Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning based SpO2 prediction from PPG signal's characteristics features

Continuous monitoring of blood oxygen saturation level (SpO2) during the second triage in the high casualty event and determining the hemostability of a patient/victim until arrival to a medical facility, is essential in emergency situations. Using a SmartPatch device attached to a victim's chest that contains a Photoplethysmogram Waveforms (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the SmartPatch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the Sp02 by extracting the set of features from the PPG signal utilizing two Python toolkits, HeartPy and Neurokit, in order to model the Machine learning predictors, using multiple regressors. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 – 100%. The best experimental results - MAE (1.45), MSE (3.85), RMSE (1.96) and RMSLE (0.02) scores are achieved with the Random Forest regressor in the experiment with 7 features extracted from the both toolkits.
A Compact Model for the Variable Switching Dynamics of HfO2 Memristors

This paper presents a novel comprehensive circuit simulation model for the hafnium-dioxide (HfO2) based memristor device. HfO2 memristors exhibit stochasticity and variability in resistance values, both in Low Resistance State (LRS) and High Resistance State (HRS), limiting their usage and performance. Based on the experimental data, the LRS and HRS variability are also influenced by the current compliance and reset voltage of these devices. The proposed Verilog-A model incorporates LRS/HRS variability and stochasticity into the resistance switching dynamics to help the circuit designer assess reliability. The model is based on empirical data and verified by the experimental measurements of HfO2 memristors fabricated in a 65 nm CMOS/memristor process manufactured by SUNY Polytechnic Institute. It also captures the temperature dependency of the device making it a more comprehensive model. Simulation results show excellent agreement with experimental measurements. The model also verifies the tunabilty of LRS and HRS through the compliance current and magnitude of reset voltage, respectively. The I-V characteristic along with ITIR circuit simulation results for the device followed by benchmarking circuit simulation for demonstrating convergence in Cadence Spectre are also presented.
Tei Index Is a Useful Adjunctive Tool in the Diagnostic Workup of Patients with Acute Myocarditis

Background: Tei index (TI) is a combined myocardial performance index, which was found to be more sensitive for overall cardiac dysfunction than systolic or diastolic parameters alone. Currently, there is only limited evidence for this measure in the context of myocarditis. Thus, TI could add additional benefits to conventional diagnostic workup. Methods: TI of patients with myocarditis (n = 40), acute coronary syndrome (n = 29) and controls (n = 50) was retrospectively analyzed concerning its discriminatory ability for myocarditis. Results: TI was most pathological in patients with myocarditis (median 0.41 vs. 0.35 vs. 0.31, p < 0.0001). Its discriminatory ability was better than that of EF or E/e’ (AUCs: TI: 0.71, p < 0.0001; EF: 0.57, p = 0.112; E/e’: 0.64, p = 0.983), which was also verified in logistic regression analysis (B(SE) = 0.81(0.23), p = 0.0004). The association of TI with myocarditis remained significant even after correction for confounders in propensity score weighted analysis. Conclusions: The TI showed a better discriminatory ability for myocarditis than conventional echocardiographic parameters. Since TI is easily conducted, it might be a helpful adjunctive tool to supplement conventional diagnostic modalities in patients with suspected myocarditis.
Degradation of Methylene Blue Using Hydrothermally Synthesized α-Manganese Oxide Nanostructures as a Heterogeneous Fenton Catalyst

Lately, the upsurge in the liberation of synthetic dyes into the environment, primarily by the textile industries, is a threat to the natural habitat and existing ecosystem. Various methods such as adsorption and degradation with nanoparticles are currently being used to degrade those hazardous materials, but still, the yearning for novel methods continues. In this study, hydrothermal reactions were performed at 160°C to synthesize manganese dioxide nanostructures (MnNSs) under different incubation periods that facilitated the comparison of the size, morphology, and crystallinity of MnNSs. The study revealed the change in crystallinity over the incubation period; MnNSs prepared at 24 hrs were highly crystalline among others. Additionally, the size and morphology of MnNSs changed from the sea-urchin or flower-like structure, predominantly sheet/layer form, to nanorods as the reaction proceeded for 24 hrs. Characterization of MnNSs was followed by heterogeneous Fenton’s reaction, using α-manganese dioxide nanostructures, for the degradation of methylene blue (MB). To further understand the catalytic activity of MnNSs, the synthesized nanostructures were subjected to degrade MB at varied time intervals, both with and without hydrogen peroxide (H2O2). Catalytically, MnNSs evinced good potential for degrading MB dye in the presence of H2O2; MnNSs prepared at 24 hrs degraded MB up to 73% within 110 minutes.
Correlation among Composition, Microstructure and Hardness of 7xxx Aluminum Alloy Using Original Statistical Spatial-Mapping Method

The quantitative study of the relationship between material composition, microstructure and properties is of great importance for the improvement in material properties. In this study, the continuous data of elemental composition, recrystallization, hardness and undissolved phase distribution of the same sample in the range of 60 to 150 square millimeters were obtained by high-throughput testing instrument. The distribution characteristics and rules of a single data set were analyzed. In addition, each data set was divided into micro-areas according to the corresponding relationship of location, and the mapping between multi-source heterogeneous micro-area data sets was established to analyze and quantify the correlation between material composition, structure and hardness. The conclusions are as follows: (1) the average size of the insoluble phase in the middle of the two materials is larger than that of the surface, but due to the existence of central segregation, the average area of the T4 insoluble phase showed an abnormal decrease; (2) there was positive micro-segregation of Al, Cr, Ti, and Zr elements, and negative micro-segregation of Zn, Cu, and Fe elements in the recrystallized grains of the T5 middle segregation zone; (3) the growth process of the insoluble phase was synchronous with the recrystallization proportion and the size of the recrystallized grains; (4) the composition segregation and recrystallized coarse grains were the main reasons for the formation of low hardness zone in T4 and T5 materials, respectively.
Modeling and Synergetic Simulation of a Lower Limb Exoskeleton Robot With the Human Subject

This paper conducts the study on the dynamic modeling and simulation of a lower limb exoskeleton with the human subject in the loop. Lagrangian method is used for the theoretical derivation and formulation of the dynamics of the synergetic human-exoskeleton system. The standing-up motion of the human subject from a chair is simulated for demonstration in MSC ADAMS software. The associated joint angles of both the human subject and exoskeleton robot are recorded and compared for studying the synergetic effect between the human subject and exoskeleton robot. The simulation results show that there are certain amount of motion discrepancy between the human subject and lower limb exoskeleton during operation, which makes accurate control difficult and could even lead to possible unbalance of the entire human-exoskeleton system.
Completeness results for many-valued \Lukasiewicz modal systems and relational semantics

The paper is dedicated to the problem of adding a modality to the \Lukasiewicz many-valued logics in the purpose of obtaining completeness results for Kripke semantics. We define a class of modal many-valued logics and their corresponding Kripke models and modal many-valued algebras. Completeness results are considered through the construction of a canonical model. Completeness is obtained for modal finitely-valued logics but also for a modal many-valued system with an infinitary deduction rule. We introduce two classes of frames for the finitely-valued logics and show that they define two distinct classes of Kripke-complete logics.
β-Galactosylceramidase Deficiency Causes Upregulation of Long Pentraxin-3 in the Central Nervous System of Krabbe Patients and Twitcher Mice

Globoid cell leukodystrophy (GLD), or Krabbe disease, is a neurodegenerative sphingolipidosis caused by genetic deficiency of lysosomal β-galactosylceramidase (GALC), characterized by neuroinflammation and demyelination of the central (CNS) and peripheral nervous system. The acute phase protein long pentraxin-3 (PTX3) is a soluble pattern recognition receptor and a regulator of innate immunity. Growing evidence points to the involvement of PTX3 in neurodegeneration. However, the expression and role of PTX3 in the neurodegenerative/neuroinflammatory processes that characterize GLD remain unexplored. Here, immunohistochemical analysis of brain samples from Krabbe patients showed that macrophages and globoid cells are intensely immunoreactive for PTX3. Accordingly, Ptx3 expression increases throughout the course of the disease in the cerebrum, cerebellum, and spinal cord of GALC-deficient twitcher (Galctwi/twi) mice, an authentic animal model of GLD. This was paralleled by the upregulation of proinflammatory genes and M1-polarized macrophage/microglia markers and of the levels of PTX3 protein in CNS and plasma of twitcher animals. Crossing of Galctwi/twi mice with transgenic PTX3 overexpressing animals (hPTX3 mice) demonstrated that constitutive PTX3 overexpression reduced the severity of clinical signs and the upregulation of proinflammatory genes in the spinal cord of P35 hPTX3/Galctwi/twi mice when compared to Galctwi/twi littermates, leading to a limited increase of their life span. However, this occurred in the absence of a significant impact on the histopathological findings and on the accumulation of the neurotoxic metabolite psychosine when evaluated at this late time point of the disease. In conclusion, our results provide the first evidence that PTX3 is produced in the CNS of GALC-deficient Krabbe patients and twitcher mice. PTX3 may exert a protective role by reducing the neuroinflammatory response that occurs in the spinal cord of GALC-deficient animals.
Optimal Scheduling of Integrated Energy System Based on Price Demand Response Mechanism

With the rapid development of national economy and productivity, energy demand is also rising rapidly, and environmental pollution is becoming increasingly serious. The use of clean and efficient energy has become the mainstream of current development. Integrated energy system has become one of the effective ways to reduce environmental pollution and improve energy efficiency. This paper proposes an integrated energy system scheduling model considering the comprehensive flexibility of supply and demand side. Firstly, an integrated energy system model including electric-gas-thermal coupling is established. Through the complementary power grid and wind and solar power generation and flexible regulation of gas turbine, gas boiler, exergy boiler and other energy conversion equipment, give full play to the utilization rate of multi-energy. In the demand side, the electric and gas comprehensive demand response is added to achieve economic and efficient operation mode by reasonable regulation of interruptible and time-varying loads. Taking the minimum operating cost of the system as the objective function, the scheduling model is solved by calling CPLEX solver on MATLAB platform. The example analysis of different scenarios verifies that the low carbon dispatching model of integrated energy system can effectively balance the economic and environmental benefits of the system.
Malignant Catarrhal Fever in Sardinia (Italy): A Case Report

Simple Summary Malignant Catarrhal Fever is a globally distributed disease that is fatal to susceptible species such as cattle. Sheep represent the reservoir species, and the Mediterranean island of Sardinia, which hosts a large number of these animals, is one geographic area where virus can easily spread. The aim of our study was to investigate a case of Malignant Catarrhal Fever in a calf, also studying the prevalence of the virus responsible, Ovine Herpesvirus type 2, among sheep in Sardinia to further investigate the epidemiological aspects. The analyses performed were consistent among each other; indeed, the histological analysis revealed patterns of lesions, which are commonly reported in literature, in many tissue samples of the calf object of the study. We also found a considerable number of copies of viral genomes in all examined organs of the animal. Phylogenetic analyses suggested the possible occurrence of a unique genetic cluster that is widely distributed across the whole Italian territory. In conclusion, the present study provides a comprehensive overview on the Malignant Catarrhal Fever in an area where, despite the high prevalence of the Ovine Herpesvirus type 2 found among sheep, the sporadic occurrence of clinical disease in bovine should be still deeply investigated. Abstract Using a multidisciplinary approach, this report describes a clinical case of malignant catarrhal fever (MCF) occurring in a calf, which shared the pasture with sheep on a farm located in the island of Sardinia (Italy). We confirmed the conventional clinico-histopathological features of MCF, as well was the presence of Ovine herpesvirus type 2 (OvHV-2) DNA in several tissues, employing histological and virological investigations. The phylogenetic analysis revealed that this Sardinian OvHV-2 strain is genetically similar to all the other Italian strains. By Real Time PCR examinations of blood samples collected across Sardinia’s sheep population, which is considered the most important reservoir species, we discovered an OvHV-2 prevalence ranging from 20 to 30 percent. Despite the high prevalence of OvHV-2 in the Sardinian sheep population, clinical disease in bovine remains sporadic; further investigations are needed to understand the risk factors that regulate this epidemiological aspect.
Why won’t you leave the process alone? Exploring emotional, motivational and cognitive mechanisms triggering tampering

Quality management focuses on improvements aimed at enhancing the value created for various stakeholders. However, actions taken in response to a perceived problem sometimes lead to an inferior result rather than an improvement. Such actions have been denoted tampering. As an example, natural variation might unnecessarily be perceived as a problem, where the best way to handle that ‘problem’ would be to not act at all. However, for someone trained in quality management and striving for improvement, doing nothing might be more challenging than acting. The research question studied in this paper is: How do emotional, motivational and cognitive mechanisms influence tampering in problem-solving situations? Through interviews with 17 respondents, 33 tampering situations were identified and analysed. The findings describe emotional, motivational and cognitive mechanisms that lead individuals to take actions leading to tampering. Some examples of these mechanisms are fear of the consequences of not acting, feelings of guilt if not acting to resolve a problem, and taking actions based on experiences of past problem-solving. To counteract this, it is advised to set aside time for reflections before starting problem-solving and to establish an environment encouraging of reflections.
Adaptive Interfaces for Mobile Map-based Visualisation

— Mobile visualisation systems present many challenges for designers, due to limited screen space, processing power, memory and network bandwidth. These problems are particularly noticeable in mobile map-based visualisation systems, where a large information space must be visualised using a very small display area. Existing mobile visualisation systems use a variety of techniques to overcome these problems. Existing techniques, however, leave a lot to be desired. In this paper, adaptive user interfaces, which adapt to the individual characteristics of the user, are proposed as an alternative approach. A model is presented, which incorporates an adaptive user interface into a mobile map-based visualisation system. A prototype is then described, showing the successful implementation of the proposed model.
A soft sensor based on FBG technology for heart rate monitoring in archery

Archery is a static sport that requires high postural stability during shooting since every tiniest movement can negatively compromise the archer's performance. One of the main crucial activities responsible for involuntary movements is the heart beating. However, keeping low HR is not always implementable especially during biggest tournaments. Innovative technology such as wearables can serve as an important opportunity to quantitatively measure HR during practice and competition without limiting the archer movements. In the literature, wearable inertial units (IMUs) have been proposed for monitoring HR but their rigidness and low body compliance limit the use in the field. Soft sensors can overcome this issue by improving the adherence to the skin through the sensor encapsulation into flexible substrates. Soft sensors based on Fiber Bragg gratings (FBGs) are reaching growing interest for HR monitoring due to their superior properties (e.g., miniaturized size, high sensitivity to strain, good frequency response and capability of working in harsh environments) over conventional methods. This study exploits the advantages of an FBG-based soft for HR monitoring in archery. Firstly, the fabrication and the metrological characterization of the soft sensor was described. Then, a preliminary assessment was carried out to investigate the sensor performance in HR monitoring. The promising results together with the possibility of automatically identifying the shooting session on the FBG signal will be useful for driving personalized training strategies according to the way in which the athlete responses to stressful conditions during both practice and official tournaments.
Mechano-Magnetic Telemetry For Urban Infrastructure Monitoring

Many cities seek utilities monitoring with centrally managed Internet of Things (IoT) systems. This requires the development of numerous reliable low-cost wireless sensors, such as water temperature and flow meters, that can transmit information from subterranean pipes to surface-mounted receivers. Traditional radio communication systems are either unable to penetrate through multiple feet of earthen and manmade material, or have impractically large energy requirements which necessitate either frequent replacement of batteries, or a complex (and expensive) built-in energy harvesting system. Magnetic signaling systems do not suffer from this drawback: low-frequency electromagnetic waves have been shown to penetrate well through several feet of earth and water. In the past, these signals were too weak for practical use; however, this has changed with the recent proliferation of high-sensitivity magnetometers and compact rare-earth magnets. A permanent magnet can be either rotated or vibrated to create an oscillating magnetic field. Utilizing this phenomenon, two types of magnetic transmitter are investigated in this study: one which uses a propeller to directly rotate a diametrically magnetized neodymium magnet; and a second in which a permanent magnet is oscillated back-and-forth across a novel soft-magnet Y-stator, which projects a switching magnetic field. In principle, these oscillating magnetic fields can be used for communication from subterranean infrastructure sensors—such as flow meters and leak detection devices—to an aboveground long range (LoRa) radio-networked Arduino receiver equipped with a magnetometer. Simulation software models the oscillating electromagnetic fields produced by the Y-stator configuration. Laboratory performance and field tests establish the capability of two IoTlinked leak-detection sensors that use magnetic telemetry. Remote datalogging demonstrates the viability of integrating many sensors and surface receivers into a single LoRa wireless IoT network.
Expression of MicroRNAs in Sepsis-Related Organ Dysfunction: A Systematic Review

Sepsis is a critical condition characterized by increased levels of pro-inflammatory cytokines and proliferating cells such as neutrophils and macrophages in response to microbial pathogens. Such processes lead to an abnormal inflammatory response and multi-organ failure. MicroRNAs (miRNA) are single-stranded non-coding RNAs with the function of gene regulation. This means that miRNAs are involved in multiple intracellular pathways and thus contribute to or inhibit inflammation. As a result, their variable expression in different tissues and organs may play a key role in regulating the pathophysiological events of sepsis. Thanks to this property, miRNAs may serve as potential diagnostic and prognostic biomarkers in such life-threatening events. In this narrative review, we collect the results of recent studies on the expression of miRNAs in heart, blood, lung, liver, brain, and kidney during sepsis and the molecular processes in which they are involved. In reviewing the literature, we find at least 122 miRNAs and signaling pathways involved in sepsis-related organ dysfunction. This may help clinicians to detect, prevent, and treat sepsis-related organ failures early, although further studies are needed to deepen the knowledge of their potential contribution.
Global Semantic Descriptors for Zero-Shot Action Recognition

The success of zero-shot action recognition (ZSAR) methods is intrinsically related to the nature of semantic side information used to transfer knowledge, although this aspect has not been primarily investigated in the literature. This work introduces a new ZSAR method based on the relationships of actions-objects and actions-descriptive sentences. We demonstrate that representing all object classes using descriptive sentences generates an accurate object-action affinity estimation when a paraphrase estimation method is used as an embedder. We also show how to estimate probabilities over the set of action classes based only on a set of sentences without hard human labeling. In our method, the probabilities from these two global classifiers (i.e., which use features computed over the entire video) are combined, producing an efficient transfer knowledge model for action classification. Our results are state-of-the-art in the Kinetics-400 dataset and are competitive on UCF-101 under the ZSAR evaluation. Our code is available at https://github.com/valterlej/objsentzsar.