On correlation distribution of Niho-type decimation d=3(pm−1)+1d=3(p^m-1)+1

The cross-correlation problem is a classic problem in sequence design. In this paper we compute the cross-correlation distribution of the Niho-type decimation $d=3(p^m-1)+1$ over $\mathrm{GF}(p^{2m})$ for any prime $p \ge 5$. Previously this problem was solved by Xia et al. only for $p=2$ and $p=3$ in a series of papers. The main difficulty of this problem for $p \ge 5$, as pointed out by Xia et al., is to count the number of codewords of "pure weight" 5 in $p$-ary Zetterberg codes. It turns out this counting problem can be transformed by the MacWilliams identity into counting codewords of weight at most 5 in $p$-ary Melas codes, the most difficult of which is related to a K3 surface well studied in the literature and can be computed. When $p \ge 7$, the theory of elliptic curves over finite fields also plays an important role in the resolution of this problem

The Differential Spectrum of the Power Mapping xpn−3

Let n be a positive integer and p a prime. The power mapping xpn−3 over Fpn has desirable differential properties, and its differential spectra for p=2,3 have been determined. In this paper, for any odd prime p , by investigating certain quadratic character sums and some equations over Fpn , we determine the differential spectrum of xpn−3 with a unified approach. The obtained result shows that for any given odd prime p , the differential spectrum can be expressed explicitly in terms of n . Compared with previous results, a special elliptic curve over Fp plays an important role in our computation for the general case p≥5.acceptedVersio

Local cooling and warming effects of forests based on satellite observations

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies

Biochemical, Molecular, and Clinical Characterization of Patients With Primary Carnitine Deficiency via Large-Scale Newborn Screening in Xuzhou Area

Background: Primary carnitine deficiency (PCD) is attributed to a variation in the SLC22A5 (OCTN2) gene which encodes the key protein of the carnitine cycle, the OCTN2 carnitine transporter. PCD is typically identified in childhood by either hypoketotic hypoglycemia, or skeletal and cardiac myopathy. The aim of this study was to the clinical, biochemical, and molecular characteristics of PCD patients via newborn screening with tandem mass spectrometry (MS/MS).Methods: MS/MS was performed to screen newborns for inherited metabolic diseases. SLC22A5 gene mutations were detected in the individual and/or their family member by DNA mass array and next-generation sequencing (NGS).Results: Among the 236,368 newborns tested, ten exhibited PCD, and six others were diagnosed with low carnitine levels caused by their mothers, who had asymptomatic PCD. The incidence of PCD in the Xuzhou area is ~1:23,637. The mean initial free carnitine (C0) concentration of patients was 6.41 ± 2.01 μmol/L, and the follow-up screening concentration was 5.80 ± 1.29 μmol/L. After treatment, the concentration increased to 22.8 ± 4.13 μmol/L.Conclusion: This study demonstrates the important clinical value of combining MS/MS and NGS for the diagnosis of PCD and provides new insight into the diagnosis of PCD and maternal patients with PCD using C0 concentration and SLC22A5 mutations

Capturing secchi disk depth by using sentinel-2 msi imagery in Jiaozhou Bay, China from 2017 to 2021

Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19)

Breath-, air- and surface-borne SARS-CoV-2 in hospitals

The COVID-19 pandemic has brought an unprecedented crisis to the global health sector. When discharging COVID-19 patients in accordance with throat or nasal swab protocols using RT-PCR, the potential risk of reintroducing the infection source to humans and the environment must be resolved. Here, 14 patients including 10 COVID-19 subjects were recruited; exhaled breath condensate (EBC), air samples and surface swabs were collected and analyzed for SARS-CoV-2 using reverse transcription-polymerase chain reaction (RT-PCR) in four hospitals with applied natural ventilation and disinfection practices in Wuhan. Here we discovered that 22.2% of COVID-19 patients (n = 9), who were ready for hospital discharge based on current guidelines, had SARS-CoV-2 in their exhaled breath (~10⁵ RNA copies/m³). Although fewer surface swabs (3.1%, n = 318) tested positive, medical equipment such as face shield frequently contacted/used by healthcare workers and the work shift floor were contaminated by SARS-CoV-2 (3–8 viruses/cm²). Three of the air samples (n = 44) including those collected using a robot-assisted sampler were detected positive by a digital PCR with a concentration level of 9–219 viruses/m³. RT-PCR diagnosis using throat swab specimens had a failure rate of more than 22% in safely discharging COVID-19 patients who were otherwise still exhaling the SARS-CoV-2 by a rate of estimated ~1400 RNA copies per minute into the air. Direct surface contact might not represent a major transmission route, and lower positive rate of air sample (6.8%) was likely due to natural ventilation (1.6–3.3 m/s) and regular disinfection practices. While there is a critical need for strengthening hospital discharge standards in preventing re-emergence of COVID-19 spread, use of breath sample as a supplement specimen could further guard the hospital discharge to ensure the safety of the public and minimize the pandemic re-emergence risk

Unexpected Xe anions in XeLi

The reactivity of Xe is important in both fundamental chemistry and geological science. The discovery of the reductive reactivity of Xe extended the doctrinal boundary of chemistry for which a completed shell is inert to reaction. The oxidation of Xe by various elements has been explored. On the other hand, the opposite chemical inclination, i.e., gaining electrons and forming anions, has not been thoroughly studied for Xe or other noble-gas elements. In this work, we demonstrate, using first-principles calculations and an efficient structure prediction method, that Xe can form stable $\text{XeLi}_{n}\ (n=1\text{--}5)$ compounds under high pressure. These compounds are intermetallic and Xe are negatively charged. The stability of these compounds indicates that atoms or ions with completely filled shell may still gain electrons in chemical reactions

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

Abstract For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy