Objective This work aims to create an automatic detection process of cardiac structures in both short-axis and long-axis views. A workflow inspired by human thinking process, for better explainability. Methods we began by separating the images into two classes: long axis and short axis, using a Residual Network model. Then, we used Particle Swarm Optimization for general segmentation. After segmentation, a characterization step based on shape descriptors calculated from bounding box and ANOVA for features selection were applied on the binary images to detect the location of each region of interest: lung, left and right ventricle in the short-axis view, the aorta, the left heart (left atrium and ventricle), and the right heart (right atrium and ventricle) in the long axis view. Results we achieved a 90% accuracy on view separation. We have selected: Elongation, Compactness, Circularity, Type Factor, for short axis identification; and:Area, Centre of Mass Y, Moment of Inertia XY, Moment of Inertia YY, for long axis identification. Conclusion a successful separation of long axis and short axis views allows for a better characterization and detection of segmented cardiac structures. After that, any method can be applied for segmentation, attribute selection, and classification. Significance an attempt to introduce explainability into cardiac image segmentation, we tried to mimic the human workflow while computerizing each step. The process seems to be valid and added clarity and interpretability to the detection.
The Zn-Al-CO3²⁻ hydrotalcite (HT) conversion film was developed on hot-dip galvanized steel (HDG), ZnAl coated steel (ZA), and Zn-Al-Mg coated steel (ZAM) substrates by “in situ” growth process for Al³⁺/Zn²⁺ ratio of 5/3 at pH 12. The characteristics of all the ZnAl HT film-coated samples before and after exposure to 0.1 M NaCl for 24 h were investigated by means of Fourier-transform infrared (FT-IR), X-Ray Diffraction (XRD), electron microscopy/energy dispersive X-ray spectroscopy SEM/EDS, and X-ray photoelectron spectroscopy (XPS). The corrosion properties of all the samples were studied by open circuit potential measurements (OCP), polarization curves, and electrochemical impedance spectroscopy (EIS). The electrochemical tests indicated that the HT film-coated can significantly restrain the corrosion of the HDG, ZA, and ZAM substrates but this efficiency was depending on the alloying compounds of the sacrificial layer. The inhibitive effect of HT film-coated on all the corrosion substrates was discussed in detail.
This paper addresses the problem of forecasting, over a daily horizon, quarter hourly profiles of residential photovoltaic (PV) power production for sites with no historical data available. Typically, such forecasts are required for improving the local operation of low-voltage systems, where observability is still a practical challenge. In this context, we develop a cross-learning forecasting approach to predict unobserved PV sites, which exploits common patterns learned from neighboring monitored PV production profiles. Concretely, the proposed approach fits a single, generic forecasting function across the entire panel of monitored PV time series based only on series-specific features – i.e., the peak power installed, geographical position, orientation and inclination – and local numerical weather predictions. This allows to enlarge the dataset for training more complex data-driven techniques, while ensuring scalability for predicting each PV site. The proposed approach is evaluated using a k-nearest neighbors algorithm, different variants of neural networks and gradient boosted trees on five new residential PV sites. Outcomes highlight the ability of the cross-learning forecasting models to better generalize on new PV sites in comparison with a clear sky-based physical approach, without needing any adjustment of the models.
This article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted after obtaining 343 observations with ten different attributes. The predictions are achieved using six immensely used ML techniques. Stratified K-fold cross-validation is mentioned to measure the expertise of proposed models to unsighted data, and Precision, Recall, F-Measure, and AUC Score matrices are determined to assess the efficiency of each model. A comprehensive investigation of this article indicates that the resampling strategy derived from the combination of edited nearest neighbor (ENN) and borderline SVM-based SMOTE and SVM model achieved prominent performance. Additionally, the borderline SVM-based SMOTE and the Adaboost model performs as the second-highest performing model.
The COVID-19 pandemic has inevitably changed people's lifestyles and demands for urban green space and public open space. The National Landscape Garden Cities in China (NLGCC) policy is one of the key development models in China aimed at building sustainable cities and society. In this paper, the development of the study's selection criteria and the significance and benefits of the NLGCC policy are first summarised. 391 cities were chosen from the NLGCC list to analyse the spatial distribution and construction of driving factors. The results show that the NLGCC's selection criteria have shifted from a focus on quantity to overall habitat quality. During the COVID-19 pandemic, city resilience has been examined more closely. The NLGCC policies have boosted to address ecological and environmental crises and enhanced urban disaster preparedness. The spatial distribution analysis shows that the NLGCC is spatially unevenly distributed and has a clustering trend. A total of 54.96% of the NLGCC is concentrated in China's eastern and central regions. The natural environment and socioeconomics are two main categories of driving factors. This study provides significant value to the understanding of the spatial pattern of the NLGCC offers a reference for decision-making about the construction of urban environments worldwide.
Offshore wind generation has developed rapidly in the past few years, leading to an increasing importance in power systems. Therefore, it becomes essential to properly account for aerodynamic effects that affect the power extracted from the wind, and to assess their impact on the power system adequacy. In adequacy studies, due to computational constraints, the power output of offshore wind farms is currently modelled in a simple and approximate way, neglecting important factors such as turbulence and wake effects. This may lead to erroneous, and thus misleading adequacy estimations. Hence, the focus of this paper is to develop data-driven proxy models able to learn the complex relation between free flow wind information and the aggregated power of wind farms. Those Machine Learning-based models are used as fast and reliable surrogates of numerical simulations based on computational fluid dynamics. The developed models are then included in an adequacy study built upon sequential Monte-Carlo simulations. The obtained outcomes are compared with traditional modelling approaches, which allows to quantify the value of the proposed procedure.
The current work outlines the impact of acetylacetone (AcAc) in the alkaline surface treatment bath on durability and the anti-corrosion properties of electro-galvanized steel (EGS) coated with a hybrid silane composition. The surface characteristics of the EGS samples before and after modifications were appraised using atomic force microscopy, field emission scanning electron microscopy, and contact angle measurements confirming the changes in roughness, morphology and wettability properties imposed by surface treatments. To elucidate the chemical composition of the protecting layer developed on the surface of EGS, X-ray photoelectron spectroscopy was utilized which evidently endorsed forming a film consisting of zinc hydroxide, zinc oxide, and zinc acetylacetonate. The corrosion resistance of the specimens during immersion in 3.5 % NaCl medium was determined using electrochemical impedance spectroscopy and polarization experiments. The low-frequency impedance (at 0.01 Hz) of silane-coated samples modified in the optimal condition (0.5 M NaOH bath) in the presence and absence of AcAc was respectively ca. 15,900, 5800 Ω·cm². The electrochemical results corroborated the overriding role of a trace amount of AcAc in the corrosion protection of silane-coated EGS samples. The novel surface treatment proposed in this work provides improved corrosion protection of silane coating on EGS having the lowest icorr and presenting inhibition efficiency of 74 % in polarization experiment.
The present Cerebellar Classic highlights a paper published in 1908 by the American pathologist Simeon Burt Wolbach (1880–1954), in which he reported multiple hernias of the cerebellum for the first time in 9 cases of increased intracranial pressure. The importance of the meninges and the anatomy of involved compartments is emphasized.
The subsurface provides multiple resources of which the exploitation has a lasting impact on future potential provision. Establishing sustainability in terms of fundamental principles, and fitting these principles into a practical framework, is an ongoing endeavour focused mainly on surface activities. The principles of ecological economics lead to six challenges that summarize the current limitations of implementing science-based sustainable management of geological resources in the medium to deep subsurface: integrating value pluralism, defining sustainable scale, evaluating interferences in the subsurface, guaranteeing environmental justice, optimising environmental and economic efficiency, and handling uncertainties. Assessing and managing geological reservoirs is particularly intriguing because of slow resource regeneration, complex spatial and temporal interactions, concealment, and naturally dictated opportunities. In answer to the challenges, visions are proposed that outline how an indicator framework is needed for guidance, how indicators require reservoir models with extended spatial and temporal scope, how environmental inequity of social values are to be considered, and how real option games combined with life cycle assessment can be used for optimising efficiency. These individual solutions are different facets of the same problem, and can be integrated into one overarching solution that takes the form of dynamic multi-criteria decision analysis.
Objectives To investigate effectiveness of olfactory training (OT) in COVID-19 patients with persistent olfactory dysfunction (OD).Methods From March 2020 to March 2022, COVID-19 patients with OD were prospectively followed in three European medical centers for a period of 18 months. A standardized OT protocol were recommended to patients. Patient-reported outcome questionnaires and psychophysical evaluations were used to evaluate olfaction at baseline, 6, 12, and 18 months after the start of OT. The evolution of olfactory outcome was compared according to the adherence to the OT protocol.ResultsFifty-seven patients completed the evaluations. Thirty-two patients fully adhered to the OT, while 25 did not adhere. The psychophysical scores significantly improved from baseline to 6-month post-infection in both groups. In the OT group, the psychophysical scores continued to significantly improve from 6 to 12 months after the start of OT (p = 0.032). The mean duration of OT was 15.4 weeks. The mean delay of patient recovery perception was comparable between groups (27.4 weeks). The occurrence of cacosmia (35.1%) and parosmia (43.9%) throughout the follow-up period was comparable between groups. There proportion of phantosmia was higher in training (34.4%) compared with no-OT (16.0%; p = 0.007) group. The baseline Sniffin’Sticks tests was positively associated with the 6-month Sniffin’Sticks tests (rs = 0.685; p < 0.001) and negatively associated with the time of recovery (rs = − 0.369; p = 0.034).Conclusions The adherence to an OT protocol was associated with better mid-term improvement of psychophysical scores. Future large-cohort randomized-controlled studies are needed to confirm the effectiveness of OT in COVID-19 patients.
We explicitly establish the equivalence between the magnetic Carrollian limit of Einstein gravity defined through the Hamiltonian formalism and the Carrollian theory of gravity defined through a gauging of the Carroll algebra along the lines of standard Poincaré (or (A)dS) gaugings.
Temporary biocompatible and degradable cell scaffolds - the new weapon of tissue engineering in the face of personalized medicine are emerging as one of the most powerful tools for guided self-regeneration of injured, diseased or malfunctioning tissues. In the current study, CPA Ti:sapphire fs laser system (τ = 150 fs, λ = 800 nm, ѵ=0.5 kHz) was used for surface modification of Poly Lactic Acid (PLA) temporary cell scaffolds at fluence F = 0.8 J/cm2 and scanning velocity V = 3.8 mm/s. Additional thin layer of chitosan (Ch)/hydroxyapatite (HAp) (up to 30 ÷ 60 nm thickness) was deposited on the laser-modified PLA matrices by spin coating method for cell scaffolds surface functionalization. In order to observe the complementary impact of fs structuring and spin coating on the PLA scaffolds’ properties, both surface modification methods were applied on the prepared by compression molding PLA samples. Each laser processed sample was analyzed in respect of the corresponding control – laser-treated and untreated PLA surface, spin-coated with Ch or HAp. The microstructured scaffolds were characterized by SEM, EDX, FTIR, roughness, and WCA analyses. The results obtained from characterization of scaffold properties, show that such combined methods application for functionalization of the bone PLA scaffolds could be applied to improve the biocompatibility of the as created PLA-chitosan and PLA- hydroxyapatite hybrid cell matrices.
Elasmobranchs are characterised by the presence of placoid scales on their skin. These scales, structurally homologous to gnathostome teeth, are thought to have various ecological functions related to drag reduction, predator defense or abrasion reduction. Some scales, particularly those present in the ventral area, are also thought to be functionally involved in the transmission of bioluminescent light in deep-sea environments. In the deep parts of the oceans, elasmobranchs are mainly represented by squaliform sharks. This study compares ventral placoid scale morphology and elemental composition of more than thirty deep-sea squaliform species. Scanning Electron Microscopy and Energy Dispersive X-ray spectrometry, associated with morphometric and elemental composition measurements were used to characterise differences among species. A maximum likelihood molecular phylogeny was computed for 43 shark species incuding all known families of Squaliformes. Character mapping was based on this phylogeny to estimate ancestral character states among the squaliform lineages. Our results highlight a conserved and stereotypical elemental composition of the external layer among the examined species. Phosphorus-calcium proportion ratios (Ca/P) slightly vary from 1.8-1.9, and fluorine is typically found in the placoid scale. By contrast, there is striking variation in shape in ventral placoid scales among the investigated families. Character-mapping reconstructions indicated that the shield-shaped placoid scale morphotype is likely to be ancestral among squaliform taxa. The skin surface occupied by scales appears to be reduced in luminous clades which reflects a relationship between scale coverage and the ability to emit light. In luminous species, the placoid scale morphotypes are restricted to pavement, bristle-and spine-shaped except for the only luminescent somniosid, Zameus squamulosus, and the dalatiid Mollisquama mississippiensis. These results, deriving from an unprecedented sampling, show extensive morphological diversity in placoid scale shape but little variation in elemental composition among Squaliformes.
By sharing common assets such as the power grid, prosumers are closely interrelated by their actions and interests. Game theory provides powerful tools for increased coordination among the prosumers to optimize the energy resources. However, depending on the prosumer profiles and the market rules, the individual bills may notably differ and prove to be unfair. In this work, we analyze the outcomes of three relevant game-theoretical billing methods, which are innovatively transposed to the day-ahead scheduling of energy exchange within a liberalized residential community dominated by distributed energy resources. The first two approaches rely on a (static) daily billing scheme, while the third considers a multi-temporal (continuous) billing. The Nash equilibria are computed using distributed algorithms, hence ensuring individual decision-making and avoiding third-party dependencies. The cost distributions are assessed using both a qualitative and a quantitative comparison based on various prosumer profiles in a modern smart grid. It is shown that, depending on the billing option, either the contribution towards the entity (i.e., the ability to improve the global solution) or the individual empowerment (i.e., the ability to bargain) can be preferentially incentivized.
Fifty-five patients who suffered from COVID-19, who were still very ill after several months, with extreme fatigue, effort exhaustion, brain fog, anomia, memory disorder, anosmia, dysgeusia, and other multi-systemic health problems have been followed in a family practice setting between May 2021 and July 2022. Data extracted from the medical records of the 55 patients (40 women), mean age 42.4 (12 to 79 years), and a qualitative study of 6 of them using a semi-open-ended questionnaire allowed to highlight the clinical picture described by WHO as post-acute COVID-19 syndrome (PACS) also known as long COVID. We used brain single-photon emission computed tomography (SPECT-CT) in thirty-two patients with a high severity index and a highly impaired functional status, demonstrating vascular encephalopathy in twenty nine patients and supporting the hypothesis of a persistent cerebral vascular flow disorder in post COVID-19 condition. The patients will benefit from the consortium COVID Human Genetic Effort (covidhge.com) to explore the genetic and immunological basis of their problem, as 23/55 cases don’t have immunological certainty of a COVID-19 infection. There is no known verified treatment. Analyzing the data from the first 52 patients, three categories of patients emerged over time: 16 patients made a full recovery after 6–8 months, 15 patients were able to return to life and work after 12–18 months with some sequelae, both groups being considered cured. In the third group, 21 patients are still very ill and unable to resume their work and life after 18 months. The biopsychosocial consequences on patients’ lives are severe and family doctors are left out in the cold. It is necessary to test the reproducibility of this description, conducted on a small number of patients. Nevertheless, identifying, monitoring and supporting these patients is a necessity in family medicine.
The aging of the world's population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT) i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT is one of domains of IoT where the real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture associating a Things layer where data is retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to addresses concerns of IoMT. Data is processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC and Cloud while a global blockchain secures exchanges and data sharing by means of smart contracts. Our architecture allows to follow elderly person and patient during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion.
Stereochemical control during polymerization is a key strategy of polymer chemistry to achieve semicrystalline engineered plastics. The stereoselective ring-opening polymerization (ROP) of racemic lactide (rac-LA), which can lead to highly isotactic polylactide (PLA), is one of the emblematic examples in this area. Surprisingly, stereoselective ROP of rac-LA employing chiral organocatalysts has been under-leveraged. Here we show that a commercially available chiral thiourea (TU1), or its urea homologue (U1), can be used in conjunction with an appropriately selected N-heterocyclic carbene (NHC) to trigger the stereoselective ROP of rac-LA at room temperature in toluene. Both a high organic catalysis activity (>90% monomer conversion in 5-9 h) and a high stereoselectivity (probability of formation of meso dyads, Pm, in the range 0.82-0.93) can be achieved by thus pairing a NHC and a chiral amino(thio)urea. The less sterically hindered and the more basic NHC, that is, a NHC bearing tert-butyl substituents (NHCtBu), provides the highest stereoselectivity when employed in conjunction with the chiral TU1 or U1. This asymmetric organic catalysis strategy, as applied here in polymerization chemistry, further expands the field of possibilities to achieve bioplastics with adapted thermomechanical properties.
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