bluuebunny/crossref_metadata_2025 huggingface dataset:
The minimum wage in the national economy: reasons and changes in Poland
Authors: Rosiński Rafał
Published: October 2021

Motivation: The minimum wage protect employees from excessively low wages. It helps ensure fair participation in economic development for the general public. The minimum wage in the economy is an essential element in a country’s economic policy. Decisions concerning the implementation and changes in the minimum wage are part of the socio-economic policy of the state. Research on minimum wage relate to its overall impact on the level and structure of wages, employment, productivity at work, the size of the gray economy, the level of poverty and public finances. Factors affecting changes in the minimum wage are formal and non-formal. The question therefore arises which factors are decisive in the Polish economy.

Aim: The aim of this paper is to present the essence and causes of changes in the minimum wage in Poland in 2003–2020 with particular regard to formal and non-formal aspects.

Results: On the basis of research carried out in Poland, a link between minimum wage and inflation was observed. There is also a fundamental relationship between the minimum wage and average wages in the economy. It has been shown that the minimum wage is not without significance for the impact on public finance sector in Poland.
What Questions Are You Inclined to Answer? Effects of Hierarchy in Corporate Q&A Communities
Authors: Pu Jingchuan , Liu Yang , Chen Yuan , Qiu Liangfei , Cheng Hsing Kenneth
Published: October 2021

Are employees willing to voluntarily share knowledge with their higher-ups? The existing studies show that the answer is no—employees are less likely to share knowledge with their higher-ups in the offline setting, corporate wikis, and online discussion groups. We answer the same question in a corporate question-and-answer (Q&A) community and argue that the answer can be yes. A potential-dyads approach and a quasi-natural experiment jointly demonstrate that employees are inclined to answer a question from their higher-ups and even exert more effort in those answers. Using an instrumental-variable design, we show that users who post more answers to higher-ranked individuals and who display greater effort in those answers are more likely to get promoted in subsequent years, meaning that employees do not need to worry about their careers when sharing knowledge with their higher-ups in corporate Q&A communities. Our research, together with research on other contexts, are useful for companies to take the role of the managers into account when considering which type of online community to adopt. Community designers can use our findings to better motivate knowledge sharing by considering users’ different job ranks.
SRPT Scheduling Discipline in Many-Server Queues with Impatient Customers
Authors: Dong Jing , Ibrahim Rouba
Published: October 2021

The shortest-remaining-processing-time (SRPT) scheduling policy has been extensively studied, for more than 50 years, in single-server queues with infinitely patient jobs. Yet, much less is known about its performance in multiserver queues. In this paper, we present the first theoretical analysis of SRPT in multiserver queues with abandonment. In particular, we consider the [Formula: see text] queue and demonstrate that, in the many-sever overloaded regime, performance in the SRPT queue is equivalent, asymptotically in steady state, to a preemptive two-class priority queue where customers with short service times (below a threshold) are served without wait, and customers with long service times (above a threshold) eventually abandon without service. We prove that the SRPT discipline maximizes, asymptotically, the system throughput, among all scheduling disciplines. We also compare the performance of the SRPT policy to blind policies and study the effects of the patience-time and service-time distributions.

This paper was accepted by Baris Ata, stochastic models & simulation.
The Sound of Silence: What Do We Know When Insiders Do Not Trade?
Authors: Gao George P. , Ma Qingzhong , Ng David T. , Wu Ying
Published: October 2021

This paper examines the information content of insider silence, periods of no insider trading. We hypothesize that, to avoid litigation risk, rational insiders do not sell own-company shares when they anticipate bad news; neither would they buy, given unfavorable prospects. Thus, they keep silent. By contrast, insiders sell shares when they do not anticipate significant bad news. Future stock returns are significantly lower following insider silence than following insider net selling, especially among firms with higher litigation risk. We examine two quasinatural experiments where new laws result in changes in shareholder litigation risks for insiders. In both cases, with higher shareholder litigation risks, stocks where insiders stay silent earn significantly lower returns than other stocks.

This paper was accepted by Karl Diether, finance.
A Case Study on Stochastic Games on Large Graphs in Mean Field and Sparse Regimes
Authors: Lacker Daniel , Soret Agathe
Published: October 2021

We study a class of linear-quadratic stochastic differential games in which each player interacts directly only with its nearest neighbors in a given graph. We find a semiexplicit Markovian equilibrium for any transitive graph, in terms of the empirical eigenvalue distribution of the graph’s normalized Laplacian matrix. This facilitates large-population asymptotics for various graph sequences, with several sparse and dense examples discussed in detail. In particular, the mean field game is the correct limit only in the dense graph case, that is, when the degrees diverge in a suitable sense. Although equilibrium strategies are nonlocal, depending on the behavior of all players, we use a correlation decay estimate to prove a propagation of chaos result in both the dense and sparse regimes, with the sparse case owing to the large distances between typical vertices. Without assuming the graphs are transitive, we show also that the mean field game solution can be used to construct decentralized approximate equilibria on any sufficiently dense graph sequence.
High conductivity n-Al0.6Ga0.4N by ammonia-assisted molecular beam epitaxy for buried tunnel junctions in UV emitters
Authors: Wang Jianfeng , SaifAddin Burhan K. , Zollner Christian J. , Bonef Bastien , Almogbel Abdullah S. , Yao Yifan , Iza Michael , Zhang Yuewei , Fireman Micha N. , Young Erin C. , DenBaars Steven P. , Nakamura Shuji , Speck James S.
Published: October 2021

Highly doped n-Al0.6Ga0.4N can be used to form tunnel junctions (TJs) on deep ultraviolet (UVC) LEDs and markedly increase the light extraction efficiency (LEE) compared to the use of p-GaN/p-AlGaN. High quality Al0.6Ga0.4N was grown by NH3-assisted molecular beam epitaxy (NH3 MBE) on top of AlN on SiC substrate. The films were crack free under scanning electron microscope (SEM) for the thickness investigated (up to 1 µm). X-ray diffraction reciprocal space map scan was used to determine the Al composition and the result is in close agreement with atom probe tomography (APT) measurements. By varying the growth parameters including growth rate, and Si cell temperature, n-Al0.6Ga0.4N with an electron density of 4×1019 /cm3 and a resistivity of 3 mΩ·cm was achieved. SIMS measurement shows that a high Si doping level up to 2×1020 /cm3 can be realized using a Si cell temperature of 1450 °C and a growth rate of 210 nm/hr. Using a vanadium-based annealed contact, ohmic contact with a specific resistance of 10−6 Ω·cm2 was achieved as determined by circular transmission line measurement (CTLM). Finally, the n-type AlGaN regrowth was done on MOCVD grown UVC LEDs to form UVC TJ LED. The sample was processed into thin film flip chip (TFFC) configuration. The emission wavelength is around 278 nm and the excess voltage of processed UV LED is around 4.1 V.
1.3 µm InGaAlAs/InP laser integrated with laterally tapered SSC in a reverse mesa shape
Authors: La Xiaobo , Zhu Xuyuan , Guo Jing , Zhao Lingjuan , Wang Wei , Liang Song
Published: October 2021

We report 1.3 µm InGaAlAs/InP lasers integrated with laterally tapered spot size converter (SSC) in reverse mesa shape. Because the top width is significantly larger than the bottom width for the reverse mesa ridge, high quality SSCs having narrow tip width can be fabricated through conventional photolithography with a high reproductivity. The Threshold current of the fabricated 1000 µm long SSC integrated device is 25 mA and 44 mW single facet optical power can be obtained at 300 mA current. The lateral and vertical divergence angles are as low as 8 ° and 11°, respectively.
Full space coordinate measurement method with a portable light pen by using hybrid light field imaging
Authors: Wu Jun , Song Fengcheng , Feng Chengbin , Yu Zhijing
Published: October 2021

The light pen is one of the most useful spatial coordinate measurement methods. However, due to the principle of vision measurement, the traditional light pen has the disadvantages such as limited measurement range, large structure size and complex calibration process. In order to revolutionize the design of light pen, this paper proposes a full space coordinate measurement method with a portable light pen by using hybrid light field imaging. Firstly, a light field imaging system with depth estimation ability has been introduced to simplify the light pen structure to three target points by using P3P method. In order to improve the accuracy of the depth estimation, a hybrid imaging method and EPI method have been used. Secondly, a full spatial measurement method with a rotation platform has been presented in detail. Finally, the experiments have been designed to verify the effectiveness and accuracy of the proposed method.
Real-time optical reconstruction for a three-dimensional light-field display based on path-tracing and CNN super-resolution
Authors: Guo Xiao , Sang Xinzhu , Chen Duo , Wang Peng , Wang Huachun , Liu Xue , Li Yuanhang , Xing Shujun , Yan Binbin
Published: October 2021

Three-Dimensional (3D) light-field display plays a vital role in realizing 3D display. However, the real-time high quality 3D light-field display is difficult, because super high-resolution 3D light field images are hard to be achieved in real-time. Although extensive research has been carried out on fast 3D light-field image generation, no single study exists to satisfy real-time 3D image generation and display with super high-resolution such as 7680×4320. To fulfill real-time 3D light-field display with super high-resolution, a two-stage 3D image generation method based on path tracing and image super-resolution (SR) is proposed, which takes less time to render 3D images than previous methods. In the first stage, path tracing is used to generate low-resolution 3D images with sparse views based on Monte-Carlo integration. In the second stage, a lite SR algorithm based on a generative adversarial network (GAN) is presented to up-sample the low-resolution 3D images to high-resolution 3D images of dense views with photo-realistic image quality. To implement the second stage efficiently and effectively, the elemental images (EIs) are super-resolved individually for better image quality and geometry accuracy, and a foreground selection scheme based on ray casting is developed to improve the rendering performance. Finally, the output EIs from CNN are used to recompose the high-resolution 3D images. Experimental results demonstrate that real-time 3D light-field display over 30fps at 8K resolution can be realized, while the structural similarity (SSIM) can be over 0.90. It is hoped that the proposed method will contribute to the field of real-time 3D light-field display.
Sparse-aperture photonics-integrated interferometer (SPIN) imaging system: structural design and imaging quality analysis
Authors: Chen TianBao , Zeng XueFeng , Bai YingYing , Zhang Zhiyu , Wang YongJin , Zhang Feng , Zhang XueJun
Published: October 2021

The burgeoning field of astrophotonics, the interface between astronomy and photonics, is redefining astronomical instrumentation to replace traditional bulk optical systems with integrated optics. This drives the development of a new promising photonics-integrated interferometric imaging technique, called the segmented planar imaging detector for electro-optical reconnaissance (SPIDER). Compared to conventional imaging systems, SPIDER can reduce the size, weight, and power (SWaP) by one to two orders of magnitude for an equivalent imaging resolution in virtue of photonics-integrated technology. However, SPIDER has a dense lens distribution and tens of separated narrow wavebands demultiplexed by array waveguide gratings. In this paper, we developed a new simplified sparse-aperture photonics-integrated interferometer (SPIN) imaging system. The SPIN imaging system was no more a Michelson configuration interferometer as SPIDER and was designed as a Fizeau configuration interferometer imaging system. This transfer of configuration type affords a more concise structure; the SPIN was designed with much less apertures and fewer wavebands than those of SPIDER. Further, the SPIN yields enhanced modulation transfer function and imaging quality with equivalent aperture diameter, compared with SPIDER. The main barrier of this transfer is the elimination of coupling restriction at the tip of a waveguide, namely the apodization effect. This effect, which is caused by the coupling effect between Fourier lens and waveguide, hinders SPIN imaging systems from getting finer resolution. However, a microscope could be used to eliminate this effect. Moreover, a waveguide array is used to receive these finer details and enlarges the field of view in SPIN. The coupling efficiency of the waveguides and crosstalk errors between waveguides of array were analyzed, which are important for proper parameters setting in SPIN imaging system. Based on these analyses, the imaging principle was derived and a hyper-Laplacian-based imaging reconstruction algorithm was developed. A simulation of the SPIN imaging system with seven apertures and one imaging waveband demonstrated the high imaging quality.
Compact dark-field confocal microscopy based on an annular beam with orbital angular momentum
Authors: Liu Jian , Hua Zijie , Liu Chenguang
Published: October 2021

This study proposes a dark-field confocal microscopy (DFCM) based on fiber-mode excitation-assisted orbital angular momentum annular beam generation with a two-mode fiber to avoid diffraction distortion. The application of optical fibers compresses the DFCM volume, providing new ideas for system miniaturization. System adjustment difficulty is reduced and feasibility is verified by imaging 2D and 3D samples. High imaging contrast is achieved by fully blocking the reflected light and annular illumination with a steady central dark-spot within a propagation distance of 3 m. The application of our scheme can be further extended to detect subsurface defects in optical components and high-contrast biological imaging.
120  mJ, 1  kHz, picosecond laser at 515  nm
Authors: Hubka Zbyněk , Antipenkov Roman , Boge Robert , Erdman Emily , Greco Michael , Green Jonathan T. , Horáček Martin , Majer Karel , Mazanec Tomáš , Mazůrek Petr , Naylon Jack A. , Novák Jakub , Šobr Václav , Strkula Petr , Torun Murat , Tykalewicz Boguslaw , Bakule Pavel , Rus Bedřich
Published: October 2021

We report on a 1 kHz, 515 nm laser system, based on a commercially available 230 W average power Yb:YAG thin-disk regenerative amplifier, developed for pumping one of the last optical parametric chirped pulse amplification (OPCPA) stages of the Allegra laser system at ELI Beamlines. To avoid problems with self-focusing of picosecond pulses, the 1030 nm output pulses are compressed and frequency doubled with an LBO crystal in vacuum. Additionally, development of a thermal management system was needed to ensure stable phase matching conditions at high average power. The resulting 515 nm pulses have an energy of more than 120 mJ with SHG efficiency of 60% and an average RMS stability of 1.1% for more than 8 h.
Mitigation of the supermode noise in a harmonically mode-locked ring fiber laser using optical injection
Authors: Ribenek V. A. , Stoliarov D. A. , Korobko D. A. , Fotiadi A. A.
Published: October 2021

We report on a new, to the best of our knowledge, technique enabling mitigation of the supermode noise (and timing jitter) in a soliton harmonically mode-locked (HML) fiber laser built on the nonlinear polarization evolution (NPE). An optical injection of an external continuous wave (CW) into the HML laser cavity results in an increase of the supermode noise suppression level (SSL) by a two-three order of magnitude for harmonics between 25th and 135th. The operation mechanism involves phase-locking between the injected light and soliton pulses and exhibits strong resonant dependence on the CW laser wavelength. Our findings offer important insights into the HML laser dynamics associated with an interaction between solitons and CW background in the laser cavity.
Pulse photothermal optical coherence tomography for multimodal hemodynamic imaging
Authors: Wu Jiayi , Wu Nanshou , Tang Peijun , Lin Jiayi , Lian Yi , Tang Zhilie
Published: October 2021

To realize multimodal hemodynamic imaging, pulse photothermal optical coherence tomography (P-PTOCT) is proposed in this Letter to solve the separation problem of photothermal phase and Doppler phase, which is difficult to solve in traditional PTOCT. This technique can obtain blood flow distribution, light absorption distribution, and concentration images simultaneously. Based on the difference between pulse photothermal phase and Doppler phase, we propose an even number differential demodulation algorithm that can separate the photothermal phase and Doppler phase from the same scanning data set. The separated photothermal phase can characterize the trend of drug concentration, which provides the possibility for quantitative measurement of plasma concentration. The combination of photothermal phase and Doppler phase is helpful for potential clinical research on hemodynamics of cerebral ischemia and provides a technical reference for the rapid acquisition of perfusion volume and plasma concentration at one time.
Raman-scattering-assistant large energy dissipative soliton and multicolor coherent noise-like pulse complex in an Yb-doped fiber laser
Authors: Chang Shuo , Zheng Yameng , Wang Zhaokun , Shen Changyu
Published: October 2021

In this Letter, we have demonstrated the generation of dissipative solitons (DSs) or multi-wavelength noise-like pulses (NLPs) directly from a common linear Yb-doped fiber laser in the presence of stimulated Raman scattering (SRS). For the DSs, the pulse energy of the solitons with a pulse width of 74.2 ps reaches 21.2 nJ. For the NLPs, the generation of the main NLP (1032 nm) together with the first-order Raman NLP (1080 nm) is realized. The narrow peak of the double-scale autocorrelation trace is characterized by quasi-periodic beat pulses with a pulse beating of 40.6 fs and a pulse separation of 79 fs, indicating that the generated solitons at dual wavelengths are mutually coherent. Furthermore, a three-color stable NLP complex with a broader spectrum is also obtained. The results contribute to an in-depth understanding of nonlinear dynamics and ultrafast physics.
The glucose-sensing transcription factor MLX balances metabolism and stress to suppress apoptosis and maintain spermatogenesis
Authors: Carroll Patrick A. , Freie Brian W. , Cheng Pei Feng , Kasinathan Sivakanthan , Gu Haiwei , Hedrich Theresa , Dowdle James A. , Venkataramani Vivek , Ramani Vijay , Wu Xiaoying , Raftery Daniel , Shendure Jay , Ayer Donald E. , Muller Charles H. , Eisenman Robert N.
Published: October 2021

Male germ cell (GC) production is a metabolically driven and apoptosis-prone process. Here, we show that the glucose-sensing transcription factor (TF) MAX-Like protein X (MLX) and its binding partner MondoA are both required for male fertility in the mouse, as well as survival of human tumor cells derived from the male germ line. Loss ofMlxresults in altered metabolism as well as activation of multiple stress pathways and GC apoptosis in the testes. This is concomitant with dysregulation of the expression of male-specific GC transcripts and proteins. Our genomic and functional analyses identify loci directly bound by MLX involved in these processes, including metabolic targets, obligate components of male-specific GC development, and apoptotic effectors. These in vivo and in vitro studies implicate MLX and other members of the proximal MYC network, such as MNT, in regulation of metabolism and differentiation, as well as in suppression of intrinsic and extrinsic death signaling pathways in both spermatogenesis and male germ cell tumors (MGCTs).
Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits
Authors: Johnson Ruth , Burch Kathryn S. , Hou Kangcheng , Paciuc Mario , Pasaniuc Bogdan , Sankararaman Sriram
Published: October 2021

The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs.
pyActigraphy: Open-source python package for actigraphy data visualization and analysis
Authors: Hammad Grégory , Reyt Mathilde , Beliy Nikita , Baillet Marion , Deantoni Michele , Lesoinne Alexia , Muto Vincenzo , Schmidt Christina
Published: October 2021

Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.
Decreasing mitochondrial RNA polymerase activity reverses biased inheritance of hypersuppressive mtDNA
Authors: Corbi Daniel , Amon Angelika
Published: October 2021

Faithful inheritance of mitochondrial DNA (mtDNA) is crucial for cellular respiration/oxidative phosphorylation and mitochondrial membrane potential. However, how mtDNA is transmitted to progeny is not fully understood. We utilized hypersuppressive mtDNA, a class of respiratory deficientSaccharomyces cerevisiaemtDNA that is preferentially inherited over wild-type mtDNA (rho+), to uncover the factors governing mtDNA inheritance. We found that some regions ofrho+mtDNA persisted while others were lost after a specific hypersuppressive takeover indicating that hypersuppressive preferential inheritance may partially be due to active destruction ofrho+mtDNA. From a multicopy suppression screen, we found that overexpression of putative mitochondrial RNA exonucleasePET127reduced biased inheritance of a subset of hypersuppressive genomes. This suppression requiredPET127binding to the mitochondrial RNA polymeraseRPO41but notPET127exonuclease activity. A temperature-sensitive allele ofRPO41improvedrho+mtDNA inheritance over a specific hypersuppressive mtDNA at semi-permissive temperatures revealing a previously unknown role forrho+transcription in promoting hypersuppressive mtDNA inheritance.
Incorporation of uncertainty to improve projections of tidal wetland elevation and carbon accumulation with sea-level rise
Authors: Buffington Kevin J. , Janousek Christopher N. , Dugger Bruce D. , Callaway John C. , Schile-Beers Lisa M. , Borgnis Sloane Evyan , Thorne Karen M.
Published: October 2021

Understanding the rates and patterns of tidal wetland elevation changes relative to sea-level is essential for understanding the extent of potential wetland loss over the coming years. Using an enhanced and more flexible modeling framework of an ecosystem model (WARMER-2), we explored sea-level rise (SLR) impacts on wetland elevations and carbon sequestration rates through 2100 by considering plant community transitions, salinity effects on productivity, and changes in sediment availability. We incorporated local experimental results for plant productivity relative to inundation and salinity into a species transition model, as well as site-level estimates of organic matter decomposition. The revised modeling framework includes an improved calibration scheme that more accurately reconstructs soil profiles and incorporates parameter uncertainty through Monte Carlo simulations. Using WARMER-2, we evaluated elevation change in three tidal wetlands in the San Francisco Bay Estuary, CA, USA along an estuarine tidal and salinity gradient with varying scenarios of SLR, salinization, and changes in sediment availability. We also tested the sensitivity of marsh elevation and carbon accumulation rates to different plant productivity functions. Wetland elevation at all three sites was sensitive to changes in sediment availability, but sites with greater initial elevations or space for upland transgression persisted longer under higher SLR rates than sites at lower elevations. Using a multi-species wetland vegetation transition model for organic matter contribution to accretion, WARMER-2 projected increased elevations relative to sea levels (resilience) and higher rates of carbon accumulation when compared with projections assuming no future change in vegetation with SLR. A threshold analysis revealed that all three wetland sites were likely to eventually transition to an unvegetated state with SLR rates above 7 mm/yr. Our results show the utility in incorporating additional estuary-specific parameters to bolster confidence in model projections. The new WARMER-2 modeling framework is widely applicable to other tidal wetland ecosystems and can assist in teasing apart important drivers of wetland elevation change under SLR.
Rise of the war machines: Charting the evolution of military technologies from the Neolithic to the Industrial Revolution
Authors: Turchin Peter , Hoyer Daniel , Korotayev Andrey , Kradin Nikolay , Nefedov Sergey , Feinman Gary , Levine Jill , Reddish Jenny , Cioni Enrico , Thorpe Chelsea , Bennett James S. , Francois Pieter , Whitehouse Harvey
Published: October 2021

What have been the causes and consequences of technological evolution in world history? In particular, what propels innovation and diffusion of military technologies, details of which are comparatively well preserved and which are often seen as drivers of broad socio-cultural processes? Here we analyze the evolution of key military technologies in a sample of pre-industrial societies world-wide covering almost 10,000 years of history usingSeshat:Global History Databank. We empirically test previously speculative theories that proposed world population size, connectivity between geographical areas of innovation and adoption, and critical enabling technological advances, such as iron metallurgy and horse riding, as central drivers of military technological evolution. We find that all of these factors are strong predictors of change in military technology, whereas state-level factors such as polity population, territorial size, or governance sophistication play no major role. We discuss how our approach can be extended to explore technological change more generally, and how our results carry important ramifications for understanding major drivers of evolution of social complexity.
Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network
Authors: Lee Seung-Yeon , Kang Hyeon , Jeong Jong-Hun , Kang Do-young
Published: October 2021

High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer’s disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. Therefore, we created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer’s Disease Neuroimaging Initiative dataset. Spatial normalization, count normalization, and skull stripping preprocessing were performed on the DAUH and external datasets. However, smoothing was only performed on the external dataset. Three types of models were used, depending on their structure: Inception3D, ResNet3D, and VGG3D. After training with 80% of the DAUH dataset, an appropriate model was selected, and the rest of the DAUH dataset was used for model evaluation. The generalization potential of the selected model was then validated using the external dataset. The accuracy of the model evaluation for Inception3D, ResNet3D, and VGG3D was 95.4%, 92.0%, and 97.7%, and the accuracy of the external validation was 76.7%, 67.1%, and 85.3%, respectively. Inception3D and ResNet3D were retrained with the external dataset; then, the area under the curve was compared to determine the binary classification performance with a significance level of less than 0.05. When external validation was performed again after fine tuning, the performance improved to 15.3%p for Inception3D and 16.9%p for ResNet3D. In [18F]Florbetaben amyloid brain PET scan classification using CNN, the generalization potential can be seen through external validation. When there is a significant difference between the model classification performance and the external validation, changing the model structure or fine tuning the model can help improve the classification performance, and the optimal model can also be found by collaborating through a web-based open platform.
A novel MADM algorithm for landfill site selection based on q-rung orthopair probabilistic hesitant fuzzy power Muirhead mean operator
Authors: Ren Yaojun , Yuan Xiujiu , Lin Ruojing
Published: October 2021

With the rapid development of economy and the acceleration of urbanization, the garbage produced by urban residents also increases with the increase of population. In many big cities, the phenomenon of "garbage siege" has seriously affected the development of cities and the lives of residents. Sanitary landfill is an important way of municipal solid waste disposal. However, due to the restriction of social, environmental and economic conditions, landfill site selection has become a very challenging task. In addition, landfill site selection is full of uncertainty and complexity due to the lack of cognitive ability of decision-makers and the existence of uncertain information in the decision-making process. Therefore, a novel multi-attribute decision making method based on q-rung orthopair probabilistic hesitant fuzzy power weight Muirhead mean operator is proposed in this paper, which can solve the problem of landfill site selection well. This method uses probability to represent the hesitance of decision maker and retains decision information more comprehensively. The negative effect of abnormal data on the decision result is eliminated by using the power average operator. Muirhead mean operator is used to describe the correlation between attributes. Then, an example of landfill site selection is given to verify the effectiveness of the proposed method, and the advantages of the proposed method are illustrated by parameter analysis and comparative analysis. The results show that this method has a wider space for information expression, gives the decision maker a great degree of freedom in decision-making, and has robustness.
Cross-sectional survey of asymptomatic malaria in Dak Nong province in the Central Highlands of Vietnam for the malaria elimination roadmap
Authors: Quang Huynh Hong , Chavchich Marina , Trinh Nguyen Thi Minh , Manh Nguyen Duc , Edstein Michael D. , Martin Nicholas J. , Edgel Kimberly A.
Published: October 2021

Asymptomatic parasite carriers represent a “silent” infective reservoir for malaria transmission and contributes to malaria persistence. However, limited data are available on asymptomatic malaria in Vietnam. Between November 2018 and March 2019, we conducted a malaria epidemiological survey of asymptomatic people (children ≥ 10 years old and adults ≥18 years old, n = 2,809) residing in three communes in Tuy Duc district, Dak Nong province in the Central Highlands of Vietnam. Based on the national stratification of malaria risk, Dak Buk So, Dak Ngo and Quang Truc communes were classified by the National Malaria Control Programme as low, moderate and high malaria endemic areas, respectively. Using participants’ finger prick blood samples, malaria parasites were detected by one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The median age (Interquartile Range) for adults and children were 35 years (26–50) and 12 years (11–14), respectively. The prevalence of asymptomatic malaria was 1.7% (22/1,328), 3.5% (31/890) and 12.2% (72/591) for participants from Dak Buk So, Dak Ngo and Quang Truc, respectively. The prevalence of asymptomatic malaria was lower in children compared to adults: 2.6% (9/352) versus 4.7% (116/2,457) (Odds Ratio 0.53, 95% Confidence Interval 0.28 to1.02). Ownership of long-lasting insecticide-treated bed nets and hammocks was 97.1%, 99.0% and 94.7% for participants in Dak Buk So, Dak Ngo and Quang Truc, respectively, however, only 66.0%, 57.3% and 42.8% of the participants reported using bed nets every night. Of the several risk factors examined, going to the forest two weeks prior to enrolment into the study and sleeping in the forest had a significant association with participants being infected with asymptomatic malaria in Quang Truc, but not in the other two communes. Knowledge of the prevalence and distribution of asymptomatic malaria will help design and evaluate future intervention strategies for malaria elimination in Vietnam.
An improved deep learning model for hierarchical classification of protein families
Authors: Sandaruwan Pahalage Dhanushka , Wannige Champi Thusangi
Published: October 2021

Although genes carry information, proteins are the main role player in providing all the functionalities of a living organism. Massive amounts of different proteins involve in every function that occurs in a cell. These amino acid sequences can be hierarchically classified into a set of families and subfamilies depending on their evolutionary relatedness and similarities in their structure or function. Protein characterization to identify protein structure and function is done accurately using laboratory experiments. With the rapidly increasing huge amount of novel protein sequences, these experiments have become difficult to carry out since they are expensive, time-consuming, and laborious. Therefore, many computational classification methods are introduced to classify proteins and predict their functional properties. With the progress of the performance of the computational techniques, deep learning plays a key role in many areas. Novel deep learning models such as DeepFam, ProtCNN have been presented to classify proteins into their families recently. However, these deep learning models have been used to carry out the non-hierarchical classification of proteins. In this research, we propose a deep learning neural network model named DeepHiFam with high accuracy to classify proteins hierarchically into different levels simultaneously. The model achieved an accuracy of 98.38% for protein family classification and more than 80% accuracy for the classification of protein subfamilies and sub-subfamilies. Further, DeepHiFam performed well in the non-hierarchical classification of protein families and achieved an accuracy of 98.62% and 96.14% for the popular Pfam dataset and COG dataset respectively.
An extended super/subloading surface model for soft rock considering structure degradation
Authors: Cui Kai , Hu Bin , Cui Aneng , Li Jing , Wei Erjian , Zhang Zhen
Published: October 2021

The strain-softening and dilatancy behavior of soft rock is affected by the loading history and the development of structure. This study regards soft rock as a structured and overconsolidated soil and develops a new elastoplastic model based on the classical super yield surface Cam-clay model. The proposed model is capable of capturing the effect of yield surface shape on the mechanical behavior of soft rock by introducing a new yield function. The proposed model is validated against the triaxial test results on different types of soft rocks under drained condition. The comparison results indicate that the proposed model is suitable for describing the constitutive behavior of soft rock.
The HDR CARE Scale, Inpatient Version: A validated survey instrument to measure environmental affordance for nursing tasks in inpatient healthcare settings
Authors: Rich Renae K. , Jimenez Francesqca E. , Bohacek Cheryl , Moore Alexandra , Heithoff Abigail J. , Conley Deborah M. , Brittin Jeri
Published: October 2021

Rigorous healthcare design research is critical to inform design decisions that improve human experience. Current limitations in the field include a lack of consistent and valid measures that provide feedback about the role of the built environment in producing desirable outcomes. Research findings about nurses’ efficiency, quality of care, and satisfaction related to inpatient unit designs have been mixed, and there was previously no validated instrument available to quantitatively measure nurses’ ability to work efficiently and effectively in their environment. The objective of this study was to develop, refine, and validate a survey instrument to measure affordance of the care environment to nurse practice, based on various aspects of their work in inpatient units. The HDR Clinical Activities Related to the Environment (CARE) Scale Inpatient Version was developed using item design, refinement, and reliability and validity testing. Psychometric methods from classical test theory and item response theory, along with statistical analyses involving correlations and factor analysis, and thematic summaries of qualitative data were conducted. The four-phase process included (1) an initial pilot study, (2) a content validation survey, (3) cognitive interviews, and (4) a final pilot study. Results from the first three phases of analysis were combined to inform survey scale revisions before the second pilot survey, such as a reduction in the number and rewording of response options, and refinement of scale items. The updated 9-item scale showed excellent internal consistency and improved response distribution and discrimination. The factor analysis revealed a unidimensional measure of nurse practice, as well as potential subscales related to integration, efficiency, and patient care. Within the healthcare design industry, this scale is much needed to generate quantitative and standardized data and will facilitate greater understanding about the aspects of an inpatient healthcare facility that best support nurses’ ability to provide quality patient care.
Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative
Authors: Tack Alexander , Ambellan Felix , Zachow Stefan
Published: October 2021

Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN-based approaches. To foster such developments we make all segmentations publicly available together with this publication.
Region-aggregated attention CNN for disease detection in fruit images
Authors: Han Chang Hee , Kim Eal , Doan Tan Nhu Nhat , Han Dongil , Yoo Seong Joon , Kwak Jin Tae
Published: October 2021

Background

Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images.

Methods

Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps.

Results

The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN.

Conclusions

The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.
Epidemiology of measles cases, vaccine effectiveness, and performance towards measles elimination in The Gambia
Authors: Sowe Alieu , Njie Mbye , Sowe Dawda , Fofana Sidat , Ceesay Lamin , Camara Yaya , Tesfaye Brook , Bah Samba , Bah Alieu K. , Baldeh Abdoulie K. , Dampha Bakary D. , Baldeh Samba N. , Touray Alagie
Published: October 2021

Introduction

In 2011, member states of the World Health Organization (WHO) Africa Regional Office (AFRO) resolved to eliminate Measles by 2020. Our study aims to assess The Gambia’s progress towards the set AFRO measles elimination target and highlight surveillance and immunisation gaps to better inform future measles prevention strategies.

Material and methods

A retrospective review of measles surveillance data for the period 2011–2019, was extracted from The Gambia case-based measles surveillance database. WHO—UNICEF national coverage estimates were used for estimating national level MCV coverage. Measles post campaign coverage survey coverage estimates were used to estimate national measles campaign coverage.

Results

One hundred and twenty-five of the 863 reported suspected cases were laboratory confirmed as measles cases. More than half (53.6%) of the confirmed cases have unknown vaccination status, 24% of cases were vaccinated, 52.8% of cases occurred among males, and 72.8% cases were among urban residents. The incidence of measles cases per million population was lowest (0) in 2011–2012 and highest in 2015 and 2016 (31 and 23 respectively). The indicator for surveillance sensitivity was met in all years except in 2016 and 2019. Children aged 5–9 years (Incidence Rate Ratio—IRR = 0.6) and residents of Central River region (IRR = 0.21) had lower measles risk whilst unvaccinated (Adjusted IRR = 5.95) and those with unknown vaccination status (IRR 2.21) had higher measles risk. Vaccine effectiveness was 89.5%.

Conclusion

The Gambia’s quest to attain measles elimination status by 2020 has registered significant success but it is unlikely that all target indicators will be met. Vaccination has been very effective in preventing cases. There is variation in measles risk by health region, and it will be important to take it into account when designing prevention and control strategies. The quality of case investigations should be improved to enhance the quality of surveillance for decision making.