bluuebunny/crossref_metadata_2025 huggingface dataset:
Vertical stratification of the air microbiome in the lower troposphere
Authors: Drautz-Moses Daniela I. , Luhung Irvan , Gusareva Elena S. , Kee Carmon , Gaultier Nicolas E. , Premkrishnan Balakrishnan N. V. , Lee Choou Fook , Leong See Ting , Park Changsook , Yap Zhei Hwee , Heinle Cassie E. , Lau Kenny J. X. , Purbojati Rikky W. , Lim Serene B. Y. , Lim Yee Hui , Kutmutia Shruti Ketan , Aung Ngu War , Oliveira Elaine L. , Ng Soo Guek , Dacanay Justine , Ang Poh Nee , Spence Sam , Phung Wen Jia , Wong Anthony , Kennedy Ryan J. , Kalsi Namrata , Sasi Santhi Puramadathil , Chandrasekaran Lakshmi , Uchida Akira , Junqueira Ana Carolina M. , Kim Hie Lim , Hankers Rudolf , Feuerle Thomas , Corsmeier Ulrich , Schuster Stephan C.
Published: February 2022

The troposphere constitutes the final frontier of global ecosystem research due to technical challenges arising from its size, low biomass, and gaseous state. Using a vertical testing array comprising a meteorological tower and a research aircraft, we conducted synchronized measurements of meteorological parameters and airborne biomass (
n
= 480) in the vertical air column up to 3,500 m. The taxonomic analysis of metagenomic data revealed differing patterns of airborne microbial community composition with respect to time of day and height above ground. The temporal and spatial resolution of our study demonstrated that the diel cycle of airborne microorganisms is a ground-based phenomenon that is entirely absent at heights >1,000 m. In an integrated analysis combining meteorological and biological data, we demonstrate that atmospheric turbulence, identified by potential temperature and high-frequency three-component wind measurements, is the key driver of bioaerosol dynamics in the lower troposphere. Multivariate regression analysis shows that at least 50% of identified airborne microbial taxa (
n
= ∼10,000) are associated with either ground or height, allowing for an understanding of dispersal patterns of microbial taxa in the vertical air column. Due to the interconnectedness of atmospheric turbulence and temperature, the dynamics of microbial dispersal are likely to be impacted by rising global temperatures, thereby also affecting ecosystems on the planetary surface.
A comprehensive map of genetic relationships among diagnostic categories based on 48.6 million relative pairs from the Danish genealogy
Authors: Athanasiadis Georgios , Meijsen Joeri J. , Helenius Dorte , Schork Andrew J. , Ingason Andrés , Thompson Wesley K. , Geschwind Daniel H. , Werge Thomas , Buil Alfonso
Published: February 2022

Significance

The ability to extract multigenerational family relationships from large-scale population cohorts provides a powerful means to understand the heritability of a wide range of diseases and their genetic relationships to each other. By showing how the heritability of broad diagnostic categories changes over time and how said categories are related on the genetic level, our analysis of the Danish genealogy and linked national patient registers illustrates the vast potential of this resource in current biomedical research.
Light emission by free electrons in photonic time-crystals
Authors: Dikopoltsev Alex , Sharabi Yonatan , Lyubarov Mark , Lumer Yaakov , Tsesses Shai , Lustig Eran , Kaminer Ido , Segev Mordechai
Published: February 2022

Significance

Recent experimental developments pave the way for the realization of photonic time-crystals (PTCs): photonic media with strong periodic variations of the optical properties occurring at single-cycle timescales. We study the radiation of free electrons in PTCs and show that the electron emission at half the modulation frequency is enhanced exponentially, drawing its energy from the modulation. When the electron is slower than the speed of light in the medium, the emitted light is fully controlled by the modulation. This radiation and its nonresonant nature hold promise for widely tunable laser sources, drawing their energy from the modulation, efficient laser-based particle accelerators, and particle detectors with adjustable angle-resolving sensitivity.
Thermodynamics of evolution and the origin of life
Authors: Vanchurin Vitaly , Wolf Yuri I. , Koonin Eugene V. , Katsnelson Mikhail I.
Published: February 2022

Significance

We employ the conceptual apparatus of thermodynamics to develop a phenomenological theory of evolution and of the origin of life that incorporates both equilibrium and nonequilibrium evolutionary processes within a mathematical framework of the theory of learning. The threefold correspondence is traced between the fundamental quantities of thermodynamics, the theory of learning, and the theory of evolution. Under this theory, major transitions in evolution, including the origin of life, represent specific types of physical phase transitions.
Setting a Q-uestionable attribute agenda
Authors: Funk Marcus , Speakman Burton
Published: February 2022

The QAnon conspiracy blends ancient and malleable anti-Semitic bigotry with modern social media. Attribute intermedia agenda setting has rarely considered conspiracy theories. Conspiracies like QAnon are not fact-based and challenge conventional agenda setting methodologies. This study explores attribute IAS among national, regional, and local media coverage of QAnon-supporting congressional candidates in Georgia and Colorado in 2020. It introduces notions of rational and irrational agenda setting domains to fully analyze the transfer of irrational attributes across diverse media agendas.
Widening the North/South Divide? Representations of the role of the EU during the Covid-19 crisis in Spanish
media
Authors: Filardo-Llamas Laura , Perales-García Cristina
Published: February 2022

In this article we study the discursive construction of the EU managerial role in the early stages of the Covid-19 pandemic in four Spanish newspapers: two
published in mainland Spain – El Mundo and El País – and two published in Catalonia – La
Vanguardia and ARA. By doing a qualitative study of newspapers, this article aims to identify which
discourse strategies are used when informing about the actions and decisions taken in European political and economic fora. The
analysis identifies the three main generic frames which are used in news pieces: morality (mostly as a call for solidarity),
economic (mostly as preventing possible harm), and conflict (focused on divide(s) within (EU)rope). The activation of these frames
in the news pieces contribute to an implicit legitimation of the existence of (EU)rope as based on two core values – solidarity
and cooperation – together with a delegitimation of the actions performed by some European institutions.
Arabic Dislocation
Authors: Alzayid Ali A.
Published: February 2022

Since the early years of generative grammar (Chomsky 1977, inter alia), the phenomenology of dislocation has proved to be a fertile area of research. This, however, has not been the case for Modern Standard Arabic (MSA), and hence this thorough monograph intends to fill this lacuna. Three aspects of this linguistic phenomenon stand out: the taxonomy of possible dislocated configurations, syntax and interpretation. Though the structure in itself has been extensively studied in various languages, including varieties of spoken Arabic, this monograph shows that MSA presents properties that set it apart from known varieties and cannot be captured by an extension or modification of existing analyses. Moreover, existing analyses are not fully satisfactory as there are open analytical questions regarding the interpretation and syntactic analysis of dislocation structures crosslinguistically. Particularly, the optimal path to follow concerning dislocation structures in MSA is to argue for the claim that contrast, as an information-structural notion, underlies the interpretation of dislocated elements, and these elements are best syntactically analyzed as being involved in a bisentential configuration, contra monoclausal approaches to dislocation. This monograph should be relevant to anyone with an interest in the Arabic language, and also to syntacticians and typologists with an interest in sentence structure.
Commercialising the struggle: the organisational and ideological effects of democracy assistance on opposition activism in Zimbabwe
Authors: Mataruse Prolific S. , Matthews Sally
Published: February 2022

One of the ways in which opposition activists in Zimbabwe receive funding is through democracy assistance. Focusing on the late 1990s to 2016, this article explores the effect the receipt of such aid had on the ways in which opposition activists organise and on their ideological orientation. The authors show that the availability of such funds contributed to the commercialisation of the struggle whereby opposition activists began to view activism as a way to earn a living. Furthermore, this funding led to a decline in trust, passion and voluntarism among opposition activists. And, finally, dependence on foreign funding resulted in ideological shifts in Zimbabwean opposition parties and organisations whereby radical, left-leaning positions were abandoned in order to secure funding. The authors suggest that local strategies for funding such struggles for democracy must be given greater consideration to promote transformational possibilities and new participatory forms of democracy.
An All-Fiberized Chirped Pulse Amplification System Based on Chirped Fiber Bragg Grating Stretcher and Compressor
Authors: Wang Ming-Xiao , Li Ping-Xue , Xu Yang-Tao , Zhu Yun-Chen , Li Shun , Yao Chuan-Fei
Published: February 2022

We report an all-fiberized chirped pulse amplification system without any bulk devices. The stretcher and compressor are chirped fiber Bragg gratings inscribed in a 6/125 μm single-mode fiber and a 30/250 μm large-mode-area fiber. The fabrication system of chirped fiber Bragg gratings was designed and built by ourselves. The width of the linear exposure spot was controlled according to the different fiber sizes to improve the fabrication quality, and the parameters of chirped fiber Bragg gratings were fine-tuned during the fabrication to achieve the overall system’s spectral matching. Two fiber circulators with the same fiber sizes as the chirped fiber Bragg gratings were employed to auxiliarily achieve the pulse stretching and compression. The dispersion accumulations provided by the stretcher and compressor are 129.8 ps and 90.8 ps. The power amplifiers were composed of the two-stage 10/130 μm fiber pre-amplifier and the 30/250 μm fiber main amplifier. The proposed chirped pulse amplification system with no spatial light is the true sense of an all-fiberized chirped pulse amplification structure and shows the main trend in development of ultrashort pulse fiber lasers.
Improved accuracy of optical distance sensor based on artificial neural network applied to real-time systems
Authors: Bui Ngoc-Thang , Nguyen Thi My Tien , Nguyen Bang Le-Huy , Vu Thi Thu Ha , Nguyen Cong Hoan , Bui Quoc Cuong , Park Sumin , Choi Jaeyeop , Truong Trong Toai
Published: February 2022

Optical time-of-flight sensors have potential in the revolution of distance measurement. These sensors can continuously monitor the distance and track the movement of objects. However, the existing sensing methods for such distance optical sensors mainly calculate the flight time, e.g. pulse transmission and receiving time, without considering the environmental effects. Therefore, the measurement accuracy is severely reduced. There are other technologies with higher accuracy in distance measurement. Nonetheless, they are too expensive due to the high accurate power supply. In this paper, we innovatively improve the accuracy of continuous distance measurement using the artificial neural network (ANN) technique. The proposed method can be applied for very cheap optical distance sensors with analog output in a real-time system. Moreover, the proposed method can self-calibrate and be miniaturized for cheap analog sensor applications. The prototype is built with the infrared sensor GP2Y0A02YK0F and an Arduino control board (ESP32_DevC), and the ANN is implemented using the deep learning algorithm. The test results show that the distance measurement accuracy is significantly improved and the measuring range is increased from 15 to 150 cm. In addition, we calculate mean squared error, mean absolute error, mean bias error, and R
2 for further performance evaluation. The experimental results have proven the superiority of the proposed ANN method in optical distance measurement. The proposed method can be applied to many types of sensors.
Development of an experimental apparatus for flat plate drag measurements and considerations for such measurements
Authors: van Nesselrooij M , van Campenhout O W G , van Oudheusden B W , Schrijer F F J , Veldhuis L L M
Published: February 2022

Accurately measuring small changes in aerodynamic drag over a flat surface stands at the core of the development of technologies capable of reducing turbulent friction drag. A wind tunnel drag measurement system was developed which improves significantly on the state of the art. Experimental tests demonstrated that an uncertainty of less than 0.5% ofCDat a 95% confidence level was typically achieved, already at drag values below 1 N. This was replicated in two different wind tunnels. A match with literature on riblet performance within 1% ofCDwas obtained. A crucial aspect of the design is the implementation of a correction for the pressure forces on the streamwise-facing surfaces of the test plate assembly. The flexible architecture of the system in the present realisation makes it suitable for most wind tunnels having a test section width of 400 mm or larger, which allows for accelerated development of turbulent drag reduction concepts from moderate-size low-cost facilities towards flow conditions relevant to the intended industrial application.
Nondestructive detection and analysis based on data enhanced thermography
Authors: Li Xiaoyuan , Ying Xiaowei , Zhu Wen , Liu Wei , Hou Beiping , Zhou Le
Published: February 2022

Machine learning and data fusion methods have played a significant role in the nondestructive testing (NDT) of composite materials in modern industries. Among them, feature extraction thermography models such as principal component thermography have been well studied and applied due to their advantages of dimensionality reduction and discriminative representation. However, current pulse thermal imaging technology can only collect limited images since the tested materials cannot usually be overheated, which also decreases the performance of the feature extraction thermography models. In this paper, an improved denoising convolutional autoencoder with U-net architecture is proposed for data enhancement purposes. Using the developed structure, enhanced images are generated, in which the deep spatial information in the original images is maintained while measurement noises are decreased simultaneously. Using the original and enhanced images together, the NDT performance based on feature extraction thermography is further improved. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by two defect detection experiments on carbon fiber reinforced polymer.
Enhanced photoresponse of PVP:GaSe nanocomposite thin film based photodetectors
Authors: Demirtaş Tugay , Odacı Cem , Aydemir Umut
Published: February 2022

Two-dimensional materials have become the focus of attention of researchers in recent years. The demand for two-dimensional materials is increasing day by day, especially with the inadequacy of graphene in optical applications. In this context, the optical and electrical characteristics of the PVP:GaSe thin film nanocomposites were investigated. The surface morphologies of the samples were characterized by SEM, the thin film thicknesses and refractive index parameters were measured by the Ellipsometer method, the structural characteristics were obtained by XRD, and Raman and PL spectroscopy was used to determine the optical characteristics. Critical parameters of Au/PVP:GaSe/n-Si photodetector were calculated under various illumination intensities. It is observed that photodetector with PVP:%5GaSe thin film has the best performance results. According to the experimental results, its responsivity, external quantum efficiency, and detectivity values are 0.485 A W−1, %86, and 1.14 × 107 cm Hz1/2 W−1 respectively.
Local magnetic behaviour of highly disordered undoped and Co-doped Bi2Se3 nanoplates: a muon spin relaxation study
Authors: Gupta Anu , Hillier A D , Telling M T F , Srivastava S K
Published: February 2022

Magnetism induced by defects in nominally non-magnetic solids has attracted intense scientific interest in recent years. The local magnetism in highly disordered undoped and Co-doped topological insulator (TI) Bi2Se3 nanoplates has been investigated by muon spin relaxation (μSR). Using μSR spectroscopy, together with other macroscopic characterizations, we find that these nanoplates are composed of a core with both static fields and dynamically fluctuating moments, and a shell with purely dynamically fluctuating moments. The fluctuations in the core die out at low temperatures, while those in the shell continue till 2 K. When Bi2Se3 is doped with Co, the static magnetic component increases, whilst keeping the dual (static-plus-dynamic) nature intact. The findings indicate that highly disordered TI’s could constitute a new class of promising magnetic materials that can be engineered by magnetic impurity doping.
An estimation of phase transition
*
Authors: Ishaq Shamsa , Leplaideur Renaud
Published: February 2022

In (2015 Ann. Sci. Éc. Norm. Supér.
48 739–763), Bruin and Leplaideur studied a class of potentials such that the pressure function exhibit a phase transition at a parameter β

c
> 0. This paper will prove that the transition in pressure function cannot appear within the interval  

0
,
2

.
Two-species competition model with chemotaxis: well-posedness, stability and dynamics
Authors: Li Guanlin , Yao Yao
Published: February 2022

We study a system of PDEs modelling the population dynamics of two competitive species whose spatial movements are governed by both diffusion and mutually repulsive chemotaxis effects. We prove that solutions to this system are globally well-posed, without any smallness assumptions on the chemotactic coefficients. Moreover, in the weak competition regime, we prove that neither species can be driven to extinction as the time goes to infinity, regardless of how strong the chemotaxis coefficients are. Finally, long-time behaviours of the system are studied both analytically in the weakly nonlinear regime, and numerically in the fully nonlinear regime.
DeepUCT: Complex cascaded deep learning network for improved ultrasound tomography
Authors: Prasad S , Almekkawy M
Published: February 2022

Ultrasound computed tomography is an inexpensive and radiation-free medical imaging technique used to quantify the tissue acoustic properties for advanced clinical diagnosis. Image reconstruction in ultrasound tomography is often modeled as an optimization scheme solved by iterative methods like full-waveform inversion. These iterative methods are computationally expensive, while the optimization problem is ill-posed and nonlinear. To address this problem, we propose to use deep learning to overcome the computational burden and ill-posedness, and achieve near real-time image reconstruction in ultrasound tomography. We aim to directly learn the mapping from the recorded time-series sensor data to a spatial image of acoustical properties. To accomplish this, we develop a deep learning model using two cascaded convolutional neural networks with an encoder–decoder architecture. We achieve a good representation by first extracting the intermediate mapping-knowledge and later utilizing this knowledge to reconstruct the image. This approach is evaluated on synthetic phantoms where simulated ultrasound data are acquired from a ring of transducers surrounding the region of interest. The measurement data is acquired by forward modeling the wave equation using the k-wave toolbox. Our simulation results demonstrate that our proposed deep-learning method is robust to noise and significantly outperforms the state-of-the-art traditional iterative method both quantitatively and qualitatively. Furthermore, our model takes substantially less computational time than the conventional full-wave inversion method.
Pedestal analysis of MAST ELMy regimes
Authors: Smith S F , Kirk A , Chapman-Oplopoiou B , Clark J G , Ham C J , Horvath L , Maggi C F , Scannell R , Saarelma S
Published: February 2022

Mega Ampere Spherical Tokamak (MAST) pedestal data has been analysed, where a pedestal database of 892 shots was obtained, using the upgraded MAST Thomson scattering (TS) diagnostic. Various edge localised mode (ELM) types are discussed, where characteristics and trends of MAST pedestals are shown. The data from the upgraded TS diagnostic confirms pedestal characteristics found in earlier analysis, using previous TS systems. Using the database, calculations of the bootstrap current are obtained using the different analytical formulae (Sauter and Redl), and comparisons are performed. The upgraded MAST TS system now spans the full plasma mid-plane, such that direct comparisons between inboard and outboard pedestals can be obtained, and asymmetries in the density pedestal width were found. To increase confidence in spherical tokamak pedestal predictions, the assumptions of Europed have been validated from the MAST pedestal data, and a value for the kinetic ballooning mode constraint (

C
∼
0.145

) has been obtained. The first spherical tokamak pedestal predictions were performed in Europed and compared to experimental values. Using C = 0.145 the temperature pedestal height was predicted to within 10% of the experimental value. In addition type II ELMs on MAST are analysed, and stability analysis and parameter scans have been performed using ESSIVE. Similar magneto-hydrodynamic (MHD) stability properties are observed for type I and type II ELMs, originating from the mixed ELM regimes; it is therefore difficult to distinguish these ELMs using the ideal MHD codes.
Probing depth in diffuse reflectance spectroscopy of biotissues: a Monte Carlo study
Authors: Kurakina Daria , Perekatova Valeriya , Sergeeva Ekaterina , Kostyuk Alexey , Turchin Ilya , Kirillin Mikhail
Published: February 2022

Diffuse reflectance spectroscopy (DRS) is an optical imaging modality based on extraction of tissue structural and functional information from back-reflectance spectra. In this paper we analyze the spectral dependence of DRS probing depth for different source-detector separations (SDSs) in the range of 1.5–7.0 mm by means of Monte Carlo simulations. The simulated spectra are employed to analyze the effect of the selected spectral range on the accuracy of oxygen saturation (StO2) reconstruction for different parameters of skin. It is shown that the probing depth varies in the range of 1–4 mm depending on SDS and tissue parameters, and in the hemoglobin absorption band for particular medium configuration it demonstrates a 2-fold decrease as compared to the neighboring spectral ranges. Comparison of different spectral ranges for StO2 reconstruction from the measured spectra at different SDSs demonstrated that the range of 480–600 nm and the full range of 480–900 nm benefit over near infrared (NIR) range (700–900 nm) in the reconstruction accuracy. The 480–600 nm range provides the best reconstruction accuracy for low blood volume content, while the full range of 480–900 nm provides better accuracy for larger blood volume content. The comprehensive study of the spectral dependency of probing depth in DSR for SDSs in the range of 1.5–7.0 mm based on MC simulations for multi-layered skin model depending on skin layers properties and numerical aperture combined with analysis of StO2 reconstruction accuracy was conducted for the first time to our knowledge.
Increasing the ⋅OH radical concentration synergistically with plasma electrolysis and ultrasound in aqueous DMSO solution
Authors: Li Chao , Xu De-Long , Xie Wen-Quan , Zhang Xian-Hui , Yang Si-Ze
Published: February 2022

In recent years, significant increases in waste processing and material engineering have been seen by using advanced oxidation processes. The treatment results and energy yields of these processes are largely determined by the generation and retention of reactive oxygen species (ROS). However, increasing the amount of ROS remains a key challenge because of the unavailability of performance- and energy-efficient techniques. In this study, plasma electrolysis, ultrasound, and plasma electrolysis combined with ultrasound were used to treat dimethyl sulfoxide (DMSO) solutions, and the results showed that the two methods can synergistically convert filament discharge into spark discharge, and the conversion of the discharge mode can significantly increase the concentration of OH radicals and effectively improve the efficiency of DMSO degradation. We verified the rationality of the results by analyzing the mass transfer path of ROS based on the reaction coefficients and found that the ⋅OH radicals in aqueous solution were mainly derived from the decomposition of hydrogen peroxide. These findings indicated that the synergistic action of plasma electrolysis and ultrasound can enhance the production of chemically reactive species, and provide new insights and guiding principles for the future translation of this combined strategy into real-life applications. Our results demonstrated that the synergistic strategy of ultrasound and plasma electrolysis is feasible in the switching mode and increasing the ROS, and may open new routes for materials engineering and pollutant degradation.
Second harmonic generation from precise diamond blade diced ridge waveguides
Authors: Xu Hui , Li Ziqi , Pang Chi , Li Rang , Li Genglin , Akhmadaliev Sh. , Zhou Shengqiang , Lu Qingming , Jia Yuechen , Chen Feng
Published: February 2022

Carbon ion irradiation and precise diamond blade dicing are applied to fabricate Nd:GdCOB ridge waveguides. The propagation properties of the fabricated Nd:GdCOB waveguides are investigated through experiments and theoretical analysis. Micro-Raman analysis reveals that the Nd:GdCOB crystal lattice expands during the irradiation process. Micro-second harmonic spectroscopic analysis suggests that the original nonlinear properties of the Nd:GdCOB crystal are greatly enhanced within the waveguide volume. Under pulsed 1064 nm laser pumping, second harmonic generation (SHG) at 532 nm has been achieved in the fabricated waveguides. The maximum SHG conversion efficiencies are determined to be ∼ 8.32 %⋅W−1 and ∼ 22.36 %⋅W−1 for planar and ridge waveguides, respectively.
Dynamic brain functional network based on EEG microstate during sensory gating in schizophrenia
Authors: Chang Qi , Li Cancheng , Zhang Jicong , Wang Chuanyue
Published: February 2022

Objective. Cognitive impairment is one of the core symptoms of schizophrenia, with an emphasis on dysfunctional information processing. Sensory gating deficits have consistently been reported in schizophrenia, but the underlying physiological mechanism is not well-understood. We report the discovery and characterization of P50 dynamic brain connections based on microstate analysis. Approach. We identify five main microstates associated with the P50 response and the difference between the first and second click presentation (S1-S2-P50) in first-episode schizophrenia (FESZ) patients, ultra-high-risk individuals (UHR) and healthy controls (HCs). We used the signal segments composed of consecutive time points with the same microstate label to construct brain functional networks. Main results. The microstate with a prefrontal extreme location during the response to the S1 of P50 are statistically different in duration, occurrence and coverage among the FESZ, UHR and HC groups. In addition, a microstate with anterior–posterior orientation was found to be associated with S1-S2-P50 and its coverage was found to differ among the FESZ, UHR and HC groups. Source location of microstates showed that activated brain regions were mainly concentrated in the right temporal lobe. Furthermore, the connectivities between brain regions involved in P50 processing of HC were widely different from those of FESZ and UHR. Significance. Our results indicate that P50 suppression deficits in schizophrenia may be due to both aberrant baseline sensory perception and adaptation to repeated stimulus. Our findings provide new insight into the mechanisms of P50 suppression in the early stage of schizophrenia.
Adaptive polymer fiber neural device for drug delivery and enlarged illumination angle for neuromodulation
Authors: Sui Kunyang , Meneghetti Marcello , Kaur Jaspreet , Sørensen R J F , Berg Rune W , Markos Christos
Published: February 2022

Objective. Optical fiber devices constitute significant tools for the modulation and interrogation of neuronal circuitry in the mid and deep brain regions. The illuminated brain area during neuromodulation has a direct impact on the spatio-temporal properties of the brain activity and depends solely on the material and geometrical characteristics of the optical fibers. In the present work, we developed two different flexible polymer optical fibers (POFs) with integrated microfluidic channels (MFCs) and an ultra-high numerical aperture (UHNA) for enlarging the illumination angle to achieve efficient neuromodulation. Approach. Three distinct thermoplastic polymers: polysulfone, polycarbonate, and fluorinated ethylene propylene were used to fabricate two step-index UHNA POF neural devices using a scalable thermal drawing process. The POFs were characterized in terms of their illumination map as well as their fluid delivery capability in phantom and adult rat brain slices. Main results. A 100-fold reduced bending stiffness of the proposed fiber devices compared to their commercially available counterparts has been found. The integrated MFCs can controllably deliver dye (trypan blue) on-demand over a wide range of injection rates spanning from 10 nl min−1 to 1000 nl min−1. Compared with commercial silica fibers, the proposed UHNA POFs exhibited an increased illumination area by 17% and 21% under 470 and 650 nm wavelength, respectively. In addition, a fluorescent light recording experiment has been conducted to demonstrate the ability of our UHNA POFs to be used as optical waveguides in fiber photometry. Significance. Our results overcome the current technological limitations of fiber implants that have limited illumination area and we suggest that soft neural fiber devices can be developed using different custom designs for illumination, collection, and photometry applications. We anticipate our work to pave the way towards the development of next-generation functional optical fibers for neuroscience.
Algorithm and hardware considerations for real-time neural signal on-implant processing
Authors: Zhang Zheng , Savolainen Oscar W , Constandinou Timothy G
Published: February 2022

Objective. Various on-workstation neural-spike-based brain machine interface (BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems. However, the effect of these factors is currently still unclear. Approaches. Here we have proposed a novel real-time 128 channel spike detection algorithm and optimised it on microcontroller (MCU) and field programmable gate array (FPGA) platforms towards consuming minimal power and memory/resources. It is presented as a use case to explore the different considerations in system design. Main results. The proposed spike detection algorithm achieved over 97% sensitivity and a smaller than 3% false detection rate. The MCU implementation occupies less than 3 KB RAM and consumes 31.5 µW ch−1. The FPGA platform only occupies 299 logic cells and 3 KB RAM for 128 channels and consumes 0.04 µW ch−1. Significance. On the spike detection algorithm front, we have eliminated the processing bottleneck by reducing the dynamic power consumption to lower than the hardware static power, without sacrificing detection performance. More importantly, we have explored the considerations in algorithm and hardware design with respect to scalability, portability, and costs. These findings can facilitate and guide the future development of real-time on-implant neural signal processing platforms.
Low-cost portable system for measuring thermal conductivity of building
Authors: Palo-Tejada E , Puma A , Campos-Falcon V , Guzman A , Perez R , Tunti J
Published: February 2022

The developed system allows to measure the thermal conductivity of solid materials used as thermal insulators in building bioclimatic houses on high Andean zones. A unidirectional heat flow model through a sample of dimensions 4.0 cm x 4.2 cm x 1.35 cm has been considered. K-type thermocouples were used to measure the temperature’s gradient. The obtained values were: 0.97 Wm−1°C−1 for glass, 0.38 Wm−1°C−1 in the ignimbrite, 0.58 Wm−1°C−1 for the roof brick and 0.34 Wm−1°C−1 in the adobe with Stipa ichu. These materials have been evaluated considering their abundance and use in house building on the Arequipa department high zones.
Energy and economic evaluation of a middle-sized CSP parabolic field in an agro-industrial company
Authors: Torres Vidaurre A V , Saettone E
Published: February 2022

The energy assessment carried out in a middle-sized CSP parabolic field of 120 m2 is presented; it provides pre-heating water at 70°C to the boilers of the lemon essential oil extraction process in an agro-industrial company located in Piura, at the border of Sechura desert in Peru. The parabolic cylindrical solar concentrators are made with glass reflectors, an automatic solar tracking system, and collector tubes through which water flows. It was measured that each collector converts solar energy and transfer it as a thermal energy to the water with an average efficiency of 18.1%. Considering this result, theoretically the whole solar field could provide 550.37 GJ/year for water preheating, which is equivalent to saving 28151.8 liters of LPG, and 46.87 tons of CO2 released to the environment. However, after a year of operation, the solar field provided only 99.59 GJ for water preheating, saving 4 705.26 liters of LPG, equivalent to stop emitting 7.69 tons of CO2 to the environment. An overall thermal energy efficiency of the CSP solar field in 18.1% was estimated. According to the financial assessment with the actual annual data, the period of recovery of the investment is estimated to be 4.4 years, 3.6 more years than theoretical projection.
PV generator nominal power estimation using a ground sensor and the PVLIB online irradiance database
Authors: Angulo J R , Conde L A , Muñoz E. , de la Casa J , Töfflinger J A
Published: February 2022

The nominal power of a photovoltaic generator is a crucial parameter. For instance, it is a required input parameter for modelling the energy produced by a PV system. Determining the nominal power in-situ requires measuring the irradiance and module temperature. Usually, the irradiance is measured with a sensor placed on the ground and at the plane of the array. The present work first compares the irradiance measurements of a calibrated PV module with the openly available irradiance database PVLIB. Second, it contrasts the daily estimated nominal power values considering both irradiance sources. The results indicate that the irradiance database is suitable to calculate the nominal power.
Agricultural residues-based activated carbons impregnated with chitosan nanoparticles by hydrothermal treatment
Authors: Cruz G J F , Aguirre D , Canepa C , Gómez M M , Solís J L , Santiago J , Solís-Castro R L , Cruz J F , Keiski R L
Published: February 2022

Much research related to the production of materials with high-quality adsorption capacity from agricultural residues has been done. However, the adsorbents themselves do not have the antibacterial capacity, an essential property in different water treatment applications. Hydrothermal treatment is an environmentally friendly approach to impregnated nanoparticles over a substrate. This paper aims to obtain and characterize activated carbon made of corn cob and red mombin seed impregnated with chitosan via the hydrothermal treatment to be tested as an antibacterial material for aqueous phase applications. The FESEM micrographics, RAMAN spectroscopy, and FTIR-ATR analyses confirmed the presence of chitosan nanoparticles (NPs) over the surface of the activated carbons. The impregnation of Chitosan NPs affected the pore size distribution in the activated carbon, but not total pore surface area or pore volume. The impregnation of chitosan increases the antibacterial capacity against Escherichia coli and Shigella flexneri.
Design of an autonomous photovoltaic system for desalination of groundwater by electrodialysis
Authors: Oré D G , Pretell V H , Pilco A W
Published: February 2022

Ultrafiltration-electrodialysis (UF-ED) systems for water desalination at different scales require electrical energy. The objective of this work is to size and select the components of an autonomous photovoltaic system (APVS) to supply energy to the UF-ED system at laboratory scale in a continuous process and to determine the percentage of salinity removal (PSR) and the specific energy consumption (SEC) at different conditions of the ED system. Two PV modules and a battery were selected. A maximum power of 150 Wp was chosen for each PV module and a capacity of 200 Ah and 12 V for the battery. A charge controller was selected with a power greater than 300 Wp and a nominal charge current greater than 18.52 A and an input voltage greater than 21.6 V. An inverter with an output power greater than 112 W was selected. The PSR and SEC were determined for different operating conditions (6 V and 25 mL/min, 12 V and 25 mL/min, 6 V and 50 mL/min, 12 V and 50 mL/min). The UF-ED system reduced the salinity of the groundwater, obtaining 64% removal at 12V and 25 mL/min in a continuous process.
Multi-level interactions for RGB-D object detection
Authors: Li Qi , Dai Meng , Zhang Qing
Published: February 2022

In order to efficiently utilize high-level information and depth information in RGB-D saliency object detection, multi-level fusion is studied. Different from existing methods which ignore feature dilution in the process of feature downward transmission, a multi-level interactive fusion method is designed and compared with five advanced models through four evaluation indexes. The experimental results show that the model in this paper is advanced.
Insulator defect detection algorithm based on a lightweight network
Authors: Lan Ying , Xu Wenxiang
Published: February 2022

Insulators are the key components of transmission lines. The identification and detection of insulator defects are directly related to the stable operation of transmission lines. In order to improve the efficiency of the insulator and its defect location, a faster defect detection algorithm based on YOLOv5 is proposed. Firstly, a lightweight Ghost module was introduced in the YOLOv5 backbone network, which significantly improved the detection speed with ensuring accuracy. Secondly, Secondly, CBAM is introduced into YOLOv5 Neck network to further improve the detection accuracy. The experimental results show that the model of the improved post-network is smaller compared to the YOLOv5 original network, and the detection speed improves greatly while ensuring the detection accuracy. It is of great significance to power grid operation and maintenance.
A New Method for Spinning Projectile Aerodynamic Estimation: Extreme Learning Machine Optimized by Cuckoo search algorithm
Authors: Xia Youran , Yi Wenjun
Published: February 2022

Aiming at the problem of aerodynamic parameter identification of spinning projectile, a Cuckoo search algorithm for extreme learning machine algorithm is proposed in this paper. The algorithm uses Cuckoo search algorithm to optimize the hidden layer weight and threshold of the extreme learning machine, so as to avoid the problem of unstable identification results caused by the random weight and threshold of the traditional extreme learning machine. The simulation results show that the algorithm can effectively identify the aerodynamic parameters of projectile, with high identification accuracy and fast convergence speed. The proposed algorithm is practical in engineer applications.
An Adaptive Low-illumination Image Enhancement Algorithm based on Weighted Least Squares Optimization
Authors: Zhao Erxun , Gao Jingmin
Published: February 2022

An adaptive low-illumination image enhancement algorithm based on the weighted least squares optimization is proposed to solve the difficulty of detailed feature recognition in low-illumination images that collected by visible light imaging equipment. First, the image is converted from RGB channel to LAB channel. Second, we use an edge-preserving smoothing operator based on the weighted least squares optimization to coarsen smooth base layer and extract multi-scale details in brightness channel. Then, an adaptive weight is proposed and applied to the weighted fusion of smooth base and detail features. Finally, the Retinex enhancement is performed to obtain a ultimate enhanced image. Experiments result show that the image enhanced by this method has suitable visual brightness and clear details. In terms of objective indicators, it has good and stable performance in NIQE, TMQI, and information entropy.
Marine ship detection and classification based on YOLOv5 model
Authors: Zhang Xuan , Yan Mingzhong , Zhu Daqi , Guan Yang
Published: February 2022

An improved deep learning neural model YOLOv5-DN based on YOLOv5 is proposed for marine ship detection and classification in the area of harbours and heavy traffic waterways. The CSP-DarkNet module in YOLOv5 is replaced by CSP-DenseNet to promote the accuracy of target detection and classification in the proposed model. Sample marine ships in the data set are divided into six classes: ore carriers, general cargo ships, bulk cargo ships, container ships, passenger ships, and fishing ships to meet the detection needs in the areas of ports and waterways. The data set are grouped into a training set, testing set, and validating set by the proportion of 6:2:2. Experiments show that the improved model has better average accuracy, from 62.2% to 71.6%.
An Optimization of Image Super-Resolution Method Based on Residual Network
Authors: Pan Yue
Published: February 2022

In this paper, an optimization with deep residual networks for the super-resolution method will be proposed. Meanwhile, the optimized method will be used into MNIST database to improve the image resolution, and the accuracy of the image identification of the improved database will be test by the residual network. In the optimization design, there are 4 convolutional layers, and 5 residual blocks will be used to replace the 3 convolutional layers of initial design, and there are 3 convolutional layers in each residual block. After the residual network testing, the experiment is positive and effective, and total number of errors is 34, and the accuracy is 99.43%.
Research on Grey Neural Network Optimal Combination in Wind Power Generation Forecasting Technique
Authors: Liu Yue , Liu Ming , Sun Jia
Published: February 2022

In this paper, combining BP neural network prediction model with grey prediction model effectively as an optimal combination forecasting technique is proposed. This model not only considers the influence factors such as wind power, wind direction and temperature, but also comprehensively considers the historical wind power generation data, through the effective combination of BP neural network prediction model and grey prediction model, the prediction accuracy is improved. The results show that the error of the new grey neural network optimal combination prediction is lower than that of the single grey prediction and BP neural network prediction, which has a certain research value.
Denoising Adversarial Examples Using CNN Models
Authors: Jing Juntong
Published: February 2022

It has always been a complicated problem to resolve adversarial attacks because figures with adversarial attacks look similar to the original figures so that models can be fooled. With deceptive data, adversarial attacks can be a threat to neural networks. There are various ways to generate adversarial attacks. For instance, they are using one-step perturbation and using multi-step perturbation. In both methods, noise is added to the images. Therefore, a question pops up: are adversarial attacks similar to normal random noise? This paper aims to find if there is anything in common between random noise and adversarial attacks. A normal denoising CNN model is trained with random noise. Then groups of adversarial examples are collected by training on LeNet. Next, the denoising CNN model has been used to denoise those adversarial examples. Finally, after denoising the adversarial examples with the CNN model trained on normal random noise, the classification accuracy increases. Thus, it is reasonable to conclude that normal random noise and adversarial tracks have some common patterns.
Morphologic recovery method of product label noises based on image features and application in defect detection
Authors: Chen Yuanhao , Yi Jianjun , Zhang Yajun , Tao Sheng
Published: February 2022

To ensure the reliability of carbon fiber material products and eliminate all kinds of possible defects, it is necessary to take effective means to check their quality. X-ray based nondestructive testing technology combined with image recognition algorithm is considered to be a fast and efficient solution. However, the surface of the processed material is often accompanied by labels which contains various information. These labels are confused in the image and make most of the defect detection methods easy to misidentify. This paper mainly studies the recovery method of product label noises based on image features and application in defect detection. What’s more, the proposed method can effectively and efficiently eliminate noises without affecting the rest of the image information which ensures the correct identification of defects in materials.
Performance Analysis of Three kinds of Neural Networks in the Classification of Mask Images
Authors: Chi Jinkui , Liu Yuxi , Wang Victor , Yan Jiaxuan
Published: February 2022

The global spread of COVID-19 has led to a massive increase in public demand for effective measures to slow the spread of the virus. In response, the World Health Organization has advised people to wear face masks every day to stop the spread of the virus. In facial mask recognition, traditional manual feature extraction methods are cumbersome and inaccurate, considering the various types of masks and other practical problems. This paper considers various neural network models comprehensively and selects three classical CNN networks, including VGG, ResNet, and DenseNet. After using the data expansion and optimization methods, good data results are obtained for the three network models. Remarkably, the improved DenseNet accuracy can reach 99.55%.