Our daily activities hinge on the flexibility of our fingers, and a fractured finger can significantly disrupt these routines. The finger bones enable us to bend and fold the fingers and thumb to pick up or grasp objects and do all of our daily activities. A broken finger can cause adverse effects on our daily life activities. It is important to treat broken finger as soon as possible. Swift and precise treatment begins with capturing multiple X-rays, followed by the critical step of fracture detection in these images. Relying on the naked eye for this task carries the risk of overlooking small fractures. To address this issue an automated diagnoses of fractured fingers from images is required for which the current research employs advanced deep learning models—ResNet34, ResNet50, ResNet101, ResNet152, VGG-16, and VGG-19—to classify finger images from the Musculoskeletal Radiographs (MURA) dataset as either fractured or non-fractured. The results emphasize the consistently strong performance of ResNet models, attaining an impressive accuracy of 81.9%. This surpasses VGG models by 3.4% and establishes ResNet as the new benchmark for state-of-the-art accuracy.

Measuring magnetic fields in the inner corona, the interface between the solar chromosphere and outer corona, is of paramount importance if we aim at understanding the energetic transformations taking place there and which is at the origin of processes that lead to coronal heating, solar wind acceleration, and of most of the phenomena relevant to space weather. However, these measurements are more difficult than mere imaging because polarimetry requires differential photometry. The Coronal Magnetograph mission (CMAG) has been designed to map the vector magnetic field, line-of-sight velocities, and plane-of-the-sky velocities of the inner corona with unprecedented spatial and temporal resolutions from space. This will be achieved through full vector spectropolarimetric observations using a coronal magnetograph as the sole instrument on board a spacecraft, combined with an external occulter installed on another spacecraft. The two spacecraft will maintain a formation flight distance of 430 meters for coronagraphic observations, which requires a 2 m occulter disk radius. The mission will be preferentially located at the Lagrangian L5 point, offering a significant advantage for solar physics and space weather research. Existing ground-based instruments face limitations such as atmospheric turbulence, solar scattered light, and long integration times when performing coronal magnetic field measurements. CMAG overcomes these limitations by performing spectropolarimetric measurements from space with an external occulter and high-image stability maintained over time. It achieves the necessary sensitivity and offers a spatial resolution of 2.5 arcseconds and a temporal resolution of approximately one minute, in its nominal mode, covering the range from 1.02 solar radii to 2.5 radii. CMAG relies on proven European technologies and can be adapted to enhance any other solar mission, offering potential significant advancements in coronal physics and space weather modeling and monitoring.

Stemming from the object overlap and undertraining from the few samples, the road dense object detection is confronted with the poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects in the road has been proposed. Firstly, Darknet-53 network structure is adopted to obtain pre-trained YOLOv3 model, then the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles; in the proposed model, one random function is adapted to intialize and optimize the weights of the transfer training model, which is seperately designed from the pre-trained YOLOv3; and the object detection classifier replaces the fully connected layer, which further improves the detection effect, the reduced size of the network model can further reduce the training and detection time, and can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the traditional YOLOv2 by 3.05% and 11.15%, respectively. Besides, the test was carried out using UA-DETRAC, a public road vehicle detection dataset, the object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the speed is 4.3 Fps/s faster; the proposed YOLOv3 performs 79.38Bn of floating point operations per second in detecting video, which obviously surpasses the traditional YOLOv3 and the newer object detection algorithm YOLOv5.

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts the image analysis technology based on artificial intelligence, and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and can effectively deal with the problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.

There is only a very short reaction time for people to find the best way out of a building in a fire outbreak. Software applications can be used to assist the rapid evacuation of victims; however, this is an arduous task, which requires an understanding of advanced technologies. Since well-known pathway algorithms (such as, Dijkstra, Bellman-Ford and A*) can lead to serious problems over performance, we decided to make use of deep reinforcement learning techniques. A wide range of strategies including a random initialization of replay buffer and transfer learning were assessed in three projects involving schools of different sizes. The results showed the proposal was viable and that in most cases the performance of transfer learning was superior. In addition, the study raised challenges that had to be faced in the future.

The phycobilisomes (PBS) of cyanobacteria and red algae are the primary light-harvesting an-tennas, absorbing solar energy and transporting it to photosynthetic reaction centres with ex-traordinary efficiency and rate. The mechanism of energy transfer in PBS should be investigated in conjunction with biological structural information, as the functions of proteins result from structures. Here, we report the energy transfer study in PBS from a thermophilic cyanobacte-rium Thermosynechococcus vulcanus NIES 2134 (T. 2134), with the Cryo-EM model resolved at near-atomic-resolution recently. The time-resolved fluorescence spectroscopy of PBS with the sub-picosecond resolution was discovered at 77K. Deconvolution of the fluorescence decay curve was then used to reveal the energy transfer channels and the associated transfer rates. Expert for the fluorescence lifetimes of terminal emitters, four time-components, i.e., 9 ps, 13 ps, 23 ps, and 55 ps, were recognised in the energy transfer in PBS. The energy transfer dynamics in PBS were further analysed by combining the cryo-EM structure and the spectral property. The findings aid our understanding of the energy transfer mechanisms in PBS.

Somatic cell nuclear transfer (SCNT) is a reproductive biotechnology with great potential in the reproduction of different species of zootechnical interest, including sheep. This study aimed to carry out a bibliometric analysis of scientific papers published on the application of SCNT in sheep reproduction in the period 1997-2023. The search involved the Science Citation Index Expanded and Social Sciences Citation Index databases of the Main Collection of Web of Sciences with different descriptors. A total of 124 scientific papers were analyzed for different bibliometric indicators using VOSviewer software. Since 2001, the number of SCNT-related documents published concerning sheep reproduction increased and has fluctuated in ensuing years. The main authors, research groups, institutions, countries, documents and journals with the highest number of publications related to SCNT in sheep reproduction were identified, as well as the topics that address the research papers according to the terms: somatic cell, embryo, oocyte, gene expression, SCNT, and sheep.

We present the update of the Snow and Ice (SICE) property retrieval algorithm proposed initially by Kokhanovsky et al. (2019). The algorithm is based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The algorithm is available as python and Fortran packages at https://github.com/GEUS-SICE/pySICE. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra.

Sparse reward long horizon task is a major challenge for deep reinforcement learning algorithm. One of the key barriers is data-inefficiency. Even in the simulation environment, it usually takes weeks to training the agent. In this study, a data-efficiency training framework is proposed, where a curriculum learning is design for the agent in the simulation scenario. Different distributions of the initial state are set for the agent to get more informative reward during the whole training process. A fine-tuning of the parameters in the output layer of the neural network for value function is conduct to bridge the gap between sim-to-real. An experiment of UAV maneuver control is conducted in the proposed training framework to verify the method more efficient. We demonstrate that data-efficiency is different for the same data in different training stages.

Controlled deposition of CoCrFeNiMo0.2 high entropy alloy (HEA) micro-particles was achieved using laser induced forward transfer (LIFT). Ultra-short laser pulses, 230 fs of 515 nm wavelength, were tightly focused into ∼ 2.4 μm focal spots on the ∼50 nm thick plasma-sputtered films of CoCrFeNiMo0.2. The HTA films were transferred onto glass substrates by magnetron sputtering in vacuum (10−8 atm) from the thermal spray coated substrates. The absorption coefficient of CoCrFeNiMo0.2 α ≈ 6 × 105 cm−1 was determined at 600 nm wavelength. The real and imaginary parts of refractive index (n + iκ) of HEA were determined from reflectance and transmittance using nano-films.

Automatic ship detection provides an essential function towards maritime domain awareness for security or economic monitoring purposes. This work presents an approach for training a deep learning ship detector in Sentinel-2 multispectral images with few labeled examples. We design a network architecture for detecting ships with a backbone that can be pre-trained separately. By using Self Supervised Learning, an emerging unsupervised training procedure, we learn good features on Sentinel-2 images, without requiring labeling, to initialize our network’s backbone. The full network is then fine-tuned to learn to detect ships in challenging settings. We evaluated this approach versus pre-training on ImageNet and versus a classical image processing pipeline. We examined the impact of variations in the self-supervised learning step and we show that in the few-shot learning setting self-supervised pre-training achieves better results than ImageNet pre-training. When enough training data is available, our self-supervised approach is as good as ImageNet pre-training. We conclude that a better design of the self-supervised task and bigger non-annotated dataset sizes can lead to surpassing ImageNet pre-training performance without any annotation costs.

Three-dimensional transfer functions (3D TFs) are generally assumed to fully describe the transfer behavior of optical topography measuring instruments such as coherence scanning interferometers in the spatial frequency domain. Therefore, 3D TFs are supposed to be independent of the surface under investigation resulting in a clear separation of surface properties and transfer characteristics. In this paper we show that the 3D TF of an interference microscope differs depending on whether the object is specularly reflecting or consists of point scatterers. In addition to the 3D TF of a point scatterer, we will derive an analytical expression for the 3D TF corresponding to specular surfaces and demonstrate this as being most relevant in practical applications of coherence scanning interferometry (CSI). We additionally study the effects of temporal coherence and disclose, that in conventional CSI temporal coherence effects dominate. However, narrowband light sources are advantageous if high spatial frequency components of weak phase objects are to be resolved, whereas for low-frequency phase objects of higher amplitude the temporal coherence is less affecting. Finally, we present an approach that explains the different transfer characteristics of coherence peak and phase detection in CSI signal analysis.

Pantoea stewartii subsp. indologenes (Psi) is a causative agent of leafspot of foxtail millet and pearl millet; however, novel strains were recently identified that are pathogenic on onion. Our recent host range evaluation study identified two pathovars; P. stewartii subsp. indologenes pv. cepacicola pv. nov. and P. stewartii subsp. indologenes pv. setariae pv. nov. that are pathogenic on onion and millets or on millets only, respectively. In the current study we developed a pan-genome using the whole genome sequencing of newly identified/classified Psi strains from both pathovars [pv. cepacicola (n= 4) and pv. setariae (n=13)]. The full spectrum of the pan-genome contained 7,030 genes. Among these, 3,546 (present in genomes of all 17 strains) were the core genes that were a subset of 3,682 soft-core genes (present in ≥16 strains). The accessory genome included 1,308 shell genes and 2,040 cloud genes (present in ≤ 2 strains). The pan-genome showed a clear liner progression with >6,000 genes, suggesting the pan-genome of Psi is open. Comparative phylogenetic analysis showed differences in phylogenetic clustering of Pantoea spp. using PAVs/wgMLST approach in comparison to core genome SNP-based phylogeny. Further, we conducted a horizontal gene transfer (HGT) study including four other Pantoea species namely, P. stewartii subsp. stewartii LMG 2715T, P. ananatis LMG 2665T, P. agglomerans LMG L15, and P. allii LMG 24248T. A total of 317 HGT events among four Pantoea species were identified with most gene transfers observed between Psi pv. cepacicola and Psi pv. setariae. Pan-GWAS analysis predicted a total of 154 genes including seven cluster of genes associated with the pathogenicity phenotype on onion. One of the clusters contain 11 genes with known functions and are found to be chromosomally located.

Meta-analysis has increasingly been used to synthesize the ecosystem services literature, with some testing of the use of such analyses to transfer benefits. These are typically based on local primary studies. However, meta-analyses associated with ecosystem services are a potentially powerful tool for transferring benefits, especially for environmental assets for which no primary studies are available. In this study we use the Ecosystem Service Valuation Database (ESVD), which brings together 1350 value estimates from more than 320 studies around the world, to estimate meta-regression functions for provisioning, regulating & maintenance and cultural ecosystem services across 12 biomes. We tested the reliability of these meta-regression functions and found that even using variables with high explanatory power, transfer errors could still be large. We show that meta-analytic transfer performs better than simple value transfer and, in addition, that local meta-analytical transfer (i.e. based on local explanatory variable values) provides more reliable estimates than global meta-analytical transfer (i.e. based on mean global explanatory variable values). Thus, we conclude that when taking into account the characteristics of the study area under analysis, including explanatory variables such as income, population density and protection status, we can determine the value of ecosystem services with greater accuracy.

Melanoma is considered the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in prognosticating this type of cancer. With the emergence of new therapeutic strategies for metastatic melanoma that have shown improvement in patient survival, we developed a transfer learning-based biomarker discovery model that could help in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results reveal that the genes we found show consistency with other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set, and our methods found novel biomarker genes as well. Our ensemble model achieved Area Under the Receiver Operating Characteristic (AUC) of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB). We also assessed the potential sources of bias for our model and confirmed some of them by the model's performance.

A series of chiral complexes of the form Ir(NHC)2(aa)(H)(X) (NHC = N-heterocyclic carbene, aa = chelated amino acid, X = halide) was synthesized by oxidative addition of -amino acids to iridium(I) bis-NHC compounds and screened for asymmetric transfer hydrogenation of ketones. Following optimization of the reaction conditions, NHC, and amino acid ligands, high enantioselectivity was achieved when employing the Ir(IMe)2(l-Pro)(H)(I) catalyst (IMe = 1,3-dimethylimidazol-2-ylidene), which asymmetrically reduces a range of acetophenone derivatives in up to 95% enantiomeric excess.

Building roofs are sources of unwanted heat for buildings situated in zones with a warm climate. Thus, reflective coatings have emerged as an alternative to reject a significant fraction of solar energy received by roofs. In this research, the thermal behavior of concrete slab-type roofs with traditional and solar reflective coatings was simulated using a computational tool. Weather data from four cities in Mexico with a warm climate were used as boundary conditions. This tool is an in-house code based on the Finite Volume Method developed by the author to perform building components simulations. The code was validated with experimental data from previous work. A series of comparative simulations were developed, taking a gray roof as a control case. The results showed that for the roof without thermal insulation (single roof), the solar reflective coatings reduced the exterior surface between 11 and 16∘C. Consequently, the single roofs’ daily heat gain was reduced by a factor ranging between 41 and 54%. On the other hand, for the insulated roof, the reflective coatings reduced the exterior surface temperature between 17 and 21∘C. At the same time, the daily heat gain of composite roofs was reduced between 37 and 56%.

Nanofluids are a novel class of heat transfer fluid that plays a vital role in industries. In mathematical investigations, these fluids are modeled in terms of traditional integer-order partial differential equations (PDEs). It is recognized that traditional PDEs cannot decode the complex behavior of physical flow parameters and memory effects. Therefore, this article intends to study the mixed convection heat transfer in nanofluid over an inclined vertical plate via fractional derivatives approach. The problem in hand is modeled in connection with Atangana-Baleanu fractional derivatives without singular and local kernel having strong memory. The human blood is considered as base fluid dispersing carbon nanotube (CNTs) (single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes(MWCNTs )) into it to form blood-CNTs nanofluid. The nanofluids are considered to flow in a saturated porous medium under the influence of an applied magnetic field. The exact analytical expressions for velocity and temperature profiles are acquired using the Laplace transform technique and plotted in various graphs. The empirical results indicate that the memory effect decreases with increasing fractional parameters in the case of both temperature and velocity profiles. Moreover, the temperature profile is higher for blood-SWCNTs by reason of higher thermal conductivity whereas, this trend is opposite in case of velocity profile due densities difference.

This paper emphasises on the analogous modelling of hardwood (Acacia) pyrolysis. The effect of physical characteristics of hardwood chips on the pyrolysis is examined through the conservation of solid mass fraction of biomass. The chip size of G30 and G50 are used in the pyrolysis reactor. In the analogous situation, the fixed bed is assumed to be a wooden slab with a porosity equivalent to the voidage of bed. The bulk density, the length of the bed and the porosity are some of the physical attributes of a fixed bed used to determine the variation of solid mass of the hardwood across the fixed bed. The four-temperature sensors separated from each other by 80 mm are used to determine the temperature along the length of the pyrolysis unit. The heating element of 2 kWe is used to initiate the pyrolysis of biomass. The experiments are conducted in three different stages. The ONORM standard chips, G30 and G50, and the combination of them are separately pyrolysis to determine the validity of a model for different sizes of chips. The proposed model is also used to establish the relationship between the kinetics of pyrolysis and decomposition of the hardwood.

This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D CNNs, 3D CNNs can be applied directly on consecutive frames to extract spatio-temporal features. The aim of this work is to fuse the convolution layers from 2D and 3D CNNs to allow temporal encoding with fewer parameters than 3D CNNs. We adopt transfer learning from pre-trained 2D CNNs for spatial extraction, followed by temporal encoding, before connecting to 3D convolution layers at the top of the architecture. We construct our fusion architecture, semi-CNN, based on three popular models: VGG-16, ResNets and DenseNets, and compare the performance with their corresponding 3D models. Our empirical results evaluated on the action recognition dataset UCF-101 demonstrate that our fusion of 1D, 2D and 3D convolutions outperforms its 3D model of the same depth, with fewer parameters and reduces overfitting. Our semi-CNN architecture achieved an average of 16 – 30% boost in the top-1 accuracy when evaluated on an input video of 16 frames.

Relying on the USA, Canada and Mexico extract from the cross-national data sample on the environmental affection and cognition of adolescent students (Niankara, 2019), along with seemingly unrelated bivariate weighted ordered probit regression modeling (Niankara and Zoungrana, 2018), this study reports on the convergence of technological awareness and expectations within the context of international trade. We achieve this by adopting a regional perspective in investigating the effects of affective, cognitive and situational factors on youth's awareness and expectations about genetically modified organisms (GMOs) and nuclear power technology (NPT) within the North American free trade block. Identification of model parameters is achieved using maximum simulated likelihood methods. The findings show that although it has been over 20 years as of 2015 that USA, Canada, and Mexico ratified the north American free trade agreement (NAFTA), the diffusion of technology and information within the trade block has not succeeded in homogenizing awareness and expectations about GMOs and Nuclear power technology, as observed in the youth population across the three countries. Indeed, with regards to technological awareness, compared to youth from the USA, those from Canada show 15% (GMOs) and 7.1% (NPT) more awareness respectively; while those in Mexico are respectively 34.4% and 19.5% less aware about GMOs and NPT. With respect to technological expectations, compared to youth from the USA, those from Canada and Mexico are respectively 34.4% and 39.9% more optimistic about GMOs, while 15% and 49.7% more optimistic about NPT. Overall, youth within NAFTA country members are respectively 2.5% and 6.7% more optimistic about GMOs and NPT for every level increase in their awareness about the two technologies.

Antimicrobial resistance (AMR) is one of the greatest public health challenges we are currently facing. To develop effective interventions against this, it is essential to understand the processes behind the spread of AMR. These are partly dependent on the dynamics of horizontal transfer of resistance genes between bacteria, which can occur by conjugation (direct contact), transformation (uptake from the environment) or transduction (mediated by bacteriophages). Mathematical modelling is a powerful tool to investigate the dynamics of AMR, however its application to study the horizontal transfer of AMR genes is currently unclear. In this systematic review, we searched for mathematical modelling studies which focused on horizontal transfer of AMR genes. We compared their aims and methods using a list of predetermined criteria, and utilized our results to assess the current state of this research field. Of the 43 studies we identified, most focused on the transfer of single genes by conjugation in Escherichia coli in culture, and its impact on the bacterial evolutionary dynamics. Our findings highlight the existence of an important research gap on the dynamics of transformation and transduction, and the overall public health implications of horizontal transfer of AMR genes. To further develop this field and improve our ability to control AMR, it is essential that we clarify the structural complexity required to study the dynamics of horizontal gene transfer, which will require cooperation between microbiologists and modellers.

This paper predict and effectively control the temperature distribution of the steady-state and transient states of anisotropic four-layer composite materials online, knowing the density, specific heat, heat conductivity and thickness of the composite materials. Based on the transfer function, a mathematical model was established to study the dynamic characteristics of heat transfer of the composite materials. First of all, the Fourier heat transfer law was used to establish a one-dimensional Fourier heat conduction differential equation for each composite layer, and the Laplace transformation was carried out to obtain the system function. Then the approximate second-order transfer function of the system was obtained by Taylor expansion, and the Laplace inverse transformation was carried out to obtain the transfer function of the whole system in the time domain. Finally, the accuracy of the simplified analytical solutions of the first, second and third order approximate transfer functions was compared with computer simulation. The results showed that the second order approximate transfer functions can describe the dynamic process of heat transfer better than others. The research on the dynamic characteristics of heat transfer in the composite layer and the dynamic model of heat transfer in composite layer proposed in this paper have a reference value for practical engineering application. It can effectively predict the temperature distribution of composite layer material and reduce the cost of experimental measurement of heat transfer performance of materials.

Cancer is a leading cause of morbidity and mortality worldwide, yet much is still unknown about its mechanism of establishment and destruction. Recently, studies had shown that tumor cells cannot survive under the high temperature conditions. This treatment technique is called Hyperthermia. This report presents the case of radiative microwave heating of hyperthermia therapy on breast cancer in a porous medium. In this study, the steady state is solved analytically while unsteady state is solved using semi-implicit finite difference to get a more accurate prediction of blood temperature distributions within the breast tissues. A moderate temperature hyperthermia treatment is apply which results into cell death due to an increase in the level of cell sensitivity to radiation therapy and blood flow in tumor and oxygen. The results show that by applying metabolic heat generation rate of $3.97 X 10^{5}Wm^{-3}$, it takes upto 2 minutes for the tumor cells to get the require therapeutic temperature point.

Catalytic upgrading of bio-based platform molecules is one of promising approaches for biomass valorization. However, most solid catalysts are thermally and/or chemically unstable and difficult to prepare. In this study, a stable organic phosphonate-hafnium solid catalyst (PPOA-Hf) was synthesized, and acid-base bifunctional sites were found to play a cooperative role in the cascade transfer hydrogenation and cyclization of ethyl levulinate (EL) to γ-valerolactone (GVL). Under relatively mild reaction conditions of 160 ºC for 6 h, EL was completely converted to GVL in a good yield of 85%. The apparent activation energy was calculated to be 53 kJ/mol, which was lower than other solid catalysts for the same reaction. In addition, the PPOA-Hf solid catalyst did not significantly decrease its activity after five recycles, and no evident leaching of Hf was observed, indicating its high stability and potential practical application.

The polarised emission from AGN jets carries information about the physical conditions at the emitting plasma elements while its temporal evolution probes the physical processes that introduce variability and dynamically modify the local conditions. Here we present the analysis of multi-frequency radio linear and circular polarisation datasets with the aim to exactly quantify the conditions in blazar jets. Our analysis includes both the careful treatment of observational datasets and numerical modelling for the reproduction of synthetic polarisation curves that can be compared to the observed ones. In our approach the variability is attributed to traveling shocks. The emission from the cells of our jet model is computed with radiative transfer of all Stokes parameters. The model also accounts for Faraday effects which map the low-energy particle populations. We present two extreme cases in terms of the significance of Faraday conversion in the production of circular polarisation. As we show, in both regimes the model gives realistic reproduction of the observed emission.

GNSS have been widely used in navigation, positioning and timing. Nowadays, the multipath errors previously considered detrimental may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), e.g. GPS- Multipath Reflectometry (GPS-MR). In this paper, a new element describing bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model linking the GPS observables (SNR, phase and pseudorange) with detailed vegetation parameters, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that wheat will decrease the amplitudes of GPS multipath observables, if we increase the vegetation moisture contents or the scatters sizes (stem or leaf), the amplitudes of GPS multipath observables (SNR, phase and code) decrease. Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with published results for environmental parameter detections with GPS-MR.

Using Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) in their information version, this paper extends their theoretical framework to incorporate instantaneous Granger Causality (iGC)’s frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger Connectivity where interactions mediated without delay between time series can be easily detected.

African scientific research faces formidable challenges, particularly with limited access to state-of-the-art measurement instruments. The high cost associated with these devices presents a significant barrier for regional research laboratories, impeding their ability to conduct sophisticated experiments and gather precise data. This predicament not only hampers the individual laboratories but also has broader implications for the African scientific community and the advancement of knowledge in developing nations—the financial cost barrier considerably impacts the research quality of these laboratories. Reflection on technical and economical solutions needs to be quickly found to help these countries advance their research. In civil Engineering, the thermal conductivity property is the most important measurement for characterising heat transfer in construction materials. Existing devices (i.e., conductometers) in a laboratory are expensive (approximately 30,000 euros) and unavailable for some African laboratories. The study proposes a new and affordable device to evaluate thermal conductivity in construction materials. The method involves establishing a thermal flux between a heat source ( from the Joule effect provided by steel wool where a current is circulating) and a cold source (generated by ice cube) under steady-state conditions. The development of the cylindrical prototype is based on the comparative flux-meter method outlined in the measuring protocol of the ASTM E1225 standard document. Experiments were conducted on four distinct materials (polystyrene, wood, agglomerated wood, and rigid foam). The results indicate a correct correlation between the experimental values obtained from the newly developed prototype and the reference values found in the literature. For example, concerning the experimental polystyrene study, the detailed case analysis reveals a good correlation, with a deviation of only 4%. The percent error found falls within the acceptable range indicated by the standards recommendations of the ASTM E1225 standard, i.e., within 5% acceptable error.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

Monolayer graphene absorbs 2.3 percent of the incident visible light. This “small” absorption has been used to emphasize the visual transparency of graphene, but it in fact means that multilayer graphene absorbs a sizable fraction of incident light, which causes non-negligible fluorescence. In this paper, we formulate the light emission properties of multilayer graphene composed of tens to hundreds of layers using a transfer matrix method and confirm the method’s validity experimentally. We could quantitatively explain the measured contrasts of multilayer graphene on SiO2/Si substrates and found sizable corrections, which cannot be classified as incoherent light emissions, to the reflectance of visible light. The new component originates from coherent emission caused by absorption at each graphene layer. Multilayer graphene thus functions as a partial coherent light source of various wavelengths, and it may have surface-emitting laser applications.

In recent years, there has been a growing interest in the prediction of facial age due to its diverse applications in fields such as security, entertainment, and healthcare. The current scourge of underaged voting in Nigeria is a problem and this research delves into the realm of facial age prediction by employing four well-known convolutional neural network (CNN) architectures, namely VGG16, ResNet50, Mobile Net, and VGG19 to predict chronological ages from facial pictures of person. The objective is to achieve precise age estimation from facial images, utilizing two datasets: UTKFace and CASIA African Facial Datasets. The results of this investigation are noteworthy. Specifically, the VGG16 model which demonstrated remarkable performance, yielding a Mean Absolute Error (MAE) of 1.76 when applied to the UTK-Face dataset. Additionally, when utilizing Mobile-Net, an unprecedented MAE of 1.10 was achieved for the Casia Africa Face Dataset. Notably, this marks the first instance of employing the dataset for facial age detection with CNNs, and this approach outperformed previous works, yielding the lowest MAE among all the studies reviewed.

The present review critically examines the advancements in the past 5 years concerning the re-search on the bioavailability of the nanoplastics (NPLs) to freshwater plankton. With a specific emphasis on two bioavailability components: uptake availability and toxico-availability, we dis-cuss the recent progress in the understanding of the adsorption, absorption, trophic transfer and biological effects in phyto- and zooplankton induced by NPLs exposure. The influence of the plankton on the NPLs bioavailability via excretion of the biomolecules and formation of eco-corona is also examined. In spite of the important research developments, there are still con-siderable knowledge gaps with respect to NPLs biouptake and trophic transfer by plankton, as well as a potential adverse effect in natural aquatic systems. As plankton play a critical role in primary production, nutrient cycling and food web structure, understanding the interaction be-tween NPLs and plankton is essential in assessing the potential implications of the nanoplastics pollution for aquatic ecosystem biodiversity and services.

Mass transfer and phase transition have important effect on the velocity of bubble migration in deepwater wellbore, and accurate prediction of bubble migration velocity is crucial for calculating the safe shut-in period of deepwater oil and gas well. Therefore, the effect of bubble dissolution mass transfer and hydrate phase transition on bubble migration behavior in deepwater environment have attracted extensive attention from researchers in the fields of energy, marine chemistry, and marine engineering safety. In this work, a new model of bubble migration velocity in deepwater is developed, which considers the effect of hydrate phase transition and gas-water bidirectional cross-shell mass transfer during bubble migration. Based on the observation data of bubble migration in deepwater, the reliability of the model in predicting bubble migration velocity is verified. Then, the model is used to calculate and analyze the bubble migration velocity and bubble migration cycle under different initial bubble size, different annular fluid viscosity and density. The results show that the initial size of bubble and the viscosity of annulus fluid are the main factors affecting the migration velocity of bubble, but the density of annulus fluid has little effect on the migration velocity of hydrated bubble and clean bubble. In addition, the migration velocity of clean bubble gradually increases during the migration process from the bottom to the wellhead, while the migration velocity of hydrated bubble is divided into gradually decreasing stage and slowly increasing stage. The gas consumption and the thickening of hydrate shell in the gradually decreasing stage play a dominant role, and the increase of bubble volume caused by the decrease of pressure in the slowly increasing stage is the most important factor. The formation of hydrated bubble can significantly reduce the migration velocity of bubble and effectively prolong the safe shut-in period. This study provides a reference for quantitative description and characterization of complex bubble migration behavior with phase change and mass transfer in deepwater environment.

Pool boiling is extensively utilized in various fields, such as electronics cooling, battery cooling, nuclear power plants, and chemical processing. In the process of temperature control through boiling, the generation and transportation of bubbles play a crucial role in calculating the heat transfer capacity of the system. Therefore, it holds immense significance to gain a profound understanding of the mechanisms underlying bubble formation, growth, and detachment. Currently, numerous mechanistic explanations and empirical correlations have been proposed to elucidate various parameters of bubbles during the boiling process. These findings were considered valuable references in selecting appropriate boiling media and designing efficient heating surfaces. To comprehensively present the progress of bubble formation and heat transfer during the boiling process, the forces exerted on the bubbles was highlighted in this article. And a meticulous review on bubble force analysis and correlation formulas pertaining to various relevant parameters (e.g. nucleation sites density, bubble growth rate, detachment diameter, detachment frequency) were conducted. This review article also were expected to provide a novel foundation for further exploration of enhanced boiling heat transfer.

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.

According to circular economy principles, the recycling and reuse of waste tyre rubber is one of the most advanced and ecological waste disposal technologies. Each year about 19 million tons of tyres are produced, and this amount is increasing each year. One of the most innovative ways to recycle rubber waste is devulcanization. There are many methods of rubber devulcanization, but the most popular are grinding and chemical. In this article devulcanized rubber granules were used for the preparation of rubber samples. Two of them were obtained by the grinding method and one by chemical devulcanization. 15 different rubber samples were produced for the experimental measurements. Multilayer constructions with two solid layers of plasterboard on both sides (GKB and GKFI) and porous acoustic material of rubber sample inside were produced. Measurements were made in an impedance tube and compared with the results of TMM analysis. The same trends of resonant frequencies were determined. According to the results, the resonant frequencies depended on the thickness of the material, since transmission loss values depended on the mass of construction. According to the test results of transmission loss, constructions with 50mm thick rubber samples had on average 3dB better results than the structures with 25mm samples and 5dB better results than structures with 12 mm thick rubber samples. In addition, it was found that higher density plasterboards (GKFI) increased the overall transmission loss value of the structure by 5 dB. The same trends were determined by the TMM method. The test results showed that multi-layered constructions with devulcanized waste rubber had high transmission loss result and could be used for sound insulating structures.

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.

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.

We have presented a controllable and human-readable polynomial neural network (CR-PNN) that is the first human-readable neural network. One can imagine its influence on system identification. Subsequently, we developed a relation spectrum in a medical application, which is likely to stand alongside the Fourier spectrum. However, the system analysis methodology is incomplete in contrast to signal processing methodology. Here, we presented the system filters for the first time. In this paper, we used the simulation system to verify the availability of the system analysis methodology. The system analysis methodology showed great properties in system identification and filter. The contribution of this paper is the system analysis methodology: transform method (CR-PNN), relation spectrum, and system filter design.

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.

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.

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.

In the broad context of vulnerability detection, deep learning has achieved considerable progress but faces generalization challenges in multilingual environments . We introduce a novel approach named AST-FMVD, which leverages transfer learning and abstract syntax trees. By employing semantic similarity clustering and context-aware technology, the method constructs node mapping relationships between different languages, enabling zero-shot learning in vulnerability detection. The method was validated by applying Java's vulnerability detection model in the Python domain, successfully demonstrating that AST-FMVD retains the original model's detection capabilities in the target domain. In conclusion, the proposed method offers a promising solution to the inherent problems in multi-language vulnerability detection, signifying a potential leap in the application of deep learning, transfer learning, and abstract syntax trees for improved cross-domain performance.

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.

This study aims to theoretically address the design and analysis of an efficient pressure sensor designed by using a polymer-based defective 1D annular photonic crystal (APC). The 1D APC comprises an alternate arrangement of Si and SiO2 in a cylindrical fashion, incorporating a central defect layer. The investigation of the reflectance characteristics of the proposed structure is conducted by separately considering the polystyrene (PS) and the polymethyl methacrylate (PMMA) polymer materials as the defect layer. The pressure-sensitive refractive index of the polymers and the constituent materials of the APC plays a vital role in envisaging the pressure-sensing application. The cornerstone of this study is represented by the shift analysis in the wavelength of the defect mode inside the band gap using different applied pressures by employing the modified transfer matrix method (MTMM). Various geometrical parameters like the defect polymer layer’s thickness and the APC period were carefully optimized to achieve higher sensing performance. The proposed design demonstrated a remarkable pressure sensitivity and FoM of 51.29 nm/GPa and 301.7 GPa-1, respectively, which is considerably high in the current research scenario. It is believed that the proposed structure can be an apt candidate as an innovative high-performance pressure sensor, and it can play a key role in photonic integrated circuits.

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.

Experimental test facilities are generally characterised using linear transfer functions to relate the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. Second and third order correction methods are becoming available but are limited to certain ranges of waves in their applicability. Artificial intelligence has been shown to be a suitable tool to find even highly nonlinear functional relationships. This paper reports on a numerical wavetank implemented using the OpenFOAM software package which is characterised using artificial intelligence. The aim of the research is to train neural networks to represent non-linear transfer functions mapping a desired surface-elevation time-trace at a probe to the wavemaker input required to create it. These first results already demonstrate the viability of the approach and the suitability of a single setup to find solutions over a wide range of sea states and wave characteristics.

The Unmanned Aerial Vehicles (UAVs), used in civilian applications such as emergency medical deliveries, precision agriculture, wireless communication provisioning, etc., face the challenge of limited flight time due to their reliance on the on-board battery. Therefore, developing efficient mechanisms for in-situ power transfer to recharge UAV batteries hold potential in extending their mission time. In this paper, we study the use of far-field wireless power transfer (WPT) technique from specialized, transmitter UAVs (tUAVs) carrying Multiple Input Multiple Output (MIMO) antennas for transferring wireless power to receiver UAVs (rUAVs) in a mission. The tUAVs can fly and adjust their distance to the rUAVs to maximize energy transfer. The use of MIMO antennas further boost the energy reception by narrowing the energy beam toward the rUAVs. The complexity of their dynamic operating environment increases with the growing number of tUAVs, and rUAVs with varying levels of energy consumption and residual power. We propose an intelligent trajectory selection algorithm for the tUAVs based on a deep reinforcement learning model called Proximal Policy Optimization (PPO) to optimize the energy transfer gain. Simulation results demonstrate that with the use of PPO, the system achieves a tenfold flight time extension compared to no wireless recharging. Further, PPO outperforms the benchmark movement strategies of ’Traveling Salesman Problem’ and ’Low Battery First’ when used by the tUAVs.

This study mainly experimentally investigates and explores the effects of local low-frequency vibrations on the starting-up and heat transfer characteristics of the pulsating heat pipe. A micro motors with the vibration frequency of 200 Hz were imposed on the external surface of evaporation, condensation and adiabatic section of the pulsating heat pipe, respectively, and the starting-up temperature and the average temperatures along the evaporation section as well as the thermal performances of the vibrating heat pipe were experimentally scrutinized under the local vibrations of different positions. The following important conclusions can be achieved by the experimental study: 1) The effect of vibrations at the evaporation section and at the adiabatic section on the starting-up time of pulsating heat pipe is more significant than that at the condensation section. 2) The vibrations at different positions can reduce the starting-up temperature of the pulsating heat pipe. The effect of the vibrations at the evaporation section is the best as the heating power is lower, and the effect of the vibration at the adiabatic section is the best as the heating power is higher. 3) The vibrations at the evaporation section and at the adiabatic section can reduce the thermal resistance of the pulsating heat pipe. However, the vibrations at the condensation section have little effect on the thermal resistance of the pulsating heat pipe. 4) The vibrations at the evaporation section and at the adiabatic section can effectively reduce the temperature of evaporation section of the pulsating heat pipe, but the vibrations at the condensation section have no effect on the temperature of evaporation section of the pulsating heat pipe.

In this paper we compare using computational fluid dynamics the aerothermal performance of two candidate casing manifolds for supplying an impingement-actuated active tip clearance control system for an aero-engine high-pressure turbine. The two geometries are (a) single-entry: an annular manifold fed at one circumferential location; (b) multiple-entry: a casing manifold split into four annular sectors, each sector supplied separately from an annular ring main. Both the single-entry and multiple-entry systems analysed in this paper are idealised version of active clearance control systems in current production engines. Aerothermal performance is quantitatively assessed on the basis of the heat transfer coefficient distribution, driving temperature difference for heat transfer between the jet and casing wall, and total pressure loss within the high-pressure turbine active clearance control system. We predict that the mean heat transfer coefficient (defined with respect to the inlet temperature and local wall temperature) of the single-entry active clearance control system is 77% greater than the multiple-entry system; primarily because the coolant in the multiple-entry case picks up approximately 40 K of temperature from the ring main walls, and secondarily because the average jet Reynolds number of impingement holes in the single entry system is 1.2 times greater than in the multiple entry system. The multiple-entry system exhibits many local hot and cold spots, depending on the position of the transfer boxes, while the single-entry case has a more predictable aerothermal field across the system. The multiple-entry feed system uses an average of 20% of the total available pressure drop, while the feed system for the single-entry geometry uses only 2% of the total available pressure drop. From the aerothermal results of this computational study, and in consideration of holistic aero-engine design factors, we conclude that a single-entry system is closer to an optimal solution than a multiple-entry system.

Background: It is unknown whether prolonged artificial hormonal environment during early fetal development affects the birthweight of singletons born after frozen-thawed embryo transfer (FET). Methods: A retrospective observational study included singleton births>22 weeks of gestation obtained after FET between 2013-2019 after endometrial preparation with ovulatory cycle (OC) or artificial cycle (AC). Our primary objective was to compare birthweight of singletons after FET between endometrial preparation by OC or AC. Secondary objectives included prolonged pregnancies, high birthweight, low birthweight, SGA and LGA rates. Multivariate analyses were performed considering potential confounding factors. Results: Among 198 singleton live births after FET, 112 were obtained with OC and 86 with AC. Prolonged pregnancies rate was higher in AC (25.6% vs. 7.1%, respectively, p=0.001). Mean birthweight was higher (+219g) in AC (3386g vs. 3167g, p=0.01; adjusted-p=0.052), as well as the rate of babies exceeding 4000g (16.3% vs. 2.7%, p=0.03, adjusted-p=0.015). The rate of babies <2500g was lower in AC (3.5% vs. 11.6%, respectively, p=0.050, adjusted-p=0.049). Conclusions: Since OC does not strain the chances of pregnancy and in the incomplete knowledge of the consequences of neonatal overweight on the future health of children, OC preparation could be advocated as first-line endometrial preparation in FET.

The aim of this paper is to contribute to the body of knowledge about Project-Based Learning (PBL) methodology in higher education by describing and analysing interrelations between competencies, and their contribution to knowledge transfer (KT) and students’ rating of the project. The sample consisted of 464 students from the Universities of Huelva (N=347; 74.8%) and Murcia (N= 117; 25.2%), enrolled in the second year of a degree in either Infant or Primary Education. Data was collected through a self-administered questionnaire comprising a total of 53 items measuring General, Specific and Transversal competencies, as well as students’ rating of the project. Competencies were selected from the course programmes for the degrees in Infant and Primary Education. Preliminary results showed that competencies were moderately to highly acquired after PBL, and that students reported notable KT as well as a positive assessment of the project. KT showed a high degree of association with students’ ratings and was established as a key factor in learning and learner satisfaction in higher education.

The recent outbreak of a new Coronavirus has developed into a global pandemic with about 10,5 million reported cases and over 500,000 deaths worldwide. Our prospective paper reports an updated analysis of the impact that this pandemic had on the Italian agri-food sector during the national lockdown and discusses why and how this unprecedented economic crisis could be a turning point to deal with the overall sustainability of food and agricultural systems in the frame of the forthcoming European Green Deal. Its introductory part includes a wide-ranging examination of the first quarter of pandemic emergency, with a specific focus on the primary production, to be understood as agriculture (i.e. crops and livestock, and their food products), fisheries and forestry. The effect on the typical food and wine exports, and the local environment tourism segments is also taken into account in this analysis, because of their old and deep roots into the cultural and historical heritage of the country. The subsequent part of the paper is centered on strategic lines and research networks for an efficient socio-economic and territorial restart, and a faster transition to sustainability in the frame of a circular bio-economy. Particular emphasis is given to the urgent need of investments in research and development concerning agriculture, in terms of not only a fruitful penetration of the agro-tech for a next-generation agri-food era, but also a deeper attention to the natural and environmental resources, including forestry. As for the rest of Europe, Italy demands actions to expand knowledge and strengthen research applied to technology transfer for innovation activities aimed at providing solutions for a climate neutral and resilient society, in reference to primary production to ensure food security and nutrition quality. Our expectation is that science and culture return to play a central role in national society, as their main actors are capable of making a pivotal contribution to renew and restart the whole primary sector and agri-food industry, addressing also social and environmental issues, and so accelerating the transition to sustainability.