bluuebunny/crossref_metadata_2025 huggingface dataset:
Compact Implementation of ARIA on 16-Bit MSP430 and 32-Bit ARM Cortex-M3 Microcontrollers
Authors: Seo Hwajeong , Kim Hyunjun , Jang Kyoungbae , Kwon Hyeokdong , Sim Minjoo , Song Gyeongju , Uhm Siwoo
Published: April 2021

In this paper, we propose the first ARIA block cipher on both MSP430 and Advanced RISC Machines (ARM) microcontrollers. To achieve the optimized ARIA implementation on target embedded processors, core operations of ARIA, such as substitute and diffusion layers, are carefully re-designed for both MSP430 (Texas Instruments, Dallas, TX, USA) and ARM Cortex-M3 microcontrollers (STMicroelectronics, Geneva, Switzerland). In particular, two bytes of input data in ARIA block cipher are concatenated to re-construct the 16-bit wise word. The 16-bit word-wise operation is executed at once with the 16-bit instruction to improve the performance for the 16-bit MSP430 microcontroller. This approach also optimizes the number of required registers, memory accesses, and operations to half numbers rather than 8-bit word wise implementations. For the ARM Cortex-M3 microcontroller, the 8×32 look-up table based ARIA block cipher implementation is further optimized with the novel memory access. The memory access is finely scheduled to fully utilize the 3-stage pipeline architecture of ARM Cortex-M3 microcontrollers. Furthermore, the counter (CTR) mode of operation is more optimized through pre-computation techniques than the electronic code book (ECB) mode of operation. Finally, proposed ARIA implementations on both low-end target microcontrollers (MSP430 and ARM Cortex-M3) achieved (209 and 96 for 128-bit security level, respectively), (241 and 111 for 192-bit security level, respectively), and (274 and 126 for 256-bit security level, respectively). Compared with previous works, the running timing on low-end target microcontrollers (MSP430 and ARM Cortex-M3) is improved by (92.20% and 10.09% for 128-bit security level, respectively), (92.26% and 10.87% for 192-bit security level, respectively), and (92.28% and 10.62% for 256-bit security level, respectively). The proposed ARIA–CTR implementation improved the performance by 6.6% and 4.0% compared to the proposed ARIA–ECB implementations for MSP430 and ARM Cortex-M3 microcontrollers, respectively.
Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment—Using MEC/SDN
Authors: Ateya Abdelhamied A. , Algarni Abeer D. , Hamdi Monia , Koucheryavy Andrey , Soliman Naglaa. F.
Published: April 2021

The Internet of things (IoT) is the third evolution of the traditional Internet that enables interaction and communication among machines. Many IoT platforms and networks have been developed, and recently, market sectors have started to develop specific IoT applications and services. Integrating heterogeneous IoT networks with the existing ones, mainly with the cellular networks, is a great demand. IoT represents one of the main use cases of the fifth-generation (5G) cellular system as announced by the 3rd Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU). Integrating IoT networks with 5G networks face many challenges related to dense deployment and a massive number of expected connected devices. Thus, IoT network availability and scalability are the main requirements that should be achieved. To this end, this work provides a framework for integrating heterogeneous IoT networks with the 5G networks. The proposed system considers dense deployment and system scalability and availability requirements as announced by ITU and 3GPP. Our proposed structure deploys three main communication paradigms; mobile edge computing (MEC), device-to-device communications (D2D), and software-defined networking (SDN). Our proposed system is evaluated over a reliable environment for various deployment scenarios, and the results validate the proposed structure. The proposed IoT/5G reduces the percentage of blocked tasks by an average of 30% than other traditional IoT networks. This increases the overall system availability and scalability since IoT networks can have more devices and tasks than existing IoT networks. Furthermore, our proposed structure reduces the overall consumed energy by an average of 20% than existing IoT networks, which is an effective metric for IoT networks.
Practical Consistency of Ethernet-Based QoS with Performance Prediction of Heterogeneous Microwave Radio Relay Transport Network
Authors: Zlatar Slađan , Lipovac Vlatko , Lipovac Adriana , Hamza Mirza
Published: April 2021

Microwave line-of-sight radio relay (RR) systems are a constitutive part of a telecom operator transport network, as an alternative to optical transmission systems when the latter are not technically possible or rational to implement. Nowadays, RR links are quite often used in the access network for connecting mobile radio base stations, thus also enabling traffic aggregation, and so on. In this paper, we focus on a practical, real-life, five-section heterogeneous RR network, comprising classic synchronous digital hierarchy (SDH) and SDH new generation network (NGN) architecture, hybrid parallel and mutually independent transmission of native Ethernet and TDM services, and all-IP network parts. Specifically, the main task of this work is to answer whether such a diverse RR system could satisfy the quality norms for Ethernet-based services, meaning whether a tolerable RR unavailability will necessarily imply the according Ethernet quality of service (QoS) degradation. This question is addressed by the comprehensive in-service and out-of-service testing of an operational hybrid RR transmission system. After extensive practical testing and appropriate analysis of the achieved results, it came out that the impact of RR-level impairments that determine the performance prediction affected the Ethernet QoS to the extent that BER values increased to the acceptability threshold values. We believe that the preliminary results reported here could serve as a hint and a framework for a more comprehensive cross-layer test strategy in terms of both test diversity and repeating rate, which contemporary network operators need to implement in order to enable the appropriate quality of experience for users of their services.
A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms
Authors: Alreshidi Eissa , Ramadan Rabie , Sharif Md. , Ince Omer , Ince Ibrahim
Published: April 2021

Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.
Broadband Reflective Polarization Rotator Built on Single Substrate
Authors: Yang Xiaofan , Qi Tao , Zeng Yonghu , Liu Xiaoming , Lu Gan , Cai Qing
Published: April 2021

A broadband polarization rotator built on single substrate is presented in this work. The device is designed for operation in the K and Ka bands. A slant array is used to achieve polarization rotation by 90° in a reflective manner. Broadband has been obtained, with the operation frequency range covering 15–45 GHz for 3 dB criteria, which is almost 100% fractional bandwidth. In addition, the insertion loss is less than 0.3 dB over a moderate broad incident angle from 0°–20°. Furthermore, the polarization conversion ratio can be as high as 0.95. By using a bi-static method, the fabricated prototype is measured, and the measured results demonstrate satisfactory agreement with the simulation ones. In comparison with other reflective designs in the literature, this design provides good bandwidth as well as polarization conversion ratio. Miniaturization can be investigated to increase the angular stability.
Design Considerations and Selection of Cost-Effective Switched Reluctance Drive for Radiator Cooling Fans
Authors: Son Ickjin , Lukman Grace Firsta , Shah Mazahir Hussain , Jeong Kwang-Il , Ahn Jin-Woo
Published: April 2021

Switched reluctance motors (SRMs) are simple in structure, easy to manufacture, magnet-less, brushless, and highly robust compared to other AC motors which makes them a good option for applications that operate in harsh environment. However, the motor has non-linear magnetic characteristics, and it comes with various pole-phase combinations and circuit topologies that causes many difficulties in deciding on which type to choose. In this paper, the viability of SRM as a low-cost, rugged machine for vehicle radiator cooling fan is considered. First, necessary design considerations are presented, then three commonly use types of SRM are analyzed: A 3-phase 6/4, 3-phase 12/8, and a 4-phase 8/6 to find their static and dynamic characteristics so the most suitable type can be selected. Simulation results show that the 8/6 SRM produces the highest efficiency with less phase current which reduces the converter burden. However, with asymmetric half bridge converter, eight power switches are required for 8/6 SRM and thus put a burden on the overall drive cost. As a solution, the Miller converter with only six switches for four phase SRM. To verify the proposed idea, the 8/6 SRM was manufactured and tested. The results show that Miller converter can be used for the proposed SRM with slightly reduced efficiency at 80.4%.
An Overview on Electric-Stress Degradation Empirical Models for Electrochemical Devices in Smart Grids
Authors: Rodríguez Licea Martín Antonio , Pérez Pinal Francisco Javier , Soriano Sánchez Allan Giovanni
Published: April 2021

The conversion from existing electrical networks into an all-renewable and environmentally friendly electrification scenario is insufficient to produce and distribute energy efficiently. Electrochemical devices’ premature degradation as a whole caused by electrical stressors in smart grids is incipient from an energy management strategies (EMS) perspective. Namely, few electrical-stress degradation models for photovoltaic panels, batteries, fuel cells, and super/ultra-capacitors (SCs), and particular stressors can be found in the literature. In this article, the basic operating principles for such devices, existing degradation models, and future research hints, including their incorporation in novel EMS, are condensed. The necessity of extending these studies to other stressors and devices is also emphasized. There are many other degradation models by non-electrical stressors, such as climatic conditions and mechanical wear. Although novel EMS should manage both electrical and non-electrical degradation mechanisms and include non-electrochemical devices, models with pure non-electrical-stressors are not the subject of this review since they already exist. Moreover, studies for the degradation of non-electrochemical devices by electrical stressors are very scarce.
Numerical Analysis of the Influence of Different Flow Patterns on Power and Reactant Transmission in Tubular-Shaped PEMFC
Authors: Yuan Lei , Jin Zunlong , Yang Penghui , Yang Youchen , Wang Dingbiao , Chen Xiaotang
Published: April 2021

The influence of a tubular structure PEMFC (proton exchange membrane fuel cell) with different flow patterns is investigated in this study. A complete 3D non-isothermal model is constructed for square and circular tubular PEMFCs, and the distribution of oxygen and water concentration in cathode channels, current density, power density and cell net power are studied. To this end, the four arrangements of tubular PEMFC are square chordal (SC), square peripheral (SP), circular chordal (CC) and circular peripheral (CP). The calculation of the effective area and boundary conditions remains the same when performing all four configurations. The consequent results show that for the tubular structure PEMFC, compared with the co-flow mode, the counter-flow mode has better performance and provides more power. Using a counter-flow pattern, the permeability of the species increases, so a more uniform reaction occurs at the cell. The entire performance of the SP and CP model is not as good as that of the SC and CC models because the SP and CP models have a higher flow velocity. Moreover, the SC model using the counter-flow pattern has the maximum predicted net power among the other models.
Achieving Environmental Policy Objectives through the Implementation of Sustainable Development Goals. The Case for European Union Countries
Authors: Kluza Krzysztof , Zioło Magdalena , Bąk Iwona , Spoz Anna
Published: April 2021

One of the key challenges for climate policies is the identification of strategies that will effectively support the implementation of environmental goals. Environmental policies are connected with other development policies carried out by governments. In order to comprehensively shape environmental policy, it is important to understand the interactions between sustainable development goals (SDGs) as well as their impact on environmental goals. Employing econometric modeling based on the least absolute shrinkage and selection operator (Lasso) method and full-factorial analysis, the authors identify a number of statistically significant relationships between the implementation of sustainable development goals and the environmental variable represented by greenhouse gas emissions. Analysis reveals that implementation of particular sustainable development goals, namely SDG4 (Ensure inclusive and equitable quality education and promote lifelong learning opportunities) and SDG17 (Strengthen the means of implementation and revitalize the global partnership for sustainable development), explicitly facilitate the achievement of environmental policies. In addition, other SDGs exert an indirect influence on environmental goals through their reinforcing interactions with SDG4 and SDG17 variables. These are: SDG1 (End poverty), SDG3 (Ensure healthy lives and promote well-being), SDG8 (Promote sustained, inclusive and sustainable economic growth, and productive employment) and SDG15 (Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests and halt biodiversity loss). These findings have important implications for proper identification of effective government policy instruments which indirectly support the achievement of environmental goals.
Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework
Authors: Witczak Marcin , Mrugalski Marcin , Lipiec Bogdan
Published: April 2021

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.
Classification of Superimposed Partial Discharge Patterns
Authors: Adam Benjamin , Tenbohlen Stefan
Published: April 2021

Phase resolved partial discharge patterns (PRPD) are routinely used to assess the condition of power transformers. In the past, classification systems have been developed in order to automate the fault identification task. Most of those systems work with the assumption that only one source is active. In reality, however, multiple PD sources can be active at the same time. Hence, PRPD patterns can overlap and cannot be separated easily, e.g., by visual inspection. Multiple PD sources in a single PRPD represent a multi-label classification problem. We present a system based on long short-term memory (LSTM) neural networks to resolve this task. The system is generally able to classify multiple overlapping PRPD by while only being trained by single class PD sources. The system achieves a single class accuracy of 99% and a mean multi-label accuracy of 43% for an imbalanced dataset. This method can be used with overlapping PRPD patterns to identify the main PD source and, depending on the data, also classify the second source. The method works with conventional electrical measuring devices. Within a detailed discussion of the presented approach, both its benefits but also its problems regarding different repetition rates of different PD sources are being evaluated.
Evaporated MAPbI3 Perovskite Planar Solar Cells with Different Annealing Temperature
Authors: Chang Yi-Tsung , Tien Ching-Ho , Lee Kun-Yi , Tung Yu-Shen , Chen Lung-Chien
Published: April 2021

The power conversion efficiency (PCE) of an Ag/spiro-OMeTAD/CH3NH3PbI3 (MAPbI3)/PCBM/mesoporous TiO2/compact TiO2/FTO planar solar cell with different annealing temperatures of PbI2 and MAPbI3 films was investigated in this study. The morphology control of a MAPbI3 thin film plays key roles in high-efficiency perovskite solar cells. The PbI2 films were prepared by using thermal vacuum evaporation technology, and the MAPbI3 perovskite films were synthesized with two-step synthesis. The X-ray spectra and surface morphologies of the PbI2 and MAPbI3 films were examined at annealing temperatures of 80, 100, 120, and 140 °C for 10 min. The performance of the perovskite planar solar cell at an annealing temperature of 100 °C for 10 min was demonstrated. The power conversion efficiency (PCE) was about 8.66%, the open-circuit voltage (Voc) was 0.965 V, the short-circuit current (Jsc) was 13.6 mA/cm2, and the fill factor (FF) was 0.66 by scanning the density–voltage (J–V) curve.
Integration of Harvester Production Data in German Wood Supply Chains: Legal, Social and Economic Requirements
Authors: Hartsch Florian , Kemmerer Julia , Labelle Eric R. , Jaeger Dirk , Wagner Thilo
Published: April 2021

Digitalization and its associated technology are shaping the world economy and society. Data collection, data exchange, and connection throughout the wood supply chain have become increasingly important. There exist many technologies for the implementation of Industry 4.0 applications in forestry. For example, the integration of harvester production data throughout the wood supply chain seems to have strong optimization potential but it is faced with several challenges due to the high number of stakeholders involved. Therefore, the objective of this article is to analyze the legal, social, and economic conditions surrounding the integration of harvester production data integration in Germany. For analysis of the legal and economic conditions, a narrative literature analysis was performed with special consideration of the relevant German and European legal references. For determination of the social conditions, a qualitative content analysis of 27 expert interviews was performed. Results showed that legal ownership of harvester production data cannot be clearly defined in Germany, but there exist several protection rights against misuse, which can define an ownership-similar data sovereignty. Furthermore, harvester data use can be restricted in the case where personal data are traceable, based on European data protection law. From a social perspective, the stakeholders interviewed in the study had different opinions on data ownership. Stakeholders require specific criteria on the data (interfaces) and other factors for the acceptance of new structures to allow successful harvester data integration. From an economic perspective, harvester production data are tradeable through varying transaction forms but, generally, there is no accepted and valid formula in existence for calculating the value or price of harvester data. Therefore, the authors advise discussing these issues with key stakeholders to negotiate and agree on data ownership and use in order to find a suitable solution to realize optimization potentials in the German wood supply chain.
Genetic, Morphological, and Environmental Differentiation of an Arid-Adapted Oak with a Disjunct Distribution
Authors: Zumwalde Bethany A. , McCauley Ross A. , Fullinwider Ian J. , Duckett Drew , Spence Emma , Hoban Sean
Published: April 2021

The patterns of genetic and morphological diversity of a widespread species can be influenced by environmental heterogeneity and the degree of connectivity across its geographic distribution. Here, we studied Quercus havardii Rydb., a uniquely adapted desert oak endemic to the Southwest region of the United States, using genetic, morphometric, and environmental datasets over various geographic scales to quantify differentiation and understand forces influencing population divergence. First, we quantified variation by analyzing 10 eastern and 13 western populations from the disjunct distribution of Q. havardii using 11 microsatellite loci, 17 morphological variables, and 19 bioclimatic variables. We then used regressions to examine local and regional correlations of climate with genetic variation. We found strong genetic, morphological and environmental differences corresponding with the large-scale disjunction of populations. Additionally, western populations had higher genetic diversity and lower relatedness than eastern populations. Levels of genetic variation in the eastern populations were found to be primarily associated with precipitation seasonality, while levels of genetic variation in western populations were associated with lower daily temperature fluctuations and higher winter precipitation. Finally, we found little to no observed environmental niche overlap between regions. Our results suggest that eastern and western populations likely represent two distinct taxonomic entities, each associated with a unique set of climatic variables potentially influencing local patterns of diversity.
Improved Hough Transform and Total Variation Algorithms for Features Extraction of Wood
Authors: Du Weiwei , Xi Yarui , Harada Kiichi , Zhang Yumei , Nagashima Keiko , Qiao Zhiwei
Published: April 2021

Research shows that the intensity impact factors of wood, such as late timber ratio, volume density and the intensity of itself, correlate with the width of wood annual rings. Therefore, extracting wood annual ring information from wood images is helpful for evaluating wood quality. During the past few years, many researchers have conducted defect detection by studying the information of wood images. However, there are few in-depth studies on the statistics and calculation of wood annual ring information. This study proposes a new model combining the Total Variation (TV) algorithm and the improved Hough transform to accurately measure the wood annual ring information. The TV algorithm is used to suppress image noise, and the Hough transform is for detecting the center of the wood image. Moreover, the edges of wood annual rings are extracted, and the statistical ring information is calculated. The experimental results show that the new model has good denoising capability, clearly extract the edges of wood annual rings and calculate the related parameters from the indoor wood images of the processed logs and the unprocessed low-noise logs.
Soundscape Perceptions and Preferences for Different Groups of Users in Urban Recreational Forest Parks
Authors: Fang Xingyue , Gao Tian , Hedblom Marcus , Xu Naisheng , Xiang Yi , Hu Mengyao , Chen Yuxuan , Qiu Ling
Published: April 2021

Although the soundscape in cities is receiving increased attention in urban planning, there is still a lack of knowledge of how personal factors influence the perception of and preference for soundscapes. Most present studies are linked to one or a few specific soundscapes and do not have a holistic approach exploring the pros and cons of all soundscapes in a place. This study surveyed individuals to assess how soundscape perceptions and preferences may differ among various attendees of typical urban forest recreational parks in Xi’an, China, using an on-site questionnaire. The respondents (N = 2034) revealed that rare natural sounds were perceived more positively than the dominating artificial sounds. Five main dimensions of social, demographic, and behavioral attributes were found to be linked to the soundscape perceptions and preferences: (1) familiarity of the park and attendees’ age made people more tolerant towards sounds that others find annoying; (2) higher education and higher socio-economic status showed lower tolerance towards sounds; (3) having companions and specific types of recreational use increased the frequent perception of artificial sounds; (4) females generally showed higher sensitivity and lower tolerance than males towards several sounds; and (5) the longer attendees remained in the park, the more positive the overall soundscape preference was. The results indicate that numerous sounds are affecting people’s overall experience in the parks. These findings could help decision-makers and urban forest recreational park designers to formulate relevant strategies for park design that are in tune with varying public needs and expectations towards soundscape. The implementation of human-oriented soundscape design can therefore enhance people’s well-being.
Some Challenges for Forest Fire Risk Predictions in the 21st Century
Authors: Resco de Dios Víctor , Nolan Rachael H.
Published: April 2021

Global wildfire activity has experienced a dramatic surge since 2017 [...]
Magnetohydrodynamic Flow of a Bingham Fluid in a Vertical Channel: Mixed Convection
Authors: Borrelli Alessandra , Giantesio Giulia , Patria Maria Cristina
Published: April 2021

In this paper, we describe our study of the mixed convection of a Boussinesquian Bingham fluid in a vertical channel in the absence and presence of an external uniform magnetic field normal to the walls. The velocity, the induced magnetic field, and the temperature are analytically obtained. A detailed analysis is conducted to determine the plug regions in relation to the values of the Bingham number, the buoyancy parameter, and the Hartmann number. In particular, the velocity decreases as the Bingham number increases. Detailed considerations are drawn for the occurrence of the reverse flow phenomenon. Moreover, a selected set of diagrams illustrating the influence of various parameters involved in the problem is presented and discussed.
Application of Broccoli Leaf Powder in Gluten-Free Bread: An Innovative Approach to Improve Its Bioactive Potential and Technological Quality
Authors: Krupa-Kozak Urszula , Drabińska Natalia , Bączek Natalia , Šimková Kristýna , Starowicz Małgorzata , Jeliński Tomasz
Published: April 2021

In comparison to conventional bread, gluten-free bread (GF) shows many post-baking defects and a lower nutritional and functional value. Although broccoli leaves are perceived as waste products, they are characterised by a high content of nutrients and bioactive compounds. The present study evaluated the nutritional value, technological quality, antioxidant properties, and inhibitory activity against the formation of advanced glycation end-products (AGEs) of GF enriched with broccoli leaf powder (BLP). Compared to the control, gluten-free bread with BLP (GFB) was characterised by a significantly (p < 0.05) higher content of nutrients (proteins and minerals), as well as improved specific volume and bake loss. However, what needs to be emphasised is that BLP significantly (p < 0.05) improved the antioxidant potential and anti-AGE activity of GFB. The obtained results indicate that BLP can be successfully used as a component of gluten-free baked products. In conclusion, the newly developed GFB with improved technological and functional properties is an added-value bakery product that could provide health benefits to subjects on a gluten-free diet.
Wild Foods: A Topic for Food Pre-History and History or a Crucial Component of Future Sustainable and Just Food Systems?
Authors: Pieroni Andrea
Published: April 2021

The ethnobiology of wild foods has garnered increasing attention in food studies in recent years, since traditional foodways in less urbanized and globalized areas of the world are sometimes still based on often neglected or even largely unknown wild plant, animal, fungal, microorganism, and mineral ingredients, as well as their food products and culinary preparations [...]
Optimization of Instrument Design for In-Line Monitoring of Dry Matter Content in Single Potatoes by NIR Interaction Spectroscopy
Authors: Wold Jens Petter , O’Farrell Marion , Andersen Petter Vejle , Tschudi Jon
Published: April 2021

Dry matter (DM) content is one of the most important quality features of potatoes. It defines the physical properties of the potatoes and determines what kind of product the potatoes can be used for. This paper presents the results obtained by a novel prototype NIR (near-infrared) instrument designed to measure DM content in single potatoes in process. The instrument is based on interaction measurements to measure deeper into the potatoes. It measures rapidly, up to 50 measurements per second, allowing several moving potatoes to be measured per second. The instrument also enables several interactance distances to be recorded for each measurement. The instrument was calibrated based on three different potato varieties and the calibration measurements were done in a process plant, making the calibration model suitable for in-line use. A good calibration for DM was obtained by partial least squares regression (RMSECV = 0.78% DM, R2 = 0.91). The instrument was tested in-line in the process plant and several batches of potatoes were monitored for the estimation of the DM distribution per batch. Accuracy of DM determination as function of measurement position on the potato was studied, and results indicate that NIR scans along the center part of the potatoes give slightly better results compared to scans taken on either side of the center. Small differences in optical measurement geometry influence the accuracy of the calibration models, underlining the importance of optimizing instrument design for successful measurements.
Quality Characteristics and Consumer Acceptance of High-Moisture Mozzarella Obtained from Heat-Treated Goat Milk
Authors: Faccia Michele , Gambacorta Giuseppe , Pasqualone Antonella , Summo Carmine , Caponio Francesco
Published: April 2021

High-moisture mozzarella is a pasta filata cheese manufactured from cow or buffalo milk that has spread all over the world. Its manufacturing from the milk of small ruminants (goat and sheep) has been recently proposed to innovate this ailing sector. Previously, a protocol was reported for making goat mozzarella from unpasteurized milk but, according to legislation, the microbiological safety of raw milk fresh cheeses is not guaranteed. In the present research, two new protocols were tested for producing mozzarella from pasteurized milk prepared by two different low-temperature long-time treatments (67 °C or 63 °C × 30 min). The obtained cheeses were subjected to physical–chemical and microbiological analyses and to consumer testing. The results showed that the heat treatments caused longer coagulation times than those reported in the literature, despite pre-acidification (at pH 5.93 or 6.35) having been performed to counterbalance the expected worsening of the coagulation aptitude. The obtained products showed differences in the chemical composition, texture, proteolysis, and lipolysis. Both pasteurization and pre-acidification played a role in determining these variations. Consumer testing indicated that mozzarella obtained from milk heated at the lower temperature and coagulated at a higher pH reached a good level of appreciation (62%).
Gut Flora-Mediated Metabolic Health, the Risk Produced by Dietary Exposure to Acetamiprid and Tebuconazole
Authors: Liu Jingkun , Zhao Fangfang , Xu Yanyang , Qiu Jing , Qian Yongzhong
Published: April 2021

The low-level and long-term exposure of pesticides was found to induce metabolic syndrome to mice. Metabolic pathways and mechanisms were investigated by detecting gut flora with metabolites, host circulation, and their interrelations. Results showed that the abundances of flora species and their metabolism were altered, consequently leading to metabolic disorders. A correlation analysis between gut flora and their metabolic profiling further explained these changes and associations. The metabolic profiling of host circulation was also performed to characterize metabolic disorders. The associations of host circulation with gut flora were established via their significantly different metabolites. Alterations to the liver metabolism clarified potential pathways and mechanisms for the disorders. Metabolic disorders were evidently released by dietary and micro-ecological intervention, directly proving that gut flora comprise a vital medium in metabolic health risk caused by pesticide exposure. This work supplied theoretical bases and intervention approaches to body metabolic problems caused by pesticide exposure mediated by gut flora.
Rheology, Microstructure, and Storage Stability of Emulsion-Filled Gels Stabilized Solely by Maize Starch Modified with Octenyl Succinylation and Pregelatinization
Authors: Jo Myeongsu , Chang Min Jea , Goh Kelvin K. T. , Ban Choongjin , Choi Young Jin
Published: April 2021

We prepared emulsion-filled gels stabilized using octenyl succinic anhydride-modified and pregelatinized maize starch (OSA-PGS). The effect of the oil volume fraction (Φ, 0.05–0.20) and OSA-PGS concentration (3–10% w/v) on the rheological and microstructural properties of the emulsion-filled gels was evaluated. Confocal fluorescence images showed that OSA-PGS stabilized the emulsion, indicated by the formation of a thick layer surrounding the oil droplets, and simultaneously gelled the aqueous phase. All of the emulsions exhibited shear-thinning flow behavior, but only those with 10% w/v OSA-PGS were categorized as Herschel–Bulkley fluids. The rheological behavior of the emulsion-filled gels was significantly affected by both the OSA-PGS concentration and Φ. The mean diameters (D1,0, D3,2, and D4,3) of oil droplets with 10% w/v OSA-PGS were stable during 30 days of storage under ambient conditions, indicating good stability. These results provide a basis for the design of systems with potential applications within the food industry.
Screening and Identification of Host Proteins Interacting with Iris lactea var. chinensis Metallothionein IlMT2a by Yeast Two-Hybrid Assay
Authors: Wang Zhiquan , Ni Longjie , Liu Liangqin , Yuan Haiyan , Huang Suzhen , Gu Chunsun
Published: April 2021

Iris lactea var. chinensis (Fisch.) (I. lactea var. chinensis) is a well-known cadmium (Cd)-tolerant plant and we have previously shown that the metallothionein gene, IlMT2a, of the plant may be playing a key role in conferring the Cd tolerance. In this study, we have identified several proteins interacting with the IlMT2a by screening yeast two-hybrid library constructed from cDNAs isolated from Cd-treated I. lacteal var. chinensis plants. Putative functions of these proteins include those involved in photosynthesis, ROS scavenge, nutrient transport, and transcriptional regulation, to name a few. In particular, another metallothionein, which we assigned the name of IlMT3, was identified as an interacting partner of the IlMT2a. Unlike IlMT2a, it did not provide any significant protection against Cd toxicity in transgenic Arabidopsis thaliana L. (A. thaliana). To our knowledge, this is the first time ever reporting the interaction of two metallothionein proteins in plants. Learning the biological significance of the interaction between IlMT2a and IlMT3 would be the focus of future study and would be able to provide valuable insights into the understanding plant metallothionein’s diverse and complex roles in coordinating many important cellular physiologies including stress responses, gene regulations, and energy metabolisms.
Allelic Diversity at Abiotic Stress Responsive Genes in Relationship to Ecological Drought Indices for Cultivated Tepary Bean, Phaseolus acutifolius A. Gray, and Its Wild Relatives
Authors: Buitrago-Bitar María A. , Cortés Andrés J. , López-Hernández Felipe , Londoño-Caicedo Jorge M. , Muñoz-Florez Jaime E. , Muñoz L. Carmenza , Blair Matthew Wohlgemuth
Published: April 2021

Some of the major impacts of climate change are expected in regions where drought stress is already an issue. Grain legumes are generally drought susceptible. However, tepary bean and its wild relatives within Phaseolus acutifolius or P. parvifolius are from arid areas between Mexico and the United States. Therefore, we hypothesize that these bean accessions have diversity signals indicative of adaptation to drought at key candidate genes such as: Asr2, Dreb2B, and ERECTA. By sequencing alleles of these genes and comparing to estimates of drought tolerance indices from climate data for the collection site of geo-referenced, tepary bean accessions, we determined the genotype x environmental association (GEA) of each gene. Diversity analysis found that cultivated and wild P. acutifolius were intermingled with var. tenuifolius and P. parvifolius, signifying that allele diversity was ample in the wild and cultivated clade over a broad sense (sensu lato) evaluation. Genes Dreb2B and ERECTA harbored signatures of directional selection, represented by six SNPs correlated with the environmental drought indices. This suggests that wild tepary bean is a reservoir of novel alleles at genes for drought tolerance, as expected for a species that originated in arid environments. Our study corroborated that candidate gene approach was effective for marker validation across a broad genetic base of wild tepary accessions.
Karyotype Aberrations in Action: The Evolution of Cancer Genomes and the Tumor Microenvironment
Authors: Baudoin Nicolaas C. , Bloomfield Mathew
Published: April 2021

Cancer is a disease of cellular evolution. For this cellular evolution to take place, a population of cells must contain functional heterogeneity and an assessment of this heterogeneity in the form of natural selection. Cancer cells from advanced malignancies are genomically and functionally very different compared to the healthy cells from which they evolved. Genomic alterations include aneuploidy (numerical and structural changes in chromosome content) and polyploidy (e.g., whole genome doubling), which can have considerable effects on cell physiology and phenotype. Likewise, conditions in the tumor microenvironment are spatially heterogeneous and vastly different than in healthy tissues, resulting in a number of environmental niches that play important roles in driving the evolution of tumor cells. While a number of studies have documented abnormal conditions of the tumor microenvironment and the cellular consequences of aneuploidy and polyploidy, a thorough overview of the interplay between karyotypically abnormal cells and the tissue and tumor microenvironments is not available. Here, we examine the evidence for how this interaction may unfold during tumor evolution. We describe a bidirectional interplay in which aneuploid and polyploid cells alter and shape the microenvironment in which they and their progeny reside; in turn, this microenvironment modulates the rate of genesis for new karyotype aberrations and selects for cells that are most fit under a given condition. We conclude by discussing the importance of this interaction for tumor evolution and the possibility of leveraging our understanding of this interplay for cancer therapy.
The Psychological Symptoms of College Student in China during the Lockdown of COVID-19 Epidemic
Authors: Li Yin , Qin Linbo , Shi Yaobin , Han Jun
Published: April 2021

The COVID-19 epidemic has had a huge impact on the mental state of human beings due to its high infection and fatality rates in early 2020. In this paper, a cross-sectional online survey was designed to understand the mental state of college students in a university located in Wuhan city during the lockdown. Out of 1168 respondents, above 50% participants had obvious fear and anxiety symptoms; anxiety and fear were 61.64% and 58.39%, respectively. Conformity (49.49%), invulnerability (26.11%), insensitivity (21.49%) and rebelliousness (12.41%) symptoms also appeared. Meanwhile, it was revealed that the senior students experienced more anxiety than the freshmen. Moreover, it was found that the psychological symptoms (except for the insensitivity symptom) had no significant difference in gender, residence and annual household income after the one-way analysis of variance.
Why Is Green Hotel Certification Unpopular in Taiwan? An Analytic Hierarchy Process (AHP) Approach
Authors: Chen Yen-Cheng , Lee Ching-Sung , Hsu Ya-Chuan , Chen Yin-Jui
Published: April 2021

The main purpose of this study was to investigate the factors that discouraged Taiwan hoteliers from applying for green hotel certification. The analytic hierarchy process (AHP) method was used to perform a weighted analysis that comprehensively identified important hindering factors based on information from hotel industry, government, academic, and consumer representatives. Overall, in order of importance, the five dimensions of hindering factors identified by these experts and scholars were hotel internal environment, consumers’ environmental protection awareness, environmental protection incentive policy, hotel laws and regulations policy, and hotel external environment. Among the 26 examined hindering factor indices, the three highest-weighted indices overall for hoteliers applying for green hotel certification were as follows: environmental protection is not the main consideration of consumers seeking accommodations, lack of support by investment owners (shareholders), and lack of relevant subsidy incentives. The major contribution of this study is that hoteliers can understand important hindering factors associated with applying for green hotel certification; therefore, strategies that can encourage or enhance the green certification of hotels can be proposed to improve corporate image in the hotel industry, implement social responsibility in this industry, and obtain consumers’ approval of and accommodation-willingness for green hotels.
Object Semantic Segmentation in Point Clouds—Comparison of a Deep Learning and a Knowledge-Based Method
Authors: Ponciano Jean-Jacques , Roetner Moritz , Reiterer Alexander , Boochs Frank
Published: April 2021

Through the power of new sensing technologies, we are increasingly digitizing the real world. However, instruments produce unstructured data, mainly in the form of point clouds for 3D data and images for 2D data. Nevertheless, many applications (such as navigation, survey, infrastructure analysis) need structured data containing objects and their geometry. Various computer vision approaches have thus been developed to structure the data and identify objects therein. They can be separated into model-driven, data-driven, and knowledge-based approaches. Model-driven approaches mainly use the information on the objects contained in the data and are thus limited to objects and context. Among data-driven approaches, we increasingly find deep learning strategies because of their autonomy in detecting objects. They identify reliable patterns in the data and connect these to the object of interest. Deep learning approaches have to learn these patterns in a training stage. Knowledge-based approaches use characteristic knowledge from different domains allowing the detection and classification of objects. The knowledge must be formalized and substitutes the training for deep learning. Semantic web technologies allow the management of such human knowledge. Deep learning and knowledge-based approaches have already shown good results for semantic segmentation in various examples. The common goal but the different strategies of the two approaches engaged our interest in doing a comparison to get an idea of their strengths and weaknesses. To fill this knowledge gap, we applied two implementations of such approaches to a mobile mapping point cloud. The detected object categories are car, bush, tree, ground, streetlight and building. The deep learning approach uses a convolutional neural network, whereas the knowledge-based approach uses standard semantic web technologies such as SPARQL and OWL2to guide the data processing and the subsequent classification as well. The LiDAR point cloud used was acquired by a mobile mapping system in an urban environment and presents various complex scenes, allowing us to show the advantages and disadvantages of these two types of approaches. The deep learning and knowledge-based approaches produce a semantic segmentation with an average F1 score of 0.66 and 0.78, respectively. Further details are given by analyzing individual object categories allowing us to characterize specific properties of both types of approaches.