bluuebunny/crossref_metadata_2025 huggingface dataset:
Renewable Fuels from Integrated Power- and Biomass-to-X Processes: A Superstructure Optimization Study
Authors: Kenkel Philipp , Wassermann Timo , Zondervan Edwin
Published: July 2022

This work presents a superstructure optimization study for the production of renewable fuels with a focus on jet fuel. Power-to-X via the methanol (MTJ) and Fischer–Tropsch (FT) route is combined with Biomass-to-X (BtX) via an algae-based biorefinery to an integrated Power- and Biomass-to-X (PBtX) process. Possible integration by algae remnant utilization for H2/CO2 production, wastewater recycling and heat integration is included. Modeling is performed using the novel Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR). Novel methods to account for advanced mass balances and uncertain input data are included. Economic optimization proposes a PBtX process. This process combines algae processing with MTJ and depicts a highly mass- and energy integrated plant. It produces fuels at 211 EUR/MWhLHV (ca. 2530 EUR/t), a cost reduction of 21% to 11.5% compared to stand-alone electricity- or bio-based production at algae costs of 25 EUR/tAlgae-sludge and electricity costs of 72 EUR/MWh. Investigation of uncertain data indicates that a combination of BtX and MTJ is economically superior to FT for a wide parameter range. Only for high algae costs of >40 EUR/tAlgae-sludge stand-alone electricity-based MTJ is economically superior and for high MTJ costs above 2000–2400 EUR/tJet FT is the optimal option.
Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil
Authors: Pletsch Mikhaela A. J. S. , Körting Thales S. , Morita Felipe C. , Silva-Junior Celso H. L. , Anderson Liana O. , Aragão Luiz E. O. C.
Published: July 2022

MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life.
An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution
Authors: Sharma Ram C.
Published: July 2022

This paper presents two categories of features extraction and mapping suite, a very high-resolution suite and an ultra-resolution suite at 2 m and 0.5 m resolutions, respectively, for the differentiation and mapping of land cover and community-level vegetation types. The features extraction flow of the ultra-resolution suite involves pan-sharpening of the multispectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features. The performance of the ultra-resolution features extraction suite was compared with the very high-resolution features extraction suite that involves the calculation of radiometric indices and color-transformation of the multi-spectral image. This research was implemented in three mountainous ecosystems located in a cool temperate region. Three machine learning classifiers, Random Forests, XGBoost, and SoftVoting, were employed with a 10-fold cross-validation method for quantitatively evaluating the performance of the two suites. The ultra-resolution suite provided 5.3% more accuracy than the very high-resolution suite using single-date autumn images. Addition of summer images gained 12.8% accuracy for the ultra-resolution suite and 13.2% accuracy for the very high-resolution suite across all sites, while the ultra-resolution suite showed 4.9% more accuracy than the very high-resolution suite. The features extraction and mapping suites presented in this research are expected to meet the growing need for differentiating land cover and community-level vegetation types at a large scale.
Seasonal Variation in Microphysical Characteristics of Precipitation at the Entrance of Water Vapor Channel in Yarlung Zangbo Grand Canyon
Authors: Li Ran , Wang Gaili , Zhou Renran , Zhang Jingyi , Liu Liping
Published: July 2022

Mêdog is located at the entrance of the water vapor channel in the Yarlung Zangbo Grand Canyon (YGC). This area has the largest annual accumulated rainfall totals and precipitation frequency on the Tibetan Plateau (TP). This paper investigates the seasonal variation in raindrop size distribution (DSD) characteristics in Mêdog based on disdrometer observations from 1 July 2019 to 30 June 2020. The DSD characteristics are examined under six rain rate classes and two rainfall types (stratiform and convective) in the winter, premonsoon, monsoon and postmonsoon periods. The highest (lowest) concentration of small raindrops is observed in monsoon (winter) precipitation, whereas large raindrops predominate in premonsoon precipitation. For stratiform rainfall, the mean mass-weighted mean diameter (Dm) exhibits overlooked differences in the four periods, while the mean normalized intercept parameter (Nw) is significantly higher in the monsoon period than in the other three periods. The convective rainfall in the monsoon and postmonsoon periods is characterized by a high concentration of limited-size drops and can be classified as maritime-like. This is probably attributed to abundant warm and humid airflow transported by the Indian Ocean monsoon into Mêdog. The westerly winds prevail over the TP during the premonsoon period, and thereby the premonsoon convective rainfall in Mêdog has a larger mean Dm and a lower mean Nw. In addition, the relationships of radar reflectivity Z and rain rate R for different precipitation types in different periods are also derived. A better understanding of the seasonal variation in the microphysical characteristics of precipitation in Mêdog is important for improving the microphysical parameterization scheme and the precipitation forecast of models on the TP.
Combined Radar Quality Index for Quantitative Precipitation Estimation of Heavy Rainfall Events
Authors: Zhang Yang , Liu Liping , Wen Hao , Yu Benchao , Wang Huiying , Zhang Yong
Published: July 2022

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.
Evaluating Landsat-8 and Sentinel-2 Data Consistency for High Spatiotemporal Inland and Coastal Water Quality Monitoring
Authors: Hafeez Sidrah , Wong Man Sing , Abbas Sawaid , Asim Muhammad
Published: July 2022

The synergy of fine-to-moderate-resolutin (i.e., 10–60 m) satellite data of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI) provides a possibility to monitor the dynamics of sensitive aquatic systems. However, it is imperative to assess the spectral consistency of both sensors before developing new algorithms for their combined use. This study evaluates spectral consistency between OLI and MSI-A/B, mainly in terms of the top-of-atmosphere reflectance (ρt), Rayleigh-corrected reflectance (ρrc), and remote-sensing reflectance (Rrs). To check the spectral consistency under various atmospheric and aquatic conditions, near-simultaneous same-day overpass images of OLI and MSI-A/B were selected over diverse coastal and inland areas across Mainland China and Hong Kong. The results showed that spectral data obtained from OLI and MSI-A/B were consistent. The difference in the mean absolute percentage error (MAPE) of the OLI and MSI-A products was ~8% in ρt and ~10% in both ρrc and Rrs for all the matching bands, whereas the MAPE for OLI and MSI-B was ~3.7% in ρt, ~5.7% in ρrc, and ~7.5% in Rrs for all visible bands except the ultra-blue band. Overall, the green band was the most consistent, with the lowest MAPE of ≤ 4.6% in all the products. The linear regression model suggested that product difference decreased significantly after band adjustment with the highest reduction rate in Rrs (NIR band) and Rrs (red band) for the OLI–MSI-A and OLI–MSI-B comparison, respectively. Further, this study discussed the combined use of OLI and MSI-A/B data for (i) time series of the total suspended solid concentrations (TSS) over coastal and inland waters; (ii) floating algae area comparison; and (iii) tracking changes in coastal floating algae (FA). Time series analysis of the TSS showed that seasonal variation was well-captured by the combined use of sensors. The analysis of the floating algae bloom area revealed that the algae area was consistent, however, the difference increases as the time difference between the same-day overpasses increases. Furthermore, tracking changes in coastal FA over two months showed that thin algal slicks (width < 500 m) can be detected with an adequate spatial resolution of the OLI and the MSI.
Waste Bank Policy Implementation through Collaborative Approach: Comparative Study—Makassar and Bantaeng, Indonesia
Authors: Fatmawati Fatmawati , Mustari Nuryanti , Haerana Haerana , Niswaty Risma , Abdillah Abdillah
Published: July 2022

The purpose of this study is to determine the dynamics of implementing waste management policies through the waste bank innovation program in national policies. It also aims to determine the factors that influence it through a collaborative approach based on communication between community stakeholders, entrepreneurs, and the government in Makassar City and Bantaeng Regency. This is a qualitative-exploratory research study that uses a case study approach to delve into the research topic. The data collected were analyzed using the software Nvivo 12 pro to provide a systematic, factual, accurate, and in-depth picture of the implementation of waste bank program policies in eastern regions in Indonesia. The results of this study explain that the implementation of the Waste Bank Management Policy in Makassar City and Bantaeng Regency has not been run optimally, especially in the aspect of communication between stakeholders, including community participation. Although stakeholders and implementing agents have understood the intent and purpose of the waste bank program, socialization in the community is still considered less than optimal. Therefore, this research encourages local governments to implement effective and efficient waste bank program policies, with collaboration for every stakeholder in the area, to increase public and private participation.
Effects of Synthesis Gas Concentration, Composition, and Operational Time on Tubular Solid Oxide Fuel Cell Performance
Authors: Hartwell Alexander R. , Wilhelm Cole A. , Welles Thomas S. , Milcarek Ryan J. , Ahn Jeongmin
Published: July 2022

There is tremendous potential to utilize the exhaust gases and heat already present within combustion chambers to generate electrical power via solid oxide fuel cells (SOFCs). Variations in system design have been investigated as well as thorough examinations into the impacts of environmental conditions and fuel composition/concentration on SOFC performance. In an attempt to isolate the impacts of carbon monoxide and hydrogen concentration ratios within the exhaust stream, this work utilizes multi-temperature performance analyses with simulated methane combustion exhaust as fuel combined with dilute hydrogen baseline tests. These comparisons reveal the impacts of the complex reaction pathways carbon monoxide participates in when used as an SOFC fuel. Despite these complexities, performance reductions as a result of the presence of carbon monoxide are low when compared to similarly dilute hydrogen as a fuel. This provides further motivation for the continued development of SOFC-CHP systems. Stability testing performed over 80 h reveals the need for careful control of the operating environment as well as signs of carbon deposition. As a result of gas flow disruption, impacts of anode oxidation that may normally not hinder power production become significant factors in addition to coarsening of the anode material. Thermal management and strategies to minimize these impacts are a topic of future research.
Prediction of Runoff in Watersheds Located within Data-Scarce Regions
Authors: Ghanim Abdulnoor A. J. , Beddu Salmia , Abd Manan Teh Sabariah Binti , Al Yami Saleh H. , Irfan Muhammad , Mursal Salim Nasar Faraj , Mohd Kamal Nur Liyana , Mohamad Daud , Machmudah Affiani , Yavari Saba , Mohtar Wan Hanna Melini Wan , Ahmad Amirrudin , Wan Rasdi Nadiah , Khan Taimur
Published: July 2022

The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges’ prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters.
A Comparative Analysis of Plant-Based Milk Alternatives Part 1: Composition, Sensory, and Nutritional Value
Authors: Pointke Marcel , Albrecht Elke Herta , Geburt Katrin , Gerken Martina , Traulsen Imke , Pawelzik Elke
Published: July 2022

Consumers are becoming increasingly interested in reducing the consumption of animal-based foods for health, sustainability, and ethical reasons. The food industry is developing products from plant-based ingredients that mimic animal-based foods’ nutritional and sensory characteristics. In this study, the focus is on plant-based milk alternatives (PBMAs). A potential problem with plant-based diets is the deficiency of important micronutrients, such as vitamin B12, B2, and calcium. Therefore, an analysis of micronutrients in PBMAs was conducted to assess their nutritional value. The second main focus was on the sensory description of the PBMAs, done by a trained panel, and instrumental assessment to characterize the sensory attributes. Almond drinks met the daily micronutrient requirements the least, while soy drinks came closest to cow’s milk in macro- and micronutrients. The experimentally determined electronic tongue and volatile compound results confirmed the sensory panel’s evaluations and could therefore be used as a method for easy and effective assessments of PBMAs. The PBMAs evaluated in this study could not completely replace cow’s milk’s nutritional and sensory properties. They are products in their own product group and must be evaluated accordingly. Given the variety of products, consumers should experiment and make their decisions regarding the substitution of cow’s milk.
Evaluation and Influencing Factors of Network Resilience in Guangdong-Hong Kong-Macao Greater Bay Area: A Structural Perspective
Authors: Shi Jialu , Wang Xuan , Wang Chengxin , Liu Haimeng , Miao Yi , Ci Fuyi
Published: July 2022

Currently, urban crises are spreading, even tending to be magnified along the urban networks. Improving urban network resilience can effectively reduce the loss and cope with sudden disasters. Based on the dimensions of regional resilience and the framework of urban network, a new evaluation system of network resilience, including economic, social, and engineering networks, was established to assess the network resilience of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from a structural perspective. We analyzed the spatial characteristics and influencing factors of network resilience using social network analysis and quadratic assignment procedure. The results were as follows: (1) regional difference was biggest in GBA’s economic network strength while smallest in its transportation network strength, and the east bank of the Pearl River represented an extremely resilient connection axis; (2) the structures of network resilience and its subsystems were heterogeneous, and the connection paths of network resilience were more heterogeneous and diversified than those of the subsystems; (3) network resilience presented an obvious core–edge structure, and the spatial correlation and spillover effect between blocks were substantial; and (4) geographical proximity, as well as differences in economic development, urban agglomeration, and market development, had a significant impact on network resilience. This study provides a more systematic approach to evaluate the regional network resilience, and the results provide references for the construction of bay areas in developing countries.
Identifying Challenges and Drivers for Deployment of Centralized Biogas Plants in Denmark
Authors: Booker Nielsen Mark
Published: July 2022

There is an increasing need to accelerate the diffusion of biogas technology, to contribute to handling grand societal challenges. It is thus concerning that around 30% of all biogas projects are abandoned. Previous studies have found that challenges for deployment of bioenergy technology are mainly economic and financial challenges, market and infrastructure challenges, regulatory and administrative challenges, local opposition, site selection challenges and ecological aspects. Very few studies have however tried to understand how these different types of challenges specifically affect individual biogas projects. Also, no previous studies have identified where these challenges occur in the different phases of a project’s lifecycle (conceptualization, planning, and execution). A lack of understanding that limits the ability of both public institutions and project owners to ensure the success of biogas projects. The aim of this study is to fill this knowledge gap and provide a unique insight into the often very complex and long project lifecycle for the realization of centralized biogas projects. Results based on five comprehensive longitudinal case studies of attempts to realize centralized biogas projects, all taking place between 2008–2020 in Denmark, provide insight into how projects are specially affected by these different types of challenges, and shows that both successful and abandoned projects typically faced an array of challenges that project owners need to overcome. The study also from a bottom-up perspective provides insight into the implementation of national policies and initiatives assigned to the accelerated deployment of biogas technology in Denmark between 2008–2020 as well as critical factors at the local level driving the development.
Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems
Authors: Fleschutz Markus , Bohlayer Markus , Braun Marco , Murphy Michael D.
Published: July 2022

A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF’s capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources.
Delving Deeper into Market Concentration of Poultry Feed and the Driving Factors for Brand Switching: Evidence from Commercial Egg Producers in Nigeria
Authors: Sowunmi Fatai Abiola , Bello Abeeb Adetunji , Ogunniyi Adebayo Isaiah , Omotayo Abiodun Olusola
Published: July 2022

An increase in the price of branded feed has been a major problem for the poultry subsector of Nigerian agriculture, and brand switching for a cheap feed at the expense of quality is a common strategy used by egg-producing farmers. Using descriptive statistics, the Markov chain model and the logistic regression model, our study shows that almost 96% of the egg producers use branded feed while 43.1% switched feed brands because of the increase in the price of their preferred brands. Most farmers used Chikun (39.3%) and Top feed (23.2%) six months before data collection and during data collection, respectively. Our study found that approximately 37% of the feed sellers sold at least 10–50 bags per day. We revealed great inequality regarding market concentration: 50% of the feed sellers accounted for 89.5% of the total bags of feed sold per day. Hybrid had the highest customer loyalty. The study shows that Chikun gained 23.7% and 7.1% from Hybrid and Top feed, respectively, while Hybrid gained 36.0% and 35.7% from Chikun and Top feed (change in loyalty by egg producers), respectively. Membership of an association, distance to feed sellers, flock size and the average price of feed per bag were factors that influenced brand switching of poultry feed among egg producers. The study recommends that the government assists in subsidizing the price of critical ingredients (maize and soya bean) in feed production to prevent the price of eggs (the cheap source of protein) from becoming out of reach for most Nigerians.
Impact of Personality Traits and Information Privacy Concern on E-Learning Environment Adoption during COVID-19 Pandemic: An Empirical Investigation
Authors: Peng Mei-Hui , Dutta Bireswar
Published: July 2022

In response to the COVID-19 pandemic, teaching and learning processes have experienced significant changes. Higher education institutions in Taiwan employed crisis intervention measures to instantly implement unified learning methods such as online teaching and learning. However, students had no time to prepare. Thus, the study explored the relationship between personality traits and the belief in conspiracy theory as antecedents of students’ concern for information privacy (CFIP) and the subsequent relationship between students’ CFIP and behavioral intention to report their personal information to e-learning service providers concerning the adoption of the e-learning environment. This cross-sectional study employed a questionnaire to accumulate data from university students in Taiwan. A total of 285 valid responses were used for the final analysis. The research framework was evaluated by structural equation modeling (SEM). The results suggest the proposed model explains about 66.4% of the variance of behavioral intention (R2 = 0.664). The findings support that four personality traits—agreeableness, openness to experiences, conscientiousness, and neuroticism—and belief in conspiracy theory significantly influenced students’ CFIP. However, concerning extraversion, an insignificant path coefficient was reported. CFIP mediates the relationship between belief in conspiracy theory and behavioral intention. E-learning service providers should consider these determinants in improving and endorsing principles concerning e-learning environment adoption.
A Complete Information Interaction-Based Bus Passenger Flow Control Model for Epidemic Spread Prevention
Authors: Hu Xinghua , Xu Yimei , Guo Jianpu , Zhang Tingting , Bi Yuhang , Liu Wei , Zhou Xiaochuan
Published: July 2022

Because the strategy of stopping bus lines during an epidemic can negatively impact residents, this study proposes a bus passenger flow control model to optimize the safety of and access to bus transport. The information interaction environment can provide a means for the two-way regulation of buses and passengers. In this model, passengers first request their pick-up and drop-off location, and then the bus feeds back information on whether it accepts the request. Through this method, passenger flow control can be realized through complete information interaction. The study aimed to establish a multi-objective function that minimizes the weighted total cost of the safety cost, the passenger travel cost, and the bus travel cost during an epidemic. The constraints were the full load and riding rates of urban buses in peak periods under the condition of epidemic prevention and control. The results showed that, in the morning peak period, the passenger flow control scheme reduced the passenger infection probability by 17.89%, compared with no passenger flow control scheme. The weighted total cost of the epidemic safety cost, the passenger travel cost, and the bus operation cost was reduced by 8.04%. The optimization effect of the passenger flow control scheme of this model is good, and not only reduces the probability of passengers being infected, but also meets the requirements of epidemic prevention and the travel needs of residents.
Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer
Authors: Qasem Mohammad , Mohamed Omar , Abu Elhaija Wejdan
Published: July 2022

Sliding pressure control is a well-known method of controlling supercritical power plants that improves energy efficiency and reduces pressure dynamic stresses. This paper presents a novel approach for developing a supercritical cleaner coal power plant’s sliding pressure control strategy. First, using Whale Optimizer, a nonlinear identified transfer matrix model was created (WO). By comparing simulations and errors, the WO clearly outperforms the GA and Grey-Wolf Optimizer (GWO) techniques on parameter identification. The model also includes a multivariable PI/PD controller for improved plant operation. Again, WO controller tuning outperformed GA and GWO in terms of pressure deviations, power deviations, rise time, and fuel usage. It is now argued that the WO is superior to other techniques in modeling and controlling system dynamics, energy efficiency, and cleaner operation.
Sustainable Food Packaging: An Integrative Framework
Authors: Santi Romina , Garrone Paola , Iannantuoni Mattia , Del Curto Barbara
Published: July 2022

The paper proposes a comprehensive and operational definition of Sustainable Food Packaging (SFP). Sustainability is a multifaceted concept, yet most SFP conversations decline it as a mere material substitution issue. The efforts of regulators, packaging producers, food companies, and consumers towards the design and adoption of SFP products are likely to fail without a common understanding of the multiple means by which food packaging contributes to sustainability. Based on an extensive literature review and the contributions of SFP innovation experts, the paper builds a Food Packaging Sustainability Framework (FPSF) that encompasses the three main dimensions of SFP, namely environmental conservation, food safety, and social value, and operationalizes them in terms of objectives and activable levers. The framework can be used as a tool to search and evaluate food packaging products, a conceptual guide for SFP design, and a narrative platform for coordinating supply chain actors, including consumers. The experimental activities applying FPSF gathered the different actors in the supply chain to jointly adopt the integrated model that distributes environmental, social, and economic benefits along the entire production chain.
Evaluation of Machine Learning Algorithms for Classification of EEG Signals
Authors: Ramírez-Arias Francisco Javier , García-Guerrero Enrique Efren , Tlelo-Cuautle Esteban , Colores-Vargas Juan Miguel , García-Canseco Eloisa , López-Bonilla Oscar Roberto , Galindo-Aldana Gilberto Manuel , Inzunza-González Everardo
Published: July 2022

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.
Effects of an Arbuscular Mycorrhizal Fungus on the Growth of and Cadmium Uptake in Maize Grown on Polluted Wasteland, Farmland and Slopeland Soils in a Lead-Zinc Mining Area
Authors: Chen Jiaxin , Guo Jianfang , Li Zuran , Liang Xinran , You Yihong , Li Mingrui , He Yongmei , Zhan Fangdong
Published: July 2022

Arbuscular mycorrhizal fungi (AMF) exist widely in soil polluted by heavy metals and have significant effects on plant growth and cadmium (Cd) uptake. Cd contents differ among wasteland, farmland and slopeland soils in a lead-zinc mining area in Yunnan Province, Southwest China. The effects of AMF on maize growth, root morphology, low-molecular-weight organic acid (LMWOA) concentrations and Cd uptake were investigated via a root-bag experiment. The results show that AMF increased maize growth on Cd-polluted soils, resulting in increases in root length, surface area, volume and branch number, with the effects being stronger in farmland than in wasteland and slopeland soils; increased malic acid and succinic acid secretion 1.3-fold and 1.1-fold, respectively, in roots on farmland soil; enhanced the iron- and manganese-oxidized Cd concentration by 22.6%, and decreased the organic-bound Cd concentration by 12.9% in the maize rhizosphere on farmland soil; and increased Cd uptake 12.5-fold and 1.7-fold in shoots and by 25.7% and 86.6% in roots grown on farmland and slopeland soils, respectively. Moreover, shoot Cd uptake presented significant positive correlations with root surface area and volume and LMWOA concentrations. Thus, these results indicated the possible mechanism that the increased maize Cd uptake induced by AMF was closely related to their effect on root morphology and LMWOA secretion, with the effects varying under different Cd pollution levels.
Essential Trace Elements in Scalp Hair of Residents across the Caspian Oil and Gas Region of Kazakhstan
Authors: Umarova Gulmira , Batyrova Gulnara , Tlegenova Zhenisgul , Kononets Victoria , Balmagambetova Saule , Umarov Yeskendir , Yessengaliyeva Inkara , Mamyrbayev Arstan
Published: July 2022

Most of the country’s oil and gas fields are situated in West Kazakhstan, mainly on the Caspian Sea coast, causing significant damage to the local environment and contributing to an imbalance in the trace element composition of the human body. The study is aimed to evaluate the relationship between the concentration of essential trace elements in scalp hair of the western Kazakhstan adult population and the remoteness of their residence from oil and gas fields. The concentration of essential trace elements (Co, Cu, Fe, I, Mn, Se, Zn) in the hair of 850 individuals aged 18–60 years was determined by inductively coupled plasma mass spectrometry. In residents of settlements located at a distance of >110 km from oil and gas fields, the concentration of Cu and I in hair was significantly higher than in those closer to 110 km (p < 0.001). The content of Cu and I were associated with the distance to oil and gas fields (0.072 (95% CI: 0.050; 0.094)) and (0.121 (95% CI: 0.058; 0.185)), respectively. We detected a significant imbalance in the distribution of some essential trace elements in residents’ scalp hair from the Caspian region of western Kazakhstan, living near oil and gas fields. The concentrations of Cu and I were significantly interrelated with the distance to oil and gas fields. The level of copper in the hair of both inhabitants of the area most remote from oil and gas facilities and the entire population of western Kazakhstan as a whole remains significantly low. The data obtained provide evidence of the possible impact of pollutants generated by the oil and gas facilities on a shortage of essential trace elements and associated subsequent health risks.
The In Vitro Cytotoxic Effects of Ionophore Exposure on Selected Cytoskeletal Proteins of C2C12 Myoblasts
Authors: Henn Danielle , Venter Annette , Ferreira Gezina C. H. , Botha Christo J.
Published: July 2022

Carboxylic ionophores, such as monensin, salinomycin and lasalocid, are polyether antibiotics used widely in production animals for the control of coccidiosis, as well as for the promotion of growth and feed efficiency. Although the benefits of using ionophores are undisputed, cases of ionophore toxicosis do occur, primarily targeting the cardiac and skeletal muscles of affected animals. The 3-[4,5-dimethylthiazol-2yl]-2,5-diphenyl tetrazolium bromide (MTT) viability assay was used to determine the cytotoxicity of monensin, salinomycin and lasalocid on mouse skeletal myoblasts (C2C12). Immunocytochemistry and immunofluorescent techniques were, in turn, performed to investigate the effects of the ionophores on the microfilament, microtubule and intermediate filament, i.e., desmin and synemin networks of the myoblasts. Monensin was the most cytotoxic of the three ionophores, followed by salinomycin and finally lasalocid. Monensin and salinomycin exposure resulted in the aggregation of desmin around the nuclei of affected myoblasts. The synemin, microtubule and microfilament networks were less affected; however, vesicles throughout the myoblast’s cytoplasm produced gaps within the microtubule and, to a limited extent, the synemin and microfilament networks. In conclusion, ionophore exposure disrupted desmin filaments, which could contribute to the myofibrillar degeneration and necrosis seen in the skeletal muscles of animals suffering from ionophore toxicosis.
Dynamic Interactions of Post Cleaved NS2B Cofactor and NS3 Protease Identified by Integrative Structural Approaches
Authors: Quek Jun-Ping , Ser Zheng , Chew Bing Liang Alvin , Li Xin , Wang Lili , Sobota Radoslaw M. , Luo Dahai , Phoo Wint Wint
Published: July 2022

Diseases caused by flaviviruses such as dengue virus (DENV) and West Nile Virus (WNV), are a serious threat to public health. The flavivirus single-stranded RNA genome is translated into a polyprotein which is cleaved into three structural proteins and seven non-structural proteins by the viral and cellular proteases. Non-structural (NS) protein 3 is a multifunctional protein that has N-terminal protease and C-terminal helicase domains. The NS3 protease requires co-factor NS2B for enzymatic activity and folding. Due to its essential role in viral replication, NS2B-NS3 protease is an attractive target for antiviral drugs. Despite the availability of crystal structures, dynamic interactions of the N- and C-termini of NS2B co-factor have been elusive due to their flexible fold. In this study, we employ integrative structural approaches combined with biochemical assays to elucidate the dynamic interactions of the flexible DENV4 NS2B and NS3 N- and C-termini. We captured the crystal structure of self-cleaved DENV4 NS2B47NS3 protease in post cleavage state. The intermediate conformation adopted in the reported structure can be targeted by allosteric inhibitors. Comparison of our new findings from DENV4 against previously studied ZIKV NS2B-NS3 proteins reveals differences in NS2B-NS3 function between the two viruses. No inhibition of protease activity was observed for unlinked DENV NS2B-NS3 in presence of the cleavage site while ZIKV NS2B-NS3 cleavage inhibits protease activity. Another difference is that binding of the NS2B C-terminus to DENV4 eNS2B47NS3Pro active site is mediated via interactions with P4-P6 residues while for ZIKV, the binding of NS2B C-terminus to active site is mediated by P1-P3 residues. The mapping of NS2B N- and C-termini with NS3 indicates that these intermolecular interactions occur mainly on the beta-barrel 2 of the NS3 protease domain. Our integrative approach enables a comprehensive understanding of the folding and dynamic interactions of DENV NS3 protease and its cofactor NS2B.
SARS-CoV-2 Circulation during the First Year of the Pandemic: A Seroprevalence Study from January to December 2020 in Tuscany, Italy
Authors: Marchi Serena , Lanave Gianvito , Camero Michele , Dapporto Francesca , Manenti Alessandro , Benincasa Linda , Acciavatti Angela , Brogi Giulio , Viviani Simonetta , Montomoli Emanuele , Trombetta Claudia Maria
Published: July 2022

Italy was the second country affected by the SARS-CoV-2 pandemic; the virus spread mainly in Northern Italy with a subsequent diffusion to the center and southern part of the country. In this study, we aimed to assess the prevalence of antibodies against SARS-CoV-2 in the general population of the Siena province in the Tuscany region (Central Italy) during 2020. A total of 2480 serum samples collected from January to December 2020 were tested for IgM and IgG antibodies against SARS-CoV-2 by a commercial ELISA. Positive and borderline samples were further tested for the presence of anti-receptor-binding domain (RBD) IgM and IgG antibodies by an in-house ELISA and by a micro-neutralization assay. Out of the 2480 samples tested by the commercial ELISA, 81 (3.3%) were found to be positive or borderline for IgG and 58 (2.3%) for IgM in a total of 133 samples (5.4%) found to be positive or borderline for at least one antibody class. When the commercial ELISA and in-house ELISA/micro-neutralization assay results were combined, 26 samples (1.0%) were positive for RBD IgG, 11 (0.4%) for RBD IgM, and 23 (0.9%) for a neutralizing antibody. An increase in seroprevalence was observed during the year 2020, especially from the end of summer, consistent with the routine epidemiological surveillance of COVID-19 cases.
Isolation of Novel Xanthomonas Phages Infecting the Plant Pathogens X. translucens and X. campestris
Authors: Erdrich Sebastian H. , Sharma Vikas , Schurr Ulrich , Arsova Borjana , Frunzke Julia
Published: July 2022

The genus of Xanthomonas contains many well-known plant pathogens with the ability to infect some of the most important crop plants, thereby causing significant economic damage. Unfortunately, classical pest-control strategies are neither particularly efficient nor sustainable and we are, therefore, in demand of alternatives. Here, we present the isolation and characterization of seven novel phages infecting the plant-pathogenic species Xanthomonas translucens and Xanthomonas campestris. Transmission electron microscopy revealed that all phages show a siphovirion morphology. The analysis of genome sequences and plaque morphologies are in agreement with a lytic lifestyle of the phages making them suitable candidates for biocontrol. Moreover, three of the isolated phages form the new genus “Shirevirus”. All seven phages belong to four distinct clusters underpinning their phylogenetic diversity. Altogether, this study presents the first characterized isolates for the plant pathogen X. translucens and expands the number of available phages for plant biocontrol.
Pathological Findings in Cattle Slaughtered in Northeastern Algeria and Associated Risk Factors
Authors: Mimoune Nora , Hamiroune Mourad , Boukhechem Said , Mecherouk Choayb , Harhoura Khaled , Khelef Djamel , Kaidi Rachid
Published: July 2022

Meat is a food of animal origin, which can be contaminated by infectious, parasitic and other non-infectious agents responsible for diseases, which threaten the health of consumers. This still poses a public health problem in Algeria and in many countries. In order to assess the epidemiological situation of certain diseases in the Taher region in Jijel and to determine the influence of certain variation factors and to estimate the risk on public health, a study was extended over a period of 14 months on a total of 1756 cattle slaughtered at the Taher slaughterhouse. The results showed that 609 cattle (34.68%) showed lesions. The highest rate of pathological findings was observed on the liver (37.27%) followed by the lungs (30.21%). The lowest rate was recorded on the digestive system (0.33%) followed by the kidneys (1.14%). In addition, the liver and lungs were more contaminated with hydatid cyst compared to other organs (20.69%, 19.05%, respectively). Our data showed that the diseases affected more females (55.82%) than males (44.17%) (p < 0.001). Furthermore, cattle aged between 3 and 5 years were the most affected (43.51%) and local breed cattle showed more lesions (71.59%). These results testify to the real risk represented by the consumption of organs affected by diseases, and the need to recognize the agents of contamination and the mode of transmission and to implement an extension and control program in this region depending on the epidemiological aspect of the lesions.
Hydrological Time Series Clustering: A Case Study of Telemetry Stations in Thailand
Authors: Prakaisak Intouch , Wongchaisuwat Papis
Published: July 2022

Water level data from telemetry stations typically demonstrate diverse behaviors over time. Specific characteristics can be observed among distinct station groups that are different from others. Clustering time series data into a specified number of groups based on their similarity is an initial step for further analysis in water management analytics. Our main goal in this work is to develop a clustering framework based on a combination of feature representations, feature reduction techniques, as well as clustering algorithms. Thorough experiments on multiple combinations of these methods were conducted and compared. Based on collected water level data in Thailand, UMAP reduced representations of engineered features using HAC clustering with euclidean distance outperformed other methods. Its performance reached 0.8 Fowlkes-Mallows score. Out of 81 stations, only nine unclear cases were incorrectly clustered. Distinct behaviors with abrupt and frequent fluctuations could be perfectly identified.