Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Value of Net Metering Outcomes for Different Averaging Time Steps

The installation of distributed energy resources (DER) heavily impacts on the power patterns of the prosumers. In fact, the variability of the generation, together with the technical characteristics of the storage systems, may introduce a huge variety in the shape of the net power curves seen from the point of common coupling (PCC). This leads to completely rethink the definition of the time series required to create homogeneous group of prosumers, for providing useful tools to manage the emerging paradigms in the electricity system, such as energy communities and local energy markets. Moreover, the differences between the local energy production and consumption at the PCC could become hidden, if the local energy management has to be considered as a private decision of the local user. In this case, only net metering (that implies a unique measurement of the net electricity taken from the grid) will be used to evaluate the impact on the network of the net power curves. Hence, new approaches are required to properly measure the electricity exchange at the PCC. This paper addresses how the net metering outcomes depend on the time resolution of the measured data, and how the information taken from net metering can be valued by giving different price rates to positive and negative values. Specific examples are provided to remark the importance of the time resolution to properly characterise the prosumers.
Cardiovascular Diseases Prediction Using Artificial Neural Networks: A Survey

Cardiovascular diseases (CVD) are always been one of the diseases that cannot be ignored. Focusing on the field of preventive medicine related to CVD prediction, our preliminary literature has found about 891 related papers on artificial intelligence(AI), more than one-third adopted artificial neural network(ANN). As a result, this paper aims to provide such a survey of CVD prediction in ANN. After the completion of this survey, it's believed that a more effective and accurate CVD prediction system can be developed.
Proteomic analysis of Taenia spp. excretion/secretion proteins: the search for Taenia solium-specific diagnostic antigens

Tandem mass spectrometry is commonly used to identify peptides, typically by comparing the experimental spectra with those predicted from a protein sequence database and scoring these matches. The most reported quality metric for a set of peptide identifications is the false discovery rate (FDR), the fraction of expected false identifications in the set. This metric has so far only been used for completely sequenced organisms or known protein mixtures. It is worthwhile to investigate whether FDR estimations are also applicable in the case of partially sequenced organisms, where many highquality spectra fail to identify the correct peptides because the latter are not present in the searched sequence database. Using real data from human plasma and simulated partial sequence databases derived from two complete human sequence databases with different levels of redundancy, the mixture model-based approach in PeptideProphet was found to be robust for partial databases, particularly if used in combination with decoy sequences, regardless of how these decoy sequences were created (reversed, scrambled or randomised). It is therefore recommended to use this method when estimating the FDR from incompletely sequenced organisms. Adapted from: Victor, B. , Gabriël, S. , Kanobana, K. , Mostovenko, E. , Polman, K. , Dorny, P., Deelder, A. M. , and Palmblad, M. Partially Sequenced Organisms, Decoy Searches and False Discovery Rates. Journal of Proteome Research 11, 3 (2012), 1991–1995.
Development and validation of a simplified nomogram predicting individual critical illness of risk in COVID‐19: A retrospective study

This study aims to screen useful predictors of critical cases among coronavirus disease 2019 (COVID‐19) patients and to develop a simple‐to‐use nomogram for clinical utility. A retrospective study was conducted that consisted of a primary cohort with 315 COVID‐19 patients and two validation cohorts with 69 and 123 patients, respectively. Logistic regression analyses were used to identify the independent risks of progression to critical. An individualized prediction model was developed, and calibration, decision curve, and clinical impact curves were used to assess the performance of the model. External validations for the predictive nomogram were also provided. The variables of age, comorbid diseases, neutrophil‐to‐lymphocyte ratio, d‐dimer, C‐reactive protein, and platelet count were estimated to be independent predictors of progression to critical, which were incorporated to establish a model of the nomogram. It demonstrated good discrimination (with a C‐index of 0.923) and calibration. Good discrimination (C‐index, 0.882 and 0.906) and calibration were also noted on applying the nomogram in two validation cohorts. The clinical relevance of the nomogram was justified by the decision curve and clinical impact curve analysis. This study presents an individualized prediction nomogram incorporating six clinical characteristics, which can be conveniently applied to assess an individual's risk of progressing to critical COVID‐19.
Control strategy of hydraulic cylinder based on Deep Reinforcement Learning

Authors developed a novel control strategy of hydraulic cylinder based on deep reinforcement learning. The control parameters of hydraulic cylinder are difficult to regulate for practical applications, and problems of force and oil pressure disturbance occur during the operation process. A class of reinforcement learning agents developed for hydraulic systems is designed based on the deep deterministic policy gradient and proximal policy optimization algorithms. The agents are trained by a significant number of system data. After learning completion, they can automatically control the hydraulic system online and consequently the system can always maintain a good control performance. Experiments are conducted to verify the proposed control strategy. Results show that the proposed method can achieve better performance that conventional proportional-integral-derivative regulator and effectively overcome the effects of disturbance.
Effects of e‐cigarettes and vaping devices on cardiac and pulmonary physiology

E‐cigarette aerosols are exceedingly different from conventional tobacco smoke, containing dozens of chemicals not found in cigarette smoke. It is highly likely that chronic use of e‐cigarettes will induce pathological changes in both the heart and lungs. Here we review human and animal studies published to date and summarize the cardiopulmonary physiological changes caused by vaping. In terms of cardiac physiology, acute exposure to e‐cigarette aerosols in human subjects led to increased blood pressure and heart rate, similar to traditional cigarettes. Chronic exposure to e‐cigarette aerosols using animal models caused increased arterial stiffness, vascular endothelial changes, increased angiogenesis, cardiorenal fibrosis and increased atherosclerotic plaque formation. Pulmonary physiology is also affected by e‐cigarette aerosol inhalation, with increased airway reactivity, airway obstruction, inflammation and emphysema. Research thus far demonstrates that the heart and lung undergo numerous changes in response to e‐cigarette use, and disease development will depend on how those changes combine with both environmental and genetic factors. E‐cigarettes have been advertised as a healthy alternative to cigarette smoking, and users are under the impression that vaping of e‐cigarettes is harmless, but these claims that e‐cigarettes are safer and healthier are not based on evidence. Data from both humans and animal models are consistent in demonstrating that vaping of e‐cigarettes causes health effects both similar to and disparate from those of cigarette smoking. Further work is needed to define the long‐term cardiopulmonary effects of e‐cigarette use in humans.
Research status of wafer level packaging for RF MEMS switches

Compared with traditional switches, RF MEMS switches are characterized by low power consumption, low insertion loss, high isolation, good linearity and high integration. However, the high cost of packaging process has great impact on the device performance among the RF MEMS processing. This paper overviews the research status of RF MEMS switch, such as the structural design, the wafer-level packaging. The review will focus on the wafer-level packaging of RF MEMS switch and the following conclusions are drawn by comparing the different methods of wafer level packaging: 1) The packaging of RF MEMS switches mainly adopts WLP methods, which are divided into two types, cap packaging and thin film packaging. Thin film packaging is more promising due to it presenting a lower temperature than cap packaging. 2) The low temperature bonding technology is used in the cap packaging for RF MEMS switch, and the cap packaging has gradually matured. 3) The sacrificial layer release technique is needed in the thin film packaging, and the contamination problem caused by clean technology in the packaging process still needs to be solved. 4) As cap materials or sealing ring materials, polymer is a low-temperature encapsulation material, which has great development potential. But, it is too sensitive to humidity, and it still need a deeper study. The reviews are presented to get a better understanding of the state of the art work done in the field RF-MEMS package for nearly past twenty years.
Gene expression patterns in Fraxinus excelsior clones with contrasting susceptibility to Hymenoscyphus fraxineus

This study aimed at investigating gene expression patterns in resistant and susceptible clones of Fraxinus excelsior L. (European ash) in response to infection with Hymenoscyphus fraxineus. A further objective of this study was to investigate the role of ABA8-hydroxylase genes (ABA8H) in European ash under infection with H. fraxineus. To address the first aim, bark of susceptible and resistant clones were infected with H. fraxineus and samples were taken after 9 months from the margins of necrosis. Total RNA was extracted and sequenced by next generation Illumina sequencing technique. To identify differentially expressed genes (DEGs), sequences were aligned to the European ash draft genome before analyzing in the Cufflinks pipeline. The DEGs were then annotated in Blast2go. More than 87,000 transcripts were presented in the library. In the inoculated susceptible clones, up-regulation of the 2-hydroxyisoflavanone dehydratase-like (HID) gene, associated with formation of secondary metabolites, was observed. The over expression of caffeoyl- o- methyltransferase (COMT) and farnesyl diphosphate synthase was also detected in the inoculated susceptible clones. In the inoculated resistant clones, the defense response genes such as 1-aminocyclopropane-1-carboxylate oxidase (ACC), some of the ethylene-responsive transcriptional factors (ERFs) and WRKY- transcriptional factors (TFs) were up-regulated. The peroxidase-like and pathogen- related protein (PR) genes, secoisolariciresinol dehydrogenase-like, flavonol syn- thase flavanone 3-hydroxylase-like, phenylalanine ammonia-lyase and shikimate chloroplastic-like genes were up-regulated but ERFs were down-regulated in both susceptible and resistant clones in response to H. fraxineus inoculation. Shikimate chloroplastic-like acts in the secondary metabolite pathway just like flavonol syn- thase flavanone 3-hydroxylase-like. To address the second aim, susceptible genotypes of the European ash were subjected to biotic stress which included wounding and inoculation with H. fraxineus. RNA was extracted from lesions and expression of candidate genes, i.e. three ABA8H genes and two encoding NAC-TFs was quantified. Some NAC genes are known to have a role in abscisic acid (ABA) signaling pathway. The European ash ABA8H and NAC genes were up-regulated 1 day after inoculation but already by day 7, they had been down-regulated. It is possible that the NAC genes contribute to the ABA biosynthesis pathway and thereby play roles in the defense mechanism.
Micro-LED Mass Transfer Technologies

Micro-LED is an emerging optoelectronic technology with the advantages of high brightness, self-illumination, fast response time and long life. It is considered to be the next generation display technology and has attracted the attention of researchers and display companies around the world. But the further development of micro-LED still faces some difficulties, one of which is the mass transfer. A variety of technologies have been used to solve the problem of mass transfer. This article will discuss several main solutions currently being developed.
Data-driven sustainable smart manufacturing: A conceptual framework

The IoT technologies, big data analytics, cloud computing, and artificial intelligence advances fundamentally impacted today’s manufacturing systems and increased the growth of data generated from manufacturing. Big data as a driver key of intelligent manufacturing empowered companies to adopt data driven approaches to enhance competitiveness, efficiency and sustainability. In this paper a historical evolution of data in manufacturing is overviewed, the smart manufacturing its key technologies and sustainability are related, and a conceptual framework from lifecycle product perspective was proposed to show potential applications of big data analytics in sustainable smart manufacturing.
A Time-Aware Graph Neural Network for Session-Based Recommendation

Recently, Graph Neural Networks (GNNs) have attracted increasing attention in the field of session-based recommendation, due to its strong ability on capturing complex interactive transitions within sessions. However, existing GNN-based models either lack the use of user’s long-term historical behaviors or fail to address the impact of collaborative filtering information from neighbor users on the current session, which are both important to boost recommendation. In addition, previous work only focuses on the sequential relations of interactions while neglects the time interval information which can imply the correlations between different interactions. To tackle these problems, we propose a Time-Aware Graph Neural Network (TA-GNN) for session-based recommendation. Specifically, we first construct a user behavior graph by linking the interacted items of the same user according to their corresponding time order. A time-aware generator is designed to model the correlations between different nodes of the user behavior graph by considering the time interval information. Moreover, items from the neighbor sessions of the current session are selected to build a neighborhood graph. Then the two graphs are respectively processed by two different modules to learn the representation of the current session, which is applied to produce the final recommendation list. Comprehensive experiments show that our model outperforms state-of-the-art baselines on three real world datastes. We also investigate the performance of TA-GNN on different numbers of historical interactions and on different session length, finding that our model presents consistently advantages under different conditions.
Big Data Processing and Artificial Intelligence at the Network Edge

The delivery of computing services – including storage, databases, networking, software, analytics, intelligence and more is moving from the cloud towards the edge; among all technical requirements, the edge needs to be able to natively support artificial intelligence processes to enable new technological paradigms, such as smart Factory 4.0, healthcare assisted living, smart cities and advanced robotics, for example. In this paper, we will present the enabling technologies to will be developed within the “Big data processing and artificial intelligence at the network edge” (BRAINE) project, one of the largest joint activities in Europe in the area of edge computing enabling artificial intelligence. BRAINE covers the full stack of a system solution, from subsystem integration up to artificial intelligence service provisioning, and includes four use cases of direct impact in the European ecosystem.
Method of Ontology Use in OODA

The article examines the behavior of an intellectual agent in a competitive environment. An OODA loop is selected to model the behavior. The interaction of the stages of the OODA loop (observation, orientation, decision-making, action) with the ontology of the tasks and the domain within which this agent functions is considered.
A Small Study of Bacterial Contamination of Anaerobic Digestion Materials and Survival in Different Feed Stocks

If pathogens are present in feedstock materials and survive in anaerobic digestion (AD) formulations at 37 °C, they may also survive the AD process to be disseminated in digestate spread on farmland as a fertilizer. The aim of this study was to investigate the prevalence of Salmonella spp. , Escherichia coli O157, Listeria monocytogenes, Enterococcus faecalis and Clostridium spp. in AD feed and output materials and survival/growth in four formulations based on food waste, bovine slurry and/or grease-trap waste using International Organization for Standardization (ISO) or equivalent methods. The latter was undertaken in 100 mL Ramboldi tubes, incubated at 37 °C for 10 d with surviving cells enumerated periodically and the T90 values (time to achieve a 1 log reduction) calculated. The prevalence rates for Salmonella spp. , Escherichia coli O157, Listeria monocytogenes, Enterococcus faecalis and Clostridium spp. were 3, 0, 5, 11 and 10/13 in food waste, 0, 0, 2, 3 and 2/3 in bovine slurry, 1, 0, 8, 7 and 8/8 in the mixing tank, 5, 1, 17, 18 and 17 /19 in raw digestate and 0, 0, 0, 2 and 2/2 in dried digestate, respectively. Depending on the formulation, T90 values ranged from 1.5 to 2.8 d, 1.6 to 2.8 d, 3.1 to 23.5 d, 2.2 to 6.6 d and 2.4 to 9.1 d for Salmonella Newport, Escherichia coli O157, Listeria monocytogenes, Enterococcus faecalis and Clostridium sporogenes, respectively. It was concluded that AD feed materials may be contaminated with a range of bacterial pathogens and L. monocytogenes may survive for extended periods in the test formulations incubated at 37 °C.
Coleorhiza-enforced seed dormancy: a novel mechanism to control germination in grasses.

How the biophysical properties of overlaying tissues control growth, such as the embryonic root (radicle) during seed germination, is a fundamental question. In eudicot seeds the endosperm surrounding the radicle confers coat dormancy and controls germination responses through modulation of its cell wall mechanical properties. Far less is known for grass caryopses that differ in tissue morphology. Here we report that the coleorhiza, a sheath-like organ that surrounds the radicle in grass embryos, performs the same role in the grass weed Avena fatua (common wild oat). We combined innovative biomechanical techniques, tissue ablation, microscopy, tissue-specific gene and enzyme activity expression with the analysis of hormones and oligosaccharides. The combined experimental work demonstrates that in grass caryopses the coleorhiza indeed controls germination for which we provide direct biomechanical evidence. We show that the coleorhiza becomes reinforced during dormancy maintenance and weakened during germination. Xyloglucan endotransglycosylases/hydrolases may have a role in coleorhiza reinforcement through cell wall remodelling to confer coat dormancy. The control of germination by coleorhiza-enforced dormancy in grasses is an example for the convergent evolution of mechanical restraint by overlaying tissues.
Quantitative analysis of subcellular distributions with an open-source, object-based tool

ABSTRACT The subcellular localization of objects, such as organelles, proteins, or other molecules, instructs cellular form and function. Understanding the underlying spatial relationships between objects through colocalization analysis of microscopy images is a fundamental approach used to inform biological mechanisms. We generated an automated and customizable computational tool, the SubcellularDistribution pipeline, to facilitate object-based image analysis from three-dimensional (3D) fluorescence microcopy images. To test the utility of the SubcellularDistribution pipeline, we examined the subcellular distribution of mRNA relative to centrosomes within syncytial Drosophila embryos. Centrosomes are microtubule-organizing centers, and RNA enrichments at centrosomes are of emerging importance. Our open-source and freely available software detected RNA distributions comparably to commercially available image analysis software. The SubcellularDistribution pipeline is designed to guide the user through the complete process of preparing image analysis data for publication, from image segmentation and data processing to visualization. This article has an associated First Person interview with the first author of the paper. Summary: The SubcellularDistribution pipeline is an open-source, automated, and customizable tool that guides users through image segmentation, data processing and visualization to analyze object-based colocalization within fluorescence microscopy images.
Prognostic, clinical, and therapeutic importance of RANTES‐CCR5 axis in hepatitis A infection: A multiapproach study

Fulminant hepatic failure (FHF) is a lethal manifestation of hepatitis A virus (HAV) infection, whose underlying mechanisms are poorly understood. We aimed to evaluate the importance of the modulation of the RANTES‐chemokine receptor type 5 (CCR5) signaling axis and its immunomodulatory effects in directing hepatitis A disease pathogenesis using an in silico, in vitro and patient cohort‐based approach. In silico interaction studies were performed using computation approaches with suitable software. Differential expression of relevant cytokines and immune cell markers were studied using real‐time quantitative reverse transcription PCR (qRT‐PCR), enzyme‐linked immunosorbent assay, and flow‐cytometry‐based methods. In the HepG2 cell line, we studied inflammatory responses and susceptibility to HAV infection following RANTES stimulation and antibody blockade of CCR5. The HAV‐VP3 region exhibited high interaction in CCR5: HAV complexes. RANTES levels were significantly increased in FHF cases. Reduced monocyte and T‐cell activation were observed in FHF cases. RANTES expression inversely correlated with viremia but positively correlated with proinflammatory responses. Hyper Th1‐biased immune responses, marked by high interleukin (IL)‐12/IL‐10 ratio were observed in FHF cases, which were also characterized by upregulated tumor necrosis factor‐alpha (TNF‐α) expression and reduced interferon‐gamma expression. In vitro, RANTES was protective against HAV infection but resulted in upregulated TNF‐α expression. Although viral load increased upon the regulation of inflammatory responses by CCR5 blocking, it was still significantly lower compared to control HAV‐infected cells. Our study suggests the importance of RANTES‐CCR5 signaling and linked‐immunomodulation in HAV disease pathogenesis, as well as highlights the utility of CCR5 antagonists as a risk‐reduction strategy in FHF patients. Our findings, therefore, have important implications for the management of high‐risk HAV infections.