bluuebunny/crossref_metadata_2025 huggingface dataset:
Green Aspects in Molecularly Imprinted Polymers by Biomass Waste Utilization
Authors: Del Sole Roberta , Mele Giuseppe , Bloise Ermelinda , Mergola Lucia
Published: July 2021

Molecular Imprinting Polymer (MIP) technology is a technique to design artificial receptors with a predetermined selectivity and specificity for a given analyte, which can be used as ideal materials in various application fields. In the last decades, MIP technology has gained much attention from the scientific world as summarized in several reviews with this topic. Furthermore, green synthesis in chemistry is nowadays one of the essential aspects to be taken into consideration in the development of novel products. In accordance with this feature, the MIP community more recently devoted considerable research and development efforts on eco-friendly processes. Among other materials, biomass waste, which is a big environmental problem because most of it is discarded, can represent a potential sustainable alternative source in green synthesis, which can be addressed to the production of high-value carbon-based materials with different applications. This review aims to focus and explore in detail the recent progress in the use of biomass waste for imprinted polymers preparation. Specifically, different types of biomass waste in MIP preparation will be exploited: chitosan, cellulose, activated carbon, carbon dots, cyclodextrins, and waste extracts, describing the approaches used in the synthesis of MIPs combined with biomass waste derivatives.
Understanding the Reinforcement of Graphene in Poly(Ether Ether Ketone)/Carbon Fibre Laminates
Authors: Flores Araceli , Quiles-Díaz Susana , Enrique-Jimenez Patricia , Martínez-Gómez Aránzazu , Gómez-Fatou Marián A. , Salavagione Horacio J.
Published: July 2021

PEEK appears as an excellent candidate to substitute epoxy resins in carbon fibre laminates for high-performance aeronautical applications. The optimization of the properties and, in particular, of the transition region between the fibres and the matrix appear as a major issue prior to serial production. Graphene, modified with two compatibilizers, has been incorporated in the polymer layer with the purpose of imparting additional functionalities and enhancing the matrix-fibre interaction. It is found that both carbon fibres and modified graphene significantly influence the crystallization behaviour and smaller, and/or more imperfect crystals appear while the degree of crystallinity decreases. Despite this, nanoindentation studies show that the PEEK layer exhibits significant modulus improvements (≈30%) for 5 wt.% of graphene. Most importantly, the study of the local mechanical properties by nanoindentation mapping allows the identification of remarkably high modulus values close to the carbon fibre front. Such a relevant mechanical enhancement can be associated with the accumulation of graphene platelets at the polymer–fibre boundary, as revealed by electron microscopy studies. The results offer a feasible route for interlaminar mechanical improvement based on the higher density of graphene platelets at the fibre front that should promote interfacial interactions. Concerning electrical conductivity, a large anisotropy was found for all laminates, and values in the range ~10−4 S/cm were found for the through-thickness arrangement as a consequence of the good consolidation of the laminates.
Environmentally Friendly Synthesis of Poly(3,4-Ethylenedioxythiophene): Poly(Styrene Sulfonate)/SnO2 Nanocomposites
Authors: Díez-Pascual Ana M.
Published: July 2021

Conductive poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is widely used for practical applications such as energy conversion and storage devices owing to its good flexibility, processability, high electrical conductivity, and superior optical transparency, among others. However, its hygroscopic character, short durability, and poor thermoelectric performance compared to inorganic counterparts has greatly limited its high-tech applications. In this work, PEDOT:PSS/SnO2 nanocomposites have been prepared via a simple, low cost, environmentally friendly method without the use of organic solvents or compatibilizing agents. Their morphology, thermal, thermoelectrical, optical, and mechanical properties have been characterized. Electron microscopy analysis revealed a uniform dispersion of the SnO2 nanoparticles, and the Raman spectra revealed the existence of very strong SnO2-PEDOT:PSS interactions. The stiffness and strength of the matrix gradually increased with increasing SnO2 content, up to 120% and 65%, respectively. Moreover, the nanocomposites showed superior thermal stability (as far as 70 °C), improved electrical conductivity (up to 140%), and higher Seebeck coefficient (about 80% increase) than neat PEDOT:PSS. On the other hand, hardly any change in optical transparency was observed. These sustainable nanocomposites show considerably improved performance compared to commercial PEDOT:PSS, and can be highly useful for applications in energy storage, flexible electronics, thermoelectric devices, and related fields.
In Vivo Biological Evaluation of Biodegradable Nanofibrous Membranes Incorporated with Antibiofilm Compounds
Authors: Geremias Thaise C. , Sartoretto Suelen C. , Batistella Marcos A. , Souza Antônio A. Ulson de , Alves Adriana T. N. N. , Uzeda Marcelo J.P. , Calasans-Maia Jose , Montemezzi Pietro , Mourão Carlos Fernando de Almeida Barros , Calasans-Maia Monica
Published: July 2021

Guided bone regeneration involves excluding non-osteogenic cells from the surrounding soft tissues and allowing osteogenic cells originating from native bone to inhabit the defect. The aim of this work was to fabricate, analyze antibiofilm activity and evaluate in vivo biological response of poly (lactic-co-glycolic acid) (PLGA) electrospun membranes incorporated with tea tree oil and furan-2(5H)-one. Samples were exposed to Streptococcus mutans culture and after 48 h incubation, biofilm was evaluated by colony forming units (CFU/mL) followed by scanning electron microscopy. Additionally, seventy-five Balb-C mice were divided into five experimental groups for subcutaneous implantation: tea tree oil loaded PLGA electrospun fiber membrane, furanone loaded PLGA electrospun fiber membrane, neat PLGA electrospun fiber membrane, a commercially available PLGA membrane –Pratix® and Sham (no-membrane implantation). Post implantation period of each experimental group (1, 3 and 9 weeks), samples were collected and processed for by histological descriptive and semiquantitative evaluation. Results showed a significant reduction of bacterial attachment on tea tree oil and furan-2(5H)-one incorporated membranes. Macrophage counts were significant found in all the materials implanted, although giant cells were predominantly associated with electrospun fiber membranes. The incorporation of antibiofilm compounds in nanofibers membranes did not incite inflammatory response significantly different in comparison with pure PLGA electrospun membranes, indicating its potential for development of novel functionalized membranes targeting the inhibition of bacterial biofilms on membrane-grafting materials.
Waterborne Cross-Linkable Polyacrylate Latex Coatings with Good Water Resistance and Strength Stabilized by Modified Hectorite
Authors: Yang Yunfan , Chen Jinyang , Ma Guoli , Yang Dingqing
Published: July 2021

Polyacrylate emulsions were prepared by Pickering emulsion polymerization with multi-modified hectorite as a modifier. The proper wettability of modified hectorite and the stability of O/W emulsions prove that modified hectorite has good emulsification properties. The stability of polyacrylate latexes and the morphology of polymer latex particles were then investigated to explain the role of multi-modified hectorite in stabilizing polyacrylate latex. In addition, the improved mechanical properties and water resistance of the latex make it a potentially excellent coating. Multi-modified hectorite as an alternative modifier to conventional surfactants offers a potential application of nanosolid particles that meet the partial wetting conditions for water and oil as stabilizers for the production of latexes for coatings.
Multi-Scale Study of the Small-Strain Damping Ratio of Fiber-Sand Composites
Authors: Li Haiwen , Kasyap Sathwik S. , Senetakis Kostas
Published: July 2021

The use of polypropylene fibers as a geosynthetic in infrastructures is a promising ground treatment method with applications in the enhancement of the bearing capacity of foundations, slope rehabilitation, strengthening of backfills, as well as the improvement of the seismic behavior of geo-systems. Despite the large number of studies published in the literature investigating the properties of fiber-reinforced soils, less attention has been given in the evaluation of the dynamic properties of these composites, especially in examining damping characteristics and the influence of fiber inclusion and content. In the present study, the effect of polypropylene fiber inclusion on the small-strain damping ratio of sands with different gradations and various particle shapes was investigated through resonant column (macroscopic) experiments. The macroscopic test results suggested that the damping ratio of the mixtures tended to increase with increasing fiber content. Accordingly, a new expression was proposed which considers the influence of fiber content in the estimation of the small-strain damping of polypropylene fiber-sand mixtures and it can be complementary of damping modeling from small-to-medium strains based on previously developed expressions in the regime of medium strains. Additional insights were attempted to be obtained on the energy dissipation and contribution of fibers of these composite materials by performing grain-scale tests which further supported the macroscopic experimental test results. It was also attempted to interpret, based on the grain-scale tests results, the influence of fiber inclusion in a wide spectrum of properties for fiber-reinforced sands providing some general inferences on the contribution of polypropylene fibers on the constitutive behavior of granular materials.
Evaluation of the PGPR Capacity of Four Bacterial Strains and Their Mixtures, Tested on Lupinus albus var. Dorado Seedlings, for the Bioremediation of Mercury-Polluted Soils
Authors: González Daniel , Blanco Carlota , Probanza Agustín , Jiménez Pedro A. , Robas Marina
Published: July 2021

Soil contamination by mercury, which is one of the most toxic heavy metals due to its bioaccumulative capacity, poses a risk to the environment as well as health. The Almadén mining district in Ciudad Real, Spain is one of the most heavily-polluted sites in the world, making the soils unusable. Bioremediation, and more specifically phyto-rhizoremediation, based on the synergistic interaction established between plant and Plant Growth Promoting Rhizobacteria (PGPR), improves the plant’s ability to grow, mobilize, accumulate, and extract contaminants from the soil. The objective of this study is to evaluate the plant growth-promoting ability of four PGPR strains (and mixtures), isolated from the bulk soil and rhizosphere of naturally grown plants in the Almadén mining district, when they are inoculated in emerged seeds of Lupinus albus, var. Dorado in the presence of high concentrations of mercury. After 20 days of incubation and subsequent harvesting of the seedlings, biometric measurements were carried out at the root and aerial levels. The results obtained show that the seeds treatment with PGPR strains improves plants biometry in the presence of mercury. Specifically, strain B2 (Pseudomonas baetica) and B1 (Pseudomonas moraviensis) were those that contributed the most to plant growth, both individually and as part of mixtures (CS5 and CS3). Thus, these are postulated to be good candidates for further in situ phyto-rhizoremediation tests of mercury-contaminated soils.
A New Symmetrical Thiazolidinedione Derivative: In Silico Design, Synthesis, and In Vivo Evaluation on a Streptozotocin-Induced Rat Model of Diabetes
Authors: Álvarez-Almazán Samuel , Navarrete-Vázquez Gabriel , Padilla-Martínez Itzia Irene , Correa-Basurto José , Alemán-González-Duhart Diana , Tamay-Cach Feliciano , Mendieta-Wejebe Jessica Elena
Published: July 2021

By activating PPAR-γ, thiazolidinediones normalize glucose levels in animal models of type 2 diabetes and in patients with this pathology. The aim of the present study was to analyze 219 new derivatives in silico and select the best for synthesis, to be evaluated for acute oral toxicity in female rats and for control of diabetes-related parameters in a rat model of streptozotocin-induced diabetes. The best compound was chosen based on pharmacokinetic, pharmacodynamic, and toxicological parameters obtained in silico and binding orientation observed by docking simulations on PPAR-γ. Compound 1G was synthesized by a quick and easy Knoevenagel condensation. Acute oral toxicity was found at a dose greater than 2000 mg/Kg. Compound 1G apparently produces therapeutic effects similar to those of pioglitazone, decreasing glycaemia and triglyceride levels in diabetic animals, without liver damage. Moreover, it did not cause a significant weight gain and tended to reduce polydipsia and polyphagia, while diminishing systemic inflammation related to TNF-α and IL-6. It lowered the level of endogenous antioxidant molecules such as reduced glutathione and glutathione reductase. In conclusion, 1G may be a candidate for further testing as an euglycemic agent capable of preventing the complications of diabetes.
Coral Reef Mapping with Remote Sensing and Machine Learning: A Nurture and Nature Analysis in Marine Protected Areas
Authors: da Silveira Camila Brasil Louro , Strenzel Gil Marcelo Reuss , Maida Mauro , Gaspar Ana Lídia Bertoldi , Ferreira Beatrice Padovani
Published: July 2021

Mapping habitats is essential to assist strategic decisions regarding the use and protection of coral reefs. Coupled with machine learning (ML) algorithms, remote sensing has allowed detailed mapping of reefs at meaningful scales. Here we integrated WorldView-3 and Landsat-8 imagery and ML techniques to produce a map of suitable habitats for the occurrence of a model species, the hydrocoral Millepora alcicornis, in coral reefs located inside marine protected areas in Northeast Brazil. Conservation and management efforts in the region were also analyzed, integrating human use layers to the ecological seascape. Three ML techniques were applied: two to derive base layers, namely geographically weighted regressions for bathymetry and support vector machine classifier (SVM) for habitat mapping, and one to build the species distribution model (MaxEnt) for Millepora alcicornis, a conspicuous and important reef-building species in the area. Additionally, human use was mapped based on the presence of tourists and fishers. SVM yielded 15 benthic classes (e.g., seagrass, sand, coral), with an overall accuracy of 79%. Bathymetry and its derivative layers depicted the topographical complexity of the area. The Millepora alcicornis distribution model identified distance from the shore and depth as topographical factors limiting the settling and growth of coral colonies. The most important variables were ecological, showing the importance of maintaining high biodiversity in the ecosystem. The comparison of the habitat suitability model with species absence and human use maps indicated the impact of direct human activities as potential inhibitors of coral development. Results reinforce the importance of the establishment of no-take zones and other protective measures for maintaining local biodiversity.
Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs
Authors: Chen Yeou-Jiunn , Chen Pei-Chung , Chen Shih-Chung , Wu Chung-Min
Published: July 2021

For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.
Combining Sensors Information to Enhance Pneumatic Grippers Performance
Authors: Romeo Rocco Antonio , Gesino Michele , Maggiali Marco , Fiorio Luca
Published: July 2021

The gripper is the far end of a robotic arm. It is responsible for the contacts between the robot itself and all the items present in a work space, or even in a social space. Therefore, to provide grippers with intelligent behaviors is fundamental, especially when the robot has to interact with human beings. As shown in this article, we built an instrumented pneumatic gripper prototype that relies on different sensors’ information. Thanks to such information, the gripper prototype was able to detect the position of a given object in order to grasp it, to safely keep it between its fingers and to avoid slipping in the case of any object movement, even very small. The gripper performance was evaluated by means of a generic grasping algorithm for robotic grippers, implemented in the form of a state machine. Several slip tests were carried out on the pneumatic gripper, which showed a very fast response time and high reliability. Objects of various size, shape and hardness were employed to reproduce different grasping scenarios. We demonstrate that, through the use of force, torque, center of pressure and proximity information, the behavior of the developed pneumatic gripper prototype outperforms the one of the traditional pneumatic gripping devices.
Multiplexed Prostate Cancer Companion Diagnostic Devices
Authors: Aidoo-Brown Josephine , Moschou Despina , Estrela Pedro
Published: July 2021

Prostate cancer (PCa) remains one of the most prominent forms of cancer for men. Since the early 1990s, Prostate-Specific Antigen (PSA) has been a commonly recognized PCa-associated protein biomarker. However, PSA testing has been shown to lack in specificity and sensitivity when needed to diagnose, monitor and/or treat PCa patients successfully. One enhancement could include the simultaneous detection of multiple PCa-associated protein biomarkers alongside PSA, also known as multiplexing. If conventional methods such as the enzyme-linked immunosorbent assay (ELISA) are used, multiplexed detection of such protein biomarkers can result in an increase in the required sample volume, in the complexity of the analytical procedures, and in adding to the cost. Using companion diagnostic devices such as biosensors, which can be portable and cost-effective with multiplexing capacities, may address these limitations. This review explores recent research for multiplexed PCa protein biomarker detection using optical and electrochemical biosensor platforms. Some of the novel and potential serum-based PCa protein biomarkers will be discussed in this review. In addition, this review discusses the importance of converting research protocols into multiplex point-of-care testing (xPOCT) devices to be used in near-patient settings, providing a more personalized approach to PCa patients’ diagnostic, surveillance and treatment management.
Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization
Authors: Villalba-Diez Javier , Gutierrez Miguel , Grijalvo Martín Mercedes , Sterkenburgh Tomas , Losada Juan Carlos , Benito Rosa María
Published: July 2021

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.
A Comprehensive Review on Edge Caching from the Perspective of Total Process: Placement, Policy and Delivery
Authors: Wu Honghai , Fan Yizheng , Wang Yingda , Ma Huahong , Xing Ling
Published: July 2021

With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.
Multitrack Compressed Sensing for Faster Hyperspectral Imaging
Authors: Kubal Sharvaj , Lee Elizabeth , Tay Chor Yong , Yong Derrick
Published: July 2021

Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times.
Product Inspection Methodology via Deep Learning: An Overview
Authors: Kim Tae-Hyun , Kim Hye-Rin , Cho Yeong-Jun
Published: July 2021

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.
Review on Comparison of Different Energy Storage Technologies Used in Micro-Energy Harvesting, WSNs, Low-Cost Microelectronic Devices: Challenges and Recommendations
Authors: Riaz Amna , Sarker Mahidur R. , Saad Mohamad Hanif Md , Mohamed Ramizi
Published: July 2021

This paper reviews energy storage systems, in general, and for specific applications in low-cost micro-energy harvesting (MEH) systems, low-cost microelectronic devices, and wireless sensor networks (WSNs). With the development of electronic gadgets, low-cost microelectronic devices and WSNs, the need for an efficient, light and reliable energy storage device is increased. The current energy storage systems (ESS) have the disadvantages of self-discharging, energy density, life cycles, and cost. The ambient energy resources are the best option as an energy source, but the main challenge in harvesting energy from ambient sources is the instability of the source of energy. Due to the explosion of lithium batteries in many cases, and the pros associated with them, the design of an efficient device, which is more reliable and efficient than conventional batteries, is important. This review paper focused on the issues of the reliability and performance of electrical ESS, and, especially, discussed the technical challenges and suggested solutions for ESS (batteries, supercapacitors, and for a hybrid combination of supercapacitors and batteries) in detail. Nowadays, the main market of batteries is WSNs, but in the last decade, the world’s attention has turned toward supercapacitors as a good alternative of batteries. The main advantages of supercapacitors are their light weight, volume, greater life cycle, turbo charging/discharging, high energy density and power density, low cost, easy maintenance, and no pollution. This study reviews supercapacitors as a better alternative of batteries in low-cost electronic devices, WSNs, and MEH systems.
Distributed Static and Dynamic Strain Measurements in Polymer Optical Fibers by Rayleigh Scattering
Authors: Coscetta Agnese , Catalano Ester , Cerri Enis , Oliveira Ricardo , Bilro Lucia , Zeni Luigi , Cennamo Nunzio , Minardo Aldo
Published: July 2021

We demonstrate the use of a graded-index perfluorinated optical fiber (GI-POF) for distributed static and dynamic strain measurements based on Rayleigh scattering. The system is based on an amplitude-based phase-sensitive Optical Time-Domain Reflectometry (ϕ-OTDR) configuration, operated at the unconventional wavelength of 850 nm. Static strain measurements have been carried out at a spatial resolution of 4 m and for a strain up to 3.5% by exploiting the increase of the backscatter Rayleigh coefficient consequent to the application of a tensile strain, while vibration/acoustic measurements have been demonstrated for a sampling frequency up to 833 Hz by exploiting the vibration-induced changes in the backscatter Rayleigh intensity time-domain traces arising from coherent interference within the pulse. The reported tests demonstrate that polymer optical fibers can be used for cost-effective multiparameter sensing.
Analysis of Reliability of Strain Measurements Made with the Fiber Bragg Grating Sensor Rosettes Embedded in a Polymer Composite Material
Authors: Matveenko Valerii , Kosheleva Natalia , Serovaev Grigorii , Fedorov Andrey
Published: July 2021

The results of strain measuring experiments, with the help of rosettes consisting of fiber Bragg grating sensors (FBG) embedded at the manufacturing stage in a polymer composite material are considered in this paper. The samples were made by the direct pressing method from fiberglass prepregs. A cross-shaped sample was tested under loading conditions corresponding to a complex stress state. A variant of strain calculations based on experimental data is discussed. The calculations were performed under the assumption of a uniaxial stress state in an optical fiber embedded in the material. The obtained results provide a reasonable explanation of the absence in the conducted experiment of two peaks in the reflected optical spectrum, the presence of which follows from the known theoretical principles. The experimental result with two peaks in the reflected optical spectrum was obtained for the same sample under a different loading scheme. The proposed variant of the numerical model of the experiment and the results of numerical simulation made for FBG rosettes embedded in the material allowed to estimate error in the strain values calculated on the assumption of the uniaxial stress state in the optical fiber and in the presence of two peaks in the reflected optical spectrum.
User Experience in Social Robots
Authors: Shourmasti Elaheh Shahmir , Colomo-Palacios Ricardo , Holone Harald , Demi Selina
Published: July 2021

Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, business, industry, and culture. However, introducing this technology for use in conventional environments is not trivial. For users to accept social robots, a positive user experience is vital, and it should be considered as a critical part of the robots’ development process. This may potentially lead to excessive use of social robots and strengthen their diffusion in society. The goal of this study is to summarize the extant literature that is focused on user experience in social robots, and to identify the challenges and benefits of UX evaluation in social robots. To achieve this goal, the authors carried out a systematic literature review that relies on PRISMA guidelines. Our findings revealed that the most common methods to evaluate UX in social robots are questionnaires and interviews. UX evaluations were found out to be beneficial in providing early feedback and consequently in handling errors at an early stage. However, despite the importance of UX in social robots, robot developers often neglect to set UX goals due to lack of knowledge or lack of time. This study emphasizes the need for robot developers to acquire the required theoretical and practical knowledge on how to perform a successful UX evaluation.
Performance Evaluation of Bundle Adjustment with Population Based Optimization Algorithms Applied to Panoramic Image Stitching
Authors: Aguiar Maria Júlia R. , Alves Tiago da Rocha , Honório Leonardo M. , Junior Ivo C. S. , Vidal Vinícius F.
Published: July 2021

The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg–Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm and Particle Swarm Optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg–Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.
An Improved Calibration Method for the IMU Biases Utilizing KF-Based AdaGrad Algorithm
Authors: Wen Zeyang , Yang Gongliu , Cai Qingzhong
Published: July 2021

In the field of high accuracy strapdown inertial navigation system (SINS), the inertial measurement unit (IMU) biases can severely affect the navigation accuracy. Traditionally we use Kalman filter (KF) to estimate those biases. However, KF is an unbiased estimation method based on the assumption of Gaussian white noise (GWN) while IMU sensors noise is irregular. Kalman filtering will no longer be accurate when the sensor’s noise is irregular. In order to obtain the optimal solution of the IMU biases, this paper proposes a novel method for the calibration of IMU biases utilizing the KF-based AdaGrad algorithm to solve this problem. Three improvements were made as the following: (1) The adaptive subgradient method (AdaGrad) is proposed to overcome the difficulty of setting step size. (2) A KF-based AdaGrad numerical function is derived and (3) a KF-based AdaGrad calibration algorithm is proposed in this paper. Experimental results show that the method proposed in this paper can effectively improve the accuracy of IMU biases in both static tests and car-mounted field tests.
A Spectrum Correction Algorithm Based on Beat Signal of FMCW Laser Ranging System
Authors: Hao Yi , Song Ping , Wang Xuanquan , Pan Zhikang
Published: July 2021

The accuracy of target distance obtained by a frequency modulated continuous wave (FMCW) laser ranging system is often affected by factors such as white Gaussian noise (WGN), spectrum leakage, and the picket fence effect. There are some traditional spectrum correction algorithms to solve the problem above, but the results are unsatisfactory. In this article, a decomposition filtering-based dual-window correction (DFBDWC) algorithm is proposed to alleviate the problem caused by these factors. This algorithm reduces the influence of these factors by utilizing a decomposition filtering, dual-window in time domain and two phase values of spectral peak in the frequency domain, respectively. With the comparison of DFBDWC and these traditional algorithms in simulation and experiment on a built platform, the results show a superior performance of DFBDWC based on this platform. The maximum absolute error of target distance calculated by this algorithm is reduced from 0.7937 m of discrete Fourier transform (DFT) algorithm to 0.0407 m, which is the best among all mentioned spectrum correction algorithms. A high performance FMCW laser ranging system can be realized with the proposed algorithm, which has attractive potential in a wide scope of applications.
Voltammetric E-Tongue for Honey Adulteration Detection
Authors: Ciursa Paula , Oroian Mircea
Published: July 2021

The aim of this study is to establish the usefulness of an electronic tongue based on cyclic voltammetry e-tongue using five working electrodes (gold, silver, copper, platinum and glass) in honey adulteration detection. Authentic honey samples of different botanical origin (acacia, tilia, sunflower, polyfloral and raspberry) were adulterated with agave, maple, inverted sugar, corn and rice syrups in percentages of 5%, 10%, 20% and 50%. The silver and copper electrodes provided the clearest voltammograms, the differences between authentic and adulterated honey samples being highlighted by the maximum current intensity. The electronic tongue results have been correlated with physicochemical parameters (pH, free acidity, hydroxymethylfurfural content—5 HMF and electrical conductivity—EC). Using statistical methods such as Linear discriminant analysis (LDA) and Support vector machines (SVM), an accuracy of 94.87% and 100% respectively was obtained in the calibration step and 89.65% and 100% respectively in the validation step. The PLS-R (Partial Least Squares Regression) model (constructed from the minimum and maximum current intensity obtained for all electrodes) was used in physicochemical parameters prediction; EC reached the highest regression coefficients (0.840 in the calibration step and 0.842 in the validation step, respectively), being followed by pH (0.704 in the calibration step and 0.516 in the validation step, respectively).
Analysis of Predistortion Techniques on Fresnel Zone Plates in Ultrasound Applications
Authors: Fuster José Miguel , Pérez-López Sergio , Belmar Francisco , Candelas Pilar
Published: July 2021

In this work, we analyze the effect of predistortion techniques on the focusing profile of Fresnel Zone Plates (FZPs) in ultrasound applications. This novel predistortion method is based on either increasing or decreasing the width of some of the FZP Fresnel rings by a certain amount. We investigate how the magnitude of the predistortion, as well as the number and location of the predistorted rings, influences the lens focusing profile. This focusing profile can be affected in different ways depending on the area of the lens where the predistortion is applied. It is shown that when the inner area of the lens, closer to its center, is predistorted, this technique allows the control of the focal depth at the main focus. However, when the predistortion is applied to an area farther from the center of the lens, the acoustic intensity distribution among the main focus and the closest adjacent secondary foci can be tailored at a certain degree. This predistortion technique shows great potential and can be used to control, modify and shape the FZP focusing profile in both industrial and therapeutic applications.
Noise Sources and Requirements for Confocal Raman Spectrometers in Biosensor Applications
Authors: Jahn Izabella J. , Grjasnow Alexej , John Henry , Weber Karina , Popp Jürgen , Hauswald Walter
Published: July 2021

Raman spectroscopy probes the biochemical composition of samples in a non-destructive, non-invasive and label-free fashion yielding specific information on a molecular level. Nevertheless, the Raman effect is very weak. The detection of all inelastically scattered photons with highest efficiency is therefore crucial as well as the identification of all noise sources present in the system. Here we provide a study for performance comparison and assessment of different spectrometers for confocal Raman spectroscopy in biosensor applications. A low-cost, home-built Raman spectrometer with a complementary metal-oxide-semiconductor (CMOS) camera, a middle price-class mini charge-coupled device (CCD) Raman spectrometer and a laboratory grade confocal Raman system with a deeply cooled CCD detector are compared. It is often overlooked that the sample itself is the most important “optical” component in a Raman spectrometer and its properties contribute most significantly to the signal-to-noise ratio. For this purpose, different representative samples: a crystalline silicon wafer, a polypropylene sample and E. coli bacteria were measured under similar conditions using the three confocal Raman spectrometers. We show that biosensor applications do not in every case profit from the most expensive equipment. Finally, a small Raman database of three different bacteria species is set up with the middle price-class mini CCD Raman spectrometer in order to demonstrate the potential of a compact setup for pathogen discrimination.
The Economic Aspect of Digital Sustainability: A Systematic Review
Authors: Cricelli Livio , Strazzullo Serena
Published: July 2021

In recent years, sustainability and Industry 4.0 have become crucial aspects of the global economy. Numerous studies focus on the link between environmental aspects of sustainability and Industry 4.0. On the contrary, few studies address the issue of the integration of economic sustainability and digital technologies. This paper aims to fill this gap through a systematic analysis of the literature. In particular, 32 articles were selected and following a descriptive analysis to evaluate the evolution of the theme, a content analysis was performed. The findings of this study highlight and categorize the main sustainability metrics associated with digital technologies. Specifically, the digitalization process enhances the connection of products and factories, the value chain and users to achieve a production cycle as sustainable as possible. The new technologies developed allow companies to foster innovation and entrepreneurship, increase the market share, reduce energy waste, recover and reuse the material, etc. Finally, managerial and academic contributions were identified.
Flexible Fuzzy Goal Programming Approach in Optimal Mix of Power Generation for Socio-Economic Sustainability: A Case Study
Authors: Khan Mohammad Faisal , Pervez Asif , Modibbo Umar Muhammad , Chauhan Jahangir , Ali Irfan
Published: July 2021

The demand for cost-efficient and clean power energy cannot be overemphasised, especially in a developing nation like India. COVID-19 has adversely affected many nations, power sector inclusive, and resiliency is imperative via flexible and sustainable power generation sources. Renewable energy sources are the primary focus of electricity production in the world. This study examined and assessed the optimal cost system of electricity generation for the socio-economic sustainability of India. A sustainable and flexible electricity generation model is developed using the concept of flexible fuzzy goal programming. This study is carried out with the aim of achieving the government’s intended nationally determined contribution goals of reducing emission levels, increasing the capacity of renewable sources and the must-run status of hydro and nuclear, and technical and financial parameters. The result shows an optimal cost solution and flexibility in how increased electricity demand would be achieved and sustained via shifting to renewable sources such as solar, wind and hydro.
Effect of Pre-Harvest Organic Cytokinin Application on the Post-Harvest Physiology of Pepper (Capsicum annuum L.)
Authors: Cavusoglu Seyda , Sensoy Suat , Karatas Arzu , Tekin Onur , Islek Firat , Yilmaz Nurettin , Kipcak Selma , Ercisli Sezai , Skrovankova Sona , Adamkova Anna , Mlcek Jiri
Published: July 2021

Peppers are cultivated in almost all provinces in Turkey, making up approximately 7% of the world’s production. In this study, the effects of pre-harvest cytokinin application on the postharvest physiology of pepper (Capsicum annuum L.) fruits (cv. Akra and Melek F1) grown in ecological conditions in Turkey (Van, eastern Turkey) were investigated. During the growing period, organic cytokinin(nytrozine) at two concentrations (50 and 100 ppm doses) was applied by spraying. Peppers were harvested in green maturity and then stored at 12 °C for 28 days. Changes in color, respiration rate, exogenous ethylene amount, antioxidative enzyme analysis (SOD, CAT and APX), lipid peroxidation, total phenolic content (TP) and antioxidant activity (AA) were determined just after the harvest and then at the 7th, 14th, 24th and 28th days. L* values for both varieties and the control sample had fluctuations during storage, and they slightly decreased after 28 days, except for the end value of cv. Melek F1 with a 50 ppm addition. In terms of respiration rate values, the difference between applications was found to be statistically significant, except for the 14th and 24th days of storage, for cv. Akra F1; and the 0th and 28th days for cv. Melek F1, respectively. It was observed that cytokinin can delay aging in peppers of both cultivars stored for 28 days. As a result, as the used cytokinin has an organic origin, is not synthetic and does not have a negative effect on human health, it can be used safely and without a significant loss of quality characteristics for peppers. These applications have positive effects on in-package gas composition, antioxidative enzymes, lipid peroxidation, total phenolics and total antioxidant activity.
A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions
Authors: Bodor Marius
Published: July 2021

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.
Food Industries Wastewater Recycling for Biodiesel Production through Microalgal Remediation
Authors: Amit  , Dahiya Divakar , Ghosh Uttam K. , Nigam Poonam S. , Jaiswal Amit K.
Published: July 2021

This article is an overview of a biosystem of food-industry wastewater (WW) treatment using microalgae towards circular bioeconomy through biosynthesis of compounds of added-value. Focusing on circular bioeconomy with concern to environmental pollution, the management of water-resource and energy-crisis could be combined; by upgrading conventional WW treatment and simultaneously producing a renewable and sustainable source of energy algal-lipids for biodiesel production. Phyco-remediation of food WW using microalgae has revealed many advantages that can fulfill new demands for the WW treatment. WWs can be valuable resources of micronutrients and organic content (carbon source) for algal cultivation. In this review, prospective routes for the production of value-added compounds (polysaccharides, amino acids, biofuels, and biopigments) along with the bioremediation of food industry WW have been discussed. Furthermore, limitations and issues of phyco-remediation of WW using microalgae have also been reviewed with perspectives for further research and development.