bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study of Encapsulation Temperature Field of Dry-type Air-core Reactor with the Structure of Equivalent and Aluminum Wire-Insulation
Authors: Wan Xiaorong , Wang Han , Li Yingna , Li Chuan , Zhao Zhengang , Cui Zhigang
Published: July 2022

During the operation of the Dry-type Air-core Reactor (DAR), the insulation material deteriorates due to local overheating of the encapsulation, and the reactor is burned in severe conditions. Therefore, studying the temperature rise of the reactor plays an important role in the reliability of its operation and the economy of design and production. According to the structural characteristics of the reactor, the three-dimensional thermal field model of the aluminum wire-insulation structure of the reactor is established, and the encapsulation temperature field distribution of the reactor under steady state is obtained. Compare the simulation result with the encapsulation temperature field distribution of the equivalent structure. The results show that the highest temperature of the aluminum wire-insulation structure encapsulation rises to 73.51∘∘C, which is located in the middle of the reactor. Each encapsulation hotspot is located about 20% from the upper edge of the encapsulation. In the temperature rise test, a Fiber Bragg Grating (FGB) temperature sensor is installed about 20% from the upper edge of the encapsulation. The test shows that the most significant value of temperature rise of the reactor is 73∘∘C, which is located in the middle of the reactor. The temperature rise test results verify the accuracy of the simulated calculation value of the temperature field of the aluminum wire-insulated reactor and provide a reference for the temperature rise calculation and temperature rise monitoring of the DAR.
Magnetic Coupling Characteristics and Efficiency Analysis of Spiral Magnetic Power Pads for Inductive WPT System
Authors: Kishan Dharavath
Published: July 2022

The inductive wireless power transfer system (IWPT) for electric vehicle battery charging works based on the principle of mutual induction (MI). The amount of power transfer from source to vehicle battery be contingent on the mutual inductance (MI) within the inductively coupled pads. This mutual inductance depends on the type of the inductive power pads, the distance among them, their positioning etc. This paper develops and study the inductive coupling characteristics of identical spiral circular and square inductive power pads. The coupling characteristics at various misalignments with different vertical distance between the coils is presented. In this work, the inductive power pads without using ferrite bars, and with ferrite bars are considered. The coupling characteristics of the spiral circular and square are computed using FEM simulations and validated with experimental results. This paper also investigated the power loss and efficiency analysis of the spiral inductive pads of the resonant IWPT system.
Anomaly Detection of Smart Grid Equipment Using Machine Learning Applications
Authors: Rajasekaran Arun Sekar , Kalyanchakravarthi P. , Subudhi Partha Sarathi
Published: July 2022

Many application systems in today’s smart grid network comprise a variety of middleware components, such as vibrating or spinning electrical equipment, data bases, storage, caches, and identification services, among others. Each component is a discrete bundle of physical or virtual computers that will generate a large amount of data in the form of logs and metrics, and failure of these high-vibrating machines will result in the system’s entire shutdown. As a result, the condition monitoring system for this smart grid equipment is more dependable and efficient in predicting the machine’s health ahead of time. To analyze the data and derive any inferences for future analysis and anomaly detection, we’ll need a separate system, which will take longer to handle data given by each component and will demand additional processing resources. As a result, in this chapter, machine learning methods are used to identify abnormalities or condition monitoring for smart grid equipment and machines. Here, we used two separate methods: multivariate statistical analysis by calculating Mob distance and auto encoders, which is an artificial neural network approach. Furthermore, the findings demonstrate that these apps are effective in identifying anomaly ahead of time, i.e. before a few days.
Implementation and Analysis of Grey Wolf Optimization Technique for PV Fed Switched Coupled Inductor Quasi Z-Source Cascaded Multilevel Inverter
Authors: Rajan V. Raghavendra , Premalatha L. , Krismadinata
Published: July 2022

A new type of photovoltaic (PV) fed cascaded multilevel topology is proposed in this work. The proposed topology is an integration of Switched Coupled Inductor (SCL) quasi Z-Source network (qZS) to Cascaded Multilevel Inverter (CMI). In order to extract maximum power with high tracking efficiency under various conditions from the PV system, Grey Wolf Optimization (GWO) algorithm is implemented in the proposed topology. The main aspect of GWO is to control the duty cycle through iterations for better performance. The GWO has the potential to achieve global peak under any climatic conditions. The iterations are done in three steps mainly hunting, encircling for prey and, attacking prey. To prove the effectiveness of GWO, its performance is compared with traditional Perturb & Observe (P & O) MPPT technique. The proposed technique is implemented, tested for various Partial Shading Conditions (PSC) using MATLAB/ Simulink results are verified with experimental set up of 1 kW by utilizing My-RIO embedded FPGA processor board.
Optimal Electricity Price of TOU Demand Response Program for Distribution Grid Reliability Improvement
Authors: Tahmasebi Mehrdad , Pasupuleti Jagadeesh , Foodaji Hamed , Askari Mohammad Tolou
Published: July 2022

Nowadays, the power systems and distribution network’s reliability are critical issues from electric companies’ and consumers’ perspectives. As suppliers of the power required by the consumers, electric companies are trying to deliver the power to customers on a continuous and reliable basis. The existence of customers’ power interruptions causes damage to the customer; moreover, it may cause damage to the electric company due to the lack of energy sales during the hours of a power outage. In this paper, the reliability issue was investigated by examining the two indices EENS and ECOST, on the test distribution network to evaluate the impact of electricity prices in TOU demand response on the reliability of the distribution network has been investigated. Also find the optimal electricity prices for the best distribution network reliability. In this research, simulation studies were conducted on the Roy Billinton Test System (RBTS) distribution network using the GAMS software. From the analysis of the simulation results, the distribution grid reliability was improved by implementing optimal electricity prices of the TOU demand response program.
Power Industry Big Data Privacy Protection Processing Method Based on Fuzzy Logic and Intelligent Clustering
Authors: Hang Feilu , Xie Linjiang , Zhang Zhenhong , Guo Wei , Li Hanruo
Published: July 2022

The power industry is the corporate globe’s backbone, delivering vital energy to industrial, manufacturing, promotional, and residential clients worldwide. Investment has been prompted by the shift of fuel and energy sources, rising environmental laws, and an ageing generating fleet and transmission infrastructure in industrialized nations with mature power systems. The evolving requirement in the power industry is causing several workforce challenges, including massive shifts in skills required, a skills gap for delivering. Performing newer technologies, changes occur when the Industry is experiencing high retirements and challenges recruiting a strengthening workforce. In this paper, Big Data based on the fuzzy logic method has been suggested to protect sensitive information. BD-FLM proposes a large data clustering-based privacy preservation probabilistic model that aims to cause the least disruption while maintaining the greatest amount of privacy. To alter or generalize sensitive data, use a methodology that secures sensitive information after detecting sensitive data clusters. In terms of conventional performance assessment metrics, the model’s privacy protection of individual data in large data with little disruption and effective reconstruction underlines its importance. This paper can automatically cluster based on the power characteristic factor and efficiently identify the power-related aspects of distinct user groups. The simulation results demonstrate that the proposed technique outperforms the comparison algorithm regarding prediction accuracy. As a result of anticipating the accurate values, individual privacy is preserved, but data accuracy is improved.
Research on Privacy Protection Based on Joint Learning in Power Industry Big Data Analysis
Authors: Hang Feilu , Xie Linjiang , Zhang Zhenhong , Guo Wei , Li Hanruo
Published: July 2022

In the era of big data, protecting the privacy of smart grid data is critical in ensuring the integrity and confidentiality of that data. Utilizing large amounts of energy data to gain insight into electricity consumers’ consumption patterns helps develop power supply strategies. This article presents a Big Data-assisted Joint learning process (BDA-JLP), taking data security issues posed by big data in the electric power industry into consideration for privacy protection using K-anonymity and L-diversity as a foundation. A blockchain with JLP electric utility investigation is being conducted, part of the existing trading model split into phases. To begin, an attribute is chosen to categorize the input database. The comparable class number K and sensitive attribute value category L are limited by the number of original predecessors in the source data table, simplifying the calculation. A mathematical equation is then developed to determine the distance between first cousins multiplied by their combined weight. Linear and clustering with binary K are used to categorize data tables. Cluster and generalize initial data sets, considering how the attribute values’ internal range changes. The asymmetric encryption method uses two distinct keys for encryption and decryption ensuring that the blockchain system is completely secure. Simulated data show that the BDA-JLP mechanism proposed here has a privacy ratio of 98.3 percent, scalability of 97.0%, improved data management and data protection ratio of 98.2 percent, customer satisfaction ratio of 98.4 percent, and a low energy consumption ratio of 23.9% when compared to other methods currently available.
STeSH: Intelligent Speech Technology Enabled Smart Home Automation Using IoT
Authors: Sahoo Santosh Kumar , Pattnaik Sushant Kumar , Samal Soumya Ranjan , Nayak Chinmay Kumar , Das Jitendra Kuma , Poulkov Vladimir
Published: July 2022

Due to the advancement in recent technologies and emergence of Internet of Things (IoT), Smart Home Automation (SHA) plays a vital role in today’s lifestyle. Many users are showing their keen interest to move towards this era of digital transformation in order to take the potential benefits of such intelligent and smart application. Traditional smart home systems have several limitations e.g. connectivity, coverage area, device dependability, platform supportability etc. which makes the system less sustainable. In this paper, the proposed technique introduces a novel scheme to operate the devices in an intelligent manner. The scheme implements a system named as “Speech Technology enabled Smart Home (STeSH)”, which supports voice assisted home automation with extreme security and range-free localization. The idea of the proposed technique is to integrate the power of IoT with classical home automation by using an advanced speech technology service. The entire processing of Speech Enabled Services (SES) is applied with this proposed technique and the detailed analysis is presented with proper experimental proof.
Distributed JPEG Compression and Decompression for Big Image Data Using Map-Reduce Paradigm
Authors: Raju U. S. N. , Barman Hillol , Kishor Netalkar Rohan , Kumar Sanjay , Kumar Hariom
Published: July 2022

Digital data is primarily created and delivered in the form of images and videos in today’s world. Storing and transmitting such a large number of images necessitates a lot of computer resources, such as storage and bandwidth. So, rather than keeping the image data as is, the data could be compressed and then stored, which saves a lot of space. Image compression is the process of removing as much redundant data from an image as feasible while retaining only the non-redundant data. In this paper, the traditional JPEG compression technique is executed in the distributed environment with map-reduce paradigm on big image data. This technique is carried out in serial as well as in parallel fashion with different number of workers in order to show the time comparisons between these setups with the self-created large image dataset. In this, more than one Lakh (121,856) images are compressed and decompressed and the execution times are compared with three different setups: single system, Map-Reduce (MR) with 2 workers and MR with 4 workers. Compression on more than one Million (1,096,704) images using single system and MR with 4 workers is also done. To evaluate the efficiency of JPEG technique, two performance measures such as Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR) are used.
Optical analysis of Müller glia cells as light transporters through the retina
Authors: Szabó Levente , Erdei Gábor , Maák Pál Andor
Published: July 2022

One and half decades ago, Müller glia cells of the retina became subjects of extended research as optical waveguides. It was demonstrated that outside the fovea, these cells are capable of providing light transmission through the thicker parts of the retina from the vitreous surface to the photoreceptor cells. We combined optical modeling of the eye’s optical system with numerical methods that describe light guiding within Müller cells to analyze efficiency of light capture and guidance at different peripheral positions. We show that higher order guided modes play an important role, especially in the case of higher incidence angles and extended geometry of the electromagnetic field distributions predicted by the eye’s optical model. We analyze the mode structure excited at different retinal peripheral positions and show that actual construction of these cells optimizes light guiding. Our results refine previously published modeling results regarding Müller cells as waveguides and provide extension to the whole area of the retina.
Inhomogeneous nonlinearity meets parity–time-symmetric Bragg structures: route to ultralow power steering and peculiar stable states
Authors: Sudhakar S. , Vignesh Raja S. , Govindarajan A. , Batri K. , Lakshmanan M.
Published: July 2022

In the context of parity–time (

P

T

)-symmetric fiber Bragg gratings, tailoring the nonlinear profile along the propagation coordinate serves as a new direction for realizing low-power all-optical switches. The scheme is fruitful only when the nonlinearity profile will be either linearly decreasing or increasing form. If the rate of variation of the nonlinearity profile is high, the critical intensities fall below the input power of value 0.01 in the unbroken regime provided that the light launching direction is right. Nowadays, every new theoretical inception into the parity–time fiber Bragg grating (PTFBG) has started making sense of switching in the broken

P

T

-symmetric regime, which was once believed to be the instability regime. When the inhomogeneous nonlinearity acts together with the broken

P

T

symmetry and right light incidence, it leads to two peculiar settings. First, the switch-up intensities are ultralow. Second, the switch-down action takes place at zero critical intensities. Such optical bistability curves are unprecedented in the context of conventional gratings and found only in plasmonic devices and anti-directional couplers. Even though the nonlinearity is inhomogeneous, the ramp-like first stable states persist in the broken

P

T

-symmetric regime, giving an additional indication that the broken PTFBG is closely associated with the plasmonic structures. In the existing PTFBG systems, the switching intensities are relatively higher in the broken regime. However, the proposed system records the lowest switching intensities in the broken regime. The reported intensities (

<

0.005

) are also the lowest-ever switching intensities recorded in the perspective of PTFBGs to date.
Multispectral LiDAR point cloud highlight removal based on color information
Authors: Liu Zhongzheng , Song Shalei , Wang Binhui , Gong Wei , Ran Yanhong , Hou Xiaxia , Chen Zhenwei , Li Faquan
Published: July 2022

With the rapid development of light detection and ranging (LiDAR) technology, multispectral LiDAR (MSL) can realize three-dimensional (3D) imaging of the ground object by acquiring rich spectral information. Although color restoration has been achieved on the basis of the full-waveform data of MSL, further improvement of the visual effect of color point clouds still faces many challenges. In this paper, a highlight removal method for MSL color point clouds is proposed to explore the potential of 3D visualization. First, the MSL reflection model are introduced according to radar equation and Phong model, and the restored color of the MSL point clouds is determined to comprise diffuse and specular components. Second, a data conversion method is proposed to improve the massive point cloud processing efficiency by spatial dimension reduction and data compression. Then, the visual saliency map after color denoising is used to obtain the highlight region, the unknown information of which is recovered based on the global or local color information. Finally, three representative targets are selected and evaluated by qualitative and quantitative validation, which verifies that the method can effectively recover the high-quality highlight-free point clouds of MSL.
Ultra compact bend-less Mach-Zehnder modulator based on GSST phase change material
Authors: Mohammadi-Pouyan Sohrab , Afrouzmehr Mohammadmoein , Abbott Derek
Published: July 2022

Optical phase change materials (O-PCMs) are emerging as promising active materials for exploitation in silicon photonics platforms, due to their compatibility with CMOS fabrication technology and the tunability of their optical characteristics via external excitation. Despite their advantages, O-PCMs suffer from relatively high insertion loss hindering efficient modulation. Also, the change of the imaginary part of the refractive index in O-PCMs is large and the realization of a Mach-Zehnder modulator (MZM) based on O-PCM materials becomes challenging. To overcome these issues, we consider the variation of both real and imaginary parts of the refractive index, facilitated by a GSST-based MZM design. To achieve this, we design an active waveguide that is constructed via depositing an ITO layer (as the microheater) surrounding the pre-fabricated GSST layer on a silicon rib-waveguide. The active length of the proposed MZM is designed ∼4.3 µm at the wavelength of 1.55 µm. The simulations indicate that a compact MZM can be achieved by eliminating the S-bends in the MZM structure without affecting the modulation. The proposed bend-less MZM demonstrates an insertion loss less than 1.7 dB and an extinction ratio greater than 35 dB over the entire optical C-band.
Experimental characterization of a silicon nitride photonic crystal light sail
Authors: Lien Max R. , Meng Deming , Liu Zerui , Sakib Mashnoon A. , Tang Yongkui , Wu Wei , Povinelli Michelle L.
Published: July 2022

The Breakthrough Starshot Initiative, established in 2016, aims to propel an ultra-lightweight spacecraft to Alpha Centauri using radiation pressure from a high-power, ground-based laser. Nanopatterned silicon nitride has been proposed as a candidate material for the laser sail. In this work, we design and fabricate a silicon nitride photonic crystal with high reflectivity around a laser wavelength of 1064 nm. We demonstrate the ability to shift the resonant features of the laser sail using titanium dioxide coatings and increase the longwave infrared emissivity using polymer coatings. We also characterize the response of the sail to temperature and optical power.
Fragmentomics of urinary cell-free DNA in nuclease knockout mouse models
Authors: Chen Meihui , Chan Rebecca W. Y. , Cheung Peter P. H. , Ni Meng , Wong Danny K. L. , Zhou Ze , Ma Mary-Jane L. , Huang Liangbo , Xu Xinzhou , Lee Wing-Shan , Wang Guangya , Lui Kathy O. , Lam W. K. Jacky , Teoh Jeremy Y. C. , Ng Chi-Fai , Jiang Peiyong , Chan K. C. Allen , Chiu Rossa W. K. , Lo Y. M. Dennis
Published: July 2022

Urinary cell-free DNA (ucfDNA) is a potential biomarker for bladder cancer detection. However, the biological characteristics of ucfDNA are not well understood. We explored the roles of deoxyribonuclease 1 (DNASE1) and deoxyribonuclease 1-like 3 (DNASE1L3) in the fragmentation of ucfDNA using mouse models. The deletion of Dnase1 in mice (Dnase1-/-) caused aberrations in ucfDNA fragmentation, including a 24-fold increase in DNA concentration, and a 3-fold enrichment of long DNA molecules, with a relative decrease of fragments with thymine ends and reduction of jaggedness (i.e., the presence of single-stranded protruding ends). In contrast, such changes were not observed in mice with Dnase1l3 deletion (Dnase1l3-/-). These results suggested that DNASE1 was an important nuclease contributing to the ucfDNA fragmentation. Western blot analysis revealed that the concentration of DNASE1 protein was higher in urine than DNASE1L3. The native-polyacrylamide gel electrophoresis zymogram showed that DNASE1 activity in urine was higher than that in plasma. Furthermore, the proportion of ucfDNA fragment ends within DNase I hypersensitive sites (DHSs) was significantly increased in Dnase1-deficient mice. In humans, patients with bladder cancer had lower proportions of ucfDNA fragment ends within the DHSs when compared with participants without bladder cancer. The area under the curve (AUC) for differentiating patients with and without bladder cancer was 0.83, suggesting the analysis of ucfDNA fragmentation in the DHSs may have potential for bladder cancer detection. This work revealed the intrinsic links between the nucleases in urine and ucfDNA fragmentomics.
A scoping review of burden of disease studies estimating disability-adjusted life years due to Taenia solium
Authors: Larkins Andrew , Bruce Mieghan , Di Bari Carlotta , Devleesschauwer Brecht , Pigott David M. , Ash Amanda
Published: July 2022

Background

Taenia solium is the most significant global foodborne parasite and the leading cause of preventable human epilepsy in low and middle-income countries in the form of neurocysticercosis.

Objectives

This scoping review aimed to examine the methodology of peer-reviewed studies that estimate the burden of T. solium using disability-adjusted life years.

Eligibility criteria

Studies must have calculated disability-adjusted life years relating to T. solium.

Charting methods

The review process was managed by a single reviewer using Rayyan. Published data relating to disease models, data sources, disability-adjusted life years, sensitivity, uncertainty, missing data, and key limitations were collected.

Results

15 studies were included for review, with seven global and eight national or sub-national estimates. Studies primarily employed attributional disease models that relied on measuring the occurrence of epilepsy before applying an attributable fraction to estimate the occurrence of neurocysticercosis-associated epilepsy. This method relies heavily on the extrapolation of observational studies across populations and time periods; however, it is currently required due to the difficulties in diagnosing neurocysticercosis. Studies discussed that a lack of data was a key limitation and their results likely underestimate the true burden of T. solium. Methods to calculate disability-adjusted life years varied across studies with differences in approaches to time discounting, age weighting, years of life lost, and years of life lived with disability. Such differences limit the ability to compare estimates between studies.

Conclusions

This review illustrates the complexities associated with T. solium burden of disease studies and highlights the potential need for a burden of disease reporting framework. The burden of T. solium is likely underestimated due to the challenges in diagnosing neurocysticercosis and a lack of available data. Advancement in diagnostics, further observational studies, and new approaches to parameterising disease models are required if estimates are to improve.
Why link diverse citizen science surveys? Widespread arboreal habits of a terrestrial amphibian revealed by mammalian tree surveys in Britain
Authors: Petrovan Silviu O. , Al-Fulaij Nida , Christie Alec , Andrews Henry
Published: July 2022

Terrestrial anurans, with their typically short limbs, heavy-set bodies and absent claws or toe pads are incongruous tree climbers, but even occasional arboreal locomotion could offer substantial advantages for evading predators or accessing new shelter or food resources. Despite recent interest, arboreal behaviour remains rarely and unsystematically described for terrestrial amphibians in Europe, likely due to fundamental differences in survey methods and therefore a lack of field data. However, other taxa surveys specifically target trees and tree cavities. We undertook collaborations and large-scale data searches with two major citizen science projects surveying for arboreal mammals in Britain to investigate potential tree climbing by amphibians at a national scale. Remarkably, we found widespread arboreal usage by amphibians in England and Wales, with occupancy of hazel dormouse (Muscardinus avellenarius) nest boxes, tree cavities investigated as potential bat roosts and even a bird nest, by common toads (Bufo bufo), but few additional records of frogs or newts. Of the 277–400 sites surveyed annually for dormice since 2009 at least 18 sites had amphibians recorded in nest boxes while of the 1388 trees surveyed for bats a total 1.4% (19 trees) had toads present. Common toads were found using cavities in seven tree species and especially goat willow (Salix caprea). Toads are potentially attracted to tree cavities and arboreal nests because they provide safe and damp microenvironments which can support an abundance of invertebrate prey but the importance of such tree microhabitats for toad conservation remains unknown and our results should be interpreted cautiously. We encourage expanding and linking of unrelated biodiversity monitoring surveys and citizen science initiatives as valuable tools for investigating ecological traits and interactions.
A qualitative examination of the factors affecting the adoption of injury focused wearable technologies in recreational runners
Authors: Lacey Aisling , Whyte Enda , O’Keeffe Sinéad , O’Connor Siobhán , Moran Kieran
Published: July 2022

Purpose

Understanding the perceived efficacy and ease of use of technologies will influence initial adoption and sustained utilization. The objectives of this study were to determine the metrics deemed important by runners for monitoring running-related injury (RRI) risk, and identify the facilitators and barriers to their use of injury focused wearable technologies.

Methods

A qualitative focus group study was undertaken. Nine semi-structured focus groups with male (n = 13) and female (n = 14) recreational runners took place. Focus groups were audio and video recorded, and transcribed verbatim. Transcripts were thematically analysed. A critical friend approach was taken to data coding, and multiple methods of trustworthiness were executed.

Results

Excessive loading and inadequate recovery were deemed the most important risk factors to monitor for RRI risk. Other important factors included training activities, injury status and history, and running technique. The location and method of attachment of a wearable device, the design of a smartphone application, and receiving useful injury-related information will affect recreational runners’ adoption of injury focused technologies.

Conclusions

Overtraining, training-related and individual-related risk factors are essential metrics that need to be monitored for RRI risk. RRI apps should include the metrics deemed important by runners, once there is supporting evidence-based research. The difficulty and/or ease of use of a device, and receiving useful feedback will influence the adoption of injury focused running technologies. There is a clear willingness from recreational runners to adopt injury focused wearable technologies whilst running.
Serological testing of blood donors to characterise the impact of COVID-19 in Melbourne, Australia, 2020
Authors: Machalek Dorothy A. , Vette Kaitlyn M. , Downes Marnie , Carlin John B. , Nicholson Suellen , Hirani Rena , Irving David O. , Gosbell Iain B. , Gidding Heather F. , Shilling Hannah , Aung Eithandee , Macartney Kristine , Kaldor John M.
Published: July 2022

Rapidly identifying and isolating people with acute SARS-CoV-2 infection has been a core strategy to contain COVID-19 in Australia, but a proportion of infections go undetected. We estimated SARS-CoV-2 specific antibody prevalence (seroprevalence) among blood donors in metropolitan Melbourne following a COVID-19 outbreak in the city between June and September 2020. The aim was to determine the extent of infection spread and whether seroprevalence varied demographically in proportion to reported cases of infection. The design involved stratified sampling of residual specimens from blood donors (aged 20–69 years) in three postcode groups defined by low (<3 cases/1,000 population), medium (3–7 cases/1,000 population) and high (>7 cases/1,000 population) COVID-19 incidence based on case notification data. All specimens were tested using the Wantai SARS-CoV-2 total antibody assay. Seroprevalence was estimated with adjustment for test sensitivity and specificity for the Melbourne metropolitan blood donor and residential populations, using multilevel regression and poststratification. Overall, 4,799 specimens were collected between 23 November and 17 December 2020. Seroprevalence for blood donors was 0.87% (90% credible interval: 0.25–1.49%). The highest estimates, of 1.13% (0.25–2.15%) and 1.11% (0.28–1.95%), respectively, were observed among donors living in the lowest socioeconomic areas (Quintiles 1 and 2) and lowest at 0.69% (0.14–1.39%) among donors living in the highest socioeconomic areas (Quintile 5). When extrapolated to the Melbourne residential population, overall seroprevalence was 0.90% (0.26–1.51%), with estimates by demography groups similar to those for the blood donors. The results suggest a lack of extensive community transmission and good COVID-19 case ascertainment based on routine testing during Victoria’s second epidemic wave. Residual blood donor samples provide a practical epidemiological tool for estimating seroprevalence and information on population patterns of infection, against which the effectiveness of ongoing responses to the pandemic can be assessed.
Correlation among clinical, functional and morphological indexes of the respiratory system in non-cystic fibrosis bronchiectasis patients
Authors: Perossi Jéssica , Koenigkam-Santos Marcel , Perossi Larissa , dos Santos Daniele Oliveira , Simoni Letícia Helena de Souza , de Souza Hugo Celso Dutra , Gastaldi Ada Clarice
Published: July 2022

Background

Non-cystic fibrosis bronchiectasis (NCFB) is a heterogeneous disease, which assessment and severity can’t be defined by one particular instrument but using a multidimensional score. Thus, in additional to traditional methods, alternative tools have been developed to assist these patients’ evaluation.

Objective

To correlate functional and morphological indexes with severity and dyspnea in NCFB patients, focusing on the correlation between the impulse oscillometry system (IOS) and the quantitative analysis of computed tomography (CT).

Methods

Clinically stable NCFB patients, between 18 and 80 years old were submitted to clinical, functional and morphological evaluations assessed by Bronchiectasis Severity Index (BSI) and Medical Research Council (MRC) scale; spirometry and IOS; and subjective and quantitative Chest CT scans analysis, respectively.

Results

This study included 38 patients. The best correlations obtained between functional and morphological airway indexes were: resistance at 5 Hz—R5 and the normalized thickness of bronchial walls—Pi10 (r = 0.57), and the mean forced expiratory flow (FEF25-75%) and CT score (r = -0.39). BSI as well as MRC showed higher correlations with the quantitative automated analysis of CT (BSI and Pi10: r = 0.41; MRC and Pi10: r = 0.35) than with subjective CT score (BSI and CT score: r = 0.41; MRC and CT score: r = 0.15); and moderate and weak correlations were obtained on both functional airway indexes (BSI and peripheral airways resistance - R5-R20: r = 0.53; BSI and forced expiratory volume at the first second—FEV1: R = -0,64; MRC and R5-R20: r = 0.42; and MRC and VEF1: r = -0.45).

Conclusion

In NCFB patients, compartmentalized methods for assessing the respiratory system (IOS and the automated quantitative CT analysis) have a good correlation with severity and dyspnea.
COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation
Authors: Raschke Robert A. , Rangan Pooja , Agarwal Sumit , Uppalapu Suresh , Sher Nehan , Curry Steven C. , Heise C. William
Published: July 2022

Background

An accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions.

Research objective

To develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to acute physiology and chronic health evaluation (APACHE IVa) and sequential organ failure assessment (SOFA).

Methods

A retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. Area under the receiver operating curve (AUROC) was calculated for C-TIME, APACHE IVa and SOFA.

Results

The median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO2/FiO2 ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72–0.79), vs 0.67 (0.64–0.71) and 0.59 (0.55–0.62) for APACHE and SOFA, respectively (Chi2 P<0.0001).

Conclusions

C-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.
The gender pay gap is smaller in occupations with a higher ratio of men: Evidence from a national panel study
Authors: Schneider Sarah , Rentzsch Katrin , Schütz Astrid
Published: July 2022

Gender differences in career success are still an issue in society and research, and men typically earn higher incomes than women do. Building on previous theorizing and findings with the Theory of Gendered Organizations and the Theory of Tokenism, we used a large sample of the adult starting cohort in the German National Educational Panel Study and a multilevel approach to test how the interaction between gender and the gender ratio in occupations was associated with income. We wanted to know whether the male advantage in terms of income would be equal in magnitude across occupations (as suggested by the Theory of Gendered Organizations) or if it would vary with the gender ratio in occupations (as suggested by the Theory of Tokenism and reasoning regarding person-job fit), such that people benefit either (a) from resembling the majority of employees in a field by working in a gender-typical occupation or (b) from standing out by working in a gender-atypical occupation. Analyses supported the hypothesis that employees’ incomes may benefit if they belong to the gender minority in an occupation, but this finding applied only to women. By contrast, men did not benefit from working in a gender-atypical occupation. Thus, women earned less than men earned overall, but the gender pay gap was smaller in occupations with a higher ratio of male employees. The findings can advance the understanding of gender-related career decisions for both employers and employees.
Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India
Authors: Paul Ranjit Kumar , Yeasin Md. , Kumar Pramod , Kumar Prabhakar , Balasubramanian M. , Roy H. S. , Paul A. K. , Gupta Ajit
Published: July 2022

Background

Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions.

Methods

In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India.

Results

An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models.

Conclusions

Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.
Genetic polymorphisms associated with susceptibility to COVID-19 disease and severity: A systematic review and meta-analysis
Authors: Dieter Cristine , Brondani Letícia de Almeida , Leitão Cristiane Bauermann , Gerchman Fernando , Lemos Natália Emerim , Crispim Daisy
Published: July 2022

Although advanced age and presence of comorbidities significantly impact the variation observed in the clinical symptoms of COVID-19, it has been suggested that genetic variants may also be involved in the disease. Thus, the aim of this study was to perform a systematic review with meta-analysis of the literature to identify genetic polymorphisms that are likely to contribute to COVID-19 pathogenesis. Pubmed, Embase and GWAS Catalog repositories were systematically searched to retrieve articles that investigated associations between polymorphisms and COVID-19. For polymorphisms analyzed in 3 or more studies, pooled OR with 95% CI were calculated using random or fixed effect models in the Stata Software. Sixty-four eligible articles were included in this review. In total, 8 polymorphisms in 7 candidate genes and 74 alleles of the HLA loci were analyzed in 3 or more studies. The HLA-A*30 and CCR5 rs333Del alleles were associated with protection against COVID-19 infection, while the APOE rs429358C allele was associated with risk for this disease. Regarding COVID-19 severity, the HLA-A*33, ACE1 Ins, and TMPRSS2 rs12329760T alleles were associated with protection against severe forms, while the HLA-B*38, HLA-C*6, and ApoE rs429358C alleles were associated with risk for severe forms of COVID-19. In conclusion, polymorphisms in the ApoE, ACE1, TMPRSS2, CCR5, and HLA loci appear to be involved in the susceptibility to and/or severity of COVID-19.
Knowledge, perception, and fear among the global population towards newly evoked variant Omicron (B.1.1.529)
Authors: Qureshi Ali , Syed Sulaiman Syed Azhar , Kumar Narendar , Qureshi Pir Abdul Ahad Aziz
Published: July 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-1), notoriously known as COVID-19, emerged in 2019 and was responsible for causing acute respiratory collapse. Moreover, in September 2020, new variant cases of severe acute respiratory syndrome coronavirus 2 were reported in the United Kingdom, with many patients and deaths. This study aimed to see knowledge, perception, and fear among the global population towards a new variant of severe acute respiratory syndrome coronavirus 2, known as Omicron (B.1.1.529). This online cross-sectional global study was conducted during the emergence of the B.1.1.529 variant, also known as the Omicron variant. The survey was carried out from 2nd December 2021 to 3rd January 2022. The descriptive analysis was presented as frequencies (N), percentages (%), and mean ± standard deviation (m ± SD). The association between dependent and categorical independent variables was determined using the Chi-square test (x2). Statistical analysis was performed by using SPSS version 23. Of 353 respondents, approximately 61% were females. One hundred fifty-four respondents were in the age group of 18–27 years. The average age was 31.53±10.3 (mean± SD). The majority of respondents (43.9%) were from Indonesia. The mean knowledge score about the Omicron variant was 3.18±1.14. Our study suggests that people have some knowledge about the new variant, Omicron (B.1.1.529). Besides, there was a significant association (p = 0.05) for the perception of the fatality rate of Omicron among the respondents from different countries. However, there is still an ample research gap in enlightening people about this infection (B.1.1.529).
A survey on text classification: Practical perspectives on the Italian language
Authors: Gasparetto Andrea , Zangari Alessandro , Marcuzzo Matteo , Albarelli Andrea
Published: July 2022

Text Classification methods have been improving at an unparalleled speed in the last decade thanks to the success brought about by deep learning. Historically, state-of-the-art approaches have been developed for and benchmarked against English datasets, while other languages have had to catch up and deal with inevitable linguistic challenges. This paper offers a survey with practical and linguistic connotations, showcasing the complications and challenges tied to the application of modern Text Classification algorithms to languages other than English. We engage this subject from the perspective of the Italian language, and we discuss in detail issues related to the scarcity of task-specific datasets, as well as the issues posed by the computational expensiveness of modern approaches. We substantiate this by providing an extensively researched list of available datasets in Italian, comparing it with a similarly sought list for French, which we use for comparison. In order to simulate a real-world practical scenario, we apply a number of representative methods to custom-tailored multilabel classification datasets in Italian, French, and English. We conclude by discussing results, future challenges, and research directions from a linguistically inclusive perspective.
Do bromeliads affect the arboreal ant communities on orange trees in northwestern Costa Rica?
Authors: Rost-Komiya Beatrice , Smith M. Alex , Rogy Pierre , Srivastava Diane S.
Published: July 2022

Ants often interact with other invertebrates as predators or mutualists. Epiphytic bromeliads provide nesting sites for ants, and could increase ant abundances in the tree canopy. We surveyed ants in the foliage of orange trees that either hosted bromeliads or did not. To determine if observed associations between bromeliads and tree ants were causal, we removed bromeliads from half of the trees, and resurveyed ants six weeks later. Our results show that bromeliad presence is correlated with higher ant abundances and different species of ants on orange trees during the dry season. This increase in ant abundance was driven primarily by Solenopsis ants, which were both numerous and found to facultatively nest in bromeliads. Bromeliad removal did not affect either ant abundance or composition, potentially because this manipulation coincided with the transition from dry to wet season. Other ant species were never encountered nesting in bromeliads, and the abundances of such ants on tree leaves were unaffected by bromeliad presence or removal. Considering the importance of ants in herbivore regulation, our findings suggest that bromeliads–through their association with ants–could indirectly be associated with biological control in agricultural systems.
The Effectiveness of Intradermal Suture Technique on Hypertrophic Scar Prevention in Rats
Authors: Wihastyoko Herman Yosef Limpat , Wuryanjono Wicaksono Patria
Published: July 2022

Introduction : Hypertrophic scarring is a complication that occurs in post-surgical wounds. There are many ways to prevent scarring, but there have been no satisfactory methods yet. Moreover, no studies investigated the effectiveness of intradermal sutures. Polypropylene thread can be used to repair a scar after surgery. This study aimed to prove the effectiveness of intradermal sutures using polypropylene thread on surgical scar quality.Method : The study was conducted using a randomized, controlled trial, and post-test only with 20 rats (Rattus norvegicus) as animal subjects. The wounds were made on the back, approximately 6 mm x 2 cm. Then, the rats were divided into two groups: a control group without intradermal sutures (K) and treatment group with intradermal sutures (I). Histopathological examination using Hematoxylin and Eosin (H&E) staining was used to identify the fibroblast number on the 21st day. The fibrocytes number were also observed on the 12th week and the Vancouver Scar Scale (VSS) was used to assess the scar quality.Result : The number of fibroblasts and fibrocytes in the control group (K) was significantly smaller (53.60 ± 14.571; 3.20 ± 0.447) than the treatment (I) (243.20 ± 75.334; 171.40 ± 13.221). The VSS value was significantly greater in the control than the treatment.Conclusion: The intradermal sutures using polypropylene thread produced a better-quality scar after surgery compared to wounds without intradermal sutures.