bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Performance and Stability of Interlayer-Free Mesoporous Silica Membranes for Wetland Saline Water Pervaporation
Authors: Elma Muthia , Bilad Muhammad Roil , Pratiwi Amalia Enggar , Rahma Aulia , Asyyaifi Zaini Lambri , Hairullah Hairullah , Syauqiah Isna , Arifin Yulian Firmana , Lestari Riani Ayu
Published: February 2022

Wetland water is an alternative water resource around wetland areas. However, it is typically saline due to seawater intrusion and contains high natural organic matter (NOM) that is challenging to treat. This study evaluated the stability of interlayer-free mesoporous silica matrix membranes employing a dual acid–base catalyzed sol–gel process for treatment of saline wetland water. The silica sols were prepared under a low silanol concentration, dip-coated in 4 layers, and calcined using the rapid thermal processing method. The membrane performance was initially evaluated through pervaporation under various temperatures (25–60 °C) using various feeds. Next, the long-term stability (up to 400 h) of wetland saline water desalination was evaluated. Results show that the water flux increased at higher temperatures up to 6.9 and 6.5 kg·m−2·h−1 at the highest temperature of 60 °C for the seawater and the wetland saline water feeds, respectively. The long-term stability demonstrated a stable performance without flux and rejection decline up to 170 h operation, beyond which slow declines in water flux and rejection were observed due to fouling by NOM and membrane wetting. The overall findings suggest that an interlayer-free mesoporous silica membrane offers excellent performance and high salt rejection (80–99%) for wetland saline water treatments.
How Effective Are Antimicrobial Agents on Preventing the Adhesion of Candida albicans to Denture Base Acrylic Resin Materials? A Systematic Review
Authors: Bajunaid Salwa Omar
Published: February 2022

Denture stomatitis is a common oral infection caused by Candid albicans. It occurs under removable dentures due to several causative and contributing factors. If not treated, it can lead to fatal systemic candida infections. Different materials and techniques have been used to treat denture stomatitis; however, no single treatment has succeeded. The purpose of this study was to review novel techniques for incorporating antimicrobial and protein repellent agents into denture acrylic resin materials and their effect on the adhesion of Candida albicans to denture base acrylic resin materials to prevent and/or treat denture stomatitis. A systematic review was conducted in which an electronic search was undertaken using different databases and relevant keywords. The literature search revealed numerous studies describing different antifungal materials incorporated into different denture acrylic resin materials. The investigated materials showed significant antimicrobial activity with slight or no effect on the physical and mechanical properties; however, the optical properties were particularly affected with higher concentrations. The incorporation of antimicrobial agents to reduce or prevent Candida albicans biofilm formation on acrylic resin proved to be very effective, and this effect was found to be proportional to the percentage of the material used. The latter should be considered carefully not to alter the physical, mechanical or optical characteristics of the denture base material.
Reproductive Abuse in the Context of Clergy Sexual Abuse in the Catholic Church
Authors: Reisinger Doris
Published: February 2022

In a significant number of cases, clerical sex offenders impregnate their victims and force them into hiding, abortion, or adoption. This phenomenon is referred to in this paper as reproductive abuse. Clearly, most victims of reproductive abuse are adults, but even among minor victims of clerical child abuse, between 1 and 10 percent may have experienced reproductive abuse. On the basis of pertinent studies, this paper explores archival material on several dozen allegations of reproductive abuse in the context of clergy sexual abuse of minors in the US Catholic Church. Besides some tentative estimates of the general frequency of the phenomenon, this paper offers a distinction of three different types of reproductive abuse and an analysis of the interplay of clericalist and secular misogyny, which appears to be largely responsible for the silencing of victims as well as for the impunity of perpetrators and leads to the invisibility of this phenomenon, despite the high importance attributed to reproductive issues in the Catholic context.
Automatic Generation of Urban Road 3D Models for Pedestrian Studies from LiDAR Data
Authors: Fernández-Arango David , Varela-García Francisco-Alberto , González-Aguilera Diego , Lagüela-López Susana
Published: February 2022

The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research.
A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images
Authors: Wang Yazhen , Liu Guojun , Zhang Rui , Liu Junmin
Published: February 2022

The pansharpening (PS) of remote-sensing images aims to fuse a high-resolution panchromatic image with several low-resolution multispectral images for obtaining a high-resolution multispectral image. In this work, a two-stage PS model is proposed by integrating the ideas of component replacement and the variational method. The global sparse gradient of the panchromatic image is extracted by variational method, and the weight function is constructed by combining the gradient of multispectral image in which the global sparse gradient can provide more robust gradient information. Furthermore, we refine the results in order to reduce spatial and spectral distortions. Experimental results show that our method had high generalization ability for QuickBird, Gaofen-1, and WorldView-4 satellite data. Experimental results evaluated by seven metrics demonstrate that the proposed two-stage method enhanced spatial details subjective visual effects better than other state-of-the-art methods do. At the same time, in the process of quantitative evaluation, the method in this paper had high improvement compared with that other methods, and some of them can reach a maximal improvement of 60%.
Varying Amplitude Vibration Phase Suppression Algorithm in ISAL Imaging
Authors: Yin Hongfei , Guo Liang , Li Yachao , Han Liang , Xing Mengdao , Zeng Xiaodong
Published: February 2022

Platform vibration introduces sinusoidal modulation in inverse synthetic aperture lidar (ISAL) imaging, which causes paired echoes in ISAL imaging. In this paper, a varying amplitude vibration phase suppression algorithm is proposed. Working without prior knowledge, the proposed algorithm can suppress paired echoes under the condition of varying vibration amplitude and will not introduce new phase errors. Furthermore, the method is suitable for the imaging scene without isolated points. Both the simulated and real experiment results of ISAL turntable data demonstrate the effectiveness of the proposed algorithm.
Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions
Authors: Merchant Michael Allan , Obadia Mayah , Brisco Brian , DeVries Ben , Berg Aaron
Published: February 2022

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.
Design and Development of an Autonomous Underwater Helicopter for Ecological Observation of Coral Reefs
Authors: Zhou Jing , Zhou Nanxi , Che Yuchao , Gao Jian , Zhao Liming , Huang Haocai , Chen Ying
Published: February 2022

Real-time status monitoring is an important prerequisite for coral reef ecological protection. Existing equipment does not provide an ocean observation platform with adequate mobility and efficiency. This paper describes the design considerations of a proposed autonomous underwater helicopter (AUH) dedicated for ecological observation of coral reefs, including the system architecture, electronic devices, sensors and actuators, and explains the path control algorithm and controller to follow a specific path for ocean exploration. The structure and dynamic model of the AUH are first introduced, and then the corresponding simplification is made for motion analysis. Furthermore, computational fluid dynamics (CFD) simulation is carried out to evaluate the dynamic performance of the AUH. Fuzzy-PID control algorithm is utilized to achieve a good antidisturbance effect. In order to validate the performance of the proposed underwater vehicle, a field test was performed, and results confirmed the feasibility of the proposed prototype.
A Novel Floating High-Voltage Level Shifter with Pre-Storage Technique
Authors: Li Qiang , Yang Yuan , Ma Haohao , Zhou Yangle , You Guolong , Zhang Minmin , Xiang Wei
Published: February 2022

This paper proposes a novel floating high-voltage level shifter (FHV-LS) with the pre-storage technique for high speed and low deviation in propagation delay. With this technology, the transmission paths from input to output are optimized, and thus the propagation delay of the proposed FHV-LS is reduced to as low as the sub-nanosecond scale. To further reduce the propagation delay, a pull-up network with regulated strength is introduced to reduce the fall time, which is a crucial part of the propagation delay. In addition, a pseudosymmetrical input pair is used to improve the symmetry of FHV-LS structurally to balance between the rising and falling propagation delays. Moreover, a start-up circuit is developed to initialize the output state of FHV-LS during the VDDH power up. The proposed FHV-LS is implemented using 0.3-µm HVCMOS technology. Post-layout simulation shows that the propagation delays and energy per transition of the proposed FHV-LS are 384 ps and 77.7 pJ @VH = 5 V, respectively. Finally, the 500-points Monte Carlo are performed to verify the performance and the stability.
Part-Per-Billion Level Chemical Sensing with a Gold-Based SERS-Active Substrate
Authors: Zhang Tingting , Wu Liyun , Pei Junchang , Li Xuefeng , Li Haowen , Inscore Frank
Published: February 2022

We used surface-enhanced Raman spectroscopy (SERS) for the rapid and sensitive detection and quantification of caffeine in solution. Such a technique incorporated into a portable device is finding wide applications in trace chemical analysis in various fields, including law enforcement, medicine, environmental monitoring, and food quality control. To realize such applications, we are currently developing portable and handheld trace chemical analyzers based on SERS, which are integrated with a sensor embedded with activated gold nanoparticles in a porous glass matrix. In this study, we used this gold SERS-active substrate to measure aqueous solutions of the drug caffeine as a test chemical to benchmark sensor performance by defining sensitivity (lowest measured concentration (LMC) and estimated limit of detection (LOD)), determining concentration dependence and quantification capabilities by constructing calibration curves; by evaluating the effects of pH values of 3, 7, and 11; and by examining the reproducibility of the SERS measurements. The results demonstrate that the SERS sensor is sensitive, with caffeine detected at an LMC of 50 parts per billion (ppb) with an LOD of 0.63 ppb. The results further show that the sensor is very stable and can be used to make reproducible measurements, even under extremely acidic to basic pH conditions. Vibrational assignments of all observed SERS peaks are made and reported for the first time for caffeine on a gold substrate.
DBGC: Dimension-Based Generic Convolution Block for Object Recognition
Authors: Patel Chirag , Bhatt Dulari , Sharma Urvashi , Patel Radhika , Pandya Sharnil , Modi Kirit , Cholli Nagaraj , Patel Akash , Bhatt Urvi , Khan Muhammad Ahmed , Majumdar Shubhankar , Zuhair Mohd , Patel Khushi , Shah Syed Aziz , Ghayvat Hemant
Published: February 2022

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.
Comparison of the Nonlinear Dynamic Pre- and Post-LED Equalization
Authors: Siuzdak Jerzy
Published: February 2022

Visible Light Communications (VLC) have gained much popularity lately. In such a system, a white LED (Light-Emitting Diode) plays a double role as a light source and a transmitter. The main problem here is that the LED exhibits a low bandwidth and high nonlinearity, so the equalization of the LED nonlinear dynamic response is necessary. For this, various equalizers are used. This paper compares the pre- and post-equalizer performance in terms of the received signal quality for a channel that includes a nonlinear element of limited bandwidth, such as an LED. Multilevel Pulse Amplitude Modulation (PAM) was selected as the signal format, as well as a variant of the Volterra series equalizer as the compensating element. The results obtained may be used for the correction of the dynamic characteristics of LEDs applied in VLC systems. For the sake of comparison, we used Modulation Error Ratio (MER) values at the receiver output. The dynamic nonlinear behavior was modeled by a Wiener–Hammerstein device, whereas the post/pre-equalizer was based on the dynamic deviation reduction-based Volterra series. The obtained results indicate that the post- and pre-equalizer performed comparably for the linear/moderately nonlinear channels and for a high noise level. In the case of high nonlinearity and a large SNR (Signal–to–Noise Ratio) values, the post-equalizer performed somewhat better in terms of MER by a few dB at maximum.
Robust Speed Tracking Control for Future Electric Vehicles under Network-Induced Delay and Road Slope Variation
Authors: Zhang Jie , Fan Qianrong , Wang Ming , Zhang Bangji , Chen Yuanchang
Published: February 2022

Integrated motor-transmission (IMT) powertrain systems are widely used in future electric vehicles due to the advantages of their simple structure configuration and high controllability. In electric vehicles, precise speed tracking control is critical to ensure good gear shifting quality of an IMT powertrain system. However, the speed tracking control design becomes challenging due to the inevitable time delay of signal transmission introduced by the in-vehicle network and unknown road slope variation. Moreover, the system parameter uncertainties and signal measurement noise also increase the difficulty for the control algorithm. To address these issues, in this paper a robust speed tracking control strategy for electric vehicles with an IMT powertrain system is proposed. A disturbance observer and low-pass filter are developed to decrease the side effect from the unknown road slope variation and measurement noise and reduce the estimation error of the external load torque. Then, the network-induced delay speed tracking model is developed and is upgraded considering the damping coefficient uncertainties of the IMT powertrain system, which can be described through the norm-bounded uncertainty reduction method. To handle the network-induced delay and parameter uncertainties, a novel and less-conservative Lyapunov function is proposed to design the robust speed tracking controller by the linear matrix inequality (LMI) algorithm. Meanwhile, the estimation error and measurement noise are considered as the external disturbances in the controller design to promote robustness. Finally, the results demonstrate that the proposed controller has the advantages of strong robustness, excellent speed tracking performance, and ride comfort over the current existing controllers.
Determination of Peak Purity in HPLC by Coupling Coulometric Array Detection and Two-Dimensional Correlation Analysis
Authors: Oney-Montalvo Julio Enrique , Morozova Ksenia , Ramírez-Sucre Manuel Octavio , Scampicchio Matteo , Rodríguez-Buenfil Ingrid Mayanin
Published: February 2022

This work aims to evaluate the purity of chromatographic peaks by a two-dimensional correlation (2D-corr) analysis. Such an analysis leads to two contour plots: synchronous and asynchronous. The synchronous contour plot provides information on the number of peaks present in the chromatogram. The asynchronous contour plot reveals the presence of overlapping species on each peak. The utility of 2D-corr analysis was demonstrated by the chromatographic analysis of Capsicum chili extracts obtained by HPLC coupled with a coulometric array of sixteen detectors. Thanks to 16 electrochemical sensors, each poised at increasing potentials, the resulting 2D-corr analysis revealed the presence of at least three species on the peak located at a retention time of 0.93 min. Mass spectrometry (MS) analysis was used to analyze the coeluting species, which were identified as: quinic acid (3.593 min), ascorbic acid (3.943 min), and phenylalanine (4.229 min). Overall, this work supports the use of 2D-corr analysis to reveal the presence of overlapping compounds and, thus, verify the signal purity of chromatographic peaks.
Study on a Fault Mitigation Scheme for Rub-Impact of an Aero-Engine Based on NiTi Wires
Authors: Pan Qiang , He Tian , Liu Wendong , Liu Xiaofeng , Chen Haibing
Published: February 2022

The aim of this study was to solve the frequently occurring rotor-stator rub-impact fault in aero-engines without causing a significant reduction in efficiency. We proposed a fault mitigation scheme, using shape memory alloy (SMA) wire, whereby the tip clearance between the rotor and the stator is adjusted. In this scheme, an acoustic emission (AE) sensor is utilized to monitor the rub-impact fault. An active control actuator is designed with pre-strained two-way SMA wires, driven by an electric current via an Arduino control board, to mitigate the rub-impact fault once it occurs. In order to investigate the feasibility of the proposed scheme, a series of tests on the material properties of NiTi wires, including heating response rate, ultimate strain, free recovery rate, and restoring force, were carried out. A prototype of the actuator was designed, manufactured, and tested under various conditions. The experimental result verifies that the proposed scheme has the potential to mitigate or eliminate the rotor-stator rub-impact fault in aero-engines.
Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review
Authors: Giannakopoulou Konstantina-Maria , Roussaki Ioanna , Demestichas Konstantinos
Published: February 2022

Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson’s disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.
Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques
Authors: Farooq Muhammad Shoaib , Arooj Ansif , Alroobaea Roobaea , Baqasah Abdullah M. , Jabarulla Mohamed Yaseen , Singh Dilbag , Sardar Ruhama
Published: February 2022

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.
Aging Characteristics of Rubber Modified Bitumen Mixed with Sulfur after Terminal Blend Process
Authors: Wang Sheng , Huang Weidong , Liu Xueyan , Lin Peng
Published: February 2022

The influence of sulfur on the chemical, rheological, and aging resistance of terminal blend rubberized bitumen (TBRB) was studied. TB hybrid bitumen (TBHB) was prepared from with different sulfur contents (0.1, 0.2, 0.3, 0.4 wt%) and TBRB prepared with neat bitumen and crumb rubbers at 20% content. TBHB binders were aged by rolling thin film oven test (RTFOT) and pressure aging vessel (PAV), respectively. The chemical composition of TBHB binders was monitored by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Rheological properties of all TBHB samples were tested. Chemical composition results show that TBHB can inhibit the degradation of polybutadiene compared with TBRB in the RTFOT stage, and the polybutadiene degradation is the main process of TBHB in the RTFOT stage, while the TBHB is mainly desulfurized after PAV aging. Meanwhile, the increase in sulfur content in the TBHB can improve the desulfurization degree of the TBHB binder after PAV. TBRB containing sulfur can improve the mechanical properties and elasticity and reduce the hardening degree during aging. Moreover, blending sulfur into the TBRB caused a lower complex modulus aging index after aging, which indicates that TBHB has superior aging resistance.
Practitioners’ Perceptions of Co-Product Allocation Methods in Biorefinery Development—A Case Study of the Austrian Pulp and Paper Industry
Authors: Wenger Julia , Pichler Stefan , Näyhä Annukka , Stern Tobias
Published: February 2022

The utilization of coproducts is a strategy that can be applied to increase the economic and environmental performance of industrial processes and thus reach an objective targeted in several environmental policies. In multi-output production processes, allocation needs to be performed to assess the products’ environmental and economic performance. It is crucial to choose an adequate allocation method, because this choice has been shown to strongly influence overall outcomes. Consequently, rash choices can lead to poor decision-making. Various ways to apply and combine allocation methods can be found in the academic literature, but it is often difficult to find sufficient guidance on how to choose an allocation method for a specific context. This study explores practitioners’ perceptions of the cost and environmental impact allocation methods used in biorefinery development (lignin, fiber fines) by applying the analytic hierarchy process (AHP). Results indicate that professional background represents a major factor influencing individual preferences and, thus, the selection of specific allocation methods. Policy makers should be aware that practitioners with different professional backgrounds have varying preferences for different allocation methods and that this influences the overall assessments. These factors, in turn, affect the interpretation of results, further decision-making and, ultimately, the realization of environmentally sound and economically viable biorefinery projects. This issue deserves more attention in biorefineries, but also in other multi-output production processes. The findings indicate a need to consider multidisciplinary, diverse views and knowledge when conducting such assessments and to display the underlying approaches transparently.
Electricity Consumption and Operational Carbon Emissions of European Telecom Network Operators
Authors: Lundén Dag , Malmodin Jens , Bergmark Pernilla , Lövehagen Nina
Published: February 2022

This study presents operational electricity consumption and greenhouse gas emissions for named European telecom network operators during 2015–2018. These results are also compared to data for 2010–2015. The study provides an extensive primary data set, collected from European Telecommunication Network Operators (ETNO) members, covering operations in Europe and beyond, providing data with higher granularity than publicly available sources. The collected data set corresponds to roughly 36 percent of European subscriptions and 8 percent of global subscriptions. This data set was used to calculate the aggregated annual electricity consumption for the assessed operators, as well as associated subscription intensities, in total, for Europe and per network type. Moreover, aggregated electricity-related carbon emissions and emissions from other sources were calculated. Finally, estimates were made for the overall network operation in Europe for 2018 and 2020. The study concludes that the electricity consumption and number of subscriptions for the reporting telecom network operators remained nearly constant (+1 percent and −3 percent, respectively) between 2015 and 2018, while data traffic increased by a factor of three. For the extended period of 2010–2018, the electricity consumption per subscription remained quite stable, slightly below 30 kWh/subscription despite substantial data traffic growth (by a factor of 12).
A Multi-Perspective Assessment of the Introduction of E-Scooter Sharing in Germany
Authors: König Alexandra , Gebhardt Laura , Stark Kerstin , Schuppan Julia
Published: February 2022

Electric scooter sharing (e-scooter sharing) is a new urban micro-mobility service that is expected to shape individual urban mobility. The introduction of e-scooter sharing systems poses challenging questions for cities and transportation planners regarding their effects on their transportation system. This study addresses the question concerning the strategies which are applied for the introduction of e-scooter sharing systems in different operation areas in Germany. An interview study with 21 stakeholders with different backgrounds (local transport authorities, public transport providers, e-scooter sharing operators, municipalities, associations, planning offices and consulting companies, and other mobility providers) was conducted to reflect upon the introduction of e-scooter sharing systems in Germany and stakeholders’ involvement in planning. The qualitative content analysis provides insights into the stakeholders’ assessment of the introduction process and thus contributes to a multi-perspective understanding on the topic. Derived hypotheses and recommendations further contribute to knowledge sharing and learning from experience. The paper concludes with a description of three introduction styles: protective, pro-active, and laissez-faire.
Influences of Silica Fume on Compressive Strength and Chemical Resistances of High Calcium Fly Ash-Based Alkali-Activated Mortar
Authors: Sothornchaiwit Kantiya , Dokduea Warayut , Tangchirapat Weerachart , Keawsawasvong Suraparb , Thongchom Chanachai , Jaturapitakkul Chai
Published: February 2022

Although elevated temperature curing can increase the compressive strength of alkali-activated mortar, its field applications are still limited. In this study, alkali-activated mortars were prepared using high calcium fly ash (FA) as a precursor. Small amounts of silica fume were used to partially replace high calcium fly ash at 3–9% by weight to increase the strength of alkali-activated mortar. All mixtures had a liquid to binder ratio of 0.60 and sand to binder ratio of 2.75 by weight. A ratio of NaOH to Na2SiO3 solution was kept at 2:1 by weight. Mortar flow was also held between 105–115 using a superplasticizer. Compressive strength and durability were investigated in terms of acid and sulfate resistance. The effects of silica fume addition and curing temperature on compressive strength were found to be significant. The optimum content of silica fume was 6%, providing compressive strength as high as that of alkali-activated mortars cured at 45 °C. The weight loss of alkali-activated mortar due to sulfuric acid attack decreased with increasing silica fume content and curing temperature. All alkali-activated mortars were found to have a better performance than (ordinary) Portland cement (OPC) mortars and mortars containing 40% FA. Alkali-activated mortars immersed in magnesium sulfate solutions had compressive strength that decreased with an increase in curing temperature. The expansion of alkali-activated mortar due to sodium sulfate attack increased with increasing silica fume content, and the expansion decreased with increased curing temperature. All alkali-activated mortars performed better than OPC mortars after 98 days of sulfate attack.
An Initial Qualitative Exploration of Economic, Cultural, and Language Changes in Telok Melano, Sarawak, Malaysia
Authors: Shin Chong , Tuah Dilah , Yusriadi Yusriadi
Published: February 2022

This research focuses on the present economic, cultural, and linguistic issues of Telok Melano, a hamlet in Sarawak that was formerly isolated from nearby communities. Telok Melano is changing dramatically as a result of the Pan Borneo Highway construction and the nature of cultural obsolescence in modern society. This qualitative study has been conducted through face-to-face structured and semi-structured interviews, as well as participant observation. This study found that the highway construction benefits the villagers. An economic overturn has begun among them, and basic infrastructure has also been greatly enhanced as a result of the project’s spin-off. Are these changes beneficial to their cultural practices? Ethnographic techniques are delivering a number of interesting results. For instance, the changes have solidified the local Malays’ religious beliefs. Although the locals have abandoned many traditional practices in order to comply with Islamic beliefs, they have preserved certain traditional etiquette. In terms of language, a generational gap emerged between three linguistic varieties spoken in this area. Their dominant mother tongue, the Kuching Malay dialect, is becoming more commonly spoken, particularly with outsiders. This study successfully presents the picture of economic and social changes in Telok Melano following the development of this new road system.
Simulation and Optimization of Supply and Demand Pattern of Multiobjective Ecosystem Services—A Case Study of the Beijing-Tianjin-Hebei Region
Authors: Wu Aibin , Zhang Jianwen , Zhao Yanxia , Shen Huitao , Guo Xiaoping
Published: February 2022

Assessing and predicting the impact of land use/cover changes on ecosystem service supply and demand are crucial to formulating effective sustainable land use policies. In this study, we use the ecosystem service (ES) score matrix, ES supply rate, and ES supply/demand ratio to analyze the supply/demand pattern of ecosystem services based on land use/cover changes in the Beijing-Tianjin-Hebei region from 1990 to 2020. The Conversion of Land Use and Its Effects-Simulation (CLUE-S) model is used to simulate the spatiotemporal patterns of land use change in three scenarios of natural development, ecological priority development, and economic priority development and to predict and simulate the evolution of the ES supply and demand patterns in these different scenarios from 2030 to 2050. It was found that the main land use types are farmland and woodland in the Beijing-Tianjin-Hebei region, accounting for more than 67% from 1990 to 2020, the proportion of farmland decreased from 51.79% to 46.11%, and the proportion of woodland increased from 20.99% to 21.34%; the land use transformation was mainly from farmland to construction land from 1990 to 2020. The supply of ES in the Beijing-Tianjin-Hebei region was at a high level, the supply rate of ES increased from 0.78 to 0.81, the supply/demand ratio of ES decreased from 0.33 to 0.16 from 1990 to 2020, and the supply and demand of ES in the northern and western parts of the Beijing-Tianjin-Hebei region were in surplus. In the natural development scenario, the ES in the Beijing-Tianjin-Hebei region would remain in a high supply state from 2030 to 2050, but the pressure would be greater than before. The deficit, centered on urban construction, would widen, and the ecological situation would deteriorate. In the ecological priority development scenario, the pressure on the ES would be relieved, and the rate of deficit expansion would be reduced. In the economic development priority scenario, the pressure on the ES would increase sharply, and the deficit area would expand rapidly.
Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets
Authors: Abulaiti Adilai , Nurmemet Ilyas , Muhetaer Nuerbiye , Xiao Sentian , Zhao Jing
Published: February 2022

Currently, soil salinization is one of the main forms of land degradation and desertification. Soil salinization not only seriously restricts the development of agriculture and the economy, but also poses a threat to the ecological environment. The main purpose of this study is to map soil salinity in Keriya Oasis, northwestern China using the PALSAR-2 fully polarized synthetic aperture radar (PolSAR) L-band data and Landsat-8-OLI (OLI) optical data combined with deep learning (DL) methods. A field survey is conducted, and soil samples are collected from 20 April 2015 to 1 May 2015. To mine the hidden information in the PALSAR-2 data, multiple polarimetric decomposition methods are implemented, and a wide range of polarimetric parameters and synthetic aperture radar discriminators are derived. The radar vegetation index (RVI) is calculated using PALSAR-2 data, while the normalized difference vegetation index (NDVI) and salinity index (SI) are calculated using OLI data. The random forest (RF)-integrated learning algorithm is used to select the optimal feature subset composed of eight polarimetric elements. The RF, support vector machine, and DL methods are used to extract different degrees of salinized soil. The results show that the OLI+PALSAR-2 image classification result of the DL classification was relatively good, having the highest overall accuracy of 91.86% and a kappa coefficient of 0.90. This method is helpful to understand and monitor the spatial distribution of soil salinity more effectively to achieve sustainable agricultural development and ecological stability.
The Impact of Big Data Technical Skills on Novel Business Model Innovation Based on the Role of Resource Integration and Environmental Uncertainty
Authors: Dai Bingqin , Liang Wenquan
Published: February 2022

Taking the dynamic capability theory as the research framework, using a two-stage questionnaire survey and 318 Chinese enterprises as samples, this study investigates the impact mechanism of big data technical skills on novel business model innovation, as well as the mediator of resource integration and the moderator of environmental uncertainty. The hierarchical regression results show that: (1) big data technical skills have a significant positive impact on novel business model innovation; (2) resource integration is a partial mediator between big data technical skills and novel business model innovation; (3) environmental uncertainty regulates the relationship between big data technical skills and resource integration, that is, the higher the environmental uncertainty, the stronger the positive relationship between big data technical skills and resource integration; (4) environmental uncertainty has no significant moderating effect on the mediating role of resource integration between big data technology skills and novel business model innovation. The conclusion of this study develops the driving factors of novel business model innovation and provides theoretical guidance for enterprises to improve the innovative activities of novel business models by using big data technology skills.
LogLS: Research on System Log Anomaly Detection Method Based on Dual LSTM
Authors: Chen Yiyong , Luktarhan Nurbol , Lv Dan
Published: February 2022

System logs record the status and important events of the system at different time periods. They are important resources for administrators to understand and manage the system. Detecting anomalies in logs is critical to identifying system faults in time. However, with the increasing size and complexity of today’s software systems, the number of logs has exploded. In many cases, the traditional manual log-checking method becomes impractical and time-consuming. On the other hand, existing automatic log anomaly detection methods are error-prone and often use indices or log templates. In this work, we propose LogLS, a system log anomaly detection method based on dual long short-term memory (LSTM) with symmetric structure, which regarded the system log as a natural-language sequence and modeled the log according to the preorder relationship and postorder relationship. LogLS is optimized based on the DeepLog method to solve the problem of poor prediction performance of LSTM on long sequences. By providing a feedback mechanism, it implements the prediction of logs that do not appear. To evaluate LogLS, we conducted experiments on two real datasets, and the experimental results demonstrate the effectiveness of our proposed method in log anomaly detection.
Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering
Authors: Almotairi Khaled H. , Abualigah Laith
Published: February 2022

Data clustering is a complex data mining problem that clusters a massive amount of data objects into a predefined number of clusters; in other words, it finds symmetric and asymmetric objects. Various optimization methods have been used to solve different machine learning problems. They usually suffer from local optimal problems and unbalance between the search mechanisms. This paper proposes a novel hybrid optimization method for solving various optimization problems. The proposed method is called HRSA, which combines the original Reptile Search Algorithm (RSA) and Remora Optimization Algorithm (ROA) and handles these mechanisms’ search processes by a novel transition method. The proposed HRSA method aims to avoid the main weaknesses raised by the original methods and find better solutions. The proposed HRSA is tested on solving various complicated optimization problems—twenty-three benchmark test functions and eight data clustering problems. The obtained results illustrate that the proposed HRSA method performs significantly better than the original and comparative state-of-the-art methods. The proposed method overwhelmed all the comparative methods according to the mathematical problems. It obtained promising results in solving the clustering problems. Thus, HRSA has a remarkable efficacy when employed for various clustering problems.
Infrasonic, Acoustic and Seismic Waves Produced by the Axion Quark Nuggets
Authors: Budker Dmitry , Flambaum Victor V. , Zhitnitsky Ariel
Published: February 2022

We advocate the idea that Axion Quark Nuggets (AQN) hitting the Earth can be detected by analysing the infrasound, acoustic, and seismic waves which always accompany their passage in the atmosphere and underground. Our estimates for the infrasonic frequency ν≃5 Hz and overpressure δp∼0.3 Pa for relatively large size dark matter (DM) nuggets suggest that sensitivity of presently available instruments is already sufficient to detect very intense (but very rare) events today with existing technology. A study of much more frequent but less intense events requires a new type of instrument. We propose a detection strategy for a systematic study to search for such relatively weak and frequent events by using distributed acoustic sensing and briefly mention other possible detection methods.
Isolation and Genomic Characterization of SARS-CoV-2 Omicron Variant Obtained from Human Clinical Specimens
Authors: Yadav Pragya D. , Gupta Nivedita , Potdar Varsha , Mohandas Sreelekshmy , Sahay Rima R. , Sarkale Prasad , Shete Anita M. , Razdan Alpana , Patil Deepak Y. , Nyayanit Dimpal A. , Joshi Yash , Patil Savita , Majumdar Triparna , Dighe Hitesh , Malhotra Bharti , Shastri Jayanthi , Abraham Priya
Published: February 2022

Due to the failure of virus isolation of the Omicron variant in Vero CCL-81 from the clinical specimens of COVID-19 cases, an initial in vivo and subsequent in vitro approach was utilized for the isolation of the virus. A total of 74 oropharyngeal/nasopharyngeal specimens were collected from SARS-CoV-2 positive international travellers and a contact case at Delhi and Mumbai, India. All the specimens were sequenced using next-generation sequencing and simultaneously inoculated onto Vero CCL-81 cells for virus isolation. Subsequently, two omicron positive specimens were inoculated into Syrian hamsters for two passages. The initial passage of the positive hamster specimens was inoculated onto Vero CCL-81 cells. The clinical specimens, hamster specimens, and Vero CCL-81 passages were sequenced to assess the mutational changes in different host species. The replication of the Omicron variant in hamsters was confirmed with the presence of a high viral load in nasal turbinate and lung specimens of both passages. The successful isolation of the virus from hamster specimens with Vero CCL-81 was observed with cytopathic effect in infected cells and high viral load in the cell suspension. The genome analysis revealed the presence of L212C mutation, Tyrosine 69 deletion, and C25000T nucleotide change in spike gene of hamster passage sequences and an absence of V17I mutation in E gene in hamster passage sequences, unlike human clinical specimen and Vero CCL-81 passages. No change was observed in the furin cleavage site in any of the specimen sequences, suggesting intact pathogenicity of the virus isolate. Our data demonstrated successful isolation of the Omicron variant with the in vivo method first followed by in vitro method. The virus isolate could be used in the future to explore different aspects of the Omicron variant.