The United Nations Sustainable Development Goals call for the eradication of poverty, and China has proposed the Rural Revitalization Strategy based on the achievements of its poverty alleviation efforts in 2020. As a vital component of this strategy, the impact of land transfer on farmers' income growth has become a hot topic in both theoretical and practical sectors. This paper utilizes data from the CFPS database for the years 2010-2018 to construct OLS regression models and a Heckman two-stage model to analyze the effects of land transfers on the incomes of different types and sources among farmers. The empirical results show: First, land transfers do promote income growth among farmers, but the effect is primarily positive for those transferring out land, with negligible impact on those acquiring land. Second, the impact of land transfers varies between different types of income for transferees and transferors. Wage income contributes up to 88.26% to the income growth of transferors, a significantly higher rate than the decrease in business income; however, the increase in business income for transferees does not offset the decline in wage income, leading to no significant change in overall income. Further sensitivity analysis using the Heckman two-stage model confirms the robustness of these findings. The conclusions of this study provide theoretical and empirical evidence to optimize land transfer policies, enhance participation in land transfers, and ultimately achieve the mission of rural revitalization.

Retroviruses perpetuate their survival by incorporating a copy of their genome into the host cell, a critical step catalyzed by the virally encoded integrase. The viral capsid plays an important role during the viral life-cycle, including nuclear importation in case of lentiviruses and integration targeting events, hence, targeting the integrase and the viral capsid is a favorable therapeutic strategy. While integrase strand transfer inhibitors (INSTIs) are recommended as first-line regimens given their high efficacy and tolerability, lenacapavir is the first capsid inhibitor and the newest addition to the HIV treatment arsenal. These inhibitors are however designed for treatment of HIV-1 infection, and their efficacy against HIV-2 remains widely understudied and inconclusive, supported only by few limited phenotypic susceptibility studies. We therefore carried out inhibition profiling of a panel of second-generation INSTIs and lenacapavir against HIV-2 in cell culture, utilizing pseudovirion inhibition profiling assays. Our results show that the tested INSTIs and lenacapavir exerted excellent efficacy against ROD-based HIV-2 integrase, and interestingly, raltegravir also showed modest antiviral activity against the SARS-CoV-2 Omicron variant in antiviral assays carried out in Vero E6 cells.

Simple Summary: The distribution of typical bacterial redox enzymes such as pyruvate-ferredoxin oxidoreductase (PFOR) in protozoa remains interestingly puzzling. Previous studies have demonstrated diverse cellular localizations of PFOR in some amitochondriate anaerobic protozoa. PFOR is of particular pharmacological importance because it catalyzes the reductive bio-activation of nitro-based prodrugs to cytotoxic radical metabolites. Metronidazole was developed primarily as an antiprotozoal agent against infections caused by Trichomonas vaginalis. However, its antimicrobial spectrum was subsequently expanded to cover anaerobic bacterial infections. It has been shown that mutations in the genes encoding PFOR result in inherent resistance of PFOR-possessing anaerobic protozoa and bacteria to nitro-based prodrugs. Deciphering the evolutionary history of PFOR is crucial in deepening our understanding of the evolution of anaerobic pathogens and unfold new approaches for drug discovery and targeting in pathogen chemotherapy. Abstract: The present frontrunners in the chemotherapy of infections caused by protozoa are nitro-based prodrugs that are selectively activated by PFOR-mediated redox reactions. This study seeks to analyze the distribution of PFOR in selected protozoa and bacteria by applying comparative bioinformatics approaches to test the hypothesis that PFOR was most likely acquired through horizontal gene transfer (HGT) from bacteria. Furthermore, to identify other genes that were putatively acquired using proteome-wide and gene expression analyses. A plausible explanation for the patchy occurrence of PFOR in protozoa is based on the hypothesis that bacteria are potential sources of genes that enhance optimal adaptation of protozoa in hostile environments. The exclusively expressed proteins obtained from Entamoeba histolytica and the putative gene donor, Desulfovibrio vulgaris, showed an over-expression of eleven genes involved in intermediary metabolism. If these results can be reproduced in other PFOR-possessing protozoa, it would provide a more validated evidence to support the horizontal transfer of pfor from bacteria.

The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumor from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making processes, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging state-of-the-art DL frameworks with transfer learning, we conduct a comprehensive evaluation encompassing both classification accuracy and interpretability. Using state-of-the-art DL frameworks with transfer learning, we conduct a thorough evaluation covering both classification accuracy and interpretability. We employ adaptive path-based techniques to understand the underlying decision-making mechanisms of these models. Grad-CAM and Grad-CAM++ highlight critical image regions where the tumors are located.

The effects of anisotropic properties (wettability and roughness) on microgroove surfaces in pool boiling heat transfer using Novec-7100 as a working fluid were experimentally investigated. The idea of introducing the concept of anisotropic wettability in boiling experiments draws inspiration from biphilic surfaces. The investigation is also motivated by two-phase immersion cooling, which involves phase-change heat transfer by using dielectric liquid as a working fluid. Very few studies have focused on the effects of surfaces with anisotropic properties on boiling performance. Thus, this study aims to examine the pool boiling heat transfer performance on surfaces with microgroove-induced anisotropic properties under the saturation condition. A femtosecond laser–texturing method was employed to create microgroove surfaces with different groove spacing. The results indicated that anisotropic properties affected the heat transfer coefficient and critical heat flux. Relative to the plain surface, microgroove surfaces enhanced the heat transfer performance due to the increased number of bubble nucleation sites and higher bubble detachment frequency. An analysis of bubble dynamics under different surface conditions was conducted with the assistance of high-speed images. The microgroove surface with groove spacing of 100 μm maximally increased the BHTC by 37% compared to the plain surface. Finally, the CHF results derived from experiments were compared with related empirical correlations. Good agreement was achieved between the results and the prediction correlation.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning to address this issue. This approach utilizes multiple source domain models for collaborative fine-tuning, thereby improving the model's feature extraction capability in the target task. Specifically, this approach employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models. The objective is to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of the proposed the adaptive multi-source domain collaborative fine-tuning framework, it is analyzed and compared with the most widely used fine-tuning methods by applying them to seven publicly available visual categorization datasets commonly employed in transfer learning. Experimental results demonstrate that, in comparison with the existing fine-tuning approaches, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, resulting in state-of-the-art performance.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

In this study, we investigate the structural changes, electronic properties, and charge redistribution within azo-bithiophene (Azo-BT) chemisorbed monolayers under different light stimuli using density functional theory and molecular dynamics simulations. We consider two types of switches, Azo-BT and BT-Azo, with different arrangements of the Azo and BT blocks counting from the anchor thiol group. The chemisorbed monolayers of pure cis- and trans-isomers with a surface concentration of approximately 2.7 molecules per nm2 are modeled on a gold surface using classical all-atom molecular dynamics. Our results reveal significant shrinkage of the BT-Azo layer under UV illumination, whereas the thicknesses of the Azo-BT layer remain comparable for both isomers. This difference in behavior is attributed to the ordering of the trans-molecules in the layers, which is more pronounced for Azo-BT, leading to a narrow distribution of the inclination angle to the gold surface. Conversely, both layers consisting of cis-switches exhibit disorder, resulting in similar brush heights. To study charge transfer within the immobilized layers, we analyze each snapshot of the layer and calculate mean charge transfer integrals using Nelsen’s algorithm for a number of interacting neighboring molecules. Combining these integrals with reorganization energies defined for the isolated molecules, we evaluate the charge transfer rates and mobilities for electron and hole hopping within the layers at room temperature based on Marcus’ theory. This research offers new perspectives for the innovative design of electrode surface modifications and provides insights into controlling charge transfer within immobilized layers using light triggers. Additionally, we identify molecular properties that are enhanced through specific molecular design, which contributes to the development of more efficient molecular switches for various electronic applications.

The variation of mutual inductance and load parameters will affect the transmission power and efficiency of the inductive power transfer (IPT) system. The identification of mutual inductance and load parameters is an essential part of establishing a stable and reliable IPT system. This paper presents a joint identification method of load and mutual inductance for LCC-S IPT system, which does not require the establishment of primary and secondary communication and related control. Firstly, the resistance-inductance characteristics of the equivalent load of the rectifier are analyzed by simulation, and then the rectifier and system load are equivalent to the circuit model of resistance and inductance in series. Secondly, the characteristics of the reflected impedance are analyzed, and the functional relationship between the transmitter impedance and the rectifier impedance is established by using the ratio of the real part to the imaginary part of the reflected impedance, which realizes the decoupling of the load and the mutual inductance. Thirdly, the functional relationship between the equivalent impedance of the rectifier and the load resistance of the system is obtained by data fitting. Then, the equations of the above two functional relationships are combined. By measuring the voltage of the parallel compensation capacitor at the transmitting side, the current of the transmitting coil and the phase difference between the two, the battery load can be solved first, and then the mutual inductance can be calculated, so that the high-precision identification of the load and mutual inductance can be realized. Finally, an experimental platform of LCC-S IPT system is built for experimental verification. The experimental results show that the maximum identification errors of mutual inductance and load are 5.20 % and 5.53 %, respectively, which proves that the proposed identification method can achieve high precision identification.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Understanding how industrial agglomeration affects agricultural green production efficiency is essential for green agricultural development. This study uses the super-efficient EBM-GML to measure and analyze the spatial and temporal evolution characteristics and core sources of dynamics of agricultural green production efficiency in China by using panel data from 30 Chinese provinces from 2006 to 2020. It also empirically investigates the relationships between industrial agglomeration, land transfer, and agricultural production efficiency. By using fixed, intermediary, and threshold effects models, the internal links between the industrial agglomeration, land transfer, and agricultural green production efficiency are examined. The findings indicate that the green production efficiency of Chinese agriculture exhibits the regional characteristics of being “high in the west and low in the east, high in the south and low in the north” in terms of space; in terms of time series, the overall trend is that of growing, with an average annual growth rate of 11.45%, and the growth primarily depends on the “single-track drive” of green technological progress. By promoting land transfer, industrial agglomeration can increase the agricultural green production efficiency and decomposition index. Land transfer has a double-threshold effect on the influence of industrial agglomeration on agricultural green production efficiency. As a result, this study suggests adhering to the idea of synergistic development, promoting agricultural green development, strengthening the development of industrial agglomerations, promoting the quality and efficiency of industry, improving land-transfer mechanisms, and placing a focus on resource efficiency improvements, as well as other policy recommendations.

Background: Primary lymphedema is a chronic condition caused by a developmental abnormality of the lymphatic system and its malfunction. Different surgical options such as physiologic and excisional procedures have been proposed. The aim of this study was to present a complete algorithm for the treatment of primary lower extremity lymphedema: Primary LYmphedema Multidisciplinary Approach (P-LYMA). Methods: Nineteen patients were treated according to the P-LYMA. Patients underwent pre- and post-operative complex decongestive therapy (CDT). Different physiologic and excisional procedures were performed independently or in combination. The primary outcome was the circumferential reduction rate (CRR). The Lymphedema Quality of Life Score (LeQOLiS), reduction in the number of episodes of cellulitis, length of stay, and complications were recorded. Results: The mean CRR calculated was 72.8±19.6% after twelve months. The episodes of cellulitis per year decreased from a mean of 1.9±0.8 preoperative to 0.4± 0.6 throughtout follow-up. Two patients experienced minor complications. Mean hospitalization time was 5 days. Patients’ quality of life (LeQOLi score) drastically improved from 70.4±11.6 to 24.2 ±13.9 at twelve months post-op. Conclusion: The P-LYMA algorithm allows to maximize surgical results and improve the quality-of life in patients who suffer from primary lymphedema. CDT is mandatory to optimize results.

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-channel object detection model based on Faster RCNN. Furthermore, we develop a user-friendly web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collection with UE5 virtual reality. The results of our tests also demonstrate that the utilization of personalized virtual dataset significantly enhances the performance of the convolutional neural network (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three essential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the efficiency of library seat management.

Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and it achieved high attention in recent industrial research. Our paper focuses on the problem of time series segmentation and presents the first in-depth research about transfer learning for deep-learning based time series segmentation on the example of industrial end-of-line pump testing. In particularly, we investigate if the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models in respect to accuracy and training speed. The benefit is most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative Transfer Learning were observed as well. Thus, the research emphasizes the potential, but also the challenges of industrial Transfer Learning.

Accurate and efficient determination of excited-state polarizabilities (α) is an open problem both experimentally and computationally. Following our previous work, [Phys. Chem. Chem. Phys. 2023, 25, 2131−2141], where one can employ simple ground-state density-based functions from the information-theoretic approach (ITA) to accurately and efficiently predict the macromolecular polarizabilities, we aim to predict the lowest excited-state polarizabilities in this work. The philosophy is to use density-based functions to depict the excited-state polarizabilities. As a proof-of-principle application, employing 2-(2′-hydroxyphenyl)benzimidazole and its derivatives as model systems, we have verified that either with the ground-state (S0) or excited-state (S1) densities as input, ITA quantities can be strongly correlated with the excited-state polarizabilities. When the transition densities are considered, both S0 and S1 polarizabilities are in good relationships with some ITA quantities. Furthermore, excitation and emission energies can be predicted based on multivariant linear regression equations of ITA quantities.

Coronavirus disease since December 2019 has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. However, this research field is at an initial stage since there is a limited number of large CXR repositories regarding COVID-19. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-Ray images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data as well, that was not used for training or validation, authenticating their performance, and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall, and Accuracy. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19.

The keyless systems have replaced the old fashion methods of inserting physical keys in the keyhole to, i.e., unlock the door, because they are inconvenient and easy to be exploited by the threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, Keyless systems are susceptible to being compromised by a thread actor who intercepts the transmitted signal and performs a reply attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system remotely. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.

Intercropping systems can flexibly use resources such as sunlight, heat, water, and nutrients in time and space, improve crop yield and land utilization rates, effectively reduce continuous cropping obstacles and the occurrence of diseases and insect pests, and control the growth of weeds. Thus, intercropping is a safe and efficient ecological planting mode. The legume–cereal intercropping system is the most common planting combination. Legume crops fix nitrogen from the atmosphere through their symbiotic nitrogen fixation abilities, and the fixed nitrogen can be transferred to and utilized by cereal crops in various ways. The symbiotic nitrogen fixation efficiency of legume crops was improved by reducing the inhibition of soil nitrogen on nitrogenase activity through competitive absorption of soil nitrogen. However, the effects of nitrogen transformation and distribution in intercropping systems, and microbial community structure characteristics on nitrogen transfer need to be further explored. In this review, ⅰ) we present the transformation and distribution of nitrogen in the legume–cereal intercropping system; ⅱ) we describe the soil microbial community characteristics in intercropping systems; and ⅲ) we discuss the advantages of using modern biological molecular techniques to study soil microorganisms. We conclude that intercropping can increase the diversity of soil microorganisms, and the interaction between different plants has an important impact on the diversity and composition of the bacterial and fungal communities. The extensive application of modern biological molecular techniques in soil microbial research and the great contribution of intercropping systems to sustainable agriculture are particularly emphasized in this review.

In the field of computer vision, large-scale image classification tasks are both important and highly challenging. With the ongoing advances in deep learning and optical character recognition (OCR) technologies, neural networks designed to perform large-scale classification play an essential role in facilitating OCR systems. In this study, we developed an automatic OCR system designed to identify up to 13,070 large-scale printed Chinese characters by using deep learning neural networks and fine-tuning techniques. The proposed framework comprises four components, including training dataset synthesis and background simulation, image preprocessing and data augmentation, the process of training the model, and transfer learning. The training data synthesis procedure is composed of a character font generation step and a background simulation process. Three background models are proposed to simulate the factors of the background noise and anti-counterfeiting patterns on ID cards. To expand the diversity of the synthesized training dataset, rotation and zooming data augmentation are applied. A massive dataset comprising more than 19.6 million images was thus created to accommodate the variations in the input images and improve the learning capacity of the CNN model. Subsequently, we modified the GoogLeNet neural architecture by replacing the FC layer with a global average pooling layer to avoid overfitting caused by a massive amount of training data. Consequently, the number of model parameters was reduced. Finally, we employed the transfer learning technique to further refine the CNN model using a small number of real data samples. Experimental results show that the overall recognition performance of the proposed approach is significantly better than that of prior methods and thus demonstrate the effectiveness of proposed framework, which exhibited a recognition accuracy as high as 99.39% on the constructed real ID card dataset.

The Mediterranean is one of the regions of the world where human-induced climate warming is expected to have large impacts on water and environmental resources. To predict shifts in the current climate system, more regional climate records, including seasonal-to-century scale variability spanning longer than the instrumental periods, are needed. To help fill this gap, we provide a reconstruction of autumn precipitation variations for the Central Pyrenees range since 1500 Common Era (CE) using the varved sediments of Lake Montcortès. To assess the suitability of the calcite sublayer width of the sediments of this lake as a proxy for precipitation anomalies, we performed an analysis and smoothing of the temporal structure of the width series, calibration of the new series with the available instrumental climate records, calculation of a transfer function and testing and comparison of the reconstructed series against available empirical data.The prediction model was statistically robust and showed that the climatic signal was captured in the calcite sublayers. The reconstruction provides the first estimations of regional autumn precipitation shifts in the Central Pyrenees at annual resolution, since 1500 CE. Pronounced interdecadal shifts in precipitation were noticeable; no increasing nor decreasing linear trends or periods of extreme precipitation events were identified. The reconstructed precipitation anomalies suggest a decrease in rainfall during the coldest phase within the coldest period of the Little Ice Age and also during the 20^th century, probably associated to current Global Warming. Correlations between autumn precipitation and the North Atlantic Oscillation, Western Mediterranean Oscillation and Southern Oscillation indices were weak to moderate. A potential relationship with the Atlantic Multidecadal Oscillation pattern is suggested. The reconstructed autumn precipitation trends are coherent with other palaeohydrological reconstructions in similar Mediterranean settings, and consistent at a regional level.

Introduction: Heat is a kinetic process whereby energy flows from between two systems; hot-to-cold objects. In oro-dental implantology, conductive heat transfer/(or thermal stress) is a complex physical phenomenon to analyze and consider in treatment planning. Hence, ample research has attempted to measure heat-production to avoid over-heating during bone-cutting and -drilling for titanium (Ti) implant-site preparation and insertion, thereby preventing/minimizing early (as well as delayed) implant-related complications and failure. Objective: Given the low bone-thermal conductivity whereby heat generated by osteotomies is not effectively dissipated and tends to remain within the surrounding tissue (peri-implant), increasing the possibility of thermal-injury; this work attempts to obtain an exact analytical solution of the heat equation under exponential thermal-stress, modeling transient heat transfer and temperature changes in Ti implants upon hot-liquid intake. Materials and Methods: We investigate the impact of the material, the location point along implant length, and the exposure time of the thermal load on temperature changes. Results: Despite its simplicity, the presented solution contains all the physics and reproduces the key features obtained in previous numerical analyses studies. To the best of knowledge, this is the first introduction of the intrinsic time, a “proper” time that characterizes the geometry of the dental implant, where we show, mathematically and graphically, how the interplay between “proper” time and exposure time influences temperature changes in Ti implants, under the suitable initial and boundary conditions. Conclusions: This work aspires to accurately complement the overall clinical diagnostic and treatment plan for enhanced bone-implant interface, implant stability and success rates, whether for immediate or delayed loading strategies.

The effect of thermal radiation on the two – dimensional, steady-state, conjugate heat transfer from a circular cylinder with an internal heat source in steady laminar crossflow is investigated in this work. P₀ (Rosseland) and P₁ approximations were used to model the radiative transfer. The mathematical model equations were solved numerically. Qualitatively, P₀ and P₁ approximations show the same effect of thermal radiation on conjugate heat transfer; the increase in the radiation – conduction parameter decreases the cylinder surface temperature and increases the heat transfer rate. Quantitatively, there are significant differences between the results provided by the two approximations.

The primary distribution systems are comprised of power lines delivering power to utility feeders from substations. The inspection and maintenance of damaged and broken power system insulators are of paramount importance for continuous power supply and public safety. hence, to identify any faults and defects in advance a periodic inspection of power line insulators and other components be ensured beforehand. To automate the process and reduce operational cost and risk Unmanned Aerial Vehicles (UAVs) are being extensively utilized. As they present a safer and efficient way to examine the power system insulators and their components without closing the power distribution system ensuring continuous supply to the end-users. To achieve these objectives in this work a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. Deep Laplacian pyramids based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low light images a low light image enhancement technique is used for the robust exposure correction of the training images. Using computer vision-based object detection techniques to identify faults and classify them according to classes they belong. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. To improve the faults detection several flight path strategies are proposed for efficient inspection. Such strategies overcome the shuttering effect of insulators along with providing a less complex, time, and energy-efficient approach for capturing video stream of the power system components. Performance of different object detection models is presented for selecting the suitable one for fine-tuning on the specific faulty insulator dataset. Our proposed approach gives a less complex and more efficient flight strategy along with better results. For defect detection, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust faults recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat Pakistan.

Electric heating and solid thermal storage system (EHSTSS) is widely used in district clean heating and the flexibility adjustment of combined heat and power (CHP) unit. It has been an effective way to absorb renewable energy. Aiming at the thermal design calculation and experimental verification of EHSTSS, the thermal calculation and the heat transfer characteristics of the EHSTSS are investigated in this paper. Firstly, a thermal calculation method for the EHSTSS is proposed in the paper. The calculation flow and calculation method for key parameters of heating system, heat storage system, heat exchange system and fan-circulating system in the EHSTSS are studied. Then, the instantaneous heat transfer characteristics of the thermal storage system (TSS) in the EHSTSS are analyzed, and the heat transfer process of ESS is simulated by FLUENT software. The uniform temperature distribution in the heat storage and release process of the TSS verifies the good heat transfer characteristics of the EHSTSS. Finally, EHSTSS test verification platform is built, and the historical operation data of the EHSTSS is analyzed. During the heating and release thermal process, the maximum temperature standard deviation of each temperature measurement point is 28.3℃ and 59℃respectively. The correctness of the thermal calculation of the EHSTSS is verified.

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data. Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn’t require human annotations, such as the wav2vec model. We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently. We compare performance with other popular methods on the IEMOCAP dataset, a well-benchmarked dataset among the Speech Emotion Recognition (SER) research community. Furthermore, we demonstrate that results can be greatly improved by combining acoustic and linguistic knowledge from transfer learning. We align acoustic pre-trained representations with semantic representations from the BERT model through an attention-based recurrent neural network. Performance improves significantly when combining both modalities and scales with the amount of data. When trained on the full IEMOCAP dataset, we reach a new state-of-the-art of 73.9% unweighted accuracy (UA).

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

Underwater wireless optical communications (UWOC) have attracted considerable attention in recent years as an alternative means for acoustic communication. However, optical path loss of light propagation from attenuation is large due to absorption and scattering in various water conditions. Identification of environmental effects especially tropical storms on underwater optical path loss is the key to the success of using optics for UWOC. Underwater inherent optical properties (IOPs) such as the beam attenuation coefficient for 470 nm light in the western North Pacific Ocean were measured from the U.S. Naval Oceanographic Office sea gliders with being deployed after Super Typhoon Guchol (7-20 June 2012)‘s passage during 25-30 June 2012 and no any typhoon passage during 9 January – 28 February 2014. The observed two sets (with and without super typhoon) of IOPs are taken as input into a recently developed Radiative Transfer Equation solver. The simulated normalized received powers for the two durations show large impact of typhoon passage on UWOC.

Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

A non-Newtonian fluid model is used to investigate the two-dimensional pulsatile blood flow through a tapered artery with a stenosis. The mixed convection effects of heat and mass transfer are also taken into account. By applying non-dimensionalization and radial coordinate transformation, we simplify the system in a tube. Under the finite difference scheme, numerical solutions are calculated for velocity, temperature concentration, resistance, impedance, wall shear stress and shearing stress at the stenosis throat. Finally, Quantitative analysis is carried out.

Blood perfusion quantification is important vital parameters in different diagnostic procedure, using infrared thermography imaging; it is reliable to use this technique as non-contact, non-invasive blood flow measurement method. Therefore, we developed a measurement protocol for blood flow over the arm's anterior surface. By using the superficial brachial and radial veins to be monitored under the impact of cold-excitation of (2 °C to 5 °C), the blood perfusion signal was detected using thermal imager of long-wave infrared spectral range (LWIR, 7μm - 14 μm). The simulation of Penne's bioheat transfer equation was performed to be compared with results obtained from the infrared thermography. Furthermore, the proposed blood flow monitoring using external adjusting of the excitation temperature, by using (cold-compress, or cold air-stream) applied to the region under testing. The signal detected resembles to the hemodynamic pulse of the superficial veins, in the definition of systolic and diastolic phases of the cardiac cycle. Moreover, statistical analysis applied to the BFIRT signals from 24 subjects to estimate the skin's mean temperature after recovery from the thermal excitation.

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent's next action by introducing two new classes of games: ’invertible games’ and ‘cyclical games'. By explicitly expanding out the mutual information between past states and the next action we show under what circumstances these expressions can be simplified. These information measures are then applied to the Traveler's Dilemma game and the Prisoner's Dilemma game, the Prisoner's Dilemma being invertible, to illustrate their use. In the Prisoner's Dilemma a novel connection is made between the computational principles of logic gates and both the structure of games and the agents' decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: ‘Tit-for-Tat' and ’Win-Stay, Lose-Switch'.

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. Predictive modeling of such microbiota count data for classification of human infection from parasitic worms such as helminths can help in detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed. This feature of microbiome data produces the challenge of needing more observations for accurate predictive modeling and has been dealt with previously using different methods of feature reduction. To our knowledge, integrative methods such as transfer learning has not been explored in the microbiome domain yet, as a way to deal with data sparsity by incorporating knowledge of different but related datasets. One way of incorporating this knowledge is by using a meaningful mapping among features of these datasets. In this paper, we claim that this mapping would exist among members of each individual cluster grouped based on phylogenetic dependency among taxa and their association to the phenotype. We validate our claim by showing that models incorporating associations in such a grouped feature space result in no performance deterioration for the given classification task. In this paper, we test our hypothesis using classification models that detect helminth infection in microbiota of human fecal samples obtained from Indonesia and Liberia countries. In our experiments, we first learn binary classifiers for helminth infection detection by using Naive Bayes, Support Vector Machines, Multilayer Perceptrons, and Random Forest methods. In the next step, we add taxonomic modeling using the SMART-scan module to group the data, and learn classifiers using the same four methods, to test the validity of the achieved groupings. We observed a 6% to 23% and 7% to 26% performance improvement based on the Area Under ROC Curve (AUC) and Balanced Accuracy(Bacc) measures, respectively, over 10 runs of 10-fold cross-validation. These results show that using phylogenetic dependency for grouping our microbiota data actually results in a noticeable improvement in classification performance for helminth infection detection. These promising results from this feasibility study demonstrate that methods such as SMART-scan can be utilized in the future for knowledge transfer from different but related microbiome datasets by phylogenetically-related functional mapping, to enable novel integrative biomarker discovery.

Land Surface Temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This work analyses calibration strategies, considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis – Satellite Application Facility to calibrate its LST algorithms applied both for current and forthcoming sensors.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

With the popularity of solar energy in the electricity market, demand arises for data such as precise locations of solar panels for efficient energy planning, management, and distribution. However, this data is not easily accessible and in some cases, information such as precise locations does not exist. Furthermore, existing data sets for training semantic segmentation models of PV installations are limited, and their annotation is time-consuming and labor-intensive. Therefore, for additional remote sensing (RS) data creation, the pix2pix generative adversarial network (GAN) is utilized, enriching the original resampled training data of varying GSDs without compromising its integrity. Experiments done with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to find the optimal configuration for an accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and utilizing an optimal amount – 60% of generated RS imagery for additional training data, increasing model accuracy. The findings demonstrate the benefits of using GAN-generated images as additional training data, increasing the size of small data sets, and improving the capabilities of the segmentation model for solar panel detection in RS images.

The ability to control the temperature distribution T(t,r) and the rate of temperature change Rt,r inside glasses is important for their microstructuring. The lattice temperature is considered at time t, exceeding the electron-phonon thermalization time, and at a distance r from the center of the model spherical heating zone. In order to describe thermal excitations, the heat capacity of glasses must be considered as a function of time due to its long-term relaxation. A method for analytical calculation of T(t,r) and R(t,r) for glasses with dynamic heat capacity cdyn(t) is proposed. It is shown that during laser microstructuring the local cooling rate -R(t,r) significantly depends on the time dispersion of cdyn(t). It has been established that at the periphery of the model heating zone of the laser beam focus, the local cooling rate can reach more than 1011 K/s. Strong cooling rate gradients were found at the periphery of the heating zone, affecting the microstructure of the material. This effect is significantly enhanced by the time dispersion of cdyn(t). The effect associated with this time dispersion is significant even well above the glass transition temperature Tg, since even short relaxation times of the dynamic heat capacity cdyn(t) are significant.

Recently considered modifications to public policy and trends in technology transfer practices have underscored the need for validated instruments to assess the maturity of technologies. This paper details a study that aimed to develop and present a generalized technology readiness level (GTRL) scale that a variety of professionals and researchers could use to assess the maturity of technologies in a range of contexts. Of particular interest is developing and validating an instrument that technology transfer professionals in federal laboratories, universities, and the private sector in the United States of America can use to assess the maturity of technologies to facilitate their technology transfer efforts. The primary goal of the research study was to highlight the need for validated measurement instruments for assessing the maturity of technologies in these contexts, test methods for validating the GTRL scale and other readiness level scales, and fill the knowledge gap regarding methods for properly validating such instruments. This included producing insights to inform future efforts to validate the GTRL scale as well as efforts to develop and validate other instruments to measure technology maturity. The study provides specific recommendations relevant to determining the face validity, content validity, inter-rater reliability, and intra-rater reliability of the GTRL scale and other such instruments for assessing technology maturity.

To study the heat and mass transfer characteristics of an evaporative cooler for cooling high-temperature working fluids such as supercritical carbon dioxide, an experimental device of the evaporative cooler was built, and a corresponding test system was constructed. Then, the experimental study was carried out based on the orthogonal test method, and the law and extent of the influence of water flow in the tube, spray water flow rate, inlet water temperature in the tube, and air flow rate on heat dissipation, cooling efficiency, and heat and mass transfer coefficients have been obtained. The correlation equations of the heat and mass transfer coefficients were obtained by fitting the orthogonal experimental data, and the validity of the correlation equations was confirmed by the verification experiment.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

Antibiotic resistance has emerged as one of the major concerns to public human health due to issues in treatment and control of major infectious diseases. From discovery of penicillin in 1940, the antibiotic resistance originated and now developed the microorganisms as resistive strains to the major available antibiotics. Furthermore, studies on this captivating activity of the microbes provide insight into the complexities of microbial physiology and may offer some guidance in preventing the onset and consequent development of antibiotic resistance. Despite of initiatives taken in last few decades to overcome the issues, the trends of antibiotic resistance gained much peak. Antibiotic resistance appears to have emerged as a result of various factors including antibiotics being misused and overused in both the medical field and the agricultural sector. Important factors in the development of antibiotic resistance also include bacterial mutation, unplanned evolution, and horizontal gene transfers. In addition, antibiotics resistance imposed the financial consequences to public health and drug development research. Future advancements related to innovative antibiotics and molecular drug designing become challenge for researchers due to intensive multiple antibiotics resistance among human population. Many researches have been conducted on origin, evolutionary aspect of antibiotic resistance and mechanisms of antibiotics resistance, but its effect on future drug development is little understood. Therefore, the recent review will highlight the role of antibiotic resistance in drug designing and impacts of antibiotic resistance on drug development in future.

With metal oxide semiconductors being a promising candidate for accurate indoor air quality assessments, multiple drawbacks of the gas sensors prevent their widespread use. Examples include poor selectivity, instability over time, and sensor poisoning. Complex calibration methods and advanced operation modes can solve some of those drawbacks. However, this leads to long calibration times, which are unsuitable for mass production. In recent years, multiple attempts to solve calibration transfer have been made with the help of direct standardization, orthogonal signal correction, and many more methods. Besides those, a new promising approach is transfer learning from deep learning. This article will compare different calibration transfer methods, including direct standardization, piecewise direct standardization, transfer learning for deep learning models, and global model building. The machine learning methods to calibrate the initial models for calibration transfer are feature extraction, selection, and regression (established methods) and a custom convolutional neural network TCOCNN. It is shown that transfer learning can outperform the other calibration transfer methods regarding the root mean squared error, especially if the initial model is built with multiple sensors. It was possible to reduce the number of calibration samples by up to 99.3 % (from 10 days to approximately 2 hours) and still achieve an RMSE for acetone of around 18 ppb (15 ppb with extended individual calibration) if six different sensors were used for building the initial model. Furthermore, it was shown that the other calibration transfer methods (direct standardization and piecewise direct standardization) also work reasonably well for both machine learning approaches, primarily when multiple sensors are used for the initial model.

In this paper, four types of open service signals of BDS-3 satellite (B1I, B1C, B2a, and B3I) and B2I signals broadcasted by BDS-2 satellites were used in the time frequency transfer and positioning experiments with signal coexisting in BDS system, and the single frequency PPP (precise point positioning) method was used. Modified GRoup And PHase Ionospheric Correction (GRAPHIC) method was proposed and verified by the experiment. The results show that 18 selected stations can achieve decimeter-level positioning results with single frequency. Compared with the general observation method, the proposed modified GRAPHIC method improves the stability and precision of positioning and timing significantly, and the positioning accuracy of 5 frequency signals is increased 40.4%, 32.2%, 80.3%, 12.4%, and 10.3%, respectively. The frequency stability can reach the same accuracy as that of dual-frequency with BDS in the current state.

Nowadays, energy saving is considered a key issue worldwide, as it brings a variety of benefits: reducing greenhouse gas emissions, reducing demand for energy imports, and lowering costs on a household and economy-wide level. Researchers and building designers are looking to optimize building efficiency by means of new energy technologies. Changes in existing buildings can also be made to reduce energy consumption and costs. These may include the usage of new heat transfer fluids based on nanofluids. In this work, an extended experimental campaign (from February 2020 to March 2021) has been carried out on an educational building in the Campus of University of Salento, Lecce – Italy, in order to evaluate the increase in performance of the HVAC system, due to the usage of a nanofluid. The results show that, under optimal operating conditions, plant performance improves more than 10%, with average daily peaks of about 15%.

Abstract: Scavenging of electrogenerated superoxide radical anion (O2•−) by pyrogallol (PyH3) was investigated on the basis of cyclic voltammetry and in situ electrolytic electron spin resonance spectrum in N,N-dimethylformamide with the aid of density functional theory (DFT) calculations. Quasi-reversible dioxygen/O2•− redox coupe was modified by the presence of PyH3, suggesting that O2•− was scavenged by PyH3 through proton-coupled electron transfer (PCET) involving two proton transfer and one electron transfer. The DFT calculation suggested that the formation of hydrogen bond (HB) complex and the subsequent concerted-two-proton coupled electron transfer (2PCET) characterized by catechol moiety in PyH3 is plausible mechanism which embodies the superior kinetics of the O2•− scavenging by PyH3 as shown in the electrochemical results. Furthermore, it was clarified that three hydroxyl groups of PyH3 promote the formation of prereactive HB complex, in comparative analyses using related compounds, resulting the promotion of the O2•− scavenging.

Abstract: Elimination of superoxide radical anion (O2•−) by tocopherols (TOH), and related compounds was investigated on the basis of cyclic voltammetry and in situ electrolytic electron spin resonance spectral measurements in N,N-dimethylformamide (DMF) with the aid of density functional theory (DFT) calculations. Quasi-reversible O2/O2•− redox was modified by the presence of TOHs, suggesting that the electrogenerated O2•− was eliminated by α-, β-, γ-TOH through proton-coupled electron transfer (PCET), but not by δ-TOH. The structure–activity correlation of α-, β-, γ-, and δ-TOH characterized by methyl group on the 6-chromanol ring was experimentally confirmed, where the methyl group promotes the PCET mechanism. Furthermore, comparative analyses using some related chemical analogues suggested that methoxyl group of the 6-chromanol ring is required for a successful electron transfer (ET) to O2•− through the PCET. The electrochemical and DFT results in dehydrated DMF suggested that the PCET mechanism involves preceding proton transfer (PT) forming hydroperoxyl radical followed by a concerted PCET (ET–PT). The O2•− elimination by TOH proceeds efficiently along the net PCET mechanism involving one ET and two PTs.

The intramolecular ionic Diel-Alder (IIDA) reactions of two dieniminiums have been studied within the Molecular Electron Density Theory (MEDT) at the B97XD/6-311G(d,p) computational level. ELF topological analysis of dieniminiums shows that its electronic structure can been seen as the sum of those of butadiene and ethaniminium. The superelectrophilic character of dieniminiums accounts for the high intramolecular global electron density transfer taking place between the diene and iminium frameworks at the transition state structures (TSs) of these IIDA reactions. The activation enthalpy associated to the IIDA reaction of the experimental dieniminium, 8.7 kcal·mol-1, is closer to that of the ionic Diels-Alder (I-DA) reaction between butadiene and ethaniminium, 9.3 kcal·mol-1. However, the activation Gibbs free energy of the IIDA reaction is 12.7 kcal·mol-1 lower than that of the intermolecular I-DA reaction. The strong exergonic character of the IIDA reaction, higher than 17 kcal·mol-1, makes the reaction irreversible. These IIDA reactions present a total re/exo and si/endo diastereoselectivity, which is controlled by the most favourable chair conformation of the tetramethylene chain. Electron localization function (ELF) topological analysis of the single bond formation indicates that these IIDA reactions take place through a non-concerted two-stage one-step mechanism. Finally, ELF and atoms-in-molecules (AIM) topological analyses of the TS associated to inter and intramolecular processes show the great similitude among them.

Leaf mechanical wounding triggers a rapid, within minutes, release of a blend of volatile organic compounds (VOCs). Wounding-induced VOC blend is mainly composed of oxygenated ubiquitous stress volatiles such as methanol and volatile products of lipoxygenase (LOX) pathway (mainly C5 and C6 alcohols and aldehydes and their derivatives), but also includes multiple minor VOCs that collectively act as infochemicals inducing defences in non-damaged plant leaves, neighbouring plants and attracting herbivore enemies. Till present, interspecific variability of the rate of induction and magnitude of wounding-induced emissions, and the extent to which plant structural traits and physiological activity alter these emissions are poorly known. Particularly scarce is the information of the induced emissions in tropical agricultural plant species despite their economic importance and large area of cultivation at regional to global scales. We chose five tropical crops with varying photosynthetic activity and leaf structural characteristics: Abelmoschus esculentus, Amaranthus cruentus, Amaranthus hybridus, Solanum aethiopicum and Telfairia occidentalis to characterize the kinetics and magnitude of wounding-induced emissions, hypothesizing that the induced emission response is greater and faster in physiologically more active species with greater photosynthetic activity than in less active species. Rapid highly repeatable leaf wounds (12-mm cuts) were generated by a within-leaf-chamber cutting knife. Wounding-induced VOC emissions were measured continuously with a proton-transfer reaction time-of-flight mass spectrometer and gas-chromatography mass spectrometry was used to separate isomers. Twenty-three ion VOCs and twelve terpenoid molecule structures were identified, whereas ubiquitous stress volatiles methanol (on average 40% of total emissions), hexenal (24%), and acetaldehyde (11%) were the main compounds across the species. Emissions of low-weight oxygenated compounds (LOC, 70% of total), and LOX products (29%) were positively correlated across species, but minor VOC components, monoterpenoids and benzenoids were negatively correlated with LOC and LOX, indicating a reverse relationship between signal specificity and strength. There was a large interspecific variability in the rate of induction and emission magnitude, but the hypothesis of a stronger emission response in physiologically more active species was only partly supported. In addition, the overall emission levels were somewhat lower with different emission blend compared to the data reported for wild species, as well as different shares for the VOCs in the blend. The study demonstrates that wounding-dependent emissions from tropical agricultural crops can significantly contribute to atmospheric volatiles, and these emissions cannot be predicted based on current evidence of wild plant model systems.

The elimination of superoxide radical anions (O2•−) by 5-amino-2-hydroxybenzoic acid (mesalazine, 5-ASA), 4-amino-2-hydroxybenzoic acid (4-ASA), and related compounds used for ulcerative colitis treatment was investigated using cyclic voltammetry and electron spin resonance (ESR) analyses aided by density functional theory (DFT) calculations. Quasi-reversible O2/O2•− redox was found to be modified by the compounds, suggesting that an acid-base reaction in which a hydroperoxyl radical (HO2•) is formed from O2•− occurs. However, the deprotonated 5-ASA anion can eliminate O2•− through proton-coupled electron transfer (PCET), forming a radical product. This electron transfer (ET) was confirmed by ESR analysis. The 4-aminophenol moiety in 5-ASA plays an important role in the PCET involving two proton transfers and one ET based on -conjugation. The electrochemical and DFT results indicated that O2•− elimination by 5-ASA proceeds efficiently viathrough the PCET mechanism after deprotonation of the 1-carboxyl group. Thus, 5-ASA may act as an anti-inflammatory agent in the alkali intestine through PCET-based O2•− elimination.

Cellular therapy has emerged as an attractive option for the treatment of cancer, and adoptive transfer of chimeric antigen receptor (CAR) expressing T cells has gained FDA approval in hematologic malignancy. However, limited efficacy has been observed utilizing CAR-T therapy in solid tumors. Natural killer (NK) cells are crucial for tumor surveillance and exhibit potent killing capacity of aberrant cells in an antigen-independent manner. Adoptive transfer of unmodified allogeneic or autologous NK cells has shown limited clinical benefit due to factors including low cell number, low cytotoxicity and failure to migrate to tumor sites. To address these problems, immortalized and autologous NK cells have been genetically engineered to express high affinity receptors (CD16), CARs directed against surface proteins (PD-L1, CD19, Her2, etc.) and endogenous cytokines (IL-2 and IL-15) that are crucial for NK cell survival and cytotoxicity, with positive outcomes reported by several groups both preclinically and clinically. With a multitude of NK cell-based therapies currently in clinic trials, it is likely they will play a crucial role in next-generation cell therapy-based treatment. In this review we will highlight the recent advances and limitations of allogeneic, autologous and genetically enhanced NK cells used in adoptive cell therapy.

We propose a novel tensor-based formalism for inferring causal structures from time series. An information theoretical analysis of transfer entropy (TE), shows that TE results from transmission of information over a set of communication channels. Tensors are the mathematical equivalents of these multi-channel causal channels. A multi-channel causal channel is a generalization of a discrete memoryless channel (DMC). We consider a DMC as a single-channel causal channel. Investigation of a system comprising three variables shows that in our formalism, bivariate analysis suffices to differentiate between direct and indirect relations. For this to be true, we have to combine the output of multi-channel causal channels with the output of single-channel causal channels. We can understand this result when we consider the role of noise. Subsequent transmission of information over noisy channels can never result in less noisy transmission overall. This implies that a Data Processing Inequality (DPI) exists for transfer entropy.

Background: Ongoing molecular profiling studies enabled by advances in biomedical technologies are producing vast amounts of ‘omic’ data for early detection, monitoring, and prognosis of diverse diseases. A major common limitation is the scarcity of biological samples, necessitating integrative modeling frameworks that can make optimal use of available data for disease classification tasks. Related data sets are often available from different studies, but may have been generated using different technology platforms. Thus, there is a critical need for flexible modeling methods that can handle data from diverse sources to facilitate the discovery of robust biomarkers that underlie disease regulatory processes. Results: In this paper, we introduce a novel framework called Knowledge Augmented Rule Learning (KARL), which incorporates two sources of knowledge, domain, and data, for pattern discovery from small and high-dimensional datasets, such as transcriptomic data. We propose KARL as a transfer rule learning framework in which knowledge of the domain is transferred to the learning process on data in order to 1) improve the reliability of the discovered patterns, and 2) study the knowledge of the domain when used along with data for modeling. In this work, we generated KARL models on gene expression datasets for five types of cancer, including brain, breast, colon, lung, and prostate. As our knowledge of the domain, we used the Ingenuity Knowledge Base (IKB) to extract genes related to hallmarks of cancer and annotated these prior relationships before learning classifiers from these datasets. Conclusions: Our results show that KARL produces, on average, rule models that are more robust classifiers than the baseline without such background knowledge, for our tasks of cancer prediction using 25 publicly available gene expression datasets. Moreover, KARL helped us learn insights about previously known relationships in these gene expression datasets, along with new relationships not input as known, to enable informed biomarker discovery for cancer prediction tasks. KARL can be applied to modeling similar data from any other domain and classification task. Future work would involve extensions to KARL to handle hierarchical knowledge to derive more general hypotheses to drive biomedicine.

This paper presents a control algorithm for soft-switching series LC converters. The conventional voltage-to-voltage controller is split into a master and a slave controller. The master controller implements constant-current-constant-voltage (CCCV) control, required for demanding applications, i.e. lithium battery charging or laboratory power supplies. It defines the set-current for the open-loop current slave controller, which generates the PWM parameters. The power supply achieves fast large-signal responses, e.g. from 5 V to 24 V, where 95% of the target value is reached in less than 400 µs. The design is evaluated extensively in simulation and on a prototype. A consensus between simulation and measurement is achieved.

A growing number of studies focus on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing inter- and intra- subject variability of connectivity matrices as well as graph-theoretical measures in a large (n=1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected as opposed to directed methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra- subject variabilities in both directed and undirected connectomic measures.

In this paper, we revisit a modified version of the classical Whitehead's theory of gravity in which all possible bilinear forms are considered to define the corresponding metric. Although, this is a linear theory that fails to give accurate results for the most sophisticated predictions of general relativity, such as gravity waves, it can still provide a convenient framework to analyze some new phenomena in the Solar System. In particular, recent development in the accurate tracking of spacecraft and the ephemerides of planetary positions have revealed certain anomalies in relation with our standard paradigm for celestial mechanics. Among them the so-called flyby anomaly and the anomalous increase of the astronomical unit play a prominent role. In the first case the total energy of the spacecraft changes during the flyby and a secular variation of the semi-major axis of the planetary orbits is found in the second anomaly. For this to happen it seems that a net energy and angular momentum transfer is taken place among the orbiting and the central body. We evaluate the total transfer per revolution for a planet orbiting the Sun in order to predict the astronomical unit anomaly in the context of Whitehead's theory. This could lead to a more deeply founded hypothesis for an extended gravity model.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

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

At present, farmland abandonment（FA）is a severe phenomenon in China, imposing a serious restriction on agricultural production. In this context, it is of great significance to explore the logical relationship between high-standard farmland construction (HSFC) and FA to optimize land resource allocation and guarantee national food security. Based on 838 samples of farmers in the main rice production area of the Yangtze River Basin in China, this paper employs the Tobit model, the mediating effect model, and the moderated mediating effect model to analyze the impact of HSFC on FA at the micro level. The results show that (1) HSFC inhibits FA, and FA proportion decreases by 1.14% for every 1% increase in HSFC proportion, and the robustness test and endogeneity treatment also yield consistent conclusions. (2) Agricultural socialization services (ASS) play a positive mediating role in the influence path. HSFC promotes farmers' purchase of ASS, which in turn inhibits FA. (3) The agricultural labor transfer distance (ALTD) plays a positive moderating role in the relationship between HSFC and FA. The farther the distance, the more likely HSFC can promote farmers' purchase of ASS and inhibit FA. The results provide enlightenment on how to precisely implement HSFC policy, that is, to inhibit FA by improving the construction of high-standard farmland and the post-construction management as well as protection system. Moreover, this study helps guiding the development of ASS and optimizing the institutional environment for agricultural labor transfer, creating conditions for agricultural scale management.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Compliant amplifying mechanisms are used widely in high-precision instruments driven by piezoelectric actuators, and the dynamic and static characteristics of these mechanisms are closely related to instrument performance. Although the majority of existing research has focused on analysis of their static characteristics, the dynamic characteristics of the mechanisms affect their response speeds directly. Therefore, this paper proposes a comprehensive theoretical model of compliant amplifying mechanisms based on the multi-body system transfer matrix method to analyze the dynamic and static characteristics of these mechanisms. The effects of the main amplifying mechanism parameters on the displacement amplification ratio and the resonance frequency are analyzed comprehensively using the control variable method. An iterative optimization algorithm is also used to obtain specific parameters that meet the design requirements. Finally, simulation analyses and experimental verification tests are performed. The results indicate the feasibility of using the proposed theoretical compliant amplifying mechanism model to describe the mechanism’s dynamic and static characteristics, which represents a significant contribution to the design and optimization of compliant amplifying mechanisms.

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

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

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

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

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

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

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

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

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

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

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