The amount of heat transfer in gas turbine blades depends on cooling techniques and various flow phenomena. The effect of eddies, passing shock waves and free flow turbulence has been noticed by many researchers since the beginning. The focus of the upcoming work is on the numerical investigation of the effects of turbulence intensity and Reynolds number on the amount of heat transfer from the blade surface with internal cooling. The SST model has been used to solve this problem, which has been compared with the experimental work to ensure the correctness of the numerical simulation. In this regard, by changing the free flow turbulence intensity with values of 1, 5, 7, and 10% and at three Reynolds numbers of 150,000, 350,000, and 750,000, the changes in heat transfer coefficients have been investigated. The results show that with the increase of the turbulence intensity at different Reynolds numbers, due to the positive effect of the flow turbulence on the suppression of separation and promotion of the boundary layer transition, the blade surface heat transferincreases and this increase is more evident at higher Reynolds numbers.

Machinery degradation assessment can offer meaningful prognosis and health management in-formation. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain dis-criminator to extract invariant features from different machine operating conditions. The case studies with the FEMTO-ST bearing dataset and the Monte Carlo method for RUL prediction during the degradation process are conducted. The experiment results fully exhibited the signif-icant advantages of the proposed method compared to other state-of-the-art techniques.

Medium-deep Borehole Heat exchanger (MBHE) has been widely paid attention for building heating. In order to avoid the thermal interference of adjacent MBHEs, temperature distribution characteristics of medium-deep rock-soil was investigated in this work. The evolution on maximum rock-soil thermal affected radius (MTAR) in full life cycle was further analyzed. The results showed that the rock-soil thermal affected area (RTAA) continuously expands in both radial and vertical directions during a heating season. Several factors of thermal extraction load, fluid velocity, geothermal gradient and pipe length have main impacts on the RTAA in vertical direction. Rock-soil thermal conductivity affects the RTAA in both radial and vertical directions. In non-heating season, temperature difference of rock-soil in radial direction dominates the temperature recovery in the first year while geothermal flow nearly dominates temperature recovery after 20 years. The thermal affected radius (TAR) in the 30 th year could be determined as an effective MTAR in full life cycle. In order to decrease installed distance of adjacent MBHEs, a new well layout form by deflecting borehole was proposed. The recommended deflection angle is equal to or larger than 4 times TAR angle. This work provides scientific reference for promoting application of multiple MBHE arrays.

The paper presents theoretical, experimental and numerical studies on the thermal behavior of mineral wool used in sandwich panels. The aim of this study is to investigate the thermal properties of mineral wool at elevated temperatures and provide a simple model that would allow to determine the heat propagation in sandwich panels during fire. The paper proposes a new method to experimentally evaluate thermal diffusivity, derived from theoretical premises. Experiments are conducted in a laboratory furnace, where specimens are placed and temperatures inside specimens are measured. Different methods are used to process the test results and calculate the thermal diffusivity of mineral wool. Finally, a numerical analysis of heat transfer using finite element method (FEM) is performed to validate the obtained thermal properties.

Electroactive microorganisms have received a lot of interest in the field of environmental engineering and biotechnology because of their extracellular electron transfer (EET) capability of energy recovery and environmental remediation. However, the low efficiency of EET remains a barrier to its practical implementation. This review presents an introduction to EET, followed by addressing strategies for enhancing EET through different approaches including enhancing EET by mediator materials, external electricity, photo, and magnetism as well as integrated strategies, that is, the combination of multiple means to promote extracellular electron transport and improve the performance of microbial electrochemical systems. Finally, the challenges, perspectives, and new directions for EET enhancement will be discussed.

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional sources of information, e.g. remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forests state. In this work, we combine the new generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results. For the majority of the forest stands, we generated good reflectances with all levels forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R²=0.62). This work provides a forward modelling tool for relating forest reflectance to forest characteristics. With this tool it is possible to generate a large set of forest stands with corresponding reflectances. This opens the possibility to understand how reflectance is related to succession and different forest conditions.

Background The main aim of reconstructive surgery (RS) is to restore the integrity of soft tissues damaged by trauma, surgery, congenital deformity, burns or infection. Microsurgical techniques consist of harvesting tissues that are separated from the vascular sources of the donor site and anastomosed to the vessels of the recipient site. In these procedures, there are some preoperative modifiable factors that have the potential to influence the outcome of the flap transfer and its anastomosis. Methods Chronic inflammatory anaemia (ACD) is a constant condition in patients who have undergone RS and correlates with the perfusion of the free flap. In RS, it is important to maintain good tissue oxyporesis, avoiding blood hyperviscosity. From January 2017 to September 2019, we studied 16 patients (16 males, mean age 38 years) who underwent microsurgical procedures for RS. Haemoglobin (Hb) levels, Corpuscular Indexes, Transferring saturation (TSAT) ferritin concentrations and creatinine clearance have been measured the first day after surgery (T0), after the first week (T1) and after five weeks (T2). At T0 all patients showed low hemoglobin levels (average 7.4 g/dl, STD 0,71range 6,2-7,4 g dL-1), with an MCV of 72, MCH 28, MCHC 33, RDW 16, Sideremy 35, Ferritin 28, Ret% 1,36, TRF 277 and Creatinine Clearance 119. . We assessed all patients for clinical status, medical history and comorbidities before starting therapy. Results. In collaboration between the two departments, we started a therapeutic protocol with erythropoietic stimulating agents (ESAs) (Binocrit 6000 UI/week) and intravenous iron every other day, on the second day after surgery. Thirteen patients received ESAs and FCM (500-1000 mg per session), three patients received ESAs and iron gluconate (1 vial every other day). No patients received blood transfusions. No side effects were observed and, most importantly, no limb or flap rejection. Conclusions Preliminary data from our protocol show an optimal therapeutic response in a surgical setting with limited data in the scientific literature. The enrolment of further patients will allow us to validate this therapeutic protocol with statistically significant data.

Cross flow heat exchangers are commonly used in the thermal industry to transfer heat from hot tubes to cooling fluid. To protect the heat exchanger tubes from corrosion and dust accumulation, microscale coatings are often applied. In this study, we present machine-learning models for predicting heat transfer from hot tubes with different micro-sized coatings to cooling fluid in a turbulent flow using computational fluid dynamics simulations. A dataset of approximately 1000 cases was generated by varying the coating coverage thickness of each tube, the inlet Reynolds number, fluid flow inlet temperature, and wall temperature of tubes. The machine-learning models were generated to predict the overall heat flow rate in the heat exchanger, and it was found that combining the features based on their importance preserved the accuracy of the models while maintaining all the relevant information. The simulation results demonstrate that the proposed method increases the coefficient of determination (R2) for the models. The R2 values for unseen data for Random Forest, K-Nearest Neighbors, and Support Vector Regression were 0.9810, 0.9037, and 0.9754, respectively, indicating the usefulness of the proposed model for predicting heat transfer in various types of heat exchangers.

Luminophores featuring thermally activated delayed fluorescence (TADF luminophores) are the workhorse of the third- and fourth-generation OLEDs. While these compounds had usually been used as dopants embedded in the host, non-doped TADF OLEDs have recently shown significant progress as well reaching the performance comparable to the host-dopant ones. For efficient operation of the non-doped OLEDs, charge transport in neat films and single crystals of TADF luminophores is important; however, this issue was nearly unexplored theoretically. In the current study, we calculated charge mobilities in four TADF single crystals, which have different molecular packing motifs. Specifically, in one of them both donor and acceptor moieties form uniform π-stacks, while in the others donors (acceptors) show alternating lateral shifts along the stacks; the difference in molecular packing results in the difference of transfer integral between the molecules. Reorganization energies differ as well up to four times for the studied crystals. As a result, charge mobilities vary from 0.001 to ~0.3 cm2/(V∙s), the largest being predicted for the crystal of the luminophore consisting of rigid donor and acceptor. We anticipate that the results obtained can be useful for the design of TADF luminophores for non-doped OLEDs, OLETs and other organic light-emitting devices.

Skin cancer is an uncommon but serious malignancy. Dermoscopic images examination and biopsy are required for cancer detection. Deep learning (DL) is extremely effective in learning characteristics and predicting malignancies. However, DL requires a large number of images to train. Image augmentation and transferring learning were employed to overcome the lack of images issue. In this study we divided images into two categories: benign and malignant. To train and test our models, we used the public ISIC 2020 database. Melanoma is classified as malignant in the ISIC 2020 dataset. Along with categorization, the dataset was studied to demonstrate variation. The performance of three top pretrained models was then benchmarked in terms of training and validation accuracy. Three optimizers were employed to optimize the loss: RMSProp, SGD, and ADAM. Using ResNet, VGG16, and MobileNetV2, we obtained training accuracy of 98.73%, 99.12%, and 99.76%, respectively. Using these three pretrained models, we attained a validation accuracy of 98.39%.

This paper introduces a multi-class hand gesture recognition model developed to identify a set of defined hand gesture sequences in two-dimensional RGB video recordings. The work presents an action detection classifier that looks at both appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model uses an available dataset to then adopt a technique known as transfer learning to fine-tune the model on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (± 0.37) with a mean Jaccard index of 0.812 (± 0.105) for 22 participants. The presented model illustrates the possibility of training a model with a small set of data (113,410 fully labelled frames). The proposed pipeline embraces a small-sized architecture that could facilitate its adoption.

Natural convection is intensively explored, especially in a valley-shaped trapezoidal enclosure, because of its broad presence in both technical settings and nature. This study deals with a trapezoidal cavity, which is initially filled with linearly stratified air. Though the side walls remain adiabatic, the bottom wall is heated, and the top wall is cooled. For the stratified fluid (air), the temperature of the fluid adjacent to the top and the bottom walls is the same as that of the walls. Natural convection in the trapezoidal cavity is simulated in two dimensions using numerical simulations, by varying Rayleigh numbers (Ra) from 100 to 108 with constant Prandtl number, Pr = 0.71, and aspect ratio, A = 0.5. According to numerical results, the development of transient flow within the enclosure owing to the predefined conditions for boundary may be categorized into three distinct stages: early, transitional, and steady or unsteady. The flow characteristics at each of the three phases and the impact of the Rayleigh number on the flow’s growth are stated in this study. In addition, heat transfer through the bottom and the top surfaces is described in this study.

Quantum cryptography is the field of cryptography that explores the quantum properties of matter. Its aim is to develop primitives beyond the reach of classical cryptography or to improve on existing classical implementations. Although much of the work in this field has been dedicated to quantum key distribution (QKD), some important steps were made towards the study and development of quantum oblivious transfer (QOT). It is possible to draw a comparison between the application structure of both QKD and QOT primitives. Just as QKD protocols allow quantum-safe communication, QOT protocols allow quantum-safe computation. However, the conditions under which QOT is actually quantum-safe have been subject to a great amount of scrutiny and study. In this review article, we survey the work developed around the concept of oblivious transfer in the area of theoretical quantum cryptography, with an emphasis on some proposed protocols and their security requirements. We review the impossibility results that daunt this primitive and discuss several quantum security models under which it is possible to prove QOT security.

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems: resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both the synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.

In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the filed of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. Existing research, however, fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.

To overcome the two-phase flow instability of traditional boiling heat dissipation technologies, a porous wick was used for liquid-vapor isolation, thus realizing efficient and stable boiling heat dissipation. A pump-assisted capillary phase-change loop with methanol as working medium was established to study the effect of liquid-vapor pressure difference and heating power on its start-up and steady-state characteristics. The results indicated that the evaporator undergoes four heat transfer modes including flooded, partial flooded, thin film evaporation and overheating. The thin film evaporation mode was the most efficient one with the shortest start-up period. Besides, the heat transfer modes were determined by liquid-vapor pressure difference and power. The heat transfer coefficient could be significantly improved and the thermal resistance could be reduced by increasing liquid-vapor pressure difference as long as it did not exceed 8 kPa. However, when the liquid-vapor pressure difference exceeded 8kPa, its influence on the heat transfer coefficient weakened. In addition, a two-dimensional heat transfer mode distribution diagram considering both liquid-vapor pressure difference and power was drawn through a great number of experiments. During engineering application, the liquid-vapor pressure difference can be controlled to maintain efficient thin film evaporation in order to achieve the optimum heat dissipation effect.

Higher order femtoscopy measured to examine the heat exchanger characterization of the fluid debris produced in the collisions and investigated a remarkable suppression in the bosons interferences measurement. The analogous suppression can be analyzed to explore the coherence of boson thermal particle production sources at unprecedented energies. We illustrate the particles emissions from radiated sources with statistical coherence which induce the thermal particles interferences to probe the peculiarity of the heated sources as well as the distinctions about the heat exchangers in the collisions at higher temperature. We perspicacious that the bosons seem to the pertinent aspirant of heat exchanger, and the normalized three particles correlators evaluate the existence of such hybrid phases significantly. The key point of this research is that we analyze the three particles correlations with their normalized correlations by difference equations to determine the characteristics of heat exchanger and its applications. With such distinctive and efficient approach, we observe a significant difference in the correlation functions at higher temperature and momenta regimes.

Silk fibroin is a well-known biopolymer used in several applications in which the interaction with biological tissue is required. In fact, fibroin is extremely versatile and can be shaped to form several constructs useful in tissue engineering applications. Confocal imaging is usually per-formed to test the cells behaviour on the construct and in this context the fibroin autofluorescence is regarded as a problem. In addition, the autofluorescence is not intense enough to provide useful morphological images. In fact, to control study the constructs morphology other techniques are used (i.e. SEM, Micro-CT). In this work we propose a method based on the fluorescence energy transfer (FRET) to suppress the fibroin autofluorescence moving it to higher wavelength accessible to the confocal microscopy for a direct imaging.

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Our simulation framework could contribute to better understand performances for species discrimination and relationship between spectral variations and taxonomic and functional dimensions of biodiversity.

Scientific social networking sites like Researchgate or Academia.edu have become part of the work practice of academic researchers. These digital platforms have been designed precisely to encourage the exchange of knowledge between scholars and to help the expansion of collaborative networks among them. Even if studies on this topic have multiplied in recent years, there is a dearth of research on the actual impact of these platforms on scientific production. The goal of this study is to investigate the relation between the attitude of researchers towards scientific social networks and the use of knowledge in their scientific work. Data from users of the scientific social networking site Researchgate were collected. A total of 143 valid responses were received and structural equation modeling was applied for data analysis. Findings of this study confirmed that researchers use knowledge obtained from scientific social networking sites both incorporating it within their research products and to acquire new competences. In particular approaching the platform as a scientific community with a shared language and a shared vision was found to have a positive impact on knowledge use. To the best knowledge of the authors this is the first study investigating the actual use of knowledge from scientific social networking sites by academic researchers. Results help to understand the impact of these platforms on the work practice of a strategic sector like scientific research.

Genes coding for enzymes of the denitrification pathway appear randomly distributed among isolates of the ancestral genus Thermus, but only in few strains of the species T. thermophilus the pathway has been studied to a certain detail. Here, we review the enzymes involved in this pathway present in T. thermophilus NAR1, a strain extensively employed as a model for nitrate respiration, on the light of its full sequence recently assembled through a combination of PacBio and Illumina technologies in order to counteract the systematic errors introduced by the former technique. The genome of this strain is divided in four replicons, a chromosome of 2,021,843 pb, two megaplasmids of 370,865 and 77,135 bp and a small plasmid of 9,799 pb. Nitrate respiration is encoded in the largest megaplasmid, pTTHNP4, within a region that includes operons for O2 and nitrate sensory systems, a nitrate reductase, nitrate and nitrite transporters and a nitrate specific NADH dehydrogenase, in addition to multiple insertion sequences (IS), suggesting its mobility-prone nature. Despite nitrite is the final product of nitrate respiration in this strain, the megaplasmid encodes two putative nitrite reductases of the cd1 and Cu-containing types, apparently inactivated by IS. No nitric oxide reductase genes have been found within this region, although the NorR sensory gene, needed for its expression, is found near the inactive nitrite respiration system. These data clearly support that partial denitrification in this strain is the consequence of recent deletions and IS insertions in genes involved in nitrite respiration. Based on these data, the capability of this strain to transfer or acquire denitrification clusters by horizontal gene transfer is discussed.

To ensure the safety in aircraft flying, we aim use of the deep learning methods of nondestructive examination with multiple defect detection paradigms for X-ray image detection posed. The use of the Fast Region-based Convolutional Neural Networks (Fast R-CNN) driven model seeks to augment and improve existing automated Non-Destructive Testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers insufficient types of X-ray aeronautics engine defect data samples can thus pose another problem in training model tackling multiple detections perform accuracy. To overcome this issue, we employ a deep learning paradigm of transfer learning tackling both single and multiple detection. Overall the achieve result get more then 90% accuracy based on the AE-RTISNet retrained with 8 types of defect detection. Caffe structure software to make networks tracking detection over multiples Fast R-CNN. We consider the AE-RTISNet provide best results to the more traditional multiple Fast R-CNN approaches simpler translate to C++ code and installed in the Jetson™ TX2 embedded computer. With the use of LMDB format, all images using input images of size 640 × 480 pixel. The results scope achieves 0.9 mean average precision (mAP) on 8 types of material defect classifiers problem and requires approximately 100 microseconds.

Modulation of the amplitude of high-frequency cortical field activity locked to changes in phase of a slower brain rhythm is known as phase-amplitude coupling (PAC). The study of this phenomenon has been gaining traction in neuroscience because of several reports on its appearance in normal and pathological brain processes in humans as well as across different mammalian species. This has led to the suggestion that PAC may be an intrinsic brain process that facilitates brain inter-area communication across different spatiotemporal scales. Several methods have been proposed to measure the PAC process, but few of these enable detailed study of its time course. It appears that no studies have reported details of PAC dynamics including its possible directional delay characteristics. Here, we study and characterize the use of a novel information theoretic measure that may address this limitation: local transfer entropy. We use both simulated and actual intracranial electroencephalographic data, and in both cases we observe initial indications that local transfer entropy can be used to detect the onset and offset of modulation process periods revealed by mutual information phase-amplitude coupling (MIPAC). We review our results in the context of current theories about PAC in brain electrical activity, and discuss technical issues that must be addressed to see local transfer entropy more widely applied to PAC analysis.

The purpose of this study was to develop a mathematical model for non-contacting face seals to analyze how their performance is affected by thermoelastic phenomena. The model was used to solve thermal conductivity and thermoelasticity problems. The primary goal was to calculate the values of thermal deformations of the sealing rings in a non-contacting face seal with a flexibly mounted rotor (FMR) for a turbomachine. The model assumes conversion of mechanical energy into heat in the fluid film. The heat flux generated in the fluid film is transferred first to the sealing rings and then to the fluid surrounding them. An asymmetric distribution of temperature within the sealing rings leads to the occurrence of thermal stresses and, consequently, a change in the rings geometry. The model is solved analytically. The distributions of temperature fields for the sealing rings in the cross-sections are calculated using the Fourier-Bessel series as a superficial function of two variables (r,z). The thermoelasticity problems described by the Navier equations are solved by applying the Boussinesq harmonic functions and Goodier’s thermoelastic displacement potential function. The proposed method involves solving various theoretical and practical problems of thermoelasticity in FMR-type non-contacting face seals. The calculated thermal deformations of the sealing rings are used to determine the most important seal performance parameters such as the leakage rate and power loss.

This work probes the combined effects of magnetic field and viscous dissipation on heat field and examine the second law analysis (entropy generation) in an electrically conducting fluid under the effect of wall mass transfer over continuous stretched non-isothermal surface with variable viscosity. The viscosity of the fluid is assumed to be an inverse linear function of temperature. The governing equation for the problem are changed to dimensionless ordinary differential equations by using similarity transformation and solved numerically by using Rung Kutta and Shooting technique. Velocity, concentration and temperature distribution are obtained and used to compute the entropy generation and the Bejan number in the flow field. The effect of variable viscosity, Schmidt number, Hartman and Reynolds number on the velocity, concentration, temperature, entropy generation and Bejan number are studied and discussed.

Turbine generator operates with complex cooling system due to the challenge in controlling the peak temperature of the stator bar caused by ohm loss, which is unavoidable. Therefore, it is important to characterise and quantifies the thermal performance of the cooling system. The focus of the present research is to investigate the heat transfer and pressure loss characteristics of typical cooling system, so-called stator ventilation duct. A real scale model was built at its operating conditions for the present study. The direction of cooling air is varied to consider its operation condition, so that there are (1) outward flow and (2) inward flow cases. In addition, the effect of (3) cross flow (inward with cross flow case) is also studied. The transient heat transfer method using thermochromic liquid crystals is implemented to measure full surface heat transfer distribution. A series of Computational Fluid Dynamics analysis is also conducted to support the observation from the experiment. For the inward flow case, the results suggest that the average Nusselt number of the 2nd duct is about 30% higher than the 3rd duct. The trend is similar with the effect of cross flow. The CFD results are in good agreement with the experimental data.

Teaching through English Medium Instruction (EMI) has been a strategic move in some European and Asian countries as part of their educational internationalisation. A large number of studies on EMI teaching have appeared in the last decade. The majority of these report on issues and concerns at macro-level including: the lack of structured policy guidance, EMI lecturers and students’ low level of English proficiency, and a shortfall of research informed training programs for EMI lecturers. Up to date, there is little research into EMI in-class teaching and learning. Lived experience in EMI in-class practice has been largely ignored. To fill this gap, this research explored a group of academics’ in-class EMI practice in a Chinese university. Their teaching process through EMI was observed and recorded, with data analysed through a multiple theoretical lens. Data reveal that EMI teaching is a complicated issue and can be neither standardised nor prescribed. It needs to be addressed as a pedagogy responding to and influenced by local context, driven by language, culture and education systems. This research is expected to provide insight for the development of localised institutional guidelines for EMI teaching and lecturers’ professional development in EMI teaching.

Redox reactions are of great importance in environmental catalysis. Gold nanoparticles (NPs) have attracted much attention because of their catalytic activity and their localized surface plasmon resonance (LSPR). In the present study, we investigated in details the reduction of ferricyanide (III) ion into ferrocyanide (II) ion catalyzed by spherical gold nanoparticles of two different sizes 15 nm and 30 nm and excited at their LSPR band. Experiments were conducted in the presence (or not) of sodium thiosulfate. This catalysis is enhanced in the presence of Au-NPs under visible light excitation. This reduction takes also place even without sodium thiosulfate. Our results demonstrate the implication of hot electrons in this reduction.

Much remains to be understood for delayed ultraweak photon emission (UPE) from organism in association with oxidative burst following external perturbation, a phenomenon that has been experimented for over three decades. Delayed UPE often decays hyperbolically; yet, it is not uncommon to have delayed UPE that reveals first-order kinetic patterns characterized by single-exponential, double exponential, or multi-exponential changes. Some delayed UPEs also presented transient patterns that are characteristic of second-order responses. A soliton-based photon-storage model has addressed the hyperbolic decaying pattern of delayed UPE; however, there are questions outstanding regarding modeling other non-hyperbolic kinetics as well as the large range of temporal scales of delayed UPE that can vary from 8.5 microseconds to many hours. This work proposes an alternative, phenomenological model-framework for interpreting the various kinetic patterns of delayed UPE following stress. The delayed UPE is considered to be governed by two sequential phases: a stress-transfer phase that transforms the external stress to photo-genesis for emitting photons, and a photon-diffusion phase that transmits the photons emitted by the photo-genesis to the surface of organism for being detected as delayed UPE. Time-resolved photon diffusion analysis reveals that any delayed UPE in organism with a delay time >100ns cannot be addressed by the inherent temporal spread that realistic tissue scattering will cause. A slow stress-transfer phase is thus required to explain the delay time-scales of delayed UPE at a minimum of 8.5 µs as reported experimentally. The stress-transfer phase is hypothesized to carry the following types of responses: single or multiple 1st-order low-pass, single 2nd-order low-pass with various damping factors, and single 2nd-order band-pass with various damping factors. A single 1st–order low-pass response with a time-varying kinetic rate is also analyzed. The responses of these modeled pathways to bolus and step inputs demonstrate that a kinetic pattern other than the exact single-exponential one may have multiple causes.

Structural perturbations in the Mn4CaO5 cluster site, an oxygen-evolving complex of photosystem II, such as those induced by Ca2+/Sr2+ exchanges or Ca/Mn-removal have been known to induce long-range positive shifts (+30 mV to +150 mV) in the redox potential of the primary quinone electron acceptor plastoquinone A (QA) located 40 Å distant from the OEC. Here, we reanalyzed the crystal structure of Sr-PSII solved at 2.1 Å and compare it with the native Ca-PSII of 1.9 Å with focus on the acceptor site and report on the possible long-range interactions between the donor, Mn4Ca(Sr)O5 cluster, and acceptor sites.

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. Machine learning algorithms have achieved state-of-the-art material classification performance on benchmark hyperspectral data sets; however, these techniques often do not consider varying atmospheric conditions experienced in a real-world detection scenario. To reduce the impact of atmospheric effects in the at-sensor signal, atmospheric compensation must be performed. Radiative Transfer (RT) modeling can generate high-fidelity atmospheric estimates at detailed spectral resolutions, but is often too time-consuming for real-time detection scenarios. This research utilizes machine learning methods to perform dimension reduction on the transmittance, upwelling radiance, and downwelling radiance (TUD) data to create high accuracy atmospheric estimates with lower computational cost than RT modeling. The utility of this approach is investigated using the instrument line shape for the Mako long-wave infrared hyperspectral sensor. This study employs physics-based metrics and loss functions to identify promising dimension reduction techniques. As a result, TUD vectors can be produced in real-time allowing for atmospheric compensation across diverse remote sensing scenarios.

Diaminomaleonitrile (DAMN) is an electron-rich ligand which is commonly used in the synthesis of purines, amides and Schiff base ligands. In the last case lies the interest in this molecule, given its symmetry and apparent facility to form various ligands and complexes of interest in fields such as organic solar cells and catalysis among others. Nevertheless, its bisubstitution with aldehydes displays certain difficulty due to the double conjugated presence of the nitrile group (C≡N) in its structure, which significantly affect its coordination behavior by modulating the chemical reactivity of its amines. In addition to presenting a practical resume of guidelines for the preparation of different bisubstituted compounds from DAMN, this paper reviews the possibility of attaining a precise characterization by simple spectroscopic techniques. We provide an additional discussion for one of these techniques (commonly reported but often overlooked), given its potential to provide both the symmetry and E/Z configuration of the product. We hope the comprehensive evidence discussed and shown in this review will be helpful to further develop and understand the products derived of this interesting molecule in their broad applications.

A heat transfer model, has determined millimeter crude oil thicknesses on saltwater at night. Model inputs are calibrated thermographic imagery, weather station data, metrological observations, and crude oil thermal conductivity. Outdoor field-testing was performed at the National Oil Spill Response & Renewable Energy Test Facility (Ohmsett) to determine model accuracy. Alaskan North Slope (ANS), Hoover Offshore Oil Pipeline System Blend (HOOPS), and ROCK crude oils were placed at varying mm depths. A roof-top mounted thermal camera measured the average oil surface temperature for each target and converted to oil spill thickness. The fidelity of the thickness measurements is dependent on the accurate measurement of the atmospheric and weather parameters, sea state, heat transfer constants, crude oil evaporation rates, and calibration and stability of the thermal camera.

The Absorbing/scattering nature of aerosols affects the total radiative forcing and this absorption to total extinction ratio is quantified by single scattering albedo (SSA). Effect of SSA in the Ultraviolet (UV) irradiance is less studied and limited measurements are available. SSA retrieved at Athens, Greece during 2009-2014 from Ultraviolet Multifilter Radiometer (UVMFR) at 332 and 368 nm, were used to calculate incoming UV irradiance, alongside with ones from AERONET at visible wavelengths, from OMI satellite at 342.5 nm and from AEROCOM climatological database at 300 nm. UVA and UVB irradiances were estimated using a Radiative Transfer Model and we found that relative differences could be as high as 20%, while average relative differences varied from 2% to 8.7 % for the whole experimental period. Both UVA and UVB drop by a rate of ~12% for 0.05 aerosol absorption optical depth compared to ones estimated using SSA at visible range. Brewer irradiance measurements at 324nm were used to validate simulated irradiances and a better agreement was found when UVMFR SSAs were used with an average difference of 0.86%, while when using visible or climatological input, relative differences were estimated +4.91 and +4.15% accordingly.

There is increasing interdisciplinary interest in phytoplankton community dynamics as the growing environmental problems of water quality (particularly eutrophication) and climate change demand attention. This has led to a pressing need for improved biophysical and causal understanding of Phytoplankton Functional Type (PFT) optical signals, in order that satellite radiometry may be used to detect ecologically relevant phytoplankton assemblage changes. This understanding can best be achieved with biophysically and biogeochemically consistent phytoplankton Inherent Optical Property (IOP) models, as it is only via modelling that phytoplankton assemblage characteristics can be examined systematically in relation to the bulk optical waterleaving signal. The Equivalent Algal Populations (EAP) model is used here to investigate the source and magnitude of size- and pigment- driven PFT signals in the water-leaving reflectance, as well as the potential to detect these using satellite radiometry. This model places emphasis on explicit biophysical modelling of the phytoplankton population as a holistic determinant of IOPs, and a distinctive attribute is its comprehensive handling of the spectral and angular character of phytoplankton scattering. Selected case studies and sensitivity analyses reveal that phytoplankton spectral scattering is the primary driver of the PFT-related signal. Key findings are that the backscattering-driven signal in the 520 to 600 nm region is the critical PFT identifier at marginal biomass, and that while PFT information does appear at blue and red wavelengths, it is compromised by biomass/gelbstoff ambiguity in the blue and low signal in the red, due primarily to absorption by water. These findings are hoped to provide considerable insight into the next generation of PFT algorithms.

The air side thermal hydraulic performance of multi-louvered aluminium fin heat exchangers is investigated. A detailed study was performed to analyse the thermal performance of air over a wide range of Reynolds number i.e. from 30 to 250. Air-side heat transfer coefficient and air pressure drop were calculated and validated over the mentioned band of Reynolds numbers. Critical Reynolds number was determined numerically and the variation in flow physics along with the thermal and hydraulic performance of microchannel heat exchanger associated with R_cri has been reported. Moreover, a parametric study of the multi-louvered aluminium fin heat exchangers was also performed for 36 heat exchanger configurations with the louver angles (19-31°), fin pitches (1.0, 1.2, 1.4 mm) and flow depths (16, 20, 24 mm); and the geometric configuration exhibiting the highest thermal performance was reported. The air-side heat transfer coefficient and pressure drop results for different geometrical configurations were presented in terms of Colburn j factor and Fanning friction factor f, as a function of Reynolds number based on louver pitch.

Background: Bariatric surgery is a safe and effective procedure for treating obesity and metabolic conditions such as type 2 diabetes mellitus (T2DM). Remodeling of the extracellular matrix (ECM) supports adipose tissue expansion and its metabolic activity, where matrix metalloproteinases (MMPs) play a key role in ECM regulation. The substrates of MMPs, particularly MMP-2 and MMP-9, are elevated in patients with morbid obesity, metabolic syndrome, and T2DM. Objectives: To evaluate the effect of weight loss in bariatric surgery patients using oxidative stress markers and to compare MMP levels in patients undergoing Roux-en-Y gastric bypass (RYGB) and vertical gastrectomy (VG). Methods: This was a prospective, controlled study including 45 morbidly obese patients with T2DM (BMI > 35 kg/m²) who underwent RYGB (n=24) or VG (n=21). Weight loss was assessed through anthropometric measurements (weight, height, BMI). MMP-2 and MMP-9 levels were measured preoperatively and at 3 and 12 months postoperatively. Results: Significant and sustained weight loss was observed after surgery in both groups, with reductions in BMI. MMP-2 and MMP-9 levels decreased significantly after one year of follow-up. Conclusions: Bariatric surgery is an effective long-term intervention for weight loss and associated comorbidities, including T2DM. MMP-2 and MMP-9 proved to be effective inflammatory markers, with significant reductions following surgery.

In the article, we define a sequence F: F(0) = 2, F(1) = 10, F(2) = 16, F(3) = 22, F(4) = 26, F(4) = 28, · · · , F(i) = F(i − 1) + l, where the number l ≤ 8 is defined by using prime numbers and matrix algebra. The sequence includes only even numbers and can be used to define a series of numbers F(i) + 1, the non-Goldbach sequence, that contains all numbers that cannot be written as the sum of two primes. The result is related to Goldbach’s conjecture. The famous Goldbach conjecture states that every even integer greater than 2 can be expressed as the sum of two primes.

Melanoma is a malignant, highly metastatic neoplasm showing increasing morbidity and mortality. Tumor invasion and angiogenesis are based on remodeling of the extracellular matrix (ECM). Selective inhibition of functional components of cell-ECM interaction, such as hyaluronic acid (HA), matrix metalloproteinases (MMPs), and integrins, may inhibit tumor progression and enhance the efficacy of combination treatment with checkpoint inhibitors (ICBs), chemotherapy, or immunotherapy. In this review, we have combined the results of different approaches targeting extracellular matrix elements in melanoma in preclinical and clinical studies. The identified limitations of many approaches, including side effects, low selectivity and toxicity, indicate the need for further studies to optimize therapy. Nevertheless, significant progress in expanding our understanding of tumor biology and the development of targeted therapies holds great promise for the early approaches developed several decades ago to inhibit metastasis through ECM targeting.

We propose a universal approach for modeling complex integrated photonic resonators based on the scattering matrix method. By dividing devices into basic elements including directional couplers and connecting waveguides, our approach can be used to model integrated photonic resonators with both unidirectional and bidirectional light propagation, with the simulated spectral response showing good agreement with experimental results. A simplified form of our approach, which divides devices into several independent submodules such as microring resonators and Sagnac interferometers, is also introduced to streamline the calculation of spectral transfer functions. Finally, we discuss the deviations introduced by approximations in our modeling, along with strategies for improving modeling accuracy. Our approach is universal across different integrated platforms, providing a useful tool for designing and optimizing integrated photonic devices with complex configurations.

Rapid microscopic analysis of tissue is an essential diagnostic tool in oncological surgery. The gold standard for intraoperative histological tissue evaluation are frozen sections. However, frozen sections are prone to a variety of artefacts and require skilled staff and specialized lab equipment. A potential method for rapid intraoperative tissue evaluation that does not require fixation, freezing nor sectioning of the tissue, is ex vivo fluorescence confocal microscopy (FCM). The visualization of the structurally important extracellular matrix (ECM) in conventional ex vivo FCM lags behind the standards of conventional histology. The objective of this study was to find a stain that will improve the depiction of the ECM to resemble FFPE H&E sections as closely as possible.

Void fillers are required for osseous gaps generated after orthopedic procedures as medial open-wedge high-tibial osteotomy (MOWHTO) to provide sufficient structural support and a rapid osteosynthesis. We developed a novel three-dimensional (3D) printing-based platform technology using the customized 3D scaffolds covered with polycaprolactone (PCL)/β-tri-calcium phosphates (β-TCP)/bone decellularized extracellular matrix (dECM) for use as bone substitute scaffold, which can be effectively exploited to estimate the calculated correction angle with preoperative simulations. PCL/β-TCP/bone dECM scaffolds demonstrated significantly higher cell contain levels in cell seeding efficiency, excellent proliferation capacity, and promotion of early osteogenic differentiation compared with PCL/β-TCP scaffolds. The scaffolds promoted bone mineralization at the early time points of an in vivo study (8 weeks) and exhibited biodegradable properties (38% for 16 weeks). The correction angle measured after osteotomy using 3D printed scaffolds was estimated with high accuracy with low errors (10.3° ± 0.9°), was and was not significantly different even in the presence of lateral cortical hinge fractures. The customized 3D scaffold enriched with PCL/β-TCP/bone dECM yielded excellent cell seeding efficiency, proliferation capacity, early osteogenic differentiation, and bone mineralization outcomes. It is expected to solve the disadvantages related with bone union in MOWHTO, and to replace autografts in the future.

The continuous casting process of helical fiber reinforced metal matrix composites was proposed, and the experimental equipment was designed and manufactured. The relationship model be-tween the process parameters and the helical shape of the fibers in the metal matrix is analyzed by differential geometry. Helical carbon fiber reinforced lead matrix composites were prepared under different conditions. When the melting temperature and cooling intensity were increased, the helical radius of helical carbon fiber in lead matrix was increased. When the fiber rotation speed was increased, the helical radius was decreased, and the helical pitch was de-creased proportionally. The experimental results are consistent with the process model. Carbon fiber rein-forced aluminum composites were prepared. Scanning electron microscopy showed that the infiltration between the aluminum matrix and the carbon fiber was not complete. As the volume fraction of carbon fiber which was added in composites is too small, the mechanical properties of the composites have not been significantly improved.

Treatment of osteochondritis dissecans of the knee, has always been a challenge for orthopedic surgeons. We present a case report of a 38-year-old male with severe right knee pain after suffering from an indirect trauma and axial rotation of the knee, limiting knee functionality and impeding ability to walk, with diagnosis of osteochondritis dissecans in the trochlea of the knee, who underwent arthroscopic treatment with matrix induced autologous chondrocyte implantation (MACI). After the surgery, physical therapy protocol for MACI was implemented, magnetic imagen resonance images with cartilage mapping were used to evaluate recovery of the lesion. A total recovery was observed evaluated with the modified Cincinnati knee rating system (mCKRS). A discussion is provided with evidence and general recommendations for the use of MACI in the treatment of adult OCD of the knee.

Abstract The aim of the present review is to discuss the roles of vitamin K and vitamin K-dependent proteins, and the combined action of the vitamins K and D, for maintenance of bone health. The most relevant vitamin K-dependent proteins in this respect are osteocalcin and matrix Gla-protein (MGP). When carboxylated these proteins appear to have the ability to chelate and import calcium from the blood to the bone, thereby reducing the risk of osteoporosis. Carboxylated osteocalcin appears to contribute directly to bone quality and strength. Adequate vitamin K status is required for carboxylation of MGP and osteocalcin. In addition, vitamin K acts on bone metabolism by other mechanism, such as a ligand for the nuclear steroid and xenobiotic receptor (SXR). In this narrative review, we examine the evidence for increased bone mineralization through dietary adequacy of vitamin K (phylloquinone or menaquinone). Summarizing the evidence for a synergistic effect of vitamin K and vitamin D3 we find that an adequate supply of vitamin K on top of an optimal vitamin D status seems to add to the benefit for maintaining bone health. More research related to synergism and possible mechanisms of vitamins D3 and K interaction in bone health is needed.

Bosentan, an endothelin-receptor antagonist (ERA), has potential anti-atherosclerotic properties. We investigated the complementary effects of bosentan and atorvastatin on the progression and composition of the atherosclerotic lesions in diabetic mice. Forty-eight male apoE-/- mice were fed high-fat diet (HFD) for 14 weeks. At week 8, diabetes was induced with streptozotocin and mice were randomized into 4 groups: 1) Control/COG: no intervention. 2) ΒΟG: bosentan 100 mg/kg/day per os. 3) ATG: atorvastatin 20mg/kg/day per os). 4) BO+ATG: Combined administration of bosentan and atorvastatin. The intra-plaque contents of collagen, elastin, monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-a (TNF-a), matrix-metalloproteinases (MMP-2,-3,-9), and TIMP-1 concentrations were determined. The percentage of lumen stenosis significantly decreased across all treated groups: BOG: 19.5±2.2%, ATG: 12.8±4.8%, BO+ATG: 9.1±2.7% compared to controls (24.6±4.8%, p<0.001). Both atorvastatin and bosentan significantly increased collagen content and fibrous cap thickness versus COG (p<0.01). All intervention groups reduced the relative intra-plaque concentrations of MCP-1, MMP-3,-9, and increased TIMP-1 compared to COG (p<0.001). Importantly, bosentan showed modest but additive to atorvastatin effects on the latter parameters compared COG (p<0.05). Bosentan treatment in diabetic, atherosclerotic apoE-/- mice delayed the atherosclerosis progression and enhanced plaques’ stability, showing modest but complementary effects with atorvastatin, which are promising in atherosclerotic cardiovascular diseases.

Abstract Background: Numerous studies have demonstrated that the amalgamation of populations belonging to the same breed or closely related breeds leads to enhanced accuracies in genomic predictions (GP). Extensive experimentation with diverse Bayesian and Genome-enabled best linear unbiased prediction (GBLUP) models has been conducted to explore multi-breed genomic selection (GS) in livestock, ultimately establishing they as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to examine the efficacy of BayesR and GBLUP model with different weighted genomic relationship matrices (GRM) in making genomic predictions for three distinct beef cattle breeds. Subsequently, we conducted a comparative analysis of the predictive accuracy pertaining to various marker densities and genetic correlations across three distinct beef cattle breeds. This investigation aimed to identify the optimal approach for enhancing the predictive accuracy of multi-breed genomic selection in beef cattle. Results：Genetic relationship matrices revealed moderate similarities between YL and the other breeds, with a striking genetic similarity of 0.87 between WG and HX. In HX cattle, BayesR demonstrated an enhancement in prediction accuracy, achieving 0.52 with HD and 0.46 with WGS, a marked improvement over 0.41 with HD and 0.42 with WGS in GBLUP. In WG and YL breeds, both methods showed comparable accuracies with HD, but BayesR slightly outperformed GBLUP with WGS. Further, multi-breed GS analysis indicated that BayesR consistently surpassed GBLUP in prediction accuracy, particularly with WGS data. For instance, in a combined HX and WG reference population, BayesR achieved a superior accuracy of 0.53 with WGS in HX cattle, a significant enhancement over GBLUP models. The study also underscores the advantage of incorporating multiple breeds in the reference population, which improved prediction accuracy, underscoring the value of broad-based genomic selection strategies. Conclusion: The results show that accuracy of multi-breed genomic predictions was higher with BayesR than with GBLUP, especially for the distantly genetic relationship between reference and validation breeds. Further improvements of multi-breed accuracy of genomic predictions could be achieved by increasing the density of the SNP marker. These findings underscore that BayesR providing a substantial improvement in genomic prediction and the importance of considering genetic relationships in the development of GS strategies for multi-breed cattle populations. Further research is warranted to optimize GRM construction and to explore alternative models for genomic prediction across breeds.

This paper investigates reachable set estimation and state-feedback controller design for linear time-delay control system with bounded disturbances. First, by constructing an appropriate Lyapunov-Krasovskii functional, we obtained a delay-dependent condition, which determed the admissible bounding ellipsoid for the reachable set of the system we considered. Then, a sufficient condition in form of liner matrix inequalities is given to solve the problem of controller design with reachable set estimation. Finally, by minimizing the volume of the ellipsoid and solving the liner matrix inequality, we obtain the desired ellipsoid and controller gain. A comparative numerical example is given to verify the usefulness of our result.

Although subcutaneous islet transplantation has many advantages, transplant efficiency is still low in animal models, except in mice. We previously reported the efficacy of pretreatment using gelatin hydrogel nonwoven fabric (GHNF) in a mouse model. We investigated whether the preimplantation of GHNF could improve the subcutaneous islet transplantation outcomes in a rat model. GHNF sheets sandwiching a silicone spacer (GHNF group) and silicone spacers without GHNF sheets (control group) were implanted into the subcutaneous space of recipients three weeks before islet transplantation, and diabetes was induced seven days before islet transplantation. Syngeneic islets were transplanted into the space where the silicone spacer was removed. Blood glucose levels, glucose tolerance, immunohistochemistry, and neovascularization were evaluated. The GHNF group showed significantly better blood glucose changes than the control group (p<0.01). The cure rate was significantly higher in the GHNF group (p<0.05). The number of vWF-positive vessels was significantly higher in the GHNF group (p<0.01), and lectin angiography showed the same tendency (p<0.05). The expression of laminin and collagen III around the transplanted islets were also higher in the GHNF group (p<0.01). GHNF pretreatment was effective in a rat model, and the main mechanisms might be neovascularization and compensation of the extracellular matrices.

Applications of gradient method for nonlinear optimization in development of Gradient Neural Network (GNN) and Zhang Neural Network (ZNN) are investigated. Particularly, the solution of the matrix equation AXB=D which changes over time is studied using the novel GNN model, termed as GGNN(A,B,D). The GGNN model is developed applying GNN dynamics on the gradient of the error matrix used in the development of the GNN model. The convergence analysis shows that the neural state matrix of the GGNN(A,B,D) design converges asymptotically to the solution of the matrix equation AXB=D, for any initial state matrix. It is also shown that the convergence result is the least square solution which is defined depending on the selected initial matrix. A hybridization of GGNN with analogous modification GZNN of the ZNN dynamics is considered. The Simulink implementation of presented GGNN models is carried out on the set of real matrices.

Although Sn has been intensively studied as one of the most promising anode materials to replace commercialized graphite, its cycling performance and rate performance are still unsatisfactory owing to insufficient control of its large volume change during cycling and poor electrochemical kinetics. Herein, we propose a Sn-TiO2-C ternary composite as a promising anode material to overcome these limitations. The hybrid TiO2-C matrix synthesized via two-step high-energy ball milling effectively regulated the irreversible lithiation/delithiation of the active Sn electrode and facilitated Li-ion diffusion. At the appropriate C concentration, Sn-TiO2-C exhibited significantly enhanced cycling performance and rate capability compared to its counterparts (Sn-TiO2 and Sn-C). Sn-TiO2-C delivers good reversible specific capacities (669 mAh g-1 after 100 cycles at 200 mA g-1 and 651 mAh g-1 after 500 cycles at 500 mA g-1) and rate performance (446 mAh g-1 at 3000 mA g-1). The superiority of Sn-TiO2-C over Sn-TiO2 and Sn-C was corroborated by electrochemical impedance spectroscopy, which revealed faster Li-ion diffusion kinetics in the presence of the hybrid TiO2-C matrix than in the presence of TiO2 or C alone. Therefore, Sn-TiO2-C is a potential anode for next-generation Li-ion batteries.

The potency and challenges of sorghum development in tidal swamplands in Indonesia have yet to be well studied. Our research was the first to evaluate land suitability, economic performance, and strategies for developing sorghum in tidal swamplands in Central Kalimantan. The assessment methods for the potency and utilization of sorghum used the land suitability evaluation method, gross margin and profit analysis, break-even, and competitive analysis. As a tool for decision-making, we used SWOT followed by the quantitative strategic planning matrix (QSPM) analysis. The results showed that the potency of arable land suitable for sorghum development was 578,511 ha. Economically, sorghum farming can generate an IDR 12,894,000 per ha with a revenue-cost ratio of 1.72; the break-even price was IDR 2,447 per kg, around 42% lower than the current market price. Sorghum was also more competitive than cassava, sweet potato, and soybeans (Q values of 0.76, 0.58, and 0.61, respectively) and less competitive than maize (Q = 1.33). Based on the QSPM, there were five alternative strategies for developing sorghum in tidal swamplands, i.e., (1) Optimization of productivity, (2) Improving the quality of human resources of farmers, (3) Facilitation of partnership cooperation, (4) Application of site-specific technology, and (5) Optimization of waste utilization. Those strategies show the potential expansion of sorghum planting in the tidal swamplands and the economic value enhancement for the community.

In response to the growing demand for high-strength and high-toughness materials in industries like aerospace and automobiles, there is a need for metal matrix composites (MMCs) that can simultaneously enhance strength and toughness. This paper focuses on the design configurations of MMCs, which include both the configurations resulting from reinforcements and the inherent heterogeneity of the matrix itself. The mechanical properties of MMCs are influenced by factors such as reinforcement content, shape, size, and spatial distribution within the composite architecture. Among them, Aluminum matrix composites (AMCs) are particularly significant in aerospace, electronics, and electric vehicles due to their potential for weight reduction and enhanced performance. However, the challenge lies in the inverse relationship between strength and toughness, hindering the widespread utilization and large-scale development of MMCs. The design configuration of composites plays a critical role in achieving concurrent improvements in strength and toughness. This review explores the advantages of toughness, toughening mechanisms, reinforcement distribution characteristics, and structural parameters in the design of composite architectures. Drawing inspiration from biological composites like bone, the development of synthetic composites with homogeneous structural designs provides insights into attaining exceptional strength and toughness in lightweight engineering structures. Additionally, understanding fracture behavior and toughening mechanisms in heterogeneous nanostructures is vital for advancing the field of metal matrix composites. Summarily, the design of composite architectures holds tremendous potential for tailoring AMCs with outstanding strength and toughness, addressing the requirements of lightweight engineering structures in various industries.

Information is physically measurable as a selection from a set of possibilities, the domain of information. This defines the term "information". The domain of the information must be known together reproducibly beforehand. As a practical consequence, digital information exchange can be made globally efficient, interoperable and searchable to a large extent by online definition of application-optimized domains of information. There are even more far-reaching consequences for physics. The purpose of this article is to present prerequisites and possibilities for a physical approach that is consistent with the precise definition of information. This concerns not only the discretization of the sets of possible experimental results, but also the order of their definition along time. The access to or comparison with the domain of information is the more frequent, the earlier it was defined. The geometrical appearance of our space is apparently a delayed statistical consequence of a very frequent connection with the common primary domain of information.

It is often claimed that Einstein is wrong about quantum mechanics. However, when comparisons are made with respect to theoretical foundations rather than experimental results Einstein’s theories are found to be superior. Although quantum mechanics correctly predicts what it is possible to observe (the emissions) it ignores the other half of natural phenomena, what cannot be observed (the absorptions), thereby violating the conservation laws. By describing only one-half of radiation processes conceptual difficulties such as wave function collapse, infinite paths, and inscrutable mathematics seem to be insoluble mysteries. The deficiencies are corrected by introducing Hamilton’s principle S = ∫ Ldt and applying the methods of quantum field theory to derive relativistic equations of motion. The calculus of variations is used to show that the wave function represents an incomplete equation of motion because it equals twice the allowable action minimum.

We use a recently proposed formulation of quantum mechanics based, not on potential functions but rather, on orthogonal energy polynomials. In this context, the most important building block of a quantum mechanical system, which is the wavefunction at a given energy, is expressed as pointwise convergent series of square integrable functions in configuration space. The expansion coefficients of the series are orthogonal polynomials in the energy; they contain all physical information about the system. No reference is made at all to the usual potential function. We consider, in this new formulation, few representative problems at the level of undergraduate students who took at least two courses in quantum mechanics and are familiar with the basics of orthogonal polynomials. The objective is to demonstrate the viability of this formulation of quantum mechanics and its power in generating rich energy spectra illustrating the physical significance of these energy polynomials in the description of a quantum system. To assist students, partial solutions are given in an appendix as tables and figures.

Based on its large surface area and covering the whole human body, the skin body’s largest organ and its main function is protection. Injuries and wound healing involving the skin offer valuable lessons shared with and of relevance to other organ systems and the diseases that impact them. Arguably the most complex human body process, wound healing is a multifaceted process that involves multiple cells and the extracellular matrix (ECM), with each component playing a specific role in the different stages of the healing process. Importantly, studies indicate that cells with stem cell-like properties are present within many of the human tissues and play key roles in case of tissue and cellular injury. Cell-to-cell and cell-to-ECM interactions are salient in wound healing subsequent to an injury. Microenvironment related factors and the variations therein including hypoxia or the abundance of oxygen, the presence/absence of growth factors and cytokines add to the complexity of the wound healing process and impact cell function and result in compromised or enhanced wound healing. This expert review critically examines the advances in biochemical and analytical tools that are enable the analysis of numerous cells and molecules within the wound microenvironment, revealing great cellular heterogeneity as well as novel molecular targets of importance to enhance wound healing. In a broader angle, we emphasize the ways in which wound healing is significant in the search for perfect skin after injury and in many common complex human diseases including cancer. In all, wound healing is a centrepiece of integrative biology research and applications in medicine as well as dermatology as discussed in this review.

As one of the most promising ablative fillers, multi-walled carbon nanotubes (MWCNTs) have been used to improve the ablative resistance of Ethylene-Propylene-Diene Monomer (EPDM) insulators by facilitating the carbothermal reduction reaction of silica. However, the contribution of MWCNTs to char layer structure of the insulators was unclear. In this work, the effects of MWCNTs on char layer structure and ablative resistance were investigated in different EPDM-based insulators with and without silica. The results showed that adding only 3 phr MWCNTs can reduce the linear ablation rate of EPDM-based insulators without silica by 31.7%, while 6 phr MWCNTs are required to obtain similar results in EPDM-based insulators with silica. Both the char layer morphology of the two insulators gradually evolved into dense porous structure as MWCNTs content increasing, but their formation mechanisms are different. The XRD and Raman spectrum showed that different physicochemical reactions occurred around MWCNTs under different charring components. The proposed ablation mechanism was further verified by designing alternating multilayer distribution of MWCNTs and silica. This work can guide the construction of desirable char layer structure for increasing ablative resistance of EPDM-based insulators.

Matrix metalloproteinases (MMPs) are critical enzymes involved in a variety of cellular processes. MMPs are well known for their ability to degrade the extracellular matrix (ECM) and their extracellular role in cell migration. Recently, more research has been conducted on investigating novel subcellular localizations of MMPs and their intracellular roles at the respective locations. In this review article, we focus on the subcellular localization and novel intracellular roles of two MMPs closely related: membrane-type-1 matrix metalloproteinase (MT1-MMP) and matrix metalloproteinase-2 (MMP-2). Although MT1-MMP is commonly known to localize on the cell surface, the protease also localizes to the cytoplasm, caveolae, Golgi, cytoskeleton, centrosome, and nucleus. At these subcellular locations, MT1-MMP functions in cell migration, macrophage metabolism, invadopodia development, spindle formation and genes expression, respectively. Similar to MT1-MMP, MMP-2 localizes to the caveolae, mitochondria, cytoskeleton, nucleus and nucleolus, and functions in calcium regulation, contractile dysfunction, genes expression and ribosomal RNA transcription. Our particular interest lies in the role MMP-2 and MT1-MMP serve within the nucleus, as it may provide critical insights into cancer epigenetics and tumor migration and invasion. We suggest that targeting nuclear MT1-MMP or MMP-2 to reduce or halt cell proliferation and migration may lead to the development of new therapies in cancer and other diseases.

In this note we study the computation of the minimum polynomial of a matrix $A$ and how we can use it for the computation of the matrix $A^n$. We also describe the form of the elements of the matrix $A^{-n}$ and we will see that it is closely related with the computation of the Drazin generalized inverse of $A$. Next we study the computation of the exponential matrix and finally we give a simple proof of the Leverrier - Faddeev algorithm for the computation of the characteristic polynomial.

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

We work with reference to a well-known semiclassical model, in which quantum degrees of freedom interact with classical ones. We show that, in the classical limit, it is possible to represent classical results (e.g., classical chaos) by means a pure-state density matrix.

Inhalational anesthetics was previously reported to suppress glioma cell malignancy, but underlying mechanisms remain unclear. The present study aims to investigate the effects of sevoflurane and desflurane on glioma cell malignancy changes via microRNA (miRNA) modulation. The cultured H4 cells were exposed to 3.6% sevoflurane or 10.3% desflurane for 2 hrs. The miR-138, -210 and -335 expression were determined with qRT-PCR. Cell proliferation and migration were assessed with wound healing assay, Ki67 staining and cell count kit 8 (CCK8) assay with/without miR-138/-210/-335 inhibitor transfections. The miRNA downstream proteins, hypoxia inducible factor-1α (HIF-1α) and matrix metalloproteinase 9 (MMP9), were also determined with immunofluorescent staining. Sevoflurane and desflurane exposure to glioma cells inhibited their proliferation and migration. Sevoflurane exposure increased miR-210 expression whereas desflurane exposure upregulated both miR-138 and miR-335 expressions. The administration of inhibitor of miR-138, -210 or -335 inhibited the suppressing effects of sevoflurane or desflurane on cell proliferation and migration, in line with the HIF-1α and MMP9 expression changes. These data indicated that inhalational anesthetics, sevoflurane and desflurane, inhibited glioma cell malignancy via miRNA upregulation and their downstream effectors, HIF-1α and MMP9, downregulation. The implication of the current study warrants further study.

Stable isotope-assisted approaches can improve untargeted LC-HRMS metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve coverage of treatment-relevant metabolites and aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly-13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Ulmus species have been widely used in Korean folk medicine because of their anti-inflammatory and antimicrobial properties. We intended to investigate the wound healing effect of the powder of Ulmus parvifolia (UP) root bark in a mouse wound healing model. We also determined the mechanisms of effects of Ulmus parvifolia (UP) in skin and skin wound healing effect using keratinocyte model. in vivo experiments showed that the wound lesions in the mice decreased by U. parvifolia with 200 mesh size of root bark powder and significantly reduced by treatment with UP, compared with those treated with U. macrocarpa (UM). Results from in vitro experiments also revealed that UP extract promoted the migration of human skin keratinocytes. UP powder treatment upregulated the expression of the matrix metalloproteinase-2 and -9 protein and significantly increased transforming growth factor (TGF)-β levels. We confirmed that topical administration of the bark powder of exerted a significant effect on skin wound healing by upregulating the expression of MMP and transforming growth factor-β. TGF-β In, Our study suggests that U. parvifolia may be a potential candidate for skin wound healing including epidermal skin rejuvernation.

The article is devoted to applications of 2-dimensional hyperbolic numbers and their algebraic 2ⁿ-dimensional extensions in modeling some genetic and cultural phenomena. Mathematical properties of hyperbolic numbers and their bisymmetric matrix representations are described in a connection with their application to analyze the following structures: alphabets of DNA nucleobases; inherited phyllotaxis phenomena; Punnett squares in Mendelian genetics; the psychophysical Weber-Fechner law; long literary Russian texts (in their special binary representations). New methods of algebraic analysis of the harmony of musical works are proposed, taking into account the innate predisposition of people to music. The hypothesis is put forward that sets of eigenvectors of matrix representations of basis units of 2ⁿ-dimensional hyperbolic numbers play an important role in transmitting biological information. A general hyperbolic rule regarding the oligomer cooperative organization of different genomes is described jointly with its quantum-information model. Besides, the hypothesis about some analog of the Weber-Fechner law for sequences of spikes in single nerve fibers is formulated. The proposed algebraic approach is connected with the theme of the grammar of biology and applications of bisymmetric doubly stochastic matrices. Applications of hyperbolic numbers reveal hidden interrelations between structures of different biological and physical phenomena. They lead to new approaches in mathematical modeling genetic phenomena and innate biological structures.

Headspace solid phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry is widely employed for volatile analyses of plants, including mapping populations used in plant breeding research. Studies often employ a single internal surrogate standard, even when multiple analytes are measured, with the assumption that any relative changes in matrix effects among individuals would be similar for all compounds, i.e. matrix effects do not show Compound × Individual interactions. We tested this assumption using individuals from two plant populations, an interspecific grape (Vitis spp.) mapping population (n = 140) and a tomato (Solanum spp.) recombinant inbred line (RIL) population (n = 148). Individual plants from the two populations were spiked with a cocktail of internal standards (n = 6, 9, respectively) prior to HS-SPME-GC-MS. Variation in the relative responses of internal standards indicated that Compound × Individual interactions exist but were different between the two populations. For the grape population, relative responses among pairs of internal standards varied considerably among individuals, with a maximum of 249% relative standard deviation (RSD) for the pair of [U13C]hexanal and [U13C]hexanol. However, in the tomato population, relative responses of internal standard pairs varied much less, with pairwise RSDs ranged from 8% to 56%. The approach described in this paper could be used to evaluate the suitability of using surrogate standards for HS-SPME-GC-MS studies in other plant populations.

TGF-β signaling transduces immunosuppressive biochemical and mechanical signals in the tumor microenvironment. In addition to canonical SMAD signaling, TGF-β can promote tumor growth and survival by inhibiting proinflammatory signaling and extracellular matrix remodeling. In this article, we will review how TAK1 activation lies at the intersection of proinflammatory signaling by immune receptors and anti-inflammatory signaling by TGF-β receptors. Additionally, we will discuss the role of TGF-β in the mechanobiology of cancer. Understanding how TGF-β dampens proinflammatory responses and induces pro-survival mechanical signals throughout cancer development will be critical in designing therapeutics which inhibit tumor progression while bolstering the immune response.

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

We introduce the Concept-Model-Graph-View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

This study proposes parabolic dish-based toroidal structured fractal solar collectors. The potential of fractal geometry to increase heat transfer and the ability of the parabolic dish to concentrate solar rays form the basis of the proposed design for increasing the efficiency. In the study, the thermal and hydrodynamic behaviors of the proposed 3-row, 4-row and 5-row parabolic collectors were investigated comprehensively. Using theoretical modeling and experimental results, the performances of the proposed parabolic dish-based toroidal fractal solar collectors were evaluated and compared via numerical simulation methods. After the experimental studies of the 3-row toroidal fractal collector, the analysis studies were completed using the ANSYS-Fluent program. Then, simulations were carried out for other toroidal solar collectors using the results of these experimental studies. As a result of the converging numerical analyzes, the radiative, hydrodynamic and thermal analysis results of the toroidal absorbers in 3-row, 4-row and 5-row structures integrated with the parabolic dish were compared. In the temperature distribution analysis, it was observed that the parabolic dish effectively focuses on the sun rays and provides a gradual temperature increase of approximately 21 K for the fractal collector. It is observed that 96.84% convergence was achieved between the experimental and numerical results.

The article reviews selected solar systems that utilize solar energy for cooling production. Particular attention is given to research on individual components of these systems, aimed at improving their efficiency and performance. The article focuses on the analysis of literature concerning the design of thermal storage units, with an emphasis on the use of computational fluid dynamics (CFD) as a research tool. Conclusions from scientists’ research regarding the impact of tank shape, thermal insulation, flow parameters, and the use of stratification partitions on heat storage efficiency have been presented. The literature review indicates that thermal storage units play a key role in the efficiency of solar systems, and thermal stratification within them can significantly improve their performance.

Current-induced field-free magnetic switching using spin-orbit torque has been an important topic for decades due to both academic and industrial interest. Most research has focused on introducing symmetry breakers, such as geometrical and compositional variation, pinned layer, and symmetry-broken crystal structures, which add complexity to the magnetic structure and fabrication process. We designed relatively simple magnetic structure, composed of a [Co/Pt] multilayer and a Co layer with perpendicular and in-plane magnetic anisotropy, respectively, with a Cu layer between them. Current-induced deterministic magnetic switching was observed in this magnetic system. The system is advantageous for its easy control of the parameters to achieve the optimal condition for magnetic switching. The balance between magnetic anisotropic strength and interlayer coupling strength is found to provide the optimal condition. This simple design and easy adjustability opens various possibilities for magnetic structures in spin-based electronics applications using spin-orbit torque.

For advancing manufacturing, arising AM, with an inverse philosophical approach compared to conventional procedures, has benefits that include intricate fabrication, reduced material waste, flexible design, and more. Regardless of its potential, AM must overcome several challenges due to multi-physical processes with miscellaneous physical stimuli in diverse materials systems and situations, such as anisotropic microstructure and mechanical properties, a restricted choice of materials, defects, and high cost. Unlike conventional experimental work that requires extensive trial and error resources and FEM, which generally consumes substantial computational power, the analytical approach based on physics is an exceptional choice. Understanding the relationship between the microstructure and material properties of the fabricated parts is a crucial focus in AM research. Texture is a vital factor in almost every modern industry. This study first proposed a physics-based model to foreshadow the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions due to the importance of part geometry in real industry. The thermal distribution obtained from this function operates as the information for the single-phase crystallographic texture model. In this model, we forerun and validate the orientations of single-phase materials utilizing three Euler Angles with the principles of CET and thermodynamics, as well as the intensity of the texture by approximating them with published results. Then, we transform the single-phase texture into a dual-phase texture in Bunge calculation, illustrating visualized by pole figures of both BCC and HCP phases. This texture evolution model provides a new paradigm for future researchers to model the texture or microstructure evolution semi-analytically and save many computational resources in a real-world perspective. Others have not yet done this work about simulating the multi-phase texture in an analytical approach, so this work bridges the gap in this field. Furthermore, this paper establishes the foundation for future research on materials properties affected by microstructure or texture in academic and industrial environments. The precision and dependability of the results obtained through this method make it a valuable tool for ongoing research and advancement.

The plantar plate (PP) tear of the second metatarsophalangeal joint (MTPJ) is the most common reason for forefoot pain in clinical practice. PP is the main stabilizing structure of the joint and, together with collateral ligaments, is the key to maintaining the stability of MTPJ. Many surgical procedures have been described to repair PP tears. Currently, there is still controversy regarding which is the surgical superior option (direct versus indirect PP repair techniques). Transfer of the flexor digitorum longus tendon to the dorsum of the proximal phalanx is one of the surgical techniques described to treat PP tears associated with MTPJ instability. We present two cases that developed instability of the second MTPJ secondary to chronic PP tear with symptoms resolved after transfer of the flexor digitorum longus (FDL). Currently, the literature review shows that the procedure seems to be the most consistent surgical option in chronic cases of PP tears.

This study investigates the capabilities of high-resolution Sentinel-2 observations and deep learning (DL) segmentation models, specifically convolutional neural networks (CNNs), for accurate mapping of small and fragmented agricultural burn areas in Punjab, India. Initially, the model was trained using ICNF burn area data from Portugal to capture large fire and burn area delineation, achieving moderate accuracy. Subsequent fine-tuning using annotated data from Punjab improved the model’s ability to detect small burn patches, demonstrating higher accuracy and precision than the baseline Normalised Burn Ratio (NBR) Index method. On-ground validation using buffer zone analysis and crop field images confirms the DL approach effectiveness. Cloud interference and temporal gaps in satellite data posed some challenges. Despite these limitations, the study highlights the methodological advancements and potential of using DL models for small burn area detection in agricultural settings. The model achieved overall testing accuracy of 0.64 in Dice score assessment. This report presents a promising approach for improved mapping of the burn area, which might engender better estimation of emissions and the management practices in regions with little available ground truth data.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

This research evaluates the feasibility of addressing computer vision problems with limited resources, particularly in the context of medical data where patient privacy concerns restrict data availability. (1) Background: The study focuses on diagnosing skin diseases using five distinct transfer learning models based on convolutional neural networks. Two versions of the dataset were created, one imbalanced (4092 samples) and the other balanced (5182 samples), using simple data augmentation techniques. Preprocessing techniques were employed to enhance the quality and utility of the data, including image resizing, noise removal, and blur techniques. The performance of each model was assessed using fresh data after preprocessing. According to the research findings, (2) Methods: the VGG-19 model achieved an accuracy of 95.00% on the imbalanced dataset. After applying augmentation on the balanced data, the best-performing model was VGG-16-Aug with an accuracy of 97.07%. (3) Results: These results suggest that low-resource approaches, coupled with preprocessing techniques, can effectively identify skin diseases, particularly when utilizing the VGG-16-Aug model with a balanced dataset. (4) Conclusions: The study addresses rare skin disorders that have received limited attention in past research, including acne, vitiligo, hyperpigmentation, nail psoriasis, and SJS-TEN. The findings highlight the potential of simple data augmentation techniques moreover, explainable AI: Grad-CAM interpreted the model outcome by showing image contours visually as well as identifying uncommon skin conditions and overcoming the data scarcity challenge. The implications of these research findings are significant for the development of machine learning-based diagnostic systems in the medical field. Further investigation is necessary to explore the generalizability of these findings to other medical datasets.

Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep-learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8,222 files selected from the authors' collection and the ISIC2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy on four categories—melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi—using an average of 1,600 images per category. Adding two categories with fewer images (about 700 each)—squamous cell carcinoma and actinic keratoses—reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements.

Radial nerve palsies present a challenging clinical scenario, often leading to substantial functional impairment. This study focuses on evaluating the outcomes of tendon transfer surgeries in patients with post-traumatic radial nerve injuries. The radial nerve, vital for upper limb movements, faces various etiologies such as trauma, compression, or idiopathy. Patients with radial nerve palsy encounter difficulties in daily activities, emphasizing the need for effective management strategies. The research introduces a novel evaluation protocol, aiming to comprehensively assess tendon transfer outcomes. This protocol incorporates functional movements of wrist and finger joints, encompassing both objective and subjective parameters. The retrospective study includes eleven patients treated between 2010 and 2022, with a minimum follow-up of one year post-surgery. Tendon transfers demonstrated positive results. The evaluation protocol covers a wide range of parameters, including wrist and finger mobility, thumb function, grip strength, and patient satisfaction. The results indicate successful restoration of motor function, with an average grip strength of 70% compared to the healthy arm. The proposed evaluation protocol facilitates standardized and reproducible assessment, minimizing subjective errors in clinical evaluations. Despite the study's limitations, such as a relatively small sample size, the findings underscore the effectiveness of tendon transfers in treating radial nerve palsies. The introduced evaluation scheme provides a comprehensive and reproducible approach to assess outcomes, contributing to the global standardization of tendon transfer assessments in radial nerve injuries.

Most research on colostrum quality and colostrum management focuses on maternal IgG transfer to the newborn calf. However, in addition to being a source of antibodies, colostrum has other components that influence passive immune transfer, nutrition, gut maturation, and thermoregulation. In this review, the constituents in the bovine colostrum, as well as their variation, are explored and it is discussed the biological role of colostrum on the newborn calf. Protein is the nutrient in higher concentration and fat is the nutrient with the most variation. There is a pronounced amplitude in the total solids content of colostrum, but the average concentration does not vary much between studies (24.7%). Bovine colostrum major components can vary with several factors, such as the region, season, breed, dry period length, and nutritional aspects of the dam. Bioactive components, cells, and microbiome variability are less understood, besides IgG. Regardless, they play crucial distinct roles in the physiological development of the newborn. However, a multi-interactive effect between these components, cells, and microorganisms is more likely to occur. Recently researched components in colostrum like miRNA, nucleo(t)sides, the milk fat globule membrane, oligosaccharides, minor proteins, and the microbiome are examples of promising interest areas of study.

When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

In this paper, the integrated performance of a modular biomass boiler with an existing industrial Rankine steam heat and power cycle and a supplementary supercritical-CO2 (sCO2) Brayton cycle is analyzed. The aim is to leverage the high efficiency supplementary sCO2 cycle to increase net generation and energy efficiency from the existing biomass boiler. Two sCO2 heater configurations situated within the flue gas flow path are investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. A quasi-steady-state 1D model was developed to simulate the integrated cycle, including detailed component characteristics for the Rankine and Brayton cycles. The model solves the mass, energy, momentum, and species balance equations. The system is analyzed for three cases: (i) the existing Rankine cycle without the sCO2 integration, (ii) with the single convective-dominant sCO2 heater configuration, and (iii) the dual sCO2 heater configuration. The results show the required rate of overfiring for the sCO2 configurations, with a 15.3% increase in fuel flowrate resulting in an additional 21.2% in net power output. The model quantifies the impact of the sCO2 heaters, with reduced heat uptakes for downstream boiler heat exchangers. Furnace water wall heat uptake increased due to overfiring, offsetting the reduced heat uptakes at downstream evaporative heat exchangers. The dual configuration has more impact on Rankine cycle operation due to the radiative sCO2 heater placement in front of the second superheater, absorbing some of the direct radiation from the furnace.

Pool boiling heat transfer is recognized as one of the most effective heat transfer methods and has extensive applications in industry. The application of non-azeotropic binary mixtures can not only meet the environmental requirements of modern industrial development, but also improve the coefficient of performance (COP) of many systems. Therefore, studying the boiling heat transfer characteristics of non-azeotropic mixtures is important on improving their potential industrial application. This article selected ethylene glycol/deionized water (EG/DW) at varies concentration as the research working fluids, and conducted experimental research on the pool boiling performance. At the same time, the thermophysical parameters of the mixtures were studied and analyzed. The results showed that the concentration of EG in the mixture had a significant impact on the thermal properties and temperature glide of the mixture, where both affected the heat transfer deterioration. Based on experimental data, six widely used heat transfer coefficient prediction correlations were used for calculation, and two of them were modified. The deviation between the modified correlations and the experimental results remained within 20%.

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25%40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in auto-tuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

The application of dry lubrication in rolling contacts is challenging due to wear. In previous studies graphene platelets applied as dry lubricants on the surfaces of angular contact ball bearings demonstrated superior properties. These specific bearings, experiencing both rolling and spinning motion, create more severe conditions for dry lubricants. The applied graphene platelets have a thickness of few graphene layers in the nm range and a diameter in the µm range. Such platelets can be produced by mechanical exfoliation from graphite. To delve deeper into the lubrication effects, investigations were carried out using a milli tribometer. In the tests, a fixed steel ball is sliding against an oscillating counterpart under defined load. During the measurements, the applied load and tangential forces on the ball are recorded to calculate the friction. In our case, a steel ball with a diameter of 6 mm was used against graphene-based films on flat bearing surfaces (axial bearing washer). Comparative investigations included nano graphite particles and fullerene as dry lubricants, in addition to graphene platelets of various staple thicknesses. To increase the adhesion of the films to the surfaces, a pre-rolling process was implemented. Afterwards, the friction on the compressed films was measured. The results indicate that the pre-rolling process effectively reduces the friction of the system. After testing, the surfaces underwent analysis using laser scanning microscopy to assess the formed films, wear, and material transfer. It has been demonstrated that the pre-rolling process leads to the formation of a very thin compacted film with surface protective properties. On the ball as counterpart, the graphene platelets generate a transfer film on the contacting surface.

We report a detailed investigation of vibrational modes, structure, and dynamics of glutathione (GSH) solutions using ultrasonic relaxation spectroscopy, FT-IR vibrational spectroscopy and electronic absorption measurements. The experimental data were analyzed in view of density functional theory (DFT) and molecular docking calculations. Three distinct Debye-type relaxation processes are resolved in the acoustic spectra, which are assigned to conformational changes between GSH conformers, self-association of GSH, and protonation processes. The standard volume changes for each process were estimated both experimentally and theoretically revealing a close resemblance among them. The higher the effect of the relaxation process in the structure, the greater the induced volume changes. From the temperature dependence of specific acoustic parameters, the thermodynamic characteristics of each process were determined. The experimental FT-IR spectra were compared with the corresponding theoretically predicted vibrational spectra revealing that the GSH dimers and extended conformers dominate the structure of GSH solutions in the high concentration region. The absorption spectra in the ultraviolet region confirmed the gradual aggregation mechanism that takes place in the aqueous GSH solutions. The results of the present study were discussed and analyzed in the framework of the current phenomenological status of the field.

In the realm of the five-category classification endeavor, there has been limited exploration into applied techniques for classifying Arabic text. These methods have primarily leaned on singletask learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

Many studies on magnetorheological fluid (MRF) have been carried out over last three decades several salient advantages such as fast phase change, easy control of the yield stress and so forth. Especially, several review articles of MRF technology have been reported for last two decades summarizing the development of MRFs and their applications. As specific examples, the review articles including the optimization of the particles and carrier liquid to achieve minimum off-state viscosity and maximum yield stress at on-state, the formulation of many constitutive models in-cluding Casson model and Herschel-Bulkley (H-B) model, the sedimentation enhancement using additives and nanosized particles, many types of dampers for automotive suspension and civil structures, medical and rehabilitation devices, MRF polishing technology, the methods of mag-netic circuit design, and the synthesis of various controllers have been published. More recently, the effect of the temperature and thermal conductivity on the properties of MRFs and application systems are actively being investigated by several works. However, there is no review article on this issue so far despite of that the thermal problem is one of crucial factors to be seriously con-sidered for the development of advanced MRFs and commercial products of application systems. In this work, studies on the thermal conductivity and temperature in MRFs themselves and tem-perature-dependent application systems are reviewed, respectively, and principal results are summarized emphasizing on the followings: how to reduce the temperature effect on the field-dependent properties of MRFs and how to design application system to minimize the thermal effect. It is noted here that the review summary is organized in a chronological format using tables.

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, whereas it is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 300 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks(SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules and complex networks to deep mine subway construction safety incident reports and explore risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risk; Then, association rules are introduced to explore the causal relationships among safety risk; Finally, the key safety risk and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risk in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key nodes or cutting off the transfer path. The results of the study provide new ideas and methods for SCSR identification and influence element mining, which help safety managers propose accurate subway construction safety risk control measures.

Nonlinear thermal transport of non-Newtonian polymer flows is an increasingly important area in materials engineering. Motivated by new developments in this area which entail more re-fined and mathematical frameworks, the present analysis investigates the boundary layer ap-proximation and heat transfer persuaded by a symmetrical cylindrical surface positioned hori-zontally. To simulate thermal relaxation impacts the bioconvection cross nanofluid flow Buongiorno model is deployed. The impact of the magnetic field applied on the nanofluid using the heat generation and melting phenomena are inspected. The nonlinear effect of thermosolutal buoyant forces is incorporated into the proposed model. The novel mathematical equations comprise thermophoresis and Brownian diffusion effects. Via robust transformation techniques, the primitive resulting partial equations for momentum, energy, concentration, and motile living microorganism are rendered into nonlinear ordinary equations with convective boundary postulates. An explicit and efficient numerical solver procedure in Mathematica 11.0 programming platform is developed to engage the nonlinear equations. The consequence of multiple governing parameters on dimensionless fluid profiles is inspected through plotted visuals and tables. Finally, outcomes declared on the surface drag force, heat, and mass transfer coefficients are exhibited through 3D visuals via different influential parameters.

Chatbots are computer programs that use artificial intelligence to imitate human conversations. Recent advancements in deep learning have shown interest in utilizing language transformers, which do not rely on predefined rules and responses like traditional chatbots. This study provides a comprehensive review of previous research on chatbots that employ deep learning and transfer learning models. Specifically, it examines the current trends in using language transformers with transfer learning techniques to evaluate the ability of Arabic chatbots to understand conversation context and demonstrate natural behavior. The proposed methods explore the use of AraBERT, CAMeLBERT, AraElectra-SQuAD, and AraElectra (Generator/Discriminator) transformers, with different variants of these transformers and semantic embedding models. Two datasets were used for evaluation: one with 398 questions and corresponding documents, and another with 1395 questions and 365,568 documents sourced from Arabic Wikipedia. Extensive experimental works were conducted, evaluating both manually crafted questions and the entire set of questions, using confidence and similarity metrics. The experimental results showed that the AraElectra-SQuAD model achieved an average confidence score of 0.6422 and an average similarity score of 0.9773 on the first dataset, and an average confidence score of 0.6658 and similarity score of 0.9660 on the second dataset. The study concludes that the AraElectra-SQuAD model consistently outperformed other models, displaying remarkable performance, high confidence, and similarity scores, as well as robustness, highlighting its potential for practical applications in natural language processing tasks for Arabic chatbots. The study suggests that the AraElectra-SQuAD model can be further enhanced and applied in various tasks such as chatbots, virtual assistants, and information retrieval systems for Arabic-speaking users. By combining the power of transformer architecture with fine-tuning on SQuAD-like large data, this trend demonstrates its ability to provide accurate and contextually relevant answers to questions in Arabic.

Deep learning is improving by leaps and bounds in remote sensing images (RSIs) analysis, pre-trained convolutional neural networks (CNNs) have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, pre-trained CNNs require massive annotated data as samples for data training. When labeled samples are not sufficient, the most common solution is to the pre-trained CNNs using a great deal of natural image dataset (e.g. ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proved that the proposed algorithm improves the classification accuracy while greatly decreasing sum of model parameters and sum of training data.

Noctuidae is known to have high species diversity, although the genomic diversity of Noctuidae species have not been studied extensively. Investigation of transposable elements (TEs) in this family can improve our understanding on the genomic diversity of Noctuidae. In this study, we annotated and characterized genome-wide TEs in ten noctuid species belonging to seven genera. With multiple annotation pipelines, we constructed a consensus sequence library containing 1,038 –2,826 TE consensus. The genome content of TEs showed high variation in the ten Noctuidae genomes, ranging from 11.3% to 45.0%. The relatedness analysis indicated that the TE content, especially the content of LINEs and DNA transposons, are positively correlated with the genome size (r=0.86, p-value=0.001). We identified SINE/B2 as a lineage-specific subfamily in Trichoplusia ni, a species-specific expansion of LTR/Gypsy subfamily in Spodoptera exigua, and a recent expansion of SINE/5S subfamily in Busseola fusca. We further revealed that of the four TE classes, only LINEs showed phylogenetic signal with high confidence. We also examined how the expansion of TEs contributed to the evolution of noctuid genomes. Moreover, we identified a total of 56 horizontal transfer TE (HTT) events among the ten noctuid species and at least three HTT events between the nine Noctuidae species and 11 non-noctuid arthropods. One of HTT events caused by a Gypsy transposon might have caused the recent expansion of Gypsy subfamily in the S.exigua genome. By determining the TE content, dynamics, and HTT events in the Noctuidae genomes, our study emphasized that TE activities and HTT events had substantial impacts on the Noctuidae genome evolution.

Theoretical approaches were applied to study the effect of magnetic field and heat transfer on the flow of blood plasma through an asymmetric arterial segment. The plasma was considered to be unsteady, laminar and an incompressible fluid through non-uniform arterial segment in a two-dimensional flow. Axial velocity, pressure gradient, stream function and pressure shift per wavelength were evaluated in blood plasma flow in arteries by use of coupled linear partial differential equations, solved with the help of Finite difference method. The corresponding initial and boundary conditions were obtained by discretizing the flow channel. The effect of magnetic field on blood plasma in arteries was determined by creating a magnetic field gradient through the application of a varying strength of magnetic field on the flow. Heat transfer characteristics due to applied magnetic field and viscosity of blood was obtained by use of linear partial differential equations to determine varying temperature conditions in heat transfer characteristics. Numerical results for velocity profiles, magnetic profiles and temperature profiles were obtained to characterize blood plasma viscosity by use of various non-dimensional parameters. The results were graphically presented using MATLAB software. The results obtained helped in analyzing theoretically the effects of magnetic field and heat transfer in arterial plasma flow.

Electron can tunnel between cofactor molecules positioned along biological electron transport chains up to the distance of ≃20 Å on the millisecond time scale of enzymatic turnover. This tunneling range mostly determines the design of biological energy chains facilitating cross-membrane transport of electrons. Tunneling distance and cofactors’ redox potentials become main physical parameters of this design. The protein identity, flexibility, or dynamics are missing from this picture assigning universal charge-transport properties to all proteins. This paradigm is challenged by dynamical models of electron transfer showing that the hopping rate is constant within the crossover distance R*≃12 Å, followed with an exponential tunneling falloff at longer distances. In this view, energy chains for electron transport are best designed by placing redox cofactors near the crossover distance R*. Protein flexibility and dynamics affect the magnitude of the maximum hopping rate within the crossover radius. Protein charge transport is not driven by universal parameters anymore and protein identity matters.

Open-cell metallic foams used as catalyst supports exhibit excellent transport properties. In this work, a unique application of metallic foam, as pelletized catalyst in a packed bed reactor, is examined. By using a wall-segment Computational Fluid Dynamics (CFD) setup, parametric analyses are carried out to investigate the influence of foam morphologies (cell size ϕ=0.45-3 mm and porosity ε=0.55-0.95) and intrinsic conductivity on flow and heat transport characteristics in a slender packed bed (N=Ddp=6.78) made of cylindrical metallic foam pellets. The transport processes have been modeled using an extended version of conventional particle-resolved CFD, i.e., flow and energy in inter-particle spaces are fully resolved, whereas porous-media model is used for the effective transport processes inside highly-porous foam pellets. Simulation inputs include the processing parameters relevant to Steam Methane Reforming (SMR), analyzed for low (Rep~100) and high (Rep~5000) flow regimes. The effect of foam morphologies on packed beds has shown that the desired requirements contradict each other, i.e., increase in cell size and porosity favor the reduction in pressure drop, however lowering the heat transfer efficiency. A design study is also conducted to find the optimum foam morphology of a cylindrical foam pellet at higher Rep~5000, which yields ϕ = 0.45, ε = 0.8. Suitable correlations to predict the friction factor and the overall heat transfer coefficient in a foam packed bed have been presented, which considers the effect of different foam morphologies over a range of particle Reynolds number, 100≤Rep≤5000.

Shewanella spp. are gram-negative bacteria that thrive in aquatic niches and also cause infectious diseases as opportunistic pathogens. Chromosomal integrons (CI) and mobile integrons (MI) were previously described in some Shewanella isolates. Here, we evaluated the occurrence of integrase genes, the integron systems and their genetic surroundings in the genus. We identified 22 integrase gene types, 17 of which were newly described, in different Shewanella species, evidencing multiple gain and loss events. Phylogenetic analysis showed that most of them were strain-specific, except for Shewanella algae, which seem to have co-evolve within the host as typical CIs. Noteworthy, co-existence of up to 5 different integrase genes, as well as their wide dissemination to Alteromonadales, Vibronales, Chromatiales, Oceanospirillales and Enterobacterales was observed. Identification of novel MIs suggests that lateral genetic transfer may have occurred resembling the behavior of class 1 integrons. The constant emergence of determinants associated to antimicrobial resistance worldwide, concomitantly with novel MIs in strains capable to harbor several types of integrons may be an alarming threat for the recruitment of novel antimicrobial resistance gene cassettes in the genus Shewanella, and its evolution towards the multidrug resistance.

Our health requires continual protein synthesis for maintaining and repairing tissues. For protein synthesis to function, all the essential (indispensable) amino acids (IAA) that must be available in the diet, along with those AAs that the cells can synthesize, the dispensable amino acids. Here we review studies that have shown the location of the detector for IAA deficiency in the brain, specifically for recognition of IAA deficient diets (IAAD diets) in the anterior piriform cortex (APC), with subsequent responses in downstream brain areas. The APC is highly excitable, uniquely suited to serve as an alarm for reductions in IAAs. With a balanced diet, these neurons are kept from over-excitation by GABAergic inhibitory neurons. Because several transporters and receptors on the GABAergic neurons have rapid turnover times, they rely on intact protein synthesis to function. When an IAA is missing, its unique tRNA cannot be charged. This activates the enzyme General Control Nonderepressible 2 (GCN2) that is important in the initiation phase of protein synthesis. Without the inhibitory control supplied by GABAergic neurons, excitation in the circuitry is free to signal an urgent alarm. Studies in rodents have shown rapid recognition of IAA deficiency by quick rejection of the IAAD diet.