Miracle-dz/UniFAR-FTUs huggingface dataset:
Expectations of Anesthesiology and Intensive Care Professionals Toward Artificial Intelligence: Observational Study

Background Artificial intelligence (AI) applications offer numerous opportunities to improve health care. To be used in the intensive care unit, AI must meet the needs of staff, and potential barriers must be addressed through joint action by all stakeholders. It is thus critical to assess the needs and concerns of anesthesiologists and intensive care physicians related to AI in health care throughout Europe. Objective This Europe-wide, cross-sectional observational study investigates how potential users of AI systems in anesthesiology and intensive care assess the opportunities and risks of the new technology. The web-based questionnaire was based on the established analytic model of acceptance of innovations by Rogers to record 5 stages of innovation acceptance. Methods The questionnaire was sent twice in 2 months (March 11, 2021, and November 5, 2021) through the European Society of Anaesthesiology and Intensive Care (ESAIC) member email distribution list. A total of 9294 ESAIC members were reached, of whom 728 filled out the questionnaire (response rate 728/9294, 8%). Due to missing data, 27 questionnaires were excluded. The analyses were conducted with 701 participants. Results A total of 701 questionnaires (female: n=299, 42%) were analyzed. Overall, 265 (37.8%) of the participants have been in contact with AI and evaluated the benefits of this technology higher (mean 3.22, SD 0.39) than participants who stated no previous contact (mean 3.01, SD 0.48). Physicians see the most benefits of AI application in early warning systems (335/701, 48% strongly agreed, and 358/701, 51% agreed). Major potential disadvantages were technical problems (236/701, 34% strongly agreed, and 410/701, 58% agreed) and handling difficulties (126/701, 18% strongly agreed, and 462/701, 66% agreed), both of which could be addressed by Europe-wide digitalization and education. In addition, the lack of a secure legal basis for the research and use of medical AI in the European Union leads doctors to expect problems with legal liability (186/701, 27% strongly agreed, and 374/701, 53% agreed) and data protection (148/701, 21% strongly agreed, and 343/701, 49% agreed). Conclusions Anesthesiologists and intensive care personnel are open to AI applications in their professional field and expect numerous benefits for staff and patients. Regional differences in the digitalization of the private sector are not reflected in the acceptance of AI among health care professionals. Physicians anticipate technical difficulties and lack a stable legal basis for the use of AI. Training for medical staff could increase the benefits of AI in professional medicine. Therefore, we suggest that the development and implementation of AI in health care require a solid technical, legal, and ethical basis, as well as adequate education and training of users.
Topological and Algebraic Properties of Topological Group Cohomology and LHS-type Spectral Sequences

The aim of the current thesis is to investigate algebraic and topological properties encoded in cohomology classes of locally continuous group cohomology, and also in which cases LHS-style spectral sequences exist for this model. One of the three main results of the current thesis shows, for a large class of topological groups, that each cohomology class has a cocycle representative whose restriction to an open and dense subset is continuous. The second main result relates to lifting obstructions on principal bundles. If P is a principal G-bundle over X and we have a topological central extension K of G by some abelian group Z, one might ask when we can lift P to a K-bundle over X. We show that, under reasonable assumptions on the spaces involved, we can always have such a lift if K is topologically the product of Z and G. The third result relates to LHS spectral sequences. We prove a generalization to the locally continuous group cohomology of the classical result of Lyndon, Hochschild and Serre, in the case of finite quotients. Ziel der vorliegenden Arbeit ist es, die in Kohomologieklassen lokal-stetiger Gruppenkohomologie enkodierte algebraische und topologische Information zu untersuchen und zu verstehen, in welchen Fallen LHS-ahnliche Spektralsequenzen existieren. Eines unserer drei Hauptresultate zeigt, fur eine grosse Klasse von topologischen Gruppen, dass jede Kohomologieklasse, einen Reprasentanten hat, der, eingeschrankt auf eine offene dichte Teilmenge, stetig ist. Das zweite Hauptresultat betrifft Obstruktion von Prinzipalbundel. Ist P ein G-Prinzipalbundel uber X und K eine zentral Erweiterung von G durch eine abelsche Gruppe Z, dann ist es eine naturliche Frage, ob sich P zu einem K-Prinzipalbundel uber X hocheben lasst. Wir zeigen, dass unter milden Bedingungen eine solche Hochhebung immer existiert, solange K topologisch das Produkt von Z und G ist. Unser drittes Resultat betrifft LHS Spektralsequenzen. Wir zeigen eine Verallgemeinerugnen klassischer Resultate von Lyndon, Hoschschild and Serre zu lokal-stetigen Gruppenkohomologie unter der Annahme endlicher Quotienten.
Stability of singular discrete-time reset control systems

The intput and ouput stability of linear system is analyzed via dissipativity properties.By introducing definition of reset control to singular discrete-time system,the definition of singular discrete-time reset control system is proposed.And by using linear matrix inequality(LMI)to analysis dissipativity,a method to judge the stability of a singular discrete-time reset control systems is obtained.
Statistical Inference in Incremental and Difference Equation Formulations

Autoregressive linear equations are being used with increasing frequency in modeling political phenomena. Two forms of the autoregressive equation, the "incremental equation" and the "difference equation" are algebraically equivalent, as are their estimated regression coefficients. However, the squared (multiple) correlation coefficient, R2, will take on different values because of the difference in variance of the dependent variable. Furthermore, the R2 in autoregressive equations responds differently to increases in error variance than it does in nonautoregressive equations. The conditions under which these various results obtain are derived and we discuss some of the implications of these results for the study of autoregressive systems.
The Identification of Streptococcus pasteurianus Obtained from Six Regions in China by Multiplex PCR Assay and the Characteristics of Pathogenicity and Antimicrobial Resistance of This Zoonotic Pathogen

Streptococcus pasteurianus is a zoonotic pathogen causing meningitis and bacteremia in animals and humans. A lack of accurate and convenient detection methods hinders preventing and controlling diseases caused by S. pasteurianus. Additionally, there is limited knowledge about its pathogenicity and antimicrobial resistance characteristics, as there are only three complete genome sequences available. In this study, we established a multiplex PCR assay for the detection of S. pasteurianus, which was applied to six fecal samples from cattle with diarrhea and 285 samples from healthy pigs. Out of the samples tested, 24 were positive, including 5 from pig tonsils, 18 from pig hilar lymph nodes, and 1 from cattle feces. Two strains were isolated from positive samples, and their complete genomes were sequenced. The two strains were non-virulent in mice and multidrug-resistant by the antimicrobial susceptibility test. We first found the presence of genes tet(O/W/32/O) and lsa(E) in S. pasteurianus, leading to resistance to lincosamides and tetracyclines. The convenient and specific multiplex PCR assay provides essential technical support for epidemiological research, and the complete genome sequence of two non-virulent strains contributes to understanding this zoonotic bacterium’s genomic characteristics and pathogenesis.
Bipolaronic Point Defects and Small-Polaron Excitations in Ba1 − xKxBiO3 and BaPbxBi1 − xO3

Recent studies on spectroscopic and transport properties concerning the semiconductor-metal (SM) transition in Ba 1 − x K x BiO 3 (BKBO) and BaPb x Bi 1 − x O 3 (BPBO) are reviewed. For a three-dimensional Peierls semiconductor, BaBiO3(BBO), direct-exciton excitations correspond to the big optical response at 1.85 eV, while indirect exciton excitations reproduce a weak absorption at infrared region. When holes are doped for semiconducting single crystalline BKBO at room temperature, an infrared-absorption band has been observed centered at 0.85 eV below the big optical absorption at 1.85 eV. The new band is assigned to a transition from the lower-Peierls band to a state of the small bipolaronic point defect. With substituting K for Ba, doped holes form small bipolaron defects so that the semiconducting region extends in a wide region. At high temperatures up to 400 K, furthermore, a new excitation appears due to intraband optical excitations of small polarons for electrons and holes, which are thermally created across the indirect gap. The small polaron conductivity has a peak at twice the small-polaron binding energy. Temperature dependence of dc conductivity, thermoelectric power, and magnetic susceptibility is interpreted by excitations of small polarons of electrons and holes. The CDW amplitude, or the Szigeti-effective-charge difference between inequivalent Bi sites, which is estimated from the oscillator strength of the disproportionation mode, tends to go to zero as K or Pb content goes to a critical concentration of the SM transition.
Trait evolution during a rapid global weed invasion despite little genetic differentiation

Invasive species often possess a great capacity to adapt to novel environments in the form of spatial trait variation, as a result of varying selection regimes, genetic drift, or plasticity. We explored the geographic differentiation in several phenotypic traits related to plant growth, reproduction, and defense in the highly invasive Centaurea solstitialis by measuring neutral genetic differentiation (FST), and comparing it with phenotypic differentiation (PST), in a common garden experiment in individuals originating from regions representing the species distribution across five continents. Native plants were more fecund than non‐native plants, but the latter displayed considerably larger seed mass. We found indication of divergent selection for these two reproductive traits but little overall genetic differentiation between native and non‐native ranges. The native versus invasive PST–FST comparisons demonstrated that, in several invasive regions, seed mass had increased proportionally more than the genetic differentiation. Traits displayed different associations with climate variables in different regions. Both capitula numbers and seed mass were associated with winter temperature and precipitation and summer aridity in some regions. Overall, our study suggests that rapid evolution has accompanied invasive success of C. solstitialis and provides new insights into traits and their genetic bases that can contribute to fitness advantages in non‐native populations.
Pramipexole Augmentation for Treatment-Resistant Unipolar and Bipolar Depression in the Real World: A Systematic Review and Meta-Analysis

Background: Pramipexole is a dopamine full agonist approved for the treatment of Parkinson’s disease and restless legs syndrome. Its high affinity for the D3 receptor and neuroprotective, antioxidant, and anti-inflammatory activity provides a rationale for the treatment of depression. In this paper, we review studies on the effectiveness and safety of antidepressant pramipexole augmentation in treatment-resistant depression. Methods: This comprehensive systematic review and meta-analysis of observational studies on pramipexole–antidepressant augmentation included patients with resistant unipolar and bipolar depression. The primary outcome measure was the treatment response, measured at the study endpoint. Results: We identified 8 studies including 281 patients overall, 57% women and 39.5% with bipolar disorder and 60.5% with major depressive disorder. The mean follow-up duration was 27.3 weeks (range 8–69). The pooled estimate of treatment response was 62.5%, without significant differences between unipolar and bipolar depression. Safety was good, with nausea and somnolence the most frequent side effects. Conclusions: The findings of this systematic review, needing further confirmation, show that off-label use of pramipexole as augmentation of antidepressant treatment could be a useful and safe strategy for unipolar and bipolar treatment-resistant depression.
Residential Load Forecasting Using Modified Federated Learning Algorithm

The feasibility of utilizing electricity consumption data is increasing with the widespread use of advanced metering infrastructure among consumers. In addition, the need for consumer demand management and services is likely to increase further as distributed resources are activated. Load forecasting technology is essential for customer demand management. Forecasting technology using deep learning has been applied extensively in recent years. Deep-learning-based load forecasting requires data for training a forecasting model, and the available data may be limited by the Personal Information Protection Act. Federated learning has emerged as a solution to this problem. However, in federated learning, the global model in the central server is trained by aggregating the local model sent from the client without filtration. This may result in the global model overfitting or failure to converge by aggregating the model that is not trained effectively in the client. In this study, we introduce a modified federated learning algorithm to solve this problem and propose a method to forecast residential loads. The proposed method is analyzed and evaluated experimentally using smart meter data. The experimental results reveal that the proposed method improves forecasting performance and convergence. For the global model, when the number of clients was two, three, four, and five, the forecasting performances were 4.891, 5.228, 5.488, and 5.633, respectively, on a mean absolute percentage error (MAPE) basis, showing performance improvements of 28.4%, 10.0%, 16.3%, and 5.8%.
A sixth order Cahn-Hilliard type equation arising in oil-water-surfactant mixtures

An initial-boundary-value problem for the sixth order Cahn-Hilliard type equation in 3-D is studied. The problem describes phase transition dynamics in ternary oil-water-surfactant systems. It is based on the Landau-Ginzburg theory proposed for such systems by G. Gompper et al. We prove that the problem under consideration is well posed in the sense that it admits a unique global smooth solution which depends continuously on the initial datum.
Curvature estimation arithmetic for scatted data point cloud in surface reconstruction

The acquisition of the geometric feature information for the measured data point cloud is the foundation of the surface reconstructoin,and the estimation for normal vector and curvature is the key problem in the processing of the point cloud.Based on the scatted data,a suitable method has been proposed in this paper,in which the least eigenvector of covariance matrix for the local data points is used as the normal vector of the data point.The arithmetic for curvature estimation has also been improved according to the measured data.It has been proved in practice that by applying the improved arithmetic,the normal vector and curvature are accurately estimated,and the curvature distribution of data point is display visually.
Chitosan-Coated Solid Lipid Nanoparticles as an Efficient Avenue for Boosted Biological Activities of Aloe perryi: Antioxidant, Antibacterial, and Anticancer Potential

Aloe perryi (ALP) is an herb that has several biological activities such as antioxidant, antibacterial, and antitumor effects and is frequently used to treat a wide range of illnesses. The activity of many compounds is augmented by loading them in nanocarriers. In this study, ALP-loaded nanosystems were developed to improve their biological activity. Among different nanocarriers, solid lipid nanoparticles (ALP-SLNs), chitosan nanoparticles (ALP-CSNPs), and CS-coated SLNs (C-ALP-SLNs) were explored. The particle size, polydispersity index (PDI), zeta potential, encapsulation efficiency, and release profile were evaluated. Scanning electron microscopy was used to see the nanoparticles’ morphology. Moreover, the possible biological properties of ALP were assessed and evaluated. ALP extract contained 187 mg GAE/g extract and 33 mg QE/g extract in terms of total phenolic and flavonoid content, respectively. The ALP-SLNs-F1 and ALP-SLNs-F2 showed particle sizes of 168.7 ± 3.1 and 138.4 ± 9.5 nm and the zeta potential values of −12.4 ± 0.6, and −15.8 ± 2.4 mV, respectively. However, C-ALP-SLNs-F1 and C-ALP-SLNs-F2 had particle sizes of 185.3 ± 5.5 and 173.6 ± 11.3 nm with zeta potential values of 11.3 ± 1.4 and 13.6 ± 1.1 mV, respectively. The particle size and zeta potential of ALP-CSNPs were 214.8 ± 6.6 nm and 27.8 ± 3.4 mV, respectively. All nanoparticles exhibited PDI < 0.3, indicating homogenous dispersions. The obtained formulations had EE% and DL% in the ranges of 65–82% and 2.8–5.2%, respectively. After 48 h, the in vitro ALP release rates from ALP-SLNs-F1, ALP-SLNs-F2, C-ALP-SLNs-F1, C-ALP-SLNs-F2, and ALP-CSNPs were 86%, 91%, 78%, 84%, and 74%, respectively. They were relatively stable with a minor particle size increase after one month of storage. C-ALP-SLNs-F2 exhibited the greatest antioxidant activity against DPPH radicals at 73.27%. C-ALP-SLNs-F2 demonstrated higher antibacterial activity based on MIC values of 25, 50, and 50 µg/mL for P. aeruginosa, S. aureus, and E. coli, respectively. In addition, C-ALP-SLNs-F2 showed potential anticancer activity against A549, LoVo, and MCF-7 cell lines with IC50 values of 11.42 ± 1.16, 16.97 ± 1.93, and 8.25 ± 0.44, respectively. The results indicate that C-ALP-SLNs-F2 may be promising nanocarriers for enhancing ALP-based medicines.
Structurally hidden magnetic transitions in Fe3C at high pressures

C. Prescher,1,* L. Dubrovinsky,1 C. McCammon,1 K. Glazyrin,1 Y. Nakajima,1 A. Kantor,1,2 M. Merlini,2,3 and M. Hanfland2 1Bayerisches Geoinstitut, Universität Bayreuth, D-95440 Bayreuth, Germany 2European Synchrotron Radiation Facility, Boı̂te Postale 220, F-38043 Grenoble, France 3Dipartimento di Scienze della Terra, Università degli Studi di Milano, Via Botticelli 23, I-20133 Milano, Italy (Received 4 April 2011; revised manuscript received 1 February 2012; published 2 April 2012)
Power-Loss Ride-Through With Reduced Number of Voltage Sensors in a Cascaded H-Bridge Inverter Fed Vector Controlled Induction Motor Drive

Cascaded H-Bridge (CHB) inverter-fed drives are quite popular in high-power industrial applications. However, these drives are extremely sensitive to power supply disturbances on the input grid side, such as voltage sags or short-duration power interruptions that cause the drive to trip on under-voltage protection resulting in production losses. Therefore, it is necessary to employ techniques that improve the resilience of the drive by enabling it to smoothly ‘ride-through’ such short-duration disturbances. The traditional ride-through solutions in CHB drives typically necessitate additional hardware or multiple DC link voltage sensors, which increase the cost and complexity of the system. This paper proposes a simple and cost-effective approach for power-loss ride-through in CHB inverter based vector-controlled induction motor drives. In this method, the kinetic energy stored in the rotating mass is recovered and fed back to the DC-link capacitors to keep all the DC link voltages constant during momentary supply interruptions or voltage sags. The proposed technique works by controlling the voltage space phasor without the need for extra hardware and voltage sensors at the CHB cells. The proposed scheme is experimentally validated on a 30 kW, 7-level CHB inverter based vector controlled induction motor drive.
IrisGuideNet: Guided Localization and Segmentation Network for Unconstrained Iris Biometrics

In recent years, unconstrained iris biometrics has become more prevalent due to its wide range of user applications. However, it also presents numerous challenges to the Iris pre-processing task of Localization and Segmentation (ILS). Many ILS techniques have been proposed to address these challenges, among which the most effective is the CNN-based methods. Training the CNN is data-intensive, and most of the existing CNN-based ILS approaches do not incorporate iris-specific features that can reduce their data dependence, despite the limited labelled iris data in the available databases. These trained CNN models built upon these databases can be sub-optimal. Hence, this paper proposes a guided CNN-based ILS approach IrisGuideNet. IrisGuideNet involves incorporating novel iris-specific heuristics named Iris Regularization Term (IRT), deep supervision technique, and hybrid loss functions in the training pipeline, which guides the network and reduces the model data dependence. A novel Iris Infusion Module (IIM) that utilizes the geometrical relationships between the ILS outputs to refine the predicted outputs is introduced at network inference. The proposed model is trained and evaluated with various datasets. Experimental results show that IrisGuideNet has outperformed most models across all the database categories. The codes implementation of the proposed IrisGuideNet will be available at: https://github.com/mohdjawadi/IrisGuidenet.
Growth and characterization of van der Waals heterostuctures formed by the topological insulator Bi2Se3 and the trivial insulator SnSe2

The formation, structure and electronic properties of SnSe2–Bi2Se3 van der Waals heterostructures were studied. Both heterostructures, SnSe2 on Bi2Se3 and Bi2Se3 on SnSe2, were grown epitaxially with high crystallinity and sharp interfaces. Their electron band structures are of trivial and topological insulators, respectively. The Dirac surface states of Bi2Se3 survive under the SnSe2 overlayer. One triple layer of SnSe2 was found to be an efficient spacer for separating a Bi2Se3 topological-insulator slab into two and creating the corresponding topological surface states.
Engineering Cell Microenvironment Using Nanopattern-Derived Multicellular Spheroids and Photo-Crosslinked Gelatin/Hyaluronan Hydrogels

Cell cultures of dispersed cells within hydrogels depict the interaction of the cell–extracellular matrix (ECM) in 3D, while the coculture of different cells within spheroids combines both the effects of cell–cell and cell–ECM interactions. In this study, the cell co-spheroids of human bone mesenchymal stem cells/human umbilical vein endothelial cells (HBMSCs/HUVECs) are prepared with the assistance of a nanopattern, named colloidal self-assembled patterns (cSAPs), which is superior to low-adhesion surfaces. A phenol-modified gelatin/hyaluronan (Gel-Ph/HA-Ph) hydrogel is used to encapsulate the multicellular spheroids and the constructs are photo-crosslinked using blue light. The results show that Gel-Ph/HA-Ph hydrogels with a 5%-to-0.3% ratio have the best properties. Cells in HBMSC/HUVEC co-spheroids are more favorable for osteogenic differentiation (Runx2, ALP, Col1a1 and OPN) and vascular network formation (CD31+ cells) compared to HBMSC spheroids. In a subcutaneous nude mouse model, the HBMSC/HUVEC co-spheroids showed better performance than HBMSC spheroids in angiogenesis and the development of blood vessels. Overall, this study paves a new way for using nanopatterns, cell coculturing and hydrogel technology for the generation and application of multicellular spheroids.