The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Titania is one of the most comprehensively studied nanostructures due to its widespread applications in production of catalytic, gas- sensing and corrosion- resistance materials [1]. M-polynomial of nanotubes has been vastly investigated as it produces many degree-based topological indices which are numerical parameters capturing structural and chemical properties. These indices are used in the development of quantitative structure-activity relationships (QSARs) in which the biological activity and other properties of molecules are correlated with their structure like boiling point, stability, strain energy etc of chemical compounds. In this paper, we determine M-polynomials of single-walled titanium (SW TiO2) nanotubes and recover important topological degree based indices of them to theoretically judge these nanotubes. We also use Maple to plot surfaces associated to different types of single-walled titanium (SW TiO2) nanotubes.

Crosslinking of proteins for their irreversible immobilization on surfaces is a proven and popular method. However, many protocols lead to random orientation and the formation of undefined or even inactive by-products. Most concepts to obtain a more targeted conjugation or immobilization requires the recombinant modification of at least one binding partner, which is often impractical or prohibitively expensive. Here a novel method is presented, which is based on the chemical preactivation of Protein A or G with selected conventional crosslinkers. In a second step, the antibody is added, which is subsequently crosslinked in the Fc part. This leads to an oriented and covalent immobilization of the immunoglobulin with a very high yield. Protocols for Protein A and Protein G with murine and human IgG are presented. This method may be useful for the preparation of columns for affinity chromatography, immunoprecipitation, antibodies conjugated to magnetic particles, permanent and oriented immobilization of antibodies in biosensor systems, microarrays, microtitration plates or any other system, where the loss of antibodies needs to be avoided, and maximum binding capacity is desired. This method is directly applicable even to antibodies in crude cell culture supernatants, raw sera or protein-stabilized antibody preparations without any purification nor enrichment of the IgG. This new method delivered much higher signals as a traditional method and, hence, seems to be preferable in many applications.

Inflammatory bowel disease (IBD) is a chronic inflammatory intestinal disorder that poses significant challenges in terms of prevention and treatment due to its complex etiology. The multifaceted nature of IBD's underlying causes means that developing effective interventions requires a nuanced understanding and innovative approaches. As a result, research into new drugs and treatment strategies has become a critical area of focus in the scientific community.Among the promising areas of research are phenolic acids, which are abundant in plant-based diets. These compounds have garnered attention because of their potent antioxidant and anti-inflammatory properties, which have shown potential in alleviating colitis. This review aims to provide a comprehensive overview of phenolic acids, highlighting their structures and primary dietary sources, specifically focusing on benzoic acid and cinnamic acid.We delve into the role of phenolic acids as a nutritional therapy for the prevention and treatment of IBD. The review synthesizes findings from animal and cell experiments, which have demonstrated that phenolic acids can effectively mitigate IBD symptoms induced by drug exposure or genetic predispositions. The mechanisms through which phenolic acids exert their beneficial effects include enhancing intestinal mucosal barrier function, reducing oxidative stress, inhibiting the overactivation of the immune response, and regulating the balance of intestinal microbiota.The evidence from these studies underscores the therapeutic potential of Nerium oleander in Acetic acid-induced Ulcerative Colitis in BALB-C mice and its amelioration. However, our observations also highlight the necessity for further basic and clinical research to fully understand the capabilities and limitations of phenolic acids and their derivatives as novel therapeutic agents for IBD. Comprehensive investigations will be essential to translate these promising findings into practical treatments for individuals suffering from this debilitating condition.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions

Balancing the efficiency and robustness of food systems is a well-known trade-off process. Over-optimization of efficiency may lead to excessive use of resources. On the other hand, under-optimization of efficiency may lead to excessive waste of resources. Using non-parametric methods can help in developing actionable frameworks through which decision-makers and stakeholders can effectively balance and improve food systems' efficiency and robustness. Under this context, this paper presents a new structured approach integrating two well-suited non-parametric methods for analyzing and balancing the interconnection between the efficiency and robustness of food systems. This approach consists of three main steps. First, the slacks-based data envelopment analysis (SBM-DEA) is utilized to analyze the efficiency of five food robustness dimensions. Second, the weighted efficiency of these dimensions is computed and analyzed to provide an insight into their influence on the food system efficiency. Finally, two search methods were developed to identify opportunities for improving efficiency and robustness. The outcomes of these methods have been analyzed and validated to ensure their effectiveness and viability. The operational and mathematical properties of the proposed approach were evaluated using data from thirty-seven food-producing countries. The weights of the robustness dimensions in this study were determined using the analytic hierarchy process (AHP) method. The input matrix of the AHP was constructed using expert judgment and consultations with stakeholders. The results of this study have shown an appropriate level of applicability and effectiveness of the proposed approach. While the first search method provided valuable insights, the second method proved to be more effective in identifying the sources of inefficacy of robustness dimensions. Several conclusions have been drawn and reported with some recommendations for future research works.

The magnetic properties of 0.10 mm ultra-thin grain-oriented (UTGO) silicon steel and Fe-based amorphous (FBA) alloy under sinusoidal excitation were measured experimentally, and the magnetic field strength and iron loss of the two materials under different frequencies and magnetic densities were obtained. Based on the measured data, the magnetization and loss characteristics of the two materials were analyzed and compared. Furthermore, two Epstein square ring models of the same specification and different materials were designed, and the reliability of the models was verified. Then, the electromagnetic characteristics of the two Epstein square ring models at higher and lower frequencies were calculated by using the finite element method, and the iron loss was obtained and compared. The results show that the FBA alloy has good application characteristics at low frequency and low power, and the 0.10 mm UTGO silicon steel has good application characteristics at high frequency and high power. This research can provide important data basis to promote the application of these two materials in new energy equipment.

Infectivity assays are the key analytical technology for development of manufacturing processes for virus-based therapeutics. Here, we introduce a novel assay format that utilizes label-free bright field images to determine the kinetics of infection-dependent changes in cell morphology. In particular, cell rounding is directly proportional to the amount of infectious virus applied, enabling rapid determination of viral titers in relation to a standard curve. Our kinetic infectious virus titer (KIT) assay is stability-indicating and, due to its sensitive readout method, provides results within 24 hours post-infection. Compared to traditional infectivity assays, which depend on a single readout of an infection endpoint, cumulated analysis of kinetic data by a fit model results in precise results (CV < 20%) based on only three wells per sample. This approach allows for a high throughput with ~400 samples processed by a single operator per week. We demonstrate the applicability of the KIT assay for VSV-GP and NDV, but it can potentially be extended to a wide range of viruses that induce morphological changes upon infection. The versatility of this assay, combined with its independence from specific instruments or software, makes it a promising solution to overcome the analytical bottleneck in infectivity assays within the pharmaceutical industry and as a routine method in academic research.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Polymer-based (nano)composite foams, especially those having a microcellular structure, containing suitable conductive (nano)fillers, have been shown to act as good shielding materials, hence coming as promising to act as shields in electronic devices, limiting/avoiding electromagnetic interference (EMI) pollution. Nevertheless, due to their high (micro)structural complexity, related to their multiphase nature, there is still a lot of unawareness behind the shielding mechanisms acting in these materials, coming as a necessity to study their microstructure-cellular/porous structure-properties relations, especially in terms of their electrical conductivity and EMI shielding efficiency (EMI SE). To target and control the electrical conductivity through a controlled distribution/dispersion of conductive nanofillers, single or combined (nanohybrids), as there is a direct relation between electrical conductivity and EMI SE, as well as the main EMI shielding mechanisms working on the developed microcellular structure of the foams, which are expected to be absorption-dominated or absorption/multiple reflection-based, are two of the main objectives when combining polymer foaming with the addition of conductive nanofillers. The present review work considers the recent developments and trends on polymer-based foams containing conductive nanofillers, especially carbon-based such as carbon nanotubes or graphene-based materials, as well as similar porous structures created using trending technologies such as 3D printing, for EMI shielding applications. It is divided in different sections, starting with the strategy of microcellular foaming to develop polymer-based foams with enhanced EMI shielding characteristics, especially focusing on technologies and methods using supercritical CO2 (sCO2); continuing with the use of polymer foams as templates to synthesize carbon foams with improved EMI shielding performance for high use temperature applications; the recent strategy of combining different functional (nano)fillers to create hybrid nanofillers/nanohybrids to be used at lower amounts as conductive fillers in polymer foams; the control and selective distribution of the nanofillers in terms of favoring the formation of a more effective conductive network and hence promote the enhancement of the EMI SE; the very recent use of computational approaches to tailor the EMI shielding properties of composite foams; and the very trending possibility of creating cellular components with varied porous structures from these materials using 3D printing; to finish, some future perspectives are discussed.

Debates on climate change challenges around the world are moving in favour of nature-based solutions (NBS) as potential mitigation and adaptation options. Mangroves and associated blue carbon ecosystems are nature-based solutions against sea level rise and other coastal hazards. As such, protecting the existing mangrove forests and restoration of the degraded areas is integral in this aspect. However, conventional mangrove restoration approaches in high-energy areas result in poor performance due to the removal of seedlings by waves. Here, we assessed the efficacy of using modified Riley Encasement Methods in the restoration of eroding intertidal areas of Gazi Bay, Kenya. Vegetation and soil baseline data were collected in 49 square plots of 100m2; established along belt transects perpendicular to the waterline. The following mangrove vegetation data was collected; species composition, tree height (m), and stem diameter (cm); from which the importance value index (IV), basal area and standing density (stems/ha) were derived. Sediment cores were made in the center of each square plot for carbon and grain size analysis. Mangrove (Rhizophora mucronata) planting adopted a randomized complete block design (RCBD) in which the planting area was divided into three blocks (A, B, C). Within each block, treatments (bamboo and different-sized PVC pipes) were randomly assigned locations. Results show that seedlings grown within PVC encasements had high survival and growth rates, surpassing bamboo and control groups. This implies that PVC pipes offer better protection from external factors. These findings are critical in highlighting potential enhancements for the effectiveness of PVC pipes in mangrove restoration. They also reinforce on the need to consider the incorporation of PVC encasements as an alternative mangrove restoration technique in high-energy locations.

Reducing meat consumption is better for the environment. Unfortunately, commercial plant-based meat substitutes are often more expensive than meat and thus have not seen widespread adoption. This paper analyzes commercially-available spirulina, soy, pea, and brown rice protein isolates characteristics to provide data for non-meat protein cost reductions. Thermal and rheological properties, viscosity, density, and particle size distribution are analyzed for further study on alternative protein-based food processing. Differential scanning calorimetry analysis produced dry amorphous-shaped curves and paste curves with a more distinct endothermic peak. Extracted linear temperature ranges for processing in food production for spirulina was 70-90ºC; soy protein was 87-116ºC; pea protein was 67-77ºC; and brown rice protein was 87-97ºC. Viscosity analysis determined each protein material was shear-thinning and that viscosity increased with decreased water concentration, with rice being an exception to the latter trend. The obtained viscosity range for spirulina was 15,100-78,000cP; soy protein was 3,200-80,000cP; pea protein was 1,400-32,700cP; and brown rice protein was 600-3,500cP. The results indicate that extrusion is a viable method for further processing of the protein isolates as this technique has a large temperature operating range and variable screw speed. Data provided here can be used to make single or multi-component protein substitutes.

A small but growing body of literature suggests that outdoor experiences during childhood affect environment-related behaviors in adulthood. However, research on the magnitude of the effect (effect size) of outdoor experience on learners’ behaviors remains scarce. In this study, we explore the extent to which outdoor experiences associate with environmentally responsible behaviors. Our sample consisted of 143 ninth- and tenth-grade students living on a Greek island. The data were collected using a properly adjusted environmental literacy instrument. Two different methodological pathways, i.e., a quasi-experimental approach and correlation analysis, were used to analyse the data. A tentative variable representing the frequency and intensity of students’ experiential contact with nature was found to be the strongest available predictor of their self-reported pro-environmental behaviors. The findings of this study support the significance of outdoor, experiential learning during childhood in shaping individuals’ environmental behaviors.

The possibility of deep radiochemical purification of Li2CO3 has been examined in the frame of the purification program of AMoRE collaboration. In this experiment, commercial Li2CO3 was converted into LiNO3. Co-precipitation with inorganic salt-based carriers followed by membrane filtration and sorption using MDM inorganic sorbent methods were tested for the removal of alkaline-earth and transition metals, potassium, magnesium, aluminum, uranium, thorium, and radium. The calcium molybdate-based carrier was the most efficient for removing Th, U, and K. Subsequently, the radium, calcium, and barium contaminations were removed with MDM sorbent. After the impurities’ removal, the final Li2CO3 product was synthesized with NH4HCO3 sludge. The separation factors were derived by means of ICP-MS and HPGe analyses of the initial material, intermediate and final products. The study showed the optimum conditions of co-precipitation and sorption to reach a high yield and radiopurity of lithium carbonate used for low radioactive background experiments.

Climate and land-use changes have been contributing to increase the occurrence of extreme wild-fires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the effects of land-use change on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini index) between 1984 and 2019, which was combined with 2018 carbon stock data (above- and below-ground), and a landscape typology re-sulting from cluster analysis over land-use composition, diversity and configuration, to explore relationships between landscape types and the two major ecosystem services at stake: wildfire reduction and carbon stock. Cross tabulations, logistic and linear regressions were performed on these data and results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt forest plantations and high-hazardous fire regime but hold the highest carbon stock. Shrubland-mixed landscapes were associated with lower carbon stocks but less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agro-forestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes however, this good wildfire performance is achieved at the cost of the poorest carbon stock, de-noting land degradation, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching-Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm is utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization is investigated, and different charging profiles and probability distributions are defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources are introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

Soybean are vulnerable to drought and temperature increases potentially induced by climate change. The purpose of this study was to determine if the use of cover crops (CC) can influence the gas exchange potential of glyphosate tolerant soybean when the vapor pressure deficit (Vpd) increases. This two years study was conducted in an open experimental field comprising Direct Seeding plots without CC (DS) or with CC (DSCC). Stomatal conductance (Gs) was measured five times on the same identified leaves following GBH application. These leaves were then collected in order to observe the stomata (size, density and stomatal index) with a scanning electron microscope and to perform measurements on foliar traits (venation density, width of midrib). The Vpd was calculated concomitantly to Gs measurements at the leaf surface. The results suggest that the use of CC promotes phenotypic change in soybean leaves (more elaborate venation and a higher stomatal density), which in turn may enhance their tolerance to drier conditions. In 2019, Gs could be up to 29% higher in DSCC plots compared to DS ones along with similar Vpd values. This study shows that the benefits of using CC can be observed through the morphological development strategies of the crop plants and their higher tolerance to drought.

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent based simulator will enable the policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify the vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM ) which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population, allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of Covid-19 are used to validate the proposed model. However, it is highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

The cell concentration measurement on a culture assay using bioimpedance is a very useful tool, but it is complex to translate impedance to cell concentration values. The purpose of this work is to find a method to obtain in real time the cell concentration values for a given cell-culture assay using an oscillator as the measurement circuit. From a basic cell-electrode model, enhanced models of the cell culture immersed in a saline solution (culture medium) can be derived. These models can be used in a fitting routine to real time estimation of the cell concentration in a cell culture, using the oscillation frequency and amplitude delivered by measurement circuits proposed by the authors. Based on real experimental data (frequency and amplitude of oscillations) obtained by connecting the cell culture to an oscillator as a load, the fitting routine is simulated, and real time data of cell concentration is achieved. These results are compared with concentration data found by traditional optical methods for counting. In addition, the error obtained is divided and analyzed in two parts: in the first part of the experiment (when the few cells are adapting to the culture medium) and the second part of the experiment (when the cells grow exponentially until they completely cover the well).Low error values are obtained in the growth phase of the cell culture (the relevant phase), therefore the results obtained are considered to be promising, proving that the fitting routine is valid, and that the cell concentration can be measured in real time using an oscillator.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes, T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20101 MRI images) was used for training, while C2 (197, 11333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics models' performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95%-Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. 620 images were misclassified; Manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased on average the PFS model C-I by 14.6% compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

Autoantibodies against type 1 interferons (IFN-I) are highly specific marker for type 1 autoimmune polyglandular syndrome (APS-1). Moreover, determination of antibodies to IFN-ω and IFN-α2 allows a short-term diagnosis in patients with isolated and atypical forms of APS-1. In this study, a comparison of three different methods, namely, multiplex microarray-based, cell-based and enzyme-linked immunosorbent assays for detection of antibodies against omega-interferon and alpha2-interferon was carried out. A total of 206 serum samples from adult patients with APS-1, APS-2, isolated autoimmune endocrine pathologies, non-autoimmune endocrine disorders and healthy individuals were analyzed. In the APS-1 patient cohort (n=18), there was good agreement between the results of anti- IFN-I antibody tests performed by three methods, with 100% specificity and sensitivity for microarray-based assay. Although only the cell-based assay can determine the neutralizing activity of autoantibodies, the microarray-based assay can serve as a highly specific and sensitive screening test to identify anti- IFN-I antibody positive patients.

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the expansion of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for static and dynamic scenarios, regardless of the number of stations. Moreover, the performance difference is amplified as the number of stations increases, with DQN and DDPG showing a 27% increase in throughput with 50 stations compared to BEB. Furthermore, DQN and DDPG presented similar performances.

Acute low back pain (LBP) stands as a leading cause of activity limitation and work absenteeism, and its associated healthcare expenditures are expected to become substantial when acute LBP develops into a chronic and even refractory condition. Therefore, early intervention is crucial to prevent progression to chronic pain whose management is particularly challenging and for which the most effective pharmacological therapy is still controversial. Current guideline treatment recommendations vary and are mostly driven by expertise with opinion differing across different interventions. Thus, it is difficult to formulate evidence-based guidance when relatively few randomized clinical trials did explore the diagnosis and management of LBP while employing different selection criteria, statistical analyses, and outcome measurements. This narrative review aims to provide a critical appraisal of current acute LBP management by discussing the unmet needs and areas of improvement from bench-to-bedside and proposes multimodal analgesia as the way forward to attain an effective and prolonged pain relief and functional recovery in patients with acute LBP.

A Chinese superalloy GH4099 (~20 vol.% γ' phase), which can operate for long periods of time at temperatures of 1173-1273 K, was fabricated by electron beam melting (EBM). Argon gas atomized (AA) and plasma rotation electrode process (PREP) powders with the similar composition and size distribution were used as raw materials for comparison. Microstructure and mechanical properties of both the as-EBMed and post-treated alloy samples were investigated. The results show that the different powder characteristics resulted in the different build temperatures for AA and PREP samples, which were 1253 K and 1373 K, respectively. With increasing the building temperature, the EBM processing window shifted towards the higher scanning speed direction. Furthermore, intergranular cracking was observed for the as-fabricated PREP sample as a result of local enrichment of Si at grain boundaries. The cracks were completely eliminated by hot isostatic pressing (HIPing) and did not re-open during subsequent solution treatment and aging (STA). Fine spherical γ' phase precipitated uniformly after STA. The tensile strength of the HIP+STA samples was ~920 MPa in the building direction and ~850 MPa in the horizontal direction, comparable with that of the wrought alloy.

Place-based education (PBE) and active learning are effective strategies for geoscience education. However, traditional PBE via field trips requires significant resources, time, physical abilities, and expertise of teachers. We provide an alternative PBE experience by showing how different kinds of geoscience videos can be spatially organized into one digital interactive virtual environment. Here, we present the Google Earth Geoscience Video Library (GEGVL) which uses Google Earth and location specific videos about Earth events to create a virtual PBE experience. Using Google Earth, GEGVL organizes place-based videos by locations and links pertinent non-place-based videos and allows users to roam the globe in search of geoscientific videos that are pertinent to them or their students. Currently GEGVL contains 150 videos organized into ten different geoscience disciplines: Plate Tectonics, Minerals, Structural Geology, Metamorphism, Magmatism, Hydrology, Environmental Science, Sedimentology, Paleontology, and Paleomagnetism. Despite stability challenges with Google Earth integration, results of user surveys among lower division undergraduates show that the design logic of GEGVL is promising virtual PBE organizer for interesting students in and helping them learn about Earth sciences.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Plasmodium falciparum dihydroorotate dehydrogenase (PfDODH) is one of the enzymes currently explored in the treatment of malaria. Although there is currently no clinically approved drug targeting PfDODH, many of the compounds in clinical trials have [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine backbone structure. This study sought to design new compounds from the fragments of known experimental inhibitors of PfDODH. Nine experimental compounds retrieved from Drug Bank online were downloaded and broken into fragments using Schrodinger power shell; the fragments were recombined to generate new ligand structures using BREED algorithm. The new compounds were docked with PfDODH crystal structure, after which the compounds were filtered with extensive drug-likeness and toxicity parameters. A 2D-QSAR model was built using the multiple linear regression method and externally validated. The compounds electronic behaviours were studied using DFT calculations. Structural investigation of the six designed compounds, which had lower binding energies than the standard inhibitors, showed that five of them had [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine moieties and interacted with essential residues at the PfDODH binding site. In addition to their drug-like and pharmacokinetic properties, they also showed minimal toxicities. The externally validated 2D-QSAR model with R² and Q² values of 0.6852 and 0.6691, confirmed the inhibitory prowess of these compounds against PfDODH. The DFT calculations showed regions of the molecules prone to electrophilic and nucleophilic attack. The current study thus provides insight into the development of a new set of potent PfDODH inhibitors.

Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.

Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, Discrete Multiphysics is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sort in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect the rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure.

In mountain communities like Sebei, Uganda, that are highly vulnerable to emerging and reemerging infectious diseases, community-based surveillance plays an important role in the monitoring of public health hazards. In this survey, we explored capacities of Village Health Teams (VHTs) in Sebei communities of Mount Elgon in undertaking surveillance tasks for emerging and reemerging infectious diseases in the context of a changing climate. We used participatory epidemiology techniques to elucidate VHTs’ perceptions on climate change and public health and assess their capacities in conducting surveillance for emerging and reemerging infectious diseases. Overall, VHTs perceived climate change to be occurring with wider impacts on public health. However, they have inadequate capacities in collecting sur-veillance data. The VHTs lack transport to navigate through their communities and have in-sufficient capacities in using mobile phones for sending alerts. They do not engage in reporting other hazards related with the environment, wildlife and domestic livestock that would ac-celerate infectious disease outbreaks. Records are not maintained for disease surveillance ac-tivities and the abilities of VHTs to analyze data are also limited. However, VHTs have access to platforms that can enable them to disseminate public health information. The VHTs thus need to be retooled to conduct their work effectively and efficiently through equipping them with adequate logistics and knowledge on collecting, storing, analyzing, and relaying data, which will improve infectious disease response and mitigation efforts.

With the continuous development of Artificial Intelligence technology and Internet of Things engineering, more and more driver-less vehicles have been developed and put into the industrial production. The birth of driver-less vehicles undoubtedly brings new vitality to a large amount of industries, particularly in transportation. For the mining industry, transportation is undoubtedly an extremely important link in the whole production process. If the driver-less vehicles can be applied to the underground mines, it can not only improve the production and transportation capacity of the whole mine, but also can reduce the occurrence of many mine safety accidents. ZigBee WSN technology can play a greater role in the narrow environment like underground mines according to the relevant literature, this concept paper just like a engineering project plan mainly tries to integrate the ZigBee WSN technology and the communication-based train control (CBTC) system to explore the possibility of the driver-less vehicles to be used in the underground mines, which aims to solve practical engineering problems for the engineering projects. As the mining engineers, we put forward the concept of this integrated system in this concept paper, but we need to continue to work hard for the future of the underground mines. This concept paper serves just as a guide to the Tossing out a brick to get a jade gem, has a few implications for the development of underground mine transportation.

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently ‘reaction-prone’. The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic ‘hydrophobic ↔ polar’ mutations serve as a potential breakthrough.

Impact in the form of innovation and commercialisation is an essential component of publicly funded research projects. PHUSICOS, an H2020 Innovation Action project, aims at demonstrating the use of nature-based solutions for mitigating hydrometeorological hazards in rural and mountainous areas. The work program is built around key innovation actions, and each WP leader specifically responsible for nurturing innovation processes, maintaining market focus and ensuring relevance for the intended recipients of the project results. Key success criteria for PHUSICOS include up-scaling and mainstreaming of NBS to reach broader market access. An innovation strategy and supporting tools for implementing this within PHUSICS has been developed and key concepts forming the basis for this strategy are presented in this research note.

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system wide transition has not been researched sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric, diesel and gasoline powered vehicle models. We use the transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently we analyze the product systems’ vehicle production, use phase and End-of-Life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of the complete car based transportation in urban agglomerations and is able to analyze different transport sectors.

Feature Selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. The problem of choosing a feature is to find the most important subset of the main attributes in a specific domain, and its main purpose is removing additional or unrelated features, and ultimately improving the accuracy of the classification algorithms. As a result, the problem of FS can be considered as an optimization problem, and use metaheuristic algorithms to solve it. In this paper, a new hybrid model combining whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of Opposition based Learning (OBL) which name is HWOAFPA. In our proposed method, using natural processes of WOA and FPA, we tried to solve the problem of optimization of FS; and on the other hand, we used an OBL method to ensure the convergence rate and accuracy of the proposed algorithm. In fact, in the proposed method, WOA create solutions in their search space using the prey siege and encircling process, bubble invasion and search for prey methods, and try to improve the solutions for the FS problem; along with this algorithm, FPA improves the solution of the FS problem with two global and local search processes in an opposite space with the solutions of the WOA. In fact, we used all of the possible solutions to the FS problem from both the solution search space and the opposite of solution search space. To evaluate the performance of the proposed algorithm, experiments were carried out in two steps. In the first stage, the experiments were performed on 10 FS datasets from the UCI data repository. In the second step, we tried to test the performance of the proposed algorithm in terms of spam e-mails detection. The results obtained from the first step showed that the proposed algorithm, performed on 10 UCI datasets, was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. Also, the results from the second step showed that the proposed algorithm which was run on the spam e-mail dataset, performed much more accurately than other similar algorithms in terms of accuracy of detecting spam e-mails.

Desalination accounts for 1% of the total global water consumption and is an energy-intensive process, with the majority of operational expenses attributed to energy consumption. Moreover, at present, a significant portion of the power comes from traditional fossil fuel-fired power plants and the greenhouse gas emissions associated with power production along with concentrated brine discharge from the process, pose a severe threat to the environment. Due to the dramatic impact of climate change, there is a major opportunity to develop sustainable desalination processes to combat the issues of brine discharge, greenhouse gas emissions along with a reduction in energy consumption per unit of freshwater produced. Nanotechnology can play a vital role to achieve specific energy consumption reduction as nanofluids application increases the overall heat transfer coefficient enabling the production of more water for the same size desalination plant. Furthermore, concentrated brine discharge harms the marine ecosystems, and hence, this problem must also be solved to support the objective of sustainable desalination. Several studies have been carried out in the past several years in the field of nanotechnology applications for desalination, brine treatment and the role of renewable energy in desalination. This paper aims to review the major advances in this field of nanotechnology for desalination. Furthermore, a hypothesis for developing an integrated solar thermal and nanofluid sustainable desalination system, based on the cyclic economy model is proposed.

The article introduces a new solution approach to grid-connected single-stage three-phase PV inverter whereby a dc-link voltage can successfully achieve the desired performance. As a single-stage, the variable output of perturb and observe maximum power point tracking (P&O based-MPPT) is fully utilized as dc-link voltage reference in inverter control scheme. As a result, it is challenging for the dc-link voltage to preserve energy balance by tracking its variable dc-link voltage reference under unpredictable environmental changes. To overcome this challenge, an adaptive reference proportional-integral (ARPI) controller, self-tuning, is designed and implemented to compel the dc-link voltage tracking its voltage reference to be equal as closely as possible; hence, the capability of the dc-link under abnormal events substantially guarantees the best energy balance and transient performance enhancement. To verify the validity of the method, simulation results for multiple events scenarios on 250 KW single-stage grid-connected PV systems show the effectiveness of the ARPI controller compared with PI controller.

In this paper, we present the design, manufacturing and characterization of a soft textile-based clutch (TBC) that switches between locking and unlocking of its linear displacement by exploiting vacuum stimulation. The applied vacuum locks the relative sliding motion between two elaborated textile webbings covered by an elastic silicone rubber bag. Based on different fabrication techniques, such as silicone casting on textile, melt embossing for direct fabrication of miniature patterns on textile and sewing, we developed three groups of TBC samples based on friction and interlocking principles and we compared their performance in blocking configuration. The clutch with interlocking mechanism presented the highest withstanding force (150 N) respect to the one (54 N) recorded for the friction-based clutch. The simple and compact structure of the proposed clutch, together with the intrinsic adaptability of fabric with other clothing and soft materials, make it a proper solution for applications in soft wearable robotics and generally as locking and variable stiffness solution for soft robotic applications.

The effects of heating rate before solution treatment and cooling rate after solution treatment on the morphological distribution and evolution of precipitation phase of nickel-based single crystal superalloy were studied. The dissolution, precipitation and growth of precipitation phase and matrix phase during heat treatment were analyzed by means of high power scanning electron microscopy. The results show that the morphology of precipitated phase has nothing to do with the distribution of precipitated phase and the heating rate in the heating process, but the cooling rate in the cooling process affects the shape, size and distribution of precipitated phase. The faster the cooling rate, the smaller the precipitated phase is, the more irregular the shape is, the smaller the equivalent edge length is, and the smaller the channel width of matrix phase is.

Due to low culturing costs and high seed protein contents, legumes represent the main global source of food protein. Pea (Pisum sativum L.) is one of the major economically important legume crops, impacting both animal feed and human nutrition. Therefore, the quality of pea seeds needs to be ensured in the context of sustainable crop production and nutritional efficiency. Obviously, changes in seed protein patterns might directly affect both of these aspects. Thus, here we address the pea seed proteome in more detail and provide, to the best of our knowledge, the most comprehensive annotation of the functions and intracellular localization of pea seed proteins. Accordingly, 1938 and 1989 non-redundant proteins were identified in yellow and green pea seeds, in total. Only 35 and 44 proteins, respectively, could be additionally identified after protamine sulfate precipitation (PSP) potentially indicating the high efficiency of our experimental workflow. In total 981 protein groups could be assigned to 34 functional classes, which were to a large extent differentially represented in yellow and green seeds. Closer analysis of these differences by processing of the data in KEGG and String databases revealed their possible relation to a higher metabolic status and reduced longevity of green seeds.

This paper proposes a cognitive radio engine platform for making exploitation of available frequency channels usable for a tactical wireless sensor network in presence of incumbent communication devices known as the primary user (PU) required to be protected from undesired harmful interference. In the field of tactical communication networks, it is desperate to find available frequencies for opportunistic and dynamic access to channels in which PU is in active. This paper introduces a cognitive engine plaform for determining available channels on the basis of case-based reasoning technique deployable as core functionality on cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. Towards this, this paper introduces a plausible learning engine to characterize channel usage pattern to extract best channel candiates for the tactical cognitive radio node (TCRN). Performance of the proposed cognitive engine is verified by conducting simulation tests which confirm the reliability in functional aspect of the proposed cognitive engine covering the learning engine as well as the case-based reasoning engine with showing how well TCRN can avoid the collision against the PU operation considered as the etiquette secondary user (SU) should have.

In this short review paper we have assembled the main characteristics of partial oxidation reactions (oxidative dehydrogenation and selective oxidation to olefins or oxygenates, as aldehydes and carboxylic acids and nitriles), as well as total oxidation, particularly for depollution, environmental issues and wastewater treatments. Both gas-solid and liquid-solid media have been considered with recent and representative examples within these fields. We have also discussed about their potential and prospective industrial applications. Particular attention has been brought to new raw materials stemming from biomass and to liquid-solid catalysts cases. This review paper also summarizes the progresses made in the use of unconventional activation methods for performing oxidation reactions, highlighting the synergy of these technologies with heterogeneous catalysis. Focus has been centered on usual catalysts activation methods but also on less usual ones, such as the use of ultrasounds, microwaves, grinding (mechanochemistry) and photo-activated processes, as well as their combined use.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

While the Internet of Things (IoT) technology has been widely recognized as the essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today's AC is the centralized authorization server, which can be the performance bottleneck or the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable an effective protection for devices, services and information in large scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registering, propagating and revocating of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI node) and more powerful computing devices (i.e., laptops), and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

In Italy HPV vaccination was implemented for girls since 2007 but its coverage was lower than recommended level. Sicily is one of the Italian administrative regions with lower vaccination coverage, ranging in the birth cohorts 1996–1999 from 59% to 62%. Aim of the study was to investigate factors associated with refusal of anti-HPV vaccination among young adult women of Palermo, Italy. A cross-sectional study was conducted through the administration of a telephone questionnaire, consisting of 23 items on HPV infection and vaccination knowledge based on Health Belief Model framework. The eligible population were young women with at least a previous vaccination among all included in Sicilian Vaccination schedule, without starting or completing anti-HPV vaccination schedule. Overall, 141 young women were enrolled, of them 84.4% were unvaccinated and 15.6% had at least one dose of HPV vaccine. In multivariate analysis, factors associated with the failure to perform the HPV vaccination were degree as school level (OR = 10.2, p = 0.041), lower participation at school seminar on HPV (OR = 0.2, p = 0.047) and lower perception of anti-HPV vaccine benefits (OR = 0.4, p = 0.048). Public health educational program focusing and tailored on benefits perception of anti-HPV vaccine and HPV disease severity, especially if carried out at school, can improve HPV vaccination uptake.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.

There is increasing demand from stakeholders for tools to support outcome-based approaches in environmental management. For such tools to be useful, understanding user requirements is key. In Scotland, UK, stakeholders were engaged in the development of an Environmental Decision Support System (EDSS) to support the management of land and freshwater resources for multiple policy outcomes. A structured participatory engagement process was employed to determine stakeholder requirements, establish development principles to fulfil these requirements and road-test prototypes. The EDSS specification that emerged from this bottom-up process was spatially-explicit, free at the point of use, and touch and mobile device compatible. This application, which is under development, does not closely resemble most existing published EDSS. We suggest that there is a mismatch between the way scientists typically conceptualise EDSS and the kinds of applications that are likely to be useful to decision-makers on the ground. Interactive mobile and web-based geospatial information services have become ubiquitous in our daily lives, but their importance is not reflected in current literature on EDSS. The current focus in environmental management on adaptive, stakeholder-centred strategies based on outcomes offers an opportunity to make better use of these new technologies to aid decision-making processes.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

Meshfree particle method, which is always regarded as a pure Lagrangian approach, is easily represented complicated domain topologies, moving boundaries, and multiphase media. Solving acoustic problems with the mesfree particle method forms a branch of the acoustic wave modeling field, namely, particle-based computational acoustics (PCA). The aim of this paper is to improve the accuracy of using the PCA method to solve two-dimensional acoustic problems, and realize the particle representation with a hybrid meshfree and finite-difference time-domain (FDTD) method for acoustic boundary conditions at both the plane and curved surface. As a widely used Lagrangian meshfree method, the smoothed particle hydrodynamics (SPH) based on the support domain and the kernel function has developed rapidly in recent years. The traditional SPH method is easily implements parallel processing and has been applied in sound wave simulation. As a corrective method with higher accuracy than SPH, the acoustic propagation and scattering in the time domain is simulated with the corrective smoothed particle method (CSPM). Moreover, a hybrid meshfree-FDTD boundary treatment technique is utilized to represent different acoustic boundaries in the Lagrangian approach. In this boundary treatment technique, the parameter value of virtual particles is obtained with the FDTD method, which concerns truncation errors based on the Tayler series expansion. Soft, rigid, and Mur’s absorbing boundary conditions are developed to simulate sound waves in finite and infinite domain. Results of modeling acoustic propagation and scattering show that CSPM is accurate and convergence with exact solutions, and different acoustic boundaries are validated to be effective in the computation.

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technologies can have a significant impact and is already a relatively mature technology for wind turbines. Though hydroelectric plants can use simple regulation systems, significant benefits have been shown to accrue from the appropriate use of the same control methods designed for wind turbine plants. This represents the key point of the paper. In fact, to date, the application communities connected with wind and hydraulic energies have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to hydrodynamic systems. Therefore, this paper examines the models and the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in models and control objectives, as well as potential solutions. Key comparative reference points include the articulation of the exployed models, specification of control objectives, development of high--fidelity simulators, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and constraints under which such renewable energy systems must operate, and the need to provide reliable and robust control solutions, which respect the often remote and relatively inaccessible location of many onshore and offshore deployments.

BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.

The inherent capabilities of the K-means++ algorithm to approximate system dynamics within complex systems are subjected by constructing a network structure that captures interconnections among identified components, extending its original purpose as a data clustering method and transforming it into a tool for systems analysis. Leveraging advanced measurement technologies and sophisticated data collection systems, the K-means++ algorithm unveils hidden relationships among components and identifies critical elements. This study explored the algorithm's potential, facilitating the identification of critical components to enhance system operation, control, optimization, and decision-making and examining the practicality and resiliency of the method in real-world application with noisy and limited data. A case study conducted on power systems (IEEE 39-bus and IEEE 300-bus systems) exemplifies K-means++'s capacity to accurately identify critical components and approximate system dynamics, supported by performance metrics affirming its effectiveness and robustness in system analysis through measurements of bus similarity within clusters based on standard deviation and comparison of net tie-line flows in equivalent networks with the original network across scenarios. Performance metrics, including the Silhouette Score, Davies-Bouldin Index, and Variation of Information (VI) score, further validated K-means++'s performance, yielding reliable and consistent results.

The article delves into the landscape of the plant-based food industry, exploring innovative market entry strategies and their interplay with the evolving food market and shifting consumer preferences and lifestyles. Adopting a multidimensional approach, the study reflects diverse perspectives at play. Through a comparative analysis of strategic promotion and growth strategies employed by both innovative startups and established industry giants in the plant-based meat alternatives sector in Germany, the study aims to capture the challenges faced by the 'newer' wave of plant-based alternatives. This examination is particularly relevant for navigating the complexities of entering a competitive market. Striving to penetrate evolving consumer lifestyles, the study draws insightful comparisons with their "older" counterparts. In addition, through in-depth interviews with selected companies, the study investigates the entry methods of startups venturing into the German market, aiming to identify parallels and contrasts in market strategies with established brands. This provides valuable insights into innovation within the alternative food market. Furthermore, the study explores the process of brand "platformization" by scrutinizing the websites of the selected companies and analyzing customers' online reviews of their brand experience. This comprehensive analysis unveils distinctive patterns in communication strategies, brand positioning, and consumer engagement, offering insights into plant-based innovation shaping new, caring lifestyles.

The clustering methods of Software Defined Networking (SDN) gained popularity due to their ability to offer improved scalability, consistency, dependability, and load balancing within overlay networks and SDN partitions. This paper delved into the effects of increasing the number of OpenFlow-enabled southbound devices on the establishment and coordination of SDN-Controller clusters. Specifically, we examined the volume of east-west cluster packet communication concerning the number of southbound devices within the topology. Many research studies focused on bandwidth and the number of bytes in East-West communication. While bandwidth referred to the maximum rate at which data could be transferred, and the number of bytes reflected the volume of data being transmitted, the number of packet communications directly influenced the efficiency and responsiveness of network operations. Our investigation encompassed the impact of SDN controllerto- controller communication within the cluster concerning the rising number of OpenFlow switches connected to various topologies, including tree (star-bus network), linear, and torus configurations. This study presented data on communication patterns within Open Network Operating Systems (ONOS) and OpenDaylight (ODL) clusters, revealing differing levels of controller communication with southbound network expansions. We evaluated the scalability of ODL and ONOS controllers by scrutinising the effect of increasing the number of southbound devices on control communication volume. Our analysis revealed varied communication patterns within ONOS and ODL clusters, resulting in different volumes of control communication with southbound expansions. The findings indicated that in small to medium-sized SDNs, ODL outperformed ONOS, notably with faster cluster discovery. Conversely, ONOS demonstrated greater efficiency in larger networks owing to its centralised communication architecture. Finally, we provided recommendations for selecting the most suitable controllers based on the size of southbound networks, aiming to provide practical guidelines for optimal network performance.

The effect of H2S with high concentrations on catalytic dry reforming of methane (DRM) process has seldom been focused on previously. Herein, a thermodynamic analysis of the DRM reactions in the presence of H2S with the concentration varying in 0-20 vol% was conducted firstly. Several typical types of catalysts including MgO, NiO/MgO and LaNiO3 in different states were prepared for executing DRM at 800 C, 0.1 MPa in the feed of 20 vol% CO2 and 20 vol% CH4 balanced with N2. The catalytic performance of each catalyst for DRM process under conditions of absence and presence of H2S was compared. A promotion effect of increasing H2S concentration on both the conversions of CO2 and CH4 and the molar yields of CO and H2 was observed on all the catalysts especially the MgO and the pristine NiO/MgO. While a significant decline in catalytic activity of either the reduced NiO/MgO or the reduced LaNiO3 catalyst after adding H2S, moderate reactant conversions still sustained. The results of process analysis and catalyst structure characterization suggest that H2S participation can contribute to the increment in CO2 and CH4 conversion, and active S-adsorbed species may play the key role of catalysis in the reactions involving H2S.

The global issues of overweight and sedentary lifestyles demand comprehensive responses from health systems, yet health education still needs to be more cohesive and less sporadic. This study focuses on primary school children as a specific target for instilling healthy habits, employing ac-tive learning strategies to address healthy diet and physical activity. Through Problem-Based Learning projects, the study engaged fourth-grade primary students in acquiring healthy habits. The study was carried out over four consecutive years at a school with two lines, allowing the employment of an opportunity sample and evaluating intervention and control groups by com-paring their corresponding scores. Control groups integrated a total of 54 students, while the in-tervention group included a total of 159 students. A pre-test and post-test questionnaire were used for data collection. Evaluation differential scores revealed statistically significant higher scores in the intervention groups: 3.62 points in control groups and 6,94 in intervention groups, particu-larly in attitudinal competences. These strategies not only facilitated the development of crucial skills such as information search, synthesis, representation, analysis, decision-making, teamwork, and intrapersonal awareness but also significantly broadened the knowledge acquired in aspects related to healthy habits. This approach proved effective in helping children comprehend the im-portance of healthy choices and encouraged integrating such habits into their daily lives.

The vast array of interconnected microbes across earth’s ecosystems and within holobionts has been called the “Internet of Microbes.” Research into the fundamental microbial properties has demonstrated that bacteria and archaea are masters of energy and information collection, storage, transformation, and dissemination using both “wired” and wireless (at a distance) capacities. These features suggest that microbes are quantum entangled within and between earth’s holobionts, and that specific tools affecting energy and information functions could provide an effective strategy for managing microbial populations. Microbes use four critically important microbial networking functions (sound, light, electricity, and magnetism). This narrative review focuses on microbial management based on two of the four: sound and light (as well as related vibrations). These are examined as: 1) tools for managing microbial populations, 2) tools to support new technologies, and 3) tools for healing humans and other holobionts. Given microbial sensitivity to sound, light, and vibrations, it is critical that microbial populations and microbe-laden holobionts are protected from potentially damaging exposures. We conclude that sound, light, and vibrations are useful therapeutic tools that can help support useful microbial populations and help to address ongoing challenges of holobiont disease.

Background: After the increasingly common anterior cruciate ligament reconstruction (ACLR) procedure in competitive athletes, rehabilitation is crucial for facilitating a timely return to sports (RTS) and preventing re-injury. This pilot study investigates the biomechanical and psychological outcomes of postoperative rehabilitation in competitive athletes, comparing supervised rehabilita-tion (SVR) and home-based rehabilitation (HBR). Methods: After ACLR, 60 (out of 74 screened) athletes were recruited and equally divided into 2 groups using non-probability convenience sam-pling: 30 HBR and 30 SVR (each group comprising 15 males and 15 females). Rehabilitation out-comes in respective groups were evaluated at 8 months using measures (Tegner-Activity-Scale [TAS], International Knee Documentation Committee subjective knee form [IKDC-SKF], ACL Return to Sport after Injury [ACL-RSI]) and objective parameters (isometric muscle strength, hamstring/quadriceps asymmetry). RTS was evaluated at 9 months; with ACL re-injury rates rec-orded approximately 6 months post-RTS. Results: Both groups exhibited decreased TAS scores (HBR: 8 to 6, SVR: 8 to 7), with the SVR group demonstrating superior post-operative IKDC-SKF scores (81.82 vs. 68.43) and lower ACL-RSI scores (49.46 vs. 55.25). Isometric and isokinetic mus-cle strength, along with asymmetry values, favored the SVR group 8 months post-ACLR (p

Machine learning models are increasingly being used in critical domains, but their complexity, lack of transparency, and poor interpretability remain problematic. Decision trees (DTs) and rule-based approaches are well-known examples of interpretable models, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets; however, tree ensemble approximators do not consider interpretability. These methods are known to generate three main types of rule sets: DT-based, unordered-based, and decision list-based. However, no known metric has been devised to distinguish and compare these rule sets. Therefore, the present study proposes an interpretability metric to allow comparisons of interpretability between different rule sets, such as decision list- and DT-based rule sets, and investigates the interpretability of the rules generated by the tree ensemble approximators. To provide new insights into the reasons why decision list-based and inspired classifiers do not work well for categorical datasets consisting of mainly nominal attributes, we compare objective metrics and rule sets generated by the tree ensemble approximators and the \textit{Recursive-Rule eXtraction algorithm (Re-RX) with J48graft}. The results indicated that \textit{Re-RX with J48graft} can handle categorical and numerical attributes separately, has simple rules, and achieves high interpretability, even when the number of rules is large.

Tea infusions are the most consumed beverages in the world after water, their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories guaranteeing high sample throughputs even without the human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LV-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy acts as an AI smelling and taste machine predicting teas sensory features without the human panel intervention.

Prediabetes, a pivotal phase in glucose metabolism between normalcy and diabetes, exerts a profound influence on the aging process and the risk of age-related diseases. This comprehensive review delves into the intricate web of blood-based biomarkers that collectively expedite senescence, marking the transition from a state of health to age-related complications. Key findings underscore the significance of diverse biomarkers, such as telomere length, p16INK4a, senescence-associated secretory phenotype (SASP) factors, DNA methylation clocks, advanced glycation end products (AGEs), inflammatory and oxidative stress markers, circulating hormones, and additional factors like folate, B12, osteocalcin, and more. Not only do these biomarkers serve as indicators of senescence, but they actively fuel chronic inflammation, oxidative stress, and metabolic dysregulation, all of which contribute to accelerated aging. The implications of this understanding are profound, as prediabetes emerges as a critical period in an individual's life, influencing various physiological systems, including the vascular and neural systems, metabolic functions, hormonal regulation, and bone health. Recognizing the profound influence of prediabetes on senescence provides a foundation for personalized intervention strategies to mitigate age-related complications and promote healthy aging. Future research directions call for a more diverse array of biomarkers, in-depth exploration of their roles, and the development of tailored precision medicine strategies to ensure a holistic understanding and effective management of prediabetes-induced senescence and its implications for aging. This knowledge has far-reaching implications for public health and clinical practice, emphasizing the need for early detection and intervention in prediabetic individuals to enhance the quality of life in an aging population with diverse needs.

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things – IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where IoT, edge and cloud converge to form a computing continuum, is also known as IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems architectural approach, including: i) design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; ii) design of efficient task offloading algorithms aimed at optimizing services execution; iii) support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; iv) scalability, flexibility and reliability guarantees to face the expected mobility for IoT systems, and; v) design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges become even more significant towards the new era of sixth generation (6G) networks, which will be based on the integration of various cutting edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented, from an IEC continuum perspective.

The COVID-19 pandemic was declared in March 2020 and is still ongoing. The course of the epidemic remains uncertain, and mutations in the coronavirus, as well as the emergence of new variants of concern, make it possible that there will be periodic surges in the number of cases. Moreover, it is not clear how quickly and to what extent the effectiveness of vaccination will decline as the virus continues to mutate. One possible solution to combat the rapidly mutating coronavirus is the development of pan-coronavirus vaccines that stimulate the production of antibodies against conserved elements of the virus. Another approach could be the creation of safe vaccine platforms that can be rapidly adapted to deliver new, specific antigens in response to viral mutations. Recombinant probiotic microorganisms that can produce viral antigens by inserting specific viral DNA fragments into their genome show promise as a platform and vector for mucosal vaccine antigen delivery. With a well-established safety profile and the ability to transform into vaccine candidates, probiotic bacteria have significant research potential. The authors of this study have developed a convenient and universal technique for inserting the DNA sequences of pathogenic bacteria and viruses into the gene that encodes the pili protein of the probiotic strain E. faecium L3. The paper presents data on the immunogenic properties of two E. faecium L3 vaccine strains, which produce two different fragments of the coronavirus S1 protein, and provides an assessment of the protective efficacy of these oral vaccines against coronavirus infection in Syrian hamsters.

Deep learning methods have been widely used in PolSAR image classification. To learn the polarimetric information, many deep learning methods expect to learn high-level semantic features from original PolSAR data. However, only original data cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these shortages, we propose a double-channel CNN network combined with an edge-preserving MRF model(DCCNN-MRF) for PolSAR image classification. Firstly, to combine complex matrix data and multiple scattering features together, a double-channel convolution network(DCCNN) is developed, which consists of a Wishart-based complex matrix and multi-feature subnetworks. The Wishart-based complex matrix network can learn the statistical characteristics and channel correlation well, and the multi-feature network can learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of features to enhance advantageous features and reduce redundant ones. Finally, an edge preserving MRF model is designed to combine with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining adaptive weighted neighborhood for edge pixels. Experiments are conducted on four real PolSAR data sets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

Continuous greenhouse gas emissions are causing global warming and impacting the habitats of many animals. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable is challenging. To address the issue, we propose a grid dispatching technique that combines prediction technology, reinforcement learning, and optimization technology. Prediction technology can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the power system, the aggregation of distributed energy resources increases uncertainty, particularly due to the fluctuating generation of renewable energy. This requires the use of advanced predictive control techniques to ensure long-term economic and decarbonization goals. In this paper, we present a real-time dispatching framework that integrates deep learning-based prediction, reinforcement learning-based decision-making, and stochastic optimization techniques. The framework can rapidly adapt to target uncertainty caused by various factors in real-time data distribution and control processes. The proposed framework achieved global Champion in the NeurIPS Challenge 2022 competition and demonstrated its effectiveness in practical scenarios of intelligent building energy management.

The incidence of breast cancer and therefore need for breast reconstruction is expected to increase. The many reconstructive options available and the changing aspects of the field make this a complex area of plastic surgery, requiring knowledge and expertise. Two major types of breast reconstruction can be distinguished: Breast Implants and Autologous Flaps. Both present advantages and disadvantages. Autologous fat grafting is also commonly used. MRI is the modality of choice for evaluating breast reconstruction. Knowledge of the type of reconstruction is preferable to provide the maximum of pertinent information and avoid false positives. Early complications include seroma, hematoma, and infection. Late complications depend on the type of reconstruction. Implant rupture and implant capsular contracture are frequently encountered. Depending on the implant type, specific MRI signs can be depicted. In case of myocutaneous flap, fat necrosis, fibrosis and vascular compromise represent the most common complications. Late cancer recurrence is much less common. Rare reported late complications include breast implant associated large cell anaplastic lymphoma (BIA-ALCL) and, recently described and even rarer, Breast implant-associated squamous cell carcinoma (BIA-SCC). In this review article, the various types of breast reconstruction will be presented, with emphasis on pertinent imaging findings and complications.

Despite the fact that strong routine separation methodologies can give reliable specificity and validity at usual working pharmaceuticals concentrations, they may fail at very low concentration levels. This poses considerable challenges for researchers inves-tigating product purity and therapeutic drug monitoring. Sensitivity enhancement pro-cedures are thus required to maximize the performance of separation techniques. Large volume injection, solid phase extraction/solid phase enrichment (SPE/SPEn), pre-, post-, and in-column derivatization, as well as the use of sensitive detection devices are the simplest strategies for improving sensitivity of the separation-based analytical techniques. Large volume injection of samples with online SPE/SPEn coupled with separation techniques increased sensitivity and improved detection as well as quantification limits without affecting peak shape and system performance. Although the primary purpose of derivatization is to improve sensitivity and selectivity, greener derivatization is growing in popularity and should be considered in analytical chemistry. In general, two strategies are essential for accomplishing greener derivatization goals. The first is the search for and use of ecologically acceptable derivatizing reagents, solvents, and reaction conditions. The second is miniaturization and automation of analytical methods. This review discusses significant advances in separation-based analytical techniques, specifically enrichment approaches and detector signal improvement for pharmaceutical quantification in various matrices at very low concentration levels. As a result of improved analytical systems setup in drug assays, the possibility of high-throughput analyses was also highlighted.

Tendons and ligaments injuries are frequent in sport horses and humans, representing a great therapeutic challenge. Tissue regeneration and function recovery are the paramount of tendon and ligament lesions management. Nowadays, several regenerative treatments are being developed, based on the use of stem cell and stem cell-based therapies. In the present study, the preparation of equine synovial membrane mesenchymal stem cells (eSM-MSCs) for clinical use - collection, transport, isolation, differentiation, characterization, and application is described. These cells are fibroblast-like and grow in clusters. They retain an osteogenic, chondrogenic and adipogenic differentiation potential. 16 clinical cases of tendonitis and desmitis, treated with allogenic eSM-MSCs and autologous serum are also presented, including their evaluation, treatment and follow-up. The use of autologous serum as a vehicle concern to a decreased immunogenic response after the administration of this combination and to its healing properties, due to the presence of growth factors and immunoglobulins. Most of the cases (14/16), healed in 30 days and presented a good outcome. Treatment of tendon and ligament lesions with the mixture of eSM-MSCs and autologous serum appears to be a promising clinical option for this category of lesions in equine patients.

Plant-based diets are increasingly popular for health as well as financial, ethical, and religious reasons. The medical literature clearly demonstrates that plant-based diets can be both nutritionally sufficient and medically beneficial. However, any person on an intentionally restrictive but poorly-informed diet may predispose themselves to clinically-relevant nutritional deficiencies. For persons on a poorly-informed plant-based diet, deficiencies are possible in both macronutrients (protein, essential fatty acids) and micronutrients (vitamin B12, iron, calcium, zinc and vitamin D). Practitioner evaluation of symptomatic patients on a plant-based diet requires special consideration of 7 key nutrient concerns for plant-based diets. This article translates these concerns into 7 practical questions that all practitioners can introduce into their patient assessments and clinical reasoning. Ideally, persons on plant-based diets should be able to answer these 7 questions. Each serves as a heuristic prompt for both clinician and patient attentiveness to a complete diet. As such, these 7 questions support increased patient nutrition knowledge and practitioner capacity to counsel, refer, and appropriately focus clinical resources.

The average age of in-service bridges has increased in recent years in the United States. To address this issue, structural health monitoring and damage identification approaches can be employed to prioritize maintenance/replacement of aging bridges. Among the damage identification and operational health monitoring approaches, finite element (FE) model updating methods can offer a solution to evaluate the mechanics-based characteristics of bridges. However, in a real-world setting, unidentifiability and mutual dependency between model parameters, modeling errors, especially due to boundary conditions, as well as ill-conditioning of updating algorithms can pose challenges to the application of FE model updating methods. To address these challenges, this study presents a two-step FE model updating approach. In the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, in order to refine parameter estimation accounting for nonlinear response behavior of the bridge, a time-domain model updating is carried out. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on their corresponding final estimates in the modal-based model updating. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of decommissioned and deteriorated prestressed bridge I-girders. After carrying out the two-step FE model updating, the final estimates of concrete compressive strength are shown to provide reasonable assessment of the damage extent in the girders.

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from its limited ability to scale (i.e. low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding proved that it is one of the most promising solutions to Blockchain’s scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e. good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. Then, we briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. Finally, we evaluate the effectiveness of the proposed model via numerical analysis.

In this study, Fe-based superconductors (SCs) are described with a model showing a novel attractive force. First, we describe this novel force using an analog from electromagnetism. From electromagnetism, it was found that this force is a Lorentz force in which two electrons orbit around a Fe-ion with the same velocity. Then, we consider a wave function of an electron. Consequently, due to the property of the proposed attractive force and the quantum-field Hamiltonian, we clarified that the Bardeen–Cooper–Schrieffer (BCS) ground state can be reused. Afterward, attractive force energy was calculated according to the presented model. In Fe-based SCs, a structure transition is essential. Considering that the derived attractive force energy is relatively large, we employ the expanded T_c-equation from the BCS theory that includes the structure transition effect and the derived attractive force energy. In addition, we succeed in analytically reproducing T_c-dome diagrams in various types of Fe-based SCs. Moreover, we discuss the universal property a general SC should have as well as the quantum critical point.

Background: Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. Methods: We conducted summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are po-tentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes that were pleiotropically/potentially causally associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, PSMR=2.10×10-18), ILMN_1682717 (tagging IER3, PSMR=1.07×10-16) and ILMN_1661439 (tagging FLOT1, PSMR=1.16×10-14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, PSMR=1.44×10-10), ENSG00000186470.9 (tagging BTN3A2, PSMR=2.28×10-10), and ENSG00000224389.4 (tagging C4B, PSMR=1.23×10-9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes. Conclusion: We identified many genetic variants/loci that are potentially involved in the patho-genesis of IgAN.

Ecologists use the net biotic interactions among plants to predict fundamental ecosystem features. Following this approach, ecologists have built a giant body of theory founded on observational evidence. However, due to the limitations that a phenomenological approach raises both in empirical and theoretical studies, an increasing number of scientists claim the need for a mechanistic understanding of plant interaction outcomes, and a few studies have taken such a mechanistic approach. In this synthesis, we propose a modeling framework to study the plant interaction mechanistically. We first establish a conceptual ground to frame plant-plant interactions, and then, we propose to formalize this research line theoretically developing a family of individual-based, spatially-explicit models in which biotic interactions are an emergent property mediated by the interaction between plants’ functional traits and the environment. These models allow researchers to evaluate the strength and sign of biotic interactions under different environmental scenarios and thus constitute a powerful tool to investigate the mechanisms underlying facilitation, species coexistence, or the formation of vegetation spatial patterns.

Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signalling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high throughput data. Novel, literature-based representations of biological processes and emerging algorithms offer new opportunities for model construction. Here, we review up-to-date efforts to address challenging biological questions by incorporating omic data into logic-based models, and discuss critical difficulties in constructing and analysing integrative, large-scale, logic-based models of biological mechanisms.

The noradrenergic theory of Cognitive Reserve (Robertson, 2013-2014) postulates that the upregulation of the Locus Coeruleus - Noradrenergic System (LC-NA) originating in the Brainstem might facilitate cortical networks involved in attention, and protracted activation of this system throughout the lifespan may enhance cognitive stimulation contributing to Reserve. To test the above-mentioned theory, a study was conducted on a sample of 686 participants (395 controls, 156 Mild Cognitive Impairment, 135 Alzheimer’s Disease) investigating the relationship between LC volume, attentional performance and a biological index of brain maintenance (BrainPAD – an objective measure which compares an individual’s structural brain health, reflected by their voxel-wise grey matter density, to the state typically expected at that individual’s age). Further analyses were carried out on Reserve indices including education and occupational attainment. Volumetric variation across groups was also explored along with gender differences. Control analyses on the Serotoninergic (5-HT), Dopaminergic (DA) and Cholinergic (Ach) systems were contrasted with the Noradrenergic (NA) hypothesis. The antithetic relationships were also tested across the neuromodulatory subcortical systems.Results supported by bayesian modelling showed that LC volume disproportionately predicted higher attentional performance as well as biological brain maintenance across the three groups. These findings lend support to the role of the noradrenergic system as a key mediator underpinning the neuropsychology of Reserve, and they suggest that early prevention strategies focused on the noradrenergic system (e.g. cognitive-attentive training, physical exercise, pharmacological and dietary interventions) may yield important clinical benefits to mitigate cognitive impairment with age and disease.

The peril of a 3-dimensional, robust and sustained myocardial restoration by means of Tissue Engineering is that it still remains a largely experimental approach. Prolific protocols have been developed and tested in small and large animals, but as clinical cardiac surgeons, we have not come to the privilege of utilizing any of them in our clinical practice. The question arises: why? The heart is a unique organ, anatomically and functionally. It is not an easy target to replicate with current techniques, or even to support its viability and function. Currently available therapies fail to reverse the loss of functional cardiac tissue, the fundamental pathology remains unaddressed and a heart transplantation is an ultima ratio treatment option. Owing to equivocal results of cell-based therapies, several strategies have been pursued to overcome limitations of the current treatment options. Preclinical data as well as first-in-human studies conducted to date have provided important insights into the understanding of injection-based approaches for myocardial restoration. In the light of the available data, injectable biomaterials suitable for transcatheteter delivery appear to have the highest translational potential,. This article presents a current state-of-the-art in the field of hydrogel-based myocardial restoration therapy.

Background: Microbiome-Inspired Green Infrastructure (MIGI) was recently proposed as an integrative system to promote healthy urban ecosystems, through multidisciplinary design. Specifically, MIGI is defined as nature-centric infrastructure restored and/or designed and managed to enhance health-promoting interactions between humans and environmental microbiomes, whilst sustaining microbially-mediated ecosystem functionality and resilience. MIGI also aims to stimulate a research agenda that focuses on considerations for the importance of urban environmental microbiomes. Objectives: In this paper we provide details of what MIGI entails from a bioscience and biodesign perspective, highlighting the potential dual benefits for human and ecosystem health. We present ‘what is known’ about the relationship between urban microbiomes, green infrastructure and environmental factors that may affect urban ecosystem health (ecosystem functionality and resilience as well as human health). We discuss how to start operationalising the MIGI concept based on current available knowledge, and present a horizon scan of emerging and future considerations in research and practice. We conclude by highlighting challenges to the implementation of MIGI and propose a series of workshops to discuss multi-stakeholder needs and opportunities. Discussion: This article will enable urban landscape managers to incorporate initial considerations for the microbiome in their development projects to promote human and ecosystem health. However, overcoming the challenges to operationalising MIGI will be essential to furthering its practical development. Although the research is in its infancy, there is considerable potential for MIGI to help deliver sustainable urban development driven by considerations for reciprocal relations between humans and the foundations of our ecosystems –– the microorganisms.

The present work evaluates the food effect on the absorption of rivaroxaban (Riva), a BCS II drug, from the orally administered commercial immediate-release tablet (Xarelto IR) using physiologically based pharmacokinetic (PBPK) and conventional in vitro- in vivo correlation (IVIVC) models. The bioavailability of Riva upon oral administration of Xarelto IR tablet is reported to exhibit a positive food effect. The PBPK model for Riva was developed and verified using the previously reported in vivo data for oral solution (5 and 10 mg) and Xarelto IR tablet (5 and 10 mg dose strength). Once the PBPK model was established, the in vivo performance of the tablet formulation with the higher dose strength (Xarelto IR tablet 20 mg in fasted and fed state) was predicted using the experimentally obtained data of in vitro permeability, biorelevant solubility and in vitro dynamic dissolution data using United States Pharmacopeia (USP) IV flow-through cell apparatus. In addition, the mathematical IVIVC model was developed using the in vitro dissolution and in vivo profile of 20 mg strength Xarelto IR tablet in fasted condition. Using the developed IVIVC model, the pharmacokinetic (PK) profile of the Xarelto IR tablet in fed condition was predicted and compared with the PK parameters obtained via the PBPK model. A virtual in vivo PK study was designed using a single-dose, 3-treatment cross-over trial in 50 subjects to predict the PK profile of the Xarelto® IR tablet in the fed state. Overall, the results obtained from the IVIVC model were found to be comparable with that from the PBPK model. The outcome from both the model pointed to the positive food effect on the in vivo profile of the Riva. The developed models thus can be effectively extended to establish bioequivalence for the marketed and novel complex formulations of Riva such as amorphous solid dispersions.

Fire is a significant threat to human life and civil infrastructures. Builders and architects are hankering for safer and sustainable alternatives of concrete that do not compromise with their design intent or fire safety requirements. The aim of the present work is to improve the residual compressive performance of concrete in the post-fire exposure, by incorporating by-products from urban residues. Based on sustainability and circular economy motivations, the attention is focused on rubber tire fly ash, aged brick powder, and plastic (PET) bottle residuals used as partial sand replacement. The selected by-products from urban residues are used for the preparation of Cement-Based Composites (CBCs) in two different proportions (10 % and 15 %). The thermal CBC behaviour is thus investigated under realistic fire scenarios (i.e., Direct Flame (DF) for 1 hour (1h)), by following the ISO 834 standard provisions, but necessarily resulting in non-uniform thermal exposure for the cubic specimens. The actual thermal exposure is further explored with a Finite Element (FE) model, giving evidence of thermal boundaries effects. The post-fire residual compressive strength of heated concrete and CBC samples is hence experimentally derived, and compared to unheated specimens in ambient conditions. From the experimental study, the enhanced post-fire performance of CBCs with PET bottle residual is generally found superior to other CBCs or concrete. The structure-property relation is also established, with the support of Scanning Electron Microscopy (SEM) micrographs. Based on existing empirical models of literature for the prediction of the compressive or residual compressive strength of standard concrete, newly developed empirical relations for both concrete and CBCs are assessed.

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

This paper addresses investigation of guided-wave excitation by angle-beam wedge piezoelectric transducers in multi-layered composite plate structure with orthotropic symmetry of the material. The aim of the present study is to determine the capability of such actuators to provide the controlled generation of an acoustic wave of a desirable type with the necessary wavelength, propagation distance and directivity. The studied CFRP panel is considered as homogenous with effective elastic moduli and anisotropic structural damping, whose parameters were determined experimentally. According to the results of dispersion analysis and taking into account the data of wave attenuation in a highly damping CFRP composite, the two types of propagating waves A0 and S0 were considered theoretically and experimentally in the frequency range 10 - 100 kHz. Using the results of a previous study, the structure of the wedge actuator was reconstructed to develop its finite element (FE) model, and a modal analysis was carried out, which revealed the most intense natural vibration modes and their eigenfrequencies within the used frequency range. Both experimental and numerical studies of the generation, propagation, directivity and attenuation of waves in the orthotropic composite panel under study revealed the influence of the angular orientation of the actuator on the formation of wave patterns and allowed to determine the capabilities of the wave's directivity control.

Alpha-fetoprotein (AFP) is a major embryo- and tumor-associated protein capable of binding and transporting variety of hydrophobic ligands including estrogens. AFP has been shown to inhibit estrogen receptor (ER)-positive tumor growth and this can be attributed to its estrogen-binding ability. Despite AFP has long been investigated, its three-dimensional (3D) structure has not been experimentally resolved and molecular mechanisms underlying AFP-ligand interaction remain obscure. In our study we constructed homology-based 3D model of human AFP (HAFP) with the purpose to perform docking of ERα ligands, three agonists (17β-estradiol, estrone and diethylstilbestrol) and three antagonists (tamoxifen, afimoxifene and endoxifen) into the obtained structure. Based on ligand docked scoring function, we identified three putative estrogen- and antiestrogen-binding sites with different ligand binding affinities. Two high-affinity sites were located in (i) a tunnel formed within HAFP subdomains IB and IIA and (ii) opposite side of the molecule in a groove originating from cavity formed between domains I and III, while (iii) the third low-affinity site was found at the bottom of the cavity. 100 ns MD simulation allowed studying their geometries and showed that HAFP-estrogen interactions occur due to van der Waals forces, while both hydrophobic and electrostatic interactions were almost equally involved in HAFP-antiestrogen binding. MM/GBSA rescoring method estimated binding free energies (ΔG_bind) and showed that antiestrogens have higher affinities to HAFP as compared to estrogens. We performed in silico point substitutions of amino acid residues to confirm their roles in HAFP-ligand interactions and showed that Thr132, Leu138, His170, Phe172, Ser217, Gln221, His266, His316, Lys453, and Asp478 residues along two disulfide bonds, Cys224-Cys270 and Cys269-Cys277 have key roles in both HAFP-estrogen and HAFP-antiestrogen binding. Data obtained in our study contribute to understanding mechanisms underlying protein-ligand interactions and anti-cancer therapy strategies based on ER-binding ligands.

A series of D–π–A diketopyrrolopyrrole (DPP)-based small molecules have been designed for organic light-emitting diodes (OLEDs) and organic solar cells (OSCs) applications. Appling the PBE0/6-31G(d,p) method, the ground state geometry and relevant electronic properties were investigated. The first excited singlet state geometry and the absorption and fluorescent spectra were simulated at the TD-PBE0/6-31G(d,p) level. The calculated results reveal that the photophysical properties are affected through the introduction of different end groups. Furthermore, the electronic transitions corresponding to absorption and emission exhibit intramolecular charge transfer feature. It was disclosed that the designed molecules act not only as luminescent for OLEDs, but also donor materials in OSCs. Moreover, they also can be used as potential electron transfer materials using for OLEDs and OSCs.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, data--driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods, such as fuzzy and adaptive self--tuning controllers, were already verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. The working conditions of these systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

Cellular agriculture is defined as the production of agricultural products from cell cultures rather than from whole plants or animals. With growing interest in cellular agriculture as a means to address the public health, environmental, and animal welfare challenges of animal agriculture, the concept of producing seafood from fish cell- and tissue-cultures is emerging as a means to address similar challenges with industrial aquaculture systems and marine capture. Cell-based seafood - as opposed to animal-based seafood - can combine developments in biomedical engineering with modern aquaculture techniques. Biomedical engineering developments such as closed-system bioreactor production of land animal cells create a basis for large scale production of marine animal cells. Aquaculture techniques such as genetic modification and closed system aquaculture have achieved marked gains in production that can pave the way for innovations in cell-based seafood production. Here, we present the current state of innovation relevant to the development of cell-based seafood across multiple species as well as specific opportunities and challenges that exist for advancing this science. The authors find that the physiological properties of fish cell- and tissue- culture may be uniquely suited to cultivation in vitro. These physiological properties, including hypoxia tolerance, high buffering capacity, and low-temperature growth conditions, make marine cell culture an attractive opportunity for scale production of cell-based seafood; perhaps even more so than mammalian and avian cell cultures for cell-based meats. This, coupled with the unique capabilities of crustacean tissue-friendly scaffolding such as chitosan, a common seafood waste product and mushroom derivative, presents great promise for cell-based seafood production via bioreactor cultivation. To become fully realized, cell-based seafood research will require more understanding of fish muscle culture and cultivation; more investigation into serum-free media formulations optimized for fish cell culture; and bioreactor designs tuned to the needs of fish cells for large scale production.

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~ 182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to tree boundary while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self--tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. The scope is to analyse common points in the control objectives and the achievable results from the application of different solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

Daylight usage in buildings improves visual comfort and lowers the final energy demand for artificial lighting. The question always occurs: how much conservation can you achieve? New upcoming or rare materials and constructions have a lack of information about their application. Therefore, the current work investigates the daylight performance of a multi-layer textile membrane roof with 2 300 m² on top of a sports hall. A translucent, thermal insulation with a glass fibre fleece between the roof membranes combines daylight usage and heating demand reduction. A sports hall with built year 2017 is selected as the case study building. The optical properties of the roof construction are measured. The (visual) light transmittance amounts to 0.72 % with a clean surface. An accordingly parametrized climate-based annual daylight modeling delivers daylight indicators for different construction scenarios. The results show that in comparison to only one glass facade, the additional translucent and thermally insulated membrane construction increases the annual daylight autonomy700/ continuous DA700 from 0/ 15 % to 1.5/ 38 %. In the roof covered areas of the sport field, this results in a reduction from 19.7 to 13.8 kWhel/m²/a electricity for the artificial lighting with dim control (30 % savings). Also, the influence of soiling on the light transmittance was determined with a relevant reduction of one layer about a factor 0.81. The novel results are of great value as a comparison and benchmark for planners and future buildings of similar type.

Four methods in two different families have been constructed to derive the exact solutions to Benjamin-Bona-Mahony equation in two space dimensions. Simply defined hyperbolic tangent, hyperbolic secant and hyperbolic cosecant ansatzes and the expansion method based on the Sine-Gordon equation in two dimensions are directly substituted into the governing ODE reduced from the two dimensional BBM equation. Classical algebraic method is used to find the relations among the target parameters representing the nonzero coefficients in the predicted solutions and the wave transform parameters. Some complex and real solutions have been constructed in explicit forms.

Background: Evaluation of nutrition programs is essential to guarantee the effectiveness of community-based management of acute malnutrition (CMAM). Methods: The Rainbow Project Supplementary Feeding Programs (SFPs) in Zambia were evaluated between years 2015-17, following implementation of new recommendations based on previous evaluations (years 2012-14). Outcomes of the program were compared with International Standards and with those of 2012-14. Cox proportional risk regression analysis was performed to identify predictors of mortality and defaulting. Results: Data for 900 under age 5 years malnourished children (48.8% male; mean age 19.7months ±9.9) were analyzed. Rainbow 2015-17 program outcomes met International Standards, for general malnutrition or stratified moderate acute malnutrition (MAM) and severe acute malnutrition (SAM). When comparing with 2012-14 outcomes, better performance was noted: mortality rates were reduced by half (5.6% vs 3.1%, p = 0.01; for SAM: 12.4% vs 6.7%, p = 0.006), with significant improvement in average weight gain and mean length of stay (p<0.001), and increased awareness of HIV status (+30%; p < 0.001). HIV infection (5.5; 1.9–15.9), WAZ < −3 at baseline (4.6; 1.3–16.1) and kwashiorkor (3.5; 1.2–9.5) remained the major predictors of mortality. Conclusion: The effectiveness of the Rainbow SFPs for child malnutrition treatment and prevention in Zambia has significantly improved after evaluation and implementation activities, with impressive outcomes which resulted in a 50% reduction in mortality.

Acute myeloid leukemias (AMLs) are heterogeneous hematologic cancers that occur prevalently in older patients as the result of a complex pathobiology. The clinical outcomes of older patients with AML, often unsuitable for intensive chemotherapy and allogeneic stem cell transplantation, are generally disappointing. However, recent advances in understanding molecular pathogenesis and other biological mechanisms recognized in AML development and progression have changed the treatment approach, especially for this particularly vulnerable category of patients. Indeed, non-intensive biologically tailored approaches, such as combining venetoclax with hypomethylating agents, have changed the therapeutic paradigm in this field. Moreover, other promising compounds and treatment strategies are currently under advanced clinical development. This article delves into the latest biological understanding and therapeutic advances in the clinical management of older AML patients into new conceptual frameworks that progressively transform the clinical practice in this challenging setting.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.