bluuebunny/crossref_metadata_2025 huggingface dataset:
Bilateral Ear Acoustic Authentication: A Biometric Authentication System Using Both Ears and a Special Earphone
Authors: Yasuhara Masaki , Nambu Isao , Yano Shohei
Published: March 2022

In existing biometric authentication methods, the user must perform an authentication operation such as placing a finger in a scanner or facing a camera. With ear acoustic authentication, the acoustic characteristics of the ear canal can be used as biometric information. Therefore, a person wearing earphones does not need to perform any authentication operation. Existing studies which use the acoustic characteristics of the ear canal as biometric information only measure the characteristics of one ear. However, the acoustic characteristics of the human ear canal can be measured from both ears. Hence, we proposed a new method for acoustic authentication based on the ability to measure the acoustic characteristics of the ear canal from both ears. The acoustic characteristics of the ear canal of 52 subjects were measured. Comparing the acoustic characteristics of the left and right ear canals, a difference in the signal between the left and right ear was observed. To evaluate the authentication accuracy, we calculated the evaluation indices of biometric authentication, equal error rate (EER), and area under curve (AUC). The EER for bilateral ear acoustic authentication using signals from both ears was 0.39%, which was lower than that of a single ear. The AUC was 0.0016 higher for bilateral ear acoustic authentication. Therefore, the use of bilateral signals for ear acoustic authentication was proved to be effective in improving authentication accuracy.
Lightweight Deep Learning for Road Environment Recognition
Authors: Liang Han , Seo Suyoung
Published: March 2022

With recent developments in the field of autonomous driving, recognition algorithms for road environments are being developed very rapidly. Currently, most of the network models have good recognition rates, but as the accuracy rate increases, the models become more complex and thus lack real-time performance. Therefore, there is an urgent need to propose a lightweight recognition system for road environments to assist autonomous driving. We propose a lightweight road environment recognition system with two different detection routes based on the same backbone network for objects and lane lines. The proposed approach uses MobileNet as the backbone network to acquire the feature layer, and our improved YOLOv4 and U-Net allows the number of parameters of the model to be greatly reduced and combined with the improved attention mechanism. The lightweight residual convolutional attention network (LRCA-Net) proposed in this work allows the network to adaptively pay attention to the feature details that need attention, which improves the detection accuracy. Finally, the object detection model and the lane line detection model of this lightweight road environment detection system evaluated on the PASCAL VOC dataset and the Highway Driving dataset show that their mAP and mIoU reach 93.2% and 93.3%, respectively, achieving excellent performance compared to other methods.
Photo-Generation of Tunable Microwave Carriers at 2 µm Wavelengths Using Double Sideband with Carrier Suppression Modulation
Authors: Ji Di , Hu Zhitao , Ye Nan , Pang Fufei , Song Yingxiong
Published: March 2022

At 2 µm wavelengths (149.9 THz), hollow-core photonics band gap fibers have higher light power damage thresholds, stable polarization states, and lower losses of 0.1 dB/km. Additionally, a thulium-doped fiber amplifier can provide a gain of >35 dB. Specifically, an indium-rich InGaAs photodetector shows a naturally higher photoresponsivity at 2 µm wavelengths than the C-band. Therefore, using tunable photo-generated microwave technology at 2 µm wavelengths could achieve higher photo-to-electric power conversion efficiencies for higher RF output power applications using the same method at the same frequency. Here, a double sideband with the carrier suppression modulation method was experimentally applied on 2 µm wavelengths to generate tunable and stable microwave carriers. Comparison experiments were also applied on the 1.55 µm (193.4 THz)/1.31 µm wavelengths (228.8 THz) based on the same indium-rich InGaAs photodetector. Through normalization on the wavelength-corresponded squared external quantum efficiency to visualize the photo-to-electric power conversion efficiency at different wavelengths under the same input optical signal power, the ratio between the results at 2 µm wavelengths and C/O-band is abstracted as 1.31/1.98, approaching theoretical estimations. This corresponds to a power conversion efficiency increasement of ~1.16 dB/~2.98 dB. To our knowledge, this is the first study on 2 micron wavelengths that proves the corresponding high efficiency power conversion property.
Dynamic Finite Element Model Updating Based on Correlated Mode Auto-Pairing and Adaptive Evolution Screening
Authors: Shao Huajin , Zuo Yanfei , Jiang Zhinong
Published: March 2022

A method for dynamic finite element (FE) model updating based on correlated mode auto-pairing and adaptive evolution screening (CMPES) is proposed to overcome difficulties in pairing inaccurate analytical modal data and incomplete experimental modal data. In each generation, the correlated mode pairings (CMPs) are determined by modal assurance criterion (MAC) values and the symbiotic natural frequency errors, according to an auto-pairing strategy. The objective function values constructed by correlated and penalized subitems are calculated to screen the better individuals. Then, both the updating parameters and the CMPs can be adjusted adaptively to simultaneously approach the ideal results during the iteration of population evolution screening. Three examples (a thin plate with small holes, an F-shaped structure, and an intermediate case with multi-layer thin-walled complex structure) were presented to validate the accuracy, effectiveness, and engineering application potential of the proposed method.
The Application of Wood Species in Enology: Chemical Wood Composition and Effect on Wine Quality
Authors: Jordão António M. , Cosme Fernanda
Published: March 2022

Aging wine is a usual practice in winemaking, as the wine quality improves due to the compounds extracted from wood barrels or chips, cubes, blocks, or staves used. The wood species used are traditionally oak, namely from Quercus petraea, Q. alba, or Q. robur species. In the last years, the increasing request for oak wood has caused a significant increase in environmental and production costs. Therefore, heartwood from several alternative species has been considered a potential wood source for winemaking and aging. Thus, the main purpose of this review is the application of these alternative wood species on wine production and to discuss the advantages and disadvantages of its use compared with the traditional wood species, namely oak wood. In addition, a brief chemical characterization of several wood species with possible application in enology is also discussed in this review.
Cross-Modal Manifold Propagation for Image Recommendation
Authors: Jian Meng , Guo Jingjing , Fu Xin , Wu Lifang , Jia Ting
Published: March 2022

The growing complex user intention gap and information overload are obstacles for users to access the desired content. User interactions and the involved content indicate rich evidence of users’ interests. It is required to investigate interaction characters over user interest and information distribution, and this alleviates information overload for personalized recommendation. Therefore, this work explores user interests with interactions and visual information from users’ historical records for image recommendation. This paper introduces cross-modal manifold propagation (CMP) for personalized image recommendation. CMP investigates the trend of user preferences by propagating users’ historical records along with users’ interest distribution, which produces personalized interest-aware image candidates according to user interests. CMP simultaneously leverages visual distribution to spread users’ visual records relying on the dense semantic visual manifold. Visual manifold propagation estimates detailed semantic-level user-image correlations for ranking candidate images in recommendations. In the proposed CMP, both user interest manifold and images’ visual manifold compensate each other in propagating users’ records to predict users’ interaction. Experimental results illustrate the effectiveness of collaborative user-image propagation of CMP for personalized image recommendation. Performance improved by more than 20% compared to that of existing baselines.
Enhanced Adsorption of Methyl Orange by Mongolian Montmorillonite after Aluminum Pillaring
Authors: Chen Jiajun , Lu Jianzun , Su Lingcheng , Ruan Huada , Zhao Yijia , Lee Chiuhong , Cai Zongwei , Wu Zhihui , Jiang Yanan
Published: March 2022

This article studies the enhancement of methyl orange (MO) adsorption by Mongolian montmorillonite (MMt) modified by the intercalation of the Keggin Al13 complex, followed by calcination during the pillaring process. The properties of MMt, Al-intercalated MMt (P-MMt), and Al-pillared MMt (P-MMt-C) were determined using X-ray diffraction (XRD), thermogravimetric analysis (TGA), surface-area analysis, and a field emission scanning electron microscope (FE-SEM). The MO adsorption by modified MMt was subsequently evaluated. The XRD basal distance (d001) and the specific surface area (SSA) increased after the modification of MMt. The TGA results revealed that P-MMt and P-MMt-C had better thermal stability than MMt. The Al-pillared MMt obtained after calcination (e.g., P-MMt-C400) showed a larger basal distance and surface area than that without pillaring. The MO adsorption process of P-MMt-C400 was supposed to be dominated by chemisorption and heterogeneous multilayer adsorption, according to the kinetic and isotherm studies. The maximum adsorption capacity of P-MMt-C400 is 6.23 mg/g. The MO adsorption ability of Al-pillared MMt was contributed by the Keggin Al13 complex attracting MO and the increase in the surface area of macro-, meso- and micro-pores (>1.2 nm). The Al-pillared MMt in this study could be applied as an adsorbent in a water purification system to remove MO or other dye elements.
Effect of Magnetic and Electrical Fields on Yield, Shelf Life and Quality of Fruits
Authors: Saletnik Bogdan , Zaguła Grzegorz , Saletnik Aneta , Bajcar Marcin , Słysz Ewelina , Puchalski Czesław
Published: March 2022

The presented article is a review of the literature reports on the influence of magnetic and electric fields on the growth, yield, ripening, and durability of fruits and their quality. The article shows the potential application of MF and EF in agricultural production. Magnetic and electrical fields increase the shelf life of the fruit and improve its quality. Alternating magnetic fields (AMF) with a value of 0.1–200 mT and a power frequency of 50 Hz or 60 Hz improve plant growth parameters. MF cause an increase in firmness, the rate of maturation, the content of beta-carotene, lycopene, and fructose, sugar concentration, and a reduction in acidity and respiration. The most common is a high-voltage electric field (HVEF) of 2–3.61 kV/cm. These fields extend the shelf life and improve the quality of fruit by decreasing respiration rate and ethylene production. The presented methods seem to be a promising way to increase the quantity and quality of crops in agricultural and fruit production. They are suitable for extending the shelf life of fruit and vegetables during their storage. Further research is needed to develop an accessible and uncomplicated way of applying MF and AEF in agricultural and fruit production.
Study on Mechanical Properties of Modified Polyurethane Concrete at Different Temperatures
Authors: Lei Jianhua , Feng Fan , Xu Shu , Wen Weibin , He Xuhui
Published: March 2022

The objective of the present research was to study the effect of temperature on the mechanical properties, failure mode and uniaxial compression constitutive relationship of a modified polyurethane concrete. A total of 24 cube and 27 prism specimens were fabricated, and the uniformity of the polyurethane concrete was checked. The compressive test, splitting tensile test and static uniaxial compression test were carried out at 0, 15, 40 and 60 °C. The failure mode, cube compressive strength, splitting tensile strength, axial compressive strength, elastic modulus and the compressive stress–strain curves of the modified polyurethane concrete were obtained. Based on the experimental results, a uniaxial compression constitutive model of the modified polyurethane concrete considering temperature characteristics was proposed. The results show that the elastic modulus, cubic compressive strength, splitting tensile strength and axial compressive strength of the modified polyurethane concrete decrease with the increase of temperature, and the peak strain and ultimate strain increase significantly. When the temperature rises from 0 to 60 °C, the cubic compressive strength, splitting tensile strength and axial compressive strength are decreased by 67.1%, 66.4% and 73.3%, respectively. The calculation results of the proposed constitutive model are in good agreement with the test results. The results are expected to guide the application of the modified polyurethane concrete in bridge deck pavement.
Experimental Analysis on the Impact of Current on the Strength and Lifespan of a Ni-Ti Element
Authors: Dumitru Cernusca , Milici Laurențiu Dan , Pentiuc Radu Dumitru , Atănăsoae Pavel , Ungureanu Constantin , Hopulele Eugen
Published: March 2022

Intelligent materials, especially materials with shape memory, are an important discovery, with technical applications in the medical and aerospace field, among others, which led to the development of systems and applications with multiple advantages and disadvantages due to ignorance about their functionality. This paper presents an application developed in the research laboratory for determining and monitoring the behavior of a material element with Ni-Ti shape memory, and its lifespan. The application allows the stress level of the Ni-Ti element subjected to numerous repeated cycles of deformation to be determined by supplying it to a constant electric current. Thus, the results show the variation of the Ni-Ti element force, in the form of a spring, at the ambient temperature variations as well as force variations at different numbers of attempts. The Ni-Ti alloy has both shape retention and superelasticity properties, being the most common in the fields of applicability. Due to its unique properties, it can be used in the most demanding applications in the medical field, usually involving difficult conditions of resistance to fatigue.
Low-Cost Sensors Accuracy Study and Enhancement Strategy
Authors: Komarizadehasl Seyedmilad , Mobaraki Behnam , Ma Haiying , Lozano-Galant Jose-Antonio , Turmo Jose
Published: March 2022

Today, low-cost sensors in various civil engineering sectors are gaining the attention of researchers due to their reduced production cost and their applicability to multiple nodes. Low-cost sensors also have the advantage of easily connecting to low-cost microcontrollers such as Arduino. A low-cost, reliable acquisition system based on Arduino technology can further reduce the price of data acquisition and monitoring, which can make long-term monitoring possible. This paper introduces a wireless Internet-based low-cost data acquisition system consisting of Raspberry Pi and several Arduinos as signal conditioners. This study investigates the beneficial impact of similar sensor combinations, aiming to improve the overall accuracy of several sensors with an unknown accuracy range. The paper then describes an experiment that gives valuable information about the standard deviation, distribution functions, and error level of various individual low-cost sensors under different environmental circumstances. Unfortunately, these data are usually missing and sometimes assumed in numerical studies targeting the development of structural system identification methods. A measuring device consisting of a total of 75 contactless ranging sensors connected to two microcontrollers (Arduinos) was designed to study the similar sensor combination theory and present the standard deviation and distribution functions. The 75 sensors include: 25 units of HC-SR04 (analog), 25 units of VL53L0X, and 25 units of VL53L1X (digital).
Benchmarking Various Pseudo-Measurement Data Generation Techniques in a Low-Voltage State Estimation Pilot Environment
Authors: Békési Gergő Bendegúz , Barancsuk Lilla , Táczi István , Hartmann Bálint
Published: March 2022

Distribution system state estimation (DSSE) is a valuable step for DSOs toward tackling the challenges of transitioning to a more sustainable energy system and the evolution and proliferation of electric cars and power electronic devices. However, on the LV level, implementation has only taken place in a few pilot projects. In this paper, an LV DSSE method is presented and implemented in four real Hungarian LV supply areas, according to well-defined scenarios. Pseudo-measurement datasets are generated from AACs and SLPs, which have been used in different combinations on networks built with different accuracies in terms of load placement. The paper focuses on the critical aspects of finding accurate and coherent information on network topology with automated management of information systems, real LV network implementation for power flow calculation and managing portions of the network characterized by uncertain or inconsistent line lengths. A refining algorithm is implemented for the integrated network information system (INIS) models. The published method estimates node voltages with a relative error of less than 1% when using AACs, and a meter-placement method to reduce the maximum value of relative errors in future scenarios is also presented. It is shown that the observation of node voltages can be improved with the usage of AACs and SLPs, and with optimal meter placement.
Content Curation in E-Learning: A Case of Study with Spanish Engineering Students
Authors: Aguilar-Peña Juan D. , Rus-Casas Catalina , Eliche-Quesada Dolores , Muñoz-Rodríguez Francisco José , La Rubia M. Dolores
Published: March 2022

Over the last decade, e-learning and the use of digital tools have received a great boost in higher education. This paper presents a content curation methodology to assess the acquisition of specific content and soft skills during the attainment of a Degree in Industrial Electronic Engineering at the University of Jaén. In this teaching–learning experience, 101 engineering students were involved in activities with digital tools related to content curation, and four steps were proposed: search, select, sense making, and share. As evaluation tools, a rubric and a questionnaire of the digital tools were proposed. Moreover, a curation index was defined in order to assess the degree of achievement of the content curation. The academic results after using the rubric were better than previous years. The average content curation index obtained was 53.53. Of the four evaluated steps, search and sense making had the lowest scores and, therefore, these steps should be further developed in the future. In addition, the Kaiser–Meyer–Olkin test and Pearson’s correlation were used for analyzing the results of the questionnaires. It was concluded that the experience had a great impact on the skills related to collaborative work, digital information management, and lifelong learning, which are transversal skills at the university level. Thus, the results highlight the great educational potential of content curation.
A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
Authors: Kim Jin Hyun , Jo Eunah , Ryu Seungjae , Nam Sohee , Song Somin , Han Yong Seop , Kang Tae Seen , Lee Woongsup , Lee Seongjin , Kim Kyong Hoon , Choi Hyunju , Lee Seunghwan
Published: March 2022

Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to a person’s quality of life. It is one of the first basic tests done when an eye disease develops. VA is usually measured by using a Snellen chart or E-chart from a specific distance. However, in some cases, such as the unconsciousness of patients or diseases, i.e., dementia, it can be impossible to measure the VA using such traditional chart-based methodologies. This paper provides a machine learning-based VA measurement methodology that determines VA only based on fundus images. In particular, the levels of VA, conventionally divided into 11 levels, are grouped into four classes and three machine learning algorithms, one SVM model and two CNN models, are combined into an ensemble method in order to predict the corresponding VA level from a fundus image. Based on a performance evaluation conducted using randomly selected 4000 fundus images, we confirm that our ensemble method can estimate with 82.4% of the average accuracy for four classes of VA levels, in which each class of Class 1 to Class 4 identifies the level of VA with 88.5%, 58.8%, 88%, and 94.3%, respectively. To the best of our knowledge, this is the first paper on VA measurements based on fundus images using deep machine learning.
An Efficient Method for Biomedical Entity Linking Based on Inter- and Intra-Entity Attention
Authors: Abdurxit Mamatjan , Tohti Turdi , Hamdulla Askar
Published: March 2022

Biomedical entity linking is an important research problem for many downstream tasks, such as biomedical intelligent question answering, information retrieval, and information extraction. Biomedical entity linking is the task of mapping mentions in medical texts to standard entities in a given knowledge base. Recently, BERT-based models have achieved state-of-the-art results on the biomedical entity linking task. Although this type of method is effective, it brings challenges for fine-tuning and online services in practical industries due to a large number of model parameters and long inference time. In addition, due to the numerous surface variants of biomedical mentions, it is difficult for a single matching module to achieve good results. To address the challenge, we propose an efficient biomedical entity linking method that integrates inter- and intra-entity attention to better capture the information between medical entity mentions and candidate entities themselves and each other, and the model in this paper is more lightweight. Experimental results show that our method achieves competitive performance on two biomedical benchmark datasets, NCBI and ADR, with an accuracy rate of 91.28% and 93.13%, respectively. Moreover, it also achieves comparable or even better results compared to the BERT-based entity linking method while having far fewer model parameters and very high inference speed.
Stochastic Models and Control of Anchoring Mechanisms for Grasping in Microgravity
Authors: Wen Qingpeng , He Jun , Gao Feng
Published: March 2022

Robots equipped with anchoring mechanisms have attractive applications in asteroid exploration. However, complex application scenarios bring great challenges to the modeling and control of anchoring mechanisms. This paper presents a grasping model and control method for an anchoring mechanism for asteroid exploration. First, the structure of the anchoring mechanism is demonstrated. Second, stochastic grasping models based on surface properties are established. The effectiveness of the grasping model is verified by experiments. A stiffness-modeling method of the microspine is proposed. On this basis, the stochastic grasping model of the anchoring mechanism is established, and the grasping cloud diagram of the anchoring mechanism is drawn. Third, in order to reduce the collision force between the anchor mechanism and the asteroid surface, a control method for the anchoring mechanism in the movement process is proposed based on the motion mode of the asteroid-exploration robot. Finally, a prototype is developed, and the experimental results validate the motion ability of the robot and the control method.
Materials Separation via the Matrix Method Employing Energy-Discriminating X-ray Detection
Authors: Yokhana Viona S. K. , Arhatari Benedicta D. , Abbey Brian
Published: March 2022

The majority of lab-based X-ray sources are polychromatic and are not easily tunable, which can make the 3D quantitative analysis of multi-component samples challenging. The lack of effective materials separation when using conventional X-ray tube sources has motivated the development of a number of potential solutions including the application of dual-energy X-ray computed tomography (CT) as well as the use of X-ray filters. Here, we demonstrate the simultaneous decomposition of two low-density materials via inversion of the linear attenuation matrices using data from the energy-discriminating PiXirad detector. A key application for this method is soft-tissue differentiation which is widely used in biological and medical imaging. We assess the effectiveness of this approach using both simulation and experiment noting that none of the materials investigated here incorporate any contrast enhancing agents. By exploiting the energy discriminating properties of the detector, narrow energy bands are created resulting in multiple quasi-monochromatic images being formed using a broadband polychromatic source. Optimization of the key parameters for materials separation is first demonstrated in simulation followed by experimental validation using a phantom test sample in 2D and a small-animal model in 3D.
Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model
Authors: Karim Musarat , Missen Malik Muhammad Saad , Umer Muhammad , Sadiq Saima , Mohamed Abdullah , Ashraf Imran
Published: March 2022

Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H index, i10 index, etc., of authors and journals. However, for a fair evaluation, the qualitative aspect should be considered along with the quantitative measures. The sentiments expressed in citation play an important role in evaluating the quality of the research because the citation may be used to indicate appreciation, criticism, or a basis for carrying on research. In-text citation analysis is a challenging task, despite the use of machine learning models and automatic sentiment annotation. Additionally, the use of deep learning models and word embedding is not studied very well. This study performs several experiments with machine learning and deep learning models using fastText, fastText subword, global vectors, and their blending for word representation to perform in-text sentiment analysis. A dimensionality reduction technique called principal component analysis (PCA) is utilized to reduce the feature vectors before passing them to the classifier. Additionally, a customized convolutional neural network (CNN) is presented to obtain higher classification accuracy. Results suggest that the deep learning CNN coupled with fastText word embedding produces the best results in terms of accuracy, precision, recall, and F1 measure.
A WSN Framework for Privacy Aware Indoor Location
Authors: Tošić Aleksandar , Hrovatin Niki , Vičič Jernej
Published: March 2022

In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system.
Technology Prediction for Acquiring a Must-Have Mobile Device for Military Communication Infrastructure
Authors: Kim Sungil , Jung Byungki , Han Dongyun , Lee Choonjoo
Published: March 2022

The smartphone is a must-have mobile device for the military forces to accomplish critical missions and protect critical infrastructures. This paper explores the applicability of a technology prediction methodology to manage technological obsolescence while pursuing the acquisition of advanced commercial technology for military use. It reviews the Technology Forecasting using Data Envelopment Analysis (TFDEA) methodology and applies an author-written Stata program for smartphone technology forecasting using TFDEA. We analyzed smartphone launch data from 2005 to 2020 to predict the adoption of smartphone technology and discuss the pace of technological change. The study identifies that the market is undergoing reorganization as new smartphone models expand the market and increase their technical performance. The average rate of technological change, the efficiency change, and the technology change were 1.079, 1.004, and 1.011 each, respectively, which means that the technology progressed over the period. When dividing before and after 2017, technological change and efficiency change generally regressed except for Huawei, Xiaomi, and Oppo. This means that Chinese smartphones are expanding the global market in all directions and the technology is reaching maturity and market competition is accelerating.
Decomposition of Trend and Interdecadal Variation of Evaporation over the Tropical Indian Ocean in ERA5
Authors: Huang Bicheng , Su Tao , Zhang Zengping , Wu Yongping , Feng Guolin
Published: March 2022

Based on ERA5 from 1980 to 2018, we compare and analyze the trend and interdecadal variation of evaporation anomalies over the tropical Indian Ocean by the evaporation decomposition method. This method mainly decomposes the evaporation anomalies into the Newtonian cooling, stability, relative humidity, wind speed, and transfer coefficient terms. The annual mean evaporation anomalies show an increasing trend (0.083 mm/d/decade). The Newtonian cooling term (0.026 mm/d/decade), the relative humidity term (0.032 mm/d/decade), and the wind speed term (0.026 mm/d/decade) play a major role in the increasing trend. The interdecadal variation of evaporation anomalies shows decreases in the 1980s and after the early 2000s, and an increase in the 1990s. The decreased evaporation anomalies in the 1980s are affected by the transfer coefficient term, which is associated with the North Atlantic Oscillation (NAO). The increased evaporation anomalies in the 1990s and the decreased evaporation anomalies since the early 2000s are largely controlled by the wind speed term, which are dominated by the Atlantic Multidecadal Oscillation (AMO). The Pacific Decadal Oscillation (PDO) may have important impacts on the interdecadal increase of evaporation anomalies by affecting the wind speed in the 1990s.
Characterization and Source Discovery of Wintertime Fog on Coastal Island, Bangladesh
Authors: Nahar Kamrun , Nahian Samiha , Jeba Farah , Islam Md Safiqul , Rahman Md Safiur , Choudhury Tasrina Rabia , Fatema Konica Jannat , Salam Abdus
Published: March 2022

An extensive chemical investigation of fog water’s chemical composition, as well as source characterization, were carried out during the winter season (December to February) at an outflow location (Bhola, Bangladesh) of the Indo-Gangetic Plain (IGP). Characterization of the source involved correlational analysis, enrichment factor analysis, estimation of percentage sources, and air mass trajectory analysis. The average pH of fog water in Bhola was found to be 7.03 ± 0.02, demonstrating that acid-neutralizing components were successful in neutralizing acidifying species. The concentrations of the water-soluble ions were determined, and they were in the following order: Ca2+ > NO3− > Cl− > Na+ > SO42− > NH4+ > Mg2+ > K+ > F− > HCO3−. Of the six trace elements (Fe, Zn, Mn, Cu, Ni, Cr, Pb) that were analyzed, Zn ions were found in the highest concentration, followed by Mn ions. Neutralization factor analysis showed that the key neutralization components of fog-water were Ca2+ and NH4+. Enrichment factor (EF) calculation revealed the anthropogenic origin of NO3−, SO42−, Zn, Mn, and Cu. The percentage source contributions of NO3− (99.74%), SO42− (84.02%), and Cl− (8.30%) further support the anthropogenic origin. Backward air mass trajectory analysis was performed using the NOAA-HYSPLIT model. Long-range transport of contaminants over the IGP area was found to have a profound impact on the chemical composition of fog on the Bhola coast. This research has provided novel findings for the chemical characterization of fog water and the detection of its source at IGP outflow, and highlighted the anthropogenic contributions to local air pollution, as well as the transboundary influence on local air quality.
Trend Analysis of U. S. Tornado Activity Frequency
Authors: Cao Zuohao , Cai Huaqing
Published: March 2022

As one of the most severe and high-impact weather phenomena, tornadoes and their long-term frequency trends have received lots of attention from both the scientific community and the public. Here, we show that over the last six decades (1954–2018), U.S. (E)F1 tornadoes have a statistically significant upward trend but (E)F2–E)F4 tornadoes have statistically significant downward trends based on both solid trend analyses of three independent methods and robust verifications of reported tornado data using a recently developed approach called sample generation by replacement (SGR). Furthermore, we develop a statistical framework to quantitatively explain two-way interconnections between long-term climate trends and internal variabilities. With the support of quantile–quantile plots, we find that a large positive trend of U.S. (E)F1 tornadoes over the last six decades statistically results from a small internal variability of (E)F1 tornado activities. The long-term trends of U.S. (E)F2–(E)F4 tornadoes are also inversely proportional to their internal variabilities, as anticipated by the two-way interconnection theory developed in this study.
Demonstration of a Compact Magneto-Optical Trap on an Unstaffed Aerial Vehicle
Authors: Earl Luuk , Vovrosh Jamie , Wright Michael , Roberts Daniel , Winch Jonathan , Perea-Ortiz Marisa , Lamb Andrew , Hayati Farzad , Griffin Paul , Metje Nicole , Bongs Kai , Holynski Michael
Published: March 2022

The extraordinary performance offered by cold atom-based clocks and sensors has the opportunity to profoundly affect a range of applications, for example in gravity surveys, enabling long term monitoring applications through low drift measurements. While ground-based devices are already starting to enter the commercial market, significant improvements in robustness and reductions to size, weight, and power are required for such devices to be deployed by Unstaffed Aerial Vehicle systems (UAV). In this article, we realise the first step towards the deployment of cold atom based clocks and sensors on UAV’s by demonstrating an UAV portable magneto-optical trap system, the core package of cold atom based systems. This system is able to generate clouds of 2.1±0.2×107 atoms, in a package of 370 mm × 350 mm × 100 mm, weighing 6.56 kg, consuming 80 W of power.
Constant-Sign Green’s Function of a Second-Order Perturbed Periodic Problem
Authors: Cabada Alberto , López-Somoza Lucía , Yousfi Mouhcine
Published: March 2022

In this paper, we were interested in obtaining the exact expression and studying the regions of constant sign of Green’s function related to a second-order perturbed periodic problem coupled with integral boundary conditions at the extremes of the interval of the definition. To obtain the expression of Green’s function related to this problem, we used the theory presented in a previous paper of the authors for general non-local perturbed boundary-value problems. Moreover, we characterized the parameter set where such a Green’s function has a constant sign. To this end, we needed to consider first a related second-order problem without integral boundary conditions, obtaining the properties of its Green’s function and then using them to compute the sign of the one related to the main problem.
Exploring the Limits of Euler–Bernoulli Theory in Micromechanics
Authors: Manoli Chrysoula K. , Papatzani Styliani , Mouzakis Dionysios E.
Published: March 2022

In this study, the limits of the Euler–Bernoulli theory in micromechanics are explored. Raman spectroscopy, which is extremely accurate and reliable, is employed to study the bending of a microbeam of a length of 191 μm. It is found that at the micro-scale, the Euler–Bernoulli theory remains an exact and consistent tool, and, possibly, other elasticity theories (such as micropolar theory, gradient elasticity theory, and couple stress theory) are not always required to study this phenomenon. More specifically, good correlation was achieved between the theoretical and experimental results, the former acquired via the theoretical equations and the latter obtained with the use of atomic force microscopy and Raman spectroscopy. The exact predicted strain of an atomic force microscope microbeam under bending, by Euler–Bernoulli equations is confirmed by Raman spectroscopy.
LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining
Authors: Chintalapudi Nalini , Angeloni Ulrico , Battineni Gopi , di Canio Marzio , Marotta Claudia , Rezza Giovanni , Sagaro Getu Gamo , Silenzi Andrea , Amenta Francesco
Published: March 2022

Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers’ text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers’ Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.
Modelling Mixed Microbial Culture Polyhydroxyalkanoate Accumulation Bioprocess towards Novel Methods for Polymer Production Using Dilute Volatile Fatty Acid Rich Feedstocks
Authors: Werker Alan , Lorini Laura , Villano Marianna , Valentino Francesco , Majone Mauro
Published: March 2022

Volatile fatty acid (VFA) rich streams from fermentation of organic residuals and wastewater are suitable feedstocks for mixed microbial culture (MMC) Polyhydroxyalkanoate (PHA) production. However, many such streams have low total VFA concentration (1–10 gCOD/L). PHA accumulation requires a flow-through bioprocess if the VFAs are not concentrated. A flow through bioprocess must balance goals of productivity (highest possible influent flow rates) with goals of substrate utilization efficiency (lowest possible effluent VFA concentration). Towards these goals, dynamics of upshift and downshift respiration kinetics for laboratory and pilot scale MMCs were evaluated. Monod kinetics described a hysteresis between the upshift and downshift responses. Substrate concentrations necessary to stimulate a given substrate uptake rate were significantly higher than the concentrations necessary to sustain the attained substrate uptake rate. A benefit of this hysteresis was explored in Monte Carlo based PHA accumulation bioprocess numerical simulations. Simulations illustrated for a potential to establish continuous flow-through PHA production bioprocesses even at a low (1 gCOD/L) influent total VFA concentration. Process biomass recirculation into an engineered higher substrate concentration mixing zone, due to the constant influent substrate flow, enabled to drive the process to maximal possible PHA production rates without sacrificing substrate utilization efficiency.
Modular Bioreactor Design for Directed Tendon/Ligament Tissue Engineering
Authors: Delakowski Axel J. , Posselt Jared D. , Wagner Christopher T.
Published: March 2022

Functional tissue-engineered tendons and ligaments remain to be prepared in a reproducible and scalable manner. This study evaluates an acellular 3D extracellular matrix (ECM) scaffold for tendon/ligament tissue engineering and their ability to support strain-induced gene regulation associated with the tenogenesis of cultured mesenchymal stromal cells. Preliminary data demonstrate unique gene regulation patterns compared to other scaffold forms, in particular in Wnt signaling. However, the need for a robust bioreactor system that minimizes process variation was also evident. A design control process was used to design and verify the functionality of a novel bioreactor. The system accommodates 3D scaffolds with clinically-relevant sizes, is capable of long-term culture with customizable mechanical strain regimens, incorporates in-line load measurement for continuous monitoring and feedback control, and allows a variety of scaffold configurations through a unique modular grip system. All critical functional specifications were met, including verification of physiological strain levels from 1–10%, frequency levels from 0.2–0.5 Hz, and accurate load measurement up to 50 N, which can be expanded on the basis of load cell capability. The design process serves as a model for establishing statistical functionality and reliability of investigative systems. This work sets the stage for detailed analyses of ECM scaffolds to identify critical differentiation signaling responses and essential matrix composition and cell–matrix interactions.
Seaweed’s Role in Energetic Transition—From Environmental Pollution Challenges to Enhanced Electrochemical Devices
Authors: Pinteus Susete , Susano Patrícia , Alves Celso , Silva Joana , Martins Alice , Pedrosa Rui
Published: March 2022

Resulting from the growing human population and the long dependency on fossil-based energies, the planet is facing a critical rise in global temperature, which is affecting all ecosystem networks. With a growing consciousness this issue, the EU has defined several strategies towards environment sustainability, where biodiversity restoration and preservation, pollution reduction, circular economy, and energetic transition are paramount issues. To achieve the ambitious goal of becoming climate-neutral by 2050, it is vital to mitigate the environmental footprint of the energetic transition, namely heavy metal pollution resulting from mining and processing of raw materials and from electronic waste disposal. Additionally, it is vital to find alternative materials to enhance the efficiency of energy storage devices. This review addresses the environmental challenges associated with energetic transition, with particular emphasis on the emergence of new alternative materials for the development of cleaner energy technologies and on the environmental impacts of mitigation strategies. We compile the most recent advances on natural sources, particularly seaweed, with regard to their use in metal recycling, bioremediation, and as valuable biomass to produce biochar for electrochemical applications.
DNA Barcoding, GIS-Facilitated Seed Germination and Pilot Cultivation of Teucrium luteum subsp. gabesianum (Lamiaceae), a Tunisian Local Endemic with Potential Medicinal and Ornamental Value
Authors: Kostas Stefanos , Hatzilazarou Stefanos , Pipinis Elias , Bourgou Soumaya , Ben Haj Jilani Imtinen , Ben Othman Wafa , Megdiche-Ksouri Wided , Ghrabi-Gammar Zeineb , Libiad Mohamed , Khabbach Abdelmajid , El Haissoufi Mohamed , Lamchouri Fatima , Koundourakis Emmanouil , Greveniotis Vasileios , Papaioannou Evgenia , Sakellariou Michalia A. , Anestis Ioannis , Tsoktouridis Georgios , Krigas Nikos
Published: March 2022

In the context of plant conservation and sustainable use of unique neglected and underutilized phytogenetic resources, this study focused on the Tunisian local endemic Teucrium luteum subsp. gabesianum (Lamiaceae). Using Geographical Information Systems and online databases, detailed taxon-specific ecological profiling was produced for the first time, which illustrated the temperature and climate conditions in its wild habitats and facilitated the investigation of how temperature affects its seed germination, thus making its cultivation in anthropogenic environments possible. Following the seed propagation first reported herein (77.5–81.25% at temperatures between 15 and 25 °C), species-specific in situ and ex situ conservation efforts or sustainable exploitation strategies can be enabled. This study also reported for the first time how chemical and integrated nutrient management (INM) fertilizers affect the growth and pilot cultivation of its seedlings (INM more advantageous). The firstly-reported herein DNA barcoding may enable its traceability, allowing future product design. The multidisciplinary approach followed has paved the way to bridge important research gaps hindering conservation efforts and/or the sustainable exploitation of this local Tunisian endemic plant to date. Based on the aforementioned results, the feasibility and readiness timescale for its sustainable exploitation was overviewed and re-evaluated herein, upgrading (>two-fold) its potential value for the medicinal-cosmetic, agro-alimentary, and ornamental-horticultural sectors.