Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions

In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data. As a result, there is an increasing demand for both researchers and engineers for fault identification with scarce data. To address this issue, an innovative domain-adaptive prototype-recalibrated network (DAPRN) based on a transductive learning paradigm and prototype recalibration strategy (PRS) is proposed, which has the potential to promote the generalization ability from the source domain to target domain in a few-shot fault diagnosis. Within this scheme, the DAPRN is composed of a feature extractor, a domain discriminator, and a label predictor. Concretely, the feature extractor is jointly optimized by the minimization of few-shot classification loss and the maximization of domain-discriminative loss. The cosine similarity-based label predictor, which is promoted by the PRS, is exploited to avoid the bias of naïve prototypes in the metric space and recognize the health conditions of machinery in the meta-testing process. The efficacy and advantage of DAPRN are validated by extensive experiments on bearing and gearbox datasets compared with seven popular and well-established few-shot fault diagnosis methods. In practical application, the proposed DAPRN is expected to solve more challenging few-shot fault diagnosis scenarios and facilitate practical fault identification problems in modern manufacturing.
Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity

Several pathogens that spread through the air are highly contagious, and related infectious diseases are more easily transmitted through airborne transmission under indoor conditions, as observed during the COVID-19 pandemic. Indoor air contaminated by microorganisms, including viruses, bacteria, and fungi, or by derived pathogenic substances, can endanger human health. Thus, identifying and analyzing the potential pathogens residing in the air are crucial to preventing disease and maintaining indoor air quality. Here, we applied deep learning technology to analyze and predict the toxicity of bacteria in indoor air. We trained the ProtBert model on toxic bacterial and virulence factor proteins and applied them to predict the potential toxicity of some bacterial species by analyzing their protein sequences. The results reflect the results of the in vitro analysis of their toxicity in human cells. The in silico-based simulation and the obtained results demonstrated that it is plausible to find possible toxic sequences in unknown protein sequences.
Sensitivity of Serum Beta-D-Glucan in Candidemia According to Candida Species Epidemiology in Critically Ill Patients Admitted to the Intensive Care Unit

Serum beta-D-glucan (BDG) determination plays an important role in the diagnosis of candidemia among critically ill patients admitted to the intensive care unit (ICU). However, BDG levels measured may be lower in the case of infections caused by some non-albicans species, such as C. parapsilosis and C. auris. The aim of this single-center study was to investigate the sensitivity of serum BDG for the diagnosis of candidemia stratified according to causative Candida species in ICU patients. This was a single-center, retrospective study, including all adult patients admitted to ICU during the period 2018–2021. All episodes of candidemia with a determination of BDG available within 3 days before or after positive blood culture were recorded. The preplanned primary objective was to investigate the sensitivity of serum BDG to detect candidemia early and the effect of different Candida species. The secondary objective was to measure serum BDG in patients with candidemia from different Candida species. In total, 146 candidemia episodes in 118 patients were analyzed. Median BDG value for C. albicans candidemia (182 pg/mL) was higher than that observed for C. parapsilosis (78 pg/mL, p = 0.015) and C. auris (48 pg/mL, p = 0.022). The overall sensitivity of BDG for the diagnosis of candidemia was low (47%, 95% CI 39–55%). In conclusion, in critically ill patients admitted to ICU, serum BDG levels for candidemia were different among species, with lower levels confirmed for C. parapsilosis and C. auris. Serum BDG sensitivity for early detection of candidemia was lower than previously reported in other ICU populations.
The greatest Ricci lower bound, conical Einstein metrics and Chern number inequality

We partially confirm a conjecture of Donaldson relating the greatest Ricci lower bound $R(X)$ to the existence of conical Kahler-Einstein metrics on a Fano manifold $X$. In particular, if $D\in |-K_X|$ is a smooth simple divisor and the Mabuchi $K$-energy is bounded below, then there exists a unique conical Kahler-Einstein metric satisfying $Ric(g) = \beta g + (1-\beta) [D]$ for any $\beta \in (0,1)$. We also construct unique smooth conical toric Kahler-Einstein metrics with $\beta=R(X)$ and a unique effective Q-divisor $D\in [-K_X]$ for all toric Fano manifolds. Finally we prove a Miyaoka-Yau type inequality for Fano manifolds with $R(X)=1$.
Diffusion Process on Time-Inhomogeneous Manifolds

Let $L_t:=\Delta_t +Z_t $, $t\in [0,T_c)$ on a differential manifold equipped with time-depending complete Riemannian metric $(g_t)_{t\in [0,T_c)}$, where $\Delta_t$ is the Laplacian induced by $g_t$ and $(Z_t)_{t\in [0,T_c)}$ is a family of $C^{1,1}$-vector fields. We first present some explicit criteria for the non-explosion of the diffusion processes generated by $L_t$; then establish the derivative formula for the associated semigroup; and finally, present a number of equivalent semigroup inequalities for the curvature lower bound condition, which include the gradient inequalities, transportation-cost inequalities, Harnack inequalities and functional inequalities for the diffusion semigroup.
Searching for oscillations in the primordial power spectrum. II. Constraints from Planck data

We apply our recently developed code to search for resonance features in the Planck CMB temperature data. We search both for log spaced oscillations or linear spaced oscillations and compare our findings with results of our WMAP9 analysis and the Planck team analysis. While there are hints of log spaced resonant features present in the WMAP9 data, the significance of these features weaken with more data. With more accurate small scale measurements, we also find that the best fit frequency has shifted and the amplitude has been reduced. We confirm the presence of a several low frequency peaks, earlier identified by the Planck team, but with a better improvement of fit (delta chi^2 ~ 12). We further investigate this improvement by allowing the lensing potential to vary as well, showing mild correlation between the amplitude of the oscillations and the lensing amplitude. We find that the improvement of the fit increases even more (delta chi^2 ~ 14) for the low frequencies that modify the spectrum in a way that mimics the lensing effect. Since these features were not present in the WMAP data, they are primarily due to better measurements of Planck at small angular scales. For linear spaced oscillations we find a maximum delta chi^2 ~ 13 scanning two orders of magnitude in frequency space, and the biggest improvements are at extremely high frequencies. We recover a best fit frequency very close to the one found in WMAP9, which confirms that the fit improvement is driven by low l. Further comparisons with WMAP9 show Planck contains many more features, both for linear and log space oscillations, but with a smaller improvement of fit. We discuss the improvement as a function of the number of modes and study the effect of the 217 GHz map, which appears to drive most of the improvement for log spaced oscillations. We conclude that none of the detected features are statistically significant.
Characteristic classes of A-infinity algebras

A standard combinatorial construction, due to Kontsevich, associates to any A-infinity algebra with an invariant inner product, an inhomogeneous class in the cohomology of the moduli spaces of Riemann surfaces with marked points. We describe an alternative version of this construction based on noncommutative geometry and use it to prove that homotopy equivalent algebras give rise to the same cohomology classes. Along the way we re-prove Kontsevich's theorem relating graph homology to the homology of certain infinite-dimensional Lie algebras. An application to topological conformal field theories is given.
Epidemic models and MCMC inference

Statistical inference and model choice for partially observed epidemics provide a variety of challenges both practical and theoretical. This thesis studies some related aspects of models for epidemics and their inference. The use of the matrix exponential to facilitate exact calculations in the General Stochastic Epidemic (GSE) is demonstrated, most usefully in providing the exact marginal likelihood when infection times are unobserved. The bipartite graph epidemic is defined and shown to be a flexible framework which encompasses many existing models. It also provides a way in which a deeper understanding of the relation between existing models could be obtained. The Indian buffet epidemic is introduced as a non-parametric approach to modelling unknown heterogeneous contact structures in epidemics. Inference for the Indian buffet epidemic is a challenging problem, some progress has been made. However the algorithms which have been studied do not yet scale to the size of problem where significant differences from the GSE are apparent. Evidence confirming and demonstrating the importance of understanding the tail behaviour of proposals in importance sampling is presented. The adverse impact of heavy tailed proposals on the Grouped Independence Metropolis-Hastings (GIMH) and Monte Carlo within Metropolis (MCWM) algorithms is demonstrated. A new algorithm, the Kernel Metropolis Hastings (KMH), is proposed as an approximate algorithm for low dimensional marginal inference in situations where the GIMH algorithm fails because of sticking. The KMH is demonstrated on a challenging 2-d problem.
Thermally activated phase slips in superconducting nanowires

We reanalyze the problem of thermally activated phase slips (TAPS) which can dominate the behavior of sufficiently thin superconducting wires at temperatures close to ${T}_{C}$. With the aid of an effective action approach we evaluate the TAPS rate which turns out to exceed the rate found by McCumber and Halperin, Phys. Rev. B 1, 1054 (1970) within the time-dependent Ginzburg-Landau analysis by the factor $\ensuremath{\sim}{(1\ensuremath{-}T/{T}_{C})}^{\ensuremath{-}1}⪢1$. Additional differences in the results of these two approaches arise at bias currents close to the Ginzburg-Landau critical current where the TAPS rate becomes bigger. We also derive a simple formula for the voltage noise across the superconducting wire in terms of the TAPS rate. Our results can be verified in modern experiments with superconducting nanowires.
An Improved Method for Rainfall Forecast Based on GNSS-PWV

Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall.
Efficacy of inhaled salbutamol therapy through nebulizer versus mdi with spacer in children with acute asthmatic attack and short-term course

Introduction: Asthma is a common chronic disease in childhood and has been subject of studies for at least two decades, due to its increasing prevalence. The symptoms are persistent, recurrent, and entirely related to bronchial hyper-responsiveness to many environmental triggers. Nebulization and metered dose inhaler (MDI) are two available modalities of salbutamol administration in patients with acute asthmatic attack. Aim: The aim of the study was to compare the relative efficacy of nebulizer and MDI with spacer for the administration of salbutamol in the treatment of acute asthmatic attack. Materials and Methods: A randomized, prospective, and interventional study was conducted on 100 children aged 1–12 years with acute asthmatic attack at a tertiary care. The subjects were randomized into two groups for salbutamol administration. Group I was given salbutamol by nebulizer and Group II by MDI with spacer. Demographic data and detailed history was collected. Children were categorized into mild, moderate, and severe grade according to peak expiratory flow rate (PEFR) and pulmonary index (PI) score. Oxygen saturation, PEFR, and PI were monitored at 0, 20, 40, and 60 min of therapy. Other details such as duration of hospital stay and oxygen requirement were also noted. Results: One hundred children with male female ratio of 1.12:1 were included in the study. Highest incidence was seen in age group of 1–3 years (38%). Statistically significant change in PI and PEFR was noted with treatment in both the groups (p<0.05). On comparison between MDI and nebulizer group, no statistically significant difference was found (p>0.05). Conclusion: MDI with spacer is an effective alternative to nebulizer for the administration of salbutamol drug in acute asthmatic attack in pediatric patients.
Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis

Intracranial aneurysms (IAs) are usually asymptomatic with a low risk of rupture, but consequences of aneurysmal subarachnoid hemorrhage (aSAH) are severe. Identifying IAs at risk of rupture has important clinical and socio-economic consequences. The goal of this study was to assess the effect of patient and IA characteristics on the likelihood of IA being diagnosed incidentally versus ruptured. Patients were recruited at 21 international centers. Seven phenotypic patient characteristics and three IA characteristics were recorded. The analyzed cohort included 7992 patients. Multivariate analysis demonstrated that: (1) IA location is the strongest factor associated with IA rupture status at diagnosis; (2) Risk factor awareness (hypertension, smoking) increases the likelihood of being diagnosed with unruptured IA; (3) Patients with ruptured IAs in high-risk locations tend to be older, and their IAs are smaller; (4) Smokers with ruptured IAs tend to be younger, and their IAs are larger; (5) Female patients with ruptured IAs tend to be older, and their IAs are smaller; (6) IA size and age at rupture correlate. The assessment of associations regarding patient and IA characteristics with IA rupture allows us to refine IA disease models and provide data to develop risk instruments for clinicians to support personalized decision-making.
Climate Change and Respiratory Diseases: Relationship between SARS and Climatic Parameters and Impact of Climate Change on the Geographical Distribution of SARS in Iran

Highlights This is the first study on the effects of climate change on SARS in Iran. The result shows a positive association between the incidence of SARS and bio-climatic variables. This study shows the hospital admissions for climate-related respiratory diseases in Iran will increase by 36% in 2020–2050. The geographical distribution of the SARS will also change in 2050 in Iran. Abstract Climate change affects human health, and severe acute respiratory syndrome (SARS) incidence is one of the health impacts of climate change. This study is a retrospective cohort study. Data have been collected from the Iranian Ministry of Health and Medical Education between 17 February 2016 and17 February 2018. The Neural Network Model has been used to predict SARS infection. Based on the results of the multivariate Poisson regression and the analysis of the coexistence of the variables, the minimum daily temperature was positively associated with the risk of SARS in men and women. The risk of SARS has increased in women and men with increasing daily rainfall. According to the result, by changes in bioclimatic parameters, the number of SARS patients will be increased in cities of Iran. Our study has shown a significant relationship between SARS and the climatic variables by the type of climate and gender. The estimates suggest that hospital admissions for climate-related respiratory diseases in Iran will increase by 36% from 2020 to 2050. This study demonstrates one of the health impacts of climate change. Policymakers can control the risks of climate change by mitigation and adaptation strategists.
Growing Pleurotus ostreatus (ex. Fr) Kummer Using Gamma Radiation in Southern Ghana and its Associated Pests

Pests have been implicated in causing severe damage to food crops including mushrooms, thereby increasing the incidence of postharvest loss. This study investigated the various pests involved in the cultivation of gamma radiation aided cultivated Pleurotus ostreatus. Pests were isolated and identified by the procedure described by the Entomological Society of Canada. Cultivation of Pleurotus ostreatus was achieved by pre-treating composted sawdust substrates and spawn preparation with a gamma radiation dose of 15 kGy from a Cobalt 60 source (SLL 515, Hungary) at a dose rate of 1.7 kGyh-1 in air. A total of five pests were recorded while cropping, namely; Drosophila melanogaster (Fruit fly), Blatta germanica (cockroach), Archachatina marginata (giant snail), and Doratogonus meridionalis (millipede) and Cryptophlebia leucotreta (False codling moth). The predominant pest was Drosophila melanogaster (Fruit fly) (97%). Results recorded over four flush periods were 378g, 0.63, and 63% for Total yield, biological yield, Biological Efficiency (%), respectively. The average stipe length and pileus diameter were recorded and showed no statistical differences (p>0.05) observed in the different weeks. Other growth parameters (mushroom size, total number of primordia, total number of fruitbodies etc.) recorded showed some significant differences (p<0.05). Pests identified contributed to yield reduction and product quality.
Phenolic Profile, Antioxidant and Enzyme Inhibitory Activities of Leaves from Two Cassia and Two Senna Species

Several species within the genera Cassia or Senna have a treasure of traditional medicines worldwide and can be a promising source of bioactive molecules. The objective of the present study was to evaluate the phenolic content and antioxidant and enzyme inhibition activities of leaf methanolic extracts of C. fistula L. , C. grandis L. , S. alexandrina Mill. , and S. italica Mill. The two Cassia spp. contained higher total polyphenolic content (42.23–49.75 mg GAE/g) than the two Senna spp. , and C. fistula had significantly (p ˂ 0.05) the highest concentration. On the other hand, the Senna spp. showed higher total flavonoid content (41.47–59.24 mg rutin equivalent per g of extract) than that found in the two Cassia spp. , and S. alexandrina significantly (p ˂ 0.05) accumulated the highest amount. HPLC–MS/MS analysis of 38 selected bioactive compounds showed that the majority of compounds were identified in the four species, but with sharp variations in their concentrations. C. fistula was dominated by epicatechin (8928.75 µg/g), C. grandis by kaempferol-3-glucoside (47,360.04 µg/g), while rutin was the major compound in S. italica (17,285.02 µg/g) and S. alexandrina (6381.85). The methanolic extracts of the two Cassia species exerted significantly (p ˂ 0.05) higher antiradical activity, metal reducing capacity, and total antioxidant activity than that recorded from the two Senna species’ methanolic extracts, and C. fistula displayed significantly (p ˂ 0.05) the highest values. C. grandis significantly (p ˂ 0.05) exhibited the highest metal chelating power. The results of the enzyme inhibition activity showed that the four species possessed anti-AChE activity, and the highest value, but not significantly (p ≥ 0.05) different from those obtained by the two Cassia spp. , was exerted by S. alexandrina. The Cassia spp. exhibited significantly (p ˂ 0.05) higher anti-BChE and anti-Tyr properties than the Senna spp. , and C. grandise revealed significantly (p ˂ 0.05) the highest values. C. grandise revealed significantly (p ˂ 0.05) the highest α- amylase inhibition, while the four species had more or less the same effect against the α-glucosidase enzyme. Multivariate analysis and in silico studies showed that many of the identified phenols may play key roles as antioxidant and enzyme inhibitory properties. Thus, these Cassia and Senna species could be a promising source of natural bioactive agents with beneficial effects for human health.
Multimodal Imaging and Phototherapy of Cancer and Bacterial Infection by Graphene and Related Nanocomposites

The advancements in nanotechnology and nanomedicine are projected to solve many glitches in medicine, especially in the fields of cancer and infectious diseases, which are ranked in the top five most dangerous deadly diseases worldwide by the WHO. There is great concern to eradicate these problems with accurate diagnosis and therapies. Among many developed therapeutic models, near infra-red mediated phototherapy is a non-invasive technique used to invade many persistent tumors and bacterial infections with less inflammation compared with traditional therapeutic models such as radiation therapy, chemotherapy, and surgeries. Herein, we firstly summarize the up-to-date research on graphene phototheranostics for a better understanding of this field of research. We discuss the preparation and functionalization of graphene nanomaterials with various biocompatible components, such as metals, metal oxides, polymers, photosensitizers, and drugs, through covalent and noncovalent approaches. The multifunctional nanographene is used to diagnose the disease with confocal laser scanning microscopy, magnetic resonance imaging computed tomography, positron emission tomography, photoacoustic imaging, Raman, and ToF-SMIS to visualize inside the biological system for imaging-guided therapy are discussed. Further, treatment of disease by photothermal and photodynamic therapies against different cancers and bacterial infections are carefully conferred herein along with challenges and future perspectives.