: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

Non-Linear Simulation by Harmonic Balance Techniques of Load Modulated Power Amplifier Driven by Random Modulated Signals. Comparison with measured results.

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works including Feedback-based Optimized Fuzzy Scheduling Approach (FOFSA) algorithm, Adaptive Task Allocation Technique (ATAT) and Osmosis Load Balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision-making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based Fuzzy Healthcare System (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for efficient Industrial Internet of Healthcare Things (IIoHT) of the future.

Jumping side volley has created breathtaking moments and cherished memories for us. Regrettably, a scientific study on the skill has not been found in literature. Relying on talent of athletes to improvise on the fly can hardly be considered a viable learning strategy. This study targets to fill the gap by quantifying factors contributing to develop its coaching method. Using 3D motion capture (12-cameras, 200Hz) and full-body biomechanical modeling, our study aimed to identify elements that govern entrainment of the skill by examining jumping, kicking and falling phases of its execution. Given the rarity of players who have acquired this skill, we found five subjects for the study. Twenty-three trials were captured and quantified. The results unveil the following key elements: 1) the control of trunk rotation during the jumping, 2) the angle between thighs upon take-off, 3) the whip-like control of the kicking leg during airborne, 4) timing between ball motion and limbs’ coordination, and 5) damping mechanism during falling. An accurate kick can only be achieved through repetitive training. This underlines the need for athletes to master a safe landing technique that minimizes risk of injury during practice. Therefore, training should begin with learning a safe falling technique.

Respiratory Syncytial Virus (RSV) continues to pose a significant challenge, contributing to elevated hospitalization rates among children up to 5 years old, with a disproportionate burden on newborn and infants under 6 months old. The unique characteristics of the young immune system make it prone to altered responses to infections and vaccinations, requiring a tailored approach to disease prevention. The recent approval of the maternal RSV vaccine, (brand name ABRYSVO), represents a pivotal advancement in preventive strategies among newborns and infants, marking a milestone in RSV research as the first market-approved maternal vaccine. The present review examines clinical trial data on both recent and previous vaccine candidates, as well as the licensed vaccine, focusing on the prevention of RSV disease in newborns and young infants through the passive acquisition of antibodies following maternal immunization. Additionally, it evaluates the safety profile of these vaccines.

In recent years, the utilization of machine learning algorithms and advancements in Unmanned Aerial Vehicle (UAV) technology have marked significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV-satellite data fusion have emerged as two prominent approaches for estimating vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for mapping crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV-satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV-satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV-RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root-mean-square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian processes regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is closely observed for the UAV-Landsat and UAV-PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, maximum performance is attained with Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, RapidEye data yielded the most stable performance, with R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data which enables effective monitoring of canopy chlorophyll in small agricultural lands.

Conjugated polymers (CPs) offer potentials for sustainable semiconductor devices due to their low cost, and inherent molecular self-assembly. Enhanced crystallinity and molecular orientation in thin films of solution-processable CPs have significantly improved organic electronic device per-formance. In this work, three methods namely spin-coating, dip-coating, and unidirectional float-ing-film transfer method (UFTM) were utilized with their parametric optimization for fabricating RR-P3HT films. These films were then utilized for their characterization via optical and micro-structural analysis to elucidate dominant roles of molecular orientation, and crystallinity in con-trolling charge transport in organic field-effect transistors (OFETs). OFETs fabricated by RR-P3HT thin films using spin-coating and dip-coating displayed field-effect mobility (μ) of 8.0 × 10-4 cm2V-1s-1 and 1.3 × 10-3 cm2V-1s-1, respectively. This two-time enhancement in (µ for dip-coated films was attributed to its enhanced crystallinity. Interestingly, UFTM films based OFETs demon-strated μ of 7.0 × 10-2 cm2V-1s-1, >100 times increment as compared to its spin-coated counterpart This superior device performance is attributed to synergistic influence of higher crystallinity and molecular orientation. Since the crystallinity of dip-coated and UFTM-thin films are similar ~50 times improved µ of UFTM thin films suggest a dominant role of molecular orientation as com-pared to crystallinity in controlling the charge transport.

The critical role of global electron density transfer (GEDT) in increasing the reaction rate of polar organic reactions has been studied within the framework of Molecular Electron Density Theory (MEDT). To this end, the series of the polar Diels-Alder (P-DA) reactions of cyclopentadiene with cyanoethylene derivatives, for which experimental kinetic data are available, have been chosen. A complete linear correlation between the computed activation Gibbs free energies and the GEDT taking place at the polar transition state structures (TSs) is found; the higher the GEDT at the TS, the lower the activation Gibbs free energy. An interacting quantum atoms (IQA) energy partitioning analysis allows establishing a complete linear correlation between the electronic stabilization of the electrophilic ethylene frameworks and the GEDT taking place at the polar TSs. This finding supports Parr’s proposal for the definition of the electrophilicity  index (J. Am. Chem. Soc., 1999, 121, 1922). The present MEDT study allows establishing the critical role of the GEDT in the acceleration of polar reactions, since the electronic stabilization of the electrophilic framework with the electron density gain is greater than the destabilization of the nucleophilic one, making a net favorable electronic contribution to the decrease of the activation energy.

Many protein expression systems are primary utilised to produce a single, specific recombinant protein. In contrast, most biological processes such as virus assembly rely upon a complex of several interacting proteins rather than the activity of a sole protein. The high complexity of the baculovirus genome, coupled with a multiphase replication cycle incorporating distinct transcriptional steps, made it the ideal system to manipulate for high-level expression of a single or co-expression of multiple foreign proteins within a single cell. We have developed and utilised a series of recombinant baculovirus systems to unravel the sequential assembly process of a complex non-enveloped model virus, Bluetongue virus (BTV). The high protein yields expressed by the baculovirus system not only facilitated structure-function analysis of each viral proteins but were also advantageous to crystallography studies and supported the first atomic level resolution of a recombinant viral protein, the major BTV capsid protein. Further, the formation of recombinant double-shelled virus-like particles (VLPs) provided insights into the structure-function relationships among the four major structural proteins of the BTV whilst also representing a potential candidate for a viral vaccine. The baculovirus multi-gene expression system facilitated the study of structurally complex viruses (both non-enveloped and enveloped viruses) and heralded in a new generation of viral vaccines.

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are po-tentially applicable in artificial and spiking neural networks, memory arrays, and many other de-vices and circuits for artificial intelligence. In this paper, a complete electronic realization of ana-log circuit model of modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an im-portant step towards the forthcoming design of complex memristor-based neural networks for ar-tificial intelligence, for implementation in very high-density integrated circuits and chips.

Gut luminal dysbiosis and pathobiosis result in compositional and biodiversify alterations in the microbial and host co-metabolites. The main mechanism of bacterial evolution is horizontal gene transfer (HGT) and acquisition of new traits can be achieved through mobile genetic element (MGEs) exchange. Introducing genetically engineered microbes (GEMs) might break the harmonized balance in the intestinal compartment. The present objectives are: 1. To reveal the role played by GEMs' horizontal gene transfers in changing the landscape of the enteric microbiome eubiosis 2. To expand on the potential detrimental effects of those changes on human genome and health. A search of articles published in PubMed/MEDLINE, EMBASE, and Scielo from 2000 to august 2023, using appropriate MeSH entry terms were performed. GEMs' horizontal gene exchanges might induce multiple human diseases. The new GEMs can change the long-term natural evolution of the enteric pro- or eukaryotic cells inhabitants. The worldwide regulatory authority's safety control of GEMs is not enough to protect public health. Viability and biocontainment and many other aspects are only partially controlled and harmful consequences for public health should be avoided. Always remember that prevention is the most cost- effective strategy and "primum non nocere" is the name of the game.

A sintered wick was formed in a heat pipe through the process of sintering a mixture of copper powder with particle sizes of 100㎛ and 200㎛, mixed with a pore-forming agent. On application to the heat pipes. The thermal resistance of heat pipes containing a sintered wick was calculated for different tilting angles (0°, 45°, and 90°) according to the type of powder, thickness of the sintered wick, and the filling ratio of the working fluid. The heat transfer efficiencies were evaluated through temperature tests. The capillary force, which is closely related to heat transfer efficiency, is determined by the effective pore radius resulting from the sintering of the copper powder. Furthermore, it was observed that the heat transfer performance of the heat pipe was at its best when the working fluid filling ratio was 30%. Additionally, the width of the wick was found to have a direct correlation with the heat transfer performance.

Widely used estimates of food-insecure populations are likely to be biased upwards, lacking adjustment for global increases in sedentary behavior in recent decades. We first construct a household model to account for sedentary choices during leisure and work time decisions. The model rationalizes increasing sedentary behavior from the household by accounting for increasing returns to cognitive human capital vs physical capital, alongside increased productivity of more sedentary activities both at work and at home. The household model then informs an empirical model that makes use of our unique international pseudo-panel data on sitting time, which serves as a proxy for sedentarism. We econometrically estimate a transfer function linking sedentarism to widely available covariates, which then are used to make out-of-sample predictions and can be applied to a large set of countries. The estimated sedentary time can be used to adjust the physical activity level reflected in the minimum dietary energy requirement used to determine a cutoff for food insecurity.

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients’ long-term survival. Several computer-based methods have recently been proposed in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. This study presents a novel framework for the classification of skin lesions. The framework incorporates deep features to generate a highly discriminant feature vector, while also maintaining the integrity of the original feature space. Recent deep models including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2 are employed in our study for the purpose of feature extraction. Additionally, transfer learning is leveraged to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models is combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection using a novel entropy-controlled grey wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques is employed to initially incorporate the feature vector with a high level of information and subsequently eliminate redundant and irrelevant feature information. The efficacy of our design is substantiated through the evaluation on three benchmark dermoscopic datasets, namely PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation is conducted, including a rigorous comparison with established techniques in the field.

The relaxation processes induced by an exposure of the Ar matrices doped with CH4 (0.1 – 10%) to an electron beam were studied with a focus on the dynamics of radiolysis products – H atoms, H2 molecules, CH radicals, and energy transfer processes. Three channels of energy transfer to dopant and radiolysis products were discussed: by free charge carriers, free excitons and photons from the “intrinsic source” provided by emission of the self-trapped excitons. Radiolysis products along with the total yield of desorbing particles were monitored in a correlated manner. Analysis of methane transformation reactions induced by free excitons showed that the CH radical can be considered as a marker of the CH3 species. The competition between exciton self-trapping and energy transfer to the dopant and radiolysis products has been demonstrated. A nonlinear concentration behavior of the H atoms in doped Ar matrices has been established. Real-time correlated monitoring of optical emissions (H atom and CH3 radicals), particle ejection, and temperature revealed a nonmonotonic behavior of optical yields with a strong luminescence flash after almost an hour of exposure, which correlated with explosive pulse of particle ejection and temperature. The connection of this phenomenon with the processes of energy transfer and radical-radical recombination is discussed.

This publication describes the influence of residue monomers in synthesized pressure-sensitive adhesives based on acrylics on main performance such as tack, peel adhesion, shear strength and shrinkage in form of transfer tapes used for joining wooden elements in furniture industry. The discussed carrier-free adhesive tapes are synthesized by photo-crosslinking and photopolymerization with UV radiation of photoreactive prepolymers sandwiched between two adhesive siliconized polyester films. The simultaneous crosslinking and polymerization process under UV lamps carried out under UV lamps placed simultaneously above and below the crosslinked photoreactive polymer layer leads to the production of a carrier-free adhesive film. In this test series it were investigated to what how the intensity of UV radiation and the time of its exposure affect the viscosity of photoreactive compositions, and then the content of unreacted monomers in them and, subsequently, the method of their crosslinking and polymerization, and the concentration of the photoreactive crosslinking compound leading to the obtaining of transfer adhesive tapes, and thus on their tray, peel adhesion, shear strength and shrinkage on the surface of the wood.

Bioelectrochemical systems (BESs) have great potential in renewable energy production technologies. BES can generate electricity via Microbial Fuel Cell (MFC) or use the electric current for the synthesis of valuable commodities in Microbial Electrolysis Cells (MECs). The number of various reactor configurations and operational protocols increasing rapidly although, the industrial scale operation is still facing difficulties. This article reviews the recent BES related to literature, with special attention to electrosynthesis and the most promising reactor configurations. We also attempted to clarify the numerous definitions proposed for BESs. The main components of BES are highlighted. Although the comparison of the various fermentation systems is we collected useful and generally applicable operational parameters to be used for comparative studies. A brief overview to link the appropriate microbes to the optimal reactor design is given.

Recently, in a previous study, we have experimentally shown the existence of an optimal injected steam mass flow rate, per unit length, ϕopt, which produces the maximal recovery of con- densate in a preformed steam chamber with an elliptical cross section of horizontal semi-major axis. Mutatis mutandis, in this work we present experimental studies in preformed steam chambers, one elliptical and the other circular. In both cases, we also have found the existence of unique optimal values. These configurations try to recreate the steam condensation, at a given time lapse, as it would occur during the growth stage of the steam-assisted gravity drainage (SAGD) process, a method used in the recovery of heavy and extra-heavy oil from homogeneous reservoirs. Finding the optimal mass flow rates in actual recovery process could become useful in the design of optimized SAGD processes.

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky and the analysis of the performance of the proposed classifier. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.

The ionic Diels-Alder (I-DA) reactions of a series of six iminium cations with cyclopentadiene have been studied within the Molecular Electron Density Theory (MEDT). The superelectrophilic character of iminium cations,  > 8.20 eV, accounts for the high reactivity of these species participating in I-DA reactions. The activation energies are found between 13 and 20 kcal·mol-1 lower in energy than those associated to the corresponding Diels-Alder (DA) reactions of neutral imines. These reactions are low endo selective as a consequence of the cationic character of the TSs, but highly regioselective. Solvents have poor effects on the relative energies, and an unappreciable effect in the geometries. In dichloromethane the activation energies increase slightly as a consequence of the better solvation of the iminium cations than the cationic TSs. ELF topological analysis of the bonding changes along the I-DA reactions shows that they are very similar to those in polar DA reactions. The present MEDT study makes it possible establishing that the global electron density transfer (GEDT) taking place at the TSs of I-DA reactions, and not steric (Pauli) repulsions such as have been recently proposed, are responsible for the features of these type of DA reactions.

Poly(2-methoxyethyl acrylate) (PMEA) and poly(ethylene oxide) (PEO) have protein-antifouling properties and blood compatibility. ABA triblock copolymers (PMEAn-PEO11340-PMEAn (MEOMn)) were prepared using single-electron transfer-living radical polymerization (SET-LRP) using a bi-functional PEO macroinitiator. Two types of MEOMn composed of PMEA blocks with a degree of polymerization (DP = n) of 85 and 777 were prepared using the same PEO macroinitiator. MEOMn formed flower micelles with a hydrophobic PMEA (A) core and hydrophilic PEO (B) loop shells in diluted water with a similar appearance to petals. The hydrodynamic radii of MEOM85 and MEOM777 were 151 and 108 nm, respectively. The PMEA block with a large DP formed a tightly packed core. The aggregation number (Nagg) of the PMEA block in a single flower micelle for MEOM85 and MEOM777 was 156 and 164, respectively, which were estimated using a light scat-tering technique. The critical micelle concentrations (CMCs) for MEOM85 and MEOM777 were 0.01 and 0.002 g/L, respectively, as determined by the light scattering intensity and fluorescence probe techniques. The size, Nagg, and CMC for MEOM85 and MEOM777 were almost the same inde-pendent of hydrophobic DP of the PMEA block.

The design of more efficient photosensitizers is a matter of great importance in the field of cancer treatment by means of photodynamic therapy. One of the main processes involved in the activation of apoptosis in cancer cells is the oxidative stress on DNA once a photosensitizer is excited by light. As a consequence, it is a matter of great relevance to investigate in detail the binding modes of the chromophore with DNA, and the nature of the electronically excited states that participate in the induction of DNA damage, for example, charge-transfer states. In this work, we investigate the electronic structure of the anthraquinone photosensitizer intercalated into a double-stranded poly(dG-dC) decamer model of DNA. First, the different geometric configurations are analyzed by means of classical molecular dynamics simulations. Then, the excited states for the most relevant poses of anthraquinone inside the binding pocket are computed by an electrostatic-embedding quantum mechanics/molecular mechanics approach, where anthraquinone and one of the nearby guanine residues are described quantum mechanically to take into account intermolecular charge-transfer states. The excited states are characterized as monomer, exciton, excimer and charge-transfer states based on the analysis of the transition density matrix, and each of these contributions to the total density of states and absorption spectrum is discussed in terms of the stacking interactions. These results are relevant as they represent the footing for future studies on the reactivity of anthraquinone derivatives with DNA and give insights on possible geometrical configurations that potentially favor the oxidative stress of DNA.

Bacterial conjugation, also referred to as bacterial sex, is a major horizontal gene transfer mechanism where the DNA is transferred from a donor to a recipient bacterium by direct contact. Conjugation is universally conserved among bacteria and occurs in a wide range of environments (soil, plant surfaces, water, sewage, biofilms and host-associated bacterial communities). Within these habitats, conjugation drives the rapid evolution and adaptation of bacterial strains by mediating the propagation of various metabolic properties, including symbiotic life-style, virulence, biofilm formation, or resistance to heavy metals and, most importantly, resistance to antibiotics. These properties make of conjugation a fundamentally important process at the center of extensive study. Here, we review the key steps of conjugation by following the life-cycle of the F plasmid during transfer from the donor to the recipient cell. We also discuss our current knowledge of the extent and impact of conjugation within an environmentally and clinically relevant bacterial habitat, bacterial biofilms.

Bioelectrocatalysis provides the intrinsic catalytic-functions of redox enzymes to non-specific electrode reactions and is the most important and basic concept for biosensors. This review starts by describing fundamental characteristics of bioelectrocatalytic reactions in mediated and direct electron transfer types from a theoretical viewpoint and summarizes amperometric biosensors based on multi-enzymatic cascades and for multi-analyte detection. The review also introduces prospective aspects of two new concepts of biosensors: mass-transfer-controlled (pseudo)steady-state amperometry at microelectrodes with enhanced enzymatic activity without calibration curves and potentiometric coulometry at enzyme/mediator-immobilized biosensors for absolute determination.

Microbial growth has been of prime importance to the researchers in health and biotechnology industries. It has been known to be closely associated to the secretion of extracellular polymeric substances that help in the formation of colonies. Inter-microbial communication happens within such colonies by means of extracellular electron transfer mediated by the aforementioned polymeric substances. Conventionally, different phases of microbial growth are monitored with the aid of a traditional UV-Visible spectrophotometer by measuring the optical density of the liquid medium at 280 nm. In this paper, we have developed an alternative novel way to sense different growth phases employing electrochemical means i.e. two-terminal cyclic voltammetry. This cyclic voltammetry relies on the extracellular electron transfer mechanism taking place via the polymeric substances secreted by the microorganisms, measured by the temporal area changes in the current-voltage hysteresis curves in the inoculated nutrient broth. This work paves a new way to detect the biological activity in the medium, which can be directly correlated to the population of microorganisms. It would be of immense interest to scientists and researchers working in the field of microbiology as well as in development of biosensors, electrochemical sensors etc. which would be helpful in absence of traditional spectrophotometers.

Here, I introduce a concept called autonomous selection to refer to a source of selection that is part of the individuals upon which it acts. The concept is motivated by a set of phenomena with the following characteristics: Natural selection shaped a variant (e.g., gene, epigenetic mark, or combination thereof) to act in a manner that reduces the frequency of one or more heritable traits of the individual in which it is located if those traits are detrimental to individual or group fitness. Phenomena with these characteristics are peculiar to traditional evolutionary theory but have been identified rather frequently in recent decades. They are also relevant to adaptive evolution: By reducing the frequency of a trait detrimental to fitness, the variant accelerates the evolution of adaptations, which allows its holders to adapt better to constantly changing environments. The variant is shaped by (natural) selection, but also does (autonomous) selection. Several phenomena with these characteristics have been invoked by proponents of the extended evolutionary synthesis (EES). The concept of autonomous selection helps resolve some of the controversy surrounding the EES: EES proponents call attention to the incompleteness of contemporary theory, emphasizing individuals’ processes that influence which adaptations those individuals evolve. I argue for the special importance of individuals’ processes that do not just influence those individuals’ adaptations, but also accelerate the adaptive evolution of those individuals. All known phenomena that fit this description are examples of autonomous selection. Other phenomena raised by EES proponents do not meet this threshold.

Reemergence of deep Neural Networks (CNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is possible because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be a lot more challenging and for various reasons using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper,we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data is easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other learning competing approaches.

The hybrid personal cooling system (HPCS) consisted of ventilation fans and phase change materials (PCMs) covered with insulation pads is a promising wearable cooling system to mitigate heat strain and heat-related illnesses of occupational workers with heavy labor in hot environments. Effects of clothing characteristics (e.g., thermal resistance of insulation pads, latent heat and melting temperature of PCMs) on the thermal performance of the HPCS have been investigated in detail in our previous study. Apart from the aforementioned factors, environmental conditions, i.e., environmental temperature and relative humidity, also significantly affect the thermal performance of the HPCS. In this paper, a numerical parametric study was performed to investigate the effects of the environmental temperature and relative humidity (RH) on the thermal management of the HPCS. Five levels of air temperature under environmental RH=50% were chosen (i.e., 32, 34, 36, 38 and 40 ºC) to study the impact of environmental temperature on the HPCS’s cooling performance. In addition, four levels of environmental RH at ambient temperatures of 36 and 40 ºC were selected (i.e., 30, 50, 70 and 90%) to examine the effect of RH on cooling performance of the HPCS. Results show that high environmental temperatures could accelerate the PCM melting process and thereby weaken the cooling performance of HPCS. In the moderately hot environment (36 °C), the HPCS presented good cooling performance with the maximum core temperature at around 37.5 °C during excise when the ambient RH≤70%, whereas good cooling performance could be only seen under RH≤50% in the extremely hot environment (40 °C). Thus, it may be concluded that the maximum environmental RH for the HPCS exhibiting good cooling performance decreases with the increase in the environmental temperature.

This paper addresses the modeling of the iron ore direct reduction process, a process likely to reduce CO₂ emissions from the steel industry. The shaft furnace is divided into three sections (reduction, transition, and cooling), and the model is two-dimensional (cylindrical geometry for the upper sections and conical geometry for the lower one), to correctly describe the lateral gas feed and cooling gas outlet. This model relies on a detailed description of the main physical–chemical and thermal phenomena, using a multi-scale approach. The moving bed is assumed to be comprised of pellets of grains and crystallites. We also take into account eight heterogeneous and two homogeneous chemical reactions. The local mass, energy, and momentum balances are numerically solved, using the finite volume method. This model was successfully validated by simulating the shaft furnaces of two direct reduction plants of different capacities. The calculated results reveal the detailed interior behavior of the shaft furnace operation. Eight different zones can be distinguished, according to their predominant thermal and reaction characteristics. An important finding is the presence of a central zone of lesser temperature and conversion.

Microbial electrochemical technologies (MET) rely on the presence of the metabolic activity of electroactive bacteria for the use of solid-state electrodes for oxidizing different kind of compound, that could lead to the synthesis of chemicals, bioremediation of polluted matrices, the treatment of contaminants of interest, as well as the recovery of energy. Keeping in mind those possibilities, since the beginning of the present century, there has been a growing interest in the use of electrochemical technologies for wastewater treatment, and if possible with simultaneous power generation. In the last years, there has been a growing interest to explore the possibility of merging MET with constructed wetlands, to offer a new option of intensified wetland system that could keep a high performance with a lower footprint. Based on that interest, this paper explains the general principles of MET, and the different known extracellular electron transfer mechanisms ruling the interaction between electroactive bacteria and potential solid-state electron acceptors. Also, the adoption of those principles for the development of MET set-ups for simultaneous wastewater treatment and power generation, and the challenges that the technology face. Ultimately, the most recent developments in set-ups that merges MET with constructed wetlands are presented and discussed.

The regulation of hepatic very-low-density lipoprotein (VLDL) secretion plays an important role in the pathogenesis of dyslipidemia and fatty liver diseases. VLDL is controlled by hepatic microsomal triglyceride transfer protein (MTTP). Mttp is regulated by carbohydrate response element binding protein (ChREBP) and small heterodimer partner (SHP). However, it is unclear whether both coordinately regulate Mttp expression and VLDL secretion. Here, adenoviral overexpression of ChREBP and SHP in rat primary hepatocytes induced and suppressed Mttp mRNA, respectively. However, Mttp induction by ChREBP was much more potent than suppression by SHP. Promoter assays of Mttp and the liver type pyruvate kinase gene revealed that SHP and ChREBP did not affect the transactivity of each other. Mttp mRNA and protein levels of Shp^/– mice were similar to those of wild-type; however, those of Chrebp^–/–Shp^–/– and Chrebp^–/– mice were much lower. Consistent with this, the VLDL particle number and VLDL secretion rates in Shp^–/–- mice were similar to wild-type, but were much lower in Chrebp^–/– and Chrebp^–/–Shp^–/–- mice. These findings suggested that ChREBP rather than SHP regulates VLDL secretion and that ChREBP and SHP do not affect the transactivities of each other.

Peptides of hydrolysates of food proteins are an easily digestible source of amino acids necessary for the body to adapt to physical stress. Commercially significant hydrolysates include whey protein, casein, and other animal proteins. Hydrolysates of plant proteins are gaining popularity, but they are less common, then animal ones. Soy protein isolate is promising for obtaining the hydrolysates due to its affordable price and balanced amino acid profile. However, there are no direct studies showing an improvement in the result of physical activity when eating soy protein isolate hydrolysate (SPIH). In this work, for the first time, the study was conducted on the safety and efficacy of SPIH during physical load on model animals (rats). It was shown that the hydrolysate did not lead to pathological changes in the viscera, food intake, and weight of animals did not differ from the control group (animals consumed whey protein). Under physical load rats enteral fed SPIH showed a tendency to adapt more quickly to physical stress than the control group and the group of animals that was fed by free amino acids. The metabolites of animal blood serum were studied by NMR spectroscopy. It was shown that by the 95th minute after feeding in the group of rats receiving SPIH, the difference of proteinogenic amino acids concentrations in blood between individuals was significantly less than in the groups receiving whey protein or a mixture of amino acids. In other words, individual biochemical and physiological characteristics of individuals did not affect the assimilation of amino acids of hydrolysate.

This study provides a contribution to the research field of 3D printed earthen buildings, focusing, for the first time, on the load-bearing capacity of these structures. The study involves the entire production and testing process of the earthen elements, from design, to the preparation of the mixture and the 3D printing, up to the uniaxial compression test on a wall segment. The results indicate that 3D printed earthen elements have a compressive strength of 2.32 MPa, comparable to that of rammed earth structures. The experimental data also made it possible to draw conclusions on the action of the infill, which seems to have the function of stopping the propagation of cracks. This has a positive effect on the overall behavior of 3D printed earthen elements, since it avoids the onset of dilative behavior in the final stages of the load test and maintains ultimate load values higher than 50% of the maximum load.

Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

As on 31.03.2020, 55.4 % (205135 MW) of total installed capacity (370106 MW) in India is through coal and lignite based power plants. These plants, set up by central, state and private utilities with substantial capital investment are facing consistently reducing Plant Utilization Factor (known as Plant Load Factor, PLF, in India). In the year 2019-20 the national average thermal power PLF stood at 55.4%, down from 78.6 % in 2007-08. On the other hand, the electricity demand is consistently rising in the country and there exists a peak and energy shortage at national level. In 2019-20 energy shortage was 0.7 % and peak shortage was 0.5 %. A disturbing paradox therefore exists here. On one hand, the country is power deficit, and on the other hand, a large amount of coal based affordable power, ready to be generated by thermal power generators, remains grossly unused. Looking into the fact that considerable investment has gone into developing these thermal power generation assets in the country, the falling PLF is a matter of concern for all the key stakeholders including the power producers, lenders, regulators and consumers. This paper identifies seven major factors that are affecting PLF of thermal power plants and then makes an attempt to project future scenario of PLF so that critical stakeholders can intervene through appropriate actions. Primary research with responses from power professionals has been used to find out the major factors. Future projection of PLF has been done using Partial Least Square (PLS) regression. Projection shows that in the Business As Usual case (Factors increasing at the current CAGR rate), the thermal power plants will face very low level of PLF (14.76 %) by 2024-25. This will mean that many plants will be shut down and many will run for only few hours in a day that too at very low loads. If the future generation mix is kept as indicated by Central Electricity Authority (CEA), a Govt. of India in its report (Draft report on optimal generation capacity mix for 2029-30- CEA- Govt of India) then the thermal power plant average PLF can sustain above 68 % until 2024-25. If followed, this path can be a breather for the thermal power plants.

Considering the fault "N-1" checksum and the power flow, the single-phase power flow model is further transformed into a three-phase power flow model, and the asymmetry of the three-phase power flow is measured by the three-phase unbalance factor. The calculation model is linearized by the second-order cone relaxation and the Big-M method. At the same time, the load response and distribution network reconstruction are used to improve the reliability of the power supply network to cope with the power failure. The relationship between power supply capability and power flow constraints, main transformer capacity and distributed power parameters is analyzed by IEEE 33-node three-phase power distribution system. The feasibility of the proposed model and the accuracy of the second-order cone relaxation are verified by numerical examples, which provides a technical reference for distribution network planning.

Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.

The popular motor drive systems with a single two-level voltage source inverter (VSI) have one main problem that is the occurrence of the common-mode voltage (CMV), which is an effect of the electromagnetic interference, shaft voltage, bearing currents, leakage current. These cause the high stress, increasung temperature and early mechanical failure in machine. To overcome this problem, the technology of the dual two-level VSI fed open-end three-phase ac loads is now available to eliminate the CMV at the ac/induction motor load with the 120-degree modulation technique for controlling each inverter. In this paper, the discontinuous space vector modulation (DSVM) schemes are proposed and applied for the dual two-level VSI fed open-end load. It is based on the 120-degree modulation technique by using only 12 active voltage vectors and the 10 zero voltage vectors from the total 64 voltage vectors along with the different five-segment swicthing sequence designs with centralizing pulse width modulation technqiue in order to not only cancel the CMV in the ac load, but also reduce the switching number/switching loss of the conversion system. Among the various DSVM schemes, their performances are compared in this paper, such as the number of the switching, the step and peak value of the CMV in each inverter, and the quality of the output waveform, etc. The details of the verfication and comparison are carried out by simulation using Matlab/Simulink software.

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

Carbapenemase-producing Enterobacter spp. Serratia marcescens, Citrobacter freundii, Providencia spp. and Morganella morganii (CP-ESCPM) are increasingly identified as causative agents of nosocomial infections but are still not under systematic genomic surveillance. In this study, using a combination of whole genome sequencing and conjugation experiments, we sought to elucidate the genomic characteristics and transferability of resistance genes in clinical CP-ESCPM isolates from Bulgaria. Among the 36 sequenced isolates, NDM-1 (12/36), VIM-4 (11/36), VIM-86 (8/36) and OXA-48 (7/36) carbapenemases were identified; two isolates carried both NDM-1 and VIM-86. The majority of carbapenemase genes were found on self-conjugative plasmids. IncL plasmids were responsible for the spread of OXA-48 among E. hormaechei, C. freundii and S. marcescens. IncM2 plasmids were generally associated with the spread of NDM-1 in C. freundii and S. marcescens, and also of VIM-4 in C. freundii. IncC plasmids were involved in the spread of the recently described VIM-86 in P. stuartii isolates. IncC plasmids carrying blaNDM-1 and blaVIM-86 have been observed too. blaNDM-1 was also detected on IncX3 in S. marcescens and on IncT plasmid in M. morganii. The significant resistance transfer rates highlight the role of the ESCPM group as a reservoir of resistance determinants and stress the need for strengthening the infection control measures.

This paper introduces a new adaptation of the enthalpic lattice Boltzmann method (LBM) tailored to simulate the intricate dynamics of conjugate heat transfer in non-homogeneous media, showcasing considerable potential for enhancing heat exchanger designs. This approach expands upon the conventional enthalpic LBM framework by seamlessly incorporating tailored source terms, enabling precise representation of local and transient variations in the medium’s thermophysical properties. The resulting LBM model comprehensively captures the energy conservation equation, with exceptions made only for flow compressibility and viscous dissipation, thereby significantly augmenting its versatility in analyzing heat exchanger configurations. Verification tests, comprising assessments of both transient and steady-state solutions, unequivocally validate the accuracy of the proposed model, further bolstering its reliability for analyzing heat exchange processes within intricate heat exchanger geometries. To demonstrate its efficacy, simulations of conjugate heat transfer processes involving fluid natural convection within structured cavities were performed, imposing both Dirichlet and Neumann boundary conditions to emulate scenarios encountered in practical heat exchanger operations. The findings yield compelling insights, particularly in the transient regime, revealing the beneficial impact of structured cavities on enhancing heat transfer processes. These insights inform potential design optimizations for heat exchangers. The results emphasize the potential of the modified enthalpic LBM approach to elucidate complex heat transfer phenomena in non-homogeneous media and structured geometries, offering valuable implications for heat exchanger engineering and optimization.

C-terminal Src kinase (CSK) is the major inhibitory kinase for Src family kinases (SFKs) through phosphorylation of their C-tail tyrosine site, and regulates various cellular activities in association with SFKs functions. As a cytoplasmic protein, CSK need be recruited to the plasma membrane to regulate SFKs activities. The regulatory mechanism for CSK activity and its subcellular localization remains largely unclear. In this work, we developed a genetically encoded biosensor based on fluorescence resonance energy transfer (FRET) to visualize CSK activity in live cells. The biosensor with optimized substrate peptide confirmed crucial Arg107 site in CSK SH2 domain, and displayed sensitivity and specificity to CSK activity while little responses to co-transfected Src and Fyn. FRET measurements showed CSK having relatively mild level of kinase activity in comparison to Src and Fyn ones in rat airway smooth muscle cells. The biosensor tagged with different submembrane-targeting signals detected CSK activity at both non-lipid raft and lipid raft microregions, while showed higher FRET level at non-lipid ones. Co-transfected receptor-type protein tyrosine phosphatase alpha (PTPα) had inhibitory effect on the CSK FRET response. The biosensor didn’t detect obvious changes of CSK activity between metastatic cancer cells and normal ones. In conclusion, a novel FRET biosensor was generated to monitor CSK activity, and demonstrated CSK activity existing in both non-lipid and lipid raft membrane microregions while more present at non-lipid ones.

We consider boundary value problems for a nonlinear mass transfer model, which generalizes the classical Boussinesq approximation, under inhomogeneous Dirichlet boundary conditions for the velocity and the substance’s concentration. It is assumed that the leading coefficients of viscosity and diffusion and the buoyancy force in the model equations depend on concentration. We develop mathematical apparatus of studying inhomogeneous boundary value problems under consideration. It is based on using of a weak solution of the boundary value problem and construction of liftings of the inhomogeneous boundary data. They remove the inhomogeneity of the data and reduce original problems to equivalent homogeneous boundary value problems. Based on this apparatus we will prove the theorem of the global existence of a weak solution to boundary value problem under study and establish important properties of the solution. In particular, we will prove the validity of the maximum principle for the substance’s concentration. We will also establish sufficient conditions for the problem data, ensuring the local uniqueness of weak solutions.

The synthesis and characterization of the multicomponent crystals formed by 2,2’-thiodiacetic acid (H2tda) and 2,6-diaminopurine (Hdap) or N9-(2-hydroxyethyl)adenine (9heade) are reported. The crystals are in the salt rather than cocrystal form, as witnessed by single crystal X-ray diffractometry where a structural analysis revealed their ionic nature, confirming the proton transfer from the 2,2’-thiodiacetic acid to the basic groups of the coformers. The new multicomponent crystals have molecular formulas [(H9heade+)(Htda)] (1) and [(H2dap+)2(tda2)]·2H2O (2), and were also characterized by FTIR, elemental analysis as well as thermogravimetric/differential scanning calorimetry (TG/DSC) analyses. In the crystal packing the ions interact with each other via O–H···N, O–H···O. N–H···O and N–H···N hydrogen bonds generating cyclic hydrogen bonded motifs with graph-set notation of R22(16), R22(10), R32(10), R33(10), R22(9), R32(8) and R42(8), to form different supramolecular homo- and hetero-synthons. In addition, in the crystal packing of 2, pairs of diaminopurinium ions display an strong anti-parallel ,-stacking interaction, with short inter-centroids and interplanar distances (3.39 and 3.24 Å respectively) and fairly tight angle (17.5°). These assemblies have been further analyzed energetically by using DFT calculations, MEP surface analysis and QTAIM characterization.

This paper studies a multihop amplify-and-forward (AF) simultaneous wireless information and power transfer (SWIPT) relaying system. Each relaying node harvests energy using power splitting (PS) scheme from a part of its received signal to amplify and forward the rest the received signal to the next relay. Based on this system model and signal flow, we derived and solved the convex energy minimization problem with optimal PS ratio. The influence of processing cost was then investigated for the AF-SWIPT system with the decode-and-forward SWIPT as benchmark, where AF-SWIPT was found to be superior.

This research is a preliminary phase of a general effort to develop a generic-data transferring capability via human haptic thermal sensation (generic-data refers to a coded language like Morse or Braille). For the capability to be effective, it must include a large variety of short recognizable cues. Hence, we propose the concept of cues based on sequences of thermal pulses, i.e., combinations of warm and cool pulses with several levels of intensity. The objective of this study was to determine the feasibility of basing a generic-data-transfer capability on thermal cues composed of sequences of short pulses. The research included defining the basic characteristics of the stimuli parameters and developing practical methods for generating and measuring them. Several sequences were designed in light of the relevant data known to date, and tests were conducted. The thermal cues presented to the participants were sensed and recognized by touch. The results indicate high feasibility for a capability that is applicable in various scenarios. In addition, the low impact on human skin temperature due to short stimuli duration represents an inherent advantage for later implementation. This report presents promising findings and offers insights for further investigation.

Bis-benzimidazole derivative (BBM) molecule, consisting of two 2-(2’-Hydroxyphenyl) benzim-idazole (HBI) halves, has been synthesized and successfully utilized as a ratiometric fluorescence sensor for the sensitive detection of Cu2+ based on enol-keto excited state intramolecular proton transfer (ESIPT). In this study, we strategically implement femtosecond stimulated Raman spec-troscopy and several time-resolved electronic spectroscopies, aided by quantum chemical calcula-tions to investigate the detailed primary photodynamics of BBM molecule. The results demonstrate that the ESIPT from BBM-enol* to BBM-keto* was observed in only one of HBI halves with a time constant of 300 fs, after that, the rotation of the dihedral angle between two HBI halves generate a planarized BBM-keto* isomer in 3 ps, leading to dynamics red shift of BBM-keto* emission.

Economic valuations of ecosystem services often transfer previously estimated global unit values to the geographical setting of interest. While this approach produces quick results, its reliability depends on how representative the large-scale average unit values are for the given local context. Here, we estimate the values of three ecosystem services (ES) – water filtration, nutrient cycling and carbon sequestration – in the Grand River watershed (GRW) of southern Ontario, Canada. The watershed covers nearly 7000 km2, has a humid continental climate and a population of close to one million people. Land cover is dominated by agriculture. We compare ES valuations using locally derived (i.e., GRW-specific) unit values to valuations based on unit values from a regional database and those compiled in the global Ecosystem Services Valuation Database (ESVD). The regional database includes mean unit values from three case studies within southern Ontario and one boreal watershed in British Columbia. As expected, the regional database yields average monetary values for the three ES that are close to those obtained with the local unit values but with larger associated uncertainties. Using the ESVD, however, results in significantly higher monetary values for the ES. For water filtration, the ESVD value is more than five times higher than the regional and local estimates. We further illustrate the effect of the extent of aggregation of forested and agricultural land categories on the ES values. For example, by subdividing the forest category into three sub-categories (deciduous, coniferous, and mixed forest), the estimated value of the carbon sequestration ES of forested areas within the GRW increases by 15%. Overall, our results emphasize the importance of critically assessing the origin of unit values and the land cover resolution in ES valuation, especially when the latter is used as a policy-guiding tool.

Geriatric decisional capacity assessment is a complex medico-legal activity that evaluates normal subjects involved in specific civil matter like wealth transfer, access to treatment or voluntary hospitalization. Our objective was to evaluate how geriatric extrajudicial capacity exams impacted a general psychiatric unit over a decade. We also assessed the value of a surrogate biomarker (neutrophils to lymphocyte ratio) as a screening factor in the capacity assessment process. 72 cases were assessed in one psychiatric unit over a decade (2009-2018). As population was fast ageing, the total number of capacity exams per year doubled in time. None of the subjects had an active diagnostic of severe cognitive impairment or active psychiatric disease. The percentage of subjects receiving an “absence of capacity” conclusion remained constant over time (approx. 20% of all cases). 81% of the subjects within this subgroup had a clinical diagnostic of mild cognitive impairment compared to 46% in the “presence of capacity” subgroup (p<0.001). The surrogate biomarker neutrophils to lymphocyte ratio was significantly higher in the “absence of capacity” subgroup, mainly related to a cognitive impairment diagnostic. The ratio has to be validated by larger studies before to be included in the capacity assessment process as an early screening tool.

The exact determination of radiative exchanges between solids and surfaces has been a long sought-for question in heat transfer science. Being the canonical equation that rules such phenomena, a fourfold integral, it is extremely difficult to obtain an accurate solution like a formula or abacus. Over the last thirty years, the author has tried to integrate the canonical expression by sundry procedures and they have published two books and a dozen of articles on the matter, recently by virtue of computational geometry and graphic algorithms as a new way to solve the finite-difference problems that arise on complex geometries. In architectural engineering curved radiant emitters are customary since antiquity, especially in domes and vaults and their oculus, However, a consistent procedure to handle them was not readily available. The principles that are described hereby based on Cabeza-Lainez’ first principle for spherical fragments offer a complete panorama on the manner in which surface sources related or contained in spheres can be interpreted and accounted for without resorting to integration. The main advance is that a variety of unexplained problems of radiative heat transfer, applicable to aerospace engineering, meteorological, architectural and medical sciences can be sorted out as exactly as quickly.

The integration of bacteriophages, a particular class of viruses that specifically infect bacteria and archaea, in biosensors for the monitoring of pathogens in foods and beverages is highly desirable. To this end, an increasing focus has been set on the exploration of covalent and physical methods for the immobilization of phages on solid surfaces. This work investigates the electrostatic assembly of tailed phages, specifically anti-Listeria monocytogenes P100 phages, on an ultrathin self-assembled monolayer (SAM) of 11-amino-1-undecanethiol (AUT). The cationic properties of AUT may allow for the electrostatic capture of P100 in a capsid-down fashion, thus, exposing the specific receptor-binding proteins on their tails to the corresponding pathogens in analytical samples. The physical properties of immobilized phages have been studied using AFM, SEM, and electrochemical techniques, providing insight into the orientation of the phages and revealing that the pH plays a remarkable role in the morphology and charge transfer behavior of the adsorbed films. Overall, this research portrays SAMs of amino-akylthiols as a valid platform for the oriented immobilization of bacteriophages on surfaces for electroanalytical purposes.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3,000 digital mammograms is assembled in which 1,496 images depict malignant lesions and 1,054 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. The first scheme is developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme is built based a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes are trained and tested using a 10-fold cross-validation method. Several score fusion methods are also investigated to classify breast lesions. CAD classification performance is evaluated by the area under ROC curve (AUC). Results: ResNet50 model-based CAD scheme yields AUC = 0.85±0.02, which is significantly higher than radiomics feature-based CAD scheme with AUC = 0.77±0.02 (p < 0.01). Additionally, fusion of classification scores generated by two CAD schemes does not further improve classification performance. Conclusion: This study indicates that using deep transfer learning is more efficient to develop CAD schemes and enables to yield higher lesion classification performance than CAD schemes developed using radiomics-based technology.

Wireless power transfer promises to revolutionize the way we use and power mobile devices. However, low transfer efficiencies prevent this technology from seeing wide scale real-world adoption. The aim of this work is to use quasioptics to develop a system composed of a dielectric lens fed by a phased array to reduce spillover losses, increasing the beam efficiency, while working on the antenna system’s Fresnel zone. The DC-RF electronics, digital beamforming and beam-steering by an FPGA, and radiating 4x4 microstrip patch phased array have been developed and experimented upon, while the lens has been designed and simulated. This paper details these preliminary results, where the phased array radiation pattern was measured, showing that the beam is being generated and steered as expected, prompting the lens construction for the complete system experimentation.

The conformational dependence of the matrix element for spin-orbit coupling and of the electronic coupling for charge separation are determined for an electron donor-acceptor system containing a pyrene acceptor and a dimethylaniline donor. Different kinetic and energetic aspects that play a role in the spin-orbit charge transfer intersystem crossing (SOCT-ISC) mechanism are discussed. This includes parameters related to initial charge separation and the charge recombination pathways using the (Semi-Classical) Marcus Theory for electron transfer. The spin-orbit coupling, which plays a significant role in charge recombination to the triplet state can be probed by (TD-)DFT, using the latter as a tool to understand and predict the SOCT-ISC mechanism. The matrix elements for spin-orbit coupling for acetone and 4-thio-thymine are used for benchmarking. (Time Dependent-) Density Functional Theory (DFT and TD-DFT) calculations are applied using the quantum chemical program Amsterdam Density Functional (ADF).

Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is a challenging problem. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Identifying objects in floor plan images is also challenging due to the variety of floor plans and different objects. We faced a problem in training our network because of the lack of publicly available datasets. Currently, available public datasets do not have enough images to train deep neural networks efficiently. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10000 images to address this issue. Our proposed method conveniently surpasses the previous state-of-the-art results on the SESYD dataset and sets impressive baseline results on the proposed SFPI dataset. The dataset can be downloaded from SFPI Dataset Link. We believe that the novel dataset enables the researcher to enhance the research in this domain further.

Mutual survival among different species of living organisms is quite common in our living world. That mutual survival can produce symbiotic or parasitic relationship among different living organisms. But at the same time, some relationships are harmful to the living organisms creating pathogenic relationships. Why some mutual survivals are beneficial, whereas some relationships are harmful creating different diseases in the living world? That harmful or pathological relationship producing different diseases in both the animal and plant kingdom has been extensively studied by the scientific community several times under the heading of ‘Host-pathogen interaction’ and ‘Disease pathogenesis’. But it is still not clear why some mutual survivals are beneficial or non-harmful, whereas some co-survivals are harmful producing different disease conditions in the living world mainly due to different immune mediated reactions or direct toxic effect of substances produced by an organism. To find the answer to this question, we have to search retrospectively to the evolutionary pattern of our diverse living world. If it is assumed that we have originated from Last Universal Common Ancestor (LUCA) by different cumulative mutations, horizontal gene transfer, mobile genetic elements (MGE), transposition and natural selection, then it would be quite pragmatic to consider that two things were moving side by side in our ancient living world. On one hand it’s purpose was to create the diversification of both unicellular and multi-cellular living world and on the other hand it’s another purpose was to maintain the specific identity of the living organisms. It is the second purpose or the maintenance of specific identity that ultimately led to the development of Immune system.

This is the first study to provide a systematic monetary benefit matrix, including greenhouse gas (GHG) emissions reduction benefits and air pollution reduction health co-benefits, for a change in on-the-road transport to low-carbon types. The benefit transfer method is employed to estimate the social cost of carbon and the health co-benefits via impact pathway analysis in Taiwan. Specifically, the total emissions reduction benefits from changing all internal combustion vehicles to either hybrid electric vehicles, plug-in hybrid electric vehicles, or electric vehicles would generate an average of US$760 million from GHG emissions reduction and US$2,091 million from health co-benefits based on air pollution reduction, for a total benefit of US$2,851 million annually. For a change from combustion scooters to light- or heavy-duty electric scooters, the average GHG emissions reduction benefits would be US$96.02 million, and the health co-benefits from air pollution reduction would be US$1,008.83 million, for total benefits of US$1,104.85 million annually.

Machine learning (ML) has a large capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied; three approaches of ML were used. Once all images were analyzed, representative areas from every digital image were also processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning - support vector machine (TL-SVM) (AUROC = 0.94, SPE 88%, SEN 100%) and transfer learning – random forest (TL- RF) method (AUROC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUROC = 0.84, SPE 77%, SEN 91%) and random forest (AUROC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas.

As a candidate for bifunctional asymmetric catalysts containing a half-sandwich C–N chelating Ir(III) framework (azairidacycle), a dinuclear Ir complex with an axially chiral linkage is newly designed. An expedient synthesis of chiral 2,2’-bis(aminomethyl)-1,1’-binaphthyl (1) from 1,1-bi-2-naphthol (BINOL) was accomplished by a three-step process involving nickel-catalyzed cyanation and subsequent reduction with Raney-Ni and KBH4. The reaction of (S)-1 with an equimolar amount of [IrCl2Cp*]2 (Cp* = 5-C5(CH3)5) in the presence of sodium acetate in acetonitrile at 80 °C gave a diastereomeric mixture of new dinuclear dichloridodiiridium (5) through the double C–H bond cleavage, as confirmed by 1H NMR spectroscopy. A loss of the central chirality on the Ir centers of 5 was demonstrated by treatment with KOC(CH3)3 to generate the corresponding 16e amidoiridium complex 6. The following hydrogen transfer from 2-propanol to 6 provided diastereomers of hydrido(amine)iridium with retaining the bis(azairidacycle) architecture. The dinuclear chlorido(amine)iridium 5 can serve as a catalyst precursor for the asymmetric transfer hydrogenation of acetophenone with a substrate to catalyst ratio of 200 in the presence of KOC(CH3)3 in 2-propanol, leading to (S)-1-phenylethanol with up to 67% ee.

Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.

The coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with CGAN based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for covid-19 especially in chest CT images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the coronavirus infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The Outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%.

The coronavirus (covid-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest x-ray images is presented. The lack of benchmark datasets for covid-19 especially in chest x-rays images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of the virus from the available x-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the covid-19, normal, pneumonia bacterial, and pneumonia virus. The dataset is divided into 90% for the GAN and the training and the validation phase, while 10% used in the testing phase. The GAN helps in generating more images from the original dataset to be 30 times larger than the originally collected dataset. The GAN also help in overcoming the overfitting problem and made the proposed model more robust. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models prove it is efficiency according to validation, testing accuracy, and performance measurements such as precision, recall, and F1 score. Three case scenarios are tested through the paper, the first scenario which includes 4 classes from the dataset, while the second scenario includes 3 classes and the third scenario includes 2 classes. All the scenarios include the covid-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes 2 classes(covid-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthen the obtained results through the research. Finally, this research may be considered one of the first trails to use GAN and deep transfer models together to help in detecting coronaviruses (covid-19) within the absence of a benchmark dataset around the world, especially in x-rays chest images.

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.

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. We propose a method for automated detection and localisation of key building defects from images using deep learning and convolution neural networks. The proposed model is based on a pre-trained VGG-16 classifier with Class Activation Mapping (CAM) for object localisation. The model has proven to be robust and able to accurately detect and localise mould growth, stains, and paint deterioration defects arising from dampness in buildings. The approach is being developed with potentials to scale-up to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

Reusing the tactile knowledge of some previously explored objects helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physical properties of new objects. These experiences, or prior tactile knowledge, are built by the feature observations that the robot perceives from multiple sensory modalities, when it applies the pressing, sliding, and static contact movements on objects with different action parameters. We call our method Active Prior Tactile Knowledge Transfer (APTKT), and systematically evaluated its performance by several experiments. Results show that the robot improved the discrimination accuracy by around 10% when it used only one training sample plus the feature observations of prior objects. By incorporating the auxiliary features, the transfer learning improved the discrimination accuracy by over 20%. The results also show that the proposed method is robust against transferring irrelevant prior tactile knowledge (negative knowledge transfer).

Load shifting is one of the demand side management (DSM) strategies to support the efficiency in the electricity grid during the peak hours demand. Load shifting at the appliance level is interesting to review since the appliance usage is one of the main inputs in the load profile analysis. More review studies in load shifting at the appliance level are required as it is a specific issue of load profile research body, and still limited studies are available at this moment. It is also essential to focus on the appliance usage patterns to better understand the impacts and characteristics of the individual appliance. The existing studies in load shifting have applied common structured literature reviews, and our study addresses the transparency of each stage and sub-stage of the selection of the final list studies. The finding shows that efficiency has been achieved in the installed capacity reduction, cost including emission reductions, and peak consumption reduction. The most applied method in the load shifting at the appliance level is by developing the load shifting optimisation algorithms. This work contributes to provide a transparent process of applying a systematicity literature review, where it becomes a source of knowledge and grounded theory. It contributes to a limited and specific research of load shifting at the appliance level by highlighting and discussing the key findings for the readers. In specific, it contributes to improve energy efficiency by presenting the load shifting methods at the appliance level and identifying the controllable and uncontrollable appliances. This detail review study at the appliance level can make valuable contributions to support decision and policy-making by illuminating new dynamic system in load shifting area in specific, and demand side management in general for energy efficiency.

In this paper, the time-resolved model of photoacoustic signal for samples with a complex inner structure is derived including local non-equilibrium of structural elements with multiple degrees of freedom, i.e. structural entropy of the system. The local non-equilibrium is taken into account through the fractional operator. By analyzing the model for two types of time-dependent excitation, a very short pulse and a very long pulse, it is shown that the rates of non-equilibrium relaxations in complex samples can be measured by applying the derived model and time-domain measurements. Limitations of the model and further directions of its development are discussed.

This research paper examines the effects, advantages, challenges, and economic aspects of a research town. The study concludes that inclusion and diversity not only constitute fundamental values but also serve as crucial drivers of business success and sustained competitiveness. Universities play a central role in shaping the prosperity of the research town by functioning as knowledge hubs, fostering collaboration, nurturing talent, and contributing to the broader innovation ecosystem. The symbiotic relationship between universities and industries within the cluster establishes a dynamic and resilient environment for research and development. The presence of startups within the research cluster introduces novel ideas, enhancing the overall vibrancy and competitiveness of the local economy. In an era where social media algorithms increasingly influence people's choices, the potential for foreign interference emerges as a significant threat.

Road-embedded inductive power transfer (IPT) systems have the potential to accelerate the electrification of the transportation sector. For these systems to be economically viable however, they need to have a similar durability and lifespan compared to that of asphalt roads. One area that has lacked investigation is thermally induced stresses in a primary IPT pad, which is caused by the increase in temperature of the pad when it is energized and the differing thermal expansion of the materials within. This paper presents an experimental and a finite-element based methodology for investigating the thermal-mechanical behaviour of a ¼-scale double-D pad, which was energized while suspended in air, as well as energized when embedded in pavement. A focus was placed on the measurement and prediction of strains in the magnetic ferrite cores because of their brittleness. Ferrite strains were measured using a combination of resistive strain gauges and non-metallic Fibre Bragg Grating (FBG) sensors. Coupled electromagnetic-thermal-structural simulations were conducted to predict temperature and strains in the system, with temperature-dependent properties obtained through physical testing. At an ambient temperature of 50°C, the temperature in the middle of the copper litz wire coil was predicted to be 100°C in both the suspended and embedded case. There was excellent correlation to experimental results, with a difference of less than 10% for most temperature measurements. When energized, the pad was predicted to experience an upward bow due to its temperature rise, resulting bending strains in the ferrite cores. At an ambient temperature of 50°C, maximum tensile strain in the ferrites of the embedded pad was measured to be 62 microstrain (με), with a root-mean square error that was 18 με across three sensors. The experimental and validated numerical methodology can be applied to full-scale operational IPT pads to analyse and improve their thermal-mechanical performance.

In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance and wake distribution based on the positions of flow control fins (FCFs) applied to containerships of various sizes. Both methods utilized data collected through Computational Fluid Dynamics (CFD) analysis. The position of the flow control fin (FCF) and hull-form information were utilized as input data, and the output data included viscous resistance coefficients and components of propeller axial velocity. The base DNN model was trained and validated using a source dataset from a 1000 TEU containership. Grid search cross-validation technique was employed to optimize the hyperparameters of the base DNN model. Then, transfer learning was applied to predict the viscous resistance and wake distribution for containerships of varying sizes. To enhance the accuracy of feature prediction with a limited amount of dataset, learning rate optimization was conducted. Transfer learning involves retraining and reconfiguring the base DNN model, and the accuracy was verified based on the fine-tuning method of the learning model. The results of this study can provide hull designers for containerships with performance evaluation information by predicting wake distribution, without relying on CFD analysis.

Abstract: In this study, a computational fluid dynamics (CFD) simulation was conducted to validate the CFD model with experimental data from Zimparov et.al (2012), which investigates the heat transfer performance in a spirally corrugated tube that has a twisted tape inserted. The heat transfer was then compared to a simple corrugated tube without the twisted tape and to a smooth tube with no corrugations and no twisted tape. This simulation is compared with a previous experimental study conducted by (Zimparov et al. 2012), which focuses on tubes with a height-to-diameter ratio e/Di > 0.04 and small relative pitches of the twisted tape, H/Di. The largest improvement was noticed in the tube with e/Di= 0.057 and ridge pitch-to-height ratio p/e=6.77 with a twisted tape of H/Di= 4.7. This tube is labeled 5035. Tube 5035 was found to have the most significant enhancement, hence the focus of the CFD simulation will be on this tube, and the simulation will range for Reynolds numbers, 3.5 x 103 < Re < 5.0 x 104. The focus of this simulation is the evaluation of heat transfer and friction factors; a metric was used to obtain an empirical representation of the tube’s performance. Keywords: Spirally corrugated tube; Twisted tape; Heat transfer enhancement; Friction factor; Nusselt number; Computational fluid dynamics (CFD); Star CCM+

In the past few years, the possibility to transfer power wirelessly has experienced growing interest from the research community. Since the wireless channel is subject to a large number of random phenomena, a crucial aspect is the statistical characterization of the energy that can be harvested by a given device. For this characterization to be reliable, a powerful model of the propagation channel is necessary. The recently proposed Generalized-K model has proven to be very useful, as it encompasses the effects of path-loss, shadowing and fast fading for a broad set of wireless scenarios, and it is analytically tractable. Accordingly, the purpose of this paper is to characterize, from a statistical point of view, the energy harvested by a static device from an unmodulated carrier signal generated by a dedicated source, assuming that the wireless channel obeys the Generalized-K propagation model. Specifically, using simulation-validated analytical methods, this paper provides exact closed-form expressions for the average and variance of the energy harvested over an arbitrary time period. The derived formulation can be used to determine a power transfer plan that allows multiple or even massive numbers of low-power devices to operate continuously, as expected from future network scenarios such as IoT or 5G/6G.

The Fusarium fujikuroi species complex (FFSC) includes socioeconomically important pathogens that cause disease and/or mycotoxin contamination on numerous crops. Here, we used comparative genomics to elucidate processes underlying the ability of pine-associated and grass-associated FFSC species to colonize tissues of their respective plant hosts. We characterized the identity, possible functions, evolutionary origins, and chromosomal positions of the host-range-associated genes encoded by the two groups of fungi. The 72 and 47 genes identified as unique to the respective genome groups were potentially involved in diverse processes, ranging from transcription, regulation, and substrate transport, through to virulence/pathogenicity. Most emerged early during the evolution of Fusarium/FFSC and were subsequently retained only in some lineages, while some had origins outside Fusarium. Although differences in the densities of these genes were especially noticeable on the conditionally dispensable chromosome of F. temperatum (representing the grass-associates) and F. circinatum (representing the pine-associates), the host-range-associated genes tended to be located towards the subtelomeric regions of chromosomes. Taken together, these results demonstrate that multiple mechanisms drive the emergence of genes in grass- and pine-associated FFSC taxa examined and highlighted the diversity of molecular processes potentially underlying niche-specificity in these and other Fusarium species.

This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies.

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI) we quantify a Fluorescence Peak Height from fitting a Gaussian function and related terms into the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height from fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. The algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration and features an inherent correction of small spectral shifts, as required for OLCI measurements.

In the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc, on raster images. A deep convolutional encoder--decoder (DCED) network is the state-of-the-art semantic segmentation method for remotely sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network. Additionally, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, "channel attention'' is presented in our network in order to select the most discriminative filters (features). Third, "domain-specific transfer learning'' is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given datasets: (i) medium resolution data collected from Landsat-8 satellite and (ii) very high resolution data called the ISPRS Vaihingen Challenge Dataset. The results show that our networks outperformed DCED in terms of $F1$ for 17.48% and 2.49% on medium and very high resolution corpora, respectively.

This paper presents two control design strategies for voltage regulation in a single inductor dual output (SIDO) DC-DC Buck converter system. Based on a nominal multiple input multiple output (MIMO) plant model and performance requirements, both an LQR and a decoupled PI control laws are designed to control the power converter system, under parametric uncertainties such as voltage source variation, CPL power variation, and load resistance variation. Therefore, the SIDO converter can cascade operating, which may cause the undesired effects of voltage oscillation and reduce the system's stability margin. The control performance was assessed under both uncertainties resistive loading and power variation of a constant power load (CPL). A SIDO DC-DC Buck converter board has been developed for experimental tests. The experimental results show that the LQR strategy, compared to the PI strategy, presented a robust performance in the presence of parametric uncertainties in the resistive loads being, however, sensitive to uncertainties due to the presence of CPL loads.

Long-span steel structure trusses are widely used in factory buildings, but with the growth of service time and the increase of dynamic load fatigue, a considerable part of the long-span truss with dynamic load appears serious transverse cracks at the bottom of the middle span and oblique deformation of the abdomen in the operation process. The U-shaped cracks at the bottom and belly, as well as the mid-span down deflection of the main truss, reduced the functional function of the factory building truss structure, and had to limit the original crane load, which affected the normal safety and durability of the structure. Therefore, the application principle of the variable axial force cable system in the long-span factory building truss structure and 3D3S software modeling [1] were used. Analyzing and studying the reinforcement method of large-span powerhouse truss can provide practical experience for subsequent similar projects. In view of the above phenomenon, the large-span powerhouse trusses of Hongcheng Powerhouse 1 and No.2 located in Tonglu, Zhejiang Province are used as the research object, and the variable axial force cable method is proposed to strengthen and lift the load. Considering the span of large-span powerhouse truss, the cable system with 22m controlling force of 400kN is proposed to be selected for powerhouse 1, and the cable system with variable axial force of 24m is proposed to be selected for Powerhouse 2. The force model of large-span truss is established by using the finite element method commonly used to analyze the force of truss. Under two working conditions, The influence of reinforcement effect is analyzed and compared from three aspects: stiffness, bearing capacity and stability. And the phenomenon of uneven stress distribution is analyzed. The stress distribution characteristics of each node are understood by simulating the most disadvantageous node plates with the greatest internal force before and after reinforcement.

In humans, age-associated degrading changes are observed in molecular and cellular processes underly the time-dependent decline in spatial navigation, time perception, cognitive and psy-chological abilities, and memory. Cross talk of biological, cognitive, and psychological clocks provides an integrative contribution to healthy and advanced aging. At the molecular level, ge-nome, proteome, and lipidome instability are widely recognized as the primary causal factors in aging. We narrow attention to the roles of protein aging linked to prevalent amino acids chirali-ty, enzymatic and spontaneous (non-enzymatic) post-translational modifications (PTMs SP), and non-equilibrium phase transitions. The homochirality of protein synthesis, resulting in the steady-state non-equilibrium condition of protein structure, makes them prone to multiple types of enzymatic and spontaneous PTMs, including racemization and isomerization. Spontaneous racemization leads to the loss of the balanced prevalent chirality. Advanced biological aging re-lated to irreversible PTMs SP has been associated with the nontrivial interplay between poor so-matic and mental health conditions. Through stress response systems (SRS), the environmental and psychological stressors contribute to the age-associated “collapse” of protein homochirality. The role of prevalent protein chirality and entropy of protein folding in biological aging is mainly overlooked. In a more generalized context, the time-dependent shift from enzymatic to the non-enzymatic transformation of biochirality might represent an important and yet un-der-appreciated hallmark of aging.

We present an analysis of high-resolution, near-infrared (NIR) spectra relative to the solar transit of Venus of 5-6 June 2012, as observed with the Facility Infrared Spectropolarimeter (FIRS) at the Dunn Solar Telescope in New Mexico. These observations offer the unique opportunity to probe the upper layers (between ∼84 and 150 km in altitude) of a thick, CO2-dominated atmosphere with the transmission spectroscopy technique – a proxy for future studies of highly-irradiated atmospheres of Earth-sized exoplanets. We were able to visually identify absorption lines from the two most abundant CO2 isotopologues, and from the main isotopologue of CO in the retrieved spectrum of Venus. Furthermore, we perform a cross-correlation analysis of the transmission spectrum using transmission templates generated with petitRADTRANS. With the cross-correlation technique, it was possible to confirm detections of both CO2 isotopologues and CO. Additionally, we retrieve a cross-correlation signal for O3 on Venus. We demonstrate the feasibility of high-resolution, ground-based observations to study the chemical inventory of planetary atmospheres, employing techniques commonly used in exoplanet characterization.

Photophysics properties such as fluorescence quenching, and photoexcitation dynamics of bimolecular non-covalent systems consisting of cationic poly[(9,9-di(3,3’-N,N’-trimethyl-ammonium) propyl fluorenyl-2,7-diyl)-alt-co-(9,9-dioctyl-fluorenyl-2,7-diyl)] diiodide salt (PFN) and anionic graphene carboxylate (GC) have been discovered for the first time by the steady state and time-resolved femtosecond transient absorption (TA) spectroscopy with broadband capabilities. The steady-state fluorescence of PFN is quenched with high efficiency by GC acceptor. Fluorescence lifetime measurements revealed that the quenching mechanism of PFN by GC is static. Here, the quenching mechanisms are well proven by TA spectra of PFN/GC systems. For PFN/GC systems, the photo electron transfer (PET) and charge recombination (CR) processes are ultrafast (within few tens of ps) related to static interactions whereas for PFN/DCB systems the PET takes place in few hundreds ps (217.50 ps)1 suggesting diffusion-controlled PET process. In the latter case, the PFN+•-DCB-• radical ion pairs as the result of PET from the PFN to DCB are clearly resolved and they are long-lived. The slow CR process (in 30 ns time scales)1 suggest PFN+• and DCB-• may already form separated radical ion pairs through charge separation (CS) process, which recombined back to the initial state with a characteristic time constant of 30 ns. The advantage of the present positively charged polyfluorene used in this work is the control over the electrostatic interactions and electron transfers in non-covalent polyfluorene/quencher systems in DMSO solution.

We offered the circuit of a discrete analog filter (DAF) low frequencies of second-order on switchable capacitors, which differs from the known ones in that three capacitors with one terminal are connected to a common bus, and the frequency of the DAF pole depends not only on the ratio of the capacitances of the frequency-setting capacitors, but also on the ratio of resistances two additional resistors in the feedback. This allows at constant values ​​of the capacitances and a fixed switching frequency of electronic switches, control the pole frequency by changing the resistance ratio of additional resistors. Computer simulation of the developed circuit was performed in the Micro-Cap environment. It has been established that the DAF has a transmission coefficient close to minus one at very low frequencies and a transmission coefficient close to zero at higher frequencies.

Recently, the number of vehicles on the road, especially in urban centers, has increased dramatically due to the increasing trend of individuals towards urbanization. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturer) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based DL techniques for automatic identification of Jordanian vehicles. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish the license plate detection, character recognition, and vehicle logo detection. While VGG16 (Visual Geometry Group) model was retrained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7,035 Jordanian vehicle images, the second dataset consist of 7,176 Jordanian license plates, and the third dataset consists of 8,271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection, respectively. While the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for the vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, mean average precision (mAP) measure was used to evaluate the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, F-measure were 98%, 98%, and 98%, respectively. The performance of vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.

This brief communication intends to develop critical thinking on the development at different levels that could be evaluated in the well-being of humans from the local development towards a global scale or planetary point of view. Therefore it was presented and discussed how Artificial Intelligence tools could provide important contributions and influence in the design of critical thinking to develop ideas and insights within Research as well as funding for technology transfer. In this context it should be highlighted that the generation of funding and economy for sustainability that it should be based on critical thinking and analysis of what is need it from the close surrounding towards longer distances as well as in the inversed direction. In this regard, the main axes that support it are based on the importance of entailment of individuals in their close surrounding as well as to longer physical distances and cultural differences. An in this context it should be highlighted the existence of large diversity and varied Multicultural differences. These factors could affect the developments of technology, education; level, and quality of life. Thus the Economical and development indexes could be largely varied and different between them. For this reason it should highlighted the local development within shortened times by interacting to receive or provide materials with aggregated value or non-tangible values. Therefore, it could be accelerated the process of improvement in a targeted field looking for average homogeneous development everywhere. And there, it is where the link-up and transfer strategies of technology and education by creation of varied strategies in communication media. As for example the communication plays an important role to transfer knowledge within the different social status and imagining the development of thinking that later it will be the basis for the next generation of technology. By this manner, it could be coupled different levels of educations degrees by a factor in common related with curiosity and open minds to learn, work, and live together in the context of developments of new smart manners of living where economy and new encrypted currency are involucred and provide by different manners sustainability. In these perspectives the development and education at the University and higher degrees taking into account the transmission of important factors such as; i) entailment , ii) development of knowledge, and iii) transference, is fundamental. Finally, it should be highlighted the particular need of good human relationships, emotional intelligence, and diplomacy to afford from the simplest challenges to the higher ones.

Despite the importance of few-shot learning, the lack of labeled training data in the real world, makes it extremely challenging for existing machine learning methods as this limited data set does not represent the data variance well. In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more intra-class variations. The purpose of our research is to increase the size of the training data set using various methods to improve the accuracy and robustness of the few-shot face recognition. Specifically, we employ the VAE generator to increase the size of the training data set, including the basic and the novel sets while utilizing transfer learning as the backend. Based on extensive experimental research, we analyze various data augmentation methods to observe how each method affects the accuracy of face recognition. We conclude that the face generation method we proposed can effectively improve the recognition accuracy rate to 96.47% using both the base and the novel sets.

Research Highlights: Data of advanced-age provenance tests were reanalyzed applying a new approach, to directly estimate the growth of populations at their original sites under individually generated future climates. The results reveal surprisingly high resilience potential of fir species. Background and Objectives: The growth and survival of silver fir under future climatic scenarios is insufficiently investigated at the xeric limits. The selective signature of past climate determining the current and projected growth was investigated to analyze the prospects of adaptive silviculture and assisted transfer of silver fir populations, and of the introduction of non-autochthonous species. Materials and Methods: Hargreaves’ climatic moisture deficit was selected to model height responses of adult populations. Climatic transfer distance was used to assess the relative drought stress of populations at the test site, relating these to the past conditions to which the populations had adapted. ClimateEU and ClimateWNA pathway RCP8.5 data served to determine individually past, current, and future moisture deficit conditions. Beside silver fir, other fir species from South Europe and the American Northwest were also tested. Results: Drought tolerance profiles explained the responses of transferred provenances and predicted their future performance and survival. Silver fir displayed significant within-species differentiation regarding drought stress response. Applying the assumed drought tolerance limit of 100mm relative moisture deficit, most of the tested silver fir populations seem to survive their projected climate at their origin until the end of the century. Survival is likely also for transferred Balkan fir species and for grand fir populations, but not for the Mediterranean species. Conclusions: The projections are less dramatic than provided by usual field assessments. Some results contradict generally accepted concepts. The method fills the existing gap between experimentally determined adaptive response and the predictions needed for management decisions. It also underscores the unique potential of provenance tests.

A novel effective vibrational mode was discovered in the conventional transducer with an array of orthogonal (square) regular piezoelectric rods in 1-3 piezocomposite, containing the damping backing and front matching layers. The operational resonance in the structure was determined as the Surface Acoustic Wave (SAW) on the backing boundary excited by the adjacent piezo-rods, with its frequency typically near 3 times lower the fundamental half-lambda conventional piezocomposite resonance. Pulse-echo sensitivity and transmitting sound-pressure-level (SPL) in air showed that the signal strength is roughly comparable to the industrial similar air transducers at the frequency range 100…700 kHz, where at these frequencies the lateral and longitudinal piezoelement dimensions in the conventional transducer design are typically close to each other causing interference with unwanted coupling modes. As was determined theoretically and proved in experiments, the backing SAW resonance effect in the transducer performance is inherent just to the regular periodic 1-3 piezocomposite structure, and does occur neither with randomly located/oriented piezo-rods, nor in the homogeneous piezo-plate at least with the same lateral cross-section as the connected to it backing. The purpose of the article is to investigate a newly discovered operational vibrational mode of a SAW type in 1-3 regular piezocomposite, other than piezoelectric resonance. The investigated phenomena can improve the transceiver sensitivity, bandwidth, providing lower drive voltage, and smaller and lighter weight ultrasonic transducers. Based on the piezocomposites with thickness’ 1…1.5 mm (rod resonance near 2…3 MHz), pillar width 0.2…0.8 mm, kerf width 0.1…0.4 mm, the transceivers with an operating frequency from 140 kHz to 650 kHz were designed and fabricated with a conventional backing of a mixture of high-density tungsten powder and epoxy, and a matching layer of a mixture of low-density glass bubbles and epoxy. Experimental evaluation of their acoustical performance showed expected characteristics suitable for practical applications.

ABSTRACT Ethiopia is a developing country, where majority of the population lives in rural areas without access to electricity. 83% of the total population of the country use traditional biomass energy as a basic source of energy. In contrast, the country is endowed with sufficient renewable energy resources which can be used as a standalone electric energy supply system for electrifying remote areas of the country. These resources are mainly micro hydropower and wind which can be used individually or the best combination of one another. The application of hybrid renewable energy system has become an important alternative solution for rural electrification program. The Modeling and control of a hybrid PV-Wind-Hydro DG system is also addressed. Dynamic models for the major system components, namely, wind energy conversion system, PV energy conversion system, hydro, inverter, and overall fuzzy logic controller units are developed. Then, a simulation model for the proposed hybrid power system has been developed using MATLAB /Simulink environment. This is done by creating subsystem sets of the major dynamic component models and then assembling into a single aggregate model. The overall power management strategy for coordinating and/or controlling the different energy sources is also presented in the thesis work. Generally there are 800 households with total electric demand of 71.6KW.To satisfy this demand 52%, 35% and 13% is to be contributed from wind/hydro/solar respectively. To use the power economically fuzzy logic controller is used. The controller monitors the demand and the available sources and then switches to the appropriate power supply according to the written rules. Simulations have been carried out to verify the system dynamic performance using a practical load profile and weather data. The result shows that the overall power management strategy is effective and the load demand is balanced. To complete this work, a grid extension from the closest substation has been compared with hybrid system. Cost of the grid extension is estimated based on the data obtained from EEP office. This is done in order to compare the cost of the designed hybrid power system against the cost of grid extension. The result shows that breakeven grid extension distance to be 23.9km which indicates that grid extension is preferable.

The recent demand for reducing pollutant and CO2 emissions from internal combustion engines has created an urgent need for the development of ultra-lean burn engines that ensure combus-tion stability and reducing knocking tendency. One of the most promising methods is the Pre-chamber Spark Ignition (PCSI) system, where a jet of high-energy reactive gases, produced by pilot combustion in a pre-chamber, ignites the main combustion event in the cylinder. Given the intricate phenomena inherent to PCSI systems, conducting 3D CFD studies is imperative for a comprehensive analysis and optimal design. Furthermore, the detailed CFD model, combined with the calibrated 0-D/1-D model, is anticipated to yield a significant amount of new data that would be difficult to obtain through experimental approaches, making it essential for advancing our understanding and optimization of these systems. However, recently published papers re-port that some state-of-the-art models developed for conventional SI operation may not be pre-dictive under the challenging conditions of TJI systems, particularly under lean conditions. This review elucidates the reasons behind the widespread adoption of CFD in the optimal design of PCSI engines. It presents significant examples and delves into the potential and challenges of employing CFD not just as a predictive tool but also as a design instrument for enhancing PCSI engine performance.

Celiac disease is a gluten-sensitive immune-mediated enteropathy of the small intestine that occurs in genetically predisposed individuals. Abnormal immune response results in mucosal inflammation, villous atrophy, and crypt hyperplasia. This study was a proof-of-concept exercise that used a convolutional neural network to classify hematoxylin and eosin (H&E) histological images of celiac disease, normal small intestine control, and non-specified duodenal inflammation; 7294, 11,642, and 5966 images, respectively. The trained network classified celiac disease images with high performance (accuracy 99.7%, precision 99.6%, recall 99.3%, F1-score 99.5%, and specificity 99.8%). Interestingly, when the same network (already trained for the 3 class images), analyzed duodenal adenocarcinoma (3723 images), the new images were classified as duodenal inflammation in 63.65%, small intestine control in 34.73%, and celiac disease in 1.61% of the cases. Finally, when the network was retrained using the 4 histological subtypes of images, all performance parameters were above 99% for celiac disease. In conclusion, the convolutional neural network (CNN)-based deep neural system was able to classify medical histological images with high performance. Narrow artificial intelligence (AI) is designed to perform tasks that typically require human intelligence, but it operates within limited constraints and is task specific.

Noninvasive brain stimulation technique can be used for modulation of motor behaviors such as reaction time (RT). The main aim of this study was to investigate whether RT during a sequential visual isometric pinch task (SVIPT) can be affected by anodal transcranial direct current stimulation (a-tDCS) over three different stimulation sites such as dorsolateral prefrontal cortex (DLPFC), primary motor cortex (M1) or posterior parietal cortex (PPC). We also aimed to assess if stimulation of these cortical sites affect the transfer of learning during SVIPT. A total of 48 right-handed healthy participants were randomly assigned to one of the four a-tDCS groups: 1) left M1, 2) left DLPFC, 3) left PPC and 4) sham. A-tDCS was applied concurrent with SVIPT in which the participants were precisely controlled their forces to reach seven different target forces from 10 to 40% of maximum voluntary contraction (MVC) presenting on a computer screen with right dominant hand. Four test blocks were randomly performed at baseline and 15 min after the intervention including sequence and random blocks with either hand. Our results showed significant differences between a-tDCS groups on ratio RT in the sequence blocks of both the right trained (P = .041) and left untrained hands (P = .023) for target forces of 15% and 30 % of MVC, respectively. In sequence blocks, M1 showed significant elongation in ratio RT for the target force of 15% and 30 % MVC compared to PPC and Sham groups in the right hand. In sequence blocks with left hand, this negative effect were found between M1-DLPFC and M1-Sham groups. In random blocks, no significant differences were found among four groups at each target forces for the left hand while there were significant differences for 15% (P =. 04) and 40% MVC (P =. 035) in the right hand. Our findings suggest that three different stimulation sites (DLPFC, M1 or PPC) were differentially affected by a single session of a-tDCS at some target forces during SVIPT. The effects of a-tDCS on ratio RT were transferred in the left untrained hand for sequence blocks but not random blocks. Further research is needed to understand fundamental aspects of these main areas of the brain on temporal processing in a precision control task such as SVIPT.

Triangles are an ever present feature in nature, which the building construction industry duly echoes. However, an exact expression intended to offer the radiant field due to any triangle in an upright or inclined position had not been identified by previous research. In this case, the author has been able to solve by direct integration, the canonical expression of radiative transfer. This result alone confers a myriad of possibilities that had been inconceivable before, for studying in detail the three-dimensional heat transfer behavior of volumes and figures in which triangles do manifest, such as fins, windows, roof-gables and louvers of various kinds. Typically, shading devices, when tilted, give rise in their extremes to rhomboidal shapes which were difficult to take into account or had to be subject to discretization and subsequent Monte Carlo methods in order to perform an approximate calculation of their emission. This implied a lengthy and inexact procedure that induced many errors and consumed computing time. We can now avoid all the former downsides due to the advances hereby presented. As the novel expression can be converted into an algorithm it will be advantageously employed for simulation. This significant finding dovetails in the intricate puzzle of radiated heat and we believe that their consequences will greatly affect the conception and design of HVAC devices, aircraft manufacturing and specifically building or lighting industries among others.

Polycyclic aromatic hydrocarbons (PAHs) have emerged as promising materials for organic electronics, including organic photovoltaics (OPVs), organic field-effect transistors (OFETs), and organic light-emitting diodes (OLEDs). Particularly, non-hexagonal ring-fused PAHs are highly desirable due to their unique optoelectronic properties. Herein, a new redox-active azulene-perylene diimide triad 1 and its ring-fused counterpart, diazulenocoronene diimide 2, were synthesized and fully characterized by a combination of NMR, cyclic voltammetry, spectroelectrochemistry, and UV-visible absorption spectroscopy. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were performed to gain further insight into various electronic transitions. Interestingly, we found that the adaptive response to acids and bases manifests itself in a reversible two-color-change that can be attributed to changes in the chemical structures.

A series of derivatives, involving two dithienylethene (DTE), groups, connected by several molecular linkers (bis(ethylene-1,2-dithiolato)Ni- (NiBDT), naphthalene, quasilinear oligothiophene chains) are investigated with the aid of computational quantum chemistry methods. This involves the computation of the second- and third-order nonlinear optical properties, i.e. molecular hyperpolarizabilities of the designed/selected derivatives. These properties can be efficiently controlled by DTE switches, in connection with light of appropriate frequency. We found that NiBDT, as a linker, is associated with a greater contrast, in comparison to naphthalene, between the first and second hyperpolarizabilities of the “open-open” and the “closed-closed” isomers. This is explained by invoking the low lying excited states of NiBDT. It has been found that the second hyperpolarizability can be used as an index, which follows the structural changes induced by photochromism. The two-photon absorption, which is related to the imaginary part of second hyperpolarizability, has also been investigated for the S0S1 transition. Furthermore, the intramolecular excited-state energy transfer (EET) is studied as well as the overlap between the absorption and emission spectra of the donor and acceptor groups of the molecules. The electronic coupling, VDA, between the donor and acceptor fragments, as well as the emission spectrum of the donor and the absorption spectrum of the acceptor have been computed, assuming a Förster type transfer mechanism. EET is a critical factor governing the photochromism. Two methods were used to compute VDA, one based on linear response and one based on the distributed multipole analysis (DMA); both techniques give very similar results for the dominating Coulomb contribution. For NiBDT as the linker a low VDA value has been computed. We found that VDA is affected by the molecular geometry. Our results clearly show that the linker strongly influences the communication between the open-closed DTE groups and thus, photochromism. The present computations show that full photochromism is attained in derivatives where two DTE groups are linked by a tetrathiophene chain, in agreement with experiment. The sensitivity of molecular linear and nonlinear optical properties upon photochromism can assist with the identification of the molecular isomers.

The DNS of film boiling requires strong computational resources that are difficult to obtain for a daily CFD use by the practitioners of the industrial R&amp;D experts. On the other hand, the film boiling experiments are associated with the usage of the expensive and highly sophisticated apparatus, and the research to this end is found to be relatively difficult due to high heat flow rates that are present in the process itself. When combined with a transient heat conduction in a solid, the problem becomes significantly difficult. Therefore, a novel method in computation of conjugate heat transfer during film boiling in a quiescent liquid has been proposed in this paper. The method relies on the solution of mass, momentum and energy conservation equations in a two-fluid framework, supplemented with the appropriate closures. Furthermore, the turbulent flow has been found as an important parameter in obtaining the accurate solution of the temperature field evolution in a solid specimen, via the proper modeling of turbulent kinetic energy (TKE) value, that has been imposed as a constant value, i.e., the frozen turbulence approach. It was found, in addition, that the appropriate TKE value can be obtained by use of Kelvin-Helmholtz instability theory in conjunction with the boundary layer theory. The obtained results show excellent agreement with the experimental data within the first 15 s of the experiment, i.e., the first ca. 10 % of the total duration of the film boiling mode of heat transfer. Furthermore, the heat transfer coefficient has matched the error bands prescribed by the authors of the paper that has presented the correlations, whilst the averaged values are far beyond this band, i.e., are slightly more than 30 % higher. The further inspection has revealed a measure of similarity between the computational result of the volume fraction field distribution and the experiment, thus confirming the capability of the method to obtain realistic interface evolution in time. The method has shown full capability for further pursuing the industrial-scale film boiling problems that involve turbulent flow and the conjugate heat transfer approach.

The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks' efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem's significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effectiveness of the proposed innovative system is demonstrated experimentally in multiple complex scenarios that optimally simulate the modern energy environment. In this way, the proposed system fully automates the strategic security planning of energy networks with computational intelligence methods. It allows the complete control of the digital strategies of the potential infrastructure that frames it, thus contributing to the timely and valid decision-making during cyber-attacks.

Background: Handover is a critical process for ensuring quality and safety in healthcare. Considerable research suggests that poor handover results in significant morbidity, mortality, dissatisfaction, and excess financial costs. Despite this, little formal attention, education, and evaluation has been given to handover. There is also paucity of data on the opinions of practitioners on the safety of handover.Objectives: The aim of this study was to measure the perceived risk, degree of patient harm and the systems used to support handover, and to understand how this varied by care setting, type of clinical practice, location, or level of experience. Methods: An open, anonymous and confidential online questionnaire covering: (a) respondent characteristics; (b) peer-to-peer handover; (c) internal referrals; (d) discharges and transfers between organisations; and (e) leading and improving handover was conducted with healthcare practitioners and managers from various settings. Results: We gathered a total of 432 completed responses from 26 countries. The average reported performance of handover was rated as 3.9 out of 5.For each type of handover, 12 - 14% reported errors occurring more than weekly. Of those that knew the outcome of such errors, between 29% and 34% reported that they had witnessed moderate or severe harm. 12% and 17% of respondents believed that handover was high or very high risk (See table 4). These respondents were more likely to have witnessed moderate or severe harm, or to be more senior.A wide combination of handover systems was utilised by respondents. 28% - 32% relied exclusively on EPRs (with or without face-to-face contact). 21% used Office documents such as Word and Excel for peer-to-peer handover, and over 30% used hand-written or manual systems. Conclusions: This study suggests the need to do more — and go further — to improve communication and reduce risk during all types of handovers. Clinical leaders should find ways to train and support handover with effective systems, with less experienced staff being the primary focus. More research is needed to demonstrate the interventions that improve the safety of handover.

Education for Sustainable Development (ESD) is considered vital to the success of the United Nations’ Sustainable Development Goals. Systems Thinking has been identified as a core competency necessary to incorporate into ESD. Systems Thinking orientated ESD learning tools, established methods of assessment of sustainability skills, and studies to demonstrate effectiveness of such learning tools, are all lacking. There is a wealth of experience in the System Dynamics field regarding the application of Systems Thinking and simulation to environmental problems, sustainability and systems education. Many System Dynamicists regard simulation as essential for teaching Systems Thinking. The substantial body of research into the design of effective simulation-based learning environments (SBLEs) can also inform ESD initiatives. This research describes a randomised controlled trial (n=106) to investigate whether an online sustainability learning tool that incorporates Systems Thinking and System Dynamics simulation increases understanding of a specific problem and supports transfer of knowledge to a second problem with a similar systemic structure. The effects of Systems Thinking and simulation were tested separately and in combination. The learning tool was designed for a single online learning session. Simulation was found to increase ESD learning outcomes significantly, and also to support transfer of skills, although less significantly.

Information is commonly considered as a mathematical quantity that forms the basis of computing. In mathematics, information can propagate instantly, so its transfer speed is not the subject of information science. In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so the information’s propagation speed cannot exceed the speed of the carrier. Because of this limitation, for any implementation, one must consider the transfer time between computing units. We need a different mathematical method to take this limitation into account: classic mathematics can only describe infinitely fast and infinitely small computing system implementations. The difference between the mathematical handling methods leads to different descriptions of the behavior of the systems. The correct handling also explains why biological implementations can have lifelong learning and technological ones cannot. The conclusion about learning evidences matches others’ experimental evidence, both in technological and biological computing.

One of the key components of application performance monitoring (APM) software is 2 AI/ML empowered data analytics for predictions, anomaly detection, event correlations and root 3 cause analysis. Time series metrics, logs and traces are three pillars of observability and the valuable 4 source of information for IT operations. Accurate, scalable and robust time series forecasting and 5 anomaly detection are desirable capabilities of the analytics. Approaches based on neural networks 6 (NN) and deep learning gain increasing popularity due to their flexibility and ability to tackle complex 7 non-linear problems. However, some of the disadvantages of NN-based models for distributed cloud 8 applications mitigate expectations and require specific approaches. We demonstrate how NN-models 9 pretrained on a global time series database can be applied to customer specific data using transfer 10 learning. In general, NN-models adequately operate only on stationary time series. Application 11 to non-stationary time series requires multilayer data processing including hypothesis testing for 12 data categorization, category specific transformations into stationary data, forecasting and backward 13 transformations. We present the mathematical background of this approach and discuss experimental 14 results from the productized implementation in Wavefront by VMware (an APM software) while 15 monitoring real customer cloud environments.