; and diabetic with cell therapy (DM + hNPCs) n = nine. After eight weeks of diabetes induction and verified DR, intravitreal injection of hNPCs (1 x 106 cel/µL) was performed in the DM + hNPCs group. Optical Coherence Tomography (OCT) and Electroretinography (ERG) evaluations were done before and after diabetes induction and after cell therapy. Eye enucleation occurred four weeks after treatment for the histopathological and immunohistochemistry analyses. In the treated group, there was the repair of the retinal structures and their arrangements. hNPCs increased the thickness of neuroretina layers, especially in the ganglion cell and photoreceptor layers. The results indicate that hNPCs reduced DR progression by a neuroprotective effect and promoted retinal repair, making them potential candidates for regeneration of the neuroretinal tissue.

This study investigates the spatial urban growth patterns of cities along the Democratic Republic of Congo (DRC) and Zambia border, a region of significant economic importance characterized by cross-border trade. This activity has led to rapid, but unplanned urban growth. The objective is to quantify the spatial expansion of Congolese cities (Kipushi, Kasumbalesa, Mokambo, and Sakania) bordering Zambia and to evaluate associated landscape changes. The methodology integrates Landsat images from 1990, 2000, 2010, and 2023, coupled with Geographic Information Systems (GIS) and landscape ecology analysis tools. The results reveal substantial landscape transformations in the border cities between 1990 and 2023. These changes are primarily driven by rapid urban expansion, particularly pronounced in Kasumbalesa. Between 1990 and 2023, forest cover declined from 70% to less than 15% in Kipushi, from 80% to 10% in Kasumbalesa, from 90% to 30% in Mokambo, and from 80% to 15% in Sakania. This forest cover loss is accompanied by an increase in landscape element diversity, as indicated by the Shannon diversity index, except in Kipushi, suggesting a transition towards more heterogeneous landscapes. In these border cities, landscape dynamics are also characterized by the expansion of agriculture and savannas, highlighted by an increase in the disturbance index. Spatial pattern changes analysis shows that built-up areas, agriculture, and savannas exhibit trends of patch creation or aggregation, while forests are undergoing processes of dissection and patch attrition. Congolese cities bordering Zambia are undergoing substantial spatial changes propelled by intricate interactions between economic, demographic, and infrastructural factors. Our results highlight the necessity for sustainable development strategies to address challenges such as urban sprawl, deforestation, and fostering cross-border cooperation in border regions.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

Background: RNA helicase DHX15 plays a significant role in vasculature development and lung metastasis in vertebrates. In addition, several studies have demonstrated the overexpression of DHX15 in the context of hepatocellular carcinoma. Therefore, we hypothesized that this helicase may play a significant role in liver regeneration, physiology and pathology. Methods: Dhx15 gene deficiency was generated by CRISPR/Cas9 in zebrafish and by TALEN-RNA in mice. FANA Antisense-Oligonucleotides were used to silence Dhx15 in wild-type mice. The hepatocellular carcinoma tumor induction model was generated by subcutaneous injection of Hepa 1-6 cells. Results: Homozygous Dhx15 gene deficiency was lethal in zebrafish and mouse embryos. Dhx15 gene deficiency impaired liver organogenesis in zebrafish embryos and liver regeneration after partial hepatectomy in mice. Also, heterozygous mice presented decreased number and size of liver metastasis after Hepa 1-6 cells injection compared to wild-type mice. Dhx15 gene silencing with FANA Antisense-Oligonucleotides in wild-type mice resulted in 80% reduced expression in the liver and a significant reduction in other major organs. In addition, Dhx15 gene silencing significantly hindered primary tumor growth in the hepatocellular carcinoma experimental model. Regarding the potential use of DHX15 as a diagnostic marker for liver disease, patients with hepatocellular carcinoma showed increased levels of DHX15 in blood samples compared with subjects without hepatic affectation. Conclusion: Dhx15 is a key regulator of liver physiology and organogenesis, is increased in the blood of cirrhotic and hepatocellular carcinoma patients and has a key role in controlling hepatocellular carcinoma tumor growth and expansion in experimental models.

This article discusses collaborative ethnography as a meaningful source for spatial research, in particular, for participatory methodologies in urban planning and placemaking processes. It investigates the experiences with co-creation and co-research in different research projects to gain insight into the performance of collaborative ethnography as a technique to explore and enrich local knowledge. To better understand the possible causal relationships between the experience gained in the projects and the learnt lessons, we also identify recommendations for improving research methodologies to be applied in placemaking. The article concludes that collaborative ethnography is an effective tool for adding value to spatial co-research and co-creation processes. It opens opportunities for the co-production of space, ideas and knowledge, contributing at the same time to better informed decision-making. It also helps improve ideas and gather insights into the spatial needs of focus groups.

This study aimed to evaluate the safety and tolerability of STP1, a combination of ibudilast and bumetanide, tailored for the treatment of a specific subgroup of patients with Autism Spectrum Disorder (ASD), namely ASD Phenotype 1 (ASD-Phen1). We conducted a randomized, double-blind, placebo-controlled, parallel-group phase 1b study with two 14-day treatment phases (registered at clinicaltrials.gov as NCT04644003). Nine ASD-Phen1 patients were administered STP1, while three received a placebo. We assessed safety and tolerability, along with electrophysiological markers, such as EEG, Auditory Habituation, and Auditory Chirp Synchronization, to better understand STP1's mechanism of action. Additionally, we used several clinical scales to measure treatment outcomes. The results showed that STP1 was safe and well-tolerated, with electrophysiological markers indicating a significant and dose-related reduction of gamma power in the whole brain and in brain areas associated with executive function and memory. Treatment with STP1 also increased alpha 2 power in frontal and occipital regions and improved habituation and neural synchronization to auditory chirps. Although numerical improvements were observed in several clinical scales, they did not reach statistical significance. Overall, this study suggests that STP1 is safe and well-tolerated in ASD-Phen1 patients and shows indirect target engagement in ASD brain regions of interest.

Thermal camouflage is a highly sought-after technology to increase the survivability of military equipment against infrared (IR) detectors. Recently, two-dimensional (2D) nanomaterials have shown low IR emissivity, widely tunable opto-electronic properties and compatibility with stealth-applications. Among them, graphene and graphene-like materials are the most attractive 2D materials used for thermal camouflage applications. In particular, in multilayer graphene (MLG) charge density can be effectively tuned through sufficiently intense electric fields or through electrolytic gating. Therefore, MLG optical properties, like infrared emissivity and absorbance, can be controlled in a wide range by voltage bias. The large emissivity modulation achievable with this material makes it suitable in the design of thermal dynamic camouflage devices. Generally, the emissivity modulation in the multilayered graphene medium is governed by an intercalation process of non-volatile ionic liquids under a voltage bias. The electrically-driven reduction of emissivity leads to a decrease in the apparent temperature of the surface, which can match that of the background enabling thermal camouflaging. Since this property is common to other graphene-based materials, here we present a review specifically focused on the recent advances in the field of the thermal camouflage properties of graphene in the form of composite film and aerogel structures. A summary of the current understanding of the working principle of thermal camouflage materials, current limitations, and future opportunities is presented and discussed.

Introduction: Galectin-13 (Gal-13) is predominantly produced by the syncytiotrophoblast, while laeverin is expressed on the outgrowing extravillous trophoblast, and both are thought to be biomarkers of preeclampsia. The aim of this study was to assess the correlation between concentrations of Gal-13 and laeverin measured in maternal serum and amniotic fluid at 16-22 weeks of gestation and the sonographic assessment of the fetoplacental measurements. Materials and methods: Fetal biometric data, and placental volume and perfusion indices were measured in 62 singleton pregnancies. Serum and amniotic levels of Gal-13 and laeverin levels were measured using a sandwich ELISA. Results: Both amniotic fluid and serum Gal-13 levels expressed a negative correlation to the plasma laeverin level in mid-pregnancy. Serum laeverin level correlated positively with the gestational length at delivery (β=0.39, p

In light of the critical and emerging global challenge posed by the increasing frequency and severity of heatwaves in recent decades, this study demonstrates a practical and robust workflow that bridges the gap in understanding how the effective integration of remote sensing data with ground-based observations can facilitate a comprehensive assessment of spatiotemporal heatwave patterns and trends in the central region of Thailand. This research transcends traditional methods, utilizing air temperature (Tair) and satellite-derived land surface temperature (LST) data from 1981 to 2019. Results exhibit that an analysis of Tair indicates increasing daytime heatwaves in peri-urban areas, with significant trends in heatwave number (HWN), heatwave frequency (HWF), and heatwave duration (HWD), heatwave amplitude (HWA) rises in urban settings while nighttime heatwaves significantly intensify in rural locales. Notably, LST trends reveal varied patterns, with peri-urban areas showing marked daytime increases in heatwave magnitude (HWM), HWA, and HWF. Correlation analysis (p=0.05) highlights strong daytime associations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. Overall, this study advances approaches to the adaptable measurement and spatial-temporal pattern of heatwave-related risk areas, providing insights for decision-makers to develop sustainable practices and strategies for climate change adaptation.

The Dual-Frequency Precipitation Radar (DPR) on the Core GPM satellite provides spaceborne 3-dimensional observations of the precipitation field and surface rainfall rate with quasi global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and Drop Size Distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR derived products version 07 from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground reference. Four matching techniques between satellite estimates and ground level observations were tested, and best results were found for the so-called optimal comparison approach. Overall, GPM DPR products capture well the variability of the observed DSD at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw are observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, lowest errors were found for radar reflectivity factor and Dm, and highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested and it was found a substantial overestimation of stratiform cases compared to disdrometer observations.

Heating systems such as heat pump and combined heat and power cycle systems (CHP) are representing a key component in the future smart grid. Their capability to couple the electricity and heat sector promises a massive potential to the energy transition. Hence, these systems are continuously studied numerical and experimental to quantify their potential and develop optimal control methods. Although numerical simulations provide time and cost-effective solution for system development and optimization, they are exposed to several uncertainties. Hardware in the loop (HiL) system enables system validation and evaluation under different real-life dynamic constraints and boundary conditions. In this paper, a HiL system of heat pump testbed is presented. This system is used to present two case studies. In the first case, the conventional heat pump testbed operation method is compared to the HiL operation method. Energetic and dynamic analyses are performed to quantify the added value of the HiL and its necessity for dynamics analysis. The second case, the HiL testbed is used to validate the heat pump operation in a single family house participating in a local energy market. It enables not only the dynamics of the heat pump and the space heating circuit to be validated but also the building room temperature. The energetic analysis indicated a deviation of 2% and 5% for heat generation and electricity consumption of the heat pump, respectively. The model dynamics emphasized the model capability to present the dynamics of a real system with a temporal distortion of 3%.

Glaucoma, a global leading cause of blindness, necessitates accurate segmentation of the optic disc (OD) and optic cup (OC) for effective screening. However, existing segmentation methods based on convolutional neural networks (CNNs) suffer from high computational complexity and long inference times. In this paper, we propose an end-to-end segmentation method for the OD and OC using a lightweight MobileNetv3 as the feature extraction network. Our approach incorporates boundary branches and an adversarial learning network to achieve multi-label segmentation of the OD and OC. We evaluate the proposed method on three publicly available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental results demonstrate segmentation accuracies of 0.974/0.900, 0.966/0.875, and 0.962/0.880 for the OD and OC on the respective datasets, while significantly reducing inference time.

On 17 October 1947, Niels Bohr was made a knight of the Order of the Elephant by the King of Denmark in view of his outstanding achievements and contributions to science. Bohr designed his own coat of arms that featured a pattern of Yin and Yang (Tai Chi symbol) to symbolize the wave-particle complementarity. However, Bohr’s Yin-Yang diagram (YYD) was neither drawn based on the principles of quantum mechanics, nor did it originate from the traditional Taoist YYD. Scientists still have doubts about the legitimacy of using YYD as the icon of the wave-particle complementarity, because the YYD belonging to quantum mechanics itself is unknown so far. This article reports the YYDs existing in quantum mechanics and justifies the role of YYD in the wave-particle duality by showing that any system, whether classical or quantum, has an ideal YYD as long as it satisfies Bohr’s principle of complementarity (BPC). The deviation of a deformed YYD from the ideal YYD indicates the extent to which a real system satisfies BPC. This article constructs the quantum YYD by the complex quantum trajectory of a particle tunneling via a step barrier, which displays the continuous transition between the wave behavior and the particle behavior. It appears that the YYD designed by Bohr in his coat of arms resembles the YYD generated by tunneling motion, not only in appearance but also in the governing equation.

Price markups and firm Tobin’s Q ratios are widely believed to have been increasing in the past several decades. Various models for the calculation of price markups have been developed, but each have methodological drawbacks and some results are poorly reflective of the near past wider macroeconomic experience. This paper defines an approach for the definition and measurement of markup pricing using the measure of Economic Rent and The Capital Asset Pricing Model. The results show limited markup pricing for The UK’s FTSE 100 companies (2018-2023), but that that certain Real Estate, Technology/Media and Financial Services/Equity Investment firms have enjoyed higher price markup levels. An analysis of the business models of these firms is used to qualitatively propose explanations for such markups. Finally, this work offers a formal proof that that expected price markup is equal to Tobin’s Q and finds that the empiric market level of markup is near equivalent to the market Tobin’s Q; differences between the markup and Tobin’s Q at the level of the firm are equally assessed.

Advanced glycation end products (AGEs) play a significant role in the aging processes, further regarding as a hallmark of aging. Despite the importance, a lack of monitoring tools has hampered the study of relationship between AGEs and aging. Here, we firstly report a novel AGEs-selective probe, AGO. AGO presented its ability to detect AGEs more sensitively than a conventional approach of measuring autofluorescence from AGEs. In addition, we confirmed that AGO can detect AGEs depending on kinetics, reflecting by a preference to ribose-derived AGEs. Finally, AGO revealed its competence to visualize glycation products in glycation mimicking model, made by collagen framework. We believe that this study will enrich molecular toolboxes to understand physiological processes of AGEs during aging processes.

The utilization of solar energy technology is crucial for generating hydrogen in sustainable energy systems. While traditional methods involving natural gas, coal, and oil have been employed for hydrogen production, this study aims to explore environmentally friendly techniques. Green hy-drogen, despite being a more expensive option compared to fossil fuel-derived hydrogen, has prompted engineers to develop affordable and low-carbon technologies. The microgrid model was developed to showcase a self-contained DC islanded system designed to supply power to an electrolyzer, capitalizing on both a solar array and an energy storage unit. This comprehensive model offers a versatile platform for evaluating the operational dynamics involved in the sustainable production of green hydrogen. Over a span of seven days, it permits the as-sessment of hydrogen generation characteristics through the utilization of solar energy, either di-rectly from a solar array or through an integrated system that incorporates energy storage. The model encompasses a multi-domain framework, encompassing electrical, thermal liquid, and thermal gas aspects, facilitating a holistic exploration of the intricate processes underlying the green hydrogen production system. Through this simulation, the feasibility and efficiency of harnessing renewable energy sources for hydrogen production in an off-grid setting can be comprehensively examined and optimized, contributing to the advancement of sustainable energy solutions.

In this research work, we present a mathematical model for novel coronavirus -19 (NCOVID-19) which is consisted on three different compartments susceptible, infected and recovered classes abbreviated as under convex incident rate involving and emigration rate. We first derive the formulation of the model. Also, we give some qualitative aspects for the model including existence of equilibriums and its stability results by using various tools of nonlinear analysis. Then by mean of nonstandard finite difference scheme (NSFD), we simulate the results against the data of Wuhan city for the sixty days. By means of simulation, we show how protection, exposure, emigration, death and cure rates affect the susceptible, infected and recovered population with the passage of time involving emigration. On the basis of simulation, we observe the dynamical behavior due to emigration of susceptible and infected classes or one of these two.

We introduce a new, and quite general variational model for opinion dynamics based on pairwise interaction potentials and a range of opinion evolution protocols ranging from random interactions to global synchronous flows in the opinion state space. The model supports the concept of topic “coupling”, allowing opinions held by individuals to be changed via indirect interaction with others on different subjects. Interaction topology is governed by a graph that determines interactions. Our model, which is really a family of variational models, has, as special cases, many of the previously established models for the opinion dynamics. After introducing the model, we study the dynamics of the special case in which the potential is either a tent function or a constructed bell-like curve. We find that even in these relatively simple potential function examples there emerges interesting behavior. We also present results of preliminary numerical explorations of the behavior of the model to motivate questions that can be explored analytically

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

The methods of Artificial Intelligence (AI) have been used in the planning and operation of power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI including the use of Machine Learning (ML) to the power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way considering the supervised and unsupervised learning methods. Based on this, three complex application examples: wind power generation forecasting, smart grid security as-sessment (using two methods), and automatic system fault detection are presented and discussed in detail. Summary and outlook conclude the paper.

Antifungal agents are not always efficient in resolving vulvovaginal candidiasis (VVC), a common genital infection caused by overgrowth of Candida spp., including Candida albicans, or preventing recurrent infections. Although lactobacilli (which are dominant microorganisms constituting healthy human vaginal microbiota) are important barriers against VVC, the Lactobacillus metabolite concentration needed to suppress VVC is unknown. Therefore, we quantitatively evaluated Lactobacillus metabolite concentrations to determine their effect on Candida spp., including 27 vaginal strains of Lactobacillus crispatus, Lactobacillus jensenii, Lactobacillus gasseri, Lacticaseibacillus rhamnosus, and Limosilactobacillus vaginalis, with inhibitory abilities against biofilms of Candida clinical isolates. Lactobacillus culture supernatants suppressed viable bacteria by approximately 24%-92% relative to preformed Candida biofilms, but their suppression differed between strains, not species. Lactate production was necessary to suppress preformed biofilms and hyphal elongation of C. albicans, whereas hydrogen peroxide was not always essential. Both lactic acid and hydrogen peroxide were required to suppress Candida planktonic cell growth. Lactobacillus strains that significantly inhibited biofilm formation in culture supernatant also inhibited Candida adhesion to epithelial cells in an actual live bacterial adhesion competition test. Healthy human microflora and their metabolites may play important roles in the development of new antifungal agent against VVC caused by C. albicans.

The flow properties of pellets or granules are crucial for further processing the drug dosage forms. Optimal compression or filling the multiparticulate dosage forms into capsules is influenced by forces between discrete particles, which could be partially characterized by flow properties. Several techniques were developed for the examination of flowability, including static and dynamic methods applying empirical studies and the up-to-date chaos theory; however newest methods seem only to be powerful with the supplement of empirical principles. Our experiments try to refine both the technique of analysis and the methods by finding new, alternative ways. Our approach to the flowability measurements was to set up a dynamic time-dependent model which combines empirical observations and the chaos theory on a geometrical basis, thus find new aspects regarding flow properties of pellets and granules that could be relevant for drug developers.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

This review presents an updated overview of research concerning Leishmania protein structures, primarily sourced from the Protein Data Bank (PDB), that play a role in the metabolic pathways of the Leishmania parasite. Furthermore, we assess the current progress in the identification and development of bioactive chemical agents aimed at addressing this substantially overlooked tropical disease. We have analyzed experimental data obtained from in vitro, in vivo and in silico sources. This data has been categorized into four main areas: a) vector taxonomy and geographic distribution; b) parasite taxonomy and geographic distribution; c) enzymatic functions of proteins engaged in parasite/host interactions throughout various developmental stages (such as oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases, and cytokines); and d) established and experimental treatments employing bioactive chemical compounds. Our objective is to establish a foundational point of reference for research efforts concentrating on the elucidation of interaction mechanisms and the processes of ligand-protein activation/inactivation, specifically linked to Leishmania infections. Consequently, we emphasize enzymes recognized for their involvement in the biochemical pathways incited during Leishmania infection episodes. This review encapsulates the current understanding, offering insights to inform and direct future explorations aimed at targeting proteins and pathways to enhance the management of Leishmania-related diseases.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

In this paper we propose the explicit formulas of Average Run Length (ARL) of Exponentially Weighted Moving Average (EWMA) control chart for Autoregressive Integrated Moving Average: ARIMA (p,d,q) (P, D, Q)_L process with exponential white noise. To check the accuracy, the ARL results were compared with numerical integral equations based on the Gauss-Legendre rule. There was an excellent agreement between the explicit formulas and the numerical solutions. Additionally, we compared the computational time between our explicit formulas for the ARL with the one obtained via Gauss-Legendre numerical scheme. The computational time for the explicit formulas was approximately one second that is much less than the numerical approximations. The explicit analytical formulas for evaluating ARL0 and ARL₁ can produce a set of optimal parameters which depend on the smoothing parameter (λ) and the width of control limit (H), for designing an EWMA chart with a minimum ARL₁.

Most of the municipalities in the Gulf region are facing performance related issues in their municipal solid waste management (MSWM) systems. They lack to possess a deliberate inter-municipality benchmarking processes. Instead of identifying the performance gaps for their key components (e.g., personnel productivity, operational reliability, etc.) and adopt proactive measures, the municipalities primarily rely on an efficient emergency response. A novel hierarchical modeling framework, based on deductive reasoning, is developed for performance assessment of MSWM systems. Fuzzy rule based modeling using Simulink-MATLAB was used for performance inferencing at different levels, i.e., component, sub-components, etc. The model is capable of handling the inherent uncertainties due to limited data and imprecise knowledge base. The model’s outcomes can exclusively assist the managers working at different levels of organizational hierarchy for effective decision-making. Performance of the key component, assists the senior management to assess the overall compliance level of performance objectives. Subsequently, operation management can hone in the sub-components to acquire useful information for intra-municipality performance management. While, individual indicators are useful for inter-municipality benchmarking. The model has been implemented on two municipalities operating in Qassim Region, Saudi Arabia. The results demonstrate the model’s pragmatism for continuous performance improvement of MSWM systems in the country and elsewhere.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

Leukocyte migration is an essential function of innate and adaptive immune responses. Chemokines and their receptors control the migration system. The abundance of chemokines is controlled by atypical chemokine receptors (ACKRs), chemokine receptor-like molecules that do not couple to the G protein signaling pathways. Among them, ACKR4 regulates dendritic cell migration by controlling the ligands and is involved in tumor development in mouse models. Because no anti-mACKR4 mAb for flow cytometry has been reported, this study aimed to develop a novel monoclonal antibody (mAb) for mouse ACKR4 (mACKR4). Among the established anti-mACKR4 mAbs, A₄Mab-1 (rat IgG_2b, kappa), A₄Mab-2 (rat IgG_2b, kappa), and A₄Mab-3 (rat IgG_2b, kappa) recognized mACKR4-overexpressed Chinese hamster ovary-K1 (CHO/mACKR4) by flow cytometry. The dissociation constant (K_D) values of A₄Mab-1, A₄Mab-2, and A₄Mab-3 for CHO/mACKR4 were determined as 3.5 × 10⁻⁹ M, 6.0 × 10⁻⁹ M, and 1.6 × 10⁻⁹ M, respectively. Furthermore, A₄Mab-1 and A₄Mab-2 could detect mACKR4 by western blotting. These results indicated that A₄Mab-1, A₄Mab-2, and A₄Mab-3 help to detect mACKR4 by flow cytometry and western blotting and obtain the proof of concept in preclinical models.

Compared to the general population, patients with inflammatory bowel disease (IBD) are less likely to be vaccinated, putting them at an increased risk of vaccine-preventable illnesses. This risk is further compounded by the immunosuppressive therapies commonly used in IBD management. Therefore, developing new treatments for IBD that maintain immune function is crucial, as successful management can lead to better vaccination outcomes and overall health for these patients. Here, we investigate the potential of recombinant banana lectin (rBanLec) as a supporting therapeutic measure to improve IBD control and possibly increase vaccination rates among IBD patients. By examining the therapeutic efficacy of rBanLec in a murine model of experimental colitis, we aim to lay the foundation for its application in improving vaccination outcomes. After inducing experimental colitis in C57BL/6 and BALB/c mice with 2,4,6-trinitrobenzene sulfonic acid, we treated animals orally with varying doses of rBanLec 0.1-10 µg/ml (0.01 – 1 µg/dose) during the course of the disease. We assessed the severity of colitis and rBanLec’s modulation of the immune response compared to control groups. rBanLec administration resulted in a dose-dependent reduction in colitis severity (less pronounced weight loss, less shortening of the colon) and an improved recovery profile, highlighting its therapeutic potential. Notably, rBanLec-treated mice exhibited significant modulation of the immune response, favoring anti-inflammatory pathways (primarily reduction in a local [TNFα]/[IL-10]) crucial for effective vaccination. Our findings suggest that rBanLec could mitigate the adverse effects of immunosuppressive therapy on vaccine responsiveness in IBD patients. By improving the underlying immune response, rBanLec may increase the efficacy of vaccinations, offering a dual benefit of disease management and prevention of vaccine-preventable illnesses. Further studies are required to translate these findings into clinical practice.

The financial crisis augmented the need to predict real estate prices. The current study examines the application of Artificial Neural Network (ANN) model and Vector Autoregressive (VAR) model in forecasting real estate prices. The study uses daily observations, which accounted for a total of 2955 observations collected. We found that ANN outperformed significantly the VAR model providing evidence that these models can be used for valuation purposes in the area of real estate and financial markets. These findings are novel and important as they provide evidence of the usefulness of more sophisticated methodologies for market participants and regulators in real estate and financial markets.

Nervous systems evolved around 560 million years ago to coordinate and empower animal bodies. Ctenophores – one of the earliest-branching lineages – are thought to share few neuronal genes with bilaterians and may have evolved neurons convergently. Here we review our current understanding of the evolution of neuronal molecules in non-bilaterians. We also reanalyse single-cell sequencing data in light of new cell-cluster identities from a ctenophore and uncover evidence supporting the homology of one ctenophore neuron-type with neurons in Bilateria. The specific coexpression of the presynaptic proteins Unc13 and RIM with voltage-gated channels, neuropeptides and homeobox genes pinpoint a spiking sensory-peptidergic cell in the ctenophore mouth. Similar Unc13-RIM neurons may have been present in the first eumetazoans to rise to dominance only in stem Bilateria. We hypothesize that the Unc13-RIM lineage ancestrally innervated the mouth and conquered other parts of the body with the rise of macrophagy and predation during the Cambrian explosion.

The objective of this study was to evaluate the effect of in ovo injection of L-arginine (L-Arg) into Ross broiler eggs at different embryonic developmental stages on their survival, hatchability, and body weight (BW). Additionally, we have analyzed the levels of serum glutamic-oxaloacetic transaminase (SGOT) and serum glutamic-pyruvic transaminase (SGPT), protein expression of heat shock proteins (HSPs), also we have the determined micronuclei (MN) and nuclear abnormality (NA). Results showed that survival and hatching rates as well as body weight were increased on the 14th day incubation compared to 8th and 18th day incubation at lower concentration of L-Arg. Moreover, the levels of SGOT and SGPT were also significantly (P < 0.05) increased at 14th day incubation at the same concentration (100μg/μl/egg) of injection. In addition, IgM levels were increased on the 14th day incubation compared to other days. The protein expressions of HSP-47, HSP-60, and HSP-70 in the liver were significantly down-regulated whereas the expression of myogenin and MyoD were significantly up-regulated on the 14th day after incubation in treated with all different doses such as 100μg, 1000μg and 2500μg/μl/egg namely 3T1, 3T2 and 3T3 respectively. However, the treatment with low dose of L-Arg down-regulated expression levels of those proteins on the 14th day incubation. Histopathology of liver by hematoxylin and eosin (H&E) straining showed that the majority of liver damage, specifically intracytoplasmic vacuoles, were observed in 3T1, 3T2, and 3T3. The minimum dose of 100 μg/ml/egg on the 14th day of incubation significantly prevented intracytoplasmic vacuole damages. These results demonstrate that in ovo administration of L-Arg at (100μg/μl/egg) may be an effective method to increase chick BW, hatch rate, increasing muscle growth related proteins and promote the immune response through increasing IgM on the 14th day of incubation period.

Additive manufacturing (AM) has been the core area of sustainable manufacturing commonly recognized for its high efficiency in enabling cost-effective production towards sustainability. There are three models this research constituted: In Collection-Recycling-Manufacturing (CRM) model, technologies and processes are benchmarked followed by Business model that evaluates industrial key criteria. However, these are insufficient for AM to effectively play dominant role as the realization requires human factors such as multi-entities authorities, policy making and AM society to initiate and execute the plan. Strategy control model focuses on human-centric approaches such as demography, population control, policy, regulations, and management. It investigates each nation’s demography, and enables strategy, plan and control to relocate overcrowding population to rural areas. It also produces robust workforce to support AM and materials recycling through the appropriate applications. Through the construction of AM and materials recycling, strategy control model creates job opportunities for those unemployed people. It further builds infrastructure for the livelihood of new residents and supports AM home-based business (HBB).

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, the review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

In the rapidly evolving landscape of AI-supported programming education, there's a growing interest in leveraging such tools to support students helping about what good code makes out. This study investigates the impact of ChatGPT on code quality among part-time undergraduate students enrolled in introductory Java programming courses. With no prior Java experience, students from two separate groups completed identical programming exercises emphasizing coding conventions and code quality. Utilizing static code analysis tools, we assessed adherence to a common coding convention ruleset and calculated cyclomatic and cognitive complexity metrics for submitted code. Our comparative analysis highlights significant improvements in code quality for the ChatGPT-assisted group (treatment group), with marked reductions in rule violations and both cyclomatic and cognitive complexities. Specifically, the treatment group demonstrated greater adherence to coding standards, with fewer violations across several rules, and produced code with lower complexity. These results suggest that ChatGPT can be a valuable tool in programming education, aiding students in writing cleaner, less complex code and better adhering to coding conventions. However, the study's limitations, including the small sample size and the novice status of the participants, necessitate further research with larger, more diverse populations and in different educational contexts.

Dendritic Mesoporous Organosilica Nanoparticles (DMON) are a new class of biodegradable nanoparticles suitable for biomolecules delivery. In order to escape from the endosomes-lysosomes and to deliver biomolecules in the cytoplasm of cells, we studied photochemical internalization (PCI) and photodynamic therapy (PDT) of DMON. We added photosensitizers in the framework of DMON. DMON were also loaded with siRNA or FVIII factor protein. We made four formulations with four different photosensitizers, The photosensitizers allowed to perform imaging of DMON in cancer cells, but the presence of the tetrasulfide bond in the framework of DMON quenched the formation of singlet oxygen. Fortunately one formulation allowed to efficiently deliver proapoptotic siRNA in MCF-7 cancer cells leading to 31% of cancer cell death, without irradiation. For FVIII protein, it was loaded in two formulations with drug loading capacities (DLC) up to 25%. In conclusion DMON are versatile nanoparticles which allowed to load siRNA and to deliver it in cancer cells, and to load FVIII protein with good DLC. Due to the presence of tetrasulfide, it was not possible to perform PDT and PCI.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

Chronic inflammatory diseases are considered the most significant cause of death worldwide. Current treatments for inflammatory diseases are limited due to the lack of understanding of the biological factors involved in early-stage disease progression. Nerve growth factor (NGF) is a neurotrophic factor directly associated with inflammatory and autoimmune diseases like osteoarthritis, multiple sclerosis, and rheumatoid arthritis. It has been shown that NGF levels are significantly upregulated at the site of inflammation and play a crucial role in developing a robust inflammatory response. However, little is known about NGF's temporal expression profile during the initial progressive phase of inflammation. This study aimed to determine the temporal expression pattern of NGF in rat skin during Adjuvant-Induced Arthritis (AIA). Sprague Dawley rats were randomly divided into control and Complete Freund's Adjuvant (CFA) treated groups. Levels of NGF were evaluated following unilateral AIA at different time points and it was found that peripheral inflammation due to AIA significantly upregulated the expression of NGF mRNA and Protein in a biphasic pattern. These results suggest that NGF signaling is crucial for initiating and maintaining peripheral neurogenic inflammation in rats during AIA.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

In recent years, the implementation of artificial intelligence (AI) in accounting has grown in many domains, including auditing. AI permits auditors to evaluate large data sets and rapidly identify discrepancies and sequences in audit. This means auditors can review all of a client's transactions via a low time and effort. This entails auditing firms investing heavily in technological infrastructure as well as training their human resources to use cutting-edge AI technologies. Thus, this research sought to investigate the impact of the use of AI techniques by Jordanian external auditors in fraud risk assessment, the moderating effect of auditor size. The descriptive analytical method was used by distributing 280 questionnaires electronically to auditors employed in auditing firms operating in Jordan, The result reveals that AI techniques are extremely beneficial in fraud risk assessment at all levels, and the relationship between the use of AI techniques and the assessment of fraud risk has improved as auditor size has increased.

β-Ga2O3 is a well-known semiconductor material for power devices and other applications. Recently, β-Ga2O3 has also been reported as a scintillator material with a light yield of approximately 8400 ph./MeV, scintillation decay time of &lt;1 μs, and density of 6.44 g/cm3. In this study, 2-inch diameter β-Ga2O3 single crystals were prepared by oxide crystal growth using the cold crucible (OCCC) method under various oxygen partial pressures. In the OCCC method, as in the cold crucible method, a high frequency is applied directly to the oxide materials, which are heated and melted, and the melt is held by the outermost solid material itself that is cooled by water using a copper hearth. In the OCCC method, crystal growth is performed while rotating the seed crystal, as in the Czochralski method, to increase the crystal diameter. The optical properties and radiation responses of the crystals grown under various oxygen partial pressures were evaluated.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

Background and Objectives: To analyze survival in peritoneal and hemodialysis patients using chest radiography and biochemical parameters, determine dialysis etiologies and common mortality causes, reveal prognostic factors, and contribute to clinical practice. Materials and Methods: This retrospective cohort study was based on the analysis of data from 33 peritoneal dialysis and 37 hemodialysis patients who were followed and treated in our hospital between October 2018 and February 2020. Chest X-ray measurements (cardiothoracic index, pulmonary vascular pedicle width, right pulmonary artery diameter, diaphragmatic height) and biochemical parameters (urea, albumin, creatinine, parathormone, ferritin, hemoglobin, arterial blood gas, potassium) were analyzed to assess their impact on survival. Statistical analyses were performed using the Shapiro-Wilk, Wilcoxon signed rank test, Mann-Whitney U test, McNemar test, Kaplan-Meier survival analysis, Cox regression analysis, Spearman correlation test, Phi test, and ROC analysis. Results: The most common etiologies of dialysis were hypertension, type 2 diabetes mellitus, and urogenital disorders. The most common cause of death in peritoneal dialysis patients was peritonitis (38.5%), while in hemodialysis patients, it was cardiovascular diseases (47.4%). In chest X-ray measurements, pulmonary vascular pedicle width (p=0.003) and pulmonary artery diameter increased significantly (p=0.002) in hemodialysis patients, while pulmonary vascular pedicle width (p=0.028) and diaphragmatic height (p=0.004) increased significantly in peritoneal dialysis patients. No significant difference was found between the two groups in terms of biochemical parameters. Kaplan-Meier survival analysis showed that there was no significant difference in survival between peritoneal dialysis and hemodialysis methods (P>0.05). Significant correlations were found between both radiologic status and clinical status and between intact parathormone levels and pulmonary vascular pedicle width (p=0.016, p=0.024). In Cox regression analysis, age, intact parathormone levels, iPTH/PVPW ratio, and clinical status were found to be factors affecting survival (p

The seed habit is the most complex and successful method of sexual reproduction in vascular plants. It represents a remarkable moment in the evolution of plants that afterwards spread on land. In particular, seed size had a pivotal role as for evolutionary success and agronomic trait, especially in the field of crop domestication. Given that crop seeds constitute one of the primary products for consumption, it follows that seed size represents a fundamental determinant of crop yield. This adaptative feature is strictly controlled by genetic traits from both maternal and zygotic tissues, although seed development and growth are also affected by environmental cues. Despite being a highly exploited topic for both basic and applied research, there are still many issues to be elucidated as for developmental biology as well as for agronomic science. This review addresses a number of open questions related to cues that influence seed growth and size, and how developmental processes are influenced by them. Besides, new insights on the genetic-molecular control of this adaptive trait are presented.

This article deals with an analytical approach to determining the stress state in trochoidal profiles under shear bending. The transverse shear stresses cannot be neglected, especially in the case of (space-saving) shafts with a low aspect ratio (l/d). In addition to bending stresses, the transverse shear stresses are a significant component of the principal stresses. The approach is based on a formulation of the elastic behavior of a non-circular-profiled shaft using complex stress functions, according to Muskhelishvili. In order to determine the shear stresses, similar to the torsion load case, a conformal mapping must be found that can completely transform the unit circle in the model plane to the profile area in the real plane. For simple and higher epiotrochoidal profiles, an exact and complete mapping was determined from the contour equation. The method was applied to such profiles as an example. Equations were derived to determine the maximum shear and bending stresses and the bending deformations. For practical applications, additional equations for the main stresses that are decisive in dimensioning were determined. Accompanying numerical investigations were carried out and compared with the analytical results, where a very good agreement was observed.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

With the rapid development of technology and the advancement of Internet technology, various social networking platforms are gradually coming into people's view and occupying a higher and higher position. In the recommendation scenario, the user-item interaction naturally forms a bipartite heterogeneous graph structure and with the development of graph embedding and graph neural network technologies based on deep learning to process graph domain information, the combination of graph information and recommendation systems shows strong research potential and application prospects. The methodological improvement of the recommendation algorithm based on collaborative filtering takes advantage of the nature that user-items can form a bipartite graph in the recommendation scenario. The existing methods still have some shortcomings. The methods that only use weights or convolutional recurrent neural networks to implicitly model different historical behaviors lack explicit modeling of video switching relationships in serialized behaviors. The user's interest is changing all the time, so it is not possible to recommend based on the user's history, and it is necessary to consider both the long-term and short-term interest of the user according to the video content in order to achieve accurate recommendation of short videos. In this paper, we design a recommendation model based on graph neural network, which models users' long-term and short-term interests by two vector propagation methods, respectively.