bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Selective Spectral Sensitivity of Oppositely Placed Double-Barrier Structures
Authors: Khudaverdyan Surik , Vaseashta Ashok , Ayvazyan Gagik , Matevosyan Ler , Khudaverdyan Ashot , Khachatryan Mane , Makaryan Elya
Published: August 2022

The characteristics of an oppositely placed double potential barrier photodetector structure were investigated under longitudinal illumination. The functional abilities of the silicon n+-p-n+ structure were studied under longitudinal illumination. The choice of impurity concentration in the n+- and p- regions provided the difference in heights of the potential barriers near-surface, rear, and oppositely directed regions and their conjoining in the high-resistance p-base. The widths of the depletion regions of these barriers varied with a step change in the bias voltage. The redistribution of the fraction of absorption of electromagnetic waves between the barriers and the change in their contribution to the total photocurrent was investigated. In connection with this, short-wavelength (490 nm) and long-wavelength (830 nm) spectral maxima were formed. In the voltage range commensurate with the difference in the heights of the potential barriers, the inversion of the sign of the spectral photocurrent and, using the developed algorithm, the spectral distribution of the absorbed radiation intensity, were obtained. A physical explanation of these results is provided. The structure under investigation enables the selective registration of individual waves and their intensities. The results reported here enable an optimistic spectrophotometric outlook for oppositely placed double-barrier photonic structures.
One-Dimensional Photonic Crystals with Different Termination Layer Thicknesses and Very Narrow Bloch Surface Wave and Guided Wave Based Resonances for Sensing Applications
Authors: Fort Tomas , Kanok Roman , Hlubina Petr , Pokorny Pavel , Sobota Jaroslav
Published: August 2022

We demonstrate an efficient sensing of both gaseous and aqueous analytes utilizing Bloch surface waves (BSWs) and guided waves (GWs) excited on a truncated one-dimensional photonic crystal (1DPhC) composed of six TiO2/SiO2 bilayers with a termination layer of TiO2. For the gaseous analytes, we show that 1DPhC can support the GW excited by an s-polarized wave and the theoretical shift of the resonance wavelength is linear for small changes in the analyte refractive index (RI), giving a constant RI sensitivity of 87 nm per RI unit (RIU). In addition, for the aqueous analytes, the GW excited by s-polarized and BSW by p-polarized waves can be resolved and exploited for sensing applications. We compare two designed and realized 1DPhCs with termination layer thicknesses of 60 nm and 50 nm, respectively, and show experimentally the differences in their very narrow reflectance and phase responses. An RI sensitivity and figure of merit as high as 544.3 nm/RIU and 303 RIU−1, respectively, are obtained for the smaller thickness when both s- and p-polarized BSWs are excited. This is the first demonstration of both very deep BSW-based resonances in two orthogonal polarizations and a very narrow resonance in one of them.
Optically Transparent Bamboo: Preparation, Properties, and Applications
Authors: Li Xuelian , Zhang Weizhong , Li Jingpeng , Li Xiaoyan , Li Neng , Zhang Zhenhua , Zhang Dapeng , Rao Fei , Chen Yuhe
Published: August 2022

The enormous pressures of energy consumption and the severe pollution produced by non-renewable resources have prompted researchers to develop various environmentally friendly energy-saving materials. Transparent bamboo represents an emerging result of biomass material research that has been identified and studied for its many advantages, including light weight, excellent light transmittance, environmental sustainability, superior mechanical properties, and low thermal conductivity. The present review summarizes methods for preparing transparent bamboo, including delignification and resin impregnation. Next, transparent bamboo performance is quantified in terms of optical, mechanical, and thermal conductivity characteristics and compared with other conventional and emerging synthetic materials. Potential applications of transparent bamboo are then discussed using various functionalizations achieved through doping nanomaterials or modified resins to realize advanced energy-efficient building materials, decorative elements, and optoelectronic devices. Finally, challenges associated with the preparation, performance improvement, and production scaling of transparent bamboo are summarized, suggesting opportunities for the future development of this novel, bio-based, and advanced material.
Verification of the Influence of Processing History through Comparing High-Speed Melt Spinning Behavior of Virgin and Recycled Polypropylene
Authors: Takarada Wataru , Barique Mohammad A. , Kunimitsu Tatsuma , Kameda Takao , Kikutani Takeshi
Published: August 2022

A ‘model’ material of recycled polypropylene (PP) was prepared through the injection molding process, and the effect of processing history on the polymer characteristics was investigated through the high-speed melt spinning of virgin and recycled PP. On-line measurement of the thinning behavior of the spin-line revealed the downstream shift of solidification point for the recycled PP at the take-up velocity of 1.0 km/min, indicating the suppression of flow-induced crystallization. The difference was not clear at higher take-up velocities of up to 5 km/min. For any identical take-up velocity, no clear difference in the stress-strain curves and birefringence of the fibers from virgin and recycled PP could be observed, whereas the detailed investigation on the variation of relative amount of c-axis and a*-axis oriented crystals in the fibers prepared at varied take-up velocities suggested the deterioration of flow-induced crystallization at 1.0 km/min. It was speculated that the processing history induced the lowering of the entanglement density, which affected the melt spinning and crystallization behavior. An undistinguishable difference between the virgin and recycled PP at increased take-up velocities suggested the existence of an optimum elongational strain rate for the detection of the different states of molecular entanglement.
Punicalagin-Loaded Alginate/Chitosan-Gallol Hydrogels for Efficient Wound Repair and Hemostasis
Authors: Ju Jaewon , Kim Jungwoo , Choi Yeonsun , Jin Subin , Kim Sumin , Son Donghee , Shin Mikyung
Published: August 2022

For recently devised wound-healing materials, a variety of acute application systems with sustainable therapeutic effects on wound sites have been suggested. For example, hydrogel-type healing agents with porous structures and high drug encapsulation efficiencies have been developed for wound repair. However, challenges remain about the poor mechanical and adhesive properties of hydrogels. Herein, we propose a punicalagin (PC)-containing wound-healing hydrogel in adhesive form that is mechanically stable and has sustainable wound-healing therapeutic efficiency. The APC hydrogel, composed of alginate (ALG), PC, and chitosan–gallol (CHI–G), exhibits significant mechanical and self-healing properties, thus indicating that PC increases cross-linking in ALG/CHI–G as macromolecule. The PC-containing mechanically enhanced hydrogel demonstrates high tissue adhesiveness. Sustainable PC release for 192 h, which indicates high therapeutic effect of the released PC, and great blood compatibility are evaluated based on rapid blood coagulation and minimal hemolysis. The cytocompatibility and wound-healing abilities of the PC-containing APC hydrogel are greater than those of the non-PC hydrogel, as verified by cell compatibility and wound scratch assays. These results indicate that a suitable concentration of PC-containing hydrogel with sustainable moisture condition and PC release may inspire further polyphenol-agent-containing hydrogels as wound-healing agents with structural stability and therapeutic efficiency.
Dynamic and Ballistic Performance of Uni- and Bidirectional Pineapple Leaf Fibers (PALF)-Reinforced Epoxy Composites Functionalized with Graphene Oxide
Authors: Neves Pamela Pinto , Costa Ulisses Oliveira , Bezerra Wendell Bruno Almeida , Figueiredo André Ben-Hur da Silva , Monteiro Sergio Neves , Nascimento Lucio Fabio Cassiano
Published: August 2022

Replacing synthetic fibers with natural ones as reinforcement in polymeric composites is an alternative to contribute to sustainability. Pineapple leaf fibers (PALF) have specific mechanical properties that allow their use as reinforcement. Further, graphene oxide (GO) has aroused interest due to its distinctive properties that allow the improvement of fiber/matrix interfacial adhesion. Thus, this work aimed to evaluate the ballistic performance and energy absorption properties of PALF-reinforced composites, presenting different conditions (i.e., GO-functionalization, and variation of fibers volume fraction and arrangement) through residual velocity and Izod impact tests. ANOVA was used to verify the variability and reliability of the results. SEM was employed to visualize the failure mechanisms. The Izod impact results revealed a significant increase in the absorbed energy with the increment of fiber volume fraction for the unidirectional configuration. The ballistic results indicated that the bidirectional arrangement was responsible for better physical integrity after the projectile impact. Furthermore, bidirectional samples containing 30 vol.% of GO non-functionalized fibers in a GO-reinforced matrix showed the best results, indicating its possible application as a second layer in multilayered armor systems.
Development of a New Additive Based on Textile Fibers of End-of-Life Tires (ELT) for Sustainable Asphalt Mixtures with Improved Mechanical Properties
Authors: Valdés-Vidal Gonzalo , Calabi-Floody Alejandra , Duarte-Nass Carla , Mignolet Cristian , Díaz Cristian
Published: August 2022

End-of-life tires (ELT) are a worldwide problem. Rubber, steel, and different textile fibers are the by-products of ELT. Unlike rubber and steel, waste tire textile fibers (WTTF) are disposed of in landfills or burned. This study developed an additive made with WTTF to be incorporated into conventional hot mix asphalt (HMA), and its performance properties were evaluated. First, a characterization of the WTTF used was made and a manufacture protocol was established. Then, a reference HMA was designed and mixtures with different addition percentages (2%, 5% and 8%) of the WTTF-based additive were evaluated. The mechanical properties studied were stiffness modulus, moisture susceptibility, rutting resistance, stripping, and cracking resistance. The results indicated that the addition of the 2% and 5% WTTF-based additive improved these performance properties. Moreover, all addition percentages of the WTTF-based additive evaluated demonstrated a decrease of over 29% in permanent deformation according to the Hamburg Wheel Tracking Test. Thus, the use of the WTTF would not only be valuing a waste, but an asphalt mixture with improved properties would be obtained, contributing to the circular economy by reusing a material and prolonging the useful life of the asphalt mixture.
3D Printing Parameter Optimization Using Taguchi Approach to Examine Acrylonitrile Styrene Acrylate (ASA) Mechanical Properties
Authors: Hameed Abdul Zubar , Aravind Raj Sakthivel , Kandasamy Jayakrishna , Shahzad Muhammad Atif , Baghdadi Majed Abubakr
Published: August 2022

Polymer composites with different reinforcements have many applications. By adjusting process settings and adding fibers and fillers, composite properties can be improved. Additive manufacturing is popular in the polymer industry because it can manufacture intricately designed parts with fewer defects and greater strength with less material consumption. Composites use thermoplastics and thermosetting polymers. Thermoset plastics cannot be reused or recycled; therefore, they are disposed in landfills, creating pollution and environmental harm. In this work, thermoplastic ASA (Acrylonitrile Styrene Acrylate) polymer filament is used for FDM 3D printing. The specimens are made by varying five process parameters that affected the materials’ mechanical properties. The tensile, flexural and impact specimens are made using MINITAB software and ASTM requirements. The L18 orthogonal array experimental design, specimens and results were optimized. Infill density and layer height were most influential. Maximum tensile strength of 51.86 MPa, flexural strength of 82.56 MPa and impact strength of 0.180 J/mm2 were obtained by following the software-suggested input factors and compared with the predicted values. Final error percentage was obtained between the predicted and the experimental results and it was found to be under 3%, which is acceptable.
Quantitative Characterization of the Anisotropy of the Stress-Optical Properties of Polyethylene Terephthalate Films Based on the Photoelastic Method
Authors: He Quanyan , Wang Miaojing , Du Yitao , Qin Qinghua , Qiu Wei
Published: August 2022

Polyethylene terephthalate (PET) is one of the most commonly used substrate materials in the field of flexible electronics, and its stress-induced birefringence often has a detrimental effect on the optical properties of the device. Therefore, a deep and systematic understanding of the stress-optical properties of PET films is crucial for device design and manufacture. The photoelastic method is a direct optical measurement technique based on the stress-induced birefringence effect of materials, which has the advantages of being nondestructive and noncontact. In this work, the photoelastic method was used to quantitatively characterize the anisotropy of the stress-optical properties of PET films under the uniaxial stress state. First, a self-built reflection-transmission coaxial bidirectional photoelasticity measurement system was developed by means of a combination of transmission and reflection photoelasticity. Then, the stress-optical coefficients and isoclinic angles of PET films with different stretching angles were measured. Finally, the linear combinations of the photoelastic tensor components and refractive-index-related parameters were determined by fitting the analytical relationship between the stress-optical coefficients and isoclinic angles.
Design and Immunoinformatic Assessment of Candidate Multivariant mRNA Vaccine Construct against Immune Escape Variants of SARS-CoV-2
Authors: Hussain Mushtaq , Amanullah Anusha , Aslam Ayesha , Raza Fozia , Arzoo Shabana , Qureshi Iffat Waqar , Waheed Humera , Jabeen Nusrat , Shabbir Sanya , Sayeed Muneeba Ahsan , Quraishy Saeed
Published: August 2022

To effectively counter the evolving threat of SARS-CoV-2 variants, modifications and/or redesigning of mRNA vaccine construct are essentially required. Herein, the design and immunoinformatic assessment of a candidate novel mRNA vaccine construct, DOW-21, are discussed. Briefly, immunologically important domains, N-terminal domain (NTD) and receptor binding domain (RBD), of the spike protein of SARS-CoV-2 variants of concern (VOCs) and variants of interest (VOIs) were assessed for sequence, structure, and epitope variations. Based on the assessment, a novel hypothetical NTD (h-NTD) and RBD (h-RBD) were designed to hold all overlapping immune escape variations. The construct sequence was then developed, where h-NTD and h-RBD were intervened by 10-mer gly-ala repeat and the terminals were flanked by regulatory sequences for better intracellular transportation and expression of the coding regions. The protein encoded by the construct holds structural attributes (RMSD NTD: 0.42 Å; RMSD RBD: 0.15 Å) found in the respective domains of SARS-CoV-2 immune escape variants. In addition, it provides coverage to the immunogenic sites of the respective domains found in SARS-CoV-2 variants. Later, the nucleotide sequence of the construct was optimized for GC ratio (56%) and microRNA binding sites to ensure smooth translation. Post-injection antibody titer was also predicted (~12000 AU) to be robust. In summary, the construct proposed in this study could potentially provide broad spectrum coverage in relation to SARS-CoV-2 immune escape variants.
Statistical Optimization of Pyrolysis Process for Thermal Destruction of Plastic Waste Based on Temperature-Dependent Activation Energies and Pre-Exponential Factors
Authors: Alqarni Ali O. , Nabi Rao Adeel Un , Althobiani Faisal , Naz Muhammad Yasin , Shukrullah Shazia , Khawaja Hassan Abbas , Bou-Rabee Mohammed A. , Gommosani Mohammad E. , Abdushkour Hesham , Irfan Muhammad , Mahnashi Mater H.
Published: August 2022

The massive increase in disposable plastic globally can be addressed through effective recovery methods, and one of these methods is pyrolysis. R software may be used to statistically model the composition and yield of pyrolysis products, such as oil, gas, and waxes to deduce an effective pyrolysis mechanism. To date, no research reports have been documented employing the Arrhenius equation in R software to statistically forecast the kinetic rate constants for the pyrolysis of high-density plastics. We used the Arrhenius equation in R software to assume two series of activation energies (Ea) and pre-exponential factors (Ao) to statistically predict the rate constants at different temperatures to explore their impact on the final pyrolysis products. In line with this, MATLAB (R2020a) was used to predict the pyrolysis products of plastic in the temperature range of 370–410 °C. The value of the rate constant increased with the temperature by expediting the pyrolysis reaction due to the reduced frequency factor. In both assumed series of Ea and Ao, a significantly larger quantity of oil (99%) was predicted; however, the number of byproducts increased in the first series analysis compared to the second series analysis. It was revealed that an appropriate combination of Ea, Ao, and the predicted rate constants could significantly enhance the efficiency of the pyrolysis process. The major oil recovery in the first assumed series occurred at 390 °C to 400 °C, whereas the second assumed series of Ea and Ao occurred at 380 °C to 390 °C. In the second series at 390 °C to 400 °C, the predicted kinetic rate constants behaved aggressively after 120 min of the pyrolysis process. The second assumed series and anticipated rate constants at 380 °C to 390 °C can be applied commercially to improve oil production while saving energy and heat.
Multiphysics Numerical Simulation Model and Hydraulic Model Experiments in the Argon-Stirred Ladle
Authors: Hua Chengjian , Bao Yanping , Wang Min
Published: August 2022

The argon-stirred ladle is a standard piece of steelmaking refining equipment. The molten steel quality will improve when a good argon-stirred process is applied. In this paper, a Multiphysics model that contained fluid flow, bubble transport, alloy transport, bubble heat flux, alloy heat flux, alloy melting, and an alloy concentration species transport model was established. The fluid model and bubble transport model that were used to calculate the fluid velocity were verified by the hydraulic model of the ladle that was combined with particle image velocimetry measurement results. The numerical simulation results of the temperature fields and steel–slag interface shape were verified by a ladle that contained 25 t of molten steel in a steel plant. The velocity difference between the hydraulic model and numerical model decreased when the CL (integral time-scale constant) increased from 0 to 0.3; then, the difference increased when the CL increased from 0.3 to 0.45. The results showed that a CL of 0.3 approached the experiment results more. The bubble heat flux model was examined by the industrial practice, and the temperature decrease rate was 0.0144 K/s. The simulation results of the temperature decrease rate increased when the initial bubble temperature decreased. When the initial bubble temperature was 800 °C, the numerical simulation results showed that the temperature decrease rate was 0.0147 K/s, and the initial bubble temperature set at 800 °C was more appropriate. The average melting time of the alloy was 12.49 s and 12.71 s, and the mixture time was approximately the same when the alloy was added to two slag eyes individually. The alloy concentration had fewer changes after the alloy was added in the ladle after 100 s.
Innovative Polymer Microspheres with Chloride Groups Synthesis, Characterization and Application for Dye Removal
Authors: Wawrzkiewicz Monika , Podkościelna Beata
Published: August 2022

This article presents the synthesis and sorption characteristics of novel microspheres based on 4-vinylbenzene chloride (VBCl) with divinylbenzene (DVB) or ethylene glycol dimethylacrylate (EGDMA). To confirm the chemical structure of the homo- and co-polymers attenuated total reflectance, Fourier transform infrared spectroscopy (ATR-FTIR) was used. The presence of characteristic functional groups (−OH, −CH, −CH2, C−O, C=O and C–O–C) in obtained microspheres was confirmed. Differential scanning calorimetry (DSC) analysis confirms the good thermal resistance of the polymers. The decomposition of microspheres is closely related to the chemical structure of the monomers used. DVB-derived materials decompose in one step, whereas the decomposition of EGDMA derivatives is multi-stage. Obtained polymeric microspheres were applied for auramine O (AO) basic dye removal form aqueous solutions. Equilibrium studies confirmed that the Freundlich model described the system better than Langmuir or Temkin equations and the adsorption capacities kF ranged from 4.56 to 7.85 mg1−1/n L1/n/g. The sorption kinetic of AO from solutions of the 10 and 100 mg/L concentrations was very fast, and after 10 min, equilibrium was reached.
Rock Sanctuaries, Sacred Landscapes, and the Making of the Iberian Pantheon
Authors: Sinner Alejandro G. , Ferrer i Jané Joan
Published: August 2022

Sanctuaries are common spaces of interaction between humankind and the gods. In many religious systems, mountains and other elevated topographical features are known to have formed part of these privileged spaces of communication. It is not surprising that open-air and, in many cases, rock sanctuaries are the cultic spaces par excellence among the pre-Roman peoples of the Iberian Peninsula. In this article, we offer a more nuanced picture of these architectonically humble but culturally rich sacred spaces by studying the Palaeohispanic inscriptions recorded in rock sanctuaries located in the territories of the Iberian peoples (fourth–first centuries BCE). Special attention will be paid to the corpus of inscriptions in Cerdanya (Pyrénées-Orientales and Catalonia), where more than 150 texts have so far been identified. After a brief introduction contextualizing the Rock Sanctuaries, the Iberian language, and the epigraphic habit of its speakers, the first section of our article analyses the characteristics that enable us to interpret most of these inscriptions as religious and votive formulations. The second half of the paper discusses what these inscriptions can reveal about the Iberian pantheon and how these rock sanctuaries formed a consolidated religious landscape that was to survive the Roman conquest. The reinterpretation of the Celtiberian sanctuary of Peñalba de Villastar will be fundamental to put forward the hypothesis that, while Iberian and Celtiberian places of worship and pantheons had points of contact, they were mostly dissociated from each other prior to the Roman arrival.
A Proposal to Incorporate Experiential Education in Non-Confessional, Intercultural Religious Education: Reflections from and on the Norwegian Context
Authors: Skrefsrud Thor-André
Published: August 2022

In Norway, religious education (RE) is a non-confessional and common core subject that should be taught in an objective, critical, and pluralistic manner. As a primary school subject, students learn about a variety of religions and worldviews together in the same classroom. The inclusive framing intends to provide an intercultural space in which the students can enhance their understandings of the beliefs of people whose worldviews differ from their own. Consequently, the subject has privileged an outsider approach, wherein students should learn about religion in a non-partial way, that is, not from religion. However, the claim for objective, critical, and pluralistic teaching still calls into question the role of learning from religions. First, an outsider approach has been criticized for promoting a dated view on learning, ignoring pedagogical knowledge on how students learn. Second, the latest national curriculum states that RE should not only provide students with in-depth knowledge about world religions, but also foster personalized learning experiences. Against this background, the paper asks how the concept of experiential learning in the tradition of Freire, Dewey, and Vygotsky invites a reflection on the ways by which the Norwegian RE subject is passed on most meaningfully in a diverse learning context.
Manufacturing the Debt Republic of America: Mounting Student Loan Debt and Dismantling Its Neoliberal Political Ideology
Authors: Ahn Ilsup
Published: August 2022

This paper explores the political debate on student loan forgiveness from a religious (Christian) ethical perspective. In so doing, I answer the three specific questions. First, what are the prospects and limits of different political approaches to the issue? Second, what are the structural or ideological backgrounds that have given birth to the student loan crisis, but are not fully addressed by the president’s executive ordering? Last, what is the Christian ethical response to the issue, and how could it be theologically justified? Answering these questions, I argue that it is time now for American society to reckon with the neoliberal economy of debt that has relentlessly undertaken every aspect of our social and political lives. I also contend that student debt should be regarded as a form of social gift offered by society to the future generation.
Micro-Doppler Feature Extraction of Rotating Structures of Aircraft Targets with Terahertz Radar
Authors: Qin Xiaoyu , Deng Bin , Wang Hongqiang
Published: August 2022

The micro-Doppler features formed by the micro-motion of rotating blades of rotors and turbines are of great significance for aircraft target detection and recognition. Mastering the micro-motion features is the premise of radar target identification. The blades’ length and rotation rate are vital parameters for classifying aircraft targets. One can instantly judge the type and state of the targets by extracting micro-Doppler features. To extract the micro-Doppler features of rotating blades of the turbine target, we utilized microwave-band and terahertz-band radar to simulate the target and extract the Doppler frequency-shift information. For a turbine model with an obvious blade tip structure, we propose an algorithm based on wavelet coefficient enhancement and inverse Radon transform, integrating the time–frequency analysis with image processing. Under low SNR, this method allows for a high-accuracy parameter estimate. For a two-bladed rotor model without an obvious blade tip structure, we conducted an actual measurement experiment on the model utilizing a 120 GHz radar, and we propose a parameter estimation algorithm based on the fitting of the time–frequency distribution. By fitting the data of the time–frequency diagram, the micro-motion characteristic parameters of the rotor target were obtained. The simulation and experimental results demonstrate the benefits of terahertz radar in target detection, and indicate that the proposed algorithms have the characteristics of high extraction precision and insensitivity to noise.
Event-Based Bias Correction of the GPM IMERG V06 Product by Random Forest Method over Mainland China
Authors: Liu Zhenyu , Hou Haowen , Zhang Lanhui , Hu Bin
Published: August 2022

The Global Precipitation Measurement (GPM) IMERG V06 product showed excellent performance in detecting precipitation, but still have room to improve. This study proposed an event-based bias correction strategy through random forest (RF) method to improve accuracy of the GPM IMERG V06 product over mainland China. Results showed that, over mainland China, biases caused by ‘hits’ events are most responsible for the errors of the GPM product, and ‘falseAlarms’ events took the least responsibility for that because of the small GPM values for ‘falseAlarms’ events. Compared with the raw GPM product, the bias-corrected GPM product showed better performance in both fitting observed precipitation values and in detecting precipitation events, thus the event-based bias-strategy in this study is credible. After bias correction, the ability of the bias-corrected GPM product was significantly improved for ‘hits’ events, but showed slight deterioration in RMSE and MAE and significant improvements in FAR and CSI for ‘falseAlarms’ events. This is because the established RF classification model tends to learn characteristics of the event with larger proportion, and then performed better in correctly identifying the event with larger proportion in the subregion.
Nightlight Intensity Change Surrounding Nature Reserves: A Case Study in Orbroicher Bruch Nature Reserve, Germany
Authors: LaRoe Jillian , Holmes Christopher M. , Schad Thorsten
Published: August 2022

Persistent global urbanization has a direct relationship to measurable artificial light at night (ALAN), and the Defense Meteorological Satellite Program has served an important role in monitoring this relationship over time. Recent studies have observed significant declines in insect abundance and populations, and ALAN has been recognized as a contributing factor. We investigated changes in nightlight intensity at various spatial scales surrounding insect traps located in Orbroicher Bruch Nature Reserve, Germany. Using a time series of global nighttime light imagery (1992–2010), we evaluated pixel-level trends through linear regressions and the Mann–Kendall test. Paired with urban land cover delineation, we compared nightlight trends across rural and urban areas. We utilized high-resolution satellite imagery to identify landscape features potentially related to pixel-level trends within areas containing notable change. Approximately 96% of the pixel-level trends had a positive slope, and 22% of pixels experienced statistically significant increases in nightlight intensity. We observed that 80% of the region experienced nightlight intensity increases >1%, concurrent with the observed decline in insect biomass. While it is unclear if these trends extend to other geographic regions, our results highlight the need for future studies to concurrently investigate long-term trends in ALAN and insect population decline across multiple scales, and consider the spatial and temporal overlaps between these patterns.
Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder
Authors: Alwan Nuha A. S. , Hussain Zahir M.
Published: August 2022

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system.
Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector
Authors: Lopac Nikola , Jurdana Irena , Brnelić Adrian , Krljan Tomislav
Published: August 2022

The development of light detection and ranging (lidar) technology began in the 1960s, following the invention of the laser, which represents the central component of this system, integrating laser scanning with an inertial measurement unit (IMU) and Global Positioning System (GPS). Lidar technology is spreading to many different areas of application, from those in autonomous vehicles for road detection and object recognition, to those in the maritime sector, including object detection for autonomous navigation, monitoring ocean ecosystems, mapping coastal areas, and other diverse applications. This paper presents lidar system technology and reviews its application in the modern road transportation and maritime sector. Some of the better-known lidar systems for practical applications, on which current commercial models are based, are presented, and their advantages and disadvantages are described and analyzed. Moreover, current challenges and future trends of application are discussed. This paper also provides a systematic review of recent scientific research on the application of lidar system technology and the corresponding computational algorithms for data analysis, mainly focusing on deep learning algorithms, in the modern road transportation and maritime sector, based on an extensive analysis of the available scientific literature.
Sensing System Based on FBG for Corrosion Monitoring in Metallic Structures
Authors: Sousa Israel , Pereira Luis , Mesquita Esequiel , Souza Vitória L. , Araújo Walney S. , Cabral Antônio , Alberto Nélia , Varum Humberto , Antunes Paulo
Published: August 2022

As corrosion has slow development, its detection at an early age could be an alternative for reducing costs of structural rehabilitation. Therefore, the employment of structural health monitoring (SHM) systems, sensing configurations collecting data over time allowing for observing changes in the properties of the materials and damage emergence, for monitoring corrosion can be a good strategy to measure the damage and to decide the better moment for intervention. Nonetheless, the current corrosion sensor technology and the high costs of the sensing system implementation are limiting this application in the field. In this work, an optical fiber Bragg grating (FBG)-based sensing system is proposed for monitoring the thickness loss of a 1020 carbon steel metal plate subjected to controlled corrosion. The natural frequency of the plate was collected as a function of the corrosion time over 3744 h. To validate the experimental results, ultrasound measures and electrochemical tests were also carried out under similar conditions. The experimental results show adequate reliability, indicating the suitable functionality of the proposed system for monitoring the thickness loss caused by corrosion in metallic structures, in comparison with traditional methods, as ultrasonic and electrochemical measures.
Coordinated Control of Intelligent Fuzzy Traffic Signal Based on Edge Computing Distribution
Authors: Yu Chaodong , Chen Jian , Xia Geming
Published: August 2022

With the development of Internet of Things infrastructures and intelligent traffic systems, the traffic congestion that results from the continuous complexity of urban road networks and traffic saturation has a new solution. In this research, we propose a traffic signal control scenario based on edge computing. We also propose a chemical reaction–cooperative particle swarm optimization (CRO-CPSO) algorithm so that flexible traffic control is sunk to the edge. To implement short-term real-time vehicle waiting time prediction as a collaborative judgment of CRO-CPSO, we suggest a traffic flow prediction system based on fuzzy logic. In addition, we introduce a co-factor (collaborative factor) set based on offline learning to take into account the experiential characteristics of intersections in urban road networks for the generation of strategies by the algorithm. Furthermore, the real case of Changsha County is simulated on the SUMO simulation platform. The issue of traffic flow saturation is improved by our method. Compared with other methods, our algorithm enhances the proportions of vehicles that reach their destinations on time by 13.03%, which maximizes the driving experience for drivers. Meanwhile, our algorithm reduces the driving times of vehicles by 25.34%, thus alleviating traffic jams.
Modeling Full-Field Transient Flexural Waves on Damaged Plates with Arbitrary Excitations Using Temporal Vibration Characteristics
Authors: Wang Dan-Feng , Chuang Kuo-Chih , Liu Jun-Jie , Liao Chan-Yi
Published: August 2022

We propose an efficient semi-analytical method capable of modeling the propagation of flexural waves on cracked plate structures with any forms of excitations, based on the same group of vibration characteristics and validated by a non-contact scanning Laser Doppler Vibrometer (LDV) system. The proposed modeling method is based on the superposition of the vibrational normal modes of the detected structure, which can be applied to analyze long-time and full-field transient wave propagations. By connecting the vibration-based transient model to a power flow analysis technique, we further analyze the transient waves on a cracked plate subjected to different excitation sources and show the influence of the damage event on the path of the propagating waves. The experimental results indicate that the proposed semi-analytical method can model the flexural waves, and through that, the crack information can be revealed.
Two-Stream Mixed Convolutional Neural Network for American Sign Language Recognition
Authors: Ma Ying , Xu Tianpei , Kim Kangchul
Published: August 2022

The Convolutional Neural Network (CNN) has demonstrated excellent performance in image recognition and has brought new opportunities for sign language recognition. However, the features undergo many nonlinear transformations while performing the convolutional operation and the traditional CNN models are insufficient in dealing with the correlation between images. In American Sign Language (ASL) recognition, J and Z with moving gestures bring recognition challenges. This paper proposes a novel Two-Stream Mixed (TSM) method with feature extraction and fusion operation to improve the correlation of feature expression between two time-consecutive images for the dynamic gestures. The proposed TSM-CNN system is composed of preprocessing, the TSM block, and CNN classifiers. Two consecutive images in the dynamic gesture are used as inputs of streams, and resizing, transformation, and augmentation are carried out in the preprocessing stage. The fusion feature map obtained by addition and concatenation in the TSM block is used as inputs of the classifiers. Finally, a classifier classifies images. The TSM-CNN model with the highest performance scores depending on three concatenation methods is selected as the definitive recognition model for ASL recognition. We design 4 CNN models with TSM: TSM-LeNet, TSM-AlexNet, TSM-ResNet18, and TSM-ResNet50. The experimental results show that the CNN models with the TSM are better than models without TSM. The TSM-ResNet50 has the best accuracy of 97.57% for MNIST and ASL datasets and is able to be applied to a RGB image sensing system for hearing-impaired people.
Extending Robot Therapy for Children with Autism Using Mobile and Web Application
Authors: Ilijoski Bojan , Ackovska Nevena , Zorcec Tatjana , Popeska Zaneta
Published: August 2022

Robot treatments for children with autism have proven to be successful and effective. However, the resources needed for the treatments do not always meet the needs of the children. We overcame the lack of equipment and staff by extending the concept of robot therapy using a web and mobile application. This application enables greater availability and personification of the therapy itself. Its use in the majority of respondents contributes to improving their condition. This approach increases the flexibility of the therapy itself and makes it more accessible, enabling the patients to progress more rapidly. Although the robotic treatment presented in this paper is specific to children with autism, this approach can be generalized and applied to other areas where there are similar types of therapies.
Design of Convolutional Neural Network Processor Based on FPGA Resource Multiplexing Architecture
Authors: Yan Fei , Zhang Zhuangzhuang , Liu Yinping , Liu Jia
Published: August 2022

As CNNs are widely used in fields such as image classification and target detection, the total number of parameters and computation of the models is gradually increasing. In addition, the requirements on hardware resources and power consumption for deploying CNNs are becoming higher and higher, leading to CNN models being restricted to certain specific platforms for miniaturization and practicality. Therefore, this paper proposes a convolutional-neural-network-processor design with an FPGA-based resource-multiplexing architecture, aiming to reduce the consumption of hardware resources and power consumption of CNNs. First, this paper takes a handwritten-digit-recognition CNN as an example of a CNN design based on a resource-multiplexing architecture, and the prediction accuracy of the CNN can reach 97.3 percent by training and testing with Mnist dataset. Then, the CNN is deployed on FPGA using the hardware description language Verilog, and the design is optimized by resource multiplexing and parallel processing. Finally, the total power consumption of the system is 1.03 W and the power consumption of the CNN module is 0.03 W under the premise of guaranteeing the prediction accuracy, and the prediction of a picture is about 68,139 clock cycles, which is 340.7 us under a 200 MHz clock. The experimental results have obvious advantages in terms of resources and power consumption compared with those reported in related articles in recent years, and the design proposed in this paper.
MiniDeep: A Standalone AI-Edge Platform with a Deep Learning-Based MINI-PC and AI-QSR System
Authors: Chen Yuh-Shyan , Cheng Kuang-Hung , Hsu Chih-Shun , Zhang Hong-Lun
Published: August 2022

In this paper, we present a new AI (Artificial Intelligence) edge platform, called “MiniDeep”, which provides a standalone deep learning platform based on the cloud-edge architecture. This AI-Edge platform provides developers with a whole deep learning development environment to set up their deep learning life cycle processes, such as model training, model evaluation, model deployment, model inference, ground truth collecting, data pre-processing, and training data management. To the best of our knowledge, such a whole deep learning development environment has not been built before. MiniDeep uses Amazon Web Services (AWS) as the backend platform of a deep learning tuning management model. In the edge device, the OpenVino enables deep learning inference acceleration at the edge. To perform a deep learning life cycle job, MiniDeep proposes a mini deep life cycle (MDLC) system which is composed of several microservices from the cloud to the edge. MiniDeep provides Train Job Creator (TJC) for training dataset management and the models’ training schedule and Model Packager (MP) for model package management. All of them are based on several AWS cloud services. On the edge device, MiniDeep provides Inference Handler (IH) to handle deep learning inference by hosting RESTful API (Application Programming Interface) requests/responses from the end device. Data Provider (DP) is responsible for ground truth collection and dataset synchronization for the cloud. With the deep learning ability, this paper uses the MiniDeep platform to implement a recommendation system for AI-QSR (Quick Service Restaurant) KIOSK (interactive kiosk) application. AI-QSR uses the MiniDeep platform to train an LSTM (Long Short-Term Memory)-based recommendation system. The LSTM-based recommendation system converts KIOSK UI (User Interface) flow to the flow sequence and performs sequential recommendations with food suggestions. At the end of this paper, the efficiency of the proposed MiniDeep is verified through real experiments. The experiment results have demonstrated that the proposed LSTM-based scheme performs better than the rule-based scheme in terms of purchase hit accuracy, categorical cross-entropy, precision, recall, and F1 score.
DITES: A Lightweight and Flexible Dual-Core Isolated Trusted Execution SoC Based on RISC-V
Authors: Chen Yuehai , Chen Huarun , Chen Shaozhen , Han Chao , Ye Wujian , Liu Yijun , Zhou Huihui
Published: August 2022

A Trusted Execution Environment (TEE) is an efficient way to secure information. To obtain higher efficiency, the building of a dual-core system-on-chip (SoC) with TEE security capabilities is the hottest topic. However, TEE SoCs currently commonly use complex processor cores such as Rocket, resulting in high resource usage. More importantly, the cryptographic unit lacks flexibility and ignores secure communication in dual cores. To address the above problems, we propose DITES, a dual-core TEE SoC based on a Reduced Instruction Set Computer-V (RISC-V). At first, we designed a fully isolated multi-level bus architecture based on a lightweight RISC-V processor with an integrated crypto core supporting Secure Hashing Algorithm-1 (SHA1), Advanced Encryption Standard (AES), and Rivest–Shamir–Adleman (RSA), among which RSA can be configured to five key lengths. Then, we designed a secure boot based on Chain-of-Trust (CoT). Furthermore, we propose a hierarchical access policy to improve the security of inter-core communication. Finally, DITES is deployed on a Kintex 7 Field-Programmable-Gate-Array (FPGA) with a power consumption of 0.297 W, synthesized using TSMC 90 nm. From the results, the acceleration ratios of SHA1 and RSA1024 decryption/encryption can reach 75 and 1331/1493, respectively. Compared to exiting TEE SoCs, DITES has lower resource consumption, higher flexibility, and better security.
Prediction of Urban Taxi Travel Demand by Using Hybrid Dynamic Graph Convolutional Network Model
Authors: Zhao Jinbao , Kong Weichao , Zhou Meng , Zhou Tianwei , Xu Yuejuan , Li Mingxing
Published: August 2022

The efficient and accurate prediction of urban travel demand, which is a hot topic in intelligent transportation research, is challenging due to its complicated spatial-temporal dependencies, dynamic nature, and uneven distribution. Most existing forecasting methods merely considered the static spatial dependencies while ignoring the influence of the diversity of dynamic demand patterns and/or uneven distribution. In this paper, we propose a traffic demand forecasting framework of a hybrid dynamic graph convolutional network (HDGCN) model to deeply capture the characteristics of urban travel demand and improve prediction accuracy. In HDGCN, traffic flow similarity graphs are designed according to the dynamic nature of travel demand, and a dynamic graph sequence is generated according to time sequence. Then, the dynamic graph convolution module and the standard graph convolution module are introduced to extract the spatial features from dynamic graphs and static graphs, respectively. Finally, the spatial features of the two components are fused and combined with the gated recurrent unit (GRU) to learn the temporal features. The efficiency and accuracy of the HDGCN model in predicting urban taxi travel demand are verified by using the taxi data from Manhattan, New York City. The modeling and comparison results demonstrate that the HDGCN model can achieve stable and effective prediction for taxi travel demand compared with the state-of-the-art baseline models. The proposed model could be used for the real-time, accurate, and efficient travel demand prediction of urban taxi and other urban transportation systems.