Our understanding of the diversity of life on our planet and the possibility of finding or sustaining life elsewhere in the universe plays a central role in supporting human space settling and exploration. Astrobiology and its outcomes require a multidisciplinary and comprehensive approach, in which the microbial, geological, chemical, astronomical, and physical domains of research are interlinked. An example of the applications of astrobiology and space microbiology is the use of extremophiles for in-situ resource utilization through biomining and bioleaching. To better understand the multidisciplinary research landscape in this area, we have quantitatively reviewed the global scientific literature on astrobiology with a focus on biomining and bioleaching through bibliometric network analysis, investigating patterns and trends in its development over time. The network analysis of the keywords co-occurrence highlights different connecting and overlapping clusters, illustrating the multidisciplinary character of astrobiology. Temporal analyses show a recent focus on topics related to microbiology and geomicrobiology, emphasizing the role that these fields will play in future astrobiology research. In conclusion, astrobiology, biomining, and bioleaching research are timely responding to the identification of these techniques as tools for biotechnological applications, expected to play a crucial role in long-term human space exploration.

The core of eLoran ground-based timing navigation system is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on Back-Propagation neural network (BPNN) for complex meteorological environment, which realizes the function of directly mapping propaga-tion delay fluctuation through meteorological factors. Firstly, the theoretical influence of meteoro-logical factors on each component of propagation delay is analyzed based on calculation parame-ters. Then, through the correlation analysis of the measured data, the complex relationship be-tween the seven main meteorological factors and the propagation delay, as well as their regional differences are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with the existing linear model and simple neural network model. eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network;

For W-band long-range mm-wave wireless transmission systems, nonlinearity issues resulting from photoelectric devices, optical fiber, and wireless power amplifiers can be handled by deep learning equalization algorithms. Besides, the PS technique is considered an effective measure to further increase the capacity of the modulation-constraint channel. However, since the proba-bilistic distribution of m-QAM varies with the amplitude, there have been difficulties in learning valuable information from the minority class. This limits the gain of nonlinear equalization. To overcome the imbalanced machine learning problem, we propose a novel two-lane DNN (TLD) equalizer with the random over-sampling (ROS) technique in this paper. The combination of PS at the transmitter and ROS at the receiver improves the overall performance of the W-band wireless transmission system, and our 4.6-km ROF delivery experiment verifies its effectiveness for the W-band mm-wave PS-16QAM system. Based on our proposed equalization scheme, we realize single-channel 10-Gbaud W-band PS-16QAM wireless transmission over 100 m optical fiber link and 4.6-km wireless air-free distance. The results show that compared with the typical TLD without ROS, the TLD-ROS can improve the receiver sensitivity by 1dB. Furthermore, a reduction of 45.6% in complexity is realized, and training samples can be reduced by 15.5%. Considering the actual wireless physical layer with its requirements, there is much to be gained from the joint use of deep learning and balanced data pre-processing techniques.

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maxi-mally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7,560 proteins related to GBM disease and 3,860 proteins associated with signaling pathways involved in GBM. By integrating the results of the aforementioned analyses and performing centrality analysis again, eleven key genes involved in GBM disease were identi-fied. ProteomicsDB or Gliovis databases were used for determining the gene expression in normal or tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of iden-tified genes, miRNAs, and metabolites associated with GBM disease was done using ExpressAna-lyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was done through the pathway, joint pathway, and network analyses. The results of this study identified 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in the GBM disease development. To establish early diagnostic methods and plan personalized GBM treatment strategies, the identified top genes-miRNAs and metabolite signatures can be targeted.

Animal models of diseases in particular, the mice, are considered to be the cornerstone for translational research in immunology. The aim of the present study is to model the geometry and analyze the network structure of murine lymphatic system (LS). The algorithm for building the graph model of the LS makes use of anatomical data. To identify the edge directions of the graph model, a mass balance approach to lymph dynamics based on Hagen-Poiseuille equation is applied. It is the first study in which a geometric model of the murine LS has been developed and characterized in terms of its structural organization and the lymph transfer function. Our study meets the demand for quantitative mechanistic approaches in the growing field of immunoengineering to utilize or exploit the lymphatic system for immunotherapy.

Time series data having low signal-to-noise ratio, non-stationarity and non-linearity are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time series data. In this paper, we develop an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future time, we use only the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing involves updating only the current data into the process. We evaluate the performance of O-LGT by analyzing the high-frequency Limit Order Book (LOB) data from the China market. It shows that our model in most cases achieves similar speed with much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 40 times faster than the existing high-accuracy neural network models for the LOB data in China market.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Structure–function relationships in proteins have been one of the crucial scientific topics. Heme proteins have diverse and pivotal biological functions. Therefore, clarifying their structure–function correlation is significant to understand their functional mechanism and is informative for various fields of science. In this study, we constructed convolutional neural network models for predicting protein functions from the tertiary structures of heme-binding sites (active sites) of heme proteins to examine the structure–function correlation. As a result, we succeeded in the classification of oxygen-binding protein (OB), oxidoreductase (OR), proteins with both functions (OB–OR), and electron transport protein (ET) with high accuracy. Although the misclassification rate for OR and ET was high, the rates between OB and ET and between OB and OR were almost zero, indicating that the prediction model works well between protein groups with very different functions. However, predicting the function of proteins modified with amino acid mutation(s) remains a challenge. Our findings indicate a structure–function correlation in the active site of heme proteins. This study is expected to be applied to the prediction of more detailed protein functions such as catalytic reactions.

Ultrasound (US) is commonly used for the diagnosis of liver masses. Ensemble learning has been widely used for image classification, but its methods have not been fully optimized. This study was performed to investigate the usefulness of ensemble learning and compare a number of ensemble learning techniques using multiple convolutional neural network (CNN)-trained models for image classification of liver masses in US images. The US imaging data set was classified into four categories: benign liver tumor (BLT, 6320 images), liver cyst (LCY, 2320 images), metastatic liver cancer (MLC, 9720 images), and primary liver cancer (PLC, 7840 images). In this study, 250 test images were randomly selected for each class, for a total of 1000 images, and the remaining images were used for training. Sixteen different CNNs were used for to train and test the US images. All four types of ensemble learning—soft voting (SV), stacking (ST), weighted average voting (WAV), and weighted hard voting (WHV)—showed greater accuracy than the single CNN. All four types also showed significantly better deep learning performance than ResNeXt101 alone. For image classification of liver masses using US images, ensemble learning improved the performance of deep learning over a single CNN.

Micro-expression (ME) is one of the key psychological stress reactions. It is a modest, spontaneous facial mechanism. ME has significant applicability in a variety of psychologically-related sectors because to its precision and unpredictability with regard to psychological manifestations. Nevertheless, the current Micro-expression recognition (MER) algorithms have poor accuracy and a limited quantity of ME data, and this study issue has not been thoroughly investigated. Therefore, we present an approach for deep learning based on a Spatio-temporal capsule network (STCP-Net). STCP-Net has four components: a jitter reduction module, a differential feature extraction module, an STCP module, and a fully linked layer. The first two modules are aimed to extract diversifying differential features more precisely and to limit the influence of head jitter. The STCP module is used to extract Spatio-temporal features layer by layer, taking the temporal and geographical connection between features into account. This research runs sufficient trials using the Leave One Subject Out (LOSO) methodology for cross-validation using the CASMEII dataset. The conclusion and analysis demonstrate that the algorithm is innovative and efficient.

This study presents the algorithm ACTION, defined as Automatic Center detection of Tropical cyclone (TC) using Image processing based on the Operational radar Network. Based on the high visibility of weather radar imagery, the TC’s motion vector is calculated from the continuous image change using optical flow, producing its rotation center as the TC’s center. The algorithm’s performance was verified for typhoons (TCs in the Northwestern Pacific) affecting the Korean Peninsula from 2018–2021 and showed a high detection rate of 80.8% within an error distance of 40 km compared to the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC’s initial entry inside the observation radius even in the absence of uniform radar data. ACTION easily calculates using Open Source Computer Vision, performs in real time, and can be directly applied to rapidly generated weather radar images; hence, it is currently being utilized by the KMA. The high-temporal-resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting.

COVID-19 is a well-known respiratory disease spreading worldwide since January 2020, causing many deaths and massive pressure on health systems. This pandemic’s appearance compromised the health services’ sustainability and quality as many procedures were postponed or even canceled out, with an expected increase of adverse events like nosocomial infections, in-hospital deaths, and the worsening of the patients’ clinical status. Therefore, assessing whether the healthcare providers’ performance has changed is paramount. In this case, we evaluated the public hospitals in Portugal as the leading secondary health care providers in the country, which in turn has been highly affected by the pandemic. To this end, we developed a network Data Envelopment Analysis model serially relating efficiency and effectiveness. Using 2,795 observations from public hospitals and 18 variables, we observed consistent drops in efficiency when the pandemic started, followed by a recovery to levels above the pre-pandemic ones. Regarding effectiveness, we observed a positive trend during the evaluated period. In short, these results allow us to conclude that, apart from the great resilience of public healthcare providers, the Portuguese State was not prepared for a pandemic like COVID-19, but still the actions taken (including massive vaccination) were beneficial.

Cells respond to a myriad of stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a senescence-associated-secretory-phenotype (SASP). The heterogeneity of senescent cells (SnCs) and their SASP is vast, yet poorly characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and harmonization of their nomenclature a priority. The Cellular Senescence Network (SenNet), a NIH Common Fund initiative, will leverage emerging single cell and spatial-omics to identify and map SnCs in numerous organs across the lifespan of humans and mice. A common coordinate framework will integrate the data, using validated, standardized methods, creating public 4-dimensional SnC atlases. Key SenNet deliverables include development of innovative tools/technologies to detect SnCs, biomarker discovery, common annotations to describe SnCs and extensive public data sets. The goal is to comprehensively understand and map SnCs for diagnostic and therapeutic purposes to improve human health.

Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN models and their scope to provide a reference/baseline to the researchers interested in using CNN models in their research.

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in male and female patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33×10^-8 calculated with the Yates-corrected Chi-square test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWAS of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network. This study indicates that a number of genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The results also suggest the genetic basis of sex-specific psychiatric comorbidities of AD.

This article introduces new method using ANN to control SVC in order to correct the source power factor of low voltage network. Two ANN approaches are investigated to design accurate and fast power factor correction (PFC) controller. First approach uses simple ANN (SANNA). Second approach uses cascaded ANN (CANNA). ANN regression, performance and the calculated error for both approaches are investigated to select between the two approaches. CANNA is selected as a better solution and it used to build the ANN PFFC controller using database, generated by MATLAB-Simulink, for standard three low voltage levels (240V, 220V and 110V). Nine test cases are carried out to validate the performance of proposed ANN PFC controller with a network has variable loads and low power factor (0.6 approximately). In order to extend the use of the controller to other voltage levels not included in the training process of the cascaded ANN, only the SVC is resized. Another nine cases are carried out with the same loads using the same ANN controller, as it is, to test its performance with the extended voltage level range (415V, 230V and 120V). The results show accurate and fast response in all test cases.

Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in Network Intrusion Detection System (NIDS) technology, most implementations still depend on signature-based intrusion detection systems, which can’t identify unknown attacks. Deep learning can help NIDS to detect novel threats since it has a strong generalization ability. The deep neural network’s architecture has a significant impact on the model’s results. We propose a genetic algorithm based model to find the optimal number of hidden layers and the number of neurons in each layer of the deep neural network (DNN) architecture for the network intrusion detection binary classification problem. Experimental results demonstrate that the proposed DNN architecture shows better performance than classical machine learning algorithms at a lower computational cost.

G protein-coupled receptors (GPCRs) serve prominent roles in melanocyte lineage physiology, with an impact at all stages of development, as well as on mature melanocyte functions. GPCR ligands are present in the skin and regulate melanocyte homeostasis, including pigmentation. The role of GPCRs in the regulation of pigmentation and, consequently, protection against external aggression, such as ultraviolet radiation, has long been established. However, evidence of new functions of GPCRs directly in melanomagenesis has been highlighted in recent years. GPCRs are coupled, through their intracellular domains, to heterotrimeric G proteins, which induce cellular signaling through various pathways. Such signaling modulates essential cellular processes of melanomagenesis, such as proliferation and migration. GPCR-associated signaling in melanoma can be activated by the binding of paracrine factors to their receptors or directly by activating mutations. In this review, we present melanoma-associated alterations of GPCRs and their downstream signaling and discuss the various preclinical models used to evaluate new therapeutic approaches against GPCR activity in melanoma. Recent striking advances in our understanding of the structure, function, and regulation of GPCRs will undoubtedly broaden treatment options in melanoma in the future.

Mobility models are used to evaluated the network protocols of the ad hoc network using the simulation. The random waypoint model is a model for mobility which is usually used for performance evaluation of ad-hoc mobile network. Mobile nodes have the dynamic mobility in the ad hoc network so the mobility model plays an important role to evaluate the protocol performance.In this article, we developed modify random waypoint mobility (MRWM) model based on random waypoint for the mobile ad hoc network. In this article, the comparative analysis of modifying random waypoint mobility and random waypoint model on the ad hoc On-Demand Distance Vector (AODV) routing protocol has been done for large wireless ad hoc network (100 nodes) with the random mobile environment for the 1800s simulation time. To enhance the confidence on the protocol widespread simulations were accomplished under heavy traffic (i.e. 80 nodes) condition. The proposed model protocol has been investigated with the performance metrics: throughput; packet delivery ratio; packet dropping ratio; the end to end delay and normalized routing overhead. The obtained results revealed that proposed modify random waypoint mobility model reduces the mobility as compared to the random waypoint mobility model and it is trace is more realist.

Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.

Numerous in vitro studies on isolated cells have been conducted to uncover the molecular mechanisms of action of Panax ginseng Meyer root extracts and purified ginsenosides. However, the concentrations of ginsenosides and the extracts used in these studies were much higher than detected in pharmacokinetic studies in humans and animals orally administered with ginseng preparations at therapeutic doses. Our study aimed to assess: (a) the effects of ginsenoside Rg5, the major "rare" ginsenoside of Red Ginseng, on gene expression in the murine neuronal cell line HT22 in a wide range of concentrations, from 10-4 to 10-18 M, and (b) the effects of differentially expressed genes on cellular and physiological functions in organismal disorders and diseases. Gene expression profiling was performed by transcriptome-wide mRNA microarray analyses in HT22 cells after treatment with ginsenoside Rg5. Ginsenoside Rg5 exhibits soft-acting effects on gene expression of neuronal cells in a wide range of physiological concentrations and strong reversal impact at high (toxic) concentration: significant up- or downregulation of expression of about 300 genes at concentrations from 10-6 M to 10-18 M, and dramatically increased both the number of differentially expressed target genes (up to 1670) and the extent of their expression (fold changes compared to unexposed cells) at a toxic concentration of 10-4 M. Network pharmacology analyses of genes expression profiles using Ingenuity pathway analysis (IPA) software showed that at low physiological concentrations, ginsenoside Rg5 has the potential to activate the biosynthesis of cholesterol and to exhibit predictable effects in senescence, neuroinflammation, apoptosis, and immune response, suggesting soft-acting, beneficial effects on organismal death, movement disorders, and cancer.

The diffusion of molecules in aqueous solutions in the domain of membrane technology is very critical in the efficiency of chemical engineering and purification processes. In this study, the diffusion in high and low concentration regions is simulated with finite difference method (FDM), and then the results of numerical computations are coupled with adaptive network-based fuzzy inference system (ANFIS) and bilayered neural network method (BNNM). Machine learning approach can individually predict diffusion phenomena across the domain based on understanding of the machine instead of the discretization of an ordinary differential equation (ODE). The findings of the machine learning method are in good agreement with those of FDM at different times of the simulation. In addition to numerical computation, the error of the system is computed for different iterations. The results show that by increasing the number of iterations and training datasets, all errors reduce significantly for both training and testing. BNN method is also used to train the prediction process of diffusion for a more accurate comparison. This technique is similar to ANFIS method in terms of prediction capability. According to the findings, ANFIS approach predicts diffusion slightly better than BNN method. In this regard, ANFIS technique produces R>0.99 while BNN method produces R around 0.98. Both machine learning methods are accurate enough to predict diffusion throughout the domain for different time steps. The computational time for both algorithms is less than that of FDM method to predict low and high concentrations in the domain. Besides, based on the results, artificial intelligence (AI) can find the relationship between inputs and outputs and determine which input has the main influence on the output in this study to optimize the process. As such, future studies can be focused on AI and other methods for faster prediction and optimization processes.

This paper attempts to compare two different approaches to solve the problem of accident rates prediction based on human factors for industrial workers. One of the methods has already been done using Fuzzy c-Means Clustering and proved to be working with decent results. The second method which will be covered in this paper is using Artificial Neural Networks. The primary goal of this work is to insure that ANN will work efficiently in such prediction problem. The second goal is to reveal the fact that which one of the two selected methodologies is better at defining the estimation of accident rates among people who work in different industrial fields. The purpose has been achieved when the outcome of the ANN was obtained and compared accordingly with the output of the research previously carried out with Fuzzy c-means clustering method. Comparing the outcomes of these two different methods gave an immense insight on which features are more important than others when it comes to laborers properties with completely different background such as varying levels of health, knowledge, experience, training and physical properties. At the end of the research, it becomes clear that accident rates estimation for laborers with properly trained Artificial Neural Network gives better results when it is compared with Fuzzy c-Means Clustering method. Standard deviation method was used to calculate the validity of ANN technique. The result was compared with Fuzzy c-mean clustering technique. Impressive improvement of 8.8% in the accident rate prediction was achieved using Tailored-Made-ANN.

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

Neural networks models are mostly represented by oriented graphs where only the components, constitutive elements of the graph, are transcribed into mathematical xpression. Indeed, accurate knowledge of the full expression of the model is required in certain situations such as selecting among several reference models, the one that best fits the available data or comparing the explanatory and predictive performance of an established model with respect to some reference models. In this paper, we establish a formalism of the mathematical expression for multilayer perceptron neural network in a general framework, MLP-p-n-q, with p, n and q natural integers and show its restriction to cases where one has a hidden layer and multivariate outputs (MLP-p-1-q), and then a single output (MLP-p-1-1). Then, we give some specific cases of the most commonly used models. An application case is presented in the context of solving a nonlinear regression problem.

Missing observations constitute one of the most important issues in data analysis in applied research studies. The magnitude and their structure impact parameters estimation in the modeling with important consequences for decision-making. This study aims to evaluate the efficiency of imputation methods combined with the backpropagation algorithm in a nonlinear regression context. The evaluation is conducted through a simulation study including sample sizes (50, 100, 200, 300 and 400) with different missing data rates (10, 20, 30 40 and 50%) and three missingness mechanisms (MCAR, MAR and MNAR). Four imputation methods (Last Observation Carried Forward, Random Forest, Amelia and MICE) were used to impute datasets before making prediction with backpropagation. 3-MLP model was used by varying the activation functions (Logistic-Linear, Logistic-Exponential, TanH-Linear and TanH-Exponentiel), the number of nodes in the hidden layer (3 - 15) and the learning rate (20 - 70%). Analysis of the performance criteria (R2, r and RMSE) of the network revealed good performances when it is trained with TanH-Linear functions, 11 nodes in the hidden layer and a learning rate of 50%. MICE and Random Forest were the most appropriate for data imputation. These methods can support up to 50% of missing rate with an optimal sample size of 200.

The monitoring of maternal and child health, using wearable devices made with wireless sensor technologies, is expected to reduce maternal and child death rates. Wireless sensor technologies have been used in wireless sensor networks to enable the acquisition of data for monitoring machines, smart cities, transportation, asset tracking, and tracking of human activity. Applications based on wireless body area network (WBAN) have been used in healthcare for measuring and monitoring of patient health and activity through integration with wearable devices. Wireless sensors used in WBAN can be cost-effective, enable remote availability, and can be integrated with electronic health record (EHR) management systems. Interoperability of WBAN sensor data with other linked data has the potential to improve health for all, including maternal and child health through the improvement of data access, data quality and healthcare access. This paper presents a survey of the state-of-the-art techniques for managing WBAN sensor data interoperability. The findings in this study will provide reliable support to enable policymakers and health care providers to take action to enhance the use of e-health to improve maternal-child health and reduce the mortality rates of women and children.

Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.

Conditional generative adversarial networks (CGANs) learn a mapping from conditional input to observed image and perform tasks in image generation, manipulation and translation. In-situ monitoring uses sensors to obtain real-time information of additive manufacturing (AM) processes that relate to process stability and part quality. Understanding the correlations between process inputs and in-situ process signatures through machine learning can enable experimental-driven predictions of future process inputs. In this research, in-situ data obtained during a metallic powder bed fusion AM process is mapped with a CGAN. A single build of two turbine blades is monitored using EOSTATE Exposure OT, a near-infrared optical tomography system of the EOS M290 system. Layerwise images generated from the in-situ monitoring system were paired with a conditional image that labeled the specimen cross-section, laser-scan stripe overlap and z-distance to part surfaces. A CGAN was trained using the turbine blade data set and employed to generate new in-situ layerwise images for unseen conditional inputs.

As a complex system, our body acts as a whole system connected to the environmental incitements. It is ordered, coherent, and tries to maintain the least possible entropy, saving the greatest amount of energy. In order to explain the dynamics of the systemic regulative network, a theoretical and speculative model is proposed, with a comprehensive approach that allows seeing the entire regulative system as a continuous unicuum. This paper covers two themes: 1) the connections between the quantum level and the classical one, through some principles of the QFT and through the Coherence Domains. The system is modeled as a field described by the wave function, with synchronous and consistent events, driven in a global computing by the quantum potential Q. The quantum potential implies the non-locality, and it needs only ultra-weak waves to occur, so it may explain how the rapid and global activation of the organism in response to perturbation/punctiform information works. The initial hypothesis is that some consistent quantum phenomena are amplified through the systemic regulative network until they become macroscopic observable. This is possible because of Coherence Domains. 2) The reactions of the different systemic networks to perturbations/punctiform information, with the attempt to model and measure information in biology, going beyond the Shannon and Turing theories. Hopfield Networks and an informational point of view are used to address the crucial informational and organizational role of proteins and nucleic acids.

In an innovation driven business environment, cross-border access to resources is important for companies to improve innovation capabilities and development performance. Based on the previous research, it shows that there are barriers to cross domain communication among alliance firms because of the restriction of multidimensional ties and dyads. Simmelian ties, as a form of alliance network with ternary connections, it effectively restrained opportunism and self-interest in the cooperation process and take a crucial role to evaluate innovation related performance in corporation. Based on the theory of Simmelian, this paper builds a theoretical framework and proposes corresponding research hypotheses between Simmelian ties and enterprise innovation performance. After designing questionnaires, collecting data and conducting empirical analysis to test theoretical models and hypotheses. Results have shown that: (1) Simmelian ties generally have a positive impact on enterprise innovation performance. (2) Knowledge capturing and knowledge integration play a partial intermediary role between Simmelian ties and enterprise innovation performance, and the mediating chain formed by the two variables plays a serial mediating role in the effect. (3) Network routines significantly positively moderates the relationship between Simmelian ties and knowledge capturing. And also, the positive relationship between Simmelian ties and enterprise innovation performance is also actively moderated by network routines. The conclusion of this study is meaningful for companies to establish of Simmelian ties, improve knowledge management capabilities and further promote enterprise innovation performance.

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

Background: Three drug-combinations, ipilimumab-nivolumab (Ipi-Nivo), pembrolizumab-axitinib (Pembro-Axi) and avelumab-axitinib (Ave-Axi), have received regulatory approvals in USA and Europe for the treatment of metastatic renal cell carcinoma with clear cell component (mRCC). However, no head-to-head comparison data are available to identify the best option. Therefore, we aimed to compare these new treatments in the first-line setting. Methods: We conducted a systematic search in Pubmed, the Cochrane library and clinicaltri-al.gov website from January 2015 to October 2019, for any randomized controlled trials of treatment-naïve patients with mRCC. The process was performed according to PRISMA guide-lines. We performed a Bayesian network meta-analysis with two different approaches. The out-comes for analysis were overall survival, progression-free survival, and objective response rate. Results: Our search identified 3 published phase 3 randomized clinical trials (2835 patients). In the contrast-based model, Ave-Axi (SUCRA: 83%) and Pembro-Axi (SUCRA: 80%) exhibited the best ranking probabilities for PFS. For OS, Pembro-Axi (SUCRA: 96%) was the most pref-erable option against Ave-Axi and Ipi-Nivo. Objective response rate analysis showed Ave-Axi as the best (SUCRA= 94%) and Pembro-Axi as second best option. In the parametric models, risk of progression was comparable for Ave-Axi and Ipi-Nivo, whereas Pembro-Axi exhibited a lower risk during the first 6 months of treatment and a higher risk afterward. Furthermore, Pembro-Axi exhibited a net advantage in terms of OS over the two other regimens, while Ave-Axi was the least preferable option. Conclusions: Overall evidences suggested pembrolizumab plus axitinib may be the best option.

Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

Recent advancements in cloud-computing and social-networking are influencing how we communicate professionally, work collaboratively, and approach data-science tasks. Here we show how the groundwater modeling field is well positioned to benefit from these advancements. We present a case study detailing a vertically-integrated, collaborative modeling framework jointly developed by participants at the American Samoa Power Authority and at the University of Hawaii Water Resources Research Center. The framework components include direct collection and analysis of climatic and streamflow data, development of a water budget model, and initiation of a dynamic groundwater modeling process. The framework is entirely open-source and applies newly available data-science infrastructure using Python-based tools compiled with Jupyter Notebooks and cloud computing services such as GitHub. These resources allow for seamless integration of multiple computational components into a dynamic cloud-based workflow that is immediately accessible to stakeholders, resource managers, or anyone with an internet connection

Deep learning has achieved lots of successes in many fields, but when trainable sample are extremely limited, deep learning often under or overfitting to few samples. Meta-learning was proposed to solve difficulties in few-shot learning and fast adaptive areas. Meta-learner learns to remember some common knowledge by training on large scale tasks sampled from a certain data distribution to equip generalization when facing unseen new tasks. Due to the limitation of samples, most approaches only use shallow neural network to avoid overfitting and reduce the difficulty of training process, that causes the waste of many extra information when adapting to unseen tasks. Euclidean space-based gradient descent also make meta-learner's update inaccurate. These issues cause many meta-learning model hard to extract feature from samples and update network parameters. In this paper, we propose a novel method by using multi-stage joint training approach to post the bottleneck during adapting process. To accelerate adapt procedure, we also constraint network to Stiefel manifold, thus meta-learner could perform more stable gradient descent in limited steps. Experiment on mini-ImageNet shows that our method reaches better accuracy under 5-way 1-shot and 5-way 5-shot conditions.

Vehicular ad-hoc network (VANET) is a type of mobile network which is used for establishing connection between vehicles (M2M) and also between vehicles and nearby stationary equipment which are often road-side equipment. The main target of VANET is to provide security and convenience for the passengers. In order to achieve this goal, a special electronic device called OBU (On-Board Unit) is embedded in each vehicle which makes the connection between vehicles and between the vehicles and the road-side equipment possible. In this paper, the Location-Aware Clustering in Vehicular Ad-hoc Networks (LAC-VANET) is proposed. We try to achieve the main and major goal in VANET networks, i.e. fast propagation of security and urgent messages in ITS systems, using clustering and selecting the best cluster head based on Fuzzy logic such that the cluster head can transfer important information such as the obstacles and accidents detected on the road with a suitable speed and without creating a large traffic load in the vehicle network in order to notify other vehicles and prevent the danger and vehicle accidents. Moreover, LAC-VANET method is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the VANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), end to end delay, and packet loss rate.

The semi-enclosed estuary is very susceptible to changes in the physical and environmental characteristics of the inflow from the land and the coastal sea weather and so the continuous and comprehensive monitoring is necessary for managing the estuary. Nevertheless, the standard procedure or schematic framework has not been proposed appropriately to determine how many instruments are necessary and where they need to be deployed to detect critical changes. Therefore, the present work proposes a systematical strategy for the deployments of the monitoring array by using the combination of the graphical optimization with the objective mapping technique. In order to reflect the spatiotemporal characteristics of the bay, the representative variables and eigenvectors are determined by the Empirical Orthogonal Function (EOF), and the cosine angle among them are calculated for a design index of optimization. At the recommended locations, the sampled representative variables are interpolated to reconstruct their spatiotemporal distribution and compared with the distributions of the true values. Analysis confirms that the selected locations, even with a small number of points, can be used for on-site monitoring. Also, the present framework suggests how to determine installable regions for real-time monitoring stations, which reflect the global and local characteristics of the semi-enclosed estuary.

Various empirical models are available to evaluate the temperature effects on relative permeability of the different rock and fluid systems. However, the implementation of limited experimental data points may hinder the applicability of such models to other systems. This study aims to develop new predictive models for k_ro estimation based on multilayer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM) approaches. A database comprising of 626 data points applied to the model development. The independent variables are temperature, oil viscosity, water viscosity, water saturation ( ), and the absolute permeability. Each variable covers a wide range of variations which increases models’ potential to be applied in various systems with different characteristics. The doubtful experimental data points excluded using a leverage value approach and a sensitivity analysis carried out to determine the quantitative impact of every individual independent variable on the k_ro. Statistical error analyses demonstrated the coefficient of determination (R²) values of 0.985, 0.975, and 0.999 for MLP-ANN, ANFIS, and LSSVM, respectively. The comparative study indicated that the LSSVM had the best performance regarding both graphical and statistical error analyses among the newly proposed models and previously reported models in the literature.

In the recent decade, Building Information Modelling (BIM) has widely been adopted in the Architecture, Engineering and Construction (AEC) industry and completely upended the way we build. While BIM continues gain momentum in the industry, it has also attracted increasing attentions from researchers. However, most of the current study focuses on reviewing BIM for management, BIM for green building, BIM for infrastructure and BIM for Facilities Management (FM). There are few studies about Global BIM review and to discuss their complex inter-connections. In this study, we adopted a scientometric analysis method to review global BIM research from 2004-2019. A total of 1455 scholarly bibliographic records obtained from Web of Science Core Collection databased were established for the analysis. This study has identified the top productive and influential researchers, research institutes, regions/countries, subject categories and journals in the BIM field. In addition, 11 clusters of Global BIM research were also identified including construction project, green BIM, construction safety planning, automated IFC-based workflow and so on. Authors distinguished 11 clusters of global BIM research into 3 stages, namely formulating stage, accelerating stage and transforming stage. Furthermore, authors reviewed the BIM policy of Singapore and observed there is a co-production relationship between evolution of BIM policy and global BIM research. These findings provide valuable information for researchers, practitioners and policy makers by visualizing the current progress in the research field of BIM and highlighting future research needs.

In this paper, PEN3 electronic nose was used to detect and recognize fresh and moldy apples (inoculated with Penicillium expansum and Aspergillusniger) taken Golden Delicious apples as model subject. Firstly, the apples were divided into two groups: apples only inoculated with different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined, which can simplify the analysis process and improve the accuracy of results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM) and radial basis function neural network (RBFNN), were then applied to analyze the data obtained from the characteristic sensors, respectively, aiming at establishing the prediction model of flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W and W2W in the PEN3 electronic nose exhibited strong signal response to the flavor information, indicating were most closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors was used for modeling. The results of the four pattern recognition methods showed that BPNN presented the best prediction performance for the training and validation sets for both the Group A and Group B, with prediction accuracies of 96.29% and 90.00% (Group A), 77.70% and 72.00% (Group B), respectively. Therefore, it first demonstrated that PEN3 electronic nose can not only effectively detect and recognize the fresh and moldy apples, but also can distinguish apples inoculated with different molds.

Social plasticity, defined as the ability to adaptively change the expression of social behavior according to previous experience and to social context, is a key ecological performance trait that should be viewed as crucial for Darwinian fitness. The neural mechanisms for social plasticity are poorly understood, in part due to skewed reliance on rodent models. Fish model organisms are relevant in the field of social plasticity for at least two reasons: first, the diversity of social organization among fish species is staggering, increasing the breadth of evolutionary relevant questions that can be asked. Second, that diversity also suggests translational relevance, since it is more likely that “core” mechanisms of social plasticity are discovered by analyzing a wider variety of social arrangements than relying on a single species. We analyze examples of social plasticity across fish species with different social organizations, concluding that a “core” mechanism is the initiation of behavioral shifts through the modulation of a conserved “social decision-making network”, along with other relevant brain regions, by monoamines, neuropeptides, and steroid hormones. The consolidation of these shifts may be mediated via neurogenomic adjustments and regulation of the expression of plasticity-related molecules (transcription factors, cell cycle regulators, and plasticity products).

Based on the key molecule of humoral adaptive immunity, the antibody, evolution of the system comprises molecular genetic, cell biologic and immunologic mechanisms, and as a network the system is likely governed and can be characterized by physical rules as well. While deep sequencing can provide vast amounts of data related primarily to clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function. In this paper a novel model of structural interaction space, termed radial adjustment of system resolution, or RADARS, is proposed. The model is based on the radial growth of resolution of structural recognition, corresponding to increasing affinity of immune reactivity, and the virtual infinity of directions of growth, corresponding to the ability to respond to almost any molecular structure. Levels of interaction strength appear as shells of the sphere representing the system. B-cell development and immune responses can be readily interpreted in the model and quantitative properties of the antibody network can be inferred from the physical properties of a quasi-spherical system growing multi-radially. The system is described by double-Pareto distribution, sampling the lognormally distributed equilibrium constants at a rate of phi square. Finally, general strategies for merging antibody sequence space into structural space are outlined.

A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 10⁷ with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.

Trends such as Industrial Internet of Things (IIoT) and Industry 4.0 have increased the need to use powerfull network technologies in industrial automation. The growing communication in industrial automation is harnessing the productivity and efficiency of manufacturing and process automation with minimum human intervention. Due to the ongoing evolution of industrial networks from Fieldbus technologies to Ethernet, the new opportunity has emerged to integrate the Software Defined Networking (SDN) technique. In this paper, we provide a brief overview of SDN in the domain of industrial automation. We propose a network architecture called Software Defined Industrial Automation Network (SDIAN), with the objective of improving network scalability and efficiency. To match the specific considerations and requirements of having a deterministic system in an industrial network, we propose two solutions for flow creation: Pro-active Flow Installation Scheme (PFIS) and Hybrid Flow-Installation Scheme (HFIS). We analytically quantify the proposed solutions in alleviating the overhead incurred from the flow setup cost. The analytical model is verified through monte carlo simulations. We also evaluate the SDIAN architecture and analyze the network performance of the modified topology using an emulator called Mininet. We further list and motivate SDIAN features and in particular report on an experimental food processing plant demonstration featuring Raspberry PIs (RPIs) instead of traditional Programmable Logic Controllers (PLCs). Our demonstration exemplifies the characteristics of SDIAN.

Maximum hardness and hardened depth are the responses of interest in relation to the laser hardening process. These values define heat treatment quality and have a direct impact on mechanical performance. This paper aims to develop models capable of predicting the shape of the hardness profile depending on laser process parameters for controlling laser hardening quality (LHQ), or rather the response values. An experimental study was conducted to highlight hardened profile sensitivity to process input parameters such as laser power (P_L), beam scanning speed (V_S) and initial hardness in the core (H_C). LHQ modeling was conducted by modeling attributes extracted from the hardness profile curve using two effective techniques based on the punctual and geometrical approaches. The process parameters with the most influence on the responses were laser power, beam velocity and initial hardness in the core. The obtained results demonstrate that the geometrical approach is more accurate and credible than the punctual approach according to performance assessment criteria.

Axon guidance is a crucial process for growth of the central and peripheral nervous systems. In this study, 3 axon guidance related disorders, namely- Duane Retraction Syndrome (DRS) , Horizontal Gaze Palsy with Progressive Scoliosis (HGPPS) and Congenital fibrosis of the extraocular muscles type 3 (CFEOM3) were studied using various Systems Biology tools to identify the genes and proteins involved with them to get a better idea about the underlying molecular mechanisms including the regulatory mechanisms. Based on the analyses carried out, 7 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with DRS, HGPPS and CFEOM3 associated genes. From the PPI network, 3 have been identified as hub proteins- DRD2, UBC and CUL3.

A modular switched-capacitor (SC) DC-DC converter (MSCC) is introduced in this paper. It is designed to boost a low input voltage to a high voltage level and can be applied for photovoltaics and electric vehicles. This topology has high extensibility for high voltage gain output. The merits of the converters also lie in the fault tolerance operation and the voltage regulation with a minimum change in the duty ratio. Those features are built in when designing the modules and then integrating these into the DC-DC converter. Converter performance including voltage gain, voltage and current stress are focused and tested. The converter is modelled analytically, and its control algorithm is analyzed in detailed. Both simulation and experiment are carried out to verify the topology under normal operation and fault mode operation.

With large-scale distributed generators (DGs) in an active distribution network (ADN), conventional load flow convergence failure is incurred by heavy power transmission. The Holomorphic Embedding Load Flow Method (HELM) has proven to be more robust than the Newton–Raphson method under heavy power transmission. At present, HELM is mainly designed for balanced transmission networks. In this study, we developed a three-phase HELM model to accommodate DGs, delta connection loads, and ZIP loads for ADN. The effectiveness and better performance of the proposed method under heavy load situations were validated using modified unbalanced IEEE 13, 34, 37, and 123 test feeders.

Electronic-health applications rely on large computer networks to facilitate patients' information access and to communicate various types of medical data. To examine the effectiveness of these networks, the traffic parameters need to be analysed. Due to quantity of the information carrying packets, examining each packet's transmission parameters individually is not practical, especially when a real time operation is needed. Sampling allows a subset of packets that accurately represents the original traffic to be formed. In this study an adaptive sampling method based on regression and fuzzy inference system was developed. It dynamically updates the number of packets sampled by responding to the traffic variations. Its performance was found to be superior to the conventional non-adaptive sampling methods.

This paper proposes a novel method for optimizing the Cauer-type thermal network model considering both the temperature influence on the extraction of parameters and the errors caused by the physical structure. In terms of prediction of the transient junction temperature and the steady-state junction temperature, the parameters of conventional Cauer-type are modified, and the general method for estimating junction temperature is studied by using the adaptive thermal network model. The results show that junction temperature estimated by adaptive Cauer-type thermal network model is more accurate than that of the conventional model.

Emergency control load-shedding is a key solution to prevent blackouts in the power system. This paper proposed a new model of emergency controls load shedding based on the fast identification of the unstable state of the power system. K-means clustering algorithm divided the instability mode into the clusters. The results of analysis of this cluster were used as the basis for classification control. Building load shedding strategies is consisted of the pre-designed rules based on AHP algorithm. When the recognition of the power system “instability” is detected, the signal of load shedding control is triggered immediately, therefore the decision time is greatly shortened comparing to the traditional methods. The effectiveness of the proposed method was tested on the IEEE 39-bus to overcome the limitations of the last traditional methods.

This paper discusses about control of the linear switched reluctance machines (LSRMs) network for the zero phase-difference tracking to a sinusoidal reference. The dynamics of each LSRM is derived by online system identification and modeled as a second-order linear system. Accordingly, based on the coupled harmonic oscillators synchronization manner, a distributed control is proposed to synchronize each LSRM state to a virtual LSRM node representing the external sinusoidal reference for tracking it with zero phase-difference. Subsequently, a simulation scenario and an experimental platform with the identical parameter setup are designed to investigate the tracking performance of the LSRMs network constructed by the proposed distributed control. Finally, the simulation and experimental results verify the effectiveness of the proposed LSRMs network controller, and also prove that the coupled harmonic oscillators synchronization method can improve the synchronization tracking performance and precision.

Aim: This study aims to analyze the structure of the co-authorship network in viral hepatitis research from 2000 to 2023 using data from the Web of Science (WoS) Core Collection. The objective is to identify key researchers, evaluate the collaborative dynamics, and understand the evolution of scientific contributions in this field. Method: This study utilized network analysis techniques to evaluate 60,720 publications related to viral hepatitis research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The findings reveal that the viral hepatitis research network is characterized by low network density, indicating sparse overall collaboration. However, the clustering coefficient is high, suggesting strong collaborations within small, cohesive groups. Over the years, researchers such as Stefan Zeuzem, Maria Buti, and Fabien Zoulim have played pivotal roles, serving as central figures in the network. A noticeable increase in network density and clustering from 2020-2023 suggests growing collaboration in recent years.Conclusion: This study underscores the importance of international collaboration in advancing viral hepatitis research. Key researchers have emerged as influential contributors, facilitating global research activities. Network analysis provides valuable insight into the structure of these collaborations and helps identify potential areas for future research and partnerships to combat viral hepatitis globally. The results of this study should be used to reduce global healthcare disparities, promote research collaboration, and address viral hepatitis as a global public health problem.

Aim: This study aims to analyze the collaborative networks and identify key players in autoimmune pancreatitis (AIP) research from 2000 to 2023. Using data from the Web of Science (WoS) Core Collection, I assess the structure and evolution of co-authorship networks to understand collaboration trends and influential researchers in the field.Method: I conducted a network analysis of co-authorship patterns in AIP research using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The analysis included macro-level indicators: network density, clustering coefficient, components, and average distance, as well as micro-level indicators: degree centrality, closeness centrality, and betweenness centrality. These metrics were used to evaluate the connectivity, clustering, and key nodes within the network across three distinct periods: 2000-2009, 2010-2019, and 2020-2023.Result: The analysis revealed that the co-authorship network in AIP research evolved from a fragmented structure with limited collaboration in the early 2000s to a more interconnected but still dispersed network in recent years. Despite an increase in network density and a decrease in the number of disconnected components over time, the overall structure remains highly clustered, with many researchers operating within isolated groups. Key figures, such as Kazuichi Okazaki, Terumi Kamisawa, and Shigeyuki Kawa from Japan, consistently ranked high in centrality measures, highlighting their significant influence and leadership in the field. The findings suggest that while local collaborations are strong, broader international connections are still developing.Conclusion: This study provides a comprehensive overview of the collaborative landscape in AIP research, identifying central researchers and highlighting persistent fragmentation within the network. The prominent role of Japanese researchers in shaping the field underscores the importance of fostering greater international collaboration to bridge gaps between research clusters. Enhancing global partnerships could lead to more integrated and impactful research outcomes, ultimately advancing the understanding and management of autoimmune pancreatitis.

Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize and rank various risk factors commonly observed in whisky industry through the development of an Analytical Hierarchy Process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations, and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3) and economical (4) risks. The Analytical Hierarchy Process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision-making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices.

This research centres on advancing the utilisation of a novel Deep Ensemble model called RicEns-Net for predicting crop yields through sophisticated multi-modal data engineering. Initially, data for the study were acquired from Ernst \& Young (EY), a prominent global technology and management consulting firm, as part of their annual data science challenge (EY Open Science Challenge 2023). The predictive models leverage multi-modal data by integrating radar and optical-type remote sensing data alongside meteorological data to create supplementary features essential for predicting crop yield. The primary data features are derived from spectral indices extracted from Sentinel 2 satellites, while more advanced features are generated through combinations and interactions of these fundamental features. Furthermore, data from Sentinel 1 and 3, along with meteorological measurements such as surface temperature and rainfall, account for the intricate natural factors influencing crop yield, creating a comprehensive set of data features based on existing research. To tackle the complexity introduced by numerous data features, extensive data engineering was conducted to identify the most predictive features among 100+ and mitigate the "curse of dimensionality," resulting in 15 features from 5 different modalities. The proposed RicEns-Net comprises multiple machine learning algorithms in a deep ensemble approach to leverage the best features of each of these techniques, achieving optimal performance. The proposed model achieves an MAE value of 341 kg/Ha for prediction, surpassing the performance of the EY Open Science Challenge 2023 winner models and state-of-the-art deep/machine learning approaches.

This paper explores the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and traditional Mean-Variance optimization in financial portfolio management. Using a dataset comprising global financial assets, we applied both methodologies to optimize portfolios based on multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III outperforms the Mean-Variance method in achieving a more diverse set of Pareto-optimal portfolios. NSGA-III portfolios exhibited superior performance in balancing risk and return, demonstrated by higher Sharpe ratios, more favorable skewness, and lower kurtosis. Additionally, NSGA-III's ability to simultaneously optimize across multiple conflicting objectives highlights its robustness in navigating complex financial landscapes, offering enhanced portfolio resilience. In contrast, the Mean-Variance approach, while effective in achieving balanced risk and return, was limited in addressing higher-order moments of the return distribution. These results underscore NSGA-III's potential as a powerful tool for portfolio optimization, providing a comprehensive alternative to traditional methods in modern financial markets where multiple objectives must be considered.

This paper investigates the applicability of deep learning models for predicting the severity of forest wildfires, utilizing an innovative benchmark dataset called EO4WildFires. EO4WildFires integrates multispectral imagery from Sentinel-2, SAR data from Sentinel-1 and meteorological data from NASA Power annotated with EFFIS data for forest fire detection and size estimation. Resulting in a coverage of 45 countries (Figure \ref{Figure1-events}) with a total of 31730 wildfire events from 2018 to 2022. All these various sources of data are archived into data cubes, with the intention of assessing wildfire severity by considering both current and historical forest conditions, utilizing a broad range of data including temperature, precipitation, and soil moisture. The experimental setup has been arranged to test different deep learning architectures’ effectiveness in predicting the size and shape of wildfire burned areas. The study incorporates both Image Segmentation networks and Visual Transformers, employing a consistent experimental design across various models to ensure comparability of results. Adjustments were made to the training data, such as the exclusion of empty labels and very small events, to refine the focus on more significant wildfire events and potentially improve prediction accuracy. The models’ performance was evaluated using metrics like F1 Score, IoU Score, and Average Percentage Difference (aPD). These metrics offer a multi-faceted view of model performance, assessing aspects such as precision, sensitivity, and the accuracy of the burned area estimation. Through extensive testing with Image Segmentation networks and Visual Transformers, the research not only aims at enhancing the accuracy of estimating the burned area but also underscores the significance of quality training data and the comparative effectiveness of traditional segmentation methods over transformer-based models for this specific application. The dataset is published with an Open Access license and is hosted at: \url{10.5281/zenodo.7762564}.

This article introduces a new method for modelling and verifying the execution of timed security protocols (TSP) and their time-dependent security properties. The method, which is both novel and reliable, uses an extension of interpreted systems, accessible semantics in multi-agent systems, and timed interpreted systems (TIS) with dense time semantics to model TSP executions. We enhance the models of TSPs by incorporating delays and varying lifetimes to capture real-life aspects of protocol executions. To illustrate the method, we model a timed version of the Needham-Schroeder Public Key Authentication Protocol. We have also developed a new SMT-based bounded model checking reachability algorithm for the proposed structures and implemented it with the tool. The method comprises a new procedure for modelling TSP executions, a translation of TSP into TIS, and a translation of TIS's reachability problem into the SMT problem. The paper also includes thorough experimental results for nine protocols modelled by TIS and discusses the findings in detail.

Classification of pre-processed fMRI dataset using the functional connectivity(FC) based features is considered a challenging task due to high dimensional FC features set and smaller dataset size. To tackle this specific FC high dimensional features set and a smaller dataset size , We have proposed here a GAN based dataset augmenter to firstly train the GAN on the NYU Connectivity Features dataset and used the trained GAN to generate synthetic features per category and after getting sufficient number of features per category , A multi-head attention mechanism was used as a head for the classification . We name our proposed approach as "ASD-GANNet" which is End-To-End and does not require hand-crafted features as the multi-head attention mechanism focuses on the features that are more relevant . Moreover , We compared our results with the 06 available state-of-the-art techniques from the literature and Our proposed approach results using "NYU" site as a training set for generating GAN based synthetic dataset are promising. We achieved an overall 10-fold cross validation based accuracy of 82% ,sensitivity 82% and specificity 81% outperforming the available state-of-the art approaches. Sitewise comparison of our proposed approach also outperformed the the available state-of-the-art as out of the 17 sites our proposed approach has better results in the 10 sites.

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach.

Current trends in systemic risk literature provide new insights into multi-hazard risks that interact and compound with built environments, creating more significant impacts on socio-economic and human systems. Recurrent natural hazards and extreme weather events are more likely to compound and cascade into more impactful events, especially in vulnerable societies. In small-island communities (SICS), including large archipelagos and Small Island Developing States (SIDS), the long-term impact of multi-, compounding-, and cascading-hazards (MCC hazards) can result in persistent vulnerability and residual risks. This is due to delayed responses, limited resources, poverty, fewer evacuation options, and inadequate markets and infrastructure. Using Ternate, a densely populated small volcanic island in North Maluku, Indonesia, as a case study, this paper assesses the impacts of seven types of natural hazards: flash floods, landslides, extreme weather, extreme waves and abrasion, earthquakes, tsunamis, and volcanic eruptions. The research focuses on the multi-hazard impact on population, land use, and infrastructure exposures in 60 villages in Ternate. The findings highlight population density, land use, and infrastructure exposure to multi-hazard risks, providing valuable information on potential losses in future hazard events.

To curb the impacts arising from the agricultural sector the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint (EF) 3.0 life cycle impact assessment (LCIA) method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is constructed utilizing neighborhood interactions, through which a parameter known as farmer's green consciousness (GC) is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e. when farmers’ GC is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as EF “single score”) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried using the EF single score impact in the objective function, though this results in an 8% reduction in profitability over ten years.

Melanin is extensively distributed in organisms and believed to be synthetized by tyrosinase (TYR), leading to aberrant coloring and skin conditions when over-produced. Loquat (Eriobotrya japonica (Thunb.) Lindl.) flowers contain large amounts of bioactive substances currently few studied on their suppressive capabilities to melanin synthesis. Its isolate product (LFP) was obtained by ethanol solution extraction and resin purification, of which the inhibitory mechanism on TYR activity was elaborated by enzyme kinetics and multi-spectroscopy determinations and the impact on related proteins’ expression in mouse melanoma B16 cells was analyzed with Western blotting. HPLC-MS analysis indicated that LFP was identified into 137 compounds, with 12 including flavonoids (quercetin, isorhamnoin, p-coumaric acid, etc.), cinnamic acid and its derivatives as well as benzene and its derivatives owning TYR inhibitory activities. LFP inhibited TYR activity in a concentration-dependent manner with its IC50 value being 2.8 mg/mL and an anti-competitive mechanism involved altering the enzyme's conformation. LFP reduced the expression of TYR, TRP1, and TRP2 in melanoma B16 cells, hence inhibiting the synthesis of melanin. Considered collectively, LFP would be a viable option for treating hyperpigmentation caused by tyrosinase, providing a theoretical basis for the value-added utilization of loquat flower resource in beauty and aging related functional products.

The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

Wind turbines (WTs) are clean a renewable energy source that has been growing in popularity in recent years. Gearboxes are complex, expensive, and critical components of WT, which are subject to high maintenance costs and several stresses including high loads and harsh environments that can lead to failure with significant downtime and financial losses. This paper focuses on the development of a digital twin-based approach for the modelling and simulation of WT gearboxes with the aim to improve their design, diagnosis, operation, and maintenance by providing insights into their behavior under different operating conditions. Pow-erful commercial computer-aided design tools (CAD) and computer-aided engineering (CAE) software are embedded into a computationally efficient multi-objective optimization framework (modeFrontier) with the aim of maximizing the power density, compactness, performance, and reliability of the WT gearbox. This entails minimizing its weight, volume, and maximum stresses and strains achieved without compromising efficiency. The 3D CAD model of the WT gearbox is carried out using SolidWorks, the Finite Element Analysis (FEA) to obtain the stresses and strains fields are modelled using Ansys Workbench, while the multibody kinematic and dynamic system is analyzed using MSC Adams. The method has been successfully applied to different case studies to find the optimal design and analyze the performance of the WT gearboxes. Simulation results can be used to identify potential failure modes and optimize gearbox design to improve performance, extend service life and relia-bility, thereby ensuring proper operation over its lifetime and reducing maintenance costs.

Background: Locally advanced rectal cancers (10-20%) can present with infiltration into adjacent viscera. R0 resections remain the key factor for better survival. Traditionally these cases have been managed with open surgery. There are some limitations of laparoscopic surgery in these case, which can potentially be overcome by robotic surgery. Objectives: To determine the feasibility and outcome results of robotic multi-visceral resection of stage T4 rectal carcinoma. Methods: Consecutive patients having locally advanced carcinoma of rectum, who undergone robotic multi-visceral resection as the primary treatment between 2015 -2018 were included, through our database which was maintained prospectively. All perioperative and intraoperative parameters were analyzed. Patient demographics, co-morbidities, preoperative imaging, endoscopic findings, operative data, histopathology results, postoperative hospital course and procedure related postoperative adverse events were noted and analyzed. Results: Twenty-one patients with the diagnosis of nonmetastatic locally advanced rectal cancers were operated with Robotic Si/X platform. The median body mass index was 28.5 kg/m2 (range 21-42). The mean blood loss during operation was 20 ml (range 5-200), and the mean duration of surgery was 280 min (range 240-430). The median hospital stay after operation was 6 days (range 4-25 days). On postoperative histopathology, R0 resection rate was 90.5% (19/21). Conclusions: The robotic multi-visceral en-bloc resection of stage T4 rectal carcinoma is technically feasible and oncologically safe. Advanced technical skills, accurate radiological staging and careful patient selection can allow the extension of minimal access surgery to this challenging group of patients.

In order to solve the problem of fault diagnosis for CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and ensemble learning model is proposed. First, various monitoring signals including vibration signal, noise signal and current signal are collected. Then, the monitoring signals are preprocessed and the time-domain, frequency-domain and time-frequency domain feature indexes are extracted to construct a multi-dimensional mixed domain feature set. Finally, the feature set is putting into the constructed DoubleEnsemble-LightGBM model to realize the fault diagnosis of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the public data set and the feed system test bench data set, and the average overall accuracy is 91.07% and 98.06% respectively. Compared with XGBoost and other advanced ensemble learning models, the method has better accuracy. It provides technical support for the stable operation and intelligent of CNC machine tools..

This study investigated the presence, distribution, and antimicrobial resistance profiles of extend-ed-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae in a dairy herd located in Northern Italy. A questionnaire was administered, and the feces of clinically healthy pre-weaned dairy calves, their mothers, and the cows treated for mastitis, waste milk, water, and environmental samples, were collected and inoculated on Chromagar ESBL™ medium. The questionnaire identi-fied several risk factors including administration of waste milk to male calves, lack of routine cleaning of calf feeding and housing equipment, and blanket dry cow therapy. Escherichia coli was identified by MALDI-TOF MS in the feces of 28 of 37 (75.67%) calves, 2 of 3 (66.67%) treated cows, 8 of 14 (57.15%) environmental samples, and the waste milk. Six of 14 (42.85%) environmental samples were also positive for ESBL Acinetobacter baumannii, while all fecal samples were negative. ESBL production was confirmed for all E. coli isolates based on the double disk susceptibility test (DDST), while none were carbapenemase producers. Upon minimal inhibitory concentration (MIC) analysis, all of them showed multiple resistances and were categorized as multi-drug resistant (MDR). This study confirms the widespread presence of MDR, ESBL-E. coli in dairy calves and highlights their diffusion in the farm environment.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

Spectral image analysis has the potential to replace traditional approaches for assessing plant responses to different types of stress including herbicide, through non-destructive and high-throughput screening (HTS). Therefore, this study was conducted to develop a rapid bioassay method using a multi-well plate and spectral image analysis for diagnosis of herbicide activity and mode of action. Crabgrass (Digitaria ciliaris) as a model weed was cultivated in multi-well plates and subsequently treated with 6 herbicides with different modes of action when they reached the 1-leaf stage, using only a quarter of the recommended dose. To detect plant response to herbicides, plant spectral images were acquired after herbicide treatment using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors and analyzed for diagnosing herbicide efficacy and mode of action. Principal Component Analysis (PCA) using all spectral data successfully distinguished herbicides and clustered depending on their modes of action. The performed experiments have shown that the multi-well plate assay combined with spectral image analysis can be successfully applied for herbicide bioassay. In addition, the use of spectral image sensors, especially in CF image, would facilitate HTS by enabling rapid observation of herbicide response, as early as 3 hours after herbicide treatment.

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom-up approach. District energy systems with a building scale resolution are modelled in a mixed integer linear programming problem. The Dantzig-Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale revealed a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Since the national PV capacity needed is forecast at 15.4 GW peak, coordinated investments between local and national actors are needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems by 43.2% and curtails 48.2% of local renewable electricity.

Background Candida auris is an emerging multidrug-resistant fungal pathogen that represents a serious threat to healthcare settings currently. Objective: Its objective was to determine the prevalence of C. auris in the hospital since its initial detection in late 2019. Methods: Using an adapted risk assessment tool, we reviewed the charts. and medical files of all suspected? and confirmed cases of C. auris cases reported at King Khalid University Hospital, Riyadh between November 2019 and December 2022. Anonymized data were retrieved in a pre-established datasheet and analyzed to determine the epidemiological characteristics of C. auris infection in our facility. We established our initial prevalence by age, gender, risk factors, and according to sampling source. Results: Of the 53 confirmed cases positive for C. auris during the study period, 33 (62%) were males. Their ages ranged between 15 and 98; most positive cases occurred in 50 and above. Only one of the confirmed cases was hospital-acquired. All patients had at least one risk factor, and Urine samples yielded the greatest number of positive cases while admission to healthcare facilities constituted the highest risk in our study. Conclusion: Establishing a local prevalence could serve as our baseline/benchmark to compare with regional and international benchmarks.

This study presents a research plan that utilizes data obtained from wearable devices to identify human activities and gain insights into human behavior. We developed a model capable of classifying activities similar to human behavior and evaluated the effectiveness and generalization capabilities of this model. The data underwent initial preprocessing, including standardization and normalization. Additionally, recognizing the inherent similarities between human activity behaviors, we introduced a multi-layer classifier model. The first layer is a random forest model based on stepwise regression, which may encounter reduced accuracy for similar activities. The second layer employs a Support Vector Machine (SVM) model based on Kernel Fisher Discriminant Analysis (KFDA). KFDA is used to reduce the dimensionality of data points with potential confusion, followed by SVM for classification. The model was experimentally evaluated and applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that our approach achieved recognition accuracies of 99.71%, 98.71%, 99.12%, and 97.6% on these datasets, indicating excellent recognition performance. Furthermore, to assess the model's generalization ability, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

The electricity generated from fossil fuel and nuclear energy has negative impacts on the environment and has shown a need to search for and promote the use of clean, renewable and sustainable energy resources such as wind, solar and geothermal resources. Nowadays, wind energy resource has emerged as the fastest-growing source of energy on an overall scale. However, the selection of the best location for constructing a wind farm is a vital issue that can be considered a site selection problem, which involves various conflicting factors. Therefore, it is classified as a multi-criteria decision-making (MCDM) problem. In this research, the site selection of wind power plants is conducted in Burundi country. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. The main factors affecting optimal wind farm location considered in this study are wind speed, slope, distance from the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map. The obtained results show that 20.91% of the total study area is suitable; nevertheless, only 1.96% is highly suitable for wind farm placement. The western part of Burundi is concluded to be the most suitable area for wind farm construction and the most area is situated in Lake Tanganyika.

Non-alcoholic steatohepatitis (NASH) is a complex disease resulting from chronic liver injury associated with obesity, type 2 diabetes, and inflammation. Recently, multi-target drugs have been proposed to treat complex diseases including NASH; however, their manufacturing pro-cesses remain time- and cost-intensive and inefficient. Therefore, to overcome these limitations, we developed UniStac, a novel enzyme-mediated conjugation platform for multi-specific drug development. UniStac demonstrated high conjugation yields, optimal thermal stabilities, and robust biological activities. Through such platform, we designed a tetra-specific compound, C-192, targeting Glucagon-like peptide 1 (GLP-1), Glucagon (GCG), Fibroblast growth factor 21 (FGF21), and Interleukin-1 receptor antagonist (IL-1RA) simultaneously for the treatment of NASH. The biological activity and treatment efficacy of C-192 were confirmed both in vitro and in vivo using a methionine–choline-deficient (MCD) diet-induced mouse model. C-192 exhibited profound therapeutic efficacies compared to conventional drugs, including liraglutide and dulaglutide. C-192 significantly improved alanine transaminase levels, triglyceride accumula-tion, and the non-alcoholic fatty liver disease activity score. Our results collectively provide clin-ical feasibility of UniStac for developing multi-specific drugs, and C-192 as an innovative thera-peutic opportunity for severe NASH, as it exerts synergistic effects across multiple targets.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

Agriculture uses a lot of fuel, fertilizers, pesticides and other substances, while emitting large amounts of GHGs. It is important to optimize these inputs and outputs. One such way is increas-ing crop biodiversity. For this reason, single crops and mixtures of maize, hemp and faba bean as binary and ternary crops were investigated at the Experimental Station of Vytautas Magnus Uni-versity, Lithuania. Results showed that consumption of diesel fuel was by the 31-46% higher than in single and 22-35% higher than in binary cultivations was found in a ternary crop. This influenced on the highest energy input or near twice higher than in maize and hemp single crops and maize+hemp binary crop, but similar with binary crops with faba bean. Despite this, the productivity of the trinary crop and, at the same time, the energy output were 2-5 times higher than in other treat-ments. This compensated for higher energy inputs and the energy efficiency ratio. In ternary crop, energy productivity was from 1.1 to 2.8 times higher and net energy was by 1.9-5.3 times higher than in other tested cultivations. The highest total GHG emissions were obtained in binary maize+hemp and maize+faba bean cultivations (1729.84 and 2067.33 CO2eq ha-1). Ternary cultiva-tion with the highest energy inputs initiated average GHG emission - 1541.90 kg ha-1 CO2eq. For higher efficiency, ternary crop could be sown and harvested in one machine pass. Faba beans should be included in the ter-nary crops, as their biomass takes a significant part of the total biomass produced. We recommend to review the inter-cropped faba bean seeding rates as faba bean seeds have a high energy input equivalent.

It is important for food recognition to separate each ingredient within a food image at the pixel level. In this paper, we propose a new approach to segment ingredients by utilizing a CNN-based Single-Ingredient Classification Model. In detail, we firstly introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model based on a novel convolutional neural network (CNN) architecture that utilizes an attention mechanism. Afterwards, we propose a new framework for segmentation using the above single-ingredient classification model as the backbone. In this framework, two methods are involved in segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an maximal mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 on the FoodSeg103 dataset. The results confirm that our CNN-based architecture achieves higher segmentation performance compared to ResNet18 and EfficientNet-B0 when used as the backbone for ingredient segmentation. We believe that our ingredient segmentation approach lays the foundation for subsequent ingredient recognition.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

At the highest level of endurance sports, the differences in finishing times are small, so equipment is constantly being constantly improved to enhance the athletes' performance. For instance, one dominant resistive force in connection with cross-country skiing is the friction between the skis and snow and since the 1930s, research designed to understand and reduce this friction has been ongoing. The mechanisms involved in ski-snow friction include compaction, micro-ploughing, adhesion, viscosity, and water-bridging. Of these, adhesive and viscous friction have been most studied, while much less is presently known about compaction, micro-ploughing, and water-bridging. At the macro-scale, the ski-camber profile plays a key role in determining whether, with respect to friction, a particular cross-country ski is suitable for use under a given set of conditions. Through its influence on where ski-snow contact occurs, this ski-camber profile has a profound impact on adhesive and viscous friction, micro-ploughing, the formation of water bridges, and the rate of compaction. Different contact zones with different apparent areas of contact and pressure have been found to be most suitable for different conditions. In addition, the micro-scale texture of the ski base is also an important determinant of ski-snow friction. Topographical measurements of this texture can be achieved with white-light interferometer and recent characterisation has focused on the mechanical properties of contact. In numerous tribological studies the interactions between features at different scales are modelled. In most tribological systems, features on several scales influence friction and ski-snow contact is certainly no exception. Accordingly, the current investigation was designed to evaluate the multi-scale properties of different combinations of two skis with two different base textures. The real area of contact and the average interfacial separation and total average reciprocal interfacial separation between the ski and snow were taken into consideration. We found that different macro- and micro-scale properties of the ski favour different mechanisms of friction. Both the profile of the ski camber and texture of the base play decisive roles in determining viscous friction. At the same time, the texture of the ski base exerts a greater impact on the average real contact pressure, real contact area and minimal average interfacial separation between the ski and snow than does the ski-camber profile.

To predict the remaining useful life (RUL) of proton exchange membrane fuel cell (PEMFC) in advance, a prediction method based on the voltage recovery model and Bayesian optimization of a multi-kernel relevance vector machine (MK-RVM) is proposed in this paper. First, the empirical mode decomposition (EMD) method was used to preprocess the data, and then the MK-RVM was used to train the model. Then, the Bayesian optimization algorithm was used to optimize the weight coefficient of the kernel function to complete the parameter update of the prediction model, and the voltage recovery model was added to the prediction model to realize the rapid and accurate prediction of the RUL of PEMFC. Finally, the method proposed in this paper was applied to the open data set of PEMFC provided by FCLAB, and the prediction accuracy of the RUL of PEMFC was obtained by 95.35%, which showed that the method had good gen-eralization ability and verified the accuracy of the prediction for the RUL of PEMFC.

Eco-labels are an instrument for enabling informed food choices and supporting a demand-sided change towards a more and urgently needed sustainable food system. Lately, novel eco-labels that depict a product’s environmental life-cycle assessment on a multi-level scale are being tested across Europe’s retailers. This study elicits consumers preferences and willingness to pay (WTP) for a multi-level eco-label. A Discrete Choice Experiment was conducted in Austria. Individual partworth utilities were estimated by means of Hierarchical Bayes. Results show higher WTP for a positive evaluated multi-level label, revealing consumers’ perceived benefits of colorful multi-level labels over binary black-and-white designs. Even a negative evaluated multi-level label showed higher WTP compared to no label, pointing towards limited effectiveness of eco-labels. Respondents’ preferences for eco-labels were independent from their subjective eco-label knowledge, health consciousness, and environmental concern. The attribute “protein source” was most important, and preferences for animal-based protein source (beef) was strongly correlated with consumers’ meat attachment, implying that a shift towards more sustainable protein sources is challenging and sustainability labels have only a small impact on the meat product choice of average consumers.

Electrospinning is a simple versatile method to generate nanofibres. Remarkable progress has been made in the development of electrospinning process. The production of nanofibers is affected by many parameters, which influence the final material properties. Electrospun fibers have a wide range of applications such as energy storage and biomedical scaffolds. Polyacrylonitrile (PAN) has been widely used in the production of carbon nanofibers. PAN has received increasing interest in recent years, due to its excellent characteristics, such as spinnability, biocompatibility, and commercial viability, opening new applications in the biotechnological field. This paper provides an overview of the electrospinning process, its main parameters, and operation modes that affect nanofibers fabrication. A review of their biological applications is proposed. A focus is made on PAN fibers formation, functionalization, and application as scaffolds to allow cell growth. Overall, nanofibre scaffolds, made of stabilized PAN, appear as a powerful tool in medical applications needing controlled cell culture.

In response to reducing the energy cost of unmanned surface vehicles (USVs) while overcoming the low accuracy problem in surface target detection, a lightweight detection algorithm with multi-scale feature fusion is proposed. Based on the popular one-stage lightweight Yolov7-tiny target detection model, a lightweight extraction module is designed first by introducing the multiscale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the at-tention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and it satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs bet-ter in a variety of difficult water surface circumstances.

We found an important fact that the beam footprint line of Ping will intersect with the real water depth when the initial incidence angle is about 45°, and sound velocity error hardly has an effect on it. This is contrary to our previous experience that the soundings of the central beam of multi-beam bathymetric system (MBS) is the most accurate. Firstly, the influence of sound velocity errors on central beam and seafloor topography distortion was analyzed. Secondly, the process of proving this fact was given, and the topography correction method was proposed by compensating the sound velocity profile area (SVP area). Finally, we showed in the first simulation of four type errors in SVPs that this fact is verified. The topography correction method used in the second simulation to correct the topography distortion of the first simulation is also verified.

The method propose in this study for stock market multi-objective mathematical model was linear programming using simplex algorithms to optimize the portfolio in the Nigerian Stock Market. The study selected five banks from the list of operators in the market and data were gathered from the banks to have individual market performances over a period of 5years. The data collected contains: Geometric Mean of Monthly Capital Gain Yields and Annual Capital Gain Yields from which Dividend Yields (%) were deducted to arrive Actual rate of return for the banks and the expected rate of return. Furthermore, the risk of semi-absolute deviation below the expected return is reduced. The data was analysed using Python programming because of some clauses in the data gathered. At first, the data assumes integer (-or +) and random in nature. As such, Python programming is one of the software suitable for such solution since the barrier for additivity is broken. Based on the analysis, the study therefore conclude that the potential investor(s) should invest in 13 units of investment x(GTbank Plc), 3 units of First Bank Plc`s Investment (y), 450 units of Zenith bank`s investment (z), and 8 units of Wema bank`s investment (m), and no units of investment of (n) Access bank. These investment quantities will result in the Optimal profit of p= 12797.902 billion naira.

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

Most existing cancer therapies negatively affect normal tissue as well as cancerous tissue. A potentially effective strategy for treating cancer that precludes off-target damage and could be an option for most patients would involve targeting one or more mutations that are ubiquitous in the given patient’s tumor(s). To effect this strategy, one would employ multi-region sequencing of a patient’s primary tumor and metastases to seek out mutations that are shared between all or at least most regions. Once the target or targets are known, one would ideally rapidly generate a molecular switch for at least one of said ubiquitous mutations that can distinguish the mutated DNA, RNA, or protein from the wild-type version and subsequently trigger a therapeutic response. I propose that the therapeutic response involve the replication of an oncolytic virus or intracellular bacterium, as any mutation can theoretically be detected by a vector that enters the cell - and automatic propagation could be very helpful. Moreover, the mutation “signal” can be easily enhanced through transcriptional and translational (if the target is an intracellular protein) enhancement. Importantly, RNA may make the best target for the molecular switches in terms of amplification of the signal and ease of targeting.

Bayesian techniques for engineering problems, that rely on Gaussian process (GP) regression, are known for their ability to quantify epistemic and aleatory uncertainties and for being data efficient. The mathematical elegance of applying these methods usually comes at a high computational cost when compared to deterministic and empirical Bayesian methods. Furthermore, using these methods becomes practically infeasible in scenarios characterized by a large number of inputs and thousands of training data. The focus of this work is on enhancing Gaussian Process-based metamodeling and model calibration tasks, when the size of the training datasets is significantly large. To achieve this goal, we employ a stochastic variational inference algorithm that enables rapid statistical learning of the calibration parameters and hyperparameter tuning, while retaining the rigor of Bayesian inference. The numerical performance of the algorithm is demonstrated on multiple metamodeling and model calibration problems with thousands of training data.

Masons are often overlooked part of impact basins, but play an important role in revealing the lunar history. Previous study in masons were usually limited to gravity data. Few researches were reported on morphology features and chronology, which hampers the construction of a complete geological interpretation for the evolution of each mascons. We use multi source remote sensing data to identify the details characteristic of mascons. Result of topography, gravity and characteristic are combined to prove that a mason beside Copernicus crater is a buried peak-ring basin which is about 130km and 260km in diameter. The underground structure is of confirmed as 890m thick mare basalts by analyzing the spectral feature of the material in a geological outcrop called Copernicus H. Geology evolution analysis joint crater size-frequency distribution (CSDF) dating demonstrate that the buried basin impact event occurred in 3.6Ga. Then a hawaiian-style eruption in late Imbrian formed Sinus Aestuum Ⅰ Dark Mantling Deposit (DMD). Mare basalts filling in 3.4Ga. After that, ejecta from Copernicus impact event in about 820Ma and weathering processes cause the disappearing from lunar surface of the buried basin.

This paper presents a study on the idle vibration reduction of a diesel sport utility vehicle (SUV). To reduce idle vibration, the transmission paths of vibration from the engine to the driver seat floor were investigated with the vehicle components related to idle vibration. Furthermore, operational deflection shape (ODS) tests were conducted to visualize the vibration shapes during engine idling. Experimental modal analyses were performed to obtain the natural frequencies and mode shapes. Through the ODS and modal tests, the vibration characteristics of the diesel SUV during idling were identified. Considering these vibration characteristics, a multi-body dynamic model for the diesel SUV described by differential equations of motion was established to evaluate the idle vibration. To implement the dynamic model effectively, the equivalent stiffnesses and damping coefficients included in the model were determined experimentally or analytically. The established dynamic model was verified by comparing the natural frequencies and idle vibration levels between simulations. Using this dynamic model, we analyzed the effects of various design variables on idle vibration and obtained an optimal design for reducing the idle vibration level. Finally, we present a design guide to reduce the idle vibration for diesel SUVs.

The Vehicle Routing Problem (VRP) and its variants are found in many fields, especially logistics. In this study, we introduced an adaptive method to a complex VRP. It combines multi-objective optimization and several forms of VRPs with practical requirements for an urban shipment system. The optimizer needs to consider terrain and traffic conditions. The proposed model also considers customers' expectations, shipper considerations as goals, and the common goal like transportation cost. We offered compromise programming to approach the multi-objective problem by decomposing the original multi-objective problem into a minimized distance-based problem. We designed a hybrid version of the Genetic algorithm with the Local Search algorithm to solve the proposed problem. We evaluate the effectiveness of the proposed algorithm with the Tabu Search algorithm, the original Genetic algorithm on the tested dataset. The results show that our method is an effective decision-making tool for the multi-objective VRP and an effective solver for the new variation of VRP.

The total lipid and protein content of the invasive caprellid amphipod Caprella scaura, from the biofouling communities of fish farm cages in the Pagasitikos Gulf were analyzed and compared among seasons. Proteins were the most abundant component (48.5 – 49.3%). Lipid content was relatively lower, with a wider range (6.7 – 34%) and showed a distinct seasonal fluctuation with high values in the winter population and a gradual decrease in spring and summer, with the lowest values in Autumn. Composition of the fatty acids profile was consistent among the seasons, with palmitic (16:0), Oleic (18:1n-9), Eicosapentanoic (20:5n-3)(EPA) and Docosahexanoic acid (22:6n-3 )(DHA) being the most abundant fatty acids. The presence of high levels of EPA and DHA fatty acids makes the species a potential candidate for use of these organisms in aquaculture.

The General Theory of Information (GTI) tells us that information is represented, processed and communicated using physical structures. The physical universe is made up of structures combining matter and energy. According to GTI, “Information is related to knowledge as energy is related to matter.” GTI also provides tools to deal with transformation of information and knowledge. We present here, the application of these tools for the design of digital autopoietic machines with higher efficiency, resiliency and scalability than the information processing systems based on the Turing machines. We discuss the utilization of these machines for building autopoietic and cognitive applications in a multi-cloud infrastructure.