bluuebunny/crossref_metadata_2025 huggingface dataset:
Kinematic Determination of the Aerial Phase in Ski Jumping
Authors: Elfmark Ola , Ettema Gertjan , Jølstad Petter , Gilgien Matthias
Published: January 2022

The purpose of this study was to find a generic method to determine the aerial phase of ski jumping in which the athlete is in a steady gliding condition, commonly known as the ‘stable flight’ phase. The aerial phase of ski jumping was investigated from a physical point mass, rather than an athlete–action-centered perspective. An extensive data collection using a differential Global Navigation Satellite System (dGNSS) was carried out in four different hill sizes. A total of 93 jumps performed by 19 athletes of performance level, ranging from junior to World Cup, were measured. Based on our analysis, we propose a generic algorithm that identifies the stable flight based on steady glide aerodynamic conditions, independent of hill size and the performance level of the athletes. The steady gliding is defined as the condition in which the rate-of-change in the lift-to-drag-ratio (LD-ratio) varies within a narrow band-width described by a threshold τ. For this study using dGNSS, τ amounted to 0.01s−1, regardless of hill size and performance level. While the absolute value of τ may vary when measuring with other sensors, we argue that the methodology and algorithm proposed to find the start and end of a steady glide (stable flight) could be used in future studies as a generic definition and help clarify the communication of results and enable more precise comparisons between studies.
Air Quality and Comfort Characterisation within an Electric Vehicle Cabin in Heating and Cooling Operations
Authors: Russi Luigi , Guidorzi Paolo , Pulvirenti Beatrice , Aguiari Davide , Pau Giovanni , Semprini Giovanni
Published: January 2022

This work is aimed at the experimental characterisation of air quality and thermal profile within an electric vehicle cabin, measuring at the same time the HVAC system energy consumption. Pollutant concentrations in the vehicle cabin are measured by means of a low-cost system of sensors. The effects of the HVAC system configuration, such as fresh-air and recirculation mode, on cabin air quality, are discussed. It is shown that the PM concentrations observed in recirculation mode are lower than those in fresh-air mode, while VOC concentrations are generally higher in recirculation than in fresh-air mode. The energy consumption is compared in different configurations of the HVAC system. The novelty of this work is the combined measurement of important comfort parameters such as air temperature distribution and air quality within the vehicle, together with the real time energy consumption of the HVAC system. A wider concept of comfort is enabled, based on the use of low-cost sensors in the automotive field.
Application of an Intelligent Sensor and Active Packaging System Based on the Bacterial Cellulose of Acetobacter xylinum to Meat Products
Authors: Dirpan Andi , Djalal Muspirah , Kamaruddin Irma
Published: January 2022

Combining intelligent and active packaging serves the dual purpose of detecting color changes in food that reflect changes in its quality and prolonging its shelf life. This study developed an intelligent and active packaging system made from the cellulose of Acetobacter xylinum and assessed its ability to detect changes in the quality and to increase shelf-life of packaged fresh beef. The properties of the intelligent packaging’s sensor and active packaging films were determined. The application of this system to fresh beef stored at room temperature (28 ± 2 °C) for 24 h was tested. The color of the bromothymol blue (BTB) solution (pH 2.75) in the indicator of the intelligent packaging system changed from orange to dark green to indicate that beef quality changed from fresh to rotten. The meat treated with the active packaging with 10% and 15% garlic extract decayed on the 16th h. In contrast, the meat treated with the active packaging without the garlic extracts rotted on the 12th h. The shift in the indicator’s color was linearly related to the total plate count (TPC), total volatile basic nitrogen (TVBN), and pH of the meat packaged using the active packaging system. Therefore, BTB solution (pH 2.75) can be used as an intelligent packaging indicator that will allow consumers to assess the quality of packaged meat easily. As an antimicrobial agent, the addition of 10–15% garlic extract to the active packaging films can help delay the spoilage of packaged beef.
Gaze Tracking Based on Concatenating Spatial-Temporal Features
Authors: Hwang Bor-Jiunn , Chen Hui-Hui , Hsieh Chaur-Heh , Huang Deng-Yu
Published: January 2022

Based on experimental observations, there is a correlation between time and consecutive gaze positions in visual behaviors. Previous studies on gaze point estimation usually use images as the input for model trainings without taking into account the sequence relationship between image data. In addition to the spatial features, the temporal features are considered to improve the accuracy in this paper by using videos instead of images as the input data. To be able to capture spatial and temporal features at the same time, the convolutional neural network (CNN) and long short-term memory (LSTM) network are introduced to build a training model. In this way, CNN is used to extract the spatial features, and LSTM correlates temporal features. This paper presents a CNN Concatenating LSTM network (CCLN) that concatenates spatial and temporal features to improve the performance of gaze estimation in the case of time-series videos as the input training data. In addition, the proposed model can be optimized by exploring the numbers of LSTM layers, the influence of batch normalization (BN) and global average pooling layer (GAP) on CCLN. It is generally believed that larger amounts of training data will lead to better models. To provide data for training and prediction, we propose a method for constructing datasets of video for gaze point estimation. The issues are studied, including the effectiveness of different commonly used general models and the impact of transfer learning. Through exhaustive evaluation, it has been proved that the proposed method achieves a better prediction accuracy than the existing CNN-based methods. Finally, 93.1% of the best model and 92.6% of the general model MobileNet are obtained.
Litter Detection with Deep Learning: A Comparative Study
Authors: Córdova Manuel , Pinto Allan , Hellevik Christina Carrozzo , Alaliyat Saleh Abdel-Afou , Hameed Ibrahim A. , Pedrini Helio , Torres Ricardo da S.
Published: January 2022

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.
SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation
Authors: Lin Chih-Wei , Huang Xiuping , Lin Mengxiang , Hong Sidi
Published: January 2022

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.
Electropolymerised pH Insensitive Salicylic Acid Reference Systems: Utilization in a Novel pH Sensor for Food and Environmental Monitoring
Authors: Miranda Mugica Monica , McGuinness Kay Louise , Lawrence Nathan Scott
Published: January 2022

This work summarizes the electrochemical response of a salicylic acid-based carbon electrode for use as a novel solid-state reference electrode in a redox-based pH sensor. This novel reference produces a pH insensitive response over a range of pH 3–10 in solutions with low buffer concentrations, different compositions, conductivities, and ionic strengths is produced. The pH of the local environment is shown to be determined by the chemistry and the electrochemical response of the redox active species on the surface of the electrode; the local pH can be controlled by the electropolymerized salicylic acid moieties due to the acid concentration on the surface, avoiding any perturbation in environmental pH and leading to a stable novel reference system. Sensitivities of −7.1 mV/pH unit, −2.4 mV/pH unit, −0.2 mV/pH unit, and 2.5 mV/pH units were obtained for different food medias, hydroponic solution, seawater, and cell-culture media, respectively, confirming its ability to control the local pH of the electrode. This reference system is paired with a new pH sensing element based on electropolymerized flavanone to provide a calibration free, pH sensitive sensor to effectively and accurately measure the pH of various media with high viscosity, low conductivity, low/high buffer concentration or cell-culture environment, presenting a maximum error of +/−0.03 pH units.
Graphene Oxide Functionalized Biosensor for Detection of Stress-Related Biomarkers
Authors: Santiago Erican , Poudyal Shailu Shree , Shin Sung Y. , Yoon Hyeun Joong
Published: January 2022

A graphene oxide (GO)-based cortisol biosensor was developed to accurately detect cortisol concentrations from sweat samples at point-of-care (POC) sites. A reference electrode, counter electrode, and working electrode make up the biosensor, and the working electrode was functionalized using multiple layers consisting of GO and antibodies, including Protein A, IgG, and anti-Cab. Sweat samples contact the anti-Cab antibodies to transport electrons to the electrode, resulting in an electrochemical current response. The sensor was tested at each additional functionalization layer and at cortisol concentrations between 0.1 and 150 ng/mL to determine how the current response differed. A potentiostat galvanostat device was used to measure and quantify the electrochemical response in the GO-based biosensor. In both tests, the electrochemical responses were reduced in magnitude with the addition of antibody layers and with increased cortisol concentrations. The proposed cortisol biosensor has increased accuracy with each additional functionalization layer, and the proposed device has the capability to accurately measure cortisol concentrations for diagnostic purposes.
A Comparative Study between Scanning Devices for 3D Printing of Personalized Ostomy Patches
Authors: Zahia Sofia , Garcia-Zapirain Begonya , Anakabe Jon , Ander Joan , Jossa Bastidas Oscar , Loizate Totoricagüena Alberto
Published: January 2022

This papers presents a comparative study of three different 3D scanning modalities to acquire 3D meshes of stoma barrier rings from ostomized patients. Computerized Tomography and Structured light scanning methods were the digitization technologies studied in this research. Among the Structured Light systems, the Go!Scan 20 and the Structure Sensor were chosen as the handheld 3D scanners. Nineteen ostomized patients took part in this study, starting from the 3D scans acquisition until the printed ostomy patches validation. 3D mesh processing, mesh generation and 3D mesh comparison was carried out using commercial softwares. The results of the presented study show that the Structure Sensor, which is the low cost structured light 3D sensor, has a great potential for such applications. This study also discusses the benefits and reliability of low-cost structured light systems.
A FRET Approach to Detect Paraoxon among Organophosphate Pesticides Using a Fluorescent Biosensor
Authors: Rodrigues Andreia C. M. , Barbieri Maria Vittoria , Chino Marco , Manco Giuseppe , Febbraio Ferdinando
Published: January 2022

The development of faster, sensitive and real-time methods for detecting organophosphate (OP) pesticides is of utmost priority in the in situ monitoring of these widespread compounds. Research on enzyme-based biosensors is increasing, and a promising candidate as a bioreceptor is the thermostable enzyme esterase-2 from Alicyclobacillus acidocaldarius (EST2), with a lipase-like Ser–His–Asp catalytic triad with a high affinity for OPs. This study aimed to evaluate the applicability of Förster resonance energy transfer (FRET) as a sensitive and reliable method to quantify OPs at environmentally relevant concentrations. For this purpose, the previously developed IAEDANS-labelled EST2-S35C mutant was used, in which tryptophan and IAEDANS fluorophores are the donor and the acceptor, respectively. Fluorometric measurements showed linearity with increased EST2-S35C concentrations. No significant interference was observed in the FRET measurements due to changes in the pH of the medium or the addition of other organic components (glucose, ascorbic acid or yeast extract). Fluorescence quenching due to the presence of paraoxon was observed at concentrations as low as 2 nM, which are considered harmful for the ecosystem. These results pave the way for further experiments encompassing more complex matrices.
A Convolutional Neural Networks-Based Approach for Texture Directionality Detection
Authors: Kociołek Marcin , Kozłowski Michał , Cardone Antonio
Published: January 2022

The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method. We recently proposed a method based on the interpolated grey-level co-occurrence matrix (iGLCM), robust to image blur and noise but slower than the Fourier-based method. Here we test the applicability of convolutional neural networks (CNNs) to texture directionality detection. To obtain the large amount of training data required, we built a training dataset consisting of synthetic textures with known directionality and varying perturbation levels. Subsequently, we defined and tested shallow and deep CNN architectures. We present the test results focusing on the CNN architectures and their robustness with respect to image perturbations. We identify the best performing CNN architecture, and compare it with the iGLCM, the Fourier and the local gradient orientation methods. We find that the accuracy of CNN is lower, yet comparable to the iGLCM, and it outperforms the other two methods. As expected, the CNN method shows the highest computing speed. Finally, we demonstrate the best performing CNN on real-life images. Visual analysis suggests that the learned patterns generalize to real-life image data. Hence, CNNs represent a promising approach for texture directionality detection, warranting further investigation.
Recent Advances in Design and Implementation of Satellite Gateways for Massive Uncoordinated Access Networks
Authors: Isca Agostino , Alagha Nader , Andreotti Riccardo , Andrenacci Marco
Published: January 2022

This paper provides an overview of recent results of a design, development and performance evaluation study of satellite gateways to receive and manage the traffic from a large population of uncoordinated user terminals. In particular, direct satellite access scenarios for machine-to-machine communications and the Internet of Things have been targeted. Tests were carried out in a representative laboratory environment emulating realistic system scenarios. Performance results, as presented in this paper indicate that the proposed gateway architecture, based on an efficient access protocol, is capable of managing a very high number of uncoordinated terminals transmitting short messages with a low duty cycle. The applicability of the proposed solution to both geostationary and non-geostationary satellite systems has also been examined. The key concept of the gateway is based on a novel receiver architecture that implements the linear minimum mean square error (MMSE) spread spectrum signal detection and successive interference cancellation techniques. The receiver uses features such as a multi-stage detector together with a robust preamble detection. The end-to-end solution includes also the use of a new waveform with a quasi-constant envelope at the terminal to modulate and transmit data packets to be received and detected by the gateway via a satellite link.
A Robust Dirichlet Reputation and Trust Evaluation of Nodes in Mobile Ad Hoc Networks
Authors: Chiejina Eric , Xiao Hannan , Christianson Bruce , Mylonas Alexios , Chiejina Chidinma
Published: January 2022

The distributed nature of mobile ad hoc networks (MANETs) presents security challenges and vulnerabilities which sometimes lead to several forms of attacks. To improve the security in MANETs, reputation and trust management systems (RTMS) have been developed to mitigate some attacks and threats arising from abnormal behaviours of nodes in networks. Generally, most reputation and trust systems in MANETs focus mainly on penalising uncooperative network nodes. It is a known fact that nodes in MANETs have limited energy resources and as such, the continuous collaboration of cooperative nodes will lead to energy exhaustion. This paper develops and evaluates a robust Dirichlet reputation and trust management system which measures and models the reputation and trust of nodes in the network, and it incorporates candour into the mode of operations of the RTMS without undermining network security. The proposed RTMS employs Dirichlet probability distribution in modelling the individual reputation of nodes and the trust of each node is computed based on the node’s actual network performance and the accuracy of the second-hand reputations it gives about other nodes. The paper also presents a novel candour two-dimensional trustworthiness evaluation technique that categorises the behaviours of nodes based on their evaluated total reputation and trust values. The evaluation and analyses of some of the simulated behaviours of nodes in the deployed MANETs show that the candour two-dimensional trustworthiness evaluation technique is an effective technique that encourages and caters to nodes that continuously contribute to the network despite the reduction in their energy levels.
An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network
Authors: Ali Aitizaz , Almaiah Mohammed Amin , Hajjej Fahima , Pasha Muhammad Fermi , Fang Ong Huey , Khan Rahim , Teo Jason , Zakarya Muhammad
Published: January 2022

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.
Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction
Authors: Kaur Prabhjot , Harnal Shilpi , Tiwari Rajeev , Upadhyay Shuchi , Bhatia Surbhi , Mashat Arwa , Alabdali Aliaa M.
Published: January 2022

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.
Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes
Authors: Calvini Rosalba , Pigani Laura
Published: January 2022

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the “smell”, “taste”, and “color” of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review’s purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.
Health Risk Assessment of Lead in Soils from an Historic Industrial Site in North-East England
Authors: Ma Renli , Dean John R.
Published: January 2022

The former St. Antony’s Lead Works site, now the central part of an urban recreational park (Walker Riverside Park) in the east end of Newcastle upon Tyne (England, UK), has been assessed based on the Pb concentration in topsoil according to recently derived Category 4 Screening Level (C4SL) for public open space–park (POSpark, 1300 mg/kg). The site was divided into eight sampling areas following its physical layout. In total 79 soil samples were collected, characterised for acidity and organic matter content, and analysed by energy dispersive X-ray fluorescence (ED-XRF). The Pb levels in most of the samples, particularly those from the fringes of the site, are below the generic guideline value (down to 70 mg/kg). More than 16% of the samples from nearly all sampling areas, especially those sampling points around the former horizontal condenser flue and main chimney, contain levels of Pb significantly exceeding the limit (up to 206,000 mg/kg). No correlation is found between the Pb concentration in soil samples and their acidity (mostly neutral, pH 7.0 ± 0.5) or organic matter content (15.5 ± 4.1%). Using the Contaminated Land Exposure Assessment (CLEA) model (version 1.071), the site-specific risk assessment criteria (SSAC) for Lead (C4SL child), 2862 mg/kg, is obtained based on adjusted exposure frequency and occupancy period. Nearly 9% of the individual sample Pb concentrations (n = 79) across sample locations B, C, D and H are still above the specific value. Further statistical evaluation based on 95% upper confidence limit confirms that the site still represents a potential human health risk. This is because Pb concentrations, from two areas in the centre of the site (sample locations B and C), are greatly over the SSAC specific threshold (sample mean at location B is 12,350 mg/kg and at location C is 11,745 mg/kg).
Sexual Receptivity Signal of Lordosis Posture and Intra-Sexual Competition in Women
Authors: Pazhoohi Farid , Garza Ray , Kingstone Alan
Published: January 2022

Previous research has shown that women may use self-enhancement strategies to compete with one other. Lumbar curvature in women is considered to enhance a woman′s attractiveness, potentially due to its role in bipedal fetal load and sexual receptiveness. The current study investigated the role of lumbar curvature on women’s perceptions of sexual receptiveness as well as its role in women’s intrasexual competitiveness. Study 1 (N = 138) tested and confirmed that women’s intrasexual competition influences their perception of sexual receptivity of women as a function of lordosis posture depicted in a standing posture. Study 2 (N = 69) replicated these results and extended them to other postures, namely, the quadruped and supine positions. Study 3 (N = 106), using a two-alternative forced-choice task, revealed that other women perceive relatively larger arched-back postures as more threatening to their relationship and frequently as being more attractive. Collectively, this work suggests that women consider a lordotic posture in other women as a signal of sexual receptivity and perceive it as a threat to their relationship. This research provides robust support for the sexually receptivity hypothesis of lumbar curvature, questioning the alternative morphological vertebral wedging hypothesis.
Is There a Smart Sustainability Transition in Manufacturing? Tracking Externalities in Machine Tools over Three Decades
Authors: Labucay Inéz
Published: January 2022

Only one third of studies on the Industry 4.0–sustainability link have been conducted in manufacturing, despite its centrality to “ensuring sustainable consumption and production patterns” (UN Sustainable Development Goal nr. 12). The European Ecodesign Directive singled out machine tools as key to the sustainability transition, not least due to their high energy usage and their increasingly becoming enmeshed in cyber-physical production systems. This paper aims to find out whether the digital transformation underway in machine tools is sustainable as well as to identify its central technological pathways. Externalities in machine tools are tracked over three decades (1990–2018) by means of a multi-method setting: (1) mapping the Technological Innovation System (TIS) of machine tools; (2) co-occurrence analysis of transnational patent families, in order to reduce geographical and market distortions (Questel’s FAMPAT); and (3) analysis of the incidence of digital and sustainable technologies in machine tools patent applications (WIPO PATENTSCOPE). A smart sustainability transition is currently not hampered by a lack of smart technologies but rather by the sluggish introduction of sustainable machine tools. Cyber-physical and robot machine tools have been found to be central pathways to a smart sustainability transition. Implications for harnessing externalities reach beyond the machine tools industry.
Visualization of Escher-like Spiral Patterns in Hyperbolic Space
Authors: Qiu Chongyang , Li Xinfei , Pang Jianhua , Ouyang Peichang
Published: January 2022

Spirals, tilings, and hyperbolic geometry are important mathematical topics with outstanding aesthetic elements. Nonetheless, research on their aesthetic visualization is extremely limited. In this paper, we give a simple method for creating Escher-like hyperbolic spiral patterns. To this end, we first present a fast algorithm to construct Euclidean spiral tilings with cyclic symmetry. Then, based on a one-to-one mapping between Euclidean and hyperbolic spaces, we establish two simple approaches for constructing spiral tilings in hyperbolic models. Finally, we use wallpaper templates to render such tilings, which results in the desired Escher-like hyperbolic spiral patterns. The method proposed is able to generate a great variety of visually appealing patterns.
Use of Nanotechnology to Mitigate Biofouling in Stainless Steel Devices Used in Food Processing, Healthcare, and Marine Environments
Authors: Pérez Hugo , Vargas Gregorio , Silva Rodolfo
Published: January 2022

In humid environments, the formation of biofilms and microfouling are known to be the detrimental processes that first occur on stainless steel surfaces. This is known as biofouling. Subsequently, the conditions created by metabolites and the activity of organisms trigger corrosion of the metal and accelerate corrosion locally, causing a deterioration in, and alterations to, the performance of devices made of stainless steel. The microorganisms which thus affect stainless steel are mainly algae and bacteria. Within the macroorganisms that then damage the steel, mollusks and crustaceans are the most commonly observed. The aim of this review was to identify the mechanisms involved in biofouling on stainless steel and to evaluate the research done on preventing or mitigating this problem using nanotechnology in humid environments in three areas of human activity: food manufacturing, the implantation of medical devices, and infrastructure in marine settings. Of these protective processes that modify the steel surfaces, three approaches were examined: the use of inorganic nanoparticles; the use of polymeric coatings; and, finally, the generation of nanotextures.
Optimization of Surface-Enhanced Raman Spectroscopy Detection Conditions for Interaction between Gonyautoxin and Its Aptamer
Authors: Liu Yan , He Lijuan , Zhao Yunli , Cao Yongbing , Yu Zhiguo , Lu Feng
Published: January 2022

This study aimed to optimize the detection conditions for surface-enhanced Raman spectroscopy (SERS) of single-stranded DNA (ssDNA) in four different buffers and explore the interaction between gonyautoxin (GTX1/4) and its aptamer, GO18. The influence of the silver colloid solution and MgSO4 concentration (0.01 M) added under four different buffered conditions on DNA SERS detection was studied to determine the optimum detection conditions. We explored the interaction between GTX1/4 and GO18 under the same conditions as those in the systematic evolution of ligands by exponential enrichment technique, using Tris-HCl as the buffer. The characteristic peaks of GO18 and its G-quadruplex were detected in four different buffer solutions. The change in peak intensity at 1656 cm−1 confirmed that the binding site between GTX1/4 and GO18 was in the G-quadruplex plane. The relative intensity of the peak at 1656 cm−1 was selected for the GTX1/4–GO18 complex (I1656/I1099) to plot the ratio of GTX1/4 in the Tris-HCl buffer condition (including 30 μL of silver colloid solution and 2 μL of MgSO4), and a linear relationship was obtained as follows: Y = 0.1867X + 1.2205 (R2 = 0.9239). This study provides a basis for subsequent application of SERS in the detection of ssDNA, as well as the binding of small toxins and aptamers.
Fundamental Physics and Computation: The Computer-Theoretic Framework
Authors: Miguel-Tomé Sergio , Sánchez-Lázaro Ángel L. , Alonso-Romero Luis
Published: January 2022

The central goal of this manuscript is to survey the relationships between fundamental physics and computer science. We begin by providing a short historical review of how different concepts of computer science have entered the field of fundamental physics, highlighting the claim that the universe is a computer. Following the review, we explain why computational concepts have been embraced to interpret and describe physical phenomena. We then discuss seven arguments against the claim that the universe is a computational system and show that those arguments are wrong because of a misunderstanding of the extension of the concept of computation. Afterwards, we address a proposal to solve Hempel’s dilemma using the computability theory but conclude that it is incorrect. After that, we discuss the relationship between the proposals that the universe is a computational system and that our minds are a simulation. Analysing these issues leads us to proposing a new physical principle, called the principle of computability, which claims that the universe is a computational system (not restricted to digital computers) and that computational power and the computational complexity hierarchy are two fundamental physical constants. On the basis of this new principle, a scientific paradigm emerges to develop fundamental theories of physics: the computer-theoretic framework (CTF). The CTF brings to light different ideas already implicit in the work of several researchers and provides a new view on the universe based on computer theoretic concepts that expands the current view. We address different issues regarding the development of fundamental theories of physics in the new paradigm. Additionally, we discuss how the CTF brings new perspectives to different issues, such as the unreasonable effectiveness of mathematics and the foundations of cognitive science.
Searches for Violation of CPT Symmetry and Lorentz Invariance with Astrophysical Neutrinos
Authors: Moura Celio A. , Rossi-Torres Fernando
Published: January 2022

Neutrinos are a powerful tool for searching physics beyond the standard model of elementary particles. In this review, we present the status of the research on charge-parity-time (CPT) symmetry and Lorentz invariance violations using neutrinos emitted from the collapse of stars such as supernovae and other astrophysical environments, such as gamma-ray bursts. Particularly, supernova neutrino fluxes may provide precious information because all neutrino and antineutrino flavors are emitted during a burst of tens of seconds. Models of quantum gravity may allow the violation of Lorentz invariance and possibly of CPT symmetry. Violation of Lorentz invariance may cause a modification of the dispersion relation and, therefore, in the neutrino group velocity as well in the neutrino wave packet. These changes can affect the arrival time signal registered in astrophysical neutrino detectors. Direction or time-dependent oscillation probabilities and anisotropy of the neutrino velocity are manifestations of the same kind of new physics. CPT violation, on the other hand, may be responsible for different oscillation patterns for neutrino and antineutrino and unconventional energy dependency of the oscillation phase or of the mixing angles. Future perspectives for possible CPT and Lorentz violating systems are also presented.
Higher-Dimensional Regular Reissner–Nordström Black Holes Associated with Linear Electrodynamics
Authors: Wu Yu-Mei , Miao Yan-Gang
Published: January 2022

Following the interpretation of matter source that the energy-momentum tensor of anisotropic fluid can be dealt with effectively as the energy-momentum tensor of perfect fluid plus linear (Maxwell) electromagnetic field, we obtain the regular higher-dimensional Reissner–Nordström (Tangherlini–RN) solution by starting with the noncommutative geometry-inspired Schwarzschild solution. Using the boundary conditions that connect the noncommutative Schwarzschild solution in the interior of the charged perfect fluid sphere to the Tangherlini–RN solution in the exterior of the sphere, we find that the interior structure can be reflected by an exterior parameter, the charge-to-mass ratio. Moreover, we investigate the stability of the boundary under mass perturbation and indicate that the new interpretation imposes a rigid restriction upon the charge-to-mass ratio. This restriction, in turn, permits a stable noncommutative black hole only in the 4-dimensional spacetime.
EAS Observation Conditions in the SPHERE-2 Balloon Experiment
Authors: Bonvech Elena , Chernov Dmitry , Finger Miroslav , Finger Michael , Galkin Vladimir , Podgrudkov Dmitry , Roganova Tatiana , Vaiman Igor
Published: January 2022

The SPHERE project studies primary cosmic rays by detection of the Cherenkov light of extensive air showers reflected from the snow covered surface of the earth. Measurements with the aerial-based detector SPHERE-2 were performed in 2011–2013. The detector was lifted by a balloon to altitudes of up to 900 m above the snow covered surface of Lake Baikal, Russia. The results of the experiment are summarized now in a series of papers that opens with this article. An overview of the SPHERE-2 detector telemetry monitoring systems is presented along with the analysis of the measurements conditions including atmosphere profile. The analysis of the detector state and environment atmosphere conditions monitoring provided various cross-checks of detector calibration, positioning, and performance.
2021 CDC Update: Treatment and Complications of Sexually Transmitted Infections (STIs)
Authors: Silverberg Benjamin , Moyers Amy , Hinkle Tate , Kessler Roanna , Russell Nancy G.
Published: January 2022

The Centers for Disease Control and Prevention (CDC) recently updated their Sexually-Transmitted Infection (STI) Treatment Guidelines with a revision to the approach to gonococcal infections in December 2020 and other STIs in July 2021. This article reviews the new recommendations and highlights important updates from the 2015 iteration that are crucial for primary care and community health practice.
Assessing a Nation’s Competitiveness in Global Food Innovation: Creating a Global Food Innovation Index
Authors: Charlebois Sylvain , Hill Amy , Vezeau Janèle , Hunsberger Lydia , Johnston Maddy , Music Janet
Published: January 2022

While food innovation is heavily influenced by the myriad of policies, regulations and other environmental factors within a country, globalization means that food innovation is also a matter of international competitiveness. This benchmarking exercise uses 24 variables to compare the different innovation environments across ten countries: Canada, the US, Mexico, the UK, France, Germany, Italy, the Netherlands, Japan, and Australia. Quantitative and qualitative data was collected from publicly available sources only to measure each variable and ultimately provide a ranking. Qualitative data was evaluated using thematic coding to establish baseline practices and then compare each country to the baseline. Quantitative data was evaluated by constructing an average to which each country was compared. Countries whose data showed they met the average were awarded two points, and those who performed above or below average were either awarded an additional point or saw a point deducted. A final ranking was established from the scores across all four pillars, and the ranking was weighted to account for lacking data. The final weighted ranking saw the UK rank first, followed by the US, Germany, Australia, Canada, the Netherlands, Japan, Mexico, France and finally, Italy in tenth place.
Terms of sowing forage crops for haylage production under the conditions of Central Yakutia
Authors:   Yakut Scientific Research Institute of Agriculture – a separate subdivision of the Federal State Budgetary Institution of Science Federal Research Center “Yakut Scientific Center of the Siberian Branch of the Russian Academy of Sciences”, Russia, Yakutsk, Pesterevа E. S.
Published: January 2022

In recent years the agricultural production in the Republic of Sakha (Yakutia) has been introducing the technology of haylage production, which can replace part of the silage and hay in the ration of animals. However, stable raw materials of herbage are not enough for the technology of haylage production in Yakutia. Natural forage lands cannot provide a new technology of haylage production with herbage, since the entire area of haymaking goes to hay harvesting. Therefore, the selection of forage crops to create a reliable raw material base for haylage is a very relevant research topic. The purpose of the research was to study the terms of sowing forage crops for haylage production under the conditions in the Central Yakutia. The research has been carried out in the research and production permanent out-station in the M. G. Safronov Yakut Scientifi c Research Institute of Agriculture for three years. The experimental site was located on the second above-fl oodplain terrace of the Lena River Valley. The objects of research were the following varieties of zoned annual crops – oats of Pokrovsky variety 9, peas of Capital variety, spring vetch of Priobskaya variety 25, barley of Tammy variety. It has been found as a result of research that in the soil and climatic conditions in the Central Yakutia for the production of haylage for plant growth and development, dynamics of accumulation of dry matter, yield and nutritional qualities, the most effective mixtures are: vetch (1,2 million pcs/ha) + oats (2,5 million pcs/ha) and peas (0,8 million pcs/ha) + oats (2,5 million pcs/ha) germinating seeds. As the result of the conducted research, the optimal terms for sowing annual grasses for haylage have been established such as the 1st term (sowing in the third decade of May, harvesting in the first decade of August), the 2nd term (sowing in the second decade of June, harvesting in the third decade of August), the 3rd term (sowing in the first decade of July, harvesting in the first decade of September) in the phase of milk-wax ripeness in cereals and in the phase of fruit formation in legumes.
Influence of feed additives on the hematological and biochemical parameters of the blood of lactating cows
Authors:   Federal State Budgetary Educational Institution of Higher Education “South Ural State Agrarian University”, Russia, Chelyabinsk region, Troitsk, Maksimova R. A. , Ermolova E. M. ,   Federal State Budgetary Educational Institution of Higher Education “South Ural State Agrarian University”, Russia, Chelyabinsk region, Troitsk, Kosilov V. I. ,   Federal State Budgetary Educational Institution of Higher Education “Orenburg State Agrarian University”, Russia, Orenburg, Karmatskikh Yu. A. ,   Federal State Budgetary Educational Institution of Higher Education “Kurgan State Agricultural Academy named after T. S. Maltsev”, Russia, Kurgan region, Lesnikovo
Published: January 2022

Increasing the milk productivity of cows is the main task facing the livestock industry of our country. The study of some hematological and biochemical blood parameters in dairy cows when different feed additives are included in their rations is an urgent research task. The purpose of the investigations was to study the hematological and biochemical parameters of the blood of lactating cows when using different feed additives in the ration. During the investigations, the influence of feed additives such as Zaslon 2+ (at a dose of 20 g/head/day to the main ration), Diatomite (100 g/head/day to the main ration) and Monosporin (70 g/head/day to the main ration) on the hematological and biochemical blood parameters of lactating cows has been observed. The studied feed additives are used for the adsorption of mycotoxins in feed, normalization of digestive processes, and improvement of the livability and productivity of farm animals. In order to study the influence of the tested feed additives on the hematological parameters of the blood of lactating cows under the conditions of the Troitsky area in the Chelyabinsk region, on four groups of cows selected according to the principle of analogues (age, live weight, productivity, breed, physiological condition) the scientific and economic experiment has been carried out. As the result of the investigations, data have been obtained indicating that the feed additive Zaslon 2+ at the dose of 20 g/head/day has the greatest effect on the metabolism in the body of lactating cows.